Fitbit Research Library

Title	Author	Year	Journal/Proceedings	Category	Device(s)	Population	Data Used	Links
Movement toward a novel activity monitoring device details	Montgomery-Downs, H.E., Insana, S.P. and Bond, J.A.	2012	Sleep and Breathing	Validation	Classic	Adults	Sleep	URL
Accuracy of the Fitbit pedometer for self-paced and prescribed physical activity details	Ramirez, E., Peterson, C., Wu, W. and Norman, G.J.	2012	Medicine & Science in Sports & Exercise	Validation	Classic	Adults	Steps	URL
Evaluating Physical Activity Monitors for Use in Personal Health Research details	Wilcox, L. and Feiner, S.	2012	Ubicomp 2012 Workshop on Evaluating Off- the-Shelf Technologies for Personal Health Monitoring	Usability	Classic	NA	NA	URL
Functional recovery in the elderly after major surgery: assessment of mobility recovery using wireless technology. details	Cook, D.J., Thompson, J.E., Prinsen, S.K., Dearani, J.A. and Deschamps, C.	2013	The Annals of Thoracic Surgery	Usability Medical	Classic	Older Adults Patients	Steps	URL
A comparison of energy expenditure estimation of several physical activity monitors. details	Dannecker, K.L., Sazonova, N.A., Melanson, E.L., Sazonov, E.S. and Browning, R.C.	2013	Medicine and Science in Sports and Exercise	Validation	Classic	Adults	Energy Expenditure	URL
Quantified Recess: Design of an Activity For Elementary Students Involving Analyses of Their Own Movement Data details	Lee, V.R. and Drake, J.	2013	Proceedings of the 12th International Conference on Interaction Design and Children	Usability	Ultra	Children	Steps	URL
An evaluation of wearable activity monitoring devices details	Guo, F., Li, Y., Kankanhalli, M.S. and Brown, M.S.	2013	Proceedings of the 1st ACM international workshop on Personal data meets distributed multimedia - PDM '13	Validation	Ultra One Zip	Adults	Steps Distance	URL
Phases of Accuracy Diagnosis: (In)visibility of System Status in the Fitbit details	Mackinlay, M.Z.	2013	Intersect: The Stanford Journal of Science, Technology and Society	Usability	One	Older Adults	NA	URL
Live Long and Prosper: Potentials of Low-Cost Consumer Devices for the Prevention of Cardiovascular Diseases details	Meyer, J. and Hein, A.	2013	Medicine 2.0	Usability Measurement	Ultra	Adults	Steps Sleep	URL
Comparison of steps and energy expenditure assessment in adults of Fitbit Tracker and Ultra to the Actical and indirect calorimetry. details	Noah, A.J., Spierer, D.K., Gu, J. and Bronner, S.	2013	Journal of Medical Engineering & Technology	Validation	Classic Ultra	Adults	Steps Energy Expenditure	URL
Monitoring daily function in persons with transfemoral amputations using a commercial activity monitor: A feasibility study details	Albert, M.V., Deeny, S., McCarthy, C., Valentin, J. and Jayaraman, A.	2014	PM and R	Usability	One	Patients Adults	Steps Energy Expenditure Distance	URL
Enhancing physical activity promotion in midlife women with technology-based self-monitoring and social connectivity: A pilot study. details	Butryn, M.L., Arigo, D., Raggio, G.a., Colasanti, M. and Forman, E.M.	2014	Journal of Health Psychology	Intervention	Flex	Adults	Steps Sleep Weight Food	URL
P. HealthyTogether: Exploring Social Incentives for Mobile Fitness Applications details	Chen, Y. and Pu, P.	2014	Proceedings of the Second International Symposium of Chinese CHI	Intervention	Ultra One	Adults	Steps
Feasibility of Using Fitbit Flex to Motivate People with Rheumatoid Arthritis to BE Physically Active details	Feehan, L., Clayton, C., Carruthers, E. and Li, L.	2014	Annals of the Rheumatic Diseases	Usability	Flex	Patients Adults	Steps Energy Expenditure Intensity	URL
Validity of using tri-axial accelerometers to measure human movement - Part II: Step counts at a wide range of gait velocities. details	Fortune, E., Lugade, V., Morrow, M. and Kaufman, K.	2014	Medical Engineering & Physics	Validation	Classic	Adults	Steps	URL
Accuracy of 2 activity monitors in detecting steps in people with stroke and traumatic brain injury. details	Fulk, G.D., Combs, S.A., Danks, K.A., Nirider, C.D., Raja, B. and Reisman, D.S.	2014	Physical Therapy	Medical Validation	Ultra	Patients Adults	Steps	URL
Comparison of FitBit® Ultra to ActiGraph™ GT1M for Assessment of Physical Activity in Young Adults During Treadmill Walking details	Gusmer, R., Bosch, T., Watkins, A., Ostrem, J. and Dengel, D.	2014	The Open Sports Medicine Journal	Validation	Classic	Adults	Steps Energy Expenditure	URL
A Qualitative Analysis of User Experiences With a Self-Tracker for Activity, Sleep, and Diet details	Kim, J.	2014	Interactive Journal of Medical Research	Usability	Classic	Adults	NA	URL
Increasing physical activity in young adults with autism spectrum disorders details	LaLonde, K.B., MacNeill, B.R., Eversole, L.W., Ragotzy, S.P. and Poling, A.	2014	Research in Autism Spectrum Disorders	Intervention	Zip	Patients Adults	Steps	URL
Validity of consumer-based physical activity monitors. details	Lee, J.-M., Kim, Y. and Welk, G.J.	2014	Medicine and Science in Sports and Exercise	Validation	One Zip	Adults	Energy Expenditure	URL
Behavior Change Techniques Implemented in Electronic Lifestyle Activity Monitors: A Systematic Content Analysis details	Lyons, E.J., Lewis, Z.H., Mayrsohn, B.G. and Rowland, J.L.	2014	Journal of Medical Internet Research	Review	Force			URL
Can Patient Use of Daily Activity Monitors Change Nurse Practitioner Practice? details	Mancuso, P.J., Thompson, M., Tietze, M., Kelk, S. and Roux, G.	2014	The Journal for Nurse Practitioners	Review	One Flex	NA	NA	URL
Becoming Reflective: Designing for Reflection on Physical Performances details	Moher, T., Ching, C., Schaefer, S., Lee, V., et al.	2014	ICLS 2014 Proceedings	Usability	One Zip	Children	Steps	URL
Effect of walking speed and placement position interactions in determining the accuracy of various newer pedometers details	Park, W., Lee, V.J., Ku, B. and Tanaka, H.	2014	Journal of Exercise Science & Fitness	Validation	Ultra	Adults	Steps	URL
Comparative assessment of sleep quality estimates using home monitoring technology details	Perez-Macias, J.M., Jimison, H., Korhonen, I. and Pavel, M.	2014	2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society	Validation	One	Adults	Sleep	URL
An inexpensive device for monitoring patients' weights via automated hovering details	Shaffer, J.A., Diaz, K., Alcántara, C., Edmondson, D., Krupka, D.J., Chaplin, W.F. and Davidson, K.W.	2014	International Journal of Cardiology	Validation	Aria	Adults	Weight	URL
Caloric expenditure assessment among older adults: criterion validity of a novel accelerometry device. details	Stahl, S.T. and Insana, S.P.	2014	Journal of Health Psychology	Validation	Classic	Older Adults	Energy Expenditure	URL
Validation of the Fitbit One activity monitor device during treadmill walking. details	Takacs, J., Pollock, C.L., Guenther, J.R., Bahar, M., Napier, C. and Hunt, M.A.	2014	Journal of Science and Medicine in Sport	Validation	One	Adults	Steps Distance	URL
Indoor activity monitoring system for elderly using RFID and Fitbit Flex wristband details	Tan, T.-H., Gochoo, M., Chen, K.-H., Jean, F.-R., Chen, Y.-F., Shih, F.-J. and Ho, C.F.	2014	IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI)	Measurement	Flex	Older Adults	Steps	URL
"Go4Life" exercise counseling, accelerometer feedback, and activity levels in older people. details	Thompson, W.G., Kuhle, C.L., Koepp, G.A., McCrady-Spitzer, S.K. and Levine, J.A.	2014	Archives of Gerontology and Geriatrics	Intervention	Classic	Older Adults	NA	URL
Activity monitor intervention to promote physical activity of physicians-in-training: randomized controlled trial. details	Thorndike, A.N., Mills, S., Sonnenberg, L., Palakshappa, D., Gao, T., Pau, C.T. and Regan, S.	2014	PloS ONE	Intervention	Classic	Adults	Steps	URL
The validation of Fibit Zip™ physical activity monitor as a measure of free-living physical activity. details	Tully, M.A., McBride, C., Heron, L. and Hunter, R.F.	2014	BMC Research Notes	Usability Validation	Zip	Adults	Steps	URL
Validity and usability of low-cost accelerometers for internet-based self-monitoring of physical activity in patients with chronic obstructive pulmonary disease. details	Vooijs, M., Alpay, L.L., Snoeck-Stroband, J.B., Beerthuizen, T., Siemonsma, P.C., Abbink, J.J., Sont, J.K. and Rövekamp, T.A.	2014	Interactive Journal of Medical Research	Usability Validation	Ultra	Patients Adults	Steps Energy Expenditure	URL
StepCity: a preliminary investigation of a personal informatics-based social game on behavior change details	Walsh, G. and Golbeck, J.	2014	Proceedings of the extended abstracts of the 32nd annual ACM conference on Human factors in computing systems	Intervention	Classic	Adults	Steps	URL
Preliminary efficacy of prize-based contingency management to increase activity levels in healthy adults. details	Washington, W.D., Banna, K.M. and Gibson, A.L.	2014	Journal of Applied Behavior Analysis	Intervention	Classic	Adults	Steps	URL
Decomposing activities of daily living to discover routine clusters details	Yürüten, O., Zhang, J. and Pu, P.	2014	Proceedings of the Twenty-Eighth AAAI Conference on Artificial Intelligence	Methods	Ultra One	Adults	Energy Expenditure
Mobile Phone-Connected Wearable Motion Sensors to Assess Postoperative Mobilization details	Appelboom, G., Taylor, B.E., Bruce, E., Bassile, C.C., Malakidis, C., Yang, A., Youngerman, B., D'Amico, R., Bruce, S., Bruyère, O., Reginster, J.-Y., Dumont, E.P. and Connolly Jr, E.S.	2015	JMIR mHealth and uHealth	Usability Validation	Zip	Patients Adults	Steps	URL
Promoting physical activity among women using wearable technology and online social connectivity: a feasibility study details	Arigo, D.	2015	Health Psychology and Behavioral Medicine	Intervention	Flex	Adults	Steps Intensity Sleep	URL
Addressing barriers to physical activity among women: A feasibility study using social networking-enabled technology details	Arigo, D., Schumacher, L.M., Pinkasavage, E. and Butryn, M.L.	2015	Digital Health	Intervention	Flex	Adults	Steps Intensity Goals	URL
“Not just another walking program”: Everyday Activity Supports You (EASY) model---a randomized pilot study for a parallel randomized controlled trial details	Ashe, M.C., Winters, M., Hoppmann, C.A., Dawes, M.G., Gardiner, P.A., Giangregorio, L.M., Madden, K.M., McAllister, M.M., Wong, G., Puyat, J.H., Singer, J., Sims-Gould, J. and McKay, H.A.	2015	Pilot and Feasibility Studies	Intervention	One	Older Adults	Steps Goals	URL
An Evaluation of Commercial Pedometers for Monitoring Slow Walking Speed Populations. details	Beevi, F.H.A., Miranda, J., Pedersen, C.F. and Wagner, S.	2015	Telemedicine journal and e-health : the official journal of the American Telemedicine Association	Validation	Zip	Adults	Steps	URL
Evaluating Low-cost Activity Trackers for Use in Large-scale Data Gathering of Forestry Workers details	Bowen, J., Hinze, A., Cunningham, S.J. and Parker, R	2015	Proceedings of the Annual Meeting of the Australian Special Interest Group for Computer Human Interaction - OzCHI '15	Usability Validation	One Flex	Adults	Steps Sleep	URL
Randomized Trial of a Fitbit-Based Physical Activity Intervention for Women. details	Cadmus-Bertram, L.A., Marcus, B.H., Patterson, R.E., Parker, B.A. and Morey, B.L.	2015	American Journal of Preventive Medicine	Intervention	One	Adults	Steps Goals	URL
Use of the Fitbit to Measure Adherence to a Physical Activity Intervention Among Overweight or Obese, Postmenopausal Women: Self-Monitoring Trajectory During 16 Weeks. details	Cadmus-Bertram, L., Marcus, B.H., Patterson, R.E., Parker, B.A. and Morey, B.L.	2015	JMIR mHealth and uHealth	Intervention	One	Adults	Steps Intensity	URL
Accuracy of smartphone applications and wearable devices for tracking physical activity data. details	Case, M.A., Burwick, H.A., Volpp, K.G. and Patel, M.S	2015	JAMA	Validation	One Zip Flex	Adults	Steps	URL
mHealth Physical Activity Intervention: A Randomized Pilot Study in Physically Inactive Pregnant Women. details	Choi, J., Lee, J.H., Vittinghoff, E. and Fukuoka, Y.	2015	Maternal and Child Health Journal	Intervention	Ultra	Adults	Steps	URL
Feasibility and preliminary efficacy of a physical activity counseling intervention using Fitbit in people with knee osteoarthritis: the TRACK-OA study protocol details	Clayton, C., Feehan, L., Goldsmith, C.H., Miller, W.C., Grewal, N., Ye, J., Yoo, J.Y. and Li, L.C.	2015	Pilot and Feasibility Studies}	Intervention	Flex	Patients Adults	NA	URL
Validitiy and usability testing of the Fitbit pedometer in patients with COPD details	De Sousa Sena, R., Bourbeau, J., Zhi Li, P. and Ahmed, S.	2015	European Respiratory Journal	Usability Validation	One	Patients Adults	Steps	URL
Fitbit®: An accurate and reliable device for wireless physical activity tracking details	Diaz, K.M., Krupka, D.J., Chang, M.J., Peacock, J., Ma, Y., Goldsmith, J., Schwartz, J.E. and Davidson, K.W.	2015	International Journal of Cardiology	Validation	One Flex	Adults	Steps Energy Expenditure	URL
Measuring steps with the Fitbit activity tracker: an inter-device reliability study details	Dontje, M.L., de Groot, M., Lengton, R.R., van der Schans, C.P. and Krijnen, W.P.	2015	Journal of Medical Engineering & Technology	Validation	Ultra	Adults	Steps	URL
Are Currently Available Wearable Devices for Activity Tracking and Heart Rate Monitoring Accurate, Precise, and Medically Beneficial? details	El-Amrawy, F. and Nounou, M.I.	2015	Healthcare Informatics Research	Validation	Flex	Adults	Steps	URl
Systematic review of the validity and reliability of consumer-wearable activity trackers. details	Evenson, K.R., Goto, M.M. and Furberg, R.D.	2015	The International Journal of Behavioral Nutrition and Physical Activity	Validation Systematic Review	Classic Ultra One Zip Flex	Adults	Steps Energy Expenditure Intensity Distance Sleep	URL
The validity of consumer-level, activity monitors in healthy adults worn in free-living conditions: a cross-sectional study. details	Ferguson, T., Rowlands, A.V., Olds, T. and Maher, C.	2015	The International Journal of Behavioral Nutrition and Physical Activity	Validation	One Zip	Adults	Steps Energy Expenditure Intensity Sleep	URL
Mobile App to Reduce Inactivity in Sedentary Overweight Women details	Finkelstein, J., Bedra, M.K., Li, X., Wood, J. and Ouyang, P.	2015	Studies in Health Technology and Informatics	Intervention	One	Adults	Steps	URL
Using Commercial Activity Monitors to Measure Gait in Patients with Suspected iNPH: Implications for Ambulatory Monitoring. details	Gaglani, S., Moore, J., Haynes, M.R., Hoffberger, J.B. and Rigamonti, D.	2015	Cure¯us	Validation	Ultra	Adults	Steps	URL
A televideo exercise and nutrition program for children with acute lymphoblastic leukemia in maintenance therapy: design and methods details	Gibson, C., August, K., Greene, J.L., Herrmann, S.D., Lee, J., Harvey, S.P., Lambourne, K. and Debra, S.	2015	Open Access Journal of Clinical Trials	Methods	Zip	Adults	Steps Intensity	URL
Impact of increasing physical activity on cognitive functioning in breast cancer survivors: Rationale and study design of Memory & Motion. details	Hartman, S.J., Natarajan, L., Palmer, B.W., Parker, B., Patterson, R.E. and Sears, D.D.	2015	Contemporary Clinical Trials	Methods	One	Patients Adults	Steps Intensity	URL
Increasing physical activity of children during school recess. details	Hayes, L.B. and Van Camp, C.M.	2015	Journal of Applied Behavior Analysis	Intervention	Classic	Children	Steps	URL
The Walking Interventions Through Texting (WalkIT) Trial: Rationale, Design, and Protocol for a Factorial Randomized Controlled Trial of Adaptive Interventions for Overweight and Obese, Inactive Adults details	Hurley, J.C., Hollingshead, K.E., Todd, M., Jarrett, C.L., Tucker, W.J., Angadi, S.S. and Adams, M.A.	2015	JMIR Research Protocols	Intervention	Zip	Adults	Steps	URL
Encouraging regular physical activity for hormone-receptor positive breast cancer patients to improve mental and physical health during treatment and survivorship. details	Jordan, C. and Klepsa, A.	2015	Journal of Clinical Oncology	Intervention	Flex	Patients Adults	Steps
Electronic activity trackers encourage family fun and fitness details	Klein, D.	2015	The Australasian Medical Journal	Case Study	Charge	Children Adolescents	NA	URL
Reliability and validity of ten consumer activity trackers. details	Kooiman, T.J.M., Dontje, M.L., Sprenger, S.R., Krijnen, W.P., van der Schans, C.P. and de Groot, M.	2015	BMC Sports Science, Medicine and Rehabilitation	Validation	One Flex	Adults	Steps	URL
Opportunistic uses of the traditional school day through student examination of Fitbit activity tracker data details	Lee, V.R., Drake, J.R., Cain, R. and Thayne, J.	2015	Proceedings of the 14th International Conference on Interaction Design and Children	Usability	Ultra	Children	Steps	URL
Let's Get Physical: K-12 Students Using Wearable Devices to Obtain and Learn About Data from Physical Activities details	Lee, V.R., Drake, J. and Williamson, K.	2015	TechTrends	Usability	Ultra	Children	Steps	URL
Effects of an Intervention using Movement Technology in a University Physical Activity Class details	Mahar, M.T., Nanney, L.W., Das, B.M., Raedeke, T.D., Vick, G.A. and Rowe, D.A.	2015	Medicine & Science in Sports & Exercise	Intervention	Flex	Adults	Steps	URL
Comparison of a Commercial Accelerometer with Polysomnography and Actigraphy in Children and Adolescents details	Meltzer, L.J., Hiruma, L.S., Avis, K., Montgomery-Downs, H. and Valentin, J.	2015	Sleep	Validation	Ultra	Children Adolescents	Sleep	URL
Smart Wristbands as Inexpensive and Reliable Non-dedicated Solution for Self-Managing Type 2 Diabetes details	Militaru, A., Frandes, M. and Lungeanu, D.	2015	2015 E-Health and Bioengineering Conference (EHB)	Case Study	Charge HR	Older Adults	Steps Energy Expenditure Intensity Sleep Heart Rate	URL
Effects of Public Commitments and Accountability in a Technology-Supported Physical Activity Intervention details	Munson, S.A., Krupka, E., Richardson, C. and Resnick, P.	2015	Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems	Intervention	Zip	Adults	Steps	URL
Feasibility of popular m-health technologies for activity tracking among individuals with serious mental illness. details	Naslund, J.A., Aschbrenner, K.A., Barre, L.K. and Bartels, S.J.	2015	Telemedicine journal and e-health: the official journal of the American Telemedicine Association	Usability	Zip	Patients Adults	NA	URL
Text Messaging to Reduce Inactivity Using Real-Time Step Count Monitoring in Sedentary Overweight Females details	Ouyang, P., Stewart, K.J., Bedra, M.E., York, S., Valdiviezo, C. and Finkelstein, J.	2015	Circulation	Intervention	One	Adults	Steps	URL
Validity of the Fitbit activity tracker for measuring steps in community-dwelling older adults details	Paul, S.S., Tiedemann, A., Hassett, L.M., Ramsay, E., Kirkham, C., Chagpar, S. and Sherrington, C.	2015	BMJ Open Sport & Exercise Medicine	Validation	One Zip	Older Adults	Steps	URL
Using A Facebook Group As An Adjunct To A Pilot mHealth Physical Activity Intervention: A Mixed Methods Approach. details	Pumper, M.A., Mendoza, J.A., Arseniev-Koehler, A., Holm, M., Waite, A. and Moreno, M.A.	2015	Studies in Health Technology and Informatics	Intervention	Flex	Adolescents	Steps	URL
Exploring physical activity monitoring devices for diabetic and obese patients details	Randriambelonoro, M., Chen, Y., Geissbuhler, A. and Pu, P.	2015	Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2015 ACM International Symposium on Wearable Computers	Usability	One	Patients Adults	NA	URL
Comparison of Three Inexpensive Commercially Available Activity Monitors to a Previously Validated Tri-Axial Accelerometer details	Reid, R.E., Comptour, A.M., Insogna, J.A., Bewski, N.A., Sciortino, C.D. and Andersen, R.E.	2015	Medicine & Science in Sports & Exercise	Validation	One Force	Adults	Steps Intensity	URL
24 Hours of Sleep, Sedentary Behavior, and Physical Activity with Nine Wearable Devices details	Rosenberger, M.E., Buman, M.P., Haskell, W.L., McConnell, M.V. and Carstensen, L.L.	2015	Medicine and Science in Sports and Exercise	Validation	One	Adults	Steps Intensity Sleep	URL
Validation of the Fitbit wireless activity tracker for prediction of energy expenditure. details	Sasaki, J.E., Hickey, A., Mavilia, M., Tedesco, J., John, D., Kozey Keadle, S. and Freedson, P.S.	2015	Journal of Physical Activity & Health	Validation	Classic	Adults	Energy Expenditure	URL
Capturing step counts at slow walking speeds in older adults: comparison of ankle and waist placement of measuring device. details	Simpson, L.A., Eng, J.J., Klassen, T.D., Lim, S.B., Louie, D.R., Parappilly, B., Sakakibara, B.M. and Zbogar, D.	2015	Journal of Rehabilitation Medicine	Validation	One	Older Adults	Steps	URL
Accuracy of the FitBit at walking speeds and cadences relevant to clinical rehabilitation populations details	Singh, A.K., Farmer, C., Van Den Berg, M.L., Killington, M. and Barr, C.J.	2015	Disability and Health Journal	Validation	Zip	Patients Adults	Steps	URL
Intergenerational activity tracker program: Impact with health related outcomes on older adults details	Sookhai, L., Coppola, J.F. and Gaur, C.	2015	2015 Long Island Systems, Applications and Technology	Usability Measurement	Zip	Older Adults	Steps	URL
A Mobile Health Driven Walking Program Improves Quality of Life but not Fitness or Fatness in Obese Sedentary Women details	Stewart, K.J., Katz, J., Dobrosielski, M. and Ouyang, P.	2015	Circulation	Intervention		Adults	Steps	URL
Step Detection and Activity Recognition Accuracy of Seven Physical Activity Monitors details	Storm, F.A., Heller, B.W. and Mazzà, C.	2015	PLOS ONE	Validation	One	Adults	Steps	URL
The intervention composed of aerobic training and non-exercise physical activity (I-CAN) study: Rationale, design and methods details	Swift, D.L., Dover, S.E., Nevels, T.R., Solar, C.A., Brophy, P.M., Hall, T.R., Houmard, J.A. and Lutes, L.D.	2015	Contemporary Clinical Trials	Methods	One	Adults	Steps Intensity	URL
Evaluating an Internet-based program and a behavioral coach for increasing physical activity. details	Valbuena, D., Miltenberger, R. and Solley, E.	2015	Behavior Analysis: Research & Practice	Intervention	One Aria	Adults	Steps Weight	URL
The Effect of Fitbit Technology on Physical Activity in Inactive Adults (50+ Years of Age) details	Vasold, K.L., Groendal, E.G. and Knous, J.L.	2015	Medicine & Science in Sports & Exercise	Intervention		Older Adults	Steps
Wearable Sensor/Device (Fitbit One) and SMS Text-Messaging Prompts to Increase Physical Activity in Overweight and Obese Adults: A Randomized Controlled Trial. details	Wang, J.B., Cadmus-Bertram, L.A., Natarajan, L., White, M.M., Madanat, H., Nichols, J.F., Ayala, G.X. and Pierce, J.P.	2015	Telemedicine and e-Health	Intervention	One	Adults	Steps Intensity	URL
Validation of Fitbit-Flex as a measure of free-living physical activity in a community-based phase III cardiac rehabilitation population details	Alharbi, M., Bauman, A., Neubeck, L. and Gallagher, R.	2016	European Journal of Preventive Cardiology	Validation	Flex	Patients Adults	Steps Intensity	URL
An experimental analysis of active living technologies to review device accuracy details	Altamimi, R. and Skinner, G.	2016	2016 IEEE Region 10 Conference (TENCON)	Validation	One Flex	Adults	Steps Distance	URL
Validation of Contemporary Physical Activity Tracking Technologies through Exercise in a Controlled Environment details	Altamimi, R.I. and Skinner, G.D.	2016	International Journal of Medical, Health, Biomedical, Bioengineering and Pharmaceutical Engineering	Validation	One Flex	Adults	Distance	URL
Integrating Mobile health and Physical Activity to reduce the burden of Chronic low back pain Trial (IMPACT): a pilot trial protocol details	Amorim, A.B., Pappas, E., Simic, M., Ferreira, M.L., Tiedemann, A., Jennings, M. and Ferreira, P.H.	2016	BMC Musculoskeletal Disorders	Methods		Patients Adults	Steps Intensity Sleep	URL
How valid are wearable physical activity trackers for measuring steps? details	An, H.-S., Jones, G.C., Kang, S.-K., Welk, G.J. and Lee, J.-M.	2016	European Journal of Sport Science	Validation	Zip Flex	Adults	Steps	URL
Real-World Walking Performance of Individuals with Lower-Limb Amputation Classified as Medicare Functional Classification Level 2 and 3 details	Arch, E.S., Erol, O., Bortz, C., Madden, C., Galbraith, M., Rossi, A., Lewis, J., Higginson, J.S., Buckley, J.M. and Horne, J.	2016	Journal of Prosthetics and Orthotics	Measurement	One	Patients Adults	Steps	URL
The Electronic CardioMetabolic Program (eCMP) for Patients With Cardiometabolic Risk: A Randomized Controlled Trial. details	Azar, K.M., Koliwad, S., Poon, T., Xiao, L., Lv, N., Griggs, R. and Ma, J.	2016	Journal of Medical Internet Research	Intervention		Patients Adults	NA	URL
Assessing the Correlation Between Physical Activity and Quality of Life in Advanced Lung Cancer details	Bade, B.C., Brooks, M.C., Nietert, S.B., Ulmer, A., Thomas, D.D., Nietert, P.J., Scott, J.B. and Silvestri, G.A.	2016	Integrative Cancer Therapies	Usability Medical	Zip	Patients Adults	Steps	URL
Using passively collected sedentary behavior to predict hospital readmission details	Bae, S., Dey, A.K. and Low, C.A.	2016	Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing	Measurement	Flex	Patients Adults	Steps Intensity Distance	URL
Comparison of consumer and research monitors under semistructured settings details	Bai, Y., Welk, G.J., Nam, Y.H., Lee, J.A., Lee, J.M., Kim, Y., Meier, N.F. and Dixon, P.M.	2016	Medicine and Science in Sports and Exercise	Validation	Flex	Adults	Energy Expenditure	URL
Impact of Digital Health Methods for Weight Management on Atherosclerotic Cardiovascular Disease Risk in “at-risk” Women details	Ball, C., Carter, K., Yeung, C., Abdelmoneim, S., Bauman, J., Huang, R., Mankad, R., Iftikar, S., Jain, S., Lopez-Jimenez, F. and Mulvagh, S.	2016	Canadian Journal of Cardiology	Intervention		Adults		URL
Accuracy and precision of smartphone applications and commercially available motion sensors in multiple sclerosis details	Balto, J.M., Kinnett-Hopkins, D.L. and Motl, R.W.	2016	Multiple Sclerosis Journal - Experimental, Translational and Clinical	Validation	One Flex	Patients Adults	Steps	URL
Use of a Wearable Activity Device in Rural Older Obese Adults: A Pilot Study details	Batsis, J.A., Naslund, J.A., Gill, L.E., Masutani, R.K., Agarwal, N. and Bartels, S.J.	2016	Gerontology and Geriatric Medicine	Usability	Zip	Older Adults	Steps	URL
Using a Community Wellness Program Pairing People with Shelter Dogs to Increase Physical Activity details	Becofsky, K., Lynch, L., Evans, E.W. and Wing, R.	2016	Medicine & Science in Sports & Exercise	Intervention	Flex	Adults	Steps Intensity	URL
Comparative analysis of actigraphy performance in healthy young subjects details	Bellone, G.J., Plano, S.A., Cardinali, D.P., Chada, D.P., Vigo, D.E. and Golombek, D.A.	2016	Sleep Science	Validation	Flex	Adults	Steps	URL
Development of a Mobile Phone-Based Weight Loss Lifestyle Intervention for Filipino Americans with Type 2 Diabetes: Protocol and Early Results From the PilAm Go4Health Randomized Controlled Trial details	Bender, M.S., Santos, G.-M., Villanueva, C. and Arai, S.	2016	JMIR Research Protocols	Intervention	Zip	Patients Adults	Steps Intensity Weight Food	URL
Evaluation of pedometry as a patient-centered outcome in patients undergoing hematopoietic cell transplant (HCT): a comparison of pedometry and patient reports of symptoms, health, and quality of life details	Bennett, A.V., Reeve, B.B., Basch, E.M., Mitchell, S.A., Meeneghan, M., Battaglini, C.L., Smith-Ryan, A.E., Phillips, B., Shea, T.C. and Wood, W.A.	2016	Quality of Life Research	Measurement	Flex	Patients Adults	Steps	URL
Video and computer-based interactive exercises are safe and improve task-specific balance in geriatric and neurological rehabilitation: a randomised trial details	van den Berg, M., Sherrington, C., Killington, M., Smith, S., Bongers, B., Hassett, L. and Crotty, M.	2016	Journal of Physiotherapy	Intervention	Zip	Older Adults Patients	Steps Energy Expenditure Distance	URL
Comparison of three different physiological wristband sensor systems and their applicability for resilience- and work load monitoring details	Binsch, O., Wabeke, T. and Valk, P.	2016	2016 IEEE 13th International Conference on Wearable and Implantable Body Sensor Networks	Validation	Surge	Adults	Steps Distance Heart Rate	URL
Continuous daily assessment of multiple sclerosis disability using remote step count monitoring details	Block, V.J., Lizée, A., Crabtree-Hartman, E., Bevan, C.J., Graves, J.S., Bove, R., Green, A.J., Nourbakhsh, B., Tremblay, M., Gourraud, P.-A., Ng, M.Y., Pletcher, M.J., Olgin, J.E., Marcus, G.M., Allen, D.D., Cree, B.A.C. and Gelfand, J.M.	2016	Journal of Neurology	Usability Measurement	Flex	Patients Adults	Steps	URL
Concurrent Validity of Wearable Activity Trackers 1 in Free-living Conditions. details	Brooke, S., An, H.-S., Kang, S.-K., Noble, J., Berg, K. and Lee, J.-M.	2016	Journal of Strength and Conditioning Research	Validation	Flex Charge HR	Adults	Energy Expenditure Sleep	URL
Accuracy of the Fitbit for Measuring Preschoolers' Physical Activity details	Byun, W., Barry, A. and Lee, J.-M.	2016	Medicine & Science in Sports & Exercise	Validation	Flex	Children	Intensity	URL
Fitbit Provides Valid Measures of Sedentary and Physical Activity in Preschool Children details	Byun, W., Barry, A., Lee, J.-M. and Kim, Y.	2016	Circulation	Validation	Flex	Children	Intensity	URL
A Study to Determine the Most Popular Lifestyle Smartphone Applications and Willingness of the Public to Share Their Personal Data for Health Research. details	Chen, J., Bauman, A. and Allman-Farinelli, M.	2016	Telemedicine and e-Health	Survey Research		Adults	NA	URL
Peer Influence on the Engagement of Fitness Tracker Usage: A Diabetic and Obesity Study details	Chen, Y., Chen, Y., Randriambelonoro, M., Geissbuhler, A. and Pu, P.	2016	IEEE International Conference on Healthcare Informatics (ICHI)	Intervention	One	Patients Adults	Steps	URL
Accuracy of Wristband Activity Monitors during Ambulation and Activities. details	Chen, M.-D., Kuo, C.-C., Pellegrini, C.A. and Hsu, M.-J.	2016	Medicine & Science in Sports & Exercise	Validation	Flex	Adults	Steps	URL
Social Incentives in Pervasive Fitness Apps for Obese and Diabetic patients details	Chen, Y., Randriambelonoro, M.E., Geissbuhler, A. and Pu, P.	2016	Proceedings of the 19th ACM Conference on Computer Supported Cooperative Work and Social Computing Companion	Intervention	One	Patients Adults	Steps	URL
Validating The Latest Commercial Physical Activity Monitors details	Chow, J.J., Ward, R.E., Thom, J.M. and Parmenter, B.J.	2016	Medicine & Science in Sports & Exercise	Validation	One Flex Charge Charge HR	Adults	Intensity	URL
Integrating Population-based Patterns with Personal Routine to Re-engage Fitbit Usesh details	Chung, C.-F. and Danis, C.	2016	Proceedings of the 10th EAI International Conference on Pervasive Computing Technologies for Healthcare	Usability		Adults	Steps Goals	URL
Tweeting to Health: A Novel mHealth Intervention Using Fitbits and Twitter to Foster Healthy Lifestyles details	Chung, A.E., Skinner, A.C., Hasty, S.E. and Perrin, E.M.	2016	Clinical Pediatrics	Intervention	Zip Aria	Adults	Steps Intensity Weight	URL
Gender Differences in Regional Brain Activity in Patients with Chronic Primary Insomnia: Evidence from a Resting-State fMRI Study details	Dai, X.-J., Nie, X., Liu, X., Pei, L., Jiang, J., Peng, D.-c., Gong, H.-H., Zeng, X.-J., Wáng, Y.-X.J. and Zhan, Y.	2016	Journal of Clinical Sleep Medicine	Measurement	Flex	Patients Adults	Sleep	URL
Feasibility and acceptability of remotely monitored pedometer-guided physical activity. details	Darvall, J.N., Parker, A. and Story, D.A.	2016	Anaesthesia and Intensive Care	Usability	Zip	Adults	Steps	URL
A Study Analyzing the Effect of Using Accelerometer in a Lifestyle Intervention Program details	Das, S., Rouseff, M., Guzman, H.E., Pino, A., Agatston, A., Shafer, et al.	2016	Circulation	Intervention		Adults	NA	URL
Men on the Move-Nashville: Feasibility and Acceptability of a Technology-Enhanced Physical Activity Pilot Intervention for Overweight and Obese Middle and Older Age African American Men. details	Dean, D.A.L., Griffith, D.M., McKissic, S.A., Cornish, E.K. and Johnson-Lawrence, V.	2016	American Journal of Men's Health	Intervention	Flex	Adults	Steps Intensity	URL
Assessment, Assurance, and Actuation: The Use of Activity Trackers to Monitor Physical Activity in a Pediatric Population with Congenital Heart Disease details	Dean, P., Voss, C., De Souza, A. and Harris, K.	2016	Canadian Journal of Cardiology	Medical Intervention	Charge HR	Adolescents	Steps Intensity	URL
Validity and inter-device reliability of dominant and non-dominant wrist worn activity trackers in suburban walking details	De Man, M., Vanderploeg, E., Aimers, N., MacMahon, C., Wise, L. and Parrington, L.	2016	Sensoria: A Journal of Mind, Brain & Culture	Validation	Charge HR	Adults	Steps	URL
Accuracy of Wrist and Hip-worn Commercial Physical Activity Monitors In Free Living Conditions details	Deyarmin, K.N., Snyder, K.M., Mihalik, A.G. and Hargens, T.A.	2016	Medicine & Science in Sports & Exercise	Validation	One Charge	Older Adults Adolescents	Steps	URL
Shaffer, J.A., Ma, Y., Goldsmith, J., Schwartz, J.E. and Davidson, K.W. Validation of the Fitbit One® for physical activity measurement at an upper torso attachment site details	Diaz, K.M., Krupka, D.J., Chang, M.J., Shaffer, J.A., Ma, Y., Goldsmith, J., Schwartz, J.E. and Davidson, K.W.	2016	BMC Research Notes	Validation	One Flex	Adults	Steps Energy Expenditure	URL
A practical validation study of a commercial accelerometer using good and poor sleepers details	Dickinson, D.L., Cazier, J. and Cech, T.	2016	Health Psychology Open	Validation	Charge HR	Adults	Sleep	URL
Physical Activity Assessment Between Consumer- and Research-Grade Accelerometers: A Comparative Study in Free-Living Conditions details	Dominick, M.G., Winfree, N.K., Pohlig, T.R. and Papas, A.M.	2016	JMIR Mhealth Uhealth	Validation	Flex	Adults	Steps Energy Expenditure Intensity	URL
Comparative accuracy of fitness tracking modalities in quantifying energy expenditure details	Dondzila, C. and Garner, D.	2016	Journal of Medical Engineering & Technology	Validation	Charge	Adults	Energy Expenditure	URL
Weight management for adolescents with intellectual and developmental disabilities: Rationale and design for an 18month randomized trial details	Donnelly, J., Ptomey, L., Goetz, J., Sullivan, D., Gibson, C., Greene, J., Lee, R., Mayo, M., Honas, J. and Washburn, R.	2016	Contemporary Clinical Trials	Methods	Charge HR	Adolescents	Steps	URL
Balanced: a randomised trial examining the efficacy of two self-monitoring methods for an app-based multi-behaviour intervention to improve physical activity, sitting and sleep in adults. details	Duncan, M.J., Vandelanotte, C., Trost, S.G., Rebar, A.L., Rogers, N., Burton, N.W., Murawski, B., Rayward, A., Fenton, S. and Brown, W.J.	2016	BMC Public Health	Methods	Charge HR	Adults	Steps Sleep	URL
Behaviour change techniques and physical activity using the fitbit flex details	Dunn, E. and Robertson-Wilson, D.J.	2016	Journal of Exercise, Movement, and Sport	Measurement Survey Research	Flex	Adults	Steps	URL
Acceptability of mHealth Technology for Self-Monitoring Eating and Activity among Rural Men. details	Eisenhauer, C.M., Hageman, P.A., Rowland, S., Becker, B.J., Barnason, S.A. and Pullen, C.H.	2016	Public Health Nursing	Usability	One	Adults	Steps Food	URL
Reconsidering the device in the drawer: lapses as a design opportunity in personal informatics details	Epstein, D.A., Kang, J.H., Pina, L.R., Fogarty, J. and Munson, S.A.	2016	Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing	Usability		Adults	Steps	URL
Use of Fitness Bands by Teachers in the Classroom details	Ertzberger, J. and Martin, F.	2016	TechTrends	Usability	Flex	Adults	NA	URL
Quantifying pilot contribution to flight safety for normal and non-normal airline operations details	Etherington, T.J., Kramer, L.J., Bailey, R.E., Kennedy, K.D. and Stephens, C.L.	2016	2016 IEEE/AIAA 35th Digital Avionics Systems Conference (DASC)	Measurement	Charge HR	Adults	Heart Rate	URL
The Effect of a Fitbit Accelerometer on Physical Activity details	Farnell, G. and Westrup, R.	2016	Medicine & Science in Sports & Exercise	Measurement		Adults	Steps	URL
Effectiveness of activity trackers with and without incentives to increase physical activity (TRIPPA): a randomised controlled trial details	Finkelstein, E.A., Haaland, B.A., Bilger, M., Sahasranaman, A., Sloan, R.A., Nang, E.E.K. and Evenson, K.R.	2016	The Lancet Diabetes & Endocrinology	Intervention	Zip	Adults	Steps	URL
Validation of Consumer-Based Hip and Wrist Activity Monitors in Older Adults With Varied Ambulatory Abilities. details	Floegel, T.A., Florez-Pregonero, A., Hekler, E.B. and Buman, M.P.	2016	The Journals of Gerontology. Series A, Biological Sciences and Medical Sciences	Validation	One Flex	Older Adults	Steps	URL
Program Design for Healthy Weight in Lesbian and Bisexual Women: A Ten-City Prevention Initiative details	Fogel, S.C., McElroy, J.A., Garbers, S., McDonnell, C., Brooks, J., Eliason, M.J., Ingraham, N., Osborn, A., Rayyes, N., Redman, S.D., Wood, S.F. and Haynes, S.G.	2016	Women's Health Issues	Methods	Zip	Adults	Steps	URL
Reliability and Validity of Ten Consumer Activity Trackers Depend on Walking Speed details	Fokkema, T., Kooiman, T.J., Krijnen, W.P., van der Schans, C.P. and de Groot, M.	2016	Medicine & Science in Sports & Exercise	Validation	Charge HR	Adults	Steps	URL
Validation of the Fitbit Charge Heart Rate Device to Monitor Physical Activity in Children with Congenital Heart Disease details	Gardner, R., Voss, C., Dean, P. and Harris, K.	2016	Canadian Journal of Cardiology	Medical Validation	Charge HR	Patients Children Adolescents	Steps	URL
Efficacy, feasibility, and acceptability of a novel technology-based intervention to support physical activity in cancer survivors details	Gell, N.M., Grover, K.W., Humble, M., Sexton, M. and Dittus, K.	2016	Supportive Care in Cancer	Intervention	Charge HR	Patients Adults	Steps Intensity	URL
Impact of a Digital Activity Tracker-Based Workplace Activity Program on Health and Wellbeing details	Glance, D.G., Ooi, E., Berman, Y., Glance, C.F. and Barrett, H.R.	2016	Proceedings of the 6th International Conference on Digital Health Conference	Measurement		Adults	Steps	URL
Estimating Physical Activity and Sedentary Behavior in a Free-Living Context: A Pragmatic Comparison of Consumer-Based Activity Trackers and ActiGraph Accelerometry details	Gomersall, S.R., Ng, N., Burton, N.W., Pavey, T.G., Gilson, N.D. and Brown, W.J.	2016	Journal of Medical Internet Research	Validation	One	Adults	Steps Intensity	URL
Implementation of the physical activity guidelines in lung cancer: The CAPACITY study details	Granger, C., Irving, L., Antippa, P., Edbrooke, L., Parry, S., Lin, K.-Y., Eriksen, A., Martin, J., Steinfort, D. and Denehy, L.	2016	European Respiratory Journal	Intervention		Patients Adults	Intensity	URL
Does Non-Running Physical Activity Contribute to the Risk of Developing a Running Related Overuse Injury? details	Gruber, A.H., Murphy, S.P., Vollmar, J.E., Kennedy-Armbruster, C. and Chomistek, A.K.	2016	Medicine & Science in Sports & Exercise	Measurement	Charge	Adults	Steps Intensity	URL
A Leisure-Time Physical Activity Intervention for Adolescents With Visual Impairments details	Haegele, J.A. and Porretta, D.	2016	Research Quarterly for Exercise and Sport	Intervention	Zip	Adolescents	Steps	URL
Integrating physical activity data in videogames with user-centered dashboards details	Hagood, D., Ching, C.C. and Schaefer, S.	2016	Proceedings of the Sixth International Conference on Learning Analytics & Knowledge	Intervention	Zip	Adolescents	Steps	URL
Quantifying Physical Activity Levels and Sleep in Hemodialysis Patients Using a Commercially Available Activity Tracker details	Han, M., Williams, S., Mendoza, M., Ye, X., Zhang, H., Calice-Silva, V., Thijssen, S., Kotanko, P. and Meyring-Wösten, A.	2016	Blood Purification	Intervention Measurement	Flex	Patients Adults	Steps Distance Sleep	URL
Accuracy of Commercial Activity Trackers to Measure Energy Expenditure During a Controlled Exercise Trial details	Hargens, T.A., Deyarmin, K.N., Snyder, K.M., Mihalik, A.G. and Sharpe, L.E.	2016	Medicine & Science in Sports & Exercise	Validation	One Charge	Adults	Energy Expenditure	URL
Technology- and Phone-Based Weight Loss Intervention details	Hartman, S.J., Nelson, S.H., Cadmus-Bertram, L.A., Patterson, R.E., Parker, B.A. and Pierce, J.P.	2016	American Journal of Preventive Medicine	Intervention	One	Patients Adults	Steps	URL
Effect of affordable technology on physical activity levels and mobility outcomes in rehabilitation: a protocol for the Activity and MObility UsiNg Technology (AMOUNT) rehabilitation trial. details	Hassett, L., van den Berg, M., Lindley, R.I., Crotty, M., McCluskey, A., van der Ploeg, H.P., Smith, S.T., Schurr, K., Killington, M., Bongers, B., Howard, K., Heritier, S., Togher, L., Hackett, M., Treacy, D., Dorsch, S., Wong, S., Scrivener, K., Chagpar, S., Weber, H., Pearson, R. and Sherrington, C.	2016	BMJ Open	Methods		Patients Adults	Steps	URL
Utility of Consumer Physical Activity Trackers as an Intervention Tool in Cardiovascular Disease Prevention and Treatment details	Hickey, A.M. and Freedson, P.S.	2016	Progress in Cardiovascular Diseases	Review	Classic Ultra One Zip Flex	NA	NA	URL
Individual Differences in Diurnal Preference and Time-of-Exercise Interact to Predict Exercise Frequency details	Hisler, G.C., Phillips, A.L. and Krizan, Z.	2016	Annals of Behavioral Medicine	Measurement	Zip	Adults	Steps Intensity	URL
Promote Students’ Healthy Behavior Through Sensor and Game: A Randomized Controlled Trial details	H-Jennings, F., Clément, M.-V., Brown, M., Leong, B., Shen, L. and Dong, C.	2016	Medical Science Educator	Intervention	Flex	Adults	Steps Sleep	URL
Use of a Fitness Tracker to Promote Physical Activity in Children With Acute Lymphoblastic Leukemia details	Hooke, M.C., Gilchrist, L., Tanner, L., Hart, N. and Withycombe, J.S.	2016	Pediatric Blood & Cancer	Intervention	One	Patients Children	Steps	URL
Validity of FitBit, Jawbone UP, Nike+ and other wearable devices for level and stair walking details	Huang, Y., Xu, J., Yu, B. and Shull, P.B.	2016	Gait & Posture	Validation	One Zip Flex	Adults	Steps Distance	URL
Trajectories of objectively-measured physical activity and sedentary time over the course of pregnancy in women self-identified as inactive details	Huberty, J.L., Buman, M.P., Leiferman, J.A., Bushar, J. and Adams, M.A.	2016	Preventive Medicine Reports	Measurement	Flex	Adults Pregnant Women	Steps Intensity	URL
Dose and timing of text messages for increasing physical activity among pregnant women: a randomized controlled trial details	Huberty, J.L., Buman, M.P., Leiferman, J.A., Bushar, J., Hekler, E.B. and Adams, M.A.	2016	Translational Behavioral Medicine	Intervention	Flex	Pregnant Women	Steps	URL
Can a Home-based Cardiac Rehabilitation Program Improve the Physical Function Quality of Life in Children With Fontan Circulation? details	Jacobsen, R.M., Danduran, M., Mussatto, K., Neubauer, J. and Ginde, S.	2016	Congenital Heart Disease	Intervention Measurement	Flex	Patients Adults	Steps Intensity Distance	URL
A Pilot Comparison of a Smartphone App with or without 2-Way Messaging Among Chronic Pain Patients details	Jamison, R.N., Jurcik, D.C., Edwards, R.R., Huang, C.-C. and Ross, E.L.	2016	The Clinical Journal of Pain	Measurement	Zip	Patients Adults	Steps	URL
Effects of an mHealth Adaptive Intervention on Vascular Health and Cardiorespiratory Fitness details	Jarrett, C.L., Tucker, W.J., Hurley, J.C., Todd, M., Bhuiyan, N., Fernandez, J.A., Adams, M.A. and Angadi, S.S.	2016	Circulation	Intervention	Zip	Adults	Steps	URL
UWALK: the development of a multi-strategy, community-wide physical activity program. details	Jennings, C.A., Berry, T.R., Carson, V., Culos-Reed, S.N., Duncan, M.J., Loitz, C.C., McCormack, G.R., McHugh, T.-L.F., Spence, J.C., Vallance, J.K. and Mummery, W.K.	2016	Translational Behavioral Medicine	Methods		Adults	NA	URL
Validation of Biofeedback Wearables for Photoplethysmographic Heart Rate Tracking details	Jo, E., Lewis, K., Directo, D., Kim, M.J. and Dolezal, B.A.	2016	Journal of Sports Science & Medicine	Validation	Charge HR	Adults	Heart Rate	URL
Feasibility and preliminary results from utilizing a Fitbit device in a pediatric pain rehabilitation program details	Junghans-Rutelonis, A., Gephardt, J., Skipper, R., Timm, W. and Weiss, K.	2016	The Journal of Pain	Measurement		Children Adolescents	Steps Distance Sleep	URL
A comparison of wearable fitness devices. details	Kaewkannate, K. and Kim, S.	2016	BMC Public Health	Usability	Flex	Adults	NA	URl
An Exercise Intervention for South Asian Mothers with Risk Factors for Diabetes details	Kandula, Namratha R.; Dave, Swapna S.; De Chavez, Peter John D.; Marquez, David X.; Bharucha, Himali; Mammen, Shinu M.; Dunaif, Andrea; Ackermann, Ronald T.; Kumar, Santosh; Siddique, J.	2016	Translational Journal of the American College of Sports Medicine	Intervention	Zip	Adults	Steps	URL
Wellbeing in the Making: Peoples' Experiences with Wearable Activity Trackers. details	Karapanos, E., Gouveia, R., Hassenzahl, M. and Forlizzi, J.	2016	Psychology of Well-being	Usability	Flex	Adults	NA	URL
Validity of Wearable Fitness Trackers on Sleep Measure details	Keill, A.K., An, H.-S., Dinkel, D.M. and Lee, J.-M.	2016	Medicine & Science in Sports & Exercise	Validation	Charge HR	Adults	Sleep	URL
An exercise in nostalgia: Nostalgia promotes health optimism and physical activity details	Kersten, M., Cox, C.R. and Enkevort, E.A.V.	2016	Psychology & Health	Measurement	One	Adults	Steps	URL
SleepSight: a wearables-based relapse prevention system for schizophrenia details	Kerz, M., Folarin, A., Meyer, N., Begale, M., MacCabe, J. and Dobson, R.J.	2016	Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing	Usability	Charge HR	Patients Adults	Sleep	UR
MOVE MORE, SIT LESS, AND BE WELL: Behavioral Aspects of Activity Trackers details	Kiessling, B.I.I. and Kennedy-Armbruster, C.	2016	ACSM's Health & Fitness Journal	Usability	Flex	Adults	NA	URL
"Stepping Up" Activity poststroke: Ankle-Positioned Accelerometer Can Accurately Record Steps During Slow Walking details	Klassen, T.D., Simpson, L.A., Lim, S.B., Louie, D.R., Parappilly, B., Sakakibara, B.M., Zbogar, D. and Eng, J.J.	2016	Physical Therapy	Validation	One	Older Adults Patients	Steps	URl
The Volunteering-in-Place (VIP) Program: Providing meaningful volunteer activity to residents in assisted living with mild cognitive impairment details	Klinedinst, N.J. and Resnick, B.	2016	Geriatric Nursing	Measurement	Flex	Older Adults	Steps	URL
Consumer sleep tracking devices: a review of mechanisms, validity and utility details	Kolla, B.P., Mansukhani, S. and Mansukhani, M.P.	2016	Expert Review of Medical Devices	Review	Classic Ultra	NA	Sleep	URL
Effects of Charitable Versus Monetary Incentives on the Acceptance of and Adherence to a Pedometer-Based Health Intervention: Study Protocol and Baseline Characteristics of a Cluster-Randomized Controlled Trial details	Kowatsch, T., Kramer, J.-N., Kehr, F., Wahle, F., Elser, N. and Fleisch, E.	2016	JMIR Research Protocols	Methods		Adults	Steps	URL
Accuracy of a Wrist-Worn Wearable Device for Monitoring Heart Rates in Hospital Inpatients: A Prospective Observational Study details	Kroll, R.R., Boyd, G.J. and Maslove, M.D.	2016	Journal of Medical Internet Research	Medical Validation	Charge HR	Patients Adults	Heart Rate	URL
Sedentary Behavior in the Workplace: A Potential Occupational Hazard for Radiologists details	Lamar, D.L., Chou, S.-H.S., Medverd, J.R. and Swanson, J.O.	2016	Current Problems in Diagnostic Radiology	Measurement	One	Adults	Steps Intensity	URL
A home-based physical activity intervention using activity trackers in survivors of childhood cancer: A pilot study details	Le, A., Mitchell, H.-R., Zheng, D.J., Rotatori, J., Fahey, J.T., Ness, K.K. and Kadan-Lottick, N.S.	2016	Pediatric Blood & Cancer	Intervention	One	Patients Adults Adolescents	NA	URL
Examining The Validity Of Fitbit Charge HR For Measuring Heart Rate In Free-living Conditions details	Lee, J.-M.M., An, H., Kang, S.-k., Kim, Y. and Dinkel, D.	2016	Medicine & Science in Sports & Exercise	Validation	Charge HR	Adults	Heart Rate	URL
Evidence of Convergent Validity for Measuring Free-Living Walking Using Wearable Devices details	Lee, M., Choi, J.-Y., Lee, K., Min, J., Kim, J. and Choi, M.C.	2016	Medicine & Science in Sports & Exercise	Validation	One Charge	Adults	Steps	URL
Appropriating Quantified Self Technologies to Support Elementary Statistical Teaching and Learning details	Lee, V., Drake, J. and Thayne, J.	2016	IEEE Transactions on Learning Technologies	Usability Measurement	Ultra One	Children	Steps	URL
Validation and Accuracy of FITBIT Charge: A Pilot Study in a University Worksite Walking Program details	Leininger, L.J., Cook, B.J. and Adams, K.J.	2016	Journal of Fitness Research	Validation	Charge	Adults	Steps Intensity	URl
Validation And Accuracy Of Fitbit Charge details	Leininger, L.J., Cook, B.J., Jones, V., Bellumori, M. and Adams, K.J.	2016	Medicine & Science in Sports & Exercise	Validation	Charge	Adults	Steps Intensity	URL
A Personalized Approach for Detecting Unusual Sleep from Time Series Sleep-Tracking Data details	Liang, Z.,Martell, M.A.C., and Nishimura T.	2016	2016 IEEE International Conference on Healthcare Informatics (ICHI)	Methods	Charge HR	Adults	Sleep	URL
Sleep Tracking in the Real World: A Qualitative Study into Barriers for Improving Sleep details	Liang, Z. and Ploderer, B.	2016	Proceedings of the 28th Australian Conference on Computer-Human Interaction	Usability		Adults	Sleep	URL
SleepExplorer: a visualization tool to make sense of correlations between personal sleep data and contextual factors details	Liang, Z., Ploderer, B., Liu, W., Nagata, Y., Bailey, J., Kulik, L. and Li, Y.	2016	Personal and Ubiquitous Computing	Usability Case Study	Flex Charge Charge HR	Adults	Steps Energy Expenditure Intensity Distance Sleep	URL
Use of Fitbit Charge HR for Management of Post-Concussive Syndrome: A Case Report details	Lindsay, L.R. and Chae, H.	2016	PM&R	Medical Case Study	Charge HR	Patients Adults	Sleep Heart Rate	URL
Pedometry to Prevent Cardiorespiratory Fitness Decline—Is it Effective? details	Lystrup, R.M., West, G.F., Olsen, C., Ward, M. and Stephens, M.B.	2016	Military Medicine	Measurement	Zip	Adults	Steps	URL
Mission Possible: Using Ubiquitous Social Goal Sharing Technology to Promote Physical Activity in Children details	Mackintosh, K.	2016	Movement, Health & Exercise	Intervention	Zip	Children	Steps Intensity	URL
Toward Accessible Health and Fitness Tracking for People with Mobility Impairments details	Malu, M. and Findlater, L.	2016	Proceedings of the 10th EAI International Conference on Pervasive Computing Technologies for Healthcare	Usability	One	Patients Adults	NA	URL
Reliability of Sleep Measures from Four Personal Health Monitoring Devices Compared to Research-Based Actigraphy and Polysomnography details	Mantua, J., Gravel, N. and Spencer, R.	2016	Sensors	Validation	Flex	Adults	Sleep	URL
Workplace Indicators of Mood: Behavioral and Cognitive Correlates of Mood Among Information Workers details	Mark, G., Czerwinski, M., Iqbal, S. and Johns, P.	2016	Proceedings of the 6th International Conference on Digital Health Conference	Measurement	Flex	Adults	Steps Sleep	URL
Older Adults' Experiences Using a Commercially Available Monitor to Self-Track Their Physical Activity. details	McMahon, S.K., Lewis, B., Oakes, M., Guan, W., Wyman, J.F. and Rothman, A.J.	2016	JMIR mHealth and uHealth	Usability	One	Older Adults	NA	URL
Acceptance of Commercially Available Wearable Activity Trackers Among Adults Aged Over 50 and With Chronic Illness: A Mixed-Methods Evaluation. details	Mercer, K., Giangregorio, L., Schneider, E., Chilana, P., Li, M. and Grindrod, K.	2016	JMIR mHealth and uHealth	Usability	Zip	Older Adults Patients	NA	URL
Behavior Change Techniques Present in Wearable Activity Trackers: A Critical Analysis details	Mercer, K., Li, M., Giangregorio, L., Burns, C. and Grindrod, K.	2016	JMIR mHealth and uHealth	Review	Flex	NA	NA	URL
Statistical methods for detecting groups of patterns in daily step count activity profiles details	Meyer, E., Tran, T. and Greenwood, M.	2016	Skyline - The Big Sky Undergraduate Journal	Methods	Flex	Adults	Steps	URL
Physical Activity Measured with Accelerometer and Self-Rated Disability in Lumbar Spine Surgery: A Prospective Study. details	Mobbs, R.J., Phan, K., Maharaj, M. and Rao, P.J.	2016	Global Spine Journal	Measurement	Zip	Patients Adults	Steps Energy Expenditure Distance	URL
Consumer-based Physical Activity Monitors and Research-grade Accelerometers Underestimate Physical Activity in a Semi-structured Setting details	Montoye, A.H., Tuttle, M.S., Nelson, M.B., Steeves, J.A. and Kaminsky, L.A.	2016	Circulation	Validation	One Zip Flex	Adults	Steps Energy Expenditure	URL
Accuracy of Wearable Devices for Estimating Total Energy Expenditure details	Murakami, H., Kawakami, R., Nakae, S., Nakata, Y., Ishikawa-Takata, K., Tanaka, S. and Miyachi, M.	2016	JAMA Internal Medicine	Validation	Flex	Adults	Energy Expenditure	URL
Wearable Devices and Smartphones for Activity Tracking Among People with Serious Mental Illness. details	Naslund, J.A., Aschbrenner, K.A. and Bartels, S.J.	2016	Mental Health and Physical Activity	Usability	Zip	Patients Adults	NA	URL
Wearable devices and mobile technologies for supporting behavioral weight loss among people with serious mental illness details	Naslund, J.A., Aschbrenner, K.A., Scherer, E.A., McHugo, G.J., Marsch, L.A. and Bartels, S.J.	2016	Psychiatry Research	Intervention	Zip	Patients Adults	Steps	URL
Validity of Consumer-Based Physical Activity Monitors for Specific Activity Types. details	Nelson, M.B., Kaminsky, L.A., Dickin, D.C. and Montoye, A.H.K.	2016	Medicine and Science in Sports and Exercise	Validation	One Zip Flex	Adults	Steps Energy Expenditure	URL
Physical activity in acute ischemic stroke patients during hospitalization details	Nozoe, M., Kitamura, Y., Kanai, M., Kubo, H., Mase, K. and Shimada, S.	2016	International Journal of Cardiology	Measurement	One	Adults	Steps Intensity Distance	URL
Evaluating a community-based exercise intervention with adults living with HIV: protocol for an interrupted time series study details	O'Brien, K.K., Bayoumi, A.M., Solomon, P., Tang, A., Murzin, K., Chan Carusone, S., Zobeiry, M., Nayar, A. and Davis, A.M.	2016	BMJ Open	Intervention	Zip	Patients Adults	Steps Energy Expenditure Intensity Distance	URL
These Shoes Are Made for Walking: Sensitivity Performance Evaluation of Commercial Activity Monitors under the Expected Conditions and Circumstances Required to Achieve the International Daily Step Goal of 10,000 Steps. details	O'Connell, S., ÓLaighin, G., Kelly, L., Murphy, E., Beirne, S., Burke, N., Kilgannon, O. and Quinlan, L.R.	2016	PLOS ONE	Validation	One	Adults	Steps	URL
Workstations To Increase Workplace Physical Activity And Reduce Sitting Time details	Olenick, A., Jason, C., Schafer, M.A., Battogtokh, Z., Lyons, S.T., Brown, J. and Falls, D.	2016	Medicine & Science in Sports & Exercise	Intervention	One	Adults	Steps	URL
ActiviTeen: A Protocol for Deployment of a Consumer Wearable Device in an Academic Setting details	Ortiz, A.M., Tueller, S.J., Cook, S.L. and Furberg, R.D.	2016	JMIR Research Protocols	Methods	Zip	Adolescents	Steps Intensity	URL
A Comparison Study Of The Fitbit Activity Monitor And PSG For Assessing Sleep Patterns And Movement In Children details	Osterbauer, B., Koempel, J.A., Davidson Ward, S.L., Fisher, L.M. and Don, D.M.	2016	Journal of Otolaryngology Advances	Validation	Flex	Children	Sleep	URL
Using High-Tech and High-Touch Methods for Effective Population Health Management details	Padarthy, S., Crespo, C., Rich, K. and Srivatsan, N.	2016	Iproceedings	Medical Measurement		Adults	Steps	URL
Retrofit Weight-Loss Outcomes at 6, 12, and 24 Months and Characteristics of 12-Month High Performers: A Retrospective Analysis details	Painter, S., Ditsch, G., Ahmed, R., Hanson, N.B., Kachin, K. and Berger, J.	2016	JMIR mHealth and uHealth	Intervention	One Aria	Adults	Weight	URL
Smartwomantm: Feasibility of a Mobile Application to Improve Cardiovascular Risk Factors in Indigent Diabetic Women details	Parashar, S., Williams, O. and Wenger, N.	2016	Circulation: Cardiovascular Quality and Outcomes	Usability		Patients Adults	Steps	URL
Long-Term Objective Physical Activity Measurements using a Wireless Accelerometer Following Minimally Invasive Transforaminal Interbody Fusion Surgery details	Phan, K., Phan, K. and Mobbs, R.J.	2016	Asian Spine Journal	Case Study		Patients Adults	Steps Distance	URL
Intrinsic rewards predict exercise via behavioral intentions for initiators but via habit strength for maintainers details	Phillips, L.A., Chamberland, P.-É., Hekler, E.B., Abrams, J. and Eisenberg, M.H.	2016	Sport, Exercise, and Performance Psychology	Measurement	Zip	Adults	Steps	URL
Self-management of chronic illness: the role of 'habit' versus reflective factors in exercise and medication adherence. details	Phillips, L.A., Cohen, J., Burns, E., Abrams, J. and Renninger, S.	2016	Journal of Behavioral Medicine	Measurement	Zip	Patients Adults	Steps	URL
Exploring Path Models of Disablement in Residential Care and Assisted Living Residents details	Phillips, L.J., Petroski, G.F., Conn, V.S., Brown, M., Leary, E., Teri, L. and Zimmerman, S.	2016	Journal of Applied Gerontology	Measurement	Classic	Older Adults	Steps
Adherent Use of Digital Health Trackers Is Associated with Weight Loss details	Pourzanjani, A., Quisel, T. and Foschini, L.	2016	PLOS ONE	Measurement		Adults	Steps Weight Food	URL
Validation of the Fitbit One, Garmin Vivofit and Jawbone UP activity tracker in estimation of energy expenditure during treadmill walking and running details	Price, K., Bird, S.R., Lythgo, N., Raj, I.S., Wong, J.Y.L. and Lynch, C.	2016	Journal of Medical Engineering & Technology	Validation	One	Adults	Energy Expenditure	URL
Can Commercially Available Pedometers Be Used For Physical Activity Monitoring In Patients With COPD Following Exacerbations? details	Prieto-Centurion, V., Bracken, N., Norwick, L., Zaidi, F., Mutso, A.A., Morken, V., Coultas, D.B., Rand, C.S., Marquez, D.X. and Krishnan, J.A.	2016	Chronic Obstructive Pulmonary Diseases: Journal of the COPD Foundation	Validation	Zip Force	Patients Adults	Steps	URL
Parents' perspective on weight management interventions for adolescents with intellectual and developmental disabilities details	Ptomey, L.T., Gibson, C.A., Willis, E.A., Taylor, J.M., Goetz, J.R., Sullivan, D.K. and Donnelly, J.E.	2016	Disability and Health Journal	Usability		Children Adolescents	Steps	URL
Experiences measuring sleep and physical activity patterns across a large college cohort with fitbits details	Purta, R., Mattingly, S., Song, L., Lizardo, O., Hachen, D., Poellabauer, C. and Striegel, A.	2016	Proceedings of the 2016 ACM International Symposium on Wearable Computers	Measurement	Charge HR	Adults	Steps Sleep Heart Rate	URL
Pilot and Feasibility Test of a Mobile Health-Supported Behavioral Counseling Intervention for Weight Management Among Breast Cancer Survivors details	Quintiliani, L.M., Mann, D.M., Puputti, M., Quinn, E. and Bowen, D.J.	2016	JMIR Cancer	Intervention	Flex	Patients Adults	NA	URL
Physical activity levels and perceived benefits and barriers to physical activity in HIV-infected women living in the deep south of the United States. details	Rehm, K.E. and Konkle-Parker, D.	2016	AIDS Care	Measurement		Patients Adults	Steps Intensity	URL
Unravelling the potential mechanisms behind hospitalization-associated disability in older patients; the Hospital-Associated Disability and impact on daily Life (Hospital-ADL) cohort study protocol. details	Reichardt, L.A., Aarden, J.J., van Seben, R., van der Schaaf, M., Engelbert, R.H.H., Bosch, J.A., Buurman, B.M.	2016	BMC Geriatrics	Methods	Flex	Older Adults	Steps	URL
Validity and reliability of Fitbit activity monitors compared to ActiGraph GT3X+ with female adults in a free-living environment details	Reid, R.E., Insogna, J.A., Carver, T.E., Comptour, A.M., Bewski, N.A., Sciortino, C. and Andersen, R.E.	2016	Journal of Science and Medicine in Sport	Validation	One Flex	Adults	Steps Intensity	URL
Feasibility and Effectiveness of Using Wearable Activity Trackers in Youth: A Systematic Review details	Ridgers, N.D., McNarry, M.A. and Mackintosh, K.A.	2016	JMIR mHealth and uHealth	Review		Children Adolescents		URL
Objective Measure of Activity Level After Total Knee Arthoplasty with The Use of the 'Fitbit Device details	Roe, J., Salmon, L. and Twiggs, J.	2016	Orthopaedic Journal of Sports Medicine	Medical Measurement		Older Adults Patients	Steps	URL
Evaluation of a culturally-adapted lifestyle intervention to treat elevated cardiometabolic risk of Latino adults in primary care (Vida Sana): A randomized controlled trial details	Rosas, L.G., Lv, N., Xiao, L., Lewis, M.A., Zavella, P., Kramer, M.K., Luna, V. and Ma, J.	2016	Contemporary Clinical Trials	Methods		Adults	Steps	URL
Acceptability of Fitbit for physical activity tracking within clinical care among men with prostate cancer details	Rosenberg, D., Kadokura, E.A., Bouldin, E.D., Miyawaki, C.E., Higano, C.S. and Hartzler, A.L.	2016	AMIA Annual Symposium Proceedings	Usability	Zip	Patients Adults	Steps	URL
Impact of newer self-monitoring technology and brief phone-based intervention on weight loss: A randomized pilot study details	Ross, K.M. and Wing, R.R.	2016	Obesity	Intervention	Zip Aria	Adults	Steps Energy Expenditure Intensity Weight Food	URL
Physical activity intervention using Fitbits in an introductory college health course details	Rote, A.E.	2016	Health Education Journal	Measurement	Zip	Adults	Steps	URL
Interrogation of Patient Smartphone Activity Tracker to Assist Arrhythmia Management details	Rudner, J., McDougall, C., Sailam, V., Smith, M. and Sacchetti, A.	2016	Annals of Emergency Medicine	Case Study	Charge HR	Patients Adults	Heart Rate	URL
Devices for Self-Monitoring Sedentary Time or Physical Activity: A Scoping Review. details	Sanders, J.P., Loveday, A., Pearson, N., Edwardson, C., Yates, T., Biddle, S.J.H. and Esliger, D.W.	2016	Journal of Medical Internet Research	Review				URL
Wearing, Thinking, and Moving: Testing the Feasibility of Fitness Tracking with Urban Youth details	Schaefer, S.E., Ching, C.C., Breen, H. and German, J.B.	2016	American Journal of Health Education	Usability	One	Adolescents	Steps	URL
Strength in numbers: group fitness programming for children with cerebral palsy details	Schmit, J. and Miros, J.	2016	Developmental Medicine & Child Neurology	Measurement	Charge HR	Children Adults Adolescents	Steps Heart Rate	URL
Validation of the Fitbit Zip for monitoring physical activity among free-living adolescents details	Schneider, M. and Chau, L.	2016	BMC Research Notes	Validation	Zip	Adolescents	Steps	URL
Assessment of Distance Measurement with Selected Wearable Devices in Telemonitoring details	Schneider, Z., Shahbazian, J. and Krishnan, S.M.	2016	2016 32nd Southern Biomedical Engineering Conference (SBEC)	Validation	Flex	Adults	Distance	URL
Sleep Tracking, Wearable Technology, and Opportunities for Research and Clinical Care details	Shelgikar, A.V., Anderson, P.F. and Stephens, M.R.	2016	Chest	Review	One Flex Charge Surge	NA	Sleep	URL
Activity monitoring using a mHealth device and correlations with psychopathology in patients with chronic schizophrenia details	Shin, S., Yeom, C.-W., Shin, C., Shin, J.-H., Jeong, J.H., Shin, J.U. and Lee, Y.R.	2016	Psychiatry Research	Measurement	Flex	Patients Adults	Steps	URL
Biosensors to assess performance status in cancer (BioAPS Study). details	Shinde, A.M., Gresham, G.K., Hendifar, A.E., Tuli, R., Spiegel, B. and Figlin, R.A.	2016	Journal of Clinical Oncology	Methods	Charge HR	Patients Adults	Steps Intensity Sleep	URL
Effects of Playing a Serious Computer Game on Body Mass Index and Nutrition Knowledge in Women details	Shiyko, M., Hallinan, S., Seif El-Nasr, M., Subramanian, S. and Castaneda-Sceppa, C.	2016	JMIR Serious Games	Intervention		Adults	Steps	URL
‘Well, if the kids can do it, I can do it': older rehabilitation patients' experiences of telerehabilitation details	Shulver, W., Killington, M., Morris, C. and Crotty, M.	2016	Health Expectations	Usability		Older Adults Patients	Steps	URL
Accuracy Of Wrist-worn Activity Monitors At Three Walking Speeds On The Treadmill details	Smith, J.D.	2016	Medicine & Science in Sports & Exercise	Validation	Flex	Adults	Steps	URL
Changes in taste preference and steps taken after sleep curtailment. details	Smith, S.L., Ludy, M.-J. and Tucker, R.M.	2016	Physiology & Behavior	Measurement	One Flex	Adults	Steps Sleep	URL
Incorporation of a Physical Education and Nutrition Program Into Neurosurgery: A Proof of Concept Pilot Program. details	Spiotta, A.M., Fargen, K.M., Denham, S.L., Fulton, M.E., Kellogg, R., Young, E., Patel, S. and Turner, R.D.	2016	Neurosurgery	Measurement	Surge	Adults	Sleep Heart Rate	URL
Piece it Together: Comprehensive Wellness for Teens and Young Adults with Autism and Neurodevelopment Disorders details	Spratt, E.G., Flynn, K., Newton, J., Papa, C., Harris, K., Serpe, A., O'Connor, A., Blackmon, L. and Eckert, M.	2016	Journal of the American Academy of Child & Adolescent Psychiatry	Intervention Measurement	Charge	Adults Adolescents	Steps	URL
Unsupervised Detection and Analysis of Changes in Everyday Physical Activity Data details	Sprint, G., Cook, D.J. and Schmitter-Edgecombe, M.	2016	Journal of Biomedical Informatics	Methods	Flex	NA	Steps	URL
The Effects of Spacing, Naps, and Fatigue on the Acquisition and Retention of Laparoscopic Skills. details	Spruit, E.N., Band, G.P.H., van der Heijden, K.B., and Hamming, J.F.	2016	Journal of Surgical Education	Measurement	Flex	Adults	Steps	URL
How accurate are the wrist-based heart rate monitors during walking and running activities? Are they accurate enough? details	Stahl, S.E., An, H.-S., Dinkel, D.M., Noble, J.M. and Lee, J.-M.	2016	BMJ Open Sport & Exercise Medicine	Validation	Charge HR	Adults	Heart Rate	URL
Validity and Reliability of Fitbit Flex for Step Count, Moderate to Vigorous Physical Activity and Activity Energy Expenditure details	Sushames, A., Edwards, A., Thompson, F., McDermott, R. and Gebel, K.	2016	PLOS ONE	Validation	Flex	Adults	Steps Energy Expenditure Intensity	URL
Daily & Hourly Adherence: Towards Understanding Activity Tracker Accuracy details	Tang, L.M., Day, M., Engelen, L., Poronnik, P., Bauman, A. and Kay, J.	2016	Proceedings of the 2016 CHI Conference Extended Abstracts on Human Factors in Computing Systems	Measurement	Zip	Adults	Steps	URL
Cardiac Patients' Walking Activity Determined by a Step Counter in Cardiac Telerehabilitation: Data From the Intervention Arm of a Randomized Controlled Trial. details	Thorup, C., Hansen, J., Grønkjær, M., Andreasen, J.J., Nielsen, G., Sørensen, E.E. and Dinesen, B.I.	2016	Journal of Medical Internet Research	Intervention	Zip	Patients Adults	Steps	URL
Pedometer use and self-determined motivation for walking in a cardiac telerehabilitation program: a qualitative study details	Thorup, C.B., Grønkjær, M., Spindler, H., Andreasen, J.J., Hansen, J., Dinesen, B.I., Nielsen, G. and Sørensen, E.E.	2016	BMC Sports Science, Medicine and Rehabilitation	Measurement	Zip	Patients Adults	Steps	URL
Health coaching and pedometers to enhance physical activity and prevent falls in community-dwelling people aged 60 years and over: study protocol for the Coaching for Healthy AGEing (CHAnGE) cluster randomised controlled trial details	Tiedemann, A., Rissel, C., Howard, K., Tong, A., Merom, D., Smith, S., Wickham, J., Bauman, A., Lord, S.R., Vogler, C., Lindley, R.I., Simpson, J.M., Allman-Farinelli, M. and Sherrington, C.	2016	BMJ Open	Methods		Older Adults	Steps	URL
The monitoring of activity at home after total hip arthroplasty details	Toogood, P.A., Abdel, M.P., Spear, J.A., Cook, S.M., Cook, D.J. and Taunton, M.J.	2016	The Bone & Joint Journal	Measurement		Older Adults Patients	Steps	URL
Health Outcomes of Information System Use Lifestyles among Adolescents: Videogame Addiction, Sleep Curtailment and Cardio-Metabolic Deficiencies. details	Turel, O., Romashkin, A. and Morrison, K.M.	2016	PLOS ONE	Measurement	Ultra One	Adolescents	Sleep	URL
Association between activity limitations and pain in patients scheduled for total knee arthroplasty details	Usiskin, I.M., Yang, H.Y., Deshpande, B.R., Collins, J.E., Michl, G.L., Smith, S.R., Klara, K.M., Selzer, F., Katz, J.N. and Losina, E.	2016	BMC Musculoskeletal Disorders	Measurement	Zip	Patients Adults	Steps	URL
Feasibility of a Memory Clinic-Based Physical Activity Prescription Program details	Vidoni, E.D., Watts, A.S., Burns, J.M., Greer, C.S., Graves, R.S., Van Sciver, A., Black, J.R., Cooper, S.K., Nagely, A.C., Uphoff, E., Volmer, J.M. and Bieberle, N.A.	2016	Journal of Alzheimer's Disease	Intervention	Zip	Older Adults	Steps	URL
Validity of commercial activity trackers in children with congenital heart disease details	Voss, C., Gardner, R.F., Dean, P.H. and Harris, K.C.	2016	Canadian Journal of Cardiology	Validation	Charge HR	Adolescents	Steps Intensity	URL
Is There an Association of Physical Activity with Brain Volume, Behavior, and Day-to-day Functioning? A Cross Sectional Design in Prodromal and Early Huntington Disease. details	Wallace, M., Downing, N., Lourens, S., Mills, J., Kim, J.-I., Long, J., Paulsen, J. and Predict-Hd Investigators And Coordinators Of The Huntington Study Group	2016	PLoS Currents	Measurement	Ultra	Patients Adults	Steps Distance	URL
Accuracy of Heart Rate Watches: Implications for Weight Management details	Wallen, M.P., Gomersall, S.R., Keating, S.E., Wisløff, U. and Coombes, J.S.	2016	PLOS ONE	Validation	Charge HR	Adults	Steps Energy Expenditure Heart Rate	URL
Mobile and Wearable Device Features that Matter in Promoting Physical Activity details	Wang, J.B., Cataldo, J.K., Ayala, G.X., Natarajan, L., Cadmus-Bertram, L.A., White, M.M., Madanat, H., Nichols, J.F. and Pierce, J.P.	2016	Journal of Mobile Technology in Medicine	Usability	One	Patients Adults	Steps	URL
Accuracy of wrist-worn heart rate monitors details	Wang, R., Blackburn, G., Desai, M. and et al	2016	JAMA Cardiology	Validation	Charge HR	Adults	Heart Rate	URL
Sleep quality, but not quantity, is associated with self-perceived minor error rates among emergency department nurses details	Weaver, A.L., Stutzman, S.E., Supnet, C. and Olson, D.M.	2016	International Emergency Nursing	Measurement		Adults	Sleep	URL
A Technology-Mediated Behavioral Weight Gain Prevention Intervention for College Students: Controlled, Quasi-Experimental Study. details	West, D.S., Monroe, C.M., Turner-McGrievy, G., Sundstrom, B., Larsen, C., Magradey, K., Wilcox, S. and Brandt, H.M.	2016	Journal of Medical Internet Research	Intervention	Zip Aria	Adults	Steps Weight	URL
Distance Learning Strategies for Weight Management Utilizing Online Social Networks versus Group Phone Conference Call details	Willis, E.A., Szabo-Reed, A.N., Ptomey, L.T., Steger, F.L., Honas, J.J., Al-Hihi, E.M., Lee, R., Lee, J., Oh, Y., Washburn, R.A. and Donnelly, J.E.	2016	Obesity Science & Practice	Intervention	Flex	Adults	Steps	URL
Measures of sleep and cardiac functioning during sleep using a multi-sensory commercially-available wristband in adolescents details	de Zambotti, M., Baker, F.C., Willoughby, A.R., Godino, J.G., Wing, D., Patrick, K. and Colrain, I.M	2016	Physiology & Behavior	Validation	Charge HR	Adolescents	Sleep Heart Rate	URL
A hospital and home-based exercise program to address functional decline in people following allogeneic stem cell transplantation details	Abo, S., Ritchie, D., Denehy, L., Panek-Hudson, Y., Irving, L. and Granger, C.L.	2017	Supportive Care in Cancer	Intervention	Zip	Patients Adults	Steps
Developing a Fitbit-supported lifestyle physical activity intervention for depressed alcohol dependent women details	Abrantes, A.M., Blevins, C., Battle, C.L., Read, J.P., Gordon, A. and Stein, M.	2017	Journal of Substance Abuse Treatment	Intervention	Charge	Patients Adults	Steps	URL
Adaptive goal setting and financial incentives: a 2 × 2 factorial randomized controlled trial to increase adults' physical activity details	Adams, M.A., Hurley, J.C., Todd, M., Bhuiyan, N., Jarrett, C.L., Tucker, W.J., Hollingshead, K.E. and Angadi, S.S.	2017	BMC Public Health	Intervention	Zip	Adults	Steps	URL
The Physical Activity Patterns of Cardiac Rehabilitation Program Participants Using Three Different Measures: Exercise Capacity, Functional Activity, and Habitual Physical Activity details	Alharbi, M., Bauman, A., Neubeck, L., Naismith, S., Tofler, Yun-Hee, J., Mitchell, J., Kirkness, A., Gallagher, P., Woolaston, A. and Gallagher, R.	2017	Heart, Lung and Circulation	Measurement	Flex	Older Adults Patients	Steps Intensity	URL
Harnessing the Potential of Wearable Activity Trackers for Heart Failure Self-Care details	Alharbi, M., Straiton, N. and Gallagher, R.	2017	Current Heart Failure Reports	Review	One Flex	Patients Adults	NA	URL
How Accurate Is Your Activity Tracker? A Comparative Study of Step Counts in Low-Intensity Physical Activities details	Alinia, P., Cain, C., Fallahzadeh, R., Shahrokni, A., Cook, D. and Ghasemzadeh, H.	2017	JMIR Mhealth and Uhealth	Validation	One Zip Flex	Adults	Steps	URL
FitBit Garden: A Mobile Game Designed to Increase Physical Activity in Children details	Amresh, A., Lyles, A., Small, L. and Gary, K.	2017	Proceedings of the 2017 International Conference on Digital Health	Usability		Children		URL
Step Count Accuracy of the Stepwatch and FitBit One Among Individuals with a Unilateral Transtibial Amputation details	Arch, E.S., Sions, J.M. and Horne, J.R.	2017	American Academy of Orthotists & Prosthetists	Validation	One	Patients Adults	Steps	URL
Facilitating Partner Support for Lifestyle Change Among Adults with Serious Mental Illness: A Feasibility Pilot Study details	Aschbrenner, K.A., Mueser, K.T., Naslund, J.A., Gorin, A.A., Zawacki, S.A., Pratt, S.I., Kinney, A. and Bartels, S.	2017	Community Mental Health Journal	Usability Intervention	Zip	Patients Adults	Steps Distance	URL
Assessing the Benefits and Use of Gain-Framed Text Messaging to Improve Physical Activity in Advanced Stage Lung Cancer details	Bade, B., Pastis, A., Owens, L., Hyer, J. and Silvestri, G.	2017	Chest	Intervention	Flex	Patients Adults	Steps	URl
The Validity of Fitbit Charge in Free Living Conditions details	Bai, Y., Ellingson, L. and Welk, G.	2017	Medicine & Science in Sports & Exercise	Validation	Charge	Adults	Steps Intensity	URL
Comparative evaluation of heart rate-based monitors: Apple Watch vs Fitbit Charge HR details	Bai, Y., Hibbing, P., Mantis, C. and Welk, G.J.	2017	Journal of Sports Sciences	Validation	Charge HR	Adults	Steps Energy Expenditure Heart Rate
The Effects of a Campus Forest-Walking Program on Undergraduate and Graduate Students' Physical and Psychological Health details	Bang, K.-S., Lee, I., Kim, S., Lim, C.S., Joh, H.-K., Park, B.-J. and Song, M.K.	2017	International Journal of Environmental Research and Public Health	Intervention	Zip	Adults	Steps	URL
Assessing bouts of activity using modeled clinically validated physical activity on commodity hardware details	Barrett, C., Dominick, G. and Winfree, K.N.	2017	2017 IEEE EMBS International Conference on Biomedical & Health Informatics	Methods	Flex	Adults	Steps Intensity	URL
The Accuracy of Personal Activity Monitoring Devices details	Battenberg, A.K., Donohoe, S., Robertson, N. and Schmalzried, T.P.	2017	Seminars in Arthroplasty	Validation	One Force	Adults	Steps	URL
Does supplementation with carnosine improve cardiometabolic health and cognitive function in patients with pre-diabetes and type 2 diabetes? study protocol for a randomised, double-blind, placebo-controlled trial details	Baye, E., Menon, K., de Courten, M.P., Earnest, A., Cameron, J. and de Courten, B.	2017	BMJ Open	Methods	Charge HR	Patients Adults	Steps Energy Expenditure Intensity Heart Rate	URL
Estimation of sleep stages in a healthy adult population from optical plethysmography and accelerometer signals details	Beattie, Z., Oyang, Y., Statan, A., Ghoreyshi, A., Pantelopoulos, A., Russell, A. and Heneghan, C.	2017	Physiological Measurement	Validation	Surge	Adults	Sleep	URL
Estimation of Sleep Stages Using Cardias and Accelerometer Data from a Wrist-Worn Device details	Beattie, Z., Pantelopoulos, A., Ghoreyshi, A., Oyang, Y., Statan, A. and Heneghan, C.	2017	Sleep	Validation	Charge 2	Adults	Sleep	URL
Validity, reliability and feasibility of commercially available activity trackers in physical therapy for people with a chronic disease: a study protocol of a mixed methods research details	Beekman, E., Braun, S.M., Ummels, D., van Vijven, K., Moser, A. and Beurskens, A.J.	2017	Pilot and Feasibility Studies	Methods	One Flex	Patients Adults	Steps Intensity	URL
A Feasible and Efficacious Mobile-Phone Based Lifestyle Intervention for Filipino Americans with Type 2 Diabetes: Randomized Controlled Trial details	Bender, M.S., Cooper, B.A., Park, L.G., Padash, S. and Arai, S.	2017	JMIR Diabetes	Intervention	Zip	Patients Adults	Steps Weight Food
A Feasible and Effective Mobile Health Weight Loss Lifestyle Intervention for Filipinos With Type 2 Diabetes details	Bender, M.S., Cooper, B. and Arai, S.	2017	Circulation	Intervention	Zip	Patients Adults	Steps	URL
Exploring the Association Between Self-Reported Asthma Impact and Fitbit-Derived Sleep Quality and Physical Activity Measures in Adolescents details	Bian, J., Guo, Y., Xie, M., Parish, A.E., Wardlaw, I., Brown, R., Modave, F., Zheng, D. and Perry, T.T.	2017	JMIR mHealth and uHealth	Measurement	Charge HR	Patients Children	Steps Intensity Sleep	URL
Trial to Incentivise Adherence for Diabetes (TRIAD): study protocol for a randomised controlled trial details	Bilger, M., Shah, M., Tan, N.C., Howard, K.L., Xu, H.Y., Lamoureux, E.L. and Finkelstein, E.A.	2017	Trials	Methods	Zip	Patients Adults	Steps	URL
Daily Activity Measured With Wearable Technology as a Novel Measurement of Treatment Effect in Patients With Coronary Microvascular Dysfunction: Substudy of a Randomized Controlled Crossover Trial details	Birkeland, K., Khandwalla, R.M., Kedan, I., Shufelt, C.L., Mehta, P.K., Minissian, M.B., Wei, J., Handberg, E.M., Thomson, L.E., Berman, D.S., Petersen, J.W., Anderson, R.D., Cook-Wiens, G., Pepine, C.J. and Bairey Merz, C.N.	2017	JMIR Research Protocols	Measurement	Flex	Patients Adults	Steps
Impact of a Wireless "Wearable" Device to Measure Daily Activity in Patients With Coronary Microvascular Disease Treated With Late Na Channel Inhibition (ranolazine): a Substudy of the RWISE Clinical Trial details	Birkeland, K., Khandwalla, R.M., Kedan, I., Shufelt, C.L., Mehta, P.K., Minissian, M.B., Wei, J., Handberg, E.M., Thomson, L.E., Berman, D.S., Petersen, J.W., Anderson, R.D., Cook-Wiens, G., Pepine, C.J. and Bairey Merz, C.N.	2017	Circulation: Cardiovascular Quality and Outcomes	Clinical Care Measurement	Flex	Patients Adults	Steps	URL
Determining the Reliability of Several Consumer-Based Physical Activity Monitors details	Bock, J., Kaminsky, L., Harber, M. and Montoye, A.	2017	Technologies	Validation	One Zip Flex	Adults	Steps Energy Expenditure Intensity	URL
Accuracy of Wearable Devices for Determining Physiological Measures during Different Physical Activities details	Boudreaux, B.D., Cormier, C.L., Williams, B.M., Hebert, E.P. and Kraemer, R.R.	2017	Medicine & Science in Sports & Exercise	Validation	Charge HR	Adults	Steps Energy Expenditure Heart Rate	URL
Validity of Wearable Activity Monitors during Cycling and Resistance Exercise details	Boudreaux, B.D., Hebert, E.P., Hollander, D.B., Williams, B.M., Cormier, C.L., Naquin, M.R., Gillan, W.W., Gusew, E.E. and Kraemer, R.R.	2017	Medicine & Science in Sports & Exercise	Validation	Charge 2 Blaze	Adults	Energy Expenditure Heart Rate	URL
Validity of Fitbit's active minutes as compared with a research-grade accelerometer and self-reported measures details	Brewer, W., Swanson, B.T. and Ortiz, A.	2017	BMJ Open Sport & Exercise Medicine	Validation		Adults	Steps Intensity	URL
Establishing Linkages Between Distributed Survey Responses and Consumer Wearable Device Datasets: A Pilot Protocol details	Brinton, J.E., Keating, M.D., Ortiz, A.M., Evenson, K.R. and Furberg, R.D.	2017	JMIR Research Protocols	Measurement		Adults	Steps Energy Expenditure Intensity Distance Sleep Heart Rate	URL
Randomized trial of a clinic-based weight loss intervention in cancer survivors details	Brown, J.C., Yung, R.L., Gobbie-Hurder, A., Shockro, L., O'Connor, K., Campbell, N., Kasper, J., Mayer, E.L., Tolaney, S.M., Partridge, A.H. and Ligibel, J.A.	2017	Journal of Cancer Survivorship	Intervention	Flex	Patients Adults	Steps	URL
Does Stress Result in You Exercising Less? Or Does Exercising Result in You Being Less Stressed? Or Is It Both? Testing the Bi-directional Stress-Exercise Association at the Group and Person (N of 1) Level details	Burg, M.M., Schwartz, J.E., Kronish, I.M., Diaz, K.M., Alcantara, C., Duer-Hefele, J. and Davidson, K.W.	2017	Annals of Behavioral Medicine	Measurement	Flex	Adults	Steps Intensity	URL
Mobile Technology to Monitor Physical Activity and Wellbeing in Daily Life - Objective and Subjective Experiences of Older Adults details	Cabrita, M., Tabak, M. and Vollenbroek-Hutten, M.M.	2017	Proceedings of the 3rd International Conference on Information and Communication Technologies for Aging Well and e-Health	Usability	Zip	Older Adults	Steps Intensity Distance	URL
The Accuracy of Heart Rate Monitoring by Some Wrist-Worn Activity Trackers details	Cadmus-Bertram, L., Gangnon, R., Wirkus, E.J., Thraen-Borowski, K.M. and Gorzelitz-Liebhauser, J.	2017	Annals of Internal Medicine	Validation	Charge HR	Adults	Heart Rate	URL
A novel stepped-care approach to weight loss: The role of self-monitoring and health literacy in treatment outcomes details	Carels, R.A., Selensky, J.C., Rossi, J., Solar, C. and Hlavka, R.	2017	Eating Behaviors	Intervention	Zip	Adults	Steps Food	URL
The efficacy of wearable activity tracking technology as part of a weight loss program: a systematic review. details	Cheatham, S.W., Stull, K.R., Fantigrassi, M. and Motel, I.	2017	The Journal of sports medicine and physical fitness	Review		Adults
Short-Term Efficacy of an Innovative Mobile Phone Technology-Based Intervention for Weight Management for Overweight and Obese Adolescents: Pilot Study. details	Chen, J.-L., Guedes, C.M., Cooper, B.A. and Lung, A.E.	2017	Interactive Journal of Medical Research	Intervention	Flex	Adolescents	Steps	URL
Explainable sleep quality evaluation model using machine learning approach details	Choi, R.-H., Kang, W.-S. and Son, C.-S.	2017	2017 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI)	Methods	Charge HR	Adults	Sleep Heart Rate	URL
Accuracy of step count measured by physical activity monitors: the effect of gait speed and anatomical placement site. details	Chow, J.J., Thom., J.M., Wewege, M.A., Ward, R.E. and Parmenter, B.J.	2017	Gait & Posture	Validation	One Flex Charge HR	Adults	Steps	URL
Accuracy of Accelerometer (Fitbit® ChargeHR™) Measured Distance and Heart Rate in Patients with Pulmonary Arterial Hypertension details	Chowdhury, A., Sehgal, S., Rabih, F., Gadre, A. and Highland, K.B.	2017	American Journal of Respiratory and Critical Care Medicine	Validation	Charge HR	Patients Adults	Distance Heart Rate	URL
Assessment of laboratory and daily energy expenditure estimates from consumer multi-sensor physical activity monitors details	Chowdhury, E.A., Western, M.J., Nightingale, T.E., Peacock, O.J. and Thompson, D.	2017	PLOS ONE	Validation	Charge HR	Adults	Energy Expenditure	URL
Comparison of wrist-worn Fitbit Flex and waist-worn ActiGraph for measuring steps in free-living adults details	Chu, A.H.Y., Ng, S.H.X., Paknezhad, M., Gauterin, A., Koh, D., Brown, M.S. and Müller-Riemenschneider, F.	2017	PLOS ONE	Validation	Flex	Adults	Steps	URL
Pulmonary exposure to metal fume particulate matter cause sleep disturbances in shipyard welders details	Chuang, H.-C., Su, T.-Y., Chuang, K.-J., Hsiao, T.-C., Lin, H.-L., Hsu, Y.-T., Pan, C.-H., Lee, K.-Y., Ho, S.-C. and Lai, C.-H.	2017	Environmental Pollution	Measurement	Charge HR	Adults	Sleep
Acceptability of the Fitbit in behavioural activation therapy for depression: a qualitative study. details	Chum, J., Kim, M.S., Zielinski, L., Bhatt, M., Chung, D., Yeung, S., Litke, K., McCabe, K., Whattam, J., Garrick, L., O'Neill, L., Goyert, S., Merrifield, C., Patel, Y. and Samaan, Z.	2017	Evidence-based Mental Health	Usability	One	Patients Adults	Steps	URL
A 30-month worksite-based lifestyle program to promote cardiovascular health in middle-aged bank employees: Design of the TANSNIP-PESA randomized controlled trial details	Coffeng, J.K., van der Ploeg, H.P., Castellano, J.M., Fernández-Alvira, J.M., Ibáñez, B., García-Lunar, I., van der Beek, A.J., Fernández-Ortiz, A., Mocoroa, A., García-Leal, L., Cárdenas, E., Rojas, C., Martínez-Castro, M.I., Santiago-Sacristán, S., Fernández-Gallardo, M., Mendiguren, J.M., Bansilal, S., van Mechelen, W. and Fuster, V.	2017	American Heart Journal	Methods	Flex	Adults		URL
Applicability of consumer activity monitor data in marathon events: an exploratory study details	Constantinou, V., Felber, A.E. and Chan, J.L.	2017	Journal of Medical Engineering & Technology	Validation		Adults	Steps Energy Expenditure Distance	URL
Utility of the Fitbit Flex to Evaluate Sleep in Major Depressive Disorder: A comparison against polysomnography and wrist-worn actigraphy details	Cook, J.D., Prairie, M.L., Plante, D.T., Finn, L., Benca, R., Nielsen, G., Straume, S., Nordhus, I., Coleman, J., Lee-Chiong, T., Pancer, J., Swick, T., Committee, S. and Medicine, A.	2017	Journal of Affective Disorders	Validation	Flex	Patients Adults	Sleep	URL
Development of the Physical activity and Your Nutrition for Cancer (PYNC) smartphone app for preventing breast cancer in women details	Coughlin, S.S., Besenyi, G.M., Bowen, D. and De Leo, G.	2017	mHealth	Methods		Adults	Steps Goals	URL
Healthy lifestyle intervention for adult clinic patients with type 2 diabetes mellitus details	Coughlin, S.S., Hatzigeorgiou, C., Anglin, J., Xie, D., Besenyi, G.M., De Leo, G., Stewart, J. and Wilkins, T.	2017	Diabetes Management	Methods	Blaze	Patients Adults	Steps Intensity Sleep Heart Rate	URL
A pragmatic, phase III, multisite, double-blind, placebo-controlled, parallel-arm, dose increment randomised trial of regular, low-dose extended-release morphine for chronic breathlessness: Breathlessness, Exertion And Morphine Sulfate (BEAMS) study protocol details	Currow, D., Watts, G.J., Johnson, M., McDonald, C.F., Miners, J.O., Somogyi, A.A., Denehy, L., McCaffrey, N., Eckert, D.J., McCloud, P., Louw, S., Lam, L., Greene, A., Fazekas, B., Clark, K.C., Fong, K., Agar, M.R., Joshi, R., Kilbreath, S., Ferreira, D. and Ekström, M.	2017	BMJ Open	Methods	Charge HR	Patients Adults	Steps Intensity Sleep	URL
Using Wrist-Worn Sensors to Measure and Compare Physical Activity Changes for Patients Undergoing Rehabilitation details	Dahmne, J., Fleur, A.L., Sprint, G., Cook, D. and Weeks, D.L.	2017	The Third IEEE International Workshop on Sensing Systems and Applications Using Wrist Worn Smart Devices	Methods	Charge HR	Patients Adults	Steps Heart Rate	URL
Using Wrist-Worn Actigraphic Devices to Measure Sleep in People with Huntington's Disease details	Dao, P., Maskevich, S., Jumabhoy, R., Drummond, S. and Stout, J.	2017	Sleep	Validation	One	Patients Adults	Sleep	URL
Nurses’ Physical Activity Study: Caring For You So You Can Care For Others details	Das, B.M., Clemmons, B.S., Kemble, C.D., Schreier, A.M. and Mahar, M.T.	2017	Medicine & Science in Sports & Exercise	Intervention	Flex	Adults	Steps Intensity	URL
Performance of an Artificial Pancreas System for Young Children with Type 1 Diabetes details	DeBoer, M.D., Breton, M.D., Wakeman, C., Schertz, E.M., Emory, E.G., Robic, J.L., Kollar, L.L., Kovatchev, B.P. and Cherñavvsky, D.R.	2017	Diabetes Technology & Therapeutics	Measurement	Charge HR	Patients Children	Steps Heart Rate	URL
Does Wearable Technology Provide Accurate Heart Rate Measures While Playing Pickleball Doubles? details	Denning, M., Smith, M., Zagrodnik, J. and Ruden, T.	2017	Medicine & Science in Sports & Exercise	Validation	Charge HR	Adults	Heart Rate	URL
Learning pulse: a machine learning approach for predicting performance in self-regulated learning using multimodal data details	Di Mitri, D., Scheffel, M., Drachsler, H., Börner, D., Ternier, S. and Specht, M.	2017	Proceedings of the Seventh International Learning Analytics & Knowledge Conference	Methods	Charge HR	Adults	Steps Heart Rate	URL
An Examination of Body Mass Index Influence on Activity Tracker Accuracy details	Dondzila, C., Johnson, H. and Steckel, M.	2017	Medicine & Science in Sports & Exercise	Validation	One Charge HR	Adults	Steps Intensity	URL
Estimating Accuracy at Exercise Intensities: A Comparative Study of Self-Monitoring Heart Rate and Physical Activity Wearable Devices details	Dooley, E.E., Golaszewski, N.M. and Bartholomew, J.B.	2017	JMIR mHealth and uHealth	Validation	Charge HR	Adults	Energy Expenditure Heart Rate	URL
Varying Exercise Intensities: The Accuracy of Three Self-Monitoring Heart Rate and Physical Activity Wearable Devices details	Dooley, E.E., Golaszewski, N.M. and Bartholomew, J.B.	2017	Medicine & Science in Sports & Exercise	Validation	Charge HR	Adults	Energy Expenditure Heart Rate	URL
Accelerometer-based sleep behavior and activity levels in student athletes in comparison to student non-athletes details	Driller, M.W., Dixon, Z.T. and Clark, M.I.	2017	Sport Sciences for Health	Measurement	Flex	Adults	Steps Energy Expenditure Intensity Distance Sleep	URL
Quantitative and qualitative measures of poststroke walking activity with inertial technologies details	Duclos, N., Tavares, L.A., Parent, G., Aissaoui, R., Nadeau, S. and Duclos, C.	2017	Annals of Physical and Rehabilitation Medicine	Validation	One	Patients Adults	Steps	URL
Activity Trackers Implement Different Behavior Change Techniques for Activity, Sleep, and Sedentary Behaviors. details	Duncan, M., Murawski, B., Short, C.E., Rebar, A.L., Schoeppe, S., Alley, S., Vandelanotte, C. and Kirwan, M.	2017	Interactive Journal of Medical Research	Review		NA	NA	URL
Feasibility study protocol to examine the role of mantra meditation at reducing psychological distress in emergency department staff details	Dunne, P.J., O'Leary, C., Prihodova, L., Breen, R., Walsh, C., Freeman, L., Carroll, A., McMahon, G. and White, B.	2017	International Journal of Clinical Trials	Methods	Charge 2	Adults	Sleep Heart Rate	URL
Restoring effective sleep tranquility (REST): A feasibility and pilot study details	Eakman, A.M., Schmid, A.A., Henry, K.L., Rolle, N.R., Schelly, C., Pott, C.E. and Burns, J.E.	2017	British Journal of Occupational Therapy	Intervention	Flex	Patients Adults	Sleep	URL
The impact of E-diaries and accelerometers on young adults' perceived and objectively assessed physical activity details	Eisenberg, M.H., Phillips, L.A., Fowler, L. and Moore, P.J.	2017	Psychology of Sport and Exercise	Measurement	Zip	Adults	Steps Intensity
Use of a Fitness Tracking Device to Monitor Physical Activity and Sleep Patterns during Hospitalization for Hematopoietic Stem Cell Transplant. Results of a Prospective Observational Study details	El Jurdi, N.H., Cotton, M.J., Pinto, R., Galloway, E., Kolke, S., Cooper, B.W., Tomlinson, B.K., Metheny, L., Malek, E., Nock, N., Kolk, M., Lee, R., De Lima, M. and Caimi, P.	2017	Blood	Measurement	Charge HR	Patients Adults	Steps Energy Expenditure Intensity Sleep Heart Rate
Effect of personal activity trackers on weight loss in families enrolled in a comprehensive behavioral family-lifestyle intervention program in the federally qualified health center setting: A randomized controlled trial details	Espinoza, J., Chen, A., Orozco, J., Deavenport-Saman, A. and Yin, L.	2017	Contemporary Clinical Trials Communications	Methods	Flex	Patients Children	Steps	URL
Using Novel Technology within a School-Based Setting to Increase Physical Activity: A Pilot Study in School-Age Children from a Low-Income, Urban Community details	Evans, E.W., Abrantes, A.M., Chen, E. and Jelalian, E.	2017	BioMed Research International	Intervention	Zip Charge	Children	Steps
Assessing Validity of the Fitbit Indicators for U.S. Public Health Surveillance details	Evenson, K.R., Wen, F. and Furberg, R.D.	2017	American Journal of Preventive Medicine	Methods		Adults		URL
The Validity of Consumer-Level Activity Monitors in Healthy Older Adults in Free-Living Conditions details	Farina, N. and Lowry, R.G.	2017	Journal of Aging and Physical Activity	Validation	Charge HR	Older Adults	Steps	URL
The effect of a wearable physical activity monitor ( Fitbit One ) on physical activity behaviour in women : A pilot study. details	Farnell, G. and Barkley, J.	2017	Journal of Human Sport and Exercise	Intervention	One	Adults	Steps
Exploring Compliance: Observations from a Large Scale Fitbit Study details	Faust, L., Purta, R., Hachen, D., Striegel, A., Poellabauer, C., Lizardo, O. and Chawla, N.V.	2017	Proceedings of the 2nd International Workshop on Social Sensing - SocialSens'17	Measurement Methods	Charge HR	Adults	Steps Sleep Heart Rate	URL
Virtual weight loss program for African-American breast cancer survivors: Preliminary results. details	Ferrante, J.M., Doose, M., Bator, A., Devine, K., Ohman Strickland, P., Angelino, A., Lee, J., Koransky, A., Hwang, K. and Bandera, E.	2017	Journal of Clinical Oncology	Intervention		Patients Adults	Steps	URL
Caloric Expenditure Using Indirect Calorimetry, Apple Watch Sport, and Fitbit Zip details	Ferrara CM, Smyth S, Mullan E and Burke C	2017	Journal of Exercise Physiology	Validation	Zip	Adults	Energy Expenditure	URL
Day2day: investigating daily variability of magnetic resonance imaging measures over half a year. details	Filevich, E., Lisofsky, N., Becker, M., Butler, O., Lochstet, M., Martensson, J., Wenger, E., Lindenberger, U. and Kühn, S.	2017	BMC Neuroscience	Measurement	One	Adults	Steps Sleep	URL
Fitbit and Fitabase Technology: Tracking and Evaluating Youth Physical Activity details	Franzen-Castle, L., Dunker, T., Chai, W. and Krehbiel, M.	2017	Journal of Extension	Methods	Charge	Adolescents	Steps	URL
System identification of Just Walk: A behavioral mHealth intervention for promoting physical activity details	Freigoun, M.T., Martin, C.A., Magann, A.B., Rivera, D.E., Phatak, S.S., Korinek, E.V. and Hekler, E.B.	2017	2017 American Control Conference	Methods	Zip	Adults	Steps	URL
Comparison Between SenseWear and FitBit Physical Activity Monitors in Chronic Obstructive Pulmonary Disease details	Fuhr, D., Michaelchuk, W. and Stickland, M.	2017	American Journal of Respiratory and Critical Care Medicine	Measurement	Flex	Patients Adults	Steps Energy Expenditure	URL
Subjective Age and Physical Activity: A Daily-Dieary Study with Commercially Available Wearables details	Gabrian, M.	2017	Innovation in Aging	Measurement		Older Adults	Steps	URL
Accuracy of Fitbit Activity Trackers During Walking in a Controlled Setting details	Gamez, J.L., Gonzalez, J., Leyva, P., Figueroa, I.A., Lucio, N., Salazar, V.E., Salazar, C., Garcia, M. and Funk, M.D.	2017	Medicine & Science in Sports & Exercise	Validation	One Zip Flex Charge HR Blaze	Adults	Steps	URL
Accuracy of Smartwatches in Assessing College Students’ Energy Expenditure in Exercise with Different Intensities details	Gao, Z., Li, X., Zeng, N., Pope, Z., Yang, H., Liu, W., Xiong, H., Chen, Y., Li, J. and He, W.	2017	Medicine & Science in Sports & Exercise	Validation	Surge	Adults	Energy Expenditure	URL
A Bit of Fit: Minimalist Intervention in Adolescents Based on a Physical Activity Tracker. details	Gaudet, J., Gallant, F. and Bélanger, M.	2017	JMIR mHealth and uHealth	Intervention	Charge HR	Adolescents	Steps Intensity Heart Rate	URL
Home-Based Trials in Adolescent Migraine details	Gelfand, A.A., Qubty, W., Patniyot, I., Grimes, B., Pletcher, M.J., Goadsby, P.J. and Cummings, S.R.	2017	JAMA Neurology	Intervention		Adolescents	Sleep	URL
The Role of Fitbits in Corporate Wellness Programs: Does Step Count Matter? details	Giddens, L., Leidner, D. and Gonzalez, E.	2017	Hawaii International Conference on System Sciences (HICSS) 2017	Measurement	Flex	Adults	Steps	URL
Variable Accuracy of Wearable Heart Rate Monitors during Aerobic Exercise details	Gillinov, S., Etiwy, M., Wang, R., Blackburn, G., Phelan, D., Gillinov, A.M., Houghtaling, P., Javadikasgari, H. and Desai, M.Y.	2017	Medicine & Science in Sports & Exercise	Validation	Blaze	Adults	Heart Rate	URL
Personalized Mobile Health Intervention for Health and Weight Loss in Postpartum Women Receiving Women, Infants, and Children Benefit: A Randomized Controlled Pilot Study details	Gilmore, L.A., Klempel, M.C., Martin, C.K., Myers, C.A., Burton, J.H., Sutton, E.F. and Redman, L.M.	2017	Journal of Women's Health	Intervention	Zip	Adults	Steps	URL
Enhancing mHealth Technology in the Patient-Centered Medical Home Environment to Activate Patients With Type 2 Diabetes: A Multisite Feasibility Study Protocol. details	Gimbel, R., Shi, L., Williams, J.E., Dye, C.J., Chen, L., Crawford, P., Shry, E.A., Griffin, S.F., Jones, K.O., Sherrill, W.W., Truong, K., Little, J.R., Edwards, K.W., Hing, M. and Moss, J.B.	2017	JMIR Research Protocols	Methods	Charge	Patients Adults	Steps Sleep	URL
OPREVENT2: Design of a multi-institutional intervention for obesity control and prevention for American Indian adults. details	Gittelsohn, J., Jock, B., Redmond, L., Fleischhacker, S., Eckmann, T., Bleich, S.N., Loh, H., Ogburn, E., Gadhoke, P., Swartz, J., Pardilla, M. and Caballero, B.	2017	BMC Public Health	Methods		Adults		URL
Physical activity levels in individuals with and without patellofemoral pain details	Glaviano, N.R., Baellow, A. and Saliba, S.	2017	Physical Therapy in Sport	Measurement	Charge HR	Patients Adults	Steps Intensity	URL
Influence of the Fitbit Charge HR on physical activity, aerobic fitness and disability in non-specific back pain participants. details	Gordon, R. and Bloxham, S.	2017	The Journal of Sports Medicine and Physical Fitness	Intervention	Charge HR	Patients Adults	Steps	URL
Fitbit Charge HR Wireless Heart Rate Monitor: Validation Study Conducted Under Free-Living Conditions details	Gorny, A.W., Liew, S.J., Tan, C.S. and Müller-Riemenschneider, F.	2017	JMIR mHealth and uHealth	Validation	Charge HR	Adults	Heart Rate	URL
Reversal of type 2 diabetes in youth who adhere to a very-low-energy diet: a pilot study details	Gow, M.L., Baur, L.A., Johnson, N.A., Cowell, C.T. and Garnett, S.P.	2017	Diabetologia	Intervention	One	Children Adolescents	Steps	URL
Meaningful weight loss in obese postmenopausal women: A pilot study of high-intensity interval training and wearable technology. details	Grossman, J.A., Arigo, D. and Bachman, J.L.	2017	Menopause	Intervention	Charge HR	Adults	Steps Intensity Heart Rate	URL
Fitbit Activity Trackers Interrupt Workplace Sedentary Behavior: A New Application details	Guitar, N.A., MacDougall, A., Connelly, D.M. and Knight, E.	2017	Workplace Health & Safety	Intervention	One	Adults	Steps	URL
Can a Made-for-Consumer Activity Monitor Assess Physical Activity in Adolescents and Young Adults After Lower Extremity Limb Salvage for Osseous Tumors? details	Gundle, K.R., Punt, S.E., Mattioli-Lewis, T. and Conrad, E.U.	2017	Journal of Pediatric Orthopaedics	Measurement	One	Adolescents	Steps	URL
FitViz: A Personal Informatics Tool for Self-management of Rheumatoid Arthritis details	Gupta, A., Tong, X., Shaw, C., Li, L. and Feehan, L.	2017	2017 International Conference on Human-Computer Interaction	Usability Measurement	Flex	Patients Adults	Steps Intensity	URL
Accuracy of the Fitbit Zip for Measuring Steps for Adolescents With Visual Impairments details	Haegele, J.A., Brian, A.S. and Wolf, D.	2017	Adapted Physical Activity Quarterly	Validation	Zip	Patients Adolescents	Steps	URL
Regular exercise improves the well-being of parents of children with cancer details	Halliday, G.C., Miles, G.C.P., Marsh, J.A., Kotecha, R.S. and Alessandri, A.J.	2017	Pediatric Blood & Cancer	Intervention		Adults	Steps	URL
Physical activity among children: objective measurements using Fitbit One® and ActiGraph details	Hamari, L., Kullberg, T., Ruohonen, J., Heinonen, O.J., Díaz-Rodríguez, N., Lilius, J., Pakarinen, A., Myllymäki, A., Leppänen, V. and Salanterä, S.	2017	BMC Research Notes	Validation	One	Children	Steps	URL
Zero Cadence as a Proxy Indicator of Sitting Behaviors in Objective Monitoring details	Han, H., Aguiar, E.J., Ducharme, S., Lim, J., Moore, C., Busa, M.A., Sirard, J.R., Chipkin, S.R., Staudenmayer, J. and Tudor-Locke, C.	2017	Medicine & Science in Sports & Exercise	Methods	One	Adults	Steps	URL
Validity Of The Diagnostix 2100 And Fitbit Charge HR In Assessing Heart Rate details	Hardekopf, P. and Fountaine, C.	2017	Medicine & Science in Sports & Exercise	Validation	Charge HR	Adults	Heart Rate	URL
A Trial of Financial and Social Incentives to Increase Older Adults' Walking details	Harkins, K.A., Kullgren, J.T., Bellamy, S.L., Karlawish, J. and Glanz, K.	2017	American Journal of Preventive Medicine	Intervention	Ultra	Older Adults	Steps	URL
Randomized controlled trial of increasing physical activity on objectively measured and self-reported cognitive functioning among breast cancer survivors: The memory & motion study details	Hartman, S.J., Nelson, S.H., Myers, E., Natarajan, L., Sears, D.D., Palmer, B.W., Weiner, L.S., Parker, B.A. and Patterson, R.E.	2017	Cancer	Intervention	One	Patients Adults	Steps	URL
Tea, talk and technology: patient and public involvement to improve connected health ‘wearables' research in dementia details	Hassan, L., Swarbrick, C., Sanders, C., Parker, A., Machin, M., Tully, M.P. and Ainsworth, J.	2017	Research Involvement and Engagement	Usability	Charge HR	Older Adults	NA	URL
Determinants for Sustained Use of an Activity Tracker: Observational Study details	Hermsen, S., Moons, J., Kerkhof, P., Wiekens, C. and De Groot, M.	2017	JMIR mHealth and uHealth	Measurement	Zip	Adults	Steps	URL
Using mobile technologies with psychiatric patients: Assessing the potential to reduce risk of developing diseases related to inactivity details	Holubova, A., Schlenker, J., Socha, V., Muzik, J., Gillar, D., Doksansky, M., Polacek, M., Hana, K., Kaspar, J., Smrcka, P., Cendelinova, M. and Gojda, J.	2017	2017 IEEE 15th International Symposium on Applied Machine Intelligence and Informatics (SAMI),	Measurement	Flex	Patients Adults	Steps	URL
A Multi-Level, Mobile-Enabled Intervention to Promote Physical Activity in Older Adults in the Primary Care Setting (iCanFit 2.0): Protocol for a Cluster Randomized Controlled Trial details	Hong, Y.A., Forjuoh, S.N., Ory, M.G., Reis, M.D. and Sang, H.	2017	JMIR Research Protocols	Methods	Flex 2	Older Adults	Steps Intensity Sleep	URL
MyAirCoach: the use of home-monitoring and mHealth systems to predict deterioration in asthma control and the occurrence of asthma exacerbations; study protocol of an observational study. details	Honkoop, P.J., Simpson, A., Bonini, M., Snoeck-Stroband, J.B., Meah, S., Fan Chung, K., Usmani, O.S., Fowler, S. and Sont, J.K.	2017	BMJ Open	Methods	Charge HR	Patients Adults	Steps Intensity Heart Rate	URL
The Accuracy of Wrist-worn Heart Rate Monitors across a Range of Exercise Intensities details	Hough, P., Glaister, M. and Pledger, A.	2017	Journal of Physical Activity Research	Validation	Charge HR	Adults	Heart Rate
Elderly People Living Alone: Detecting Home Visits with Ambient and Wearable Sensing details	Hu, R., Pham, H., Buluschek, P. and Gatica-Perez, D.	2017	Proceedings of the 2nd International Workshop on Multimedia for Personal Health and Health Care - MMHealth '17	Measurement Methods	Charge HR	Older Adults	Steps Intensity Sleep	URl
A First Step Towards Behavioral Coaching for Managing Stress: A Case Study on Optimal Policy Estimation with Multi-stage Threshold Q-learning. details	Hu, X., Hsueh, P.-Y.S., Chen, C.-H., Diaz, K.M., Cheung, Y.-K.K. and Qian, M.	2017	AMIA Annual Symposium proceedings.	Measurement	Flex	Adults	Steps Intensity	URL
Validity of the Fitbit One for Measuring Activity in Community-Dwelling Stroke Survivors details	Hui, J., Heyden, R., Bao, T., Accettone, N., McBay, C., Richardson, J. and Tang, A.	2017	Physiotherapy Canada	Validation	One	Patients Adults	Steps	URL
The Accuracy of Pedometers in Measuring Walking Steps on a Treadmill in College Students details	Husted, H. and Llewellyn, T.	2017	International Journal of Exercise Science	Validation	Charge	Adults	Steps	URL
Comparison of four Fitbit and Jawbone activity monitors with a research-grade ActiGraph accelerometer for estimating physical activity and energy expenditure details	Imboden, M.T., Nelson, M.B., Kaminsky, L.A. and Montoye, A.H.	2017	British Journal of Sports Medicine	Validation	One Zip Flex	Adults	Steps Intensity	URL
Activity trackers, prior motivation, and perceived informational and motivational affordances details	Jarrahi, M.H., Gafinowitz, N. and Shin, G.	2017	Personal and Ubiquitous Computing	Survey Research		Adults	Steps Intensity	URL
Wrist-Worn Activity Monitoring Devices Overestimate Sleep Duration and Efficiency in Healthy Adults details	Jumabhoy, R., Dao, P., Maskevich, S., Stout, J. and Drummond, S.	2017	Sleep	Validation	One	Adults	Sleep	URL
Ka-HOLO Project: a protocol for a randomized controlled trial of a native cultural dance program for cardiovascular disease prevention in Native Hawaiians details	Kaholokula, J.K., Look, M.A., Wills, T.A., de Silva, M., Mabellos, T., Seto, T.B., Ahn, H.J., Sinclair, K.A. and Buchwald, D.	2017	BMC Public Health	Methods	Flex	Adults	Steps Energy Expenditure Intensity Distance	URL
Validity of a commercial wearable sleep tracker in adult insomnia disorder patients and good sleepers details	Kang, S.-G., Kang, J.M., Ko, K.-P., Park, S.-C., Mariani, S. and Weng, J.	2017	Journal of Psychosomatic Research	Validation	Flex	Patients Adults	Sleep	URL
Blood Glucose Control and Related Factors at a Camp for Korean Children and Adolescents with Type 1 Diabetes details	Kang, N., Lee, J., Park, D.-H., Lee, S.-K., Nam, S.-Y., Lee, S., Kim, M., Kang, H., Kim, E., Kim, M. and Sohn, M.	2017	Comprehensive Child and Adolescent Nursing	Measurement	Flex	Patients Children Adolescents	Steps	URL
Examination of a Wearable Activity Tracker to Assess Children’s Physical Activity details	Kang, S.-K., An, H.-S., Hong, S. and Lee, J-M.	2017	Medicine & Science in Sports & Exercise	Validation	Charge HR	Children	Intensity	URL
Physical Activities in Patients with β-Thalassemia As Measured By Fitbit in Association with Patient-Reported Outcomes details	Kattamis, A., Sutcharitchan, P., Taher, A., Cappellini, M.D., Mahmoud, D., Pariseau, J., Laadem, A., Khan, A., Hu, X.H. and Viprakasit, V.	2017	Blood	Measurement	One	Patients Adults	Steps
Physical Activity to Reduce Fatigue in Rheumatoid Arthritis: A Randomized, Controlled Trial details	Katz, P., Margaretten, M., Gregorich, S. and Trupin, L.	2017	Arthritis Care & Research	Intervention	Zip	Patients Adults	Steps	URL
The Motivational Impact of Wearable Healthy Lifestyle Technologies: A Self-determination Perspective on Fitbits With Adolescents details	Kerner, C. and Goodyear, V.A.	2017	American Journal of Health Education	Measurement	Charge	Adolescents	NA	URL
Development of an innovative mHealth platform for remote physical activity monitoring and health coaching of cardiac rehabilitation patients details	Kitsiou, S., Thomas, M., Marai, G.E., Maglaveras, N., Kondos, G., Arena, R. and Gerber, B.	2017	2017 IEEE EMBS International Conference on Biomedical & Health Informatics (BHI)	Usability	Charge HR	Patients Adults	Steps Intensity Heart Rate	URL
Consumer-Based Physical Activity Monitor as a Practical Way to Measure Walking Intensity During Inpatient Stroke Rehabilitation details	Klassen, T.D., Semrau, J.A., Dukelow, S.P., Bayley, M.T., Hill, M.D. and Eng, J.J.	2017	Stroke	Validation	One	Patients Adults	Steps	URL
Active2Gether: A Personalized m-Health Intervention to Encourage Physical Activity details	Klein, M., Manzoor, A. and Mollee, J.	2017	Sensors	Methods	One	Adults	Steps Intensity	URL
The Volunteering-in-Place Program: Meaningful Volunteer Activity for Assisted Living Residents details	Klinedinst, J.	2017	Innovation in Aging	Intervention		Older Adults		URL
Implementation of a Home Monitoring System for Heart Failure Patients: A Feasibility Study. details	Kohn, M.S., Haggard, J., Kreindler, J., Birkeland, K., Kedan, I., Zimmer, R. and Khandwalla, R.	2017	JMIR Research Protocols	Methods	Flex	Patients Adults	Steps	URL
Heart Rate Assessment by Smart Watch: Utility or Futility? details	Koshy, A., Sajeev, J., Zureik, M., Rajakariar, K., Street, M., Cosgrave, N., Wong, M., Hamer, A., Cooke, J., Roberts, L. and Teh, A.	2017	Heart, Lung and Circulation	Validation	Blaze	Patients Adults	Heart Rate	URL
Wearable technology to reduce sedentary behavior and CVD risk in older adults: Design of a randomized controlled trial details	Krehbiel, L.M., Layne, A.S., Sandesara, B., Manini, T.M., Anton, S.D. and Buford, T.W.	2017	Contemporary Clinical Trials Communications	Methods	Zip	Adults	Steps Intensity	URL
Use of wearable devices for post-discharge monitoring of ICU patients: a feasibility study details	Kroll, R.R., McKenzie, E.D., Boyd, J.G., Sheth, P., Howes, D., Wood, M. and Maslove, D.M.	2017	Journal of Intensive Care	Usability	Charge HR	Patients Adults	Steps Sleep Heart Rate	URL
N-of-1 study of weight loss maintenance assessing predictors of physical activity, adherence to weight loss plan and weight change details	Kwasnicka, D., Dombrowski, S.U., White, M. and Sniehotta, F.F.	2017	Psychology & Health	Measurement	One Aria	Adults	Steps Weight	URL
Use of a mobile social networking intervention for weight management: a mixed-methods study protocol details	Laranjo, L., Lau, A.Y.S., Martin, P., Tong, H.L. and Coiera, E.	2017	BMJ Open	Methods	Flex 2	Adults	Steps Weight	URL
Use It And Lose It: Fitbit Use, Daily Steps, And Weight Change Among Overweight Adults details	Larsen, C.A., Monroe, C.M. and West, D.S.	2017	Medicine & Science in Sports & Exercise	Intervention	Zip	Adults	Steps	URL
Comparison of Wearable Activity Tracker with Actigraphy for Sleep Evaluation and Circadian Rest-Activity Rhythm Measurement in Healthy Young Adults details	Lee, H.-A., Lee, H.-J., Moon, J.-H., Lee, T., Kim, M.-G., In, H., Cho, C.-H. and Kim, L.	2017	Psychiatry Investigation	Validation	Charge HR	Adults	Steps Sleep Heart Rate	URL
Evaluation of Commercial Self-Monitoring Devices for Clinical Purposes: Results from the Future Patient Trial, Phase I. details	Leth, S., Hansen, J., Nielsen, O.W. and Dinesen, B.	2017	Sensors	Validation	One Zip Charge HR	Adults	Steps Heart Rate	URL
A technology-enabled Counselling program versus a delayed treatment control to support physical activity participation in people with inflammatory arthritis: study protocol for the OPAM-IA randomized controlled trial details	Li, L.C., Feehan, L.M., Shaw, C., Xie, H., Sayre, E.C., Aviña-Zubeita, A., Grewal, N., Townsend, A.F., Gromala, D., Noonan, G. and Backman, C.L.	2017	BMC Rheumatology	Methods	Flex 2	Patients Adults	Steps Intensity	URL
A Community-Based Physical Activity Counselling Program for People With Knee Osteoarthritis: Feasibility and Preliminary Efficacy of the Track-OA Study details	Li, L.C., Sayre, E.C., Xie, H., Clayton, C. and Feehan, L.M.	2017	JMIR mHealth and uHealth	Intervention	Flex	Patients Adults	Steps	URL
Considering interpersonal differences in validating wearable sleep-tracking technologies details	Liang, Z. and Martell, M.A.C.	2017	2017 Tenth International Conference on Mobile Computing and Ubiquitous Network	Validation	Charge 2	Adults	Sleep
Are wearable EEG devices more accurate than fitness wristbands for home sleep Tracking? Comparison of consumer sleep trackers with clinical devices details	Liang, Z. and Nishimura, T.	2017	2017 IEEE 6th Global Conference on Consumer Electronics	Validation	Charge 2	Adults	Sleep
Metformin to Augment Strength Training Effective Response in Seniors (MASTERS): study protocol for a randomized controlled trial details	Long, D.E., Peck, B.D., Martz, J.L., Tuggle, S.C., Bush, H.M., McGwin, G., Kern, P.A., Bamman, M.M. and Peterson, C.A.	2017	Trials	Intervention	Flex	Older Adults Patients	Steps Intensity	URL
Financial incentives and health coaching to improve physical activity following total knee replacement: a randomized controlled trial details	Losina, E., Collins, J.E., Deshpande, B.R., Smith, S.R., Michl, G.L., Usiskin, I.M., Klara, K.M., Winter, A.R., Yang, H.Y., Selzer, F. and Katz, J.N.	2017	Arthritis Care & Research	Intervention	Zip	Older Adults Patients	Steps	URL
Implementation of a workplace intervention using financial rewards to promote adherence to physical activity guidelines: a feasibility study details	Losina, E., Smith, S.R., Usiskin, I.M., Klara, K.M., Michl, G.L., Deshpande, B.R., Yang, H.Y., Smith, K.C., Collins, J.E. and Katz, J.N.	2017	BMC Public Health	Intervention	Flex	Adults	Intensity	URL
Estimation of Symptom Severity During Chemotherapy From Passively Sensed Data: Exploratory Study. details	Low, C.A., Dey, A.K., Ferreira, D., Kamarck, T., Sun, W., Bae, S. and Doryab, A.	2017	Journal of Medical Internet Research	Measurement Methods	Charge HR	Patients Adults	Steps Energy Expenditure Intensity Heart Rate
Fitness band accuracy in older community dwelling adults details	Madigan, E.A.	2017	Health Informatics Journal	Validation	Flex	Older Adults	Steps	URL
Step Count Accuracy and Reliability from Activity Monitors Worn by Mobility-Intact Older Adults details	Maganja, S., Donaldson, M. and Mackey, D.	2017	Innovation in Aging	Validation	One Charge	Older Adults	Steps	URL
Users' experiences of wearable activity trackers: a cross-sectional study details	Maher, C., Ryan, J., Ambrosi, C. and Edney, S.	2017	BMC Public Health	Survey Research		Adults		URL
Aerobic exercise training in children and adolescents with inflammatory bowel disease: Influence on psychological functioning, sleep and physical performance – An exploratory trial details	Mählmanna, L., Gerber, M., Furlano, R.I., Legeret, C., Kalak, N., Holsboer-Trachsler, E. and Brand, S.	2017	Mental Health and Physical Activity	Intervention	Flex	Children Adolescents	Steps	URL
Promoting Optimal Physical Exercise for Life (PROPEL): aerobic exercise and self-management early after stroke to increase daily physical activity-study protocol for a stepped-wedge randomised trial. details	Mansfield, A., Brooks, D., Tang, A., Taylor, D., Inness, E.L., Kiss, A., Middleton, L., Biasin, L., Fleck, R., French, E., LeBlanc, K., Aqui, A. and Danells, C.	2017	BMJ Open	Methods	One Charge HR	Patients Adults	Steps Heart Rate
Validity of the Fitbit? Distance Traveled Feature Among Multiple Speed Trials details	Marton, C.J.	2017	Medicine & Science in Sports & Exercise	Validation	Charge	Adults	Distance	URL
Pilot Validation of Ambulatory Activity Monitors for Sleep Measurement in Huntington's Disease Gene Carriers details	Maskevich, S., Jumabhoy, R., Dao, P.D., Stout, J.C. and Drummond, S.P.	2017	Journal of Huntington's Disease	Validation	One	Patients Adults	Sleep	URL
Explaining System Missing: Missing Data and Experience Sampling Method details	McLean, D.C., Nakamura, J. and Csikszentmihalyi, M.	2017	Social Psychological and Personality Science	Measurement	Flex	Adults	Steps Sleep	URL
Assessing the Effects of Interpersonal and Intrapersonal Behavior Change Strategies on Physical Activity in Older Adults: a Factorial Experiment details	McMahon, S.K., Lewis, B., Oakes, J.M., Wyman, J.F., Guan, W. and Rothman, A.J.	2017	Annals of Behavioral Medicine	Intervention	One	Older Adults	Steps Intensity Distance	URL
Impact of work-hours and sleep on well-being and burnout for physicians-in-training: the prospective RATE Study details	Mendelsohn, D., Despot, I., Gooderham, P., Singhal, A., Redekop, G. and Toyota, B.	2017	Canadian Journal of Neurological Sciences	Measurement		Adults	Steps	URL
Data driven multi-agent m-health system to characterize the daily activities of elderly people details	Mendes, S., Queiroz, J. and Leitao, P.	2017	2017 12th Iberian Conference on Information Systems and Technologies	Measurement Methods	Charge HR	Older Adults	Steps Intensity Sleep Heart Rate	URL
A Fitbit and Facebook mHealth intervention for promoting physical activity among adolescent and young adult childhood cancer survivors: A pilot study details	Mendoza, J.A., Baker, K.S., Moreno, M.A., Whitlock, K., Abbey-Lambertz, M., Waite, A., Colburn, T. and Chow, E.J.	2017	Pediatric Blood & Cancer	Intervention	Flex	Adolescents	Steps	URL
Crafting a View of Self-Tracking Data in the Clinical Visit details	Mentis, H.M., Komlodi, A., Schrader, K., Phipps, M., Gruber-Baldini, A., Yarbrough, K. and Shulman, L.	2017	Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems	Usability	Zip	Patients Adults	Steps	URL
Adolescent girls' reactions to nutrition and physical activity assessment tools and insight into lifestyle habits details	Metos, J., Gren, L., Brusseau, T., Moric, E., O'Toole, K., Mokhtari, T., Buys, S. and Frost, C.	2017	Health Education Journal	Usability	Flex	Adolescents	Steps Energy Expenditure	URL
Identification and Classification of Usage Patterns in Long-Term Activity Tracking details	Meyer, J., Wasmann, M., Heuten, W., El Ali, A. and Boll, S.C.	2017	Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems	Review Methods	Ultra One Zip Flex	Adults	Steps	URL
A Validation Study of the Fitbit One in Daily Life Using Different Time Intervals details	Middelweerd, A., Van der Ploeg, H.P., Van Halteren, A., Twisk, J., Brug, J. and Te Velde, S.J.	2017	Medicine & Science in Sports & Exercise	Validation	One	Adults	Steps Intensity	URL
Increasing physical activity through an Internet-based motivational intervention supported by pedometers in a sample of sedentary students: A randomised controlled trial details	Miragall, M., Domínguez-Rodríguez, A., Navarro, J., Cebolla, A. and Baños, R.M.	2017	Psychology & Health	Intervention	One	Adults	Steps	URL
Assessing the Use of Wrist-Worn Devices in Patients With Heart Failure: Feasibility Study details	Moayedi, Y., Abdulmajeed, R., Duero Posada, J., Foroutan, F., Alba, A.C., Cafazzo, J. and Ross, H.J.	2017	JMIR Cardio	Validation	Charge HR	Patients Adults	Steps Heart Rate
Mobile Device Accuracy for Step Counting Across Age Groups. details	Modave, F., Guo, Y., Bian, J., Gurka, M.J., Parish, A., Smith, M.D., Lee, A.M. and Buford, T.W.	2017	JMIR mHealth and uHealth	Validation	Surge	Older Adults Adults	Steps	URL
Types of Social Support and Weight Change among Overweight Adults details	Monroe, C.M., Larsen, C. and West, D.	2017	Medicine & Science in Sports & Exercise	Intervention	Zip Aria	Adults	Weight	URL
Energy Expenditure and Step Count Analysis of the Fitbit Flex Activity Tracker details	Montes, J. and Navalta, J.	2017	Medicine & Science in Sports & Exercise	Validation	Flex	Adults	Steps Energy Expenditure	URL
Comparative Accuracy of a Wrist-Worn Activity Tracker and a Smart Shirt for Physical Activity Assessment details	Montoye, A.H.K., Mitrzyk, J.R. and Molesky, M.J.	2017	Measurement in Physical Education and Exercise	Validation	Charge HR	Adults	Steps Energy Expenditure Heart Rate	URL
Determining Resident Sleep during and after Call with Commercial Sleep Monitoring Devices. details	Morhardt, D.R., Luckenbaugh, A., Goldstein, C. and Faerber, G.J.	2017	Urology	Measurement	Flex	Adults	Sleep	URL
Reduction of Health Disparities in Appalachians With Multiple Cardiovascular Disease Risk Factors: A Randomized Controlled Trial details	Moser, D.K., Feltner, F., Biddle, M.J., Chung, M.L. and Lennie, T.A.	2017	Circulation	Intervention		Patients Adults	Steps	URL
Development and evaluation of two web-based interventions for the promotion of physical activity in older adults: study protocol for a community-based controlled intervention trial details	Muellmann, S., Bragina, I., Voelcker-Rehage, C., Rost, E., Lippke, S., Meyer, J., Schnauber, J., Wasmann, M., Toborg, M., Koppelin, F., Brand, T., Zeeb, H. and Pischke, C.R.	2017	BMC Public Health	Methods	Zip	Older Adults	Steps	URL
Minimal Detectable Change Thresholds and Responsiveness of Zephyr Bioharness & Fitbit Charge Devices. details	Nazari, G. and MacDermid, J.C.	2017	Journal of Strength and Conditioning Research	Validation	Charge HR	Adults	Heart Rate	URL
Feasibility of a telephone and web-based physical activity intervention for women shift workers details	Neil-Sztramko, S.E., Gotay, C.C., Sabiston, C.M., Demers, P.A. and Campbell, K.C.	2017	Translational Behavioral Medicine	Usability Intervention	Flex	Adults	Steps Intensity Distance	URL
A qualitative evaluation of breast cancer survivors' acceptance of and preferences for consumer wearable technology activity trackers details	Nguyen, N.H., Hadgraft, N.T., Moore, M.M., Rosenberg, D.E., Lynch, C., Reeves, M.M. and Lynch, B.M.	2017	Supportive Care in Cancer	Usability	One	Patients Adults	Steps	URL
Cognitive Behavior Therapy to Treat Sleep Disturbance and Fatigue After Traumatic Brain Injury: A Pilot Randomized Controlled Trial details	Nguyen, S., McKay, A., Wong, D., Rajaratnam, S.M., Spitz, G., Williams, G., Mansfield, D. and Ponsford, J.L.	2017	Archives of Physical Medicine and Rehabilitation	Intervention	Zip	Patients Adults	Steps	URL
Determining the role of Maternal Leptin and Activity in Pregnancy Outcomes details	Nishimura, T.E., Polgreen, P.M., Polgreen, L.A., Brandt, D.S., Thomas, K.M., Scroggins, S.M., Evans, N.J., Alexander, L.B., Santillan, M.K. and Santillan, D.A.	2017	The FASEB Journal	Measurement	Zip	Adults Pregnant Women	Steps	URL
The utility of personal activity trackers (Fitbit Charge 2) on exercise capacity in patients post acute coronary syndrome [UP-STEP ACS Trial]: a randomised controlled trial protocol details	Nogic, J., Thein, P.M., Cameron, J., Mirzaee, S., Ihdayhid, A. and Nasis, A.	2017	BMC Cardiovascular Disorders	Methods	Charge 2	Patients Adults	Steps Heart Rate
Accuracy of Consumer Monitors for Measuring Sleep Across Seven Nights details	Nowakowski, S., Grandner, M., Stout-Aguilar, J., Meers, J., Lewis-Powell, Z., Swartz, M., Choi, H. and Lyons, E.	2017	Sleep	Validation	Flex	Adults	Sleep	URL
Measuring and understanding adherence in a home-based exercise intervention during chemotherapy for early breast cancer details	Nyrop, K.A., Deal, A.M., Choi, S.K., Wagoner, C.W., Lee, J.T., Wood, A., Anders, C., Carey, L.A., Dees, E.C., Jolly, T.A., Reeder-Hayes, K.E. and Muss, H.B.	2017	Breast Cancer Research and Treatment	Intervention	Zip	Patients Adults	Steps	URL
When a Step Is Not a Step! Specificity Analysis of Five Physical Activity Monitors details	O’Connell, S., ÓLaighin, G. and Quinlan, L.R.	2017	PLOS ONE	Validation	One	Adults	Steps	URL
What Matters in Weight Loss? An In-Depth Analysis of Self-Monitoring. details	Painter, S.L., Ahmed, R., Hill, J.O., Kushner, R.F., Lindquist, R., Brunning, S. and Margulies, A.	2017	Journal of Medical Internet Research	Intervention	One Aria	Adults	Steps Energy Expenditure Intensity Weight	URL
Screening of Atrial Fibrillation Using Wrist Photoplethysmography from a Fitbit Tracker details	Pantelopoulos, A., Faranesh, A., Milescu, A., Hosking, P., Venkatraman, S. and Heneghan, C.	2017	Iproceedings	Validation Methods		Patients Adults	Heart Rate	URL
Sources of Error with Wearable Step Counters details	Park, S., Toth, L.P., Morton, A.L., Pittman, W.L., Sarasaltik, D. and Bassett, D.R.	2017	Medicine & Science in Sports & Exercise	Validation	Zip Charge	Adults	Steps	URL
Effect of a Game-Based Intervention Designed to Enhance Social Incentives to Increase Physical Activity Among Families: The BE FIT Randomized Clinical Trial details	Patel, M.S., Benjamin, E.J., Volpp, K.G., Fox, C.S., Small, D.S., Massaro, J.M., Lee, J.J., Hilbert, V., Valentino, M., Taylor, D.H., Manders, E.S., Mutalik, K., Zhu, J., Wang, W. and Murabito, J.M.	2017	AMA Internal Medicine	Intervention	Flex	Adults	Steps	URL
Using Wearable Devices and Smartphones to Track Physical Activity: Initial Activation, Sustained Use, and Step Counts Across Sociodemographic Characteristics in a National Sample details	Patel, M.S., Foschini, L., Kurtzman, G.W., Zhu, J., Wang, W., Rareshide, C.A. and Zbikowski, S.M.	2017	Annals of Internal Medicine	Measurement Methods		Adults	Steps	URL
A Feasibility Study for Improved Physical Activity After Total Knee Arthroplasty details	Paxton, R.J., Forster, J.E., Miller, M.J., Gerron, K.L., Stevens-Lapsley, J.E. and Christiansen, C.L.	2017	Journal of Aging and Physical Activity	Intervention	Zip	Patients Adults	Steps	URL
Physical Activity Trackers in Combination with Motivational Interviewing to Increase Activity details	Perez, M., Ellingson, L., Bai, Y., Peyer, K. and Welk, G.	2017	Medicine & Science in Sports & Exercise	Intervention	Charge	Adults	Steps	URL
Quantification of physical activity in adult patients with haemophilic arthropathy in prophylaxis treatment using a fitness tracker details	Pérez-Alenda, S., Carrasco, J.J., Megías-Vericat, J.E., Poveda, J.L., Bonanad, S. and Querol, F.	2017	Haemophilia	Measurement	Charge HR	Patients Adults	Steps Intensity Distance
Interdependent Effects of Autonomous and Controlled Regulation on Exercise Behavior details	Phillips, L.A. and Johnson, M.A.	2017	Personality and Social Psychology Bulletin	Measurement	Zip	Adults	Steps Intensity	URL
Wellness, sleep and exercise in emergency medicine residents: an observational study details	Poonja, Z., O'Brien, P., Cross, E., Desrochers, C., Jaggi, P., Dance, E., Krentz, J. and Thoma, B.	2017	Canadian Journal of Emergency Medicine	Measurement	Charge	Adults	Steps Intensity Sleep	URL
Step It Up: Higher Step Count Is Significantly Correlated with Better Exercise Capacity in Individuals with Cystic Fibrosis details	Powers, K., Herzog, T., Loosen, H., Berg, K.V., Weir, B., Riekert, K.A. and Mogayzel, P.J.	2017	American Journal of Respiratory and Critical Care Medicine	Intervention	Flex	Patients Adults	Steps	URL
Accuracy And Reliability Of The Fitbit Charge? Activity Tracker Among Older Adults details	Powers, M., Smith, M., Henderson, O. and Bodman, T.	2017	Medicine & Science in Sports & Exercise	Validation	Charge	Older Adults	Steps	URL
Validation of a self-monitoring tool for use in exercise therapy. details	Powierza, C.S., Clark, M.D., Hughes, J.M., Carneiro, K.A. and Mihalik, J.P.	2017	PM&R	Validation	Charge HR	Adults	Heart Rate	URL
A Six-Week Supervised Exercise and Educational Intervention After Total Hip Arthroplasty: A Case Series details	Pozzi, F., Madara, K. and Zeni Jr, J.A.	2017	The International Journal of Sports Physical Therapy	Intervention	Zip	Patients Adults	Steps	URL
Monitoring-Based Model for Personalizing the Clinical Process of Crohn's Disease details	de Ramón-Fernández, A., Ruiz-Fernández, D., Marcos-Jorquera, D., Gilart-Iglesias, V. and Vives-Boix, V.	2017	Sensors	Methods	Flex	Patients	Steps	URL
Can Fitness Trackers Help Diabetic and Obese Users Make and Sustain Lifestyle Changes? details	Randriambelonoro, M., Chen, Y. and Pu, P.	2017	Computer	Usability Measurement	One	Patients Adults	Steps	URL
Effectiveness of SmartMoms, a Novel eHealth Intervention for Management of Gestational Weight Gain: Randomized Controlled Pilot Trial details	Redman, L.M., Gilmore, L.A., Breaux, J., Thomas, D.M., Elkind-Hirsch, K., Stewart, T., Hsia, D.S., Burton, J., Apolzan, J.W., Cain, L.E., Altazan, A.D., Ragusa, S., Brady, H., Davis, A., Tilford, J.M., Sutton, E.F. and Martin, C.K.	2017	JMIR mHealth and uHealth	Intervention	Zip	Adults Pregnant Women	Steps	URL
A Feasibility Study Examining Use of the FitBit Zip™ vs. the Accusplit Eagle AC 120 XL Pedometer to Increase Physical Activity for Persons with Mild Cognitive Disorder details	Richeson, N.E. and Croteau, K.A.	2017	Activities, Adaptation & Aging	Usability	Zip	Older Adults	Steps	URL
A cluster-randomised controlled trial to promote physical activity in adolescents: the Raising Awareness of Physical Activity (RAW-PA) Study. details	Ridgers, N.D., Timperio, A., Brown, H., Ball, K., Macfarlane, S., Lai, S.K., Richards, K., Ngan, W. and Salmon, J.	2017	BMC Public Health	Methods	Flex	Adolescents	Steps Intensity	URL
Smartphone-Enabled Health Coaching Intervention (iMOVE) to Promote Long-Term Maintenance of Physical Activity in Breast Cancer Survivors: Protocol for a Feasibility Pilot Randomized Controlled Trial details	Ritvo, P., Obadia, M., Santa Mina, D., Alibhai, S., Sabiston, C., Oh, P., Campbell, K., McCready, D., Auger, L. and Jones, J.M.	2017	JMIR Research Protocols	Methods	Flex	Patients Adults	Steps Intensity Sleep	URL
An Acceptance and Commitment Therapy (ACT) intervention for Chronic Fatigue Syndrome (CFS): A case series approach details	Roche, L., Dawson, D.L., Moghaddam, N.G. and Abey, A.	2017	Journal of Contextual Behavioral Science	Intervention	Flex	Patients Adults	Steps	URL
A telehealth intervention to promote healthy lifestyles after stroke: The Stroke Coach protocol details	Sakakibara, B.M., Lear, S.A., Barr, S.I., Benavente, O., Goldsmith, C.H., Silverberg, N.D., Yao, J. and Eng, J.J.	2017	International Journal of Stroke	Methods	Zip	Older Adults Patients	Steps	URL
Heart rate and sentiment experimental data with common timeline details	Salamon, J. and Moucek, R.	2017	Data in Brief	Measurement	Charge HR	Adults	Heart Rate	URL
Accuracy of Fitbit Charge 2 Worn at Different Wrist Locations During Exercise details	Salazar, V.E., Lucio, N.D. and Funk, M.D.	2017	Medicine & Science in Sports & Exercise	Validation	Charge 2	Adults	Heart Rate	URL
Step count accuracy and reliability of two activity tracking devices in people after stroke details	Schaffer, S.D., Holzapfel, S.D., Fulk, G. and Bosch, P.R.	2017	Physiotherapy Theory and Practice	Validation	Zip	Patients Adults	Steps	URL
Piloting a mobile health intervention to increase physical activity for adolescents with ADHD details	Schoenfelder, E., Moreno, M., Wilner, M., Whitlock, K.B. and Mendoza, J.A.	2017	Preventive Medicine Reports	Intervention	Flex	Patients Adolescents	Steps	URL
What factors predict Fitbit adherence in Stroke and Parkinson disease? details	Schrader, K., Mentis, H., Phipps, M., Barr, E., Gruber-Baldini, A., Yarbrough, K., Coelln, F.R.V. and Shulman, L.	2017	Neurology	Usability	Charge HR	Patients Children	Heart Rate	URL
Assessing the Influence of a Fitbit Physical Activity Monitor on the Exercise Practices of Emergency Medicine Residents: A Pilot Study details	Schrager, J.D., Shayne, P., Wolf, S., Das, S., Patzer, R.E., White, M. and Heron, S.	2017	JMIR mHealth and uHealth	Intervention	Flex	Adults	Steps Intensity	URL
Understanding physical activity lapses among women: responses to lapses and the potential buffering effect of social support details	Schumacher, L.M., Arigo, D. and Thomas, C.	2017	Journal of Behavioral Medicine	Intervention	Flex	Adults	Steps Intensity	URL
Fitbit Vs Six Minute Walk in PAH details	Sehgal, S., Chowdhury, A., Rabih, F., Gadre, A. and Highland, K.B.	2017	American Journal of Respiratory and Critical Care Medicine	Measurement	Charge HR	Patients Adults	Steps	URL
Validity and reliability of the Fitbit Zip as a measure of preschool children's step count details	Sharp, C.A., Mackintosh, K.A., Erjavec, M., Pascoe, D.M. and Horne, P.J.	2017	BMJ Open Sport & Exercise Medicine	Validation	Zip	Children	Steps	URL
Accuracy in Wrist-Worn, Sensor-Based Measurements of Heart Rate and Energy Expenditure in a Diverse Cohort details	Shcherbina, A., Mattsson, C., Waggott, D., Salisbury, H., Christle, J., Hastie, T., Wheeler, M. and Ashley, E.	2017	Journal of Personalized Medicine	Validation	Surge	Adults	Energy Expenditure Heart Rate
Exercise- and Psychosocial-Based Interventions to Improve Daily Activity in Heart Failure: A Pilot Study details	Shoemaker, M.J., Oberholtzer, N.L., Jongekrijg, L.E., Bowen, T.E., Cartwright, K., Hanson, K., Serba, D., Dickinson, M.G. and Kowalk, A.	2017	Home Health Care Management & Practice	Intervention	Zip	Patients Adults	Steps	URL
"Pre-Habilitation" of Frail Candidates for Lung Transplantation Using a Mobile Health Enabled Home-Based Intervention Is Feasible and Safe details	Singer, J., Soong, A., Bruun, A., Hays, S., Kukreja, J., Bracha, A., Chin, G., Wolters, P.J., Peters, M. and Garvey, C.M.	2017	American Journal of Respiratory and Critical Care Medicine	Intervention		Patients Adults		URL
Accuracy Of Steps, Energy Expenditure, And Distance In Nine Activity Trackers details	Smith, M.A.	2017	Medicine & Science in Sports & Exercise	Validation	Charge Charge HR Surge	Adults	Steps Energy Expenditure Distance	URL
Maintained Physical Activity Induced Changes in Delay Discounting details	Sofis, M.J., Carrillo, A. and Jarmolowicz, D.P.	2017	Behavior Modification	Intervention	Charge HR	Adults	Steps	URL
Analyzing Sensor-Based Time Series Data to Track Changes in Physical Activity during Inpatient Rehabilitation details	Sprint, G., Cook, D., Weeks, D., Dahmen, J. and La Fleur, A.	2017	Sensors	Methods	Charge HR	Adults	Steps Heart Rate	URL
Activity-tracking devices for older adults: comparison and preferences details	Steinert, A., Haesner, M. and Steinhagen-Thiessen, E.	2017	Universal Access in the Information Society	Usability	Flex	Older Adults		URL
Healthy lifestyle intervention for African American uterine cancer survivors: Study protocol details	Stewart, J.L., Besenyi, G.B., Williams, L.B., Burt, V., Anglin, J.C., Ghamande, S.A. and Coughlin, S.S.	2017	Contemporary Clinical Trials Communications	Methods	Blaze	Patients Adults	Steps Energy Expenditure Intensity Distance Sleep Heart Rate	URL
Virtual Games and Real- World Communities: Environments that Constrain and Enable Physical Activity in Games for Health details	Stewart, M.K., Hagood, D. and Ching, C.C.	2017	International Journal of Game-Based Learning	Usability		Children Adolescents		URL
A multicenter controlled study for dementia prevention through physical, cognitive and social activities - GESTALT-kompakt. details	Streber, A., Abu-Omar, K., Hentschke, C. and Rütten, A.	2017	Clinical Interventions in Aging	Intervention	Zip	Older Adults	Steps
The Spread of Physical Activity Through Social Networks details	Stück, D., Hallgrímsson, H.T., Ver Steeg, G., Epasto, A. and Foschini, L.	2017	Proceedings of the 26th International Conference on World Wide Web - WWW '17	Measurement Methods		Adults	Steps	URL
A Mobile Health Application to Track Patients After Gastrointestinal Surgery: Results from a Pilot Study details	Symer, M.M., Abelson, J.S., Milsom, J., McClure, B. and Yeo, H.L.	2017	Journal of Gastrointestinal Surgery	Usability	Charge HR	Patients Adults	Steps	URL
Fitbit And Actigraph: A Comparison Of Physical Activity And Sedentary Time In Overweight/obese Adults details	Szabo-Reed, A.N., Willis, E., Ptomey, L., Washburn, R. and Donnelly, J.	2017	Medicine & Science in Sports & Exercise	Validation	Flex	Adults	Steps Intensity	URL
Effect of sleep curtailment on dietary behavior and physical activity: A randomized crossover trial details	Tajiri, E., Yoshimura, E., Hatamoto, Y., Tanaka, H. and Shimoda, S.	2017	Physiology & Behavior	Measurement	Charge HR	Adults	Sleep	URL
Harnessing Long Term Physical Activity Data—How Long-term Trackers Use Data and How an Adherence-based Interface Supports New Insights details	Tang, L.M. and Kay, J.	2017	Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies	Usability		Adults	Steps	URL
Study protocol for the FITR Heart Study: Feasibility, safety, adherence, and efficacy of high intensity interval training in a hospital-initiated rehabilitation program for coronary heart disease details	Taylor, J., Keating, S.E., Leveritt, M.D., Holland, D.J., Gomersall, S.R. and Coombes, J.S.	2017	Contemporary Clinical Trials Communications	Methods	Charge HR	Patients Adults	Steps Intensity	URL
Accuracy of a step counter during treadmill and daily life walking by healthy adults and patients with cardiac disease details	Thorup, C.B., Andreasen, J.J., Sørensen, E.E., Grønkjær, M., Dinesen, B.I. and Hansen, J.	2017	BMJ Open	Validation	Zip	Patients Adults	Steps	URL
Step Count Filters in Wearable Step Counters details	Toth, L., Park, S., Pittman, W., Sarasaltik, D., Morton, A.L. and Bassett, D.R.	2017	Medicine & Science in Sports & Exercise	Validation Methods	Zip Charge	Adults	Steps	URL
Validity of Different Activity Monitors to Count Steps in an Inpatient Rehabilitation Setting. details	Treacy, D., Hassett, L., Schurr, K., Chagpar, S., Paul, S.S. and Sherrington, C.	2017	Physical Therapy	Validation	One Charge	Patients Adults	Steps	URL
A model linking video gaming, sleep quality, sweet drinks consumption and obesity among children and youth details	Turel, O., Romashkin, A. and Morrison, K.M.	2017	Clinical Obesity	Measurement		Children	Steps	URL
Building research in diet and cognition: The BRIDGE randomized controlled trial. details	Tussing-Humphreys, L., Lamar, M., Blumenthal, J.A., Babyak, M., Fantuzzi, G., Blumstein, L., Schiffer, L. and Fitzgibbon, M.L.	2017	Contemporary clinical trials	Methods		Older Adults Patients	Steps Intensity Distance	URL
Patient-Specific Activity Level Benchmarks During Recovery for Total Knee Arthoplasty details	Twiggs, J., Roe, J., Salmon, L., Miles, B. and Theodore, W.	2017	Orthopaedic Proceedings	Measurement		Patients Adults	Steps	URL
Measurement of physical activity in the pre- and early post-operative period after total knee arthroplasty for Osteoarthritis using a Fitbit Flex device. details	Twiggs, J., Salmon, L., Kolos, E., Bogue, E., Miles, B. and Roe, J.	2017	Medical Engineering &Physics	Measurement	Flex	Patients Adults	Steps	URL
Data presentation options to manage variability in physical activity research details	Valbuena, D., Miller, B.G., Samaha, A.L. and Miltenberger, R.G.	2017	Journal of Applied Behavior Analysis	Methods	One	Adults	Steps	URL
Self-monitoring and reminder texts to increase physical activity after colorectal cancer (Smart Pace): A pilot trial. details	Van Blarigan, E., Van Loon, K., Kenfield, S.A., Chan, J.M., Fukuoka, Y., Laffan, A., Mitchell, E., Chan, H., Rodriguez-Jaquez, D., Meyerhardt, J.A. and Venook, A.P.	2017	Journal of Clinical Oncology	Intervention	Flex	Patients Adults	Steps	URL
The Fitbit One Physical Activity Tracker in Men With Prostate Cancer: Validation Study details	Van Blarigan, E.L., Kenfield, S.A., Tantum, L., Cadmus-Bertram, L.A., Carroll, P.R. and Chan, J.M.	2017	JMIR Cancer	Validation	One	Patients Adults	Steps Intensity	URL
Identifying Beneficial Physical Activity During School Recess: Utility and Feasibility of the Fitbit details	Van Camp, C.M. and Hayes, L.B.	2017	Journal of Behavioral Education	Usability Validation	Classic	Children	Steps	URL
TEEN HEED: Design of a clinical-community youth diabetes prevention intervention details	Vangeepuram, N., Williams, N., Constable, J., Waldman, L., Lopez-Belin, P., Phelps-Waldropt, L. and Horowitz, C.R.	2017	Contemporary Clinical Trials	Methods	One	Adolescents	Steps	URL
Virtual Environment for Diabetes Self-Management Education and Support: Preliminary RCT Outcomes details	Vorderstrasse, A., Melkus, G.D., Feinglos, M., Pan, W., Chang, J., Pereira, K., Reagan, L., Kreider, K., Totten, S., Burd, A., Nowlin, S., Birdsall, S. and Johnson, C.M.	2017	Circulation	Intervention		Patients Adults	Steps Intensity	URL
Criterion-Validity of Commercially Available Physical Activity Tracker to Estimate Step Count, Covered Distance and Energy Expenditure during Sports Conditions details	Wahl, Y., Düking, P., Droszez, A., Wahl, P. and Mester, J.	2017	Frontiers in Physiology	Validation	Charge Charge HR	Adults	Steps Energy Expenditure Distance	URL
Evaluation on Step Counting Performance of Wristband Activity Monitors in Daily Living Environment details	Wang, L., Liu, T., Wang, Y., Li, Q., Yi, J. and Inoue, Y.	2017	IEEE Access	Validation	Charge	Adults	Steps	URL
Polysomnography vs Consumer Devices: Comparing Common Sleep Indices details	Wedderburn-Bisshop, J., Kharatishvili-Bredt, I., Martin, I. and Tolhurst, S.	2017	Sleep	Validation	Charge HR	Patients Adults	Sleep	URL
Wearable Heart Rate Monitor Technology Accuracy in Research: A Comparative Study Between PPG and ECG Technology details	Weiler, D.T., Villajuan, S.O., Edkins, L., Cleary, S. and Saleem, J.J.	2017	Proceedings of the Human Factors and Ergonomics Society Annual Meeting	Validation	Charge HR	Adults	Heart Rate	URl
Evaluating the Consistency of Current Mainstream Wearable Devices in Health Monitoring: A Comparison Under Free-Living Conditions details	Wen, D., Zhang, X., Liu, X. and Lei, J.	2017	Journal of Medical Internet Research	Validation	Surge	Adults	Steps Energy Expenditure Intensity Distance Sleep	URL
Sensing interstitial glucose to nudge active lifestyles (SIGNAL): feasibility of combining novel self-monitoring technologies for persuasive behaviour change. details	Whelan, M.E., Kingsnorth, A.P., Orme, M.W., Sherar, L.B. and Esliger, D.W.	2017	BMJ Open	Methods	Charge 2	Patients Adults	Steps Intensity Heart Rate	URL
Physical Activity and Sleep Patterns in Hemodialysis Patients in a Suburban Environment. details	Williams, S., Han, M., Ye, X., Zhang, H., Meyring-Wösten, A., Bonner, M., Young, C., Thijssen, S., Marsh, D. and Kotanko, P.	2017	Blood Purification	Measurement	Flex	Patients Adults	Steps Sleep	URL
Modeling clinically validated physical activity using commodity hardware details	Winfree, K.N. and Dominick, G.	2017	2017 IEEE EMBS International Conference on Biomedical & Health Informatics (BHI)	Methods	Flex	Adults	Steps Intensity	URL
Improved Mental Acuity Forecasting with an Individualized Quantitative Sleep Model. details	Winslow, B.D., Nguyen, N. and Venta, K.E.	2017	Frontiers in Neurology	Methods	Charge HR	Adults	Steps Intensity Sleep	URL
Validation of Three Wearable Fitness Trackers for Measuring Physical Activity in College Students details	Witmer, C., Zhang, P., Godin, S. and Che, D.	2017	Medicine & Science in Sports & Exercise	Validation	Charge HR	Adults	Energy Expenditure	URL
An analysis of how consumer physical activity monitors are used in biomedical research details	Wright, S.P., Collier, S.R., Brown, T.S. and Sandberg, K.	2017	The FASEB Journal	Review		NA	NA	URL
How consumer physical activity monitors could transform human physiology research details	Wright, S.P., Hall Brown, T.S., Collier, S.R. and Sandberg, K.	2017	American Journal of Physiology - Regulatory, Integrative and Comparative Physiology	Review		NA	NA	URL
Using Wireless Pedometers to Measure Children's Physical Activity: How Reliable is the Fitbit Zip? details	Xu, T., Byker, E.J. and Gonzales, M.R.	2017	Movement, Health & Exercise	Validation	Zip	Children	Steps	URL
Habitual sleep duration and sleep duration variation are independently associated with body mass index details	Xu, X., Conomos, M.P., Manor, O., Rohwer, J.E., Magis, A.T. and Lovejoy, J.C.	2017	International Journal of Obesity	Measurement	Charge HR	Adults	Sleep	URL
Impact of Electronic Feedback and Peer Comparisons on Residents' Physical Activity Level details	Yeung, J., Mazloomdoost, D., Crisp, C.C., Kleeman, S. and Pauls, R.N.	2017	Journal of Graduate Medical Education	Intervention Measurement	Flex	Adults	Steps	URL
Stress moderation impacting lupus with exercise (S.M.I.L.E.): influence of daily moderate exercise and stress modification on autoimmune-mediated inflammation in mice and humans with lupus details	Young, N., Thomas, E., Snoad, B., Sharma, J., Mobeen, M., Yedimenko, J., Steigelman, H., Meara, A., Valiente, G., Ardoin, S., Powell, N., Sheridan, J. and Jarjour, W.	2017	Annals of the Rheumatic Diseases	Intervention	Flex	Patients Adults	Steps Energy Expenditure Distance Sleep	URL
A validation study of Fitbit Charge 2™ compared with polysomnography in adults details	de Zambotti, M., Goldstone, A., Claudatos, S., Colrain, I.M. and Baker, F.C.	2017	Chronobiology International	Validation	Charge 2	Adults	Sleep
A Pilot Study to Assess the Feasibility and Impact of a Home Exercise Program on Heart Rate and Heart Rate Variability in Children with Muscular Dystrophy (P4.163) details	Zelikovich, A.S., Oswald, T. and Kuntz, N.L.	2017	Neurology	Usability		Older Adults Patients Adults	Steps	URL
Exercise Among Women With Ovarian Cancer: A Feasibility and Pre-/Post-Test Exploratory Pilot Study. details	Zhang, X., McClean, D., Ko, E., Morgan, M.A. and Schmitz, K.	2017	Oncology Nursing Forum	Intervention		Patients Adults	Steps Intensity	URL
Effects of Brief Fitbit Use on Physical Activity & Cardiometabolic Risk details	Adams, M.M. and Freleng, C.	2018	Journal of Physical Activity Research	Intervention	Flex	Adults	Energy Expenditure Intensity Heart Rate	URL
Step count accuracy of StepWatch and FitBit One™ among individuals with a unilateral transtibial amputation details	Arch, E.S., Sions, J.M., Horne, J. and Bodt, B.A.	2018	Prosthetics and Orthotics International	Validation	One	Patients Adults	Steps
Assessing and enhancing the utility of low-cost activity and location sensors for exposure studies details	Asimina, S., Chapizanis, D., Karakitsios, S., Kontoroupis, P., Asimakopoulos, D.N., Maggos, T. and Sarigiannis, D.	2018	Environmental Monitoring and Assessment	Methods	Flex	Adults	Steps
Assessing personal exposure to PM using data from an integrated indoor-outdoor experiment in Athens-Greece details	Assimakopoulos, V.D., Bekiari, T., Pateraki, S., Maggos, T., Stamatis, P., Nicolopoulou, P. and Assimakopoulos, M.N.	2018	Science of The Total Environment	Measurement	Flex	Adults	Steps	URL
Clinical response to treatment in inpatients with depression correlates with changes in activity levels and psychomotor speed details	Averill, I.R., Crowe, M., Frampton, C.M., Beaglehole, B., Lacey, C.J., Jordan, J., Wilson, L.D., Douglas, K.M. and Porter, R.J.	2018	Australian & New Zealand Journal of Psychiatry	Intervention	Charge HR	Patients Adults	Intensity
A Patient-Centered Activity Regimen Improves Participation in Physical Activity Interventions in Advanced-Stage Lung Cancer details	Bade, B.C., Hyer, J.M., Bevill, B.T., Pastis, A., Rojewski, A.M., Toll, B.A. and Silvestri, G.A	2018	Integrative Cancer Therapies	Intervention	Zip Flex	Patients	Steps
Current, future and potential use of mobile and wearable technologies and social media data in the ABCD study to increase understanding of contributors to child health details	Bagot, K., Matthews, S., Mason, M., Squeglia, L.M., Fowler, J., Gray, K., Herting, M., May, A., Colrain, I., Godino, J., Tapert, S., Brown, S. and Patrick, K.	2018	Developmental Cognitive Neuroscience	Review	Charge HR Charge 2	Adolescents	Sleep Heart Rate
Feeling validated yet? A scoping review of the use of consumer-targeted wearable and mobile technology to measure and improve sleep details	Baron, K.G., Duffecy, J., Berendsen, M.A., Cheung Mason, I., Lattie, E.G. and Manalo, N.C.	2018	Sleep Medicine Reviews	Review	One Flex Charge Charge HR	NA	Sleep
Technology-Assisted Behavioral Intervention to Extend Sleep Duration: Development and Design of the Sleep Bunny Mobile App. details	Baron, K.G., Duffecy, J., Reid, K., Begale, M. and Caccamo, L.	2018	JMIR Mental Health	Intervention	Flex	Adults	Sleep
Assessment of the Fitbit Charge 2 for monitoring heart rate details	Benedetto, S., Caldato, C., Bazzan, E., Greenwood, D.C., Pensabene, V. and Actis, P.	2018	PLOS ONE	Validation	Charge 2	Adults	Heart Rate
We12BFit!-Improving lifestyle physical activity in children aged 7-12 years with developmental coordination disorder: protocol of a multicentre single-arm mixed-method study. details	Braaksma, P., Stuive, I., Boomsma, H., van der Sluis, C.K., Schoemaker, M.M. and Dekker, R.	2018	BMJ open	Usability	Zip	Children	Steps
Reliability and validity of two fitness tracker devices in the laboratory and home environment for older community-dwelling people details	Burton, E., Hill, K.D., Lautenschlager, N.T., Thøgersen-Ntoumani, C., Lewin, G., Boyle, E. and Howie, E.	2018	BMC Geriatrics	Validation	Flex Charge HR	Older Adults	Steps Intensity Distance Sleep
Impact of Coordinated-Bilateral Physical Activities on Attention and Concentration in School-Aged Children details	Buchele Harris, H., Cortina, K.S., Templin, T., Colabianchi, N. and Chen, W.	2018	BioMed Research International	Intervention	Charge HR	Children	Steps Intensity
Technology-Enhanced Classroom Activity Breaks Impacting Children’s Physical Activity and Fitness details	Buchele Harris, H. and Chen, W.	2018	Journal of Clinical Medicine	Intervention	Charge HR	Children	Steps Intensity
Assessment of step accuracy using the Consumer Technology Association standard details	Bunn, J.A., Jones, C., Oliviera, A. and Webster, M.J.	2018	Journal of Sports Sciences	Review	Surge	Adults	Steps
Classification Accuracy of a Wearable Activity Tracker for Assessing Sedentary Behavior and Physical Activity in 3–5-Year-Old Children details	Byun, W., Lee, J.-M., Kim, Y. and Brusseau, T.	2018	International Journal of Environmental Research and Public Health	Validation	Flex	Children	Intensity
The Use of a Fitbit Device for Assessing Physical Activity and Sedentary Behavior in Preschoolers. details	Byun, W., Kim, Y. and Brusseau, T.A.	2018	The Journal of pediatrics	Validation	Flex	Children	Intensity Sleep	URL
Exercise combined with Acceptance and Commitment Therapy (ExACT) compared to a supervised exercise programme for adults with chronic pain: study protocol for a randomised controlled trial details	Casey, M.-B., Smart, K., Segurado, R., Hearty, C., Gopal, H., Lowry, D., Flanagan, D., McCracken, L. and Doody, C.	2018	Trials	Methods	Zip	Patients Adults	Steps
Validation of Fitness Tracker for Sleep Measures in Women with Asthma details	Castner, J., Mammen, M.J., Jungquist, C., Licata, O., Pender, J.J., Wilding, G.E. and Sethi, S.	2018	Journal of Asthma	Validation	Charge	Adults	Sleep
Effect of physical activity trackers and daily text messages on quality-of-life in colorectal survivors (Smart Pace): A pilot randomized controlled trial. details	Chan, H., Van Loon, K., Kenfield, S.A., Chan, J.M., Mitchell, E., Zhang, L., Paciorek, A., Joseph, G., Laffan, A., Fukuoka, Y., Miaskowski, C., Meyerhardt, J.A., Venook, A.P. and Van Blarigan, E.	2018	JCO	Intervention	Flex	Patients Adults	Steps	URL
Preliminary concurrent validity of the Fitbit-Zip and ActiGraph activity monitors for measuring steps in people with polymyalgia rheumatica. details	Chandrasekar, A., Hensor, E.M.A., Mackie, S.L., Backhouse, M.R. and Harris, E.	2018	Gait & posture	Validation	Zip	Patients Adults	Steps
A Physical Therapist–Administered Physical Activity Intervention After Total Knee Replacement: Protocol for a Randomized Controlled Trial details	Christiansen, M.B., Thoma, L.M., Master, H., Schmitt, L.A., Pohlig, R. and White, D.K.	2018	Physical Therapy	Methods	Zip	Patients Adults	Steps
The role of sleep in adolescents' daily stress recovery: Negative affect spillover and positive affect bounce-back effects details	Chue, A.E., Gunthert, K.C., Kim, R.W., Alfano, C.A. and Ruggiero, A.R.	2018	Journal of Adolescence	Measurement	Flex	Adolescents	Sleep	URL
Engagement Strategies for Self-Monitoring Symptoms of Bipolar Disorder With Mobile and Wearable Technology: Protocol for a Randomized Controlled Trial details	Cochran, A., Belman-Wells, L. and McInnis, M.	2018	JMIR Research Protocols	Methods		Patients Adults	Steps Intensity Sleep Heart Rate
Peer Coaching Through mHealth Targeting Physical Activity in People With Parkinson Disease: Feasibility Study. details	Colón-Semenza, C., Latham, N.K., Quintiliani, L.M. and Ellis, T.D.	2018	JMIR mHealth and uHealth	Usability	Zip	Adults	Steps
Validation of the Fitbit Flex in an Acute Post-Cardiac Surgery Patient Population details	Daligadu, J., Pollock, C.L., Carlaw, K., Chin, M., Haynes, A., Thevaraajah Kopal, T., Tahsinul, A., Walters, K. and Colella, T.J.F.	2018	Physiotherapy Canada	Validation	Flex	Patients	Steps Distance
Free-Living Physical Activity Monitoring in Adult US Patients with Multiple Sclerosis Using a Consumer Wearable Device details	DasMahapatra, P., Chiauzzi, E., Bhalerao, R. and Rhodes, J.	2018	Digital Biomarkers	Usability Measurement	One	Patients Adults	Steps
Feasibility of using the Fitbit Charge HR in validating self-reported exercise diaries in a community setting in patients with heart failure details	Deka, P., Pozehl, B., Norman, J.F. and Khazanchi, D.	2018	European Journal of Cardiovascular Nursing	Usability	Charge HR	Patients Adults	Intensity
Utilizing wearable technology to increase physical activity in future physicians: A randomized trial details	DiFrancisco-Donoghue, J., Jung, M.-K., Stangle, A., Werner, W.G., Zwibel, H., Happel, P. and Balentine, J.	2018	Preventive Medicine Reports	Intervention		Adults	Steps Weight
Personalized Physical Activity Coaching: A Machine Learning Approach details	Dijkhuis, T.B., Blaauw, F.J., van Ittersum, M.W., Velthuijsen, H. and Aiello, M.	2018	Sensors	Methods	Flex	Adults	Steps
Validation of PiezoRx Pedometer Derived Sedentary Time details	Donahoe, K., MacDonald, D., Tremblay, M. and Saunders, T.	2018	International Journal of Exercise Science	Validation	Flex	Adults	Intensity
Congruent Accuracy of Wrist-worn Activity Trackers during Controlled and Free-living Conditions details	Dondzilla, C.J., Lewis, C., Lopez, J. and Parker, T.	2018	International Journal of Exercise Science	Validation	Charge HR	Adults	Steps Energy Expenditure Heart Rate
Individual differences in regulatory mode moderate the effectiveness of a pilot mHealth trial for diabetes management among older veterans details	Dugas, M., Crowley, K., Gao, G.G., Xu, T., Agarwal, R., Kruglanski, A.W. and Steinle, N.	2018	PLOS ONE	Intervention	One	Patients Adults	Steps
Behavior Change Techniques and Physical Activity Using the Fitbit Flex® details	Dunn, E.E. and Robertson-Wilson, J.	2018	International Journal of Exercise Science	Survey Research	Flex	Adults	NA
Effects of a 12 Week mHealth Program on Peak VO2 and Physical Activity Patterns after Completing Cardiac Rehabilitation: A Randomized Controlled Trial details	Duscha, B.D., Piner, L.W., Patel, M.P., Craig, K.P., Brady, M., McGarrah, R.W., Chen, C. and Kraus, W.E.	2018	American Heart Journal	Intervention	Charge	Patients Adults	Steps Intensity
Chronic Health Risks in Commercial Fishermen: A Cross-Sectional Analysis from a Small Rural Fishing Village in Alaska details	Eckert, C., Baker, T. and Cherry, D.	2018	Journal of Agromedicine	Measurement	One	Adults	Steps Sleep
Steps per Day Measured by Consumer Activity Trackers Worn at the Non-Dominant and Dominant Wrist Relative to a Waist-Worn Pedometer details	Edwardson, C.L., Davies, M., Khunti, K., Yates, T. and Rowlands, A.V.	2018	Journal for the Measurement of Physical Behaviour	Validation	Flex	Adults	Steps
A modified activity protocol for claudication. details	Endicott, K.M., Hynes, C.F., Amdur, R. and Macsata, R.	2018	The Journal of Cardiovascular Surgery	Intervention	One Zip	Patients Adults	Steps
Buying time: a proof-of-concept randomized controlled trial to improve sleep quality and cognitive function among older adults with mild cognitive impairment details	Falck, R.S., Davis, J.C., Best, J.R., Li, L.C., Chan, P.C.Y., Wyrough, A.B., Landry, G.J. and Liu-Ambrose, T.	2018	Trials	Intervention	Flex	Older Adults	Sleep
A Mobile Health Intervention to Reduce Pain and Improve Health (MORPH) in Older Adults With Obesity: Protocol for the MORPH Trial. details	Fanning, J., Brooks, A.K., Ip, E., Nicklas, B.J. and Rejeski, W.J.	2018	JMIR research protocols	Methods	Aria Alta	Older Adults	Steps Intensity Weight	URL
Quantifying Subjective Well-Being Using Trends in Weekend Activity details	Faust, L., Hachen, D., Lizardo, O. and Chawla, N.V.	2018	2018 IEEE International Conference on Healthcare Informatics (ICHI)	Usability	Charge HR	Adults	Steps
Acceptability and feasibility of wearable fitness technology for endometrial cancer survivors details	Frechette, L., Miller, D.T., Rossi, A., Miller, E.M., Van Arsdale, A.R., Viswanathan, S. and Nevadunsky, N.S.	2018	Gynecologic Oncology	Intervention		Older Adults	Steps
A weight loss intervention using a commercial mobile application in Latino Americans—Adelgaza Trial details	Fukuoka, Y., Vittinghoff, E. and Hooper, J.	2018	Translational Behavioral Medicine	Intervention	Zip	Adults	Steps Food
Accuracy of Fitbit Surge and Smartphone Apps at Measuring Cycling Distance and Speed details	Gamez, J.L., Figueroa, I.A. and Funk, M.D.	2018	Medicine & Science in Sports & Exercise	Validation	Surge	Adults	Distance
Intensive remote monitoring versus conventional care in type 1 diabetes: A randomized controlled trial details	Gandrud, L., Altan, A., Buzinec, P., Hemphill, J., Chatterton, J., Kelley, T. and Vojta, D.	2018	Pediatric Diabetes	Intervention		Adolescents	Steps
The Feasibility of a Gender- and Culturally-Sensitive Weight Loss Intervention Plus Mobile Health Technology for Hispanic Males details	Garcia, D.O., Valdez, L.A., Aceves, B., Campas, D., Loya, J., Hingle, M., Humphrey, K., Bell, M.L., McEwen, M. and Hooker, S.P.	2018	Medicine & Science in Sports & Exercise	Usability	Charge 2 Aria	Adults	Intensity
Determining the Validity and Accuracy of Multiple Activity-Tracking Devices in Controlled and Free-Walking Conditions details	Gaz, D.V., Rieck, T.M., Peterson, N.W., Ferguson, J.A., Schroeder, D.R., Dunfee, H.A., Henderzahs-Mason, J.M. and Hagen, P.T.	2018	American Journal of Health Promotion	Validation	One Zip Charge HR	Adults	Steps Distance
Feasibility and effects of a home-based intervention using activity trackers on achievement of individual goals, quality of life and motor performance in patients with paediatric cancer details	Götte, M., Kesting, S.V., Gerss, J., Rosenbaum, D. and Boos, J.	2018	BMJ Open Sport & Exercise Medicine	Intervention	One Flex	Patients Adults Adolescents	Steps Energy Expenditure Intensity Distance	URL
Gamifying Accelerometer Use Increases Physical Activity Levels of Sedentary Office Workers. details	Gremaud, A.L., Carr, L.J., Simmering, J.E., Evans, N.J., Cremer, J.F., Segre, A.M., Polgreen, L.A. and Polgreen, P.M.	2018	Journal of the American Heart Association	Intervention	Zip	Adults	Steps Intensity
Wearable activity monitors to assess performance status and predict clinical outcomes in advanced cancer patients details	Gresham, G., Hendifar, A.E., Spiegel, B., Neeman, E., Tuli, R., Rimel, B.J., Figlin, R.A., Meinert, C.L., Piantadosi, S. and Shinde, A.M.	2018	npj Digital Medicine	Usability	Charge HR	Patients	Steps Intensity Sleep Heart Rate
A mobile phone-based program to promote healthy behaviors among adults with prediabetes: study protocol for a pilot randomized controlled trial details	Griauzde, D.H., Kullgren, J.T., Liestenfeltz, B., Richardson, C. and Heisler, M.	2018	Pilot and Feasibility Studies	Methods	Aria	Patients Adults	Steps Weight
Assessment of the accuracy of nutrient calculations of five popular nutrition tracking applications details	Griffiths, C., Harnack, L. and Pereira, M.A.	2018	Public Health Nutrition	Validation		Adults	Food
Feasibility of Wearable Physical Activity Monitors in Patients With Cancer details	Gupta, A., Stewart, T., Bhulani, N., Dong, Y., Rahimi, Z., Crane, K., Rethorst, C. and Beg, M.S.	2018	JCO Clinical Cancer Informatics	Usability	Flex	Adults	Steps
Enhancing Lifestyle Change in Cardiac Patients Through the Do CHANGE System ("Do Cardiac Health: Advanced New Generation Ecosystem"): Randomized Controlled Trial Protocol. details	Habibović, M., Broers, E., Piera-Jimenez, J., Wetzels, M., Ayoola, I., Denollet, J. and Widdershoven, J.	2018	JMIR Research Protocols	Methods		Patients Adults	Steps Intensity
Relationships between Neighborhood Walkability and Objectively Measured Physical Activity Levels in Hemodialysis Patients. details	Han, M., Ye, X., Preciado, P., Williams, S., Campos, I., Bonner, M., Young, C., Marsh, D., Larkin, J.W., Usvyat, L.A., Maddux, F.W., Pecoits-Filho, R. and Kotanko, P.	2018	Blood Purification	Measurement	Flex	Patients Adults	Steps
Impact of Coordinated-Bilateral Physical Activities on Attention and Concentration in School-Aged Children details	Buchele Harris, Heidi; Cortina, Kai Schnabel; Templin, Thomas; Colabianchi, Natalie; Chen, Weiyun	2018	BioMed Research International	Intervention	Charge HR	Children	Steps Energy Expenditure Distance Heart Rate	URL URL URL
Patterns of Fitbit Use and Activity Levels Throughout a Physical Activity Intervention: Exploratory Analysis from a Randomized Controlled Trial details	Hartman, S.J., Nelson, S.H. and Weiner, L.S.	2018	JMIR mHealth and uHealth	Intervention	One	Patients Adults	Steps Intensity
Predicting Future Glucose Fluctuations Using Machine Learning and Wearable Sensor Data details	HAYERI, A.M.I.R.	2018	Diabetes	Validation		Children	Steps Heart Rate
Using Fitness Trackers and Smartwatches to Measure Physical Activity in Research: Analysis of Consumer Wrist-Worn Wearables. details	Henriksen, A., Haugen Mikalsen, M., Woldaregay, A.Z., Muzny, M., Hartvigsen, G., Hopstock, L.A. and Grimsgaard, S.	2018	Journal of Medical Internet Research	Review			NA
Protocol for Moving On: a randomized controlled trial to increase outcome expectations and exercise among breast cancer survivors details	Hirschey, R., Kimmick, G., Hockenberry, M., Shaw, R., Pan, W. and Lipkus, I.	2018	Nursing Open	Methods	Flex	Patients Adults	Steps Intensity
The Accuracy of Wrist-Worn Heart Rate Monitors Across a Range of Exercise Intensities details	Hough, P.	2018	Medicine & Science in Sports & Exercise	Validation	Charge	Adults	Heart Rate
A Reimbursable Counseling Intervention Improves Physical Activity and Function for Primary Care Patients with Type 2 Diabetes details	HUEBSCHMANN, A.M.Y.G., LEAVITT, I.A.N.M., GLASGOW, R.U.S.S.E.L.L., REGENSTEINER, J.U.D.I.T.H.G. and ANDREA L., D.U.N.N.	2018	Diabetes	Intervention		Adults	Steps
VALIDITY AND RELIABILITY OF THE FITBIT FLEX™ AND ACTIGRAPH GT3X+ AT JOGGING AND RUNNING SPEEDS. details	Jones, D., Crossley, K., Dascombe, B., Hart, H.F. and Kemp, J.	2018	International journal of sports physical therapy	Validation	Flex	Adults	Steps	URL
Relationship between subjective fatigue, physical activity, and sleep indices in nurses working 16-hour night shifts in a rotating two-shift system details	Kagamiyama, H. and Yano, R.	2018	Journal of Rural Medicine	Case Study	One	Adults	Energy Expenditure Sleep
Effects of Sleep, Physical Activity, and Shift Work on Daily Mood: a Prospective Mobile Monitoring Study of Medical Interns details	Kalmbach, D.A., Fang, Y., Arnedt, J.T., Cochran, A.L., Deldin, P.J., Kaplin, A.I. and Sen, S.	2018	Journal of General Internal Medicine	Measurement	One Charge	Adults	Sleep	URL
Effect of accelerometer-based feedback on physical activity in hospitalized patients with ischemic stroke: a randomized controlled trial details	Kanai, M., Izawa, K.P., Kobayashi, M., Onishi, A., Kubo, H., Nozoe, M., Mase, K. and Shimada, S.	2018	Clinical Rehabilitation	Intervention	One	Older Adults Patients	Steps
Impact Of Placement Of Wrist-worn Activity Monitors During The Lab And Free-living Settings details	Kang, M., Lee, N.H., Jung, H.C., Jeon, S. and Lee, S.	2018	Medicine & Science in Sports & Exercise	Validation		Adults	Steps Energy Expenditure Heart Rate
Use of Consumer Monitors for Estimating Energy Expenditure in Youth details	Kaplan, A.S., Lamunion, S.R., Hibbing, P.R. and Crouter, S.E.	2018	Medicine & Science in Sports & Exercise	Validation	Charge 2	Children	Energy Expenditure Intensity
Detecting short-duration ambulatory episodes in Fitbit® data details	van Kasteren, Y.F., Perimal-Lewis, L. and Maeder, A.	2018	Proceedings of the Australasian Computer Science Week Multiconference on - ACSW '18	Methods	Charge HR	Adults	Steps
Validity of wearable activity monitors for tracking steps and estimating energy expenditure during a graded maximal treadmill test details	Kendall, B., Bellovary, B. and Gothe, N.P.	2018	Journal of Sports Sciences	Validation	Flex	Adults	Steps Energy Expenditure	URL
Commonly available activity tracker apps and wearables as a mental health outcome indicator: A prospective observational cohort study among young adults with psychological distress details	Knight, A. and Bidargaddi, N.	2018	Journal of Affective Disorders	Measurement		Patients Adults	Steps
Self-tracking of Physical Activity in People With Type 2 Diabetes details	Kooiman, T.J.M., de Groot, M., Hoogenberg, K., Krijnen, W.P., van der Schans, C.P. and Kooy, A.	2018	CIN: Computers, Informatics, Nursing	Intervention	Zip	Patients Adults	Steps
Text messaging and activity tracker motivation program to increase physical activity in cancer survivors. details	Koontz, B.F., Levine, E., McSherry, F., Dale, T., Streicher, M., Chino, J.P., Kelsey, C.R., Ward, K., Little, K., Owen, L., Bennett, G., Kraus, W., Peterson, B., Pollak, K.I. and Sutton, L.	2018	Journal of Clinical Oncology	Intervention	Flex	Patients Adults	Steps	URL
Accuracy and precision of consumer-level activity monitors for stroke detection during wheelchair propulsion and arm ergometry details	Kressler, J., Koeplin-Day, J., Muendle, B., Rosby, B., Santo, E. and Domingo, A.	2018	PLOS ONE	Validation	Flex	Adults	Steps
The Physical Activity Daily (PAD) Trial: The rationale and design of a randomized controlled trial evaluating an internet walking program to improve maximal walking distance among patients with peripheral arterial disease details	Kumar, A.M., Lyden, A.K., Carlozzi, N.E., Sen, A., Richardson, C.R. and Jackson, E.A.	2018	Contemporary Clinical Trials	Intervention		Patients	Steps Distance
Accuracy of wearable physical activity trackers in people with Parkinson's disease details	Lamont, R.M., Daniel, H.L., Payne, C.L. and Brauer, S.G.	2018	Gait & Posture	Validation	Charge HR	Patients Adults	Steps Intensity
The effectiveness, reproducibility, and durability of tailored mobile coaching on diabetes management in policyholders: A randomized, controlled, open-label study details	Lee, D.Y., Park, J., Choi, D., Ahn, H.-Y., Park, S.-W. and Park, C.-Y.	2018	Scientific Reports	Intervention	Flex	Patients Adults	Steps
Clinical Feasibility of Continuously Monitored Data for Heart Rate, Physical Activity, and Sleeping by Wearable Activity Trackers in Patients with Thyrotoxicosis: Protocol for a Prospective Longitudinal Observational Study. details	Lee, J.-E., Lee, D.H., Oh, T.J., Kim, K.M., Choi, S.H., Lim, S., Park, Y.J., Park, D.J., Jang, H.C. and Moon, J.H.	2018	JMIR Research Protocols	Methods	Charge HR Charge 2	Patients Adults	Steps Intensity Distance Sleep Heart Rate
Comparison of Wearable Trackers’ Ability to Estimate Sleep details	Lee, J.-M., Byun, W., Keill, A., Dinkel, D. and Seo, Y.	2018	International Journal of Environmental Research and Public Health	Validation	Charge HR	Adults	Sleep
Lifelog-based classification of mild cognitive impairment using artificial neural networks details	Lee, S.-h., Kang, W.-s. and Moon, C.	2018	2018 International Conference on Electronics, Information, and Communication (ICEIC)	Methods	Charge 2	Patients Adults	Steps Intensity Sleep
Validation of Overall Energy Expenditure Measurements in the Fitbit Charge HR 2 and Apple Watch details	Li, K., Nuss, K., Thomson, E.A., Comstock, A., Blake, S., Reinwald, S., Pimentel, R.E. and Tracy, B.	2018	Medicine & Science in Sports & Exercise	Validation	Charge HR	Adults	Energy Expenditure
Efficacy of a Community-Based Technology-Enabled Physical Activity Counseling Program for People With Knee Osteoarthritis: Proof-of-Concept Study details	Li, L.C., Sayre, E.C., Xie, H., Falck, R.S., Best, J.R., Liu-Ambrose, T., Grewal, N., Hoens, A.M., Noonan, G. and Feehan, L.M.	2018	Journal of Medical Internet Research	Intervention	Flex	Patients Adults	Steps
Validity of Consumer Activity Wristbands and Wearable EEG for Measuring Overall Sleep Parameters and Sleep Structure in Free-Living Conditions details	Liang, Z. and Chapa Martell, M.A.	2018	Journal of Healthcare Informatics Research	Validation	Charge 2	Adults	Sleep
Internet Gaming Disorder: the interplay between physical activity and user-avatar relationship details	Liew, L.W.L., Stavropoulos, V., Adams, B.L.M., Burleigh, T.L. and Griffiths, M.D.	2018	Behaviour & Information Technology	Measurement	Flex	Adults	Steps Intensity
Beyond fitness tracking: The use of consumer-grade wearable data from normal volunteers in cardiovascular and lipidomics research details	Lim, W.K., Davila, S., Teo, J.X., Yang, C., Pua, C.J., Blöcker, C., Lim, J.Q., Ching, J., Yap, J.J.L., Tan, S.Y., Sahlén, A., Chin, C.W.-L., Teh, B.T., Rozen, S.G., Cook, S.A., Yeo, K.K. and Tan, P.	2018	PLOS Biology	Measurement	Charge HR	Adults	Steps Heart Rate
Altered connection properties of important network hubs may be neural risk factors for individuals with primary insomnia details	Liu, X., Zheng, J., Liu, B.-X. and Dai, X.-J.	2018	Scientific Reports	Measurement	Flex	Patients Adults	Sleep
Fitbit step counts during inpatient recovery from cancer surgery as a predictor of readmission details	Low, C.A., Bovbjerg, D.H., Ahrendt, S., Choudry, M.H., Holtzman, M., Jones, H.L., Pingpank, J.F., Ramalingam, L., Zeh, H.J., Zureikat, A.H. and Bartlett, D.L.	2018	Annals of Behavioral Medicine	Measurement	Flex	Patients Adults	Steps
Joint Modeling of Heterogeneous Sensing Data for Depression Assessment via Multi-task Learning details	Lu, J., Bi, J., Shang, C., Yue, C., Morillo, R., Ware, S., Kamath, J., Bamis, A., Russell, A. and Wang, B.	2018	Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies	Methods	Charge HR	Adults	Intensity Distance Sleep Heart Rate
Findings from a Diabetes Support Group--A Pilot Study details	MALONEY, L.O.R.I.M.C.L.E.E.R., FLOOD, J.E.R.E.M.Y., ALAMUDDIN, N.A.J.I. and MUKADDAM, M.O.N.A.A.L.	2018	Diabetes	Intervention		Adults	Steps Food
Increasing physical activity in cancer survivors at cardiovascular risk using Fitbits: Randomized controlled trial. details	Maxwell-Smith, C.M., Cohen, P.A., Platell, C., Tan, P., Tan, J., Salama, P., Levitt, M., Makin, G.B., Salfinger, S., Kader Ali Mohan, G.R., Kane, R., Hince, D. and Hardcastle, S.J.	2018	Journal of Clinical Oncology	Methods	Alta	Patients Adults	NA	URL
Wearable Activity Technology And Action-Planning (WATAAP) to promote physical activity in cancer survivors: Randomised controlled trial protocol details	Maxwell-Smith, C., Cohen, P.A., Platell, C., Tan, P., Levitt, M., Salama, P., Makin, G.B., Tan, J., Salfinger, S., Kader Ali Mohan, G.R., Kane, R.T., Hince, D., Jiménez-Castuera, R. and Hardcastle, S.J.	2018	International Journal of Clinical and Health Psychology	Methods		Patients Adults	Steps Intensity
Effect of a Home-Based Exercise Intervention of Wearable Technology and Telephone Coaching on Walking Performance in Peripheral Artery Disease details	McDermott, M.M., Spring, B., Berger, J.S., Treat-Jacobson, D., Conte, M.S., Creager, M.A., Criqui, M.H., Ferrucci, L., Gornik, H.L., Guralnik, J.M., Hahn, E.A., Henke, P., Kibbe, M.R., Kohlman-Trighoff, D., Li, L., Lloyd-Jones, D., McCarthy, W., Polonsky, T.S., Skelly, C., Tian, L., Zhao, L., Zhang, D. and Rejeski, W.J.	2018	JAMA	Intervention	Zip	Patients Adults	Steps Intensity
Sleep and BMI: Do (Fitbit) bands aid? [version 1; referees: 2 approved with reservations] details	McDonald, L., Mehmud, F. and Ramagopalan, S.V.	2018	PLOS Biology	Usability	Charge HR	Adults	Steps	URL
Simultaneous Aerobic Exercise and Memory Training Program in Older Adults with Subjective Memory Impairments details	McEwen, S.C., Siddarth, P., Abedelsater, B., Kim, Y., Mui, W., Wu, P., Emerson, N.D., Lee, J., Greenberg, S., Shelton, T., Kaiser, S., Small, G.W. and Merrill, D.A.	2018	Journal of Alzheimer's Disease	Intervention	Charge HR	Older Adults	Heart Rate
Reconsidering the experience machine: Self-reported versus objective measures of physical activity to increase positive affect details	McLean, D.C., Nakamura, J. and Csikszentmihalyi, M.	2018	Journal of Health Psychology	Intervention	Flex	Adults	Intensity
Promoting Healthy Diet, Physical Activity, and Life-Skills in High School Athletes: Results from the WAVE Ripples for Change Childhood Obesity Prevention Two-Year Intervention details	Meng, Y., Manore, M., Schuna, J., Patton-Lopez, M., Branscum, A. and Wong, S	2018	Nutrients	Intervention	Zip	Adolescents	Intensity Food
Validating Fitbit Zip for monitoring physical activity of children in school: a cross-sectional study details	Mooses, K., Oja, M., Reisberg, S., Vilo, J. and Kull, M.	2018	BMC Public Health	Validation	Zip	Children	Steps Intensity
Heart Rate Alters, But Does Not Improve, Calorie Predictions in Fitbit Activity Monitors details	Montoye, A.H., Vusich, J., Mitrzyk, J. and Wiersma, M.	2018	Journal for the Measurement of Physical Behaviour	Validation	Charge HR Flex 2 Charge 2 Blaze	Adults	Energy Expenditure Heart Rate
Reliability of Zephyr Bioharness and Fitbit Charge Measures of Heart Rate and Activity at Rest, During the Modified Canadian Aerobic Fitness Test and Recovery details	Nazari, G., Macdermid, J.C., Kin., K.E.S.R., Richardson, J. and Tang, A.	2018	Journal of Strength and Conditioning Research	Validation	Charge HR	Adults	Heart Rate
Veterans’ Perspectives on Fitbit Use in Treatment for Post-Traumatic Stress Disorder: An Interview Study details	Ng, A., Reddy, M., Zalta, A.K. and Schueller, S.M.	2018	JMIR Mental Health	Validation	Charge HR	Patients	Steps Sleep
Early enforced mobilization after liver resection: A prospective randomized controlled trial details	Ni, C.-y., Wang, Z.-h., Huang, Z.-p., Zhou, H., Fu, L.-j., Cai, H., Huang, X.-x., Yang, Y., Li, H.-f. and Zhou, W.-p.	2018	International Journal of Surgery	Intervention	Flex	Patients Adults	Steps Distance Sleep	URL
Patient Experiences and Opinions of a Behavioral Activation Group Intervention for Depression details	O'Neill, L., Samaan, Z., McCabe, K., Tabak, T.A., Key, B., Litke, K., Whattam, J., Garrick, L., Chalmers, S., Xie, F., Thabane, L., Patel, Y., Shahid, H., Zielinski, L. and Vanstone, M.	2018	Research on Social Work Practice	Usability	One	Patients Adults	Steps
The efficacy of a multimodal physical activity intervention with supervised exercises, health coaching and an activity monitor on physical activity levels of patients with chronic, nonspecific low back pain (Physical Activity for Back Pain (PAyBACK) trial): study protocol for a randomised controlled trial details	Oliveira, C.B., Franco, M.R., Maher, C.G., Tiedemann, A., Silva, F.G., Damato, T.M., Nicholas, M.K., Christofaro, D.G.D. and Pinto, R.Z.	2018	Trials	Methods	Flex	Patients Adults	Steps Intensity
Physical Activity in Patients With Type 2 Diabetes: The Case for Objective Measurement in Routine Clinical Care. details	Oosterom, N., Gant, C.M., Ruiterkamp, N., van Beijnum, B.-J.F., Hermens, H., Bakker, S.J.L., Navis, G., Vollenbroek-Hutten, M.M.R. and Laverman, G.D.	2018	Diabetes Care	Measurement	Flex	Patients Adults	Steps
Using the Habit App for Weight Loss Problem Solving: Development and Feasibility Study details	Pagoto, S., Tulu, B., Agu, E., Waring, M.E., Oleski, J.L. and Jake-Schoffman, D.E.	2018	JMIR mHealth and uHealth	Usability		Adults	Weight
Exercise monitoring of young adults using a Facebook application details	Pagkalos, I., Kokkinopoulou, A., Weal, M., Petrou, L. and Hassapidou, M.	2017	DIGITAL HEALTH	Intervention	Zip	Adults	Steps
Dominant Vs Non-dominant Wrist details	Park, S.	2018	Medicine & Science in Sports & Exercise	Validation	Charge	Adults	Steps
Accuracy of a Wrist-Worn Heart Rate Sensing Device during Elective Pediatric Surgical Procedures details	Pelizzo, G., Guddo, A., Puglisi, A., De Silvestri, A., Comparato, C., Valenza, M., Bordonaro, E. and Calcaterra, V.	2018	Children	Validation	Charge HR	Children Adults	Heart Rate
Changes in Physical Activity during a Pilot Weight Loss Program Before and After Knee Replacement details	Pellegrini, C.A., Chang, R.W., Dunlop, D.D., Conroy, D.E., Lee, J. and Cameron, K.A.	2018	Medicine & Science in Sports & Exercise	Intervention		Patients	Steps Intensity
Modeling Individual Differences: A Case Study of the Application of System Identification for Personalizing a Physical Activity Intervention details	Phatak, S.S., Freigoun, M.T., Martín, C.A., Rivera, D.E., Korinek, E.V., Adams, M.A., Buman, M.P., Klasnja, P. and Hekler, E.B.	2018	Journal of Biomedical Informatics	Intervention	Zip	Adults	Steps
Optimization of a technology-supported physical activity intervention for breast cancer survivors: Fit2Thrive study protocol. details	Phillips, S.M., Collins, L.M., Penedo, F.J., Courneya, K.S., Welch, W., Cottrell, A., Lloyd, G.R., Gavin, K., Cella, D., Ackermann, R.T., Siddique, J. and Spring, B.	2018	Contemporary Clinical Trials	Methods	Zip	Patients Adults	Steps Intensity
The effect of automated text messaging and goal setting on pedometer adherence and physical activity in patients with diabetes: A randomized controlled trial details	Polgreen, L.A., Anthony, C., Carr, L., Simmering, J.E., Evans, N.J., Foster, E.D., Segre, A.M., Cremer, J.F. and Polgreen, P.M.	2018	PLOS ONE	Intervention	Zip	Patients Adults	Steps
Lessons Learned Through the Implementation of an eHealth Physical Activity Gaming Intervention with High School Youth details	Pope, L., Garnett, B. and Dibble, M.	2018	Games for Health Journal	Intervention	Flex	Adolescents	Steps
Comparison Of Daily Energy Expenditure And Weekly Physical Activity Exposure Estimated Using Consumer And Research-grade Physical Activity Monitors During Officer Cadet Initial Military Training details	Powell, S.D., Siddall, A.G., S Thompson, J.E., Edwards, V.C., Jackson, S., Greeves, J.P., Wardle, S., Blacker, S.D. and Myers, S.D.	2018	Medicine & Science in Sports & Exercise	Validation	Surge	Adults	Energy Expenditure
Impact of EPOC adjustment on estimation of energy expenditure using activity monitors details	Pribyslavska, V., Caputo, J.L., Coons, J.M. and Barry, V.W.	2018	Journal of Medical Engineering & Technology	Validation	Surge	Adults	Energy Expenditure Intensity
Changes in cognitive function after a 12-week exercise intervention in adults with Down syndrome. details	Ptomey, L.T., Szabo, A.N., Willis, E.A., Gorczyca, A.M., Greene, J.L., Danon, J.C. and Donnelly, J.E.	2018	Disability and Health Journal	Intervention	Charge HR	Adults	Intensity Heart Rate
Remote Exercise for Adults with Down Syndrome details	Ptomey, L.T., Szabo, A.N., Willis, E.A., Greene, J.L., Danon, J.C., Washburn, R.A., Forsha, D.E. and Donnelly, J.E.	2018	Translational Journal of the American College of Sports Medicine	Intervention	Charge HR	Adults	Intensity Heart Rate
Development of a Just-in-Time Adaptive mHealth Intervention for Insomnia: Usability Study details	Pulantara, I.W., Parmanto, B. and Germain, A.	2018	JMIR Human Factors	Methods	Charge	Adults	Sleep	URL
Examination of the association of physical activity during pregnancy after cesarean delivery and vaginal birth among Chinese women details	Qi, X.-Y., Xing, Y.-P., Wang, X.-Z. and Yang, F.-Z.	2018	Reproductive Health	Measurement	Flex	Adults Pregnant Women	Steps Intensity	URL
Pilot Trial of an Acceptance-Based Behavioral Intervention to Promote Physical Activity Among Adolescents details	Remmert, J.E., Woodworth, A., Chau, L., Schumacher, L.M., Butryn, M.L. and Schneider, M.	2018	The Journal of School Nursing	Intervention	Flex 2	Adolescents	Steps Intensity
Physical Activity during Pregnancy Alters Gene Expression in Neonatal Tissue details	Reynolds, L.J., Chavan, N.R., Rice, B.B., Maddox, H.F., Preston, J.D., DeHoff, L.B. and Pearson, K.J.	2018	Medicine & Science in Sports & Exercise	Validation	Flex	Adults	Steps
Wearable Activity Tracker Use Among Australian Adolescents: Usability and Acceptability Study. details	Ridgers, N.D., Timperio, A., Brown, H., Ball, K., Macfarlane, S., Lai, S.K., Richards, K., Mackintosh, K.A., McNarry, M.A., Foster, M. and Salmon, J.	2018	JMIR mHealth and uHealth	Usability	Flex	Adolescents	Steps Goals Sleep
A Latino Patient-Centered, Evidence-Based Approach to Diabetes Prevention. details	Rosas, L.G., Lv, N., Lewis, M.A., Venditti, E.M., Zavella, P., Luna, V. and Ma, J.	2018	Journal of the American Board of Family Medicine : JABFM	Usability		Adults	NA	URL
Acceptability and feasibility of a Fitbit physical activity monitor for endometrial cancer survivors. details	Rossi, A., Frechette, L., Miller, D., Miller, E., Friel, C., Van Arsdale, A., Lin, J., Shankar, V., Kuo, D.Y.S. and Nevadunsky, N.S.	2018	Gynecologic Oncology	Usability		Patients Adults	Steps
Consumer Wearable Devices for Activity Monitoring Among Individuals After a Stroke: A Prospective Comparison details	Rozanski, G.M., Aqui, A., Sivakumaran, S. and Mansfield, A.	2018	JMIR Cardio	Validation	Charge HR	Patients Adults	Steps Heart Rate
How well does a commercially available wearable device measure sleep in young athletes? details	Sargent, C., Lastella, M., Romyn, G., Versey, N., Miller, D.J. and Roach, G.D.	2018	Chronobiology International	Validation	Charge HR	Adults	Sleep
Use and physiological responses of portable dynamic office workstations in an occupational setting - A field study details	Schellewald, V., Kleinert, J. and Ellegast, R.	2018	Applied Ergonomics	Usability	Charge HR	Adults	Energy Expenditure Heart Rate	URL
Validation of Activity Tracking Procedures in Elderly Patients after Operative Treatment of Proximal Femur Fractures details	Schmal, H., Holsgaard-Larsen, A., Izadpanah, K., Brønd, J.C., Madsen, C.F. and Lauritsen, J.	2018	Rehabilitation Research and Practice	Validation	Flex	Patients	Steps
Extending research on self-regulation of physical activity in older age: Role of views on aging within an intensive ambulatory assessment scheme details	Schmidt, L.I., Gabrian, M., Jansen, C.-P., Wahl, H.-W. and Sieverding, M.	2018	Journal of Self-Regulation and Regulation	Intervention	Charge HR	Older Adults	Intensity
'Wow! You're Wearing a Fitbit, You're a Young Boy Now!" details	Sharma, S., Achary, K., Kaur, H., Linna, J., Turunen, M., Varkey, B., Hakulinen, J. and Daeeyya, S.	2018	Proceedings of the 20th International ACM SIGACCESS Conference on Computers and Accessibility - ASSETS '18	Usability	Flex	Children	Steps Energy Expenditure Sleep
Feasibility Of Recording Sleep Quality And Sleep Duration Using Fitbit In Children With Asthma details	Sheth, A., Yip, H., Jaimini, U., Sridharan, V., Venkataramanan, R., Thirunarayam, K., Banerjee, T. and Kalra, M.	2018	Sleep	Usability	Flex	Children Adolescents	Sleep
Using financial incentives to promote physical activity in American Indian adolescents: A randomized controlled trial details	Short, K.R., Chadwick, J.Q., Cannady, T.K., Branam, D.E., Wharton, D.F., Tullier, M.A., Thompson, D.M. and Copeland, K.C.	2018	PLOS ONE	Intervention	Zip	Adolescents	Steps	URL
Comparison of Research- and Consumer-grade Energy Expenditure Estimation Methods during 10 Days of Military Training details	Siddall, A.G., Thompson, J.E.S., Powell, S.D., Edwards, V.C., Kefyalew, S.S., Singh, P.A., Orford, E.R., Venables, M.C., Jackson, S., Greeves, J.P., Blacker, S.D. and Myers, S.D.	2018	Medicine & Science in Sports & Exercise	Validation	Surge	Adults	Energy Expenditure
mActive-Smoke: A Prospective Observational Study Using Mobile Health Tools to Assess the Association of Physical Activity With Smoking Urges details	Silverman-Lloyd, L.G., Kianoush, S., Blaha, M.J., Sabina, A.B., Graham, G.N. and Martin, S.S.	2018	JMIR mHealth and uHealth	Measurement	Charge HR	Adults	Steps Heart Rate
A Smartphone App to Promote an Active Lifestyle in Lower-Educated Working Young Adults: Development, Usability, Acceptability, and Feasibility Study. details	Simons, D., De Bourdeaudhuij, I., Clarys, P., De Cocker, K., Vandelanotte, C. and Deforche, B.	2018	JMIR mHealth and uHealth	Usability	Charge	Adults	Steps
The influence of a consumer-wearable activity tracker on sedentary time and prolonged sedentary bouts: secondary analysis of a randomized controlled trial details	Sloan, R.A., Kim, Y., Sahasranaman, A., Müller-Riemenschneider, F., Biddle, S.J.H. and Finkelstein, E.A.	2018	BMC Research Notes	Intervention	Zip	Adults	Steps
Lessons Learned From a Feasibility Study Delivered in 2 WIC Sites to Promote Physical Activity Among Pregnant Latinas details	Soto, S.H., Sanz, S., Merchant, K.M., Nichols, J.F. and Arredondo, E.M.	2018	Journal of Nutrition Education and Behavior	Usability	Zip	Adults	Steps
Water intake reverses dehydration associated impaired executive function in healthy young women details	Stachenfeld, N.S., Leone, C.A., Mitchell, E.S., Freese, E. and Harkness, L.	2018	Physiology & Behavior	Intervention		Adults	Steps Sleep Food
Bouldering psychotherapy reduces depressive symptoms even when general physical activity is controlled for: A randomized controlled trial details	Stelzer, E.-M., Book, S., Graessel, E., Hofner, B., Kornhuber, J. and Luttenberger, K.	2018	Heliyon	Intervention	Zip	Adults	Steps
Heart Rate Equivalency of the Fitbit Charge HR During Continuous Aerobic Exercise details	Stock, J.M., Pohlig, R.T., Botieri, M.J., Edwards, D.G. and Dominick, G.M.	2018	Journal for the Measurement of Physical Behaviour	Validation	Charge HR	Adults	Steps Intensity Heart Rate
The validity and reliability of consumer-grade activity trackers in older, community-dwelling adults: A systematic review details	Straiton, N., Alharbi, M., Bauman, A., Neubeck, L., Gullick, J., Bhindi, R. and Gallagher, R.	2018	Maturitas	Review	One Zip Charge HR	Older Adults	Steps Intensity
The Influence Of Non-exercise Physical Activity During Aerobic Exercise On Cardiometabolic Risk Factors details	Swift, D.L., McGee, J.E., Barefoot, S.G., Brophy, P., Solar, C.A., Houmard, J.A. and Lutes, L.D.	2018	Medicine & Science in Sports & Exercise	Intervention	One	Adults	Intensity
Effect of carbohydrate restriction-induced weight loss on aortic pulse wave velocity in overweight men and women details	Syed-Abdul, M.M., Hu, Q., Jacome-Sosa, M., Padilla, J., Manrique-Acevedo, C., Heimowitz, C. and Parks, E.J.	2018	Applied Physiology, Nutrition, and Metabolism	Intervention	Flex	Adults	Steps	URL
Validation of Electronic Activity Monitor Devices During Treadmill Walking details	Tam, K.M. and Cheung, S.Y.	2018	Telemedicine and e-Health	Validation	Charge HR	Adults	Steps
Validity of wrist-worn consumer products to measure heart rate and energy expenditure details	Thiebaud, R.S., Funk, M.D., Patton, J.C., Massey, B.L., Shay, T.E., Schmidt, M.G. and Giovannitti, N.	2018	Digital Health	Validation	Surge	Adults	Energy Expenditure Heart Rate
SIT LESS: A prototype home-based system for monitoring older adults sedentary behavior details	Tirkel, T., Edan, Y., Khvorostyanov, N. and Bar-Haim, S.	2018	Assistive Technology	Usability	Charge HR	Older Adults	Steps
Video-Recorded Validation of Wearable Step Counters under Free-living Conditions details	Toth, L.P., Park, S., Springer, C.M., Feyerabend, M.D., Steeves, J.A. and Bassett, D.R.	2018	Medicine & Science in Sports & Exercise	Validation	Zip Charge	Adults	Steps
Sleeping Well Trial: Increasing the effectiveness of treatment with continuous positive airway pressure using a weight management program in overweight adults with obstructive sleep apnoea-A stepped wedge randomised trial protocol details	Truby, H., Edwards, B.A., O'Driscoll, D.M., Young, A., Ghazi, L., Bristow, C., Roem, K., Bonham, M.P., Murgia, C., Day, K., Haines, T.P. and Hamilton, G.S.	2018	Nutrition & Dietetics	Methods	Flex	Patients Adults	Sleep	URL
Parenting Intervention to Improve Nutrition and Physical Activity for Preschoolers with Type 1 Diabetes: A Feasibility Study details	Tully, C., Mackey, E., Aronow, L., Monaghan, M., Henderson, C., Cogen, F., Wang, J. and Streisand, R.	2018	Journal of Pediatric Health Care	Usability	One	Children Adults	NA
Using Commercial Physical Activity Trackers for Health Promotion Research: Four Case Studies details	Turner-McGrievy, G., Jake-Schoffman, D.E., Singletary, C., Wright, M., Crimarco, A., Wirth, M.D., Shivappa, N., Mandes, T., West, D.S., Wilcox, S., Drenowatz, C., Hester, A. and McGrievy, M.J.	2018	Health Promotion Practice	Review	Zip Flex Aria	Children Adults	Steps Weight
Counting Steps in Activities of Daily Living in People With a Chronic Disease Using Nine Commercially Available Fitness Trackers: Cross-Sectional Validity Study. details	Ummels, D., Beekman, E., Theunissen, K., Braun, S. and Beurskens, A.J.	2018	JMIR mHealth and uHealth	Validation	One Flex	Adults	Steps
The Wearable Technological Device as a Means of Physical Activity Monitoring details	Underhill, A.	2018	Medicine & Science in Sports & Exercise	Validation	Flex	Adults	Steps Intensity
Self-monitoring and reminder text messages to increase physical activity after colorectal cancer (CRC): A pilot randomized controlled trial. details	Van Blarigan, E., Van Loon, K., Kenfield, S.A., Chan, J.M., Mitchell, E., Chan, H., Zhang, L., Paciorek, A., Joseph, G., Laffan, A., Fukuoka, Y., Miaskowski, C., Meyerhardt, J.A. and Venook, A.P.	2018	JCO	Intervention	Flex	Patients Adults	Steps	URL
Self-Reported Physical Activity at Breast Cancer Diagnosis is Associated with Greater Physical Activity During Chemotherapy details	Wagoner, C.W., Deal, A.M., Choi, S.K., Lee, J.T., Muss, H.B. and Nyrop, K.A.	2018	Medicine & Science in Sports & Exercise	Intervention	Zip	Patients	Steps Intensity
Increasing At-Risk Youth Self-Reported and Objectively Measured Physical Activity in an Afterschool Program details	Walther, A., Dunker, T., Franzen-Castle, L. and Krehbiel, M.	2018	Journal of Family & Consumer Sciences	Intervention	Charge	Children	Steps
A Paradoxical Relationship between Physical Activity and HbA1c in Pediatric T1D Patients details	WATSON, D.A.V.I.D.A., BARNES, T.I.M.O.T.H.Y.L. and GANDRUD, L.A.U.R.A.M.	2018	Diabetes	Intervention		Patients	Steps Intensity
Sleep Tracking and Exercise in Patients With Type 2 Diabetes Mellitus (Step-D): Pilot Study to Determine Correlations Between Fitbit Data and Patient-Reported Outcomes. details	Weatherall, J., Paprocki, Y., Meyer, T.M., Kudel, I. and Witt, E.A.	2018	JMIR mHealth and uHealth	Measurement	Charge HR	Patients Adults	Steps Sleep	URL
Accuracy of Activity Trackers in Parkinson Disease: Should We Prescribe Them? details	Wendel, N., Macpherson, C.E., Webber, K., Hendron, K., Tamara, D., Cristina, C.-S. and Ellis, T.	2018	Physical Therapy	Validation	Zip Surge	Patients Adults	Steps
Modeling Clinically Validated Physical Activity Assessments Using Commodity Hardware details	Winfree, K.N. and Dominick, G.	2018	IEEE Journal of Biomedical and Health Informatics	Methods	Flex	Adults	Steps Intensity
The bit doesn't fit: Evaluation of a commercial activity-tracker at slower walking speeds. details	Wong, C.K., Mentis, H.M. and Kuber, R.	2018	Gait & Posture	Validation	Ultra	Older Adults	Steps	URL
A randomized controlled trial testing a social network intervention to promote physical activity among adolescents details	van Woudenberg, T.J., Bevelander, K.E., Burk, W.J., Smit, C.R., Buijs, L. and Buijzen, M.	2018	BMC Public Health	Intervention	Flex	Adolescents	Steps
Protocol for promoting recovery optimization of walking activity in stroke (PROWALKS): a randomized controlled trial details	Wright, H., Wright, T., Pohlig, R.T., Kasner, S.E., Raser-Schramm, J. and Reisman, D.	2018	BMC Neurology	Methods	One	Patients Adults	Steps
Evaluating the Validity of Current Mainstream Wearable Devices in Fitness Tracking Under Various Physical Activities: Comparative Study details	Xie, J., Wen, D., Liang, L., Jia, Y., Gao, L. and Lei, J.	2018	JMIR Mhealth Uhealth	Validation	Surge	Adults	Steps Energy Expenditure Distance Sleep Heart Rate
Successful adherence and retention to daily monitoring of physical activity: Lessons learned details	Xu, X., Tupy, S., Robertson, S., Miller, A.L., Correll, D., Tivis, R. and Nigg, C.R.	2018	PLOS ONE	Usability	One	Adults	Steps
Using Behavioral Analytics to Increase Exercise: A Randomized N-of-1 Study. details	Yoon, S., Schwartz, J.E., Burg, M.M., Kronish, I.M., Alcantara, C., Julian, J., Parsons, F., Davidson, K.W. and Diaz, K.M.	2018	American Journal of Preventive Medicine	Intervention	Flex	Adults	Steps
Forecasting of heart rate variability using wrist-worn heart rate monitor based on hidden Markov model details	Yun, S., Son, C.-S., Lee, S.-H. and Kang, W.-S.	2018	2018 International Conference on Electronics, Information, and Communication (ICEIC)	Methods	Charge HR	Adults	Heart Rate
Effect of Fitbit and iPad Wearable Technology in Health-Related Quality of Life in Adolescent and Young Adult Cancer Patients details	Yurkiewicz, I.R., Simon, P., Liedtke, M., Dahl, G. and Dunn, T.	2018	Journal of Adolescent and Young Adult Oncology	Intervention		Adults Adolescents	Steps Sleep
Acceptability of a Self-Regulation Theory-Based mHealth Behavior Intervention for Older Adults with Type 2 Diabetes and Obesity details	ZHENG, Y.A.G.U.A.N.G., WEINGER, K.A.T.I.E., GREGAS, M.A.T.T.C., GREENBERG, J.O.R.D.A.N., ZHUOXIN, L.I., BURKE, L.O.R.A.E., CHENFANG, Q.I., SLYNE, C.H.R.I.S.T.I.N.E., GREAVES, T.O.R.I. and MUNSHI, M.E.D.H.A.	2018	Diabetes	Intervention		Older Adults	Weight
Personalized effect of health behavior on blood pressure: Machine learning based prediction and recommendation details	Chiang, Po Han; Dey, Sujit	2018	2018 IEEE 20th International Conference on e-Health Networking, Applications and Services (Healthcom)	Validation	Charge HR	Adults	Steps
The Go-VAR (Veterans Active Recovery): An Adjunctive, Exercise-Based Intervention for Veterans Recovering from Substance Use Disorders details	Linke, Sarah E.; Hovsepians, Rita; Schnebly, Brittany; Godfrey, Kathryn; Noble, Madison; Strong, David R.; Isgro, Melodie; Lindamer, Laurie A.	2019	Journal of Psychoactive Drugs	Intervention	Charge HR	Adults	Intensity
Night Stepping: Fitbit Cracks the Case details	Somboon, Thapanee; Grigg-Damberger, Madeleine M.; Foldvary-Schaefer, Nancy	2019	Journal of Clinical Sleep Medicine	Case Study	Charge	Adults	Steps	URL
Accuracy of PurePulse photoplethysmography technology of Fitbit Charge 2 for assessment of heart rate during sleep details	Haghayegh, Shahab; Khoshnevis, Sepideh; Smolensky, Michael H.; Diller, Kenneth R.	2019	Chronobiology International	Validation	Charge 2	Adults	Heart Rate	URL
Heart rate measures from the Apple Watch, Fitbit Charge HR 2, and electrocardiogram across different exercise intensities details	Thomson, Elizabeth A.; Nuss, Kayla; Comstock, Ashley; Reinwald, Steven; Blake, Sophie; Pimentel, Richard E.; Tracy, Brian L.; Li, Kaigang	2019	Journal of Sports Sciences	Validation	Charge 2	Adults	Heart Rate	URL
Accuracy of wristband fitbit models in assessing sleep: Systematic review and meta-analysis details	Haghayegh, Shahab; Khoshnevis, Sepideh; Smolensky, Michael H.; Diller, Kenneth R.; Castriotta, Richard J.	2019	Journal of Medical Internet Research	Systematic Review		NA	NA	URL
Validation of Fitbit Charge 2 and Fitbit Alta HR Against Polysomnography for Assessing Sleep in Adults With Obstructive Sleep Apnea details	Moreno-Pino, Fernando; Porras-Segovia, Alejandro; L{\'{o}}pez-Esteban, Pilar; Art{\'{e}}s, Antonio; Baca-Garc{\'{i}}a, Enrique	2019	Journal of Clinical Sleep Medicine	Validation	Charge 2	Adults	Sleep	URL
Ability of the Fitbit Alta HR to quantify and classify sleep in patients with suspected central disorders of hypersomnolence: A comparison against polysomnography details	Cook, Jesse D.; Eftekari, Sahand C.; Dallmann, Erika; Sippy, Megan; Plante, David T.	2019	Journal of Sleep Research	Usability	Alta HR	Adults	Sleep	URL
Validation of the Fitbit Charge 2 compared to the ActiGraph GT3X+ in older adults with knee osteoarthritis in free-living conditions details	Collins, Jamie E.; Yang, Heidi Y.; Trentadue, Taylor P.; Gong, Yusi; Losina, Elena	2019	PLOS ONE		Charge 2	Older Adults	Intensity	URL
Identifying behavioral phenotypes of loneliness and social isolation with passive sensing: Statistical analysis, data mining and machine learning of smartphone and fitbit data details	Doryab, Afsaneh; Villalba, Daniella K.; Chikersal, Prerna; Dutcher, Janine M.; Tumminia, Michael; Liu, Xinwen; Cohen, Sheldon; Creswell, Kasey; Mankoff, Jennifer; Creswell, John D.; Dey, Anind K.	2019	JMIR mHealth and uHealth	Measurement	Flex 2	Adults	Intensity	URL
Is There a Benefit to Patients Using Wearable Devices Such as Fitbit or Health Apps on Mobiles? A Systematic Review details	Jo, Ara; Coronel, Bryan D.; Coakes, Courtney E.; Mainous, Arch G.	2019	American Journal of Medicine	Systematic Review		NA	NA	URL
The Effects of a Mobile Wellness Intervention with Fitbit Use and Goal Setting for Workers details	Lee, Sang-Ho; Ha, Yeongmi; Jung, Mira; Yang, Seungkyoung; Kang, Won-Seok	2019	Telemedicine and e-Health	Intervention	Charge HR	Adults	Steps	URL
Validity Evaluation of the Fitbit Charge2 and the Garmin vivosmart HR+ in Free-Living Environments in an Older Adult Cohort. details	Tedesco, Salvatore; Sica, Marco; Ancillao, Andrea; Timmons, Suzanne; Barton, John; O'Flynn, Brendan	2019	JMIR mHealth and uHealth	Validation	Charge 2	Older Adults	Steps	URL
Accuracy of fitbit wristbands in measuring sleep stage transitions and the effect of user-specific factors details	Liang, Zilu; Chapa-Martell, Mario Alberto	2019	Journal of Medical Internet Research	Validation	Charge 2	Adults	Sleep	URL
Reliability of Zephyr Bioharness and Fitbit Charge Measures of Heart Rate and Activity at Rest, During the Modified Canadian Aerobic Fitness Test, and Recovery details	Nazari, Goris; MacDermid, Joy C.; Sinden, Kathryn E.; Richardson, Julie; Tang, Ada	2019	Journal of Strength and Conditioning Research	Validation	Charge	Adults	Heart Rate	URL
Concurrent validity of the Fitbit for assessing sedentary behavior and moderate-to-vigorous physical activity details	Redenius, Nicklaus; Kim, Youngwon; Byun, Wonwoo	2019	BMC Medical Research Methodology	Validation	Flex	Adults	Intensity	URL
Fitbit Usage in Patients With Breast Cancer Undergoing Chemotherapy details	Dreher, Nickolas; Hadeler, Edward Kenji; Hartman, Sheri J.; Wong, Emily C.; Acerbi, Irene; Rugo, Hope S.; Majure, Melanie Catherine; Chien, Amy Jo; Esserman, Laura J.; Melisko, Michelle E.	2019	Clinical Breast Cancer	Usability	Charge HR	Patients
Validity of Cardiorespiratory Fitness Measured with Fitbit Compared to V˙O2max details	Klepin, Katharine; Wing, David; Higgins, Michael; Nichols, Jeanne; Godino, Job G.	2019	Medicine and science in sports and exercise	Validation	Charge 2	Adults	Heart Rate	URL
Preliminary Agreement on Tracking Sleep Between a Wrist-Worn Device Fitbit Alta and Consensus Sleep Diary details	Liu, Jiaxing; Wong, Wing To; Zwetsloot, Inez Maria; Hsu, Yu Cheng; Tsui, Kwok Leung	2019	Telemedicine and e-Health	Validation	Alta	Adults	Sleep	URL
Evaluation of Knee Cartilage Diurnal, Activity, and BMI-Related Variations Using Quantitative T2 Mapping MRI and Fitbit Activity Tracking details	Matcuk, George R.; Jones, Ian A.; McIntyre, J. Alex; Burt, Robert; Hwang, Darryl; Cen, Steven; Schein, Aaron J.; Vangsness, C. Thomas	2019	The Journal of Knee Surgery	Measurement		Adults	Steps	URL
Assessment of Accuracy of Overall Energy Expenditure Measurements for the Fitbit Charge HR 2 and Apple Watch details	Nuss, Kayla J.; Thomson, Elizabeth A.; Courtney, Jimikaye B.; Comstock, Ashley; Reinwald, Steven; Blake, Sophie; Richard, E.; Tracy, Brian L.; Li, Kaigang	2019	American Journal of Health Behavior	Validation	Charge HR	Adults	Energy Expenditure
Potential benefits of Fitbit device in managing a patient with mucopolysaccharidosis details	Oldham, Andrew; Stepien, Karolina M.; Hendriksz, Christian J.	2019	Molecular Genetics and Metabolism	Case Study		NA	Heart Rate
Validating Fitbit for Evaluation of Physical Activity in Patients with Knee Osteoarthritis: Do Thresholds Matter? details	Silva, Genevieve S.; Yang, Heidi; Collins, Jamie E.; Losina, Elena	2019	ACR Open Rheumatology	Validation	Charge 2	Patients	Intensity	URL
Offline and Online Learning Techniques for Personalized Blood Pressure Prediction and Health Behavior Recommendations details	Chiang, Po Han; Dey, Sujit	2019	IEEE Access	Usability	Charge HR	Adults	Sleep Heart Rate
Goal-directed versus outcome-based financial incentives for weight loss among low-income patients with obesity: Rationale and design of the Financial Incentives for Weight Reduction (FIReWoRk) randomised controlled trial details	Jay, Melanie; Orstad, Stephanie L.; Wali, Soma; Wylie-Rosett, Judith; Tseng, Chi Hong; Sweat, Victoria; Wittleder, Sandra; Shu, Suzanne B.; Goldstein, Noah J.; Ladapo, Joseph A.	2019	BMJ Open	Intervention	Alta HR	Adults	Intensity	URL
What is the Agreement Between Two Generations of Commercial Accelerometer in a Free-Living Environment for Young to Middle-Aged Adults? details	Jones, Denise M.; Hart, Harvi F.; Crossley, Kay M.; Ackerman, Ilana N.; Kemp, Joanne L.	2019	Journal for the Measurement of Physical Behaviour	Validation	Flex Flex 2	Adults	Steps	URL
The feasibility of using real-time, objective measurements of physiological stress among law enforcement officers in Dallas, Texas details	Reingle Gonzalez, Jennifer M.; Jetelina, Katelyn K.; Bishopp, Stephen A.; Livingston, Melvin D.; Perez, Rodolfo A.; Gabriel, Kelley Pettee	2019	Policing	Usability	Charge 2	Adults	Heart Rate	URL
Free-Living Comparison of Physical Activity and Sleep Data from Fitbit Activity Trackers Worn on the Dominant and Nondominant Wrists details	Clevenger, Kimberly A.; Molesky, Monroe J.; Vusich, John; Montoye, Alexander H. K.	2019	Measurement in Physical Education and Exercise Science	Validation	Flex Flex 2	Adults	Steps Intensity Distance Sleep	URL
Validity of Heart Rate Measurements in a Fitbit Charge 2 and Apple iWatch in a Free-Living Setting details	Dione, Dakota	2019	UVM Honors College Senior Theses	Validation Dissertation / Thesis	Charge 2	Adults	Heart Rate	URL
Agreement between the Apple Series 1, LifeTrak Core C200, and Fitbit Charge HR with Indirect Calorimetry for Assessing Treadmill Energy Expenditure details	Zhang, Peng; Burns, Ryan Donald; Fu, You; Godin, Steven; Byun, Wonwoo	2019	International Journal of Environmental Research and Public Health	Usability	Charge HR	Adults	Energy Expenditure	URL
The Influence of Wearing a fitbit on Eating Behaviors While Stressed details	Sparacino, Maria	2019	Coastal Carolina University Digital Commons	Measurement	Flex	Adults	Heart Rate	URL
Comparing Methods to Identify Wear-Time Intervals for Physical Activity With the Fitbit Charge 2 details	Claudel, Sophie E.; Tamura, Kosuke; Troendle, James; Andrews, Marcus R.; Ceasar, Joniqua N.; Mitchell, Valerie M.; Vijayakumar, Nithya; Powell-Wiley, Tiffany M.	2020	Journal of Aging and Physical Activity	Methods	Charge 2	Adults	Steps	URL
Fitbit for learning: Towards capturing the learning experience using wearable sensing details	Giannakos, Michail N.; Sharma, Kshitij; Papavlasopoulou, Sofia; Pappas, Ilias O.; Kostakos, Vassilis	2020	International Journal of Human Computer Studies	Usability		Adolescents	Heart Rate	URL
Performance assessment of new-generation Fitbit technology in deriving sleep parameters and stages details	Haghayegh, Shahab; Khoshnevis, Sepideh; Smolensky, Michael H.; Diller, Kenneth R.; Castriotta, Richard J.	2020	Chronobiology International	Validation	Charge 2	Adults	Sleep	URL
Fitbit wear-time and patterns of activity in cancer survivors throughout a physical activity intervention and follow-up: Exploratory analysis from a randomised controlled trial details	Hardcastle, Sarah J.; Jim{\'{e}}nez-Castuera, Ruth; Maxwell-Smith, Chlo{\'{e}}; Bulsara, Max K.; Hince, Dana	2020	PLOS ONE	Intervention	Alta	Patients	Intensity	URL
Estimating physical activity and sedentary behaviour in a free-living environment: A comparative study between Fitbit Charge 2 and Actigraph GT3X details	Mikkelsen, Marie-Louise K.; Berg-Beckhoff, Gabriele; Frederiksen, Peder; Horgan, Graham; O'Driscoll, Ruairi; Palmeira, Ant{\'{o}}nio L.; Scott, Sarah E.; Stubbs, James; Heitmann, Berit L.; Larsen, Sofus C.	2020	PLOS ONE	Measurement	Charge 2	Adults	Steps	URL
Participant Perceptions on a Fitbit and Facebook Intervention for Young Adult Cancer Survivors: A Qualitative Study details	Miropolsky, Evelina M.; Scott Baker, Kevin; Abbey-Lambertz, Mark; Syrjala, Karen; Chow, Eric J.; Ceballos, Rachel; Mendoza, Jason A.	2020	Journal of Adolescent and Young Adult Oncology	Intervention	Flex	Adults	Steps	URL
Fitbit-Based Interventions for Healthy Lifestyle Outcomes: Systematic Review and Meta-Analysis details	Ringeval, Mickael; Wagner, Gerit; Denford, James; Par{\'{e}}, Guy; Kitsiou, Spyros	2020	Journal of Medical Internet Research	Systematic Review			Steps	URL
Outcomes of a text message, Fitbit, and coaching intervention on physical activity maintenance among cancer survivors: a randomized control pilot trial details	Gell, Nancy M.; Grover, Kristin W.; Savard, Liliane; Dittus, Kim	2020	Journal of Cancer Survivorship	Intervention	One	Adults	Intensity	URL
Performance of a commercial multi-sensor wearable (Fitbit Charge HR) in measuring physical activity and sleep in healthy children details	Godino, Job G.; Wing, David; de Zambotti, Massimiliano; Baker, Fiona C.; Bagot, Kara; Inkelis, Sarah; Pautz, Carina; Higgins, Michael; Nichols, Jeanne; Brumback, Ty; Chevance, Guillaume; Colrain, Ian M.; Patrick, Kevin; Tapert, Susan F.	2020	PLOS ONE	Validation	Charge HR	Children	Sleep	URL
Use of Fitbit Technology Does Not Impact Health Biometrics in a Community of Older Adults details	Hong, Estee; Jakacic, Alexa N.; Sahoo, Arpan; Breyman, Erica; Ukegbu, Gabriel; Tabacof, Laura; Sachs, David; Migliaccio, John; Phipps, Colette; Schwartz, Jennifer; Capasso, Marikay; Carpenter, Mae; Putrino, David	2020	Telemedicine and e-Health	Intervention	Zip	Older Adults	Steps	URL
Investigating the within-person relationships between activity levels and sleep duration using Fitbit data details	Liao, Yue; Robertson, Michael C.; Winne, Andrea; Wu, Ivan H. C.; Le, Thuan A.; Balachandran, Diwakar D.; Basen-Engquist, Karen M.	2020	Translational Behavioral Medicine	Intervention	Flex 2	Adults	Sleep	URL
Towards a Functional Approach to the Assessment of Daily Life Physical Activity in Children: Are the PAQ-C and Fitbit Flex-2 Technically Adequate? details	Venetsanou, Fotini; Emmanouilidou, Kyriaki; Soutos, Konstantinos; Sotiriou, Sofoklis A.; Bastida, Leire; Moya, Ana; Kambas, Antonis	2020	International Journal of Environmental Research and Public Health	Usability	Flex 2	Children	Intensity	URL
Minimal Detectable Change Thresholds and Responsiveness of Zephyr Bioharness and Fitbit Charge Devices details	Nazari, Goris; MacDermid, Joy C.	2020	Journal of Strength and Conditioning Research	Validation	Charge	Adults	Heart Rate	URL
Twitter-Based Social Support Added to Fitbit Self-Monitoring for Decreasing Sedentary Behavior: Protocol for a Randomized Controlled Pilot Trial With Female Patients From a Women's Heart Clinic details	Oppezzo, Marily; Tremmel, Jennifer; Desai, Manisha; Baiocchi, Michael; Ramo, Danielle; Cullen, Mark; Prochaska, Judith J.	2020	JMIR Research Protocols	Usability		Adults	Steps	URL
Impact of placement of fitbit HR under laboratory and free-living conditions details	Jung, Hyun Chul; Kang, Minsoo; Lee, Nan Hee; Jeon, Soeun; Lee, Sukho	2020	Sustainability (Switzerland)	Validation	Charge 2	Adults	Heart Rate	URL
Using Fitbit Competitions to Increase Physical Activity in College Students details	Ramirez, Omar; Bernhardt, Vipa	2020	The Macksey Journal	Intervention	One	Adults	Steps	URL
Self-efficacy and physical activity in overweight and obese adults participating in a worksite weight loss intervention: Multistate modeling of wearable device data details	Robertson, Michael C.; Green, Charles E.; Liao, Yue; Durand, Casey P.; Basen-Engquist, Karen M.	2020	Cancer Epidemiology Biomarkers and Prevention	Intervention	Flex 2	Adults	Intensity	URL
Comparison of Physical Activity Measures Derived From the Fitbit Flex and the ActiGraph GT3X+ in an Employee Population With Chronic Knee Symptoms details	Semanik, Pamela; Lee, Jungwha; Pellegrini, Christine A.; Song, Jing; Dunlop, Dorothy D.; Chang, Rowland W.	2020	ACR Open Rheumatology	Intervention	Flex	Adults	Intensity	URL
Participant perceptions on a Fitbit and Facebook intervention for adolescent and young adult cancer survivors: A qualitative study details	Miropolsky, Evelina	2020	University of Washington	Intervention	Flex	Adults	Steps Intensity	URL
Physical activity pattern and Fitbit use in working patients with hip and/or knee osteoarthritis throughout an intervention: an exploratory descriptive study - Osteoarthritis and Cartilage details	E. Östlind, A. Sant'Anna, F. Eek, K. Stigmar, E. Ekvall Hansson	2020	Osteoarthritis and Cartilage	Intervention	Flex 2	Adults	Steps Intensity	URL
Fitbit\textregistered-derived evidence of enhanced mobility of patients with knee osteoarthritis treated with triamcinolone acetonide extended release: Results from a phase 3B, single-ARM, open-label study details	Huffman, K. M.; Spitzer, A.; Richmond, J. C.; Gomoll, A.; Kraus, V. B.; Jones, D. G.; Concoff, A.; Cinar, A.; Kelley, S.	2020	Osteoarthritis and Cartilage					URL URL URL
Accuracy and precision of three consumer-grade motion sensors during overground and treadmill walking in people with parkinson disease: Cross-sectional comparative study details	Lai, Byron; Sasaki, Jeffer E.; Jeng, Brenda; Cederberg, Katie L.; Bamman, Marcas M.; Motl, Robert W.	2020	JMIR Rehabilitation and Assistive Technologies					URL
A SECONDARY DATA ANALYSIS ON THE RELATIONSHIP BETWEEN BREAST CANCER SURVIVORS' SYMPTOMS AND PHYSICAL ACTIVITY LEVELS MEASURED BY FITBIT (STEPS/DAY) details	Merritt, Alexis	2020	East Carolina University					URL
The Physical Activity Wearables in the Police Force (PAW-Force) study: acceptability and impact details	Buckingham, Sarah Ann; Morrissey, Karyn; Williams, Andrew James; Price, Lisa; Harrison, John	2020	BMC Public Health	Intervention	Charge 2	Adults	Steps Goals	URL
Examining the Effects of a Fitbit Treatment Package on the Physical Activity for Adults with Intellectual Disabilities details	Peck, Kimberly	2020	Western Michigan University					URL
Harnessing wearable device data to improve state-level real-time surveillance of influenza-like illness in the USA: a population-based study details	Radin, Jennifer M.; Wineinger, Nathan E.; Topol, Eric J.; Steinhubl, Steven R.	2020	The Lancet Digital Health					URL
Evaluation of an Activity Tracker to Detect Seizures Using Machine Learning details	Mittlesteadt, Jackson; Bambach, Sven; Dawes, Alex; Wentzel, Evelynne; Debs, Andrea; Sezgin, Emre; Digby, Dan; Huang, Yungui; Ganger, Andrea; Bhatnagar, Shivani; Ehrenberg, Lori; Nunley, Sunjay; Glynn, Peter; Lin, Simon; Rust, Steve; Patel, Anup D.	2020	Journal of Child Neurology	Usability	Charge 2	Children Adolescents	Heart Rate	URL
Using Fitbit data to examine factors that affect daily activity levels of college students details	Wang, Cheng; Lizardo, Omar; Hachen, David S.	2021	PLOS ONE	Measurement	Charge HR	Adults	Intensity	URL
Feasibility and acceptability of a technology-based, rural weight management intervention in older adults with obesity details	Batsis, John A.; Petersen, Curtis L.; Clark, Matthew M.; Cook, Summer B.; Kotz, David; Gooding, Tyler L.; Roderka, Meredith N.; Al-Nimr, Rima I.; Pidgeon, Dawna; Haedrich, Ann; Wright, K. C.; Aquila, Christina; Mackenzie, Todd A.	2021	BMC Geriatrics	Intervention	Alta	Older Adults	Intensity	URL
Annual rhythms in adults' lifestyle and health (ARIA): protocol for a 12-month longitudinal study examining temporal patterns in weight, activity, diet, and wellbeing in Australian adults details	Curtis, Rachel G.; Olds, Timothy; Fraysse, Fran{\c{c}}ois; Dumuid, Dorothea; Hendrie, Gilly A.; Esterman, Adrian; Brown, Wendy J.; Ferguson, Ty; Lagiseti, Rajini; Maher, Carol A.	2021	BMC Public Health	Intervention		Adults	Intensity	URL
A digital lifestyle behaviour change intervention for the prevention of type 2 diabetes: a qualitative study exploring intuitive engagement with real-time glucose and physical activity feedback details	Whelan, Maxine E.; Denton, Francesca; Bourne, Claire L. A.; Kingsnorth, Andrew P.; Sherar, Lauren B.; Orme, Mark W.; Esliger, Dale W.	2021	BMC Public Health	Intervention	Charge 2	Adults	Intensity	URL
Measuring Daily Compliance with Physical Activity Tracking in Ambulatory Surgery Patients: Comparative Analysis of Five Compliance Criteria (Preprint) details	Kelly, Ryan; Jones, Simon; Price, Blaine; Katz, Dmitri; McCormick, Ciaran; Pearce, Oliver	2021	JMIR mHealth and uHealth		Zip	Adults	Steps	URL
A Prospective Observational Study Comparing Effects of Call Schedules on Surgical Resident Sleep and Physical Activity Using the Fitbit details	Kelly-Schuette, Kathrine; Shaker, Tamer; Carroll, Joseph; Davis, Alan T.; Wright, G. Paul; Chung, Mathew	2021	Journal of Graduate Medical Education	Measurement	Charge 2	Adults	Steps Sleep	URL
Phase II trial evaluating efficacy of a Fitbit program for improving the health of endometrial cancer survivors details	Rahimy, Elham; Usoz, Melissa; von Eyben, Rie; Fujimoto, Dylann; Watanabe, Darla; Karam, Amer; Jairam-Thodla, Arati; Mills, Margaret; Dorigo, Oliver; Diver, Elisabeth Jenefer; Teng, Nelson; English, Diana; Kidd, Elizabeth	2021	Gynecologic Oncology	Usability Intervention	Alta	Patients Adults	Steps Intensity Goals	URL
Performance of Fitbit Charge 3 against polysomnography in measuring sleep in adolescent boys and girls details	Menghini, Luca; Yuksel, Dilara; Goldstone, Aimee; Baker, Fiona C.; de Zambotti, Massimiliano	2021	Chronobiology International	Validation	Charge 3	Adolescents	Sleep Heart Rate	URL
A standardized framework for testing the performance of sleep-tracking technology: step-by-step guidelines and open-source code details	Menghini, Luca; Cellini, Nicola; Goldstone, Aimee; Baker, Fiona C.; de Zambotti, Massimiliano	2021	Sleep	Validation	Charge 2	Adults	Sleep	URL
Comparing the accuracy of sleep staging data from a new wearable sleep electroencephalography device versus the Fitbit Charge 3 with polysomnography as reference details	Suzuki, Yoko; Abe, Takashi; Kawana, Fusae; Okabe, Satomi; Kokubo, Toshio; Hoshino, Kazuya; Baba, Misao; Fujiwara, Masaaki; Yanagisawa, Masashi	2021	Clinical Neurophysiology	Validation	Charge 3	Adults	Sleep	URL
Monitoring Beliefs and Physiological Measures Using Wearable Sensors and Smartphone Technology Among Students at Risk of COVID-19: Protocol for a mHealth Study details	Christine; Clingan, Caroline; Gilley, Kristen; Rozwadowski, Michelle; Gainsburg, Izzy; Bradley, Christina; Barabas, Jenny; Sandford, Erin; Olesnavich, Mary; Tyler, Jonathan; Mayer, Caleb; DeMoss, Matthew; Flora, Christopher; Forger, Daniel B.; Cunningham, Julia Lee; Tewari, Muneesh; Choi, Sung Won	2021	JMIR Res Protoc 2021;10(6):e29561 https://www.researchprotocols.org/2021/6/e29561	Usability	Charge 3	Adults	Energy Expenditure Sleep Heart Rate	URL
Co-occurrence of pain and dyspnea in Veterans with COPD: Relationship to functional status and a pilot study of neural correlates using structural and functional magnetic resonance imaging details	Moy, Marilyn L.; Daniel, Rinu A.; Rivera, Paola N. Cruz; Mongiardo, Maria A.; Goldstein, Rebekah L.; Higgins, Diana M.; Salat, David H.	2021	PLOS ONE	Medical	Zip	Older Adults	Steps Intensity	URL
Feasibility, preliminary efficacy, and accessibility of a twitter-based social support group vs Fitbit only to decrease sedentary behavior in women details	Oppezzo, M. A.; Tremmel, J. A.; Kapphahn, K.; Desai, M.; Baiocchi, M.; Sanders, M.; Prochaska, J. J.	2021	Internet Interventions	Usability Intervention	Inspire	Adults	Steps Intensity	URL
Using fitbit as an mHealth intervention tool to promote physical activity: Potential challenges and solutions details	Balbim, Guilherme M.; Marques, Isabela G.; Marquez, David X.; Patel, Darshilmukesh; Sharp, Lisa K.; Kitsiou, Spyros; Nyenhuis, Sharmilee M.	2021	JMIR mHealth and uHealth	Review	Charge HR	Older Adults Adults	Steps Intensity
Implementation of a school-based Fitbit program for youth with Autism Spectrum Disorder: A feasibility study details	Garcia, J. M.; Leahy, N.; Brazendale, K.; Quelly, S.; Lawrence, S.	2021	Disability and health journal	Usability Survey Research	Inspire	Adolescents	Steps Energy Expenditure Distance Sleep	URL
Use of Fitbit Devices in Physical Activity Intervention Studies Across the Life Course: Narrative Review details	St Fleur, R. G.; St George, S. M.; Leite, R.; Kobayashi, M.; Agosto, Y.; Jake-Schoffman, D. E.	2021	JMIR mHealth and uHealth	Intervention Review Measurement	Classic Ultra One Zip Flex Charge Charge HR Flex 2 Alta	Older Adults Children Adults Adolescents	Steps Intensity Goals Sleep Heart Rate Food	URL
Feasibility of a Latin Dance Program with mHealth for Middle-Aged and Older Latinxs (BAILA TECH) details	Marques, Isabela G.; Kitsiou, Spyros; Gerber, Ben S.; Buchholz, Susan W.; Bustamante, Eduardo E.; Marquez, David X.	2021	Translational Journal of the American College of Sports Medicine	Intervention	Charge 2	Older Adults	Steps Intensity Goals Sleep Heart Rate
The Association Between Logging Steps Using a Website, App, or Fitbit and Engaging With the 10,000 Steps Physical Activity Program: Observational Study details	Rayward, A. T.; Vandelanotte, C.; Van Itallie, A.; Duncan, M. J.	2021	Journal of medical Internet research	Usability Intervention Measurement		Adults	Steps	URL
A Comparison of Sedentary Behavior as Measured by the Fitbit and ActivPAL in College Students details	Carpenter, C.; Yang, C. H.; West, D.	2021	International journal of environmental research and public health	Validation Measurement	Charge 3	Adults	Steps Energy Expenditure Intensity Sleep Heart Rate	URL
Physical Activity Monitoring Using a Fitbit Device in Ischemic Stroke Patients: Prospective Cohort Feasibility Study details	Katzan, I.; Schuster, A.; Kinzy, T.	2021	JMIR mHealth and uHealth	Medical Clinical Care	Charge HR	Older Adults Patients Adults	Steps	URL
Assessing acceptability and patient experience of a behavioral lifestyle intervention using fitbit technology in older adults to manage type 2 diabetes amid COVID-19 pandemic: A focus group study details	Jiwani, R.; Dennis, B.; Bess, C.; Monk, S.; Meyer, K.; Wang, J.; Espinoza, S.	2021	Geriatric nursing (New York, N.Y.)	Intervention		Older Adults	Steps Energy Expenditure Distance Weight Food	URL
Validity Evaluation of the Fitbit Charge2 and the Garmin vivosmart HR+ in Free-Living Environments in an Older Adult Cohort details	Tedesco, S.; Sica, M.; Ancillao, A.; Timmons, S.; Barton, J.; O'Flynn, B.	2019	JMIR mHealth and uHealth	Usability Validation	Charge 2	Older Adults	Steps Energy Expenditure Intensity Sleep	URL
Physician Monitoring of FitBit Use for Patient Health details	Shannahan Md, A.; Shah Md, A.; Wright PhD, K.; Clements Md, D. S.	2021	Global advances in health and medicine					URL
A study of the accuracy of the Fitbit Zip in measuring steps both indoors and outdoors in a mixed rehabilitation population details	Farmer, C.; van den Berg, M. E.; Vuu, S.; Barr, C. J.	2021	Clinical rehabilitation	Usability Validation Measurement	Zip	Patients Adults	Steps	URL
Predicting lying, sitting, walking and running using Apple Watch and Fitbit data details	Fuller, D.; Anaraki, J. R.; Simango, B.; Rayner, M.; Dorani, F.; Bozorgi, A.; Luan, H.; A Basset, F.	2021	BMJ open sport & exercise medicine	Validation Measurement	Charge HR	Adults	Steps Energy Expenditure Intensity Distance Heart Rate	URL
Measurement of Heart Rate Using the Polar OH1 and Fitbit Charge 3 Wearable Devices in Healthy Adults During Light, Moderate, Vigorous, and Sprint-Based Exercise: Validation Study details	Muggeridge, D. J.; Hickson, K.; Davies, A. V.; Giggins, O. M.; Megson, I. L.; Gorely, T.; Crabtree, D. R.	2021	JMIR mHealth and uHealth	Validation	Charge 3	Adults	Heart Rate	URL
Comparison of the Wrist-Worn Fitbit Charge 2 and the Waist-Worn Actigraph GTX3 for Measuring Steps Taken in Occupational Settings details	Garza, J. L.; Wu, Z. H.; Singh, M.; Cherniack, M. G.	2021	Annals of work exposures and health	Validation	Charge 2	Adults	Steps	URL
Accuracy of the ActivPAL and Fitbit Charge 2 in measuring step count in Cystic Fibrosis details	Curran, M.; Tierney, A.; Collins, L.; Kennedy, L.; McDonnell, C.; Sheikhi, A.; Walsh, C.; Casserly, B.; Cahalan, R.	2021	Physiotherapy theory and practice	Validation	Charge 2	Patients Adults	Steps	URL
Comparison of free-living physical activity data obtained from a Fitbit Zip, the Apple iPhone Health app and a modified Bouchard Activity Record details	Reynolds, R. C.; Smith, V. M.; Macniven, R.	2021	Health promotion journal of Australia : official journal of Australian Association of Health Promotion Professionals	Validation	Zip	Adults	Steps Distance	URL
Does a physical therapist-administered physical activity intervention reduce sedentary time after total knee replacement: An exploratory study? details	Coleman, Grace; White, Daniel K.; Thoma, Louise M.; Mathews, Dana; Christiansen, Meredith B.; Schmitt, Laura A.; Jakiela, Jason T.; Master, Hiral	2021	Musculoskeletal Care	Intervention	Zip	Patients Adults	Steps Intensity Goals
Management of fatigue with physical activity and behavioural change support in vasculitis: a feasibility study details	Harper, L.; Hewitt, C. A.; Litchfield, I.; Morgan, M. D.; Chanouzas, D.; Caulfield, H. K.; Coughlan, L.; Dean, C.; Fletcher, K.; Cramp, F.; Greenfield, S.; Ives, N. J.; Jowett, S.; Kodabuckus, S.; Tearne, S.; Sehmi, S.; Edwardson, C.; Dawkins, N. P.; Daley, A. J.	2021	Rheumatology (Oxford, England)	Intervention	Charge HR	Patients Adults	Goals	URL
Effect and feasibility of wearable physical activity trackers and pedometers for increasing physical activity and improving health outcomes in cancer survivors: A systematic review and meta-analysis details	Singh, B.; Zopf, E. M.; Howden, E. J.	2021	Journal of sport and health science	Systematic Review	One	Adults	Steps Intensity Distance	URL
Consumer-based activity trackers in evaluation of physical activity in myositis patients details	Saygin, D.; Rockette-Wagner, B.; Oddis, C.; Neiman, N.; Koontz, D.; Moghadam-Kia, S.; Aggarwal, R.	2021	Rheumatology (Oxford, England)	Validation Intervention	One	Patients Adults	Steps Intensity	URL
Inter-Device Agreement between Fitbit Flex 1 and 2 for Assessing Sedentary Behavior and Physical Activity details	Kwon, S.; Burns, R. D.; Kim, Y.; Bai, Y.; Byun, W.	2021	International journal of environmental research and public health	Validation	Flex Flex 2	Adults	Steps Intensity	URL
Objective Measurement of Physical Activity Attributed to a Park-Based Afterschool Program details	Booth, J. V.; Messiah, S. E.; Hansen, E.; Nardi, M. I.; Hawver, E.; Patel, H. H.; Kling, H.; Okeke, D.; D'Agostino, E. M.	2021	Journal of physical activity & health	Intervention	Charge 2	Adolescents	Steps Intensity	URL
Multilevel mHealth Intervention Increases Physical Activity of Older Adults Living in Retirement Community details	Hosteng, K. R.; Simmering, J. E.; Polgreen, L. A.; Cremer, J. F.; Segre, A. M.; Francis, S. L.; Whitaker, K. M.; Polgreen, P. M.; Carr, L. J.	2021	Journal of physical activity & health	Intervention	Zip	Older Adults	Steps	URL
Physical Activity and Sleep Measures Using a Fitness Tracking Device during Hematopoietic Cell Transplantation: A Pilot Study details	Jurdi, N. E.; Nock, N.; Fu, P.; Cao, S.; Cotton, J. M.; Ali, N.; Ravi, G.; Pinto, R.; Galloway, E.; Kolke, S.; Cooper, B.; Tomlinson, B.; Malek, E.; Lance, C.; Kolk, M. J.; Ferrari, N.; Lee, R.; de Lima, M.; Caimi, P. F.; Metheny, L.	2021	Transplantation and cellular therapy	Intervention	Charge HR	Patients	Steps Intensity Distance Sleep Heart Rate	URL
Consumer-Based Activity Trackers as a Tool for Physical Activity Monitoring in Epidemiological Studies During the COVID-19 Pandemic: Development and Usability Study details	Henriksen, A.; Johannessen, E.; Hartvigsen, G.; Grimsgaard, S.; Hopstock, L. A.	2021	JMIR public health and surveillance	Usability	Device unspecified	Adults	Steps Energy Expenditure Intensity Sleep	URL
Characterizing rates of physical activity in individuals with binge eating disorder using wearable sensor technologies and clinical interviews details	Wons, O. B.; Michael, M. L.; Lin, M.; Juarascio, A. S.	2021	European eating disorders review : the journal of the Eating Disorders Association	Intervention	Flex 2	Adults	Steps Intensity Weight	URL
Physical activity in hemodialysis patients on nondialysis and dialysis days: Prospective observational study details	Malhotra, R.; Kumar, U.; Virgen, P.; Magallon, B.; Garimella, P. S.; Chopra, T.; Kotanko, P.; Ikizler, T. A.; Trzebinska, D.; Cadmus-Bertram, L.; Ix, J. H.	2021	Hemodialysis international. International Symposium on Home Hemodialysis	Medical Intervention	Charge 2	Patients Adults	Steps	URL
Dynamics of sleep, sedentary behavior, and moderate-to-vigorous physical activity on school versus nonschool days details	Armstrong, Bridget; Beets, Michael W.; Starrett, Angela; Brazendale, Keith; Turner-McGrievy, Gabrielle; Saelens, Brian E.; Pate, Russell R.; Youngstedt, Shawn D.; Maydeu-Olivares, Alberto; Weaver, R. Glenn	2021	Sleep	Case Study	Charge 2	Children Adolescents	Steps Intensity Sleep Heart Rate	URL
Physical activity patterns, adherence to using a wearable activity tracker during a 12-week period and correlation between self-reported function and physical activity in working age individuals with hip and/or knee osteoarthritis details	Östlind, Elin; Sant’Anna, Anita; Eek, Frida; Stigmar, Kjerstin; Hansson, Eva Ekvall	2021	BMC Musculoskeletal Disorders 2021 22:1	Intervention	Flex 2	Adults	Steps Intensity	URL
Does Education Close the Black-White Physical Activity and Obesity Gaps? details	Blackshear, T. B.; Seyfried, L.	2021	Journal of American college health : J of ACH	Case Study	Zip	Adults	Steps Intensity	URL
Applying Mobile Technology to Sustain Physical Activity After Completion of Cardiac Rehabilitation: Acceptability Study details	Elnaggar, A.; von Oppenfeld, J.; Whooley, M. A.; Merek, S.; Park, L. G.	2021	JMIR human factors	Intervention	Charge 2	Patients	Steps	URL
A Pilot Randomized Controlled Trial of a Fitbit- and Facebook-Based Physical Activity Intervention for Young Adult Cancer Survivors details	Johnson, A. M.; Baker, K. S.; Haviland, M. J.; Syrjala, K. L.; Abbey-Lambertz, M.; Chow, E. J.; Mendoza, J. A.	2021	Journal of adolescent and young adult oncology	Intervention	Device unspecified	Patients Adults	Steps Goals	URL
Greater time in bed and less physical activity associate with poorer cognitive functioning performance in Huntington's disease details	McLaren, B.; Drummond, S. P. A.; Glikmann-Johnston, Y.; Loy, C.; Bellgrove, M. A.; Stout, J. C.; Andrews, S. C.	2021	Neuropsychology	Case Study Measurement	One	Adults	Steps Sleep	URL
Patterns of Fitbit Use and Activity Levels Among African American Breast Cancer Survivors During an eHealth Weight Loss Randomized Controlled Trial details	Ferrante, J. M.; Lulla, A.; Williamson, J. D.; Devine, K. A.; Ohman-Strickland, P.; Bandera, E. V.	2021	American journal of health promotion : AJHP	Usability	Charge Alta	Adults	Steps Energy Expenditure Goals	URL
Targeting self-control as a behavior change mechanism to increase physical activity: Study protocol of a randomized controlled trial details	Stieger, M.; Allemand, M.; Lachman, M. E.	2021	Contemporary clinical trials	Intervention	Charge 4	Adults	Steps	URL
Effect of Wearable Technology-Based Physical Activity Interventions on Breast Cancer Survivors' Physiological, Cognitive, and Emotional Outcomes: A Systematic Review details	Blount, Daphne S.; McDonough, Daniel J.; Gao, Zan	2021	Journal of Clinical Medicine	Systematic Review	One	Adults	Steps Intensity	URL URL URL
Examining physical activity and quality of life in adults with autism spectrum disorder and intellectual disability details	Tomaszewski, B.; Savage, M. N.; Hume, K.	2021	Journal of intellectual disabilities : JOID	Intervention	Flex 2	Adults	Steps Energy Expenditure Intensity	URL
A Mind-Body Physical Activity Program for Chronic Pain With or Without a Digital Monitoring Device: Proof-of-Concept Feasibility Randomized Controlled Trial details	Greenberg, Jonathan; Popok, Paula J.; Lin, Ann; Kulich, Ronald J.; James, Peter; Macklin, Eric A.; Millstein, Rachel A.; Edwards, Robert R.; Vranceanu, Ana Maria	2020	JMIR formative research	Intervention		Adults	Steps Goals	URL
Experiences of Wearable Technology by Persons with Knee Osteoarthritis Participating in a Physical Activity Counseling Intervention: Qualitative Study Using a Relational Ethics Lens details	Leese, Jenny; MacDonald, Graham; Backman, Catherine L.; Townsend, Anne; Nimmon, Laura; Li, Linda C.	2021	JMIR mHealth and uHealth	Intervention	Flex	Older Adults Adults	Steps Goals	URL
A Mental Health-Informed Physical Activity Intervention for First Responders and Their Partners Delivered Using Facebook: Mixed Methods Pilot Study details	McKeon, Grace; Steel, Zachary; Wells, Ruth; Newby, Jill; Hadzi-Pavlovic, Dusan; Vancampfort, Davy; Rosenbaum, Simon	2021	JMIR formative research	Measurement	Flex 2	Adults	Steps Intensity Goals	URL
Validity of Fitbit activity monitoring for adults with progressive muscle diseases details	Roberts-Lewis, Sarah F.; White, Claire M.; Ashworth, Mark; Rose, Michael R.	2021	Disability and rehabilitation	Validation	Charge 2	Adults	Steps Energy Expenditure Intensity Heart Rate	URL
Using Ecological Momentary Assessments and Fitbit Data to Examine Daily Associations Between Physical Activity, Affect and Alcohol Cravings in Patients with Alcohol Use Disorder details	Stevenson, Brittany L.; Kunicki, Zachary J.; Brick, Leslie; Blevins, Claire E.; Stein, Michael; Abrantes, Ana M.	2021	International journal of behavioral medicine	Intervention Measurement	Charge 3	Adults	Steps Intensity	URL
The Feasibility and Acceptability of the Developing Real Incentives and Volition for Exercise (DRIVE) Program: A Pilot Study for Promoting Physical Activity in African American Women details	Sweeney, Allison M.; Wilson, Dawn K.; Zarrett, Nicole; Van Horn, M. Lee; Resnicow, Kenneth	2021	Health promotion practice	Intervention Measurement	Flex 2	Adults	Steps Intensity	URL
Physical Activity Can Enhance Life (PACE-Life): results from a 10-week walking intervention for individuals with schizophrenia spectrum disorders details	Orleans-Pobee, Maku; Browne, Julia; Ludwig, Kelsey; Merritt, Carrington; Battaglini, Claudio L.; Jarskog, L. Fredrik; Sheeran, Paschal; Penn, David L.	2021	Journal of mental health (Abingdon, England)	Intervention Measurement	Charge HR	Adults	Steps Intensity Heart Rate	URL
A Feasibility Study of a Peer-Facilitated Physical Activity Intervention in Methadone Maintenance details	Abrantes, Ana M.; Van Noppen, Donnell; Bailey, Genie; Uebelacker, Lisa A.; Buman, Matthew; Stein, Michael D.	2021	Mental health and physical activity	Intervention		Adults	Steps	URL
Feasibility of a behavioral intervention using mobile health applications to reduce cardiovascular risk factors in cancer survivors: a pilot randomized controlled trial details	Chow, Eric J.; Doody, David R.; Di, Chongzhi; Armenian, Saro H.; Baker, K. Scott; Bricker, Jonathan B.; Gopal, Ajay K.; Hagen, Anna M.; Ketterl, Tyler G.; Lee, Stephanie J.; Reding, Kerryn W.; Schenk, Jeannette M.; Syrjala, Karen L.; Taylor, Sarah A.; Wang, Guangxing; Neuhouser, Marian L.; Mendoza, Jason A.	2021	Journal of cancer survivorship : research and practice	Intervention	Flex	Adults	Steps Goals	URL
A qualitative study on participants' experiences with a community-based mindful walking intervention and mobile device activity measurement details	Jones, Karyn O.; Lopes, Snehal S.; Kelly, Claire; Welsh, Ralph S.; Chen, Liwei; Wilson, Mark; Jindal, Meenu; Zinzow, Heidi; Zhang, Lingling; Shi, Lu	2021	Complementary therapies in medicine	Intervention		Adults	Steps Goals	URL
Measuring Daily Compliance With Physical Activity Tracking in Ambulatory Surgery Patients: Comparative Analysis of Five Compliance Criteria details	Kelly, Ryan; Jones, Simon; Price, Blaine; Katz, Dmitri; McCormick, Ciaran; Pearce, Oliver	2021	JMIR mHealth and uHealth	Intervention		Adults	Steps Goals	URL
Impact of the COVID-19 Pandemic on Objectively Measured Physical Activity and Sedentary Behavior Among Overweight Young Adults: Yearlong Longitudinal Analysis details	Lawhun Costello, Victoria; Chevance, Guillaume; Wing, David; Mansour-Assi, Shadia J.; Sharp, Sydney; Golaszewski, Natalie M.; Young, Elizabeth A.; Higgins, Michael; Ibarra, Anahi; Larsen, Britta; Godino, Job G.	2021	JMIR public health and surveillance	Measurement	Charge 3	Adults	Steps Intensity	URL
Feasibility of a mental health informed physical activity intervention for the carers of children with developmental and epileptic encephalopathy details	McKeon, Grace; Palmer, Elizabeth E.; Macintosh, Rebecca; Nevin, Suzanne M.; Wheatley, Lauren; Rosenbaum, Simon	2021	Epilepsy & behavior : E&B	Intervention	Inspire	Adults	Steps Goals	URL
Changes in physical activity associated with the COVID-19 pandemic in individuals with overweight and obesity: an interrupted time series analysis with historical controls details	Robertson, Michael C.; Lee, Che Young; Wu, Ivan Haw-Chong; Liao, Yue; Raber, Margaret; Parker, Nathan; Le, Thuan; Gatus, Leticia; Basen-Engquist, Karen M.	2021	Journal of behavioral medicine	Measurement	Flex 2	Adults	Steps Intensity	URL
The Feasibility of a Lifestyle Physical Activity Intervention for Black Women with Asthma details	Nyenhuis, Sharmilee M.; Shah, Nida; Kim, Hajwa; Marquez, David X.; Wilbur, Jo Ellen; Sharp, Lisa K.	2021	The journal of allergy and clinical immunology. In practice	Measurement	Charge HR	Adults	Steps Intensity	URL
Rates, patterns, and correlates of fitness tracker use among older adults with multiple sclerosis details	Silveira, Stephanie L.; Baird, Jessica F.; Motl, Robert W.	2021	Disability and health journal	Usability Validation		Older Adults	Steps	URL
Research on physical activity variability and changes of metabolic profile in patients with prediabetes using Fitbit activity trackers data details	Bliudzius, Antanas; Puronaite, Roma; Trinkunas, Justas; Jakaitiene, Audrone; Kasiulevicius, Vytautas	2021	Technology and health care : official journal of the European Society for Engineering and Medicine	Validation		Older Adults		URL
The relationship between sleep and physical activity in an in-patient rehabilitation stroke setting: a cross-sectional study details	Hei Chow, Ching; Fraysse, Francois; Hillier, Susan	2021	Topics in stroke rehabilitation	Validation	Flex	Patients	Steps Sleep	URL
Using an activity tracker to increase motivation for physical activity in patients with type 2 diabetes in primary care: a randomized pilot trial details	Pelletier, Cynthia; Gagnon, Marie Pierre; Alm{\'{e}}ras, Natalie; Despr{\'{e}}s, Jean Pierre; Poirier, Paul; Tremblay, Angelo; Chabot, Christian; Rh{\'{e}}aume, Caroline	2021	mHealth	Usability	Charge HR	Patients	Steps Energy Expenditure Distance Sleep Heart Rate	URL
Feasibility, Usability, and Effectiveness of a Machine Learning-Based Physical Activity Chatbot: Quasi-Experimental Study details	To, Quyen G.; Green, Chelsea; Vandelanotte, Corneel	2021	JMIR mHealth and uHealth	Usability	Flex	Adults	Steps	URL
Validity of Tools to Measure Physical Activity in Older Adults Following Total Knee Arthroplasty details	Jasper, Lisa; Beaupre, Lauren A.; Spence, John C.; Jones, C. Allyson	2021	Journal of aging and physical activity	Validation		Patients	Steps Energy Expenditure	URL
The Effect of Aerobic Training and Increasing Nonexercise Physical Activity on Cardiometabolic Risk Factors details	Swift, Damon L.; Nevels, Tyara R.; Solar, Chelsey A.; Brophy, Patricia M.; McGee, Joshua E.; Brewer, Savanna B.; Clark, Angela; Houmard, Joseph A.; Lutes, Lesley D.	2021	Medicine and science in sports and exercise	Intervention	One	Adults	Steps	URL
Validity of Wrist-Wearable Activity Devices for Estimating Physical Activity in Adolescents: Comparative Study details	Hao, Yingying; Ma, Xiao Kai; Zhu, Zheng; Cao, Zhen Bo	2021	JMIR mHealth and uHealth	Validation	Flex	Adolescents	Steps Energy Expenditure	URL URL URL
A meta-analysis of Fitbit devices: same company, different models, different validity evidence details	Leung, Willie; Case, Layne; Sung, Ming-Chih; Jung, Jaehun	2021	https://doi.org/10.1080/03091902.2021.2006350	Validation Systematic Review	Classic Ultra One Zip Flex Charge Charge HR Surge Flex 2 Charge 2 Blaze Alta Alta HR	Adults	Steps Energy Expenditure Heart Rate	URL
Gamifying accelerometer use increases physical activity levels of individuals pre-disposed to type II diabetes details	Francis, Shelby L.; Simmering, Jacob E.; Polgreen, Linnea A.; Evans, Nicholas J.; Hosteng, Katie R.; Carr, Lucas J.; Cremer, James F.; Coe, Sarah; Cavanaugh, Joe E.; Segre, Alberto M.; Polgreen, Philip M.	2021	Preventive medicine reports	Intervention	Zip	Adults	Steps	URL
BAILAMOS With mHealth Technology! Improving Physical Activity and Well-Being in Middle-Aged and Older Latinxs: A Pre-Post Feasibility Study details	Aguinaga, Susan; Marques, Isabela G.; Kitsiou, Spyros; Balbim, Guilherme M.; Gerber, Ben S.; Buchholz, Susan W.; Bustamante, Eduardo E.; Marquez, David X.	2021	Health education & behavior : the official publication of the Society for Public Health Education	Intervention	Charge 2	Older Adults	Steps Energy Expenditure Intensity	URL
Effectiveness of Distance Technology in Promoting Physical Activity in Cardiovascular Disease Rehabilitation: Cluster Randomized Controlled Trial, A Pilot Study details	Hakala, Sanna; Kivisto; Heikki; Paajanen; Teemu; Kankainen; Annaliisa; Anttila; Marjo Riitta; Heinonen; Ari; Sjogren; Tuulikki	2021	JMIR rehabilitation and assistive technologies	Intervention	Zip Charge HR	Patients	Steps Energy Expenditure Intensity Goals Sleep Heart Rate	URL
Mobile Health Intervention Promoting Physical Activity in Adults Post Cardiac Rehabilitation: Pilot Randomized Controlled Trial details	Park, Linda G.; Elnaggar, Abdelaziz; Lee, Sei J.; Merek, Stephanie; Hoffmann, Thomas J.; von Oppenfeld, Julia; Ignacio, Nerissa; Whooley, Mary A.	2021	JMIR formative research	Measurement	Charge 2	Patients	Steps	URL
A systematic review of the validity, reliability, and feasibility of measurement tools used to assess the physical activity and sedentary behaviour of pre-school aged children details	Phillips, Sophie M.; Summerbell, Carolyn; Hobbs, Matthew; Hesketh, Kathryn R.; Saxena, Sonia; Muir, Cassey; Hillier-Brown, Frances C.	2021	The international journal of behavioral nutrition and physical activity	Systematic Review	Zip Flex	Children	Steps Energy Expenditure Intensity Heart Rate	URL
Optimization of a technology-supported physical activity promotion intervention for breast cancer survivors: Results from Fit2Thrive details	Phillips, Siobhan M.; Penedo, Frank J.; Collins, Linda M.; Solk, Payton; Siddique, Juned; Song, Jing; Cella, David; Courneya, Kerry S.; Ackermann, Ronald T.; Welch, Whitney A.; Auster-Gussman, Lisa A.; Whitaker, Madelyn; Cullather, Erin; Izenman, Emily; Spring, Bonnie	2021	Cancer	Intervention	Zip	Adults	Steps Intensity Goals	URL URL URL
Effect of commercial wearables and digital behaviour change resources on the physical activity of adolescents attending schools in socio-economically disadvantaged areas: the RAW-PA cluster-randomised controlled trial details	Ridgers, Nicola D.; Timperio, Anna; Ball, Kylie; Lai, Samuel K.; Brown, Helen; Macfarlane, Susie; Salmon, Jo	2021	The international journal of behavioral nutrition and physical activity	Intervention	Flex	Adolescents	Intensity	URL
Stepping into survivorship pilot study: Harnessing mobile health and principles of behavioral economics to increase physical activity in ovarian cancer survivors details	Schrier, Elizabeth; Xiong, Niya; Thompson, Embree; Poort, Hanneke; Schumer, Susan; Liu, Joyce F.; Krasner, Carolyn; Campos, Susana M.; Horowitz, Neil S.; Feltmate, Colleen; Konstantinopoulos, Panagiotis A.; Dinardo, Margaret M.; Tayob, Nabihah; Matulonis, Ursula A.; Patel, Mitesh; Wright, Alexi A.	2021	Gynecologic oncology	Intervention	Charge 2	Patients	Steps Goals	URL
Feasibility of a theory-informed mobile app for changing physical activity in youth with multiple sclerosis details	Stephens, S.; Schneiderman, Jane E.; Finlayson, Marcia; Berenbaum, Tara; Motl, Robert W.; Yeh, E. Ann	2021	Multiple sclerosis and related disorders	Intervention	Charge 2	Children	Steps	URL
Using a consumer-based wearable activity tracker for physical activity goal setting and measuring steps in pregnant women with gestational diabetes mellitus: exploring acceptance and validity details	Ehrlich, Samantha F.; Maples, Jill M.; Barroso, Cristina S.; Brown, Kathleen C.; Bassett, David R.; Zite, Nikki B.; Fortner, Kimberly B.	2021	BMC Pregnancy and Childbirth	Validation	Charge 3	Pregnant Women	Steps
Relationship quality and objectively measured physical activity before and after implementation of COVID-19 stay-home orders: details	Cornelius, Talea; Denes, Amanda; Webber, Katrina T.; Guest, Chelsea; Goldsmith, Jeff; Schwartz, Joseph E.; Gorin, Amy A.	2021	https://doi.org/10.1177/13591053211042075	Measurement		Adults	Steps Intensity	URL
Heart failure and promotion of physical activity before and after cardiac rehabilitation (HF-aPProACH): a study protocol 8 details	Limpens, Marlou A. M.; Gurgoze Muhammed T.; Lenzen Mattie J.; Roest Stefan; Voortman Trudy; Kavousi Maryam; ter Hoeve Nienke; Sunamura Madoka; den Uijl Iris; van der Spek Peter J.; Brugts Jasper J.; Manintveld Olivier C.; IJpma Arne S.; Boersma Eric	2021	ESC Heart Failure	Intervention	Charge 3	Patients	Steps Goals
Quantitative activity levels and gestational age at delivery: a prospective cohort study among nulliparous women details	Booker, Whitney A.; Ekpe, Etoroabasi E.; Handal-Orefice, Roxane C.; Zhang, Yuan; Cande, Ananth; Gyamfi-Bannerman, Cynthia; Nieto, Vanessa	2022	American journal of obstetrics & gynecology MFM	Case Study	Flex 2	Pregnant Women	Steps	URL
Emerging wearable technology applications in gastroenterology: A review of the literature details	Chong, Kimberly P. L.; Woo, Benjamin K. P.	2021	World journal of gastroenterology	Review		Patients Children Adults	Steps Energy Expenditure Sleep	URL
Day-to-day associations between sleep and physical activity: a set of person-specific analyses in adults with overweight and obesity details	Chevance, Guillaume; Baretta, Dario; Romain, Ahmed J{\'{e}}r{\^{o}}me; Godino, Job G.; Bernard, Paquito	2021	Journal of behavioral medicine	Intervention	Charge 3	Adults	Steps Sleep	URL
A Technology-Based Physical Activity Intervention for Patients With Metastatic Breast Cancer (Fit2ThriveMB): Protocol for a Randomized Controlled Trial details	Phillips, Siobhan; Solk, Payton; Welch, Whitney; Auster-Gussman, Lisa; Lu, Marilyn; Cullather, Erin; Torre, Emily; Whitaker, Madelyn; Izenman, Emily; La, Jennifer; Lee, Jungwha; Spring, Bonnie; Gradishar, William	2021	JMIR research protocols	Intervention	Inspire	Adults	Steps Intensity Heart Rate	URL
Feasibility Trial of a Mind–Body Activity Pain Management Program for Older Adults With Cognitive Decline details	Mace, Ryan A.; Gates, Melissa V.; Popok, Paula J.; Kulich, Ron; Quiroz, Yakeel T.; Vranceanu, Ana-Maria	2021	The Gerontologist	Intervention	Alta HR	Older Adults	Steps Goals	URL
Feasibility and potential efficacy of commercial mHealth/eHealth tools for weight loss in African American breast cancer survivors: pilot randomized controlled trial details	Ferrante, Jeanne M.; Devine, Katie A.; Bator, Alicja; Rodgers, Ashley; Ohman-Strickland, Pamela A.; Bandera, Elisa V.; Hwang, Kevin O.	2020	Translational behavioral medicine	Usability Intervention	Charge Alta	Adults	Steps	URL
Use of consumer wearable devices to promote physical activity among breast, prostate, and colorectal cancer survivors: a review of health intervention studies details	Coughlin, Steven S.; Caplan, Lee S.; Stone, Rebecca	2020	Journal of cancer survivorship : research and practice	Systematic Review	One Zip Flex Charge	Adults	Steps Energy Expenditure Distance Sleep Heart Rate	URL
Wrist-Worn Wearables for Monitoring Heart Rate and Energy Expenditure While Sitting or Performing Light-to-Vigorous Physical Activity: Validation Study details	Duking Peter; Giessing Laura; Frenkel Marie Ottilie; Koehler Karsten; Holmberg Hans Christer; Sperlich Billy	2020	JMIR mHealth and uHealth	Validation	Versa	Adults	Energy Expenditure Heart Rate	URL
Reporting adherence, validity and physical activity measures of wearable activity trackers in medical research: A systematic review details	Chan, Alexandre; Chan, Daniella; Lee, Hui; Ng, Chiu Chin; Yeo, Angie Hui Ling	2022	International journal of medical informatics	Systematic Review		Children Adults Adolescents	Steps Energy Expenditure Intensity	URL
Functional Connectivity, Physical Activity, and Neurocognitive Performances in Patients with Vascular Cognitive Impairment, No Dementia details	Chang, Ya-Ting; Liu, Chun-Ting; Hsu, Shih-Wei; Lee, Chen-Chang; Huang, Pei-Ching	2022	Current Alzheimer research	Intervention	Charge 2	Patients	Steps Energy Expenditure Distance	URL
Accuracy and Acceptability of Wrist-Wearable Activity-Tracking Devices: Systematic Review of the Literature details	Germini, Federico; Noronha, Noella; Borg Debono, Victoria; Abraham Philip, Binu; Pete, Drashti; Navarro, Tamara; Keepanasseril, Arun; Parpia, Sameer; de Wit, Kerstin; Iorio, Alfonso	2022	Journal of medical Internet research	Systematic Review	Charge Charge HR	Older Adults Adults Adolescents	Steps Energy Expenditure Heart Rate	URL
Exploring the Use of Fitbit Consumer Activity Trackers to Support Active Lifestyles in Adults with Type 2 Diabetes: A Mixed-Methods Study details	Hodgson, William; Kirk, Alison; Lennon, Marilyn; Paxton, Gregor	2021	International journal of environmental research and public health	Methods	Charge 4	Adults	Steps Intensity Heart Rate	URL
Is physical activity before the end of the workday a drain or a gain? Daily implications on work focus in regular exercisers details	ten Brummelhuis, Lieke L.; Calderwood, Charles; Rosen, Christopher C.; Gabriel, Allison S.	2021	The Journal of applied psychology	Case Study		Adults	Steps Intensity	URL
Feasibility and Acceptability of a Physical Activity Tracker and Text Messages to Promote Physical Activity During Chemotherapy for Colorectal Cancer: Pilot Randomized Controlled Trial (Smart Pace II) details	Van Blarigan, Erin L.; Dhruva, Anand; Atreya, Chloe E.; Kenfield, Stacey A.; Chan, June M.; Milloy, Alexandra; Kim, Iris; Steiding, Paige; Laffan, Angela; Zhang, Li; Piawah, Sorbarikor; Fukuoka, Yoshimi; Miaskowski, Christine; Hecht, Frederick M.; Kim, Mi-Ok; Venook, Alan P.; Van Loon, Katherine	2022	JMIR cancer	Intervention	Flex 2	Patients	Steps	URL
Monitoring work-related physical activity and estimating lower-limb loading: a proof-of-concept study details	Wang, Xia; Perry, Thomas A.; Caroupapoull{\'{e}}, Jimmy; Forrester, Alexander; Arden, Nigel K.; Hunter, David J.	2021	BMC musculoskeletal disorders	Measurement	Versa 2	Adults	Steps	URL
Risk Factors for COVID-19 in College Students Identified by Physical, Mental, and Social Health Reported During the Fall 2020 Semester: Observational Study Using the Roadmap App and Fitbit Wearable Sensors details	Gilley, Kristen N.; Baroudi, Loubna; Yu, Miao; Gainsburg, Izzy; Reddy, Niyanth; Bradley, Christina; Cislo, Christine; Rozwadowski, Michelle Lois; Clingan, Caroline Ashley; DeMoss, Matthew Stephen; Churay, Tracey; Birditt, Kira; Colabianchi, Natalie; Chowdhury, Mosharaf; Forger, Daniel; Gagnier, Joel; Zernicke, Ronald F.; Cunningham, Julia Lee; Cain, Stephen M.; Tewari, Muneesh; Choi, Sung Won	2022	JMIR mental health	Case Study		Adults	Steps Sleep Heart Rate	URL
Moving from intention to behaviour: a randomised controlled trial protocol for an app-based physical activity intervention (i2be) details	Kokai, Lili L.; O Ceallaigh, Diarmaid T.; Wijtzes, Anne I.; Roeters Van Lennep, Jeanine E.; Hagger, Martin S.; Cawley, John; Rohde, Kirsten I. M.; Van Kippersluis, Hans; Burdorf, Alex	2022	BMJ open	Intervention	Inspire	Pregnant Women	Intensity Heart Rate	URL
Acceptability of a Mobile Health Behavior Change Intervention for Cancer Survivors With Obesity or Overweight: Nested Mixed Methods Study Within a Randomized Controlled Trial details	Groarke, Jenny M.; Richmond, Janice; Mc Sharry, Jenny; Groarke, Ann Marie; Harney, Owen M.; Kelly, Mary Grace; Walsh, Jane C.	2021	JMIR mHealth and uHealth	Intervention		Older Adults Adults	Steps Goals	URL
A 'novel' multi-component approach to promote physical activity among older cancer survivors: a pilot randomized controlled trial details	Pinto, Bernardine M.; Kindred, Madison; Franco, Regina; Simmons, Virginia; Hardin, James	2021	Acta oncologica (Stockholm, Sweden)	Usability	Charge 2	Older Adults	Steps	URL
¡Mi Vida Saludable! A randomized, controlled, 2 × 2 factorial trial of a diet and physical activity intervention among Latina breast cancer survivors: Study design and methods details	Santiago-Torres, Margarita; Contento, Isobel; Koch, Pamela; Tsai, Wei Yann; Brickman, Adam M.; Gaffney, Ann Ogden; Thomson, Cynthia A.; Crane, Tracy E.; Dominguez, Naxielly; Sepulveda, Jhack; Mar{\'{i}}n-Chollom, Amanda M.; Paul, Rachel; Shi, Zaixing; Ulanday, Kathleene T.; Hale, Christiane; Hershman, Dawn; Greenlee, Heather	2021	Contemporary clinical trials	Methods	Zip	Adults	Steps Intensity	URL
Connecting breast cancer survivors for exercise: protocol for a two-arm randomized controlled trial details	Smith-Turchyn, Jenna; McCowan, Michelle E.; O'Loughlin, Erin; Fong, Angela J.; McDonough, Meghan H.; Santa Mina, Daniel; Arbour-Nicitopoulos, Kelly P.; Trinh, Linda; Jones, Jennifer M.; Bender, Jackie L.; Culos-Reed, S. Nicole; Tomasone, Jennifer R.; Vani, Madison F.; Sabiston, Catherine M.	2021	BMC sports science, medicine & rehabilitation	Methods	Inspire 2	Adults	Steps Intensity	URL
Quality of life of colorectal cancer survivors participating in a pilot randomized controlled trial of physical activity trackers and daily text messages details	Chan, Hilary; Van Loon, Katherine; Kenfield, Stacey A.; Chan, June M.; Mitchell, Emily; Zhang, Li; Paciorek, Alan; Joseph, Galen; Laffan, Angela; Atreya, Chloe; Fukuoka, Yoshimi; Miaskowski, Christine; Meyerhardt, Jeffrey A.; Venook, Alan P.; Van Blarigan, Erin L.	2022	Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer	Intervention	Flex	Adults	Steps Intensity	URL
Charity-based incentives motivate young adult cancer survivors to increase physical activity: a pilot randomized clinical trial details	Keadle, Sarah Kozey; Meuter, Leah; Phelan, Suzanne; Phillips, Siobhan M.	2021	Journal of behavioral medicine	Usability	One	Adults	Steps Goals	URL
Feasibility and efficacy of enteral tube feeding on weight stability, lean body mass, and patient-reported outcomes in pancreatic cancer cachexia details	Gresham, Gillian; Placencio-Hickok, Veronica R.; Lauzon, Marie; Nguyen, Tyra; Kim, Haesoo; Mehta, Sejal; Paski, Shirley; Pandol, Stephen J.; Osipov, Arsen; Gong, Jun; Jamil, Laith H.; Nissen, Nicholas; Lo, Simon K.; Hendifar, Andrew E.	2021	Journal of cachexia, sarcopenia and muscle	Intervention	Charge HR	Adults	Steps Intensity Distance Sleep Heart Rate	URL
Feasibility of Fitness Tracker Usage to Assess Activity Level and Toxicities in Patients With Colorectal Cancer details	Ward, William H.; Meeker, Caitlin R.; Handorf, Elizabeth; Hill, Maureen V.; Einarson, Margret; Alpaugh, R. Katherine; Holden, Thomas L.; Astsaturov, Igor; Denlinger, Crystal S.; Hall, Michael J.; Reddy, Sanjay S.; Sigurdson, Elin R.; Dotan, Efrat; Zibelman, Matthew; Meyer, Joshua E.; Farma, Jeffrey M.; Vijayvergia, Namrata	2021	JCO clinical cancer informatics	Usability	Charge	Patients	Steps	URL
Wrist-worn devices for the measurement of heart rate and energy expenditure: A validation study for the Apple Watch 6, Polar Vantage V and Fitbit Sense details	Hajj-Boutros, Guy; Landry-Duval, Marie Anne; Comtois, Alain Steve; Gouspillou, Gilles; Karelis, Antony D.	2022	European journal of sport science	Validation	Sense	Adults	Energy Expenditure Heart Rate	URL
Accuracy of Heart Rate Measurement by the Fitbit Charge 2 During Wheelchair Activities in People With Spinal Cord Injury: Instrument Validation Study details	Hoevenaars, Dirk; Yocarini, Iris E.; Paraschiakos, Stylianos; Holla, Jasmijn F. M.; de Groot, Sonja; Kraaij, Wessel; Janssen, Thomas W. J.	2022	JMIR rehabilitation and assistive technologies	Usability Validation	Charge 2	Adults	Heart Rate	URL
Stepping Up: An Evaluation of Social Comparison of Physical Activity During Fitbit Challenges details	Nastasi, Jessica A.; Curry, Erin M.; Martinez, Romina E.; Arigo, Danielle; Raiff, Bethany R.	2022	Journal of technology in behavioral science	Case Study	Charge HR	Adults	Steps	URL
Concurrent and prospective associations between fitbit wearable-derived RDoC arousal and regulatory constructs and adolescent internalizing symptoms details	Nelson, Benjamin W.; Flannery, Jessica E.; Flournoy, John; Duell, Natasha; Prinstein, Mitchell J.; Telzer, Eva	2022	Journal of child psychology and psychiatry, and allied disciplines	Case Study	Charge 2	Children	Steps Sleep Heart Rate	URL
Heart Rate Measurement Accuracy of Fitbit Charge 4 and Samsung Galaxy Watch Active2: Device Evaluation Study details	Nissen, Michael; Slim, Syrine; Jager Katharina; Flaucher Madeleine; Huebner Hanna; Danzberger Nina; Fasching Peter A.; Beckmann Matthias W.; Gradl Stefan; Eskofier Bjoern M.	2022	JMIR formative research	Validation	Charge 4	Adults	Heart Rate	URL
Comparison of Different Physical Activity Measures in a Cardiac Rehabilitation Program: A Prospective Study details	Alharbi, Muaddi; Bauman, Adrian; Alabdulaali, Mohammed; Neubeck, Lis; Smith, Sidney; Naismith, Sharon; Jeon, Yun Hee; Tofler, Geoffrey; Surour, Atef; Gallagher, Robyn	2022	Sensors (Basel, Switzerland)	Usability	Flex	Patients	Steps Intensity	URL
Consistency With and Disengagement From Self-monitoring of Weight, Dietary Intake, and Physical Activity in a Technology-Based Weight Loss Program: Exploratory Study details	Carpenter, Chelsea A.; Eastman, Abraham; Ross, Kathryn M.	2022	JMIR formative research	Intervention	Zip Aria	Adults	Steps Weight Food	URL
HRV Monitoring Using Commercial Wearable Devices as a Health Indicator for Older Persons during the Pandemic details	Rodrigues, Eujessika; Lima, Daniella; Barbosa, Paulo; Gonzaga, Karoline; Guerra, Ricardo Oliveira; Pimentel, Marcela; Barbosa, Humberto; Maciel, {\'{A}}lvaro	2022	Sensors (Basel, Switzerland)	Validation	Inspire HR	Older Adults	Steps Sleep Heart Rate	URL
Commercial Devices Provide Estimates of Energy Balance with Varying Degrees of Validity in Free-Living Adults details	Shook, Robin P.; Yeh, Hung Wen; Welk, Gregory J.; Davis, Ann M.; Ries, Daniel	2022	The Journal of nutrition	Validation	Aria Alta HR	Adults	Heart Rate Weight	URL
Effect of tailored, daily feedback with lifestyle self-monitoring on weight loss: The SMARTER randomized clinical trial details	Burke, Lora E.; Sereika, Susan M.; Parmanto, Bambang; Bizhanova, Zhadyra; Kariuki, Jacob K.; Cheng, Jessica; Beatrice, Britney; Loar, India; Pulantara, I. Wayan; Wang, Yuhan; Cedillo, Maribel; Conroy, Molly B.	2022	Obesity (Silver Spring, Md.)	Usability	Charge 2	Adults	Intensity	URL
A multiple technology-based physical activity intervention for Latina adolescents in the USA: randomized controlled trial study protocol for Chicas Fuertes details	Larsen, Britta; Greenstadt, Emily; Olesen, Brittany; Osuna, Lilliana; Godino, Job; Marcus, Bess; Dunsiger, Shira; Meyer, Dawn; Zive, Michelle	2022	Trials	Intervention	Inspire HR	Adolescents	Steps Intensity Goals Heart Rate	URL URL URL
Fitbeat: COVID-19 estimation based on wristband heart rate using a contrastive convolutional auto-encoder details	Liu, Shuo; Han, Jing; Puyal, Estela Laporta; Kontaxis, Spyridon; Sun, Shaoxiong; Locatelli, Patrick; Dineley, Judith; Pokorny, Florian B.; Costa, Gloria Dalla; Leocani, Letizia; Guerrero, Ana Isabel; Nos, Carlos; Zabalza, Ana; Sorensen, Per Soelberg; Buron, Mathias; Magyari, Melinda; Ranjan, Yatharth; Rashid, Zulqarnain; Conde, Pauline; Stewart, Callum; Folarin, Amos A.; Dobson, Richard J. B.; Bailon, Raquel; Vairavan, Srinivasan; Cummins, Nicholas; Narayan, Vaibhav A.; Hotopf, Matthew; Comi, Giancarlo; Schuller, Bjorn; Consortium; RADAR-CNS	2022	Pattern Recognition	Medical	Charge 2 Charge 3	Adults	Heart Rate	URL URL URL
User Engagement and Abandonment of mHealth: A Cross-Sectional Survey details	Mustafa, Abdulsalam Salihu; Ali, Nor'Ashikin; Dhillon, Jaspaljeet Singh; Alkawsi, Gamal; Baashar, Yahia	2022	Healthcare	Review Survey Research		Adults	Steps Energy Expenditure Intensity Weight	URL URL URL
A randomized controlled trial of the effect of supervised exercise on functional outcomes in older African American and non-Hispanic White breast cancer survivors: Are there racial differences in the effects of exercise on functional outcomes? details	Owusu, Cynthia; Margevicius, Seunghee; Nock, Nora L.; Austin, Kristina; Bennet, Elizabeth; Ld, Rd; Cerne, Stephen; Acs-Cet, Acsm; Paul Hergenroeder; Halle; Moore, C. F.; Petkac, Jean; Schluchter; Mark; Schmitz, Kathryn H.; Monica; Hooper, Webb; Wimbley; Leonard; Ma, Ba /; Berger, Nathan A.	2022	Cancer	Usability	Blaze Versa	Older Adults	Steps	URL URL URL
Perceptions of patients and nurses regarding the use of wearables in inpatient settings: a mixed methods study details	Patel, Vikas; Moosa, Sabreena; Sundaram, Sanjana; Langer, Laura; MacMillan, Thomas E.; Cavalcanti, Rodrigo; Cram, Peter; Gunaratne, Keith; Bayley, Mark; Wu, Robert	2022	Informatics for health & social care	Usability	Charge 2 Charge 3	Patients Adults	Steps Sleep Heart Rate	URL
Improving Sleep Among Teachers: an Implementation-Intention Intervention details	Schmidt, Laura I.; Steenbock, Lisa M.; Sieverding, Monika	2022	International journal of behavioral medicine	Intervention	Charge HR	Adults	Steps Sleep	URL
Using Fitbit data to monitor the heart rate evolution patterns of college students details	Wang, Cheng; Lizardo, Omar; Hachen, David S.	2022	Journal of American college health : J of ACH	Measurement		Adults	Steps Sleep Heart Rate	URL
Accuracy and Precision of Energy Expenditure, Heart Rate, and Steps Measured by Combined-Sensing Fitbits Against Reference Measures: Systematic Review and Meta-analysis details	Chevance, Guillaume; Golaszewski, Natalie M.; Tipton, Elizabeth; Hekler, Eric B.; Buman, Matthew; Welk, Gregory; Patrick, Kevin; Godino, Job G.	2022	JMIR mHealth and uHealth	Review	Charge HR Surge Charge 2 Alta HR Ionic Versa Charge 3 Charge 4 Versa 2 Versa 3 Inspire HR Inspire 2	Older Adults Adults	Steps Energy Expenditure Heart Rate	URL
A Digital Health Program Targeting Physical Activity Among Adolescents With Overweight or Obesity: Open Trial details	Cummings, Caroline; Crochiere, Rebecca; Lansing, Amy Hughes; Patel, Riya; Stanger, Catherine	2022	JMIR pediatrics and parenting	Intervention	Charge HR	Adolescents	Steps Goals	URL
Using smartwatches to observe changes in activity during recovery from critical illness following COVID-19: a 1 year multi-centre observational study details	Hunter, Alex; Leckie, Todd; Coe, Oliver; Hardy, Benjamin; Fitzpatrick, Daniel; Gon{\c{c}}alves, Ana-Carolina; Standing, Mary-Kate; Koulouglioti, Christina; Richardson, Alan; Hodgson, Luke	2022	JMIR rehabilitation and assistive technologies	Usability	Charge 3	Patients	Steps Heart Rate	URL
Virtual AppLication-supported Environment To INcrease Exercise (VALENTINE) during cardiac rehabilitation study: Rationale and design details	Jeganathan, V. Swetha; Golbus, Jessica R.; Gupta, Kashvi; Luff, Evan; Dempsey, Walter; Boyden, Thomas; Rubenfire, Melvyn; Mukherjee, Brahmar; Klasnja, Predrag; Kheterpal, Sachin; Nallamothu, Brahmajee K.	2022	American heart journal	Usability	Versa 2	Patients Adults	Steps Distance	URL
Efficacy and acceptability of using wearable activity trackers in older adults living in retirement communities: a mixed method study details	Zhang, Zhanjia; Giordani, Bruno; Margulis, Alayna; Chen, Weiyun	2022	BMC geriatrics	Usability	Inspire HR	Older Adults	Steps Goals	URL
Exploring Families' Acceptance of Wearable Activity Trackers: A Mixed-Methods Study details	Creaser, Amy V.; Hall, Jennifer; Costa, Silvia; Bingham, Daniel D.; Clemes, Stacy A.	2022	International journal of environmental research and public health	Usability	Alta HR	Children Adults	NA	URL
On the Security of Bluetooth Low Energy in Two Consumer Wearable Heart Rate Monitors/Sensing Devices details	Kurt Peker, Yeṣem; Bello, Gabriel; Perez, Alfredo J.	2022	Sensors (Basel, Switzerland)	Validation	Charge 4	NA	Heart Rate	URL URL URL
A pilot randomized trial of simplified versus standard calorie dietary self-monitoring in a mobile weight loss intervention details	Nezami, Brooke T.; Hurley, Lex; Power, Julianne; Valle, Carmina G.; Tate, Deborah F.	2022	Obesity (Silver Spring, Md.)	Intervention	Inspire	Adults	Energy Expenditure Food	URL
Sex-based differences in remote monitoring of biometric, psychometric and biomarker indices in stable ischemic heart disease details	Barsky, Lili; Speier, William; Fuller, Garth; Cheng, Susan; Kim, Andy; Joung, Sandy; Arnold, Corey; Dhawan, Shivani; Lopez, Mayra; Mastali, Mitra; van den Broek, Irene; Wei, Janet; Spiegel, Brennan; Van Eyk, Jennifer E.; Bairey Merz, C. Noel; Shufelt, Chrisandra	2022	Biology of sex differences	Measurement	Charge 2	Older Adults	Steps Intensity Heart Rate	URL
“The Ripple Effect”: The influence of social support on participation in a small workplace health promotion program details	Bell, Alexandra; Barrett, Norma; Lamaro Haintz, Greer	2022	Health Promotion Journal of Australia	Usability Intervention	Flex	Adults	NA	URL URL URL
Impact of the COVID-19 pandemic on elementary schoolers' physical activity, sleep, screen time and diet: A quasi-experimental interrupted time series study details	Burkart, Sarah; Parker, Hannah; Weaver, R. Glenn; Beets, Michael W.; Jones, Alexis; Adams, Elizabeth L.; Chaput, Jean Philippe; Armstrong, Bridget	2022	Pediatric obesity	Measurement	Charge 2	Children	Intensity Sleep Heart Rate	URL
Relationship Between Life-Space Mobility and Health Characteristics in Older Adults Using Global Positioning System Watches details	Chung, Jane; Boyle, Joseph; Wheeler, David C.	2022	Journal of applied gerontology : the official journal of the Southern Gerontological Society	Validation	Surge	Older Adults	Distance	URL
Wrist-mounted accelerometers provide objective evidence of disease and recovery in patients with frozen shoulder details	Mackenzie, Samuel P.; McLean, Michael; Spasojevic, Milo{\v{s}}; Niu, Rui; Kruse, Lisa; Gwynne, Jasmin; Young, Allan; Cass, Benjamin	2021	JSES international	Usability	Flex 2	Patients	Steps Intensity	URL
Consumer-grade wearables identify changes in multiple physiological systems during COVID-19 disease progression details	Mayer, Caleb; Tyler, Jonathan; Fang, Yu; Flora, Christopher; Frank, Elena; Tewari, Muneesh; Choi, Sung Won; Sen, Srijan; Forger, Daniel B.	2022	Cell reports. Medicine	Review	Charge 2 Charge 3	Adults	Steps Sleep Heart Rate	URL
Validity of the Fitbit Ace and Moki Devices for Assessing Steps During Different Walking Conditions in Young Adolescents details	Sun, Xiyao; Adams, Stephanie A.; Li, Chuchu; Booth, Josephine N.; Robertson, Judy; Fawkner, Samantha	2022	Pediatric exercise science	Validation		Adolescents	Steps	URL
Impact of a Pilot Structured Mobile Technology Based Lifestyle Intervention for Patients with Nonalcoholic Fatty Liver Disease details	Tincopa, Monica A.; Lyden, Angela; Wong, Jane; Jackson, Elizabeth A.; Richardson, Caroline; Lok, Anna S.	2022	Digestive diseases and sciences	Intervention	Zip	Adults	Steps	URL
A distance-based, randomized controlled trial for reducing sedentary behavior among prostate cancer survivors: a study protocol details	Trinh, Linda; Sabiston, Catherine M.; Alibhai, Shabbir M. H.; Jones, Jennifer M.; Arbour-Nicitopoulos, Kelly P.; Mina, Daniel Santa; Campbell, Kristin; Faulkner, Guy E.	2022	BMC public health	Usability Intervention	Inspire 2	Adults	Steps Energy Expenditure Distance Sleep	URL
Responsiveness of an activity tracker as a measurement tool in a knee osteoarthritis clinical trial (ACTIVe-OA study) details	Yu, Shirley P.; Ferreira, Manuela L.; Duong, Vicky; Caroupapoull{\'{e}}, Jimmy; Arden, Nigel K.; Bennell, Kim L.; Hunter, David J.	2022	Annals of physical and rehabilitation medicine	Usability	Flex 2	Older Adults	Steps	URL
Associations of COVID-19 lockdown restrictions with longer-term activity levels of working adults with type 2 diabetes details	Brakenridge, Christian; Salim, Agus; Healy, Genevieve Nissa; Grigg, Ruth; Carver, Alison; Rickards, Kym; Owen, Neville; Dunstan, David	2022	JMIR diabetes	Intervention	Inspire HR	Adults	Steps Intensity	URL
Something Tells Me I Can't Do That No More": Experiences With Real-Time Glucose and Activity Monitoring Among Underserved Black Women With Type 2 Diabetes" details	Fritschi, Cynthia; Kim, Min Jung; Srimoragot, Manassawee; Jun, Jeehye; Sanchez, Lidia Emily; Sharp, Lisa K.	2022	The science of diabetes self-management and care	Usability Intervention	Charge	Adults	Steps Intensity	URL
Continuous Monitoring of Vital Signs With Wearable Sensors During Daily Life Activities: Validation Study details	Haveman, Marjolein E.; van Rossum, Mathilde C.; Vaseur, Roswita M. E.; van der Riet, Claire; Schuurmann, Richte C. L.; Hermens, Hermie J.; de Vries, Jean Paul P. M.; Tabak, Monique	2022	JMIR formative research	Validation	Charge 3	Adults	Heart Rate	URL
Dataset of consumer-based activity trackers as a tool for physical activity monitoring in epidemiological studies during the COVID-19 Pandemic details	Henriksen, Andre; Johannessen, Erlend; Hartvigsen, Gunnar; Grimsgaard, Sameline; Hopstock, Laila Arnesdatter	2022	Data in brief	Systematic Review	Device unspecified	Adults	Steps Energy Expenditure Intensity	URL
Evaluation of Sleep Parameters and Sleep Staging (Slow Wave Sleep) in Athletes by Fitbit Alta HR, a Consumer Sleep Tracking Device details	Kawasaki, Yu; Kasai, Takatoshi; Sakurama, Yuko; Sekiguchi, Akiko; Kitamura, Eri; Midorikawa, Itsuki; Shiroshita, Nanako; Kawana, Fusae; Ogasawara, Etsuko; Kitade, Mari; Koikawa, Natsue; Matsuda, Takao	2022	Nature and science of sleep	Validation	Alta HR	Adults	Sleep Heart Rate	URL
Reference Values for Wristband Accelerometry Data in Children Aged 6-11 Years of Age details	Lammers, Astrid E.; Romanowski, Anna Lena; Baumgartner, Helmut; Diller, Gerhard-Paul; Uebing, Anselm	2022	Frontiers in pediatrics	Usability	Charge 2	Children	Steps Heart Rate	URL
Home-based cardiac rehabilitation using information and communication technology for heart failure patients with frailty details	Nagatomi, Yuta; Ide, Tomomi; Higuchi, Tae; Nezu, Tomoyuki; Fujino, Takeo; Tohyama, Takeshi; Nagata, Takuya; Higo, Taiki; Hashimoto, Toru; Matsushima, Shouji; Shinohara, Keisuke; Yokoyama, Tomiko; Eguchi, Aika; Ogusu, Ayumi; Ikeda, Masataka; Ishikawa, Yusuke; Yamashita, Fumika; Kinugawa, Shintaro; Tsutsui, Hiroyuki	2022	ESC heart failure	Measurement	Inspire HR	Patients	Steps	URL
A Remote Health Coaching, Text-Based Walking Program in Ethnic Minority Primary Care Patients With Overweight and Obesity: Feasibility and Acceptability Pilot Study details	Smart, Mary H.; Nabulsi, Nadia A.; Gerber, Ben S.; Gupta, Itika; Di Eugenio, Barbara; Ziebart, Brian; Sharp, Lisa K.	2022	JMIR formative research	Usability Intervention	Charge 2 Charge 3	Adults	Steps	URL
Factors related to daily step counts of stroke patients during hospitalization in a convalescent rehabilitation ward details	Yamada, Ryuichiro; Shimizu, Shinobu; Suzuki, Yuta; Nakachi, Yuki; Takemura, Nami; Taira, Katsuya; Yamazato, Tomoya; Shimabukuro, Michiru; Tsunoda, Satoshi; Shimose, Ryota; Ogura, Misao; Higa, Jun; Nakanishi, Takayuki; Matsunaga, Atsuhiko	2022	Journal of stroke and cerebrovascular diseases : the official journal of National Stroke Association	Measurement	Flex 2	Patients	Steps	URL
Real-time infection prediction with wearable physiological monitoring and AI to aid military workforce readiness during COVID-19 details	Conroy, Bryan; Silva, Ikaro; Mehraei, Golbarg; Damiano, Robert; Gross, Brian; Salvati, Emmanuele; Feng, Ting; Schneider, Jeffrey; Olson, Niels; Rizzo, Anne G.; Curtin, Catherine M.; Frassica, Joseph; McFarlane, Daniel C.	2022	Scientific Reports	Usability	Garmin Fenix 6 Garmin Vivoactive 4	Adults	Heart Rate	URL
The Effectiveness of a Computer-Tailored Web-Based Physical Activity Intervention Using Fitbit Activity Trackers in Older Adults (Active for Life): Randomized Controlled Trial details	Alley, Stephanie J.; van Uffelen, Jannique; Schoeppe, Stephanie; Parkinson, Lynne; Hunt, Susan; Power, Deborah; Waterman, Natasha; Waterman, Courtney; To, Quyen G.; Duncan, Mitch J.; Schneiders, Anthony; Vandelanotte, Corneel	2022	Journal of medical Internet research	Intervention	Flex	Older Adults	Steps Intensity	URL
The effectiveness of the Structured Health Intervention For Truckers (SHIFT): a cluster randomised controlled trial (RCT) details	Clemes, Stacy A.; Varela-Mato, Veronica; Bodicoat, Danielle H.; Brookes, Cassandra L.; Chen, Yu-Ling; Edwardson, Charlotte L.; Gray, Laura J.; Guest, Amber J.; Johnson, Vicki; Munir, Fehmidah; Paine, Nicola J.; Richardson, Gerry; Ruettger, Katharina; Sayyah, Mohsen; Sherry, Aron; Paola, Ana Suazo Di; Troughton, Jacqui; Yates, Thomas; King, James A.	2022	BMC Medicine	Intervention	Charge 2	Adults	Steps Intensity Sleep	URL URL URL
Investigation of physical activity, sleep, and mental health recovery in treatment resistant depression (TRD) patients receiving repetitive transcranial magnetic stimulation (rTMS) treatment details	Griffiths, Chris; da Silva, Ksenija Maravic; Leathlean, Chloe; Jiang, Harmony; Ang, Chee Siang; Searle, Ryan	2022	Journal of affective disorders reports	Usability	Device unspecified	Adults	Steps Intensity Sleep	URL
Human-Centered Design of a Digital Health Tool to Promote Effective Self-care in Patients With Heart Failure: Mixed Methods Study details	Johnston, William; Keogh, Alison; Dickson, Jane; Leslie, Stephen J.; Megyesi, Peter; Connolly, Rachelle; Burke, David; Caulfield, Brian	2022	JMIR Formative Research	Methods	Charge 4	Older Adults Adults	Steps Sleep Heart Rate Weight	URL URL URL
Occurrence of Relative Bradycardia and Relative Tachycardia in Individuals Diagnosed With COVID-19 details	Natarajan, Aravind; Su, Hao-Wei; Heneghan, Conor	2022	Frontiers in Physiology	Survey Research	Device unspecified	Adults	Heart Rate	URL URL URL
A Facebook-Delivered Weight Loss Intervention Using Open Enrollment: Randomized Pilot Feasibility Trial details	Pagoto, Sherry L.; Schroeder, Matthew W.; Xu, Ran; Waring, Molly E.; Groshon, Laurie; Goetz, Jared M.; Idiong, Christie; Troy, Haley; DiVito, Joseph; Bannor, Richard	2022	JMIR Formative Research	Intervention Measurement	Aria	Adults	Weight	URL URL URL
Effect of Walking Steps Measured by a Wearable Activity Tracker on Improving Components of Metabolic Syndrome: A Prospective Study details	Park, Jae-Min; Choi, Ja-Eun; Lee, Hye Sun; Jeon, Soyoung; Lee, Ji-Won; Hong, Kyung-Won	2022	International Journal of Environmental Research and Public Health	Measurement	Device unspecified	Adults	Steps Heart Rate	URL URL URL
Predictors of engagement with remote sensing technologies for symptom measurement in Major Depressive Disorder details	Matcham, F.; Carr, E.; White, K. M.; Leightley, D.; Lamers, F.; Siddi, S.; Annas, P.; de Girolamo, G.; Haro, J. M.; Horsfall, M.; Ivan, A.; Lavelle, G.; Li, Q.; Lombardini, F.; Mohr, D. C.; Narayan, V. A.; Penninx, B. W. H. J.; Oetzmann, C.; Coromina, M.; Simblett, S. K.; Weyer, J.; Wykes, T.; Zorbas, S.; Brasen, J. C.; Myin-Germeys, I.; Conde, P.; Dobson, R. J. B.; Folarin, A. A.; Ranjan, Y.; Rashid, Z.; Cummins, N.; Dineley, J.; Vairavan, S.; Hotopf, M.	2022	Journal of Affective Disorders	Usability	Device unspecified	Adults	Heart Rate	URL
Predictive Modeling of Mental Illness Onset Using Wearable Devices and Medical Examination Data: Machine Learning Approach details	Saito, Tomoki; Suzuki, Hikaru; Kishi, Akifumi	2022	Frontiers in Digital Health	Intervention Measurement	Device unspecified	Adults	Steps Sleep Heart Rate	URL URL URL
Agreement of the Apple Watch\textregistered and Fitbit Charge\textregistered for recording step count and heart rate when exercising in water details	Held, Nicholas J.; Perrotta, Andrew S.; Mueller, Tyler; Pfoh-MacDonald, Selina J.	2022	Medical and Biological Engineering and Computing	Validation	Charge 3	Adults	Steps Heart Rate	URL
Longitudinal Collection of Patient-Reported Outcomes and Activity Data during CAR-T Therapy: Feasibility, Acceptability, and Data Visualization details	Oswald, Laura B.; Li, Xiaoyin; Carvajal, Rodrigo; Hoogland, Aasha I.; Gudenkauf, Lisa M.; Hansen, Doris K.; Alsina, Melissa; Locke, Frederick L.; Rodriguez, Yvelise; Irizarry-Arroyo, Nathaly; Robinson, Edmondo J.; Jim, Heather S. L.; Gonzalez, Brian D.; Kirtane, Kedar	2022	Cancers	Usability	Inspire 2	Adults	Steps Sleep	URL
Sleep habits of high school student-athletes and non-athletes during a semester details	Ungaro, Corey T.; Chavez, Peter John D. De	2022	Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine	Intervention	Device unspecified	Children	Steps Sleep	URL
Low- and High-Intensity Physical Activity Among People with HIV: Multilevel Modeling Analysis Using Sensor- and Survey-Based Predictors details	Cook, Paul; Jankowski, Catherine; Erlandson, Kristine M.; Reeder, Blaine; Starr, Whitney; Makic, Mary Beth Flynn	2022	JMIR mHealth and uHealth	Intervention	Alta HR	Adults	Steps Intensity	URL
Quality Evaluation of Free-living Validation Studies for the Assessment of 24-Hour Physical Behavior in Adults via Wearables: Systematic Review details	Giurgiu, Marco; Timm, Irina; Becker, Marlissa; Schmidt, Steffen; Wunsch, Kathrin; Nissen, Rebecca; Davidovsky, Denis; Bussmann, Johannes B. J.; Nigg, Claudio R.; Reichert, Markus; Ebner-Priemer, Ulrich W.; Woll, Alex; von Haaren-Mack, Birte	2022	JMIR mHealth and uHealth	Systematic Review	Flex	Adults	Steps Energy Expenditure Intensity	URL
Comparison of the Fitbit\textregistered charge and polysomnography for measuring sleep quality in children with sleep disordered breathing details	Hakim, Mohammed; Miller, Rebecca; Hakim, Mumin; Tumin, Dmitry; Tobias, Joseph D.; Jatana, Kris R.; Raman, Vidya T.	2022	Minerva pediatrics	Usability	Charge	Children	Sleep	URL
Integrating a physical activity coaching intervention into diabetes care: a mixed-methods evaluation of a pilot pragmatic trial details	Huebschmann, Amy G.; Glasgow, Russell E.; Leavitt, Ian M.; Chapman, Kristi; Rice, John D.; Lockhart, Steven; Stevens-Lapsley, Jennifer E.; Reusch, Jane E. B.; Dunn, Andrea L.; Regensteiner, Judith G.	2022	Translational behavioral medicine	Methods	Device unspecified	Patients	Steps	URL
Evaluation of the Immediate Effects of Web-Based Intervention Modules for Goals, Planning, and Coping Planning on Physical Activity: Secondary Analysis of a Randomized Controlled Trial on Weight Loss Maintenance details	Mattila, Elina; Horgan, Graham; Palmeira, Antonio L.; O'Driscoll, Ruairi; Stubbs, R. James; Heitmann, Berit L.; Marques, Marta M.	2022	Journal of medical Internet research	Intervention	Charge 2 Aria	Adults	Steps Energy Expenditure Intensity Distance Goals Weight	URL
Obstructive Sleep Apnea Home-Monitoring Using a Commercial Wearable Device details	Mokhtaran, Mehrshad; Sacchi, Lucia; Tibollo, Valentina; Risi, Irene; Ramella, Vittoria; Quaglini, Silvana; Fanfulla, Francesco	2022	Studies in health technology and informatics	Intervention	Charge HR	Patients	Sleep	URL
A Longitudinal Study of Fitbit Usage Behavior Among College Students details	Wang, Cheng; Lizardo, Omar; Hachen, David S.	2022	Cyberpsychology, behavior and social networking	Usability	Device unspecified	Adults	Steps Sleep	URL
Fitbit Use and Activity Levels From Intervention to 2 Years After: Secondary Analysis of a Randomized Controlled Trial details	Hartman, Sheri J.; Chen, Ruohui; Tam, Rowena M.; Narayan, Hari K.; Natarajan, Loki; Liu, Lin	2022	JMIR mHealth and uHealth	Intervention	One	Adults	Steps Intensity	URL
Identification of Healthy and Unhealthy Lifestyles by a Wearable Activity Tracker in Type 2 Diabetes: A Machine Learning-Based Analysis details	Kim, Kyoung Jin; Lee, Jung-Been; Choi, Jimi; Seo, Ju Yeon; Yeom, Ji Won; Cho, Chul-Hyun; Bae, Jae Hyun; Kim, Sin Gon; Lee, Heon-Jeong; Kim, Nam Hoon	2022	Endocrinology and metabolism (Seoul, Korea)	Case Study	Charge 2	Adults	Steps Sleep Heart Rate	URL
Comparison of free-living physical activity measurements between ActiGraph GT3X-BT and Fitbit Charge 3 in young people with haemophilia details	Matlary, Ruth Elise D.; Holme, Paal Andre; Glosli, Heidi; Rueegg, Corina Silvia; Grydeland, May	2022	Haemophilia : the official journal of the World Federation of Hemophilia	Validation	Charge 3	Patients	Steps Intensity	URL
A randomized trial to promote physical activity in adult pre-hypertensive and hypertensive patients details	Bai, Yang; Burns, Ryan; Gell, Nancy; Byun, Wonwoo	2022	Journal of sports sciences	Usability Intervention	Charge HR	Adults	NA	URL
Playing With a Purpose: The Impact of Therapeutic Recreation During Hospitalization details	Hoag, Jennifer A.; Bingen, Kristin; Karst, Jeffrey; Palou, Akasha; Yan, Ke; Zhang, Jian	2022	Journal of pediatric hematology/oncology nursing	Measurement	Charge HR	Patients Children	Steps	URL
Effects of Sleep-Extend on glucose metabolism in women with a history of gestational diabetes: a pilot randomized trial details	Reutrakul, Sirimon; Martyn-Nemeth, Pamela; Quinn, Lauretta; Rydzon, Brett; Priyadarshini, Medha; Danielson, Kirstie K.; Baron, Kelly G.; Duffecy, Jennifer	2022	Pilot and feasibility studies	Measurement	Device unspecified	Adults	Sleep	URL
Exercise motives impact on physical activities measured using wearable devices details	Hewitt, Barbara; Deranek, Kimberly; McLeod, Alex; Gudi, Arvind	2022	Health promotion international	Measurement	Charge HR	Adults	NA	URL
A Randomized Clinical Trial Demonstrating Feasibility and Preliminary Efficacy of a Videoconference-Delivered Physical Activity Lifestyle Intervention Among Adolescents With a Congenital Heart Defect details	Jackson, Jamie L.; Fox, Kristen R.; Rausch, Joseph R.; Swenski, Taylor N.; Neville, Steven P.; Marousis, Noelle C.; Korth, Christina X.; Cua, Clifford L.; Garg, Vidu; Vannatta, Kathryn	2022	Annals of behavioral medicine : a publication of the Society of Behavioral Medicine	Intervention	Device unspecified	Adolescents	Steps Intensity Heart Rate	URL
Performance of a Multisensor Smart Ring to Evaluate Sleep: In-Lab and Home-Based Evaluation of Generalized and Personalized Algorithms details	Grandner, Michael A.; Bromberg, Zohar; Hadley, Aaron; Morrell, Zoe; Graf, Arnulf; Hutchison, Stephen; Freckleton, Dustin	2022	Sleep	Validation	Charge 4	Adults	Sleep	URL
The Need for Objective Physical Activity Measurements in Routine Bariatric Care details	Kuipers, Ellen; Timmerman, Josien; van Det, Marc; Vollenbroek-Hutten, Miriam	2022	Obesity surgery	Measurement	Charge HR Charge 2 Inspire 2	Patients	Steps Intensity Heart Rate	URL
Physical Activity Evaluation Using Activity Trackers for Type 2 Diabetes Prevention in Patients with Prediabetes details	Bliudzius, Antanas; Svaikeviciene, Kristina; Puronaite, Roma; Kasiulevicius, Vytautas	2022	International journal of environmental research and public health	Usability	Inspire	Adults	Steps Distance	URL
Sign of the times: Community engagement to refine a cardiovascular mHealth intervention through a virtual focus group series during the COVID-19 Pandemic details	Brewer, LaPrincess C.; Cyriac, Jissy; Kumbamu, Ashok; Burke, Lora E.; Jenkins, Sarah; Hayes, Sharonne N.; Jones, Clarence; Cooper, Lisa A.; Patten, Christi A.	2022	Digital health	Intervention	Versa 2	Adults	Steps	URL
Daily steps are associated with walking ability in hospitalized patients with sub-acute stroke details	Kubo, Hiroki; Kanai, Masashi; Nozoe, Masafumi; Inamoto, Asami; Taguchi, Akira; Mase, Kyoshi; Shimada, Shinichi	2022	Scientific reports	Case Study	One	Adults	Steps	URL
Protocol for a multi-center randomized controlled trial to evaluate the benefits of exercise incentives and corticosteroid injections in osteoarthritis of the knee (MOVE-OK) details	Leach, William; Doherty, Caleigh; Olave, Marianna; England, Bryant R.; Wysham, Katherine; Kerr, Gail; Quinones, Mercedes; Ogdie, Alexis; White, Dan; Neogi, Tuhina; Scanzello, Carla R.; Baker, Joshua F.	2022	Trials	Intervention	Device unspecified	Older Adults	Steps	URL
Using wearables to promote physical activity in old age : Feasibility, benefits, and user friendliness details	Schmidt, Laura I.; Jansen, Carl-Philipp; Depenbusch, Johanna; Gabrian, Martina; Sieverding, Monika; Wahl, Hans-Werner	2022	Zeitschrift fur Gerontologie und Geriatrie	Usability	Charge HR	Older Adults	Steps Intensity	URL
The Effect of Tailored, Daily, Smartphone Feedback to Lifestyle Self-Monitoring on Weight Loss at 12 Months: the SMARTER Randomized Clinical Trial details	Burke, Lora E.; Sereika, Susan M.; Bizhanova, Zhadyra; Parmanto, Bambang; Kariuki, Jacob; Cheng, Jessica; Beatrice, Britney; Cedillo, Maribel; Loar, India; Pulantara, I. Wayan; Wang, Yuhan; Conroy, Molly B.	2022	Journal of medical Internet research	Intervention	Charge 2	Adults	Steps Food	URL
The structured health intervention for truckers (SHIFT) cluster randomised controlled trial: a mixed methods process evaluation details	Guest, Amber J.; Paine, Nicola J.; Chen, Yu-Ling; Chalkley, Anna; Munir, Fehmidah; Edwardson, Charlotte L.; Gray, Laura J.; Johnson, Vicki; Ruettger, Katharina; Sayyah, Mohsen; Sherry, Aron; Troughton, Jacqui; Varela-Mato, Veronica; Yates, Thomas; King, James; Clemes, Stacy A.	2022	The international journal of behavioral nutrition and physical activity	Methods	Charge 2	Adults	Steps	URL
Utilizing daily mood diaries and wearable sensor data to predict depression and suicidal ideation among medical interns details	Horwitz, Adam; Czyz, Ewa; Al-Dajani, Nadia; Dempsey, Walter; Zhao, Zhuo; Nahum-Shani, Inbal; Sen, Srijan	2022	Journal of affective disorders	Measurement	Charge 2	Adults	Steps Sleep	URL
Acute pain pathways: protocol for a prospective cohort study details	Jeffery, Molly Moore; Ahadpour, Mitra; Allen, Summer; Araojo, Richardae; Bellolio, Fernanda; Chang, Nancy; Ciaccio, Laura; Emanuel, Lindsay; Fillmore, Jonathan; Gilbert, Gregg H.; Koussis, Patricia; Lee, Christine; Lipkind, Heather; Mallama, Celeste; Meyer, Tamra; Moncur, Megan; Nuckols, Teryl; Pacanowski, Michael A.; Page, David B.; Papadopoulos, Elektra; Ritchie, Jessica D.; Ross, Joseph S.; Shah, Nilay D.; Soukup, Mat; St. Clair, Christopher O.; Tamang, Stephen; Torbati, Sam; Wallace, Douglas W.; Zhao, Yueqin; Heckmann, Rebekah	2022	BMJ open	Methods	Device unspecified	Children Adults	Steps Sleep	URL
A Smartwatch Step-Counting App for Older Adults: Development and Evaluation Study details	Boateng, George; Petersen, Curtis L.; Kotz, David; Fortuna, Karen L.; Masutani, Rebecca; Batsis, John A.	2022	JMIR aging	Validation Measurement	Flex 2	Older Adults	Steps	URL
Feasibility of Conducting Long-term Health and Behaviors Follow-up in Adolescents: Longitudinal Observational Study details	Cucchiaro, Giovanni; Ahumada, Luis; Gray, Geoffrey; Fierstein, Jamie; Yates, Hannah; Householder, Kym; Frye, William; Rehman, Mohamed	2022	JMIR formative research	Usability	Charge 4	Adolescents	Steps Sleep Heart Rate	URL
Pre S cription Digita L Th E rap E utic for P atients with I nsomnia ( SLEEP-I): a protocol for a pragmatic randomised controlled trial details	Dreyer, Rachel P.; Berkowitz, Alyssa; Yaggi, Henry Klar; Schneeberg, Lynelle; Shah, Nilay D.; Emanuel, Lindsay; Kolla, Bhanuprakash; Jeffery, Molly Moore; Deeg, Mark; Ervin, Keondae; Thorndike, Frances; Ross, Joseph S.	2022	BMJ open	Intervention	Inspire 2	Patients	Sleep	URL
Utility of Fitbit devices among children and adolescents with chronic health conditions: a scoping review details	Kasparian, Alexandra M.; Badawy, Sherif M.	2022	mHealth	Usability	Ultra One Zip Flex Charge Charge HR Charge 2	Children Adolescents	Steps Energy Expenditure Intensity Distance Sleep Heart Rate	URL
A catalog of validity indices for step counting wearable technologies during treadmill walking: the CADENCE-adults study details	Mora-Gonzalez, Jose; Gould, Zachary R.; Moore, Christopher C.; Aguiar, Elroy J.; Ducharme, Scott W.; Schuna, John M.; Barreira, Tiago V.; Staudenmayer, John; McAvoy, Cayla R.; Boikova, Mariya; Miller, Taavy A.; Tudor-Locke, Catrine	2022	The international journal of behavioral nutrition and physical activity	Validation	One Zip Ionic	Adults	Steps	URL
Feasibility, acceptability, and effectiveness of school-based dance movement psychotherapy for children with emotional and behavioral difficulties details	Moula, Zoe; Powell, Joanne; Brocklehurst, Shirley; Karkou, Vicky	2022	Frontiers in psychology	Intervention	Device unspecified	Children	Sleep	URL
Estimating Physical/Mental Health Condition Using Heart Rate Data from a Wearable Device details	Onuki, Masaki; Sato, Makito; Sese, Jun	2022	Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference	Intervention	Inspire HR	Adults	Steps Heart Rate	URL
Monitoring postoperative ambulation and sleep after head and neck surgery: A feasibility and utility study using wearable devices details	Pandrangi, Vivek C.; Jorizzo, Matthew; Shah, Suparna; Bruening, Jennifer; Wax, Mark K.; Clayburgh, Daniel; Andersen, Peter; Li, Ryan J.	2022	Head & neck	Usability	Device unspecified	Patients	Steps Sleep	URL
A method for intelligent allocation of diagnostic testing by leveraging data from commercial wearable devices: a case study on COVID-19 details	Shandhi, Md Mobashir Hasan; Cho, Peter J.; Roghanizad, Ali R.; Singh, Karnika; Wang, Will; Enache, Oana M.; Stern, Amanda; Sbahi, Rami; Tatar, Bilge; Fiscus, Sean; Khoo, Qi Xuan; Kuo, Yvonne; Lu, Xiao; Hsieh, Joseph; Kalodzitsa, Alena; Bahmani, Amir; Alavi, Arash; Ray, Utsab; Snyder, Michael P.; Ginsburg, Geoffrey S.; Pasquale, Dana K.; Woods, Christopher W.; Shaw, Ryan J.; Dunn, Jessilyn P.	2022	NPJ digital medicine	Intervention	Device unspecified	Adults	Steps Heart Rate	URL
A randomized controlled trial for reduction of risk factors for cardiovascular disease in young adults: Methods of the Aspire study details	Stephens, Janna; Randolph, Jillian; Burke, Lora; Miller, Carla; Tan, Alai; Buffington, Brenda; Melnyk, Bernadette; Perkins, Antoinette	2022	Contemporary clinical trials	Intervention	Inspire 2	Adults	Steps User Friendliness	URL
Lifestyle and health changes in wheelchair users with a chronic disability after 12 weeks of using the WHEELS mHealth application details	Hoevenaars, Dirk; Holla, Jasmijn F. M.; de Groot, Sonja; Weijs, Peter J. M.; Kraaij, Wessel; Janssen, Thomas W. J.	2022	Disability and Rehabilitation. Assistive Technology	Measurement	Charge 3	Adults	Steps Energy Expenditure Sleep	URL
Detection of Atrial Fibrillation in a Large Population Using Wearable Devices: The Fitbit Heart Study details	Lubitz, Steven A.; Faranesh, Anthony Z.; Selvaggi, Caitlin; Atlas, Steven J.; McManus, David D.; Singer, Daniel E.; Pagoto, Sherry; McConnell, Michael V.; Pantelopoulos, Alexandros; Foulkes, Andrea S.	2022	Circulation	Case Study	Ionic Versa Charge 3 Charge 4 Versa 2 Sense Inspire HR Inspire 2 Versa Lite	Adults	Sleep Heart Rate	URL
Physical Activity Dynamics During a Digital Messaging Intervention Changed After the Pandemic Declaration details	Hojjatinia, Sahar; Lee, Alexandra M.; Hojjatinia, Sarah; Lagoa, Constantino M.; Brunke-Reese, Deborah; Conroy, David E.	2022	Annals of Behavioral Medicine: A Publication of the Society of Behavioral Medicine	Measurement	Versa Versa Lite	Adults	Steps Heart Rate	URL
Effectiveness of wearable technology to optimize youth soccer players' off-training behaviour and training responses: a cluster-randomized trial details	Mateus, Nuno; Exel, Juliana; Santos, Sara; Gonçalves, Bruno; Sampaio, Jaime	2022	Science & Medicine in Football	Intervention	Charge 2	Children	Steps	URL
mHealth Support to Stimulate Physical Activity in Individuals With Intellectual Disability: Protocol for a Mixed Methods Pilot Study details	Michalsen, Henriette; Wangberg, Silje C.; Hartvigsen, Gunnar; Henriksen, André; Pettersen, Gunn; Jaccheri, Letizia; Jahnsen, Reidun Birgitta; Thrane, Gyrd; Arntzen, Cathrine; Anke, Audny	2022	JMIR research protocols	Validation Intervention Methods	Versa	Children Adults	Steps	URL
Step-count outcomes of 13 different activity trackers: Results from laboratory and free-living experiments details	Nakagata, Takashi; Murakami, Haruka; Kawakami, Ryoko; Tripette, Julien; Nakae, Satoshi; Yamada, Yosuke; Ishikawa-Takata, Kazuko; Tanaka, Shigeho; Miyachi, Motohiko	2022	Gait \& Posture	Validation	Device unspecified	Adults	Steps	URL
Activity monitoring and patient-reported outcome measures in Myalgic Encephalomyelitis/Chronic Fatigue Syndrome patients details	Rekeland, Ingrid G.; Sørland, Kari; Bruland, Ove; Risa, Kristin; Alme, Kine; Dahl, Olav; Tronstad, Karl J.; Mella, Olav; Fluge, Øystein	2022	PloS One	Case Study	Charge 3	Adults	Steps Heart Rate	URL
The use of wearable devices for predicting biphasic basal body temperature to estimate the date of ovulation in women details	Uchida, Yuki; Izumizaki, Masahiko	2022	Journal of Thermal Biology					URL
Physical activity measurement in older adults: Wearables versus self-report details	VandeBunte, Anna; Gontrum, Eva; Goldberger, Lauren; Fonseca, Corrina; Djukic, Nina; You, Michelle; Kramer, Joel H.; Casaletto, Kaitlin B.	2022	Frontiers in Digital Health	Usability	Flex 2	Older Adults	Steps Energy Expenditure Heart Rate	URL
Mediators of Effects on Physical Activity and Sedentary Time in an Activity Tracker and Behavior Change Intervention for Adolescents: Secondary Analysis of a Cluster Randomized Controlled Trial details	Verswijveren, Simone Johanna Josefa Maria; Abbott, Gavin; Lai, Samuel K.; Salmon, Jo; Timperio, Anna; Brown, Helen; Macfarlane, Susie; Ridgers, Nicola D.	2022	JMIR mHealth and uHealth	Intervention	Flex	Older Adults	User Friendliness	URL
TILES-2019: A longitudinal physiologic and behavioral data set of medical residents in an intensive care unit details	Yau, Joanna C.; Girault, Benjamin; Feng, Tiantian; Mundnich, Karel; Nadarajan, Amrutha; Booth, Brandon M.; Ferrara, Emilio; Lerman, Kristina; Hsieh, Eric; Narayanan, Shrikanth	2022	Scientific Data	Measurement	Charge 3	Adults	Steps Sleep Heart Rate	URL
A Smartphone Physical Activity App for Patients in Alcohol Treatment: Single-Arm Feasibility Trial details	Abrantes, Ana M.; Meshesha, Lidia Z.; Blevins, Claire E.; Battle, Cynthia L.; Lindsay, Clifford; Marsh, Eliza; Feltus, Sage; Buman, Matthew; Agu, Emmanuel; Stein, Michael	2022	JMIR Formative Research	Usability Medical	Device unspecified	Older Adults	Steps Sleep Heart Rate	URL
Validation of Fitbit Charge 4 for assessing sleep in Chinese patients with chronic insomnia: A comparison against polysomnography and actigraphy details	Dong, Xiaofang; Yang, Sen; Guo, Yuanli; Lv, Peihua; Wang, Min; Li, Yusheng	2022	PloS One	Usability	Charge 4	Adults	Sleep	URL
The Accuracy of Commercially Available Fitness Trackers in Patients after Stroke details	Holubová, Anna; Malá, Eliška; Hoidekrová, Kristýna; Pětioký, Jakub; Ďuriš, Andrea; Mužík, Jan	2022	Sensors (Basel, Switzerland)	Validation	Alta HR	Adults	Steps	URL
Intra-individual impact of the COVID-19 pandemic on mental health and sleep in young adults details	Knickerbocker, Kelly J.; Cox, Evelyn A.; Dhawka, Luvna; Woods, Kerri; Ingram, Krista K.	2022	PloS One	Case Study Measurement	Versa 2	Adults	Sleep	URL
Association of step counts over time with the risk of chronic disease in the All of Us Research Program details	Master, Hiral; Annis, Jeffrey; Huang, Shi; Beckman, Joshua A.; Ratsimbazafy, Francis; Marginean, Kayla; Carroll, Robert; Natarajan, Karthik; Harrell, Frank E.; Roden, Dan M.; Harris, Paul; Brittain, Evan L.	2022	Nature Medicine	Case Study	Device unspecified	Adults	Steps	URL
Fitbits for monitoring depressive symptoms in older aged persons: Qualitative outcomes of a feasibility study details	Mughal, Fiza; Raffe, William; Stubbs, Peter; Kneebone, Ian; Garcia, Jaime	2022	JMIR formative research	Usability	Alta HR	Older Adults	User Friendliness	URL
Digital phenotyping by wearable-driven artificial intelligence in older adults and people with Parkinson's disease: Protocol of the mixed method, cyclic ActiveAgeing study details	Torrado, Juan C.; Husebo, Bettina S.; Allore, Heather G.; Erdal, Ane; Fæø, Stein E.; Reithe, Haakon; Førsund, Elise; Tzoulis, Charalampos; Patrascu, Monica	2022	PloS One	Measurement	Sense	Older Adults	NA	URL
Virtual cardiac fitness training in pediatric heart transplant patients: A pilot study details	Ziebell, Daniel; Stark, Megan; Xiang, Yijin; Mckane, Megan; Mao, Chad	2022	Pediatric Transplantation	Intervention	Inspire	Children Adults	Heart Rate	URL
A qualitative study to explore the acceptability and usefulness of personalized biofeedback to motivate physical activity in cancer survivors details	Brannon, Grace E.; Ray, Madison; Cho, Patrick; Baum, Miranda; Beg, Muhammad Shaalan; Bevers, Therese; Schembre, Susan M.; Basen-Engquist, Karen; Liao, Yue	2022	Digital Health	Intervention Measurement	Device unspecified	Adults	Steps Sleep Heart Rate User Friendliness	URL
Evaluation of sleep quality and duration using wearable sensors in shift laborers of construction industry: A public health perspective details	Sathvik, S.; Krishnaraj, L.; Irfan, Muhammad	2022	Frontiers in Public Health	Case Study	Device unspecified	Adults	Sleep	URL
A Remotely Delivered Progressive Walking Intervention for Adults With Persistent Symptoms of a Mild Traumatic Brain Injury: Feasibility and Exploration of Its Impact details	Alarie, Christophe; Gagnon, Isabelle; de Guise, Elaine; McKerral, Michelle; Kersalé, Marietta; Hoog, Béatrice van Het; Swaine, Bonnie	2022	Frontiers in Rehabilitation Sciences	Intervention	Inspire 2	Adults	Steps	URL
An app-based just-in-time-adaptive self-management intervention for care partners: The CareQOL feasibility pilot study details	Carlozzi, Noelle E.; Choi, Sung Won; Wu, Zhenke; Troost, Jonathan P.; Lyden, Angela K.; Miner, Jennifer A.; Graves, Christopher M.; Wang, Jitao; Yan, Xinghui; Sen, Srijan	2022	Rehabilitation Psychology	Methods	Device unspecified	Adults	Steps Sleep
A Positive Emotion-Focused Intervention to Increase Physical Activity After Bariatric Surgery: Protocol for a Pilot Randomized Controlled Trial details	Feig, Emily H.; Harnedy, Lauren E.; Thorndike, Anne N.; Psaros, Christina; Healy, Brian C.; Huffman, Jeff C.	2022	JMIR research protocols	Intervention Measurement	Device unspecified	Adults	Steps Intensity
How is the usability of commercial activity monitors perceived by older adults and by researchers? A cross-sectional evaluation of community-living individuals details	Hofbauer, Lena M.; Rodriguez, Francisca S.	2022	BMJ open	Usability	Charge 4	Older Adults	User Friendliness
Effect of a 12-week non-contact exercise intervention on body composition and health-related physical fitness in adults: a pilot test details	Kim, Sung-Woo; Choi, Jae-Ho; Sun, Yerin; Seo, Jisoo; Jung, Won-Sang; Park, Hun-Young; Kim, Jisu; Lim, Kiwon	2022	Physical Activity and Nutrition	Intervention	Charge 4	Adults	NA
Validity and Acceptability of Wearable Devices for Monitoring Step-Count and Activity Minutes Among People With Multiple Sclerosis details	Lavelle, Grace; Norris, Meriel; Flemming, Julie; Harper, Jamie; Bradley, Joan; Johnston, Helen; Fortune, Jennifer; Stennett, Andrea; Kilbride, Cherry; Ryan, Jennifer M.	2021	Frontiers in Rehabilitation Sciences	Validation	Zip Alta Garmin Vivofit 4	Adults	Steps
A digital health intervention to improve nutrition and physical activity in breast cancer survivors: Rationale and design of the Cook and Move for Your Life pilot and feasibility randomized controlled trial details	Ueland, Katherine; Sanchez, Sofia Cobos; Rillamas-Sun, Eileen; Shen, Hanjie; Schattenkerk, Liza; Garcia, Gino; VanDoren, Matthew; Myers, Samantha A.; Santiago-Torres, Margarita; Di, Chongzhi; Dey, Neelendu; Guthrie, Katherine A.; Yung, Rachel; Davidson, Nancy E.; Greenlee, Heather	2022	Contemporary Clinical Trials	Intervention	Aria Inspire HR	Adults	Heart Rate
Correlation Between Right Ventricular Echocardiography Measurements and Functional Capacity in Patients With Pulmonary Arterial Hypertension details	Woo, Joyce L.; DiLorenzo, Michael P.; Rosenzweig, Eliana; Pasumarti, Nikhil; Villeda, Gerson Valencia; Berman-Rosenzweig, Erika; Krishnan, Usha	2022	Texas Heart Institute Journal	Clinical Care	Charge HR	Children Adults	Steps Heart Rate
Evaluation of physical health status beyond daily step count using a wearable activity sensor details	Xu, Zheng; Zahradka, Nicole; Ip, Seyvonne; Koneshloo, Amir; Roemmich, Ryan T.; Sehgal, Sameep; Highland, Kristin B.; Searson, Peter C.	2022	NPJ digital medicine	Usability	Charge HR	Adults	Heart Rate
Feasibility and preliminary efficacy of a physiotherapy-led remotely delivered physical activity intervention in cancer survivors using wearable technology. The IMPETUS trial details	Broderick, Julie; Haberlin, Ciarán; O Donnell, Dearbhaile M.	2022	Physiotherapy Theory and Practice	Intervention	One Flex	Adults	User Friendliness
Comparing three wearable accelerometers to measure early activity after cardiac surgery details	Brown, Charles H.; Yanek, Lisa; Healy, Ryan; Tsay, Tiffany; Di, Junrui; Goeddel, Lee; Young, Daniel; Zipunnikov, Vadim; Schrack, Jennifer; Cardiac Surgery Mobility Working Group	2022	JTCVS open	Usability Validation	Charge HR	Adults	Steps
HEART rate variability biofeedback for long COVID symptoms (HEARTLOC): protocol for a feasibility study details	Corrado, Joanna; Halpin, Stephen; Preston, Nick; Whiteside, Diana; Tarrant, Rachel; Davison, Jennifer; Simms, Alexander D.; O'Connor, Rory J.; Casson, Alexander; Sivan, Manoj	2022	BMJ open	Measurement		Adults	Heart Rate
Steps Ahead: Optimising physical activity in adults with cystic fibrosis: A pilot randomised trial using wearable technology, goal setting and text message feedback details	Curran, Máire; Tierney, Audrey C.; Collins, Louise; Kennedy, Lauren; McDonnell, Ciara; Jurascheck, Andrew J.; Sheikhi, Ali; Walsh, Cathal; Button, Brenda; Casserly, Brian; Cahalan, Roisin	2022	Journal of Cystic Fibrosis: Official Journal of the European Cystic Fibrosis Society	Intervention	Charge 2	Adults	Steps Sleep
mHealth-Supported Gender- and Culturally Sensitive Weight Loss Intervention for Hispanic Men With Overweight and Obesity: Single-Arm Pilot Study details	Garcia, David O.; Valdez, Luis A.; Aceves, Benjamin; Bell, Melanie L.; Rabe, Brooke A.; Villavicencio, Edgar A.; Marrero, David G.; Melton, Forest; Hooker, Steven P.	2022	JMIR formative research	Intervention	Charge 2	Adults	User Friendliness
Mining associations between glycemic variability in awake-time and in-sleep among non-diabetic adults details	Liang, Zilu	2022	Frontiers in Medical Technology	Measurement	Charge 3	Adults	Sleep
A randomized, double-blind, placebo-controlled trial investigating the effects of an Ocimum tenuiflorum (Holy Basil) extract (HolixerTM) on stress, mood, and sleep in adults experiencing stress details	Lopresti, Adrian L.; Smith, Stephen J.; Metse, Alexandra P.; Drummond, Peter D.	2022	Frontiers in Nutrition	Medical Case Study	Charge 4	Adults	Sleep
Physical Activity Intervention in Patients with Metastatic Breast Cancer During Active Treatment: Quality of Life and Function details	Shachar, Shlomit Strulov; Heiling, Hillary; Muss, Hyman B.; Meghan, Damone; Wagoner, Chad W.; Deal, Allison M.; Nyrop, Kirsten A.	2022	The Oncologist	Intervention	Zip	Patients	Steps
Feasibility, acceptability, and preliminary efficacy of a single-arm, remotely-delivered health coaching intervention to increase physical activity and reduce sedentary behavior during pregnancy details	Whitaker, Kara M.; Jones, Melissa A.; Dziewior, Jaclyn; Anderson, Megan; Anderson, Chelsie; Gibbs, Bethany Barone; Carr, Lucas J.	2022	BMC pregnancy and childbirth	Intervention	Inspire	Pregnant Women	Steps
Feasibility of Fitbit Use in Adolescent Survivors of Pediatric Cancer: Who Consistently Uses It and for How Long? details	Williamson Lewis, Rebecca; Howell, Kristen E.; Effinger, Karen E.; Meacham, Lillian R.; Wasilewski-Masker, Karen; Mertens, Ann; Marchak, Jordan Gilleland	2022	Journal of Adolescent and Young Adult Oncology	Usability	Flex	Children	Steps
Escalating schedules of incentives increase physical activity with no differences between deposit and no-deposit groups: A systematic replication details	Batchelder, Sydney R.; Van Heukelom, Justin T.; Proctor, Kaitlyn; Washington, Wendy Donlin	2022	Journal of Applied Behavior Analysis	Intervention	Zip Charge	Adults	Steps Goals
Monitoring postoperative ambulation and sleep after head and neck surgery: Feasibility and utility study using wearable devices details	Pandrangi, Vivek C.; Jorizzo, Matthew; Shah, Suparna; Bruening, Jennifer; Wax, Mark K.; Clayburgh, Daniel; Andersen, Peter; Li, Ryan J.	2022	Head \& Neck	Intervention	Inspire HR	Patients	Steps Sleep
The Development of a Novel mHealth Tool for Obstructive Sleep Apnea: Tracking Continuous Positive Airway Pressure Adherence as a Percentage of Time in Bed details	Pfammatter, Angela Fidler; Hughes, Bonnie Olivia; Tucker, Becky; Whitmore, Harry; Spring, Bonnie; Tasali, Esra	2022	Journal of Medical Internet Research	Intervention	Inspire HR	Patients	Steps Weight
Validity of a Global Positioning System-Based Algorithm and Consumer Wearables for Classifying Active Trips in Children and Adults details	Steel, Chelsea; Crist, Katie; Grimes, Amanda; Bejarano, Carolina; Ortega, Adrian; Hibbing, Paul R.; Schipperijn, Jasper; Carlson, Jordan A.	2021	Journal for the Measurement of Physical Behaviour	Validation	Alta	Adults	Distance
A remotely delivered, peer-led intervention to improve physical activity and quality of life in younger breast cancer survivors details	Weiner, Lauren S.; Nagel, Stori; Irene Su, H.; Hurst, Samantha; Levy, Susan S.; Arredondo, Elva M.; Hekler, Eric; Hartman, Sheri J.	2022	Journal of Behavioral Medicine	Intervention	Charge 3	Adults	Steps Heart Rate User Friendliness
Recommendations for Identifying Valid Wear for Consumer-Level Wrist-Worn Activity Trackers and Acceptability of Extended Device Deployment in Children details	Wing, David; Godino, Job G.; Baker, Fiona C.; Yang, Rongguang; Chevance, Guillaume; Thompson, Wesley K.; Reuter, Chase; Bartsch, Hauke; Wilbur, Aimee; Straub, Lisa K.; Castro, Norma; Higgins, Michael; Colrain, Ian M.; de Zambotti, Massimiliano; Wade, Natasha E.; Lisdahl, Krista M.; Squeglia, Lindsay M.; Ortigara, Joseph; Fuemmeler, Bernard; Patrick, Kevin; Mason, Michael J.; Tapert, Susan F.; Bagot, Kara S.	2022	Sensors (Basel, Switzerland)	Usability	Charge HR	Children	Steps Intensity Heart Rate User Friendliness
Self-expansion is positively associated with Fitbit-measured daily steps across 4-weeks details	Xu, Xiaomeng; Tupy, Samantha; Sharp, Julia L.; Miller, Ashley L.; Correll, Danielle; Nigg, Claudio R.	2022	Scientific Reports	Case Study	One	Adults	Steps
PreScription DigitaL ThErapEutic for Patients with Insomnia (SLEEP-I): a protocol for a pragmatic randomised controlled trial details	Dreyer, Rachel P.; Berkowitz, Alyssa; Yaggi, Henry Klar; Schneeberg, Lynelle; Shah, Nilay D.; Emanuel, Lindsay; Kolla, Bhanuprakash; Jeffery, Molly Moore; Deeg, Mark; Ervin, Keondae; Thorndike, Frances; Ross, Joseph S.	2022	BMJ open	Intervention	Inspire 2	Patients	Steps Sleep
Adverse Effects of Sit and Stand Workstations on the Health Outcomes of Assembly Line Workers: A Cross-sectional Study details	Moshref Javadi, Amir-Milad; Choobineh, Alireza; Razeghi, Mohsen; Ghaem, Haleh; Daneshmandi, Hadi	2022	Journal of Preventive Medicine and Hygiene	Intervention	Charge	Adults	Energy Expenditure
Identifying Predictors of Adherence to the Physical Activity Goal: A Secondary Analysis of the SMARTER Weight Loss Trial details	Bizhanova, Zhadyra; Sereika, Susan M.; Brooks, Maria M.; Rockette-Wagner, Bonny; Kariuki, Jacob K.; Burke, Lora E.	2022	Medicine and Science in Sports and Exercise	Case Study	Charge 2	Adults	Intensity
Association of daily physical activity with brain volumes and cervical spinal cord areas in multiple sclerosis details	Block, Valerie J.; Cheng, Shuiting; Juwono, Jeremy; Cuneo, Richard; Kirkish, Gina; Alexander, Amber M.; Khan, Mahir; Akula, Amit; Caverzasi, Eduardo; Papinutto, Nico; Stern, William A.; Pletcher, Mark J.; Marcus, Gregory M.; Olgin, Jeffrey E.; Hauser, Stephen L.; Gelfand, Jeffrey M.; Bove, Riley; Cree, Bruce Ac; Henry, Roland G.	2022	Multiple Sclerosis (Houndmills, Basingstoke, England)	Case Study	Flex 2	Patients	Steps
Moderate-vigorous physical activity attenuates premature senescence of immune cells in sedentary adults with obesity: a pilot randomized controlled trial details	Chen, Xiang-Ke; Zheng, Chen; Wong, Stephen Heung-Sang; Ma, Alvin Chun-Hang	2022	Aging	Intervention	Inspire 2	Adults	Steps Intensity
Comparing a Fitbit Wearable to an Electrocardiogram Gold Standard as a Measure of Heart Rate Under Psychological Stress: A Validation Study details	Gagnon, Joel; Khau, Michelle; Lavoie-Hudon, Léandre; Vachon, François; Drapeau, Vicky; Tremblay, Sébastien	2022	JMIR formative research	Usability	Versa 2	Adults	Heart Rate
Accuracy and Systematic Biases of Heart Rate Measurements by Consumer-Grade Fitness Trackers in Postoperative Patients: Prospective Clinical Trial details	Helmer, Philipp; Hottenrott, Sebastian; Rodemers, Philipp; Leppich, Robert; Helwich, Maja; Pryss, Rüdiger; Kranke, Peter; Meybohm, Patrick; Winkler, Bernd E.; Sammeth, Michael	2022	Journal of Medical Internet Research	Validation	Sense Garmin Fenix 6	Patients	Heart Rate
How Fitbit data are being made available to registered researchers in All of Us Research Program details	Master, Hiral; Kouame, Aymone; Marginean, Kayla; Basford, Melissa; Harris, Paul; Holko, Michelle	2023	Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
Wearable and mobile technology to characterize daily patterns of sleep, stress, presleep worry, and mood in adolescent insomnia details	Menghini, Luca; Yuksel, Dilara; Prouty, Devin; Baker, Fiona C.; King, Christopher; de Zambotti, Massimiliano	2022	Sleep Health	Case Study	Charge 3	Adolescents	Steps Sleep Heart Rate
Lower daily steps among U.S. adolescents during the COVID-19 pandemic: Objective findings from the Adolescent Brain Cognitive Development Study details	Nagata, Jason M.; Yu, Jiayue; Dooley, Erin E.; Baker, Fiona C.; Alsamman, Sana; Wing, David; Ganson, Kyle T.; Pettee Gabriel, Kelley	2023	Preventive Medicine Reports	Case Study	Charge	Adolescents	Steps
Medicinal cannabis improves sleep in adults with insomnia: a randomised double-blind placebo-controlled crossover study details	Ried, Karin; Tamanna, Tasnuva; Matthews, Sonja; Sali, Avni	2022	Journal of Sleep Research	Intervention	Inspire HR	Adults	Sleep
Objective and subjective measures of daily physical activity in persons with Multiple Sclerosis beginning a rehabilitation regime: A cross-sectional study details	Torchio, Alessandro; Fusari, Giulia; Perini, Gloria; Crispiatico, Valeria; Grosso, Cristina; Cattaneo, Davide; Pagliari, Chiara; Jonsdottir, Johanna	2022	Multiple Sclerosis and Related Disorders	Case Study	Versa	Adults	Steps Intensity
Personal Devices to Monitor Physical Activity and Nutritional Intake After Colorectal Cancer Surgery: Feasibility Study details	van der Linden, Manouk J. W.; Nahar van Venrooij, Lenny M. W.; Verdaasdonk, Emiel G. G.	2022	JMIR perioperative medicine	Usability	Charge 3	Adults	User Friendliness
Usability and acceptability of wearable technology in the early detection of dementia details	Wilson, Sarah; Ardle, Ríona Mc; Tolley, Clare; Slight, Sarah	2022	Alzheimer's \& Dementia: The Journal of the Alzheimer's Association	Usability	Charge 4	Adults	User Friendliness
Use of coaching and technology to improve blood pressure control in Black women with hypertension: Pilot randomized controlled trial study details	Abel, Willie M.; Efird, Jimmy T.; Crane, Patricia B.; Ferdinand, Keith C.; Foy, Capri G.; DeHaven, Mark J.	2023	Journal of Clinical Hypertension (Greenwich, Conn.)	Case Study	Inspire HR	Adults	Steps Weight
Magnetic field interactions of smartwatches and portable electronic devices with CIEDs - Did we open a Pandora's box? details	Badertscher, Patrick; Vergne, Céline; Féry, Corentin; Mannhart, Diego; Quirin, Thomas; Osswald, Stefan; Kühne, Michael; Sticherling, Christian; Knecht, Sven; Pascal, Joris	2022	International Journal of Cardiology. Heart \& Vasculature	Review	Sense	NA	NA
Detailed Versus Simplified Dietary Self-monitoring in a Digital Weight Loss Intervention Among Racial and Ethnic Minority Adults: Fully Remote, Randomized Pilot Study details	Patel, Michele L.; Cleare, Angel E.; Smith, Carly M.; Rosas, Lisa Goldman; King, Abby C.	2022	JMIR formative research	Intervention	Inspire 2	Adults	Steps Goals
Sleep and seizure risk in epilepsy: bed and wake times are more important than sleep duration details	Stirling, Rachel E.; Hidajat, Cindy M.; Grayden, David B.; D'Souza, Wendyl J.; Naim-Feil, Jodie; Dell, Katrina L.; Schneider, Logan D.; Nurse, Ewan; Freestone, Dean; Cook, Mark J.; Karoly, Philippa J.	2022	Brain: A Journal of Neurology	Case Study	Device unspecified	Adults	Sleep
Healthy hearts healthy minds: A randomized trial of online interventions to improve physical activity details	Sylvia, Louisa G.; Gold, Alexandra K.; Rakhilin, Marina; Amado, Selen; Modrow, Madelaine Faulkner; Albury, Evan A.; George, Nevita; Peters, Amy T.; Selvaggi, Caitlin A.; Horick, Nora; Rabideau, Dustin J.; Dohse, Heidi; Tovey, Roberta E.; Turner, Jon A.; Schopfer, David W.; Pletcher, Mark J.; Katz, Doug; Deckersbach, Thilo; Nierenberg, Andrew A.	2023	Journal of Psychosomatic Research	Intervention	Charge 2	Adults	Steps
Living labs for a mobile app-based health program: effectiveness of a 24-week walking intervention for cardiovascular disease risk reduction among female Korean-Chinese migrant workers: a randomized controlled trial details	Kim, Youlim; Lee, Hyeonkyeong; Chung, Misook Lee	2022	Archives of Public Health = Archives Belges De Sante Publique	Intervention	Alta	Adults	Steps Intensity
Prescribing Time in Nature for Human Health and Well-Being: Study Protocol for Tailored Park Prescriptions details	Schultz, Courtney L.; Bocarro, Jason N.; Hipp, J. Aaron; Bennett, Gary J.; Floyd, Myron F.	2022	Frontiers in Digital Health	Methods	Flex 2	Patients	Steps
Telehealth acceptance and commitment therapy for weight loss: Protocol of the WeLNES full scale randomized controlled trial details	Bricker, Jonathan B.; Mull, Kristin E.; Sullivan, Brianna M.; Forman, Evan M.; Lillis, Jason; McTiernan, Anne; Santiago-Torres, Margarita	2023	Contemporary Clinical Trials	Intervention	Inspire	Adults	Steps Food
Design and rationale of a pilot randomized clinical trial investigating the use of a mHealth app for sarcoidosis-associated fatigue details	Chandler, Jessica; Christon, Lillian M.; Benfield, Katie; Pairet, Samantha; Hoffman, Maria; Treiber, Frank; Mueller, Martina; James, W. Ennis	2023	Contemporary Clinical Trials Communications	Methods	Device unspecified	Patients	Heart Rate
The Feasibility of Implementing Remote Measurement Technologies in Psychological Treatment for Depression: Mixed Methods Study on Engagement details	de Angel, Valeria; Adeleye, Fadekemi; Zhang, Yuezhou; Cummins, Nicholas; Munir, Sara; Lewis, Serena; Laporta Puyal, Estela; Matcham, Faith; Sun, Shaoxiong; Folarin, Amos A.; Ranjan, Yatharth; Conde, Pauline; Rashid, Zulqarnain; Dobson, Richard; Hotopf, Matthew	2023	JMIR mental health	Validation	Charge 4	Adults	Steps Sleep Heart Rate
Linking Electronic Health Records With Wearable Technology From the All of Us Research Program details	Epstein, Crystal Modde; McCoy, Thomas P.	2023	Journal of obstetric, gynecologic, and neonatal nursing: JOGNN	Usability	Device unspecified	Pregnant Women	Heart Rate
Feasibility and Acceptability of a Preoperative Multimodal Mobile Health Assessment in Spine Surgery Candidates details	Greenberg, Jacob K.; Frumkin, Madelyn R.; Javeed, Saad; Zhang, Justin K.; Dai, Ruixuan; Molina, Camilo A.; Pennicooke, Brenton H.; Agarwal, Nitin; Santiago, Paul; Goodwin, Matthew L.; Jain, Deeptee; Pallotta, Nicholas; Gupta, Munish C.; Buchowski, Jacob M.; Leuthardt, Eric C.; Ghogawala, Zoher; Kelly, Michael P.; Hall, Bruce L.; Piccirillo, Jay F.; Lu, Chenyang; Rodebaugh, Thomas L.; Ray, Wilson Z.	2022	Neurosurgery	Usability	Inspire 2	Patients	Heart Rate User Friendliness
Systematic Review of Fitbit Charge 2 Validation Studies for Exercise Tracking details	Irwin, Crista; Gary, Rebecca	2022	Translational Journal of the American College of Sports Medicine	Validation Systematic Review	Charge 2	NA	Steps Intensity Heart Rate
An online mental health informed physical activity intervention for emergency service workers and their families: A stepped-wedge trial details	McKeon, Grace; Wells, Ruth; Steel, Zachary; Hadzi-Pavlovic, Dusan; Teasdale, Scott; Vancampfort, Davy; Rosenbaum, Simon	2023	Digital Health	Intervention	Inspire	Adults	Steps
Piloting HealthScore: Feasibility and acceptability of a clinically integrated health coaching program for people living with cancer details	Wood, William A.; Bailey, Carly; Castrogivanni, Brianna; Mehedint, Diana; Bryant, Ashley Leak; Lavin, Kyle; Tan, Xianming; Richardson, Jaime; Qian, Yiqing; Tan, Kelly R.; Kent, Erin E.	2023	Cancer Medicine	Intervention	Device unspecified	Patients	Steps Heart Rate User Friendliness
LifeSnaps, a 4-month multi-modal dataset capturing unobtrusive snapshots of our lives in the wild details	Yfantidou, Sofia; Karagianni, Christina; Efstathiou, Stefanos; Vakali, Athena; Palotti, Joao; Giakatos, Dimitrios Panteleimon; Marchioro, Thomas; Kazlouski, Andrei; Ferrari, Elena; Girdzijauskas, Šarūnas	2022	Scientific Data	Survey Research	Sense	Older Adults Adults	Steps Energy Expenditure Intensity Distance Sleep Heart Rate
Selection of and Response to Physical Activity-Based Social Comparisons in a Digital Environment: Series of Daily Assessment Studies details	Arigo, Danielle; Gray, Robert C.; Dallal, Diane H.; Villareale, Jennifer; Zhu, Jichen	2023	JMIR human factors	Systematic Review	Device unspecified	Adults	Steps
The development of a family-based wearable intervention using behaviour change and co-design approaches: move and connect details	Creaser, A. V.; Bingham, D. D.; Bennett, H. a. J.; Costa, S.; Clemes, S. A.	2023	Public Health	Intervention	Alta HR	Children Adults	User Friendliness
How many days are needed? Measurement reliability of wearable device data to assess physical activity details	Hilden, Patrick; Schwartz, Joseph E.; Pascual, Christian; Diaz, Keith M.; Goldsmith, Jeff	2023	PloS One	Intervention	Flex	Adults	Steps Intensity
Are Activity Wrist-Worn Devices Accurate for Determining Heart Rate during Intense Exercise? details	Martín-Escudero, Pilar; Cabanas, Ana María; Dotor-Castilla, María Luisa; Galindo-Canales, Mercedes; Miguel-Tobal, Francisco; Fernández-Pérez, Cristina; Fuentes-Ferrer, Manuel; Giannetti, Romano	2023	Bioengineering (Basel, Switzerland)	Usability Validation	Charge	Adults	Heart Rate
Challenges of using a Fitbit smart wearable among people with dementia details	O'Sullivan, Grace; Whelan, Barbara; Gallagher, Niamh; Doyle, Priscilla; Smyth, Siobhán; Murphy, Kathleen; Dröes, Rose-Marie; Devane, Declan; Casey, Dympna	2023	International Journal of Geriatric Psychiatry	Usability	Charge 3	Patients	Steps
Non-equivalent, but still valid: Establishing the construct validity of a consumer fitness tracker in persons with multiple sclerosis details	Polhemus, Ashley; Sieber, Chloé; Haag, Christina; Sylvester, Ramona; Kool, Jan; Gonzenbach, Roman; von Wyl, Viktor	2023	PLOS digital health	Validation	Inspire HR	Adults	Steps Intensity
Assessing the Suitability of a Virtual 'Pink Warrior' for Older Breast Cancer Survivors during COVID-19: A Pilot Study details	Swartz, Maria C.; Robertson, Michael C.; Christopherson, Ursela; Wells, Stephanie J.; Lewis, Zakkoyya H.; Bai, Jinbing; Swartz, Michael D.; Silva, H. Colleen; Martinez, Eloisa; Lyons, Elizabeth J.	2023	Life (Basel, Switzerland)	Intervention	Alta HR	Patients	Steps User Friendliness
Wearable sensor-based performance status assessment in cancer: A pilot multicenter study from the Alliance for Clinical Trials in Oncology (A19\_Pilot2) details	Wood, William A.; Dilip, Deepika; Derkach, Andriy; Grover, Natalie S.; Elemento, Olivier; Levine, Ross; Thanarajasingam, Gita; Batsis, John A.; Bailey, Charlotte; Kannappan, Arun; Devine, Steven M.; Artz, Andrew S.; Ligibel, Jennifer A.; Basch, Ethan; Kent, Erin; Glass, Jacob	2023	PLOS digital health	Case Study	Charge HR	Patients	Intensity Sleep Heart Rate
Long-term participant retention and engagement patterns in an app and wearable-based multinational remote digital depression study details	Zhang, Yuezhou; Pratap, Abhishek; Folarin, Amos A.; Sun, Shaoxiong; Cummins, Nicholas; Matcham, Faith; Vairavan, Srinivasan; Dineley, Judith; Ranjan, Yatharth; Rashid, Zulqarnain; Conde, Pauline; Stewart, Callum; White, Katie M.; Oetzmann, Carolin; Ivan, Alina; Lamers, Femke; Siddi, Sara; Rambla, Carla Hernández; Simblett, Sara; Nica, Raluca; Mohr, David C.; Myin-Germeys, Inez; Wykes, Til; Haro, Josep Maria; Penninx, Brenda W. J. H.; Annas, Peter; Narayan, Vaibhav A.; Hotopf, Matthew; Dobson, Richard J. B.; RADAR-CNS consortium	2023	NPJ digital medicine	Survey Research	Charge 2 Charge 3	Adults	Steps Sleep Heart Rate
Performance of the Fitbit Charge 2 and Galaxy Watch 2 compared with polysomnography in assessing patients with obstructive sleep apnoea details	Byun, Jung-Ick; Noh, Kyung Chul; Shin, Won Chul	2023	Chronobiology International	Usability	Charge 2	Patients	Sleep
Using Wearable Technology to Quantify Physical Activity Recovery: Secondary Report From the AFTER (App-Facilitated Tele-Rehabilitation) Program for COVID-19 Survivors Randomized Study details	Churchill, Laura; Morrow, Mary; Capin, Jacob J.; Jolley, Sarah E.; Hare, Kristine; MaWhinney, Samantha; Stevens-Lapsley, Jennifer E.; Erlandson, Kristine M.	2023	JMIR rehabilitation and assistive technologies	Case Study	Inspire 2	Adults	Steps
Harnessing digital health to optimise the delivery of guideline-based cardiac rehabilitation during COVID-19: an observational study details	Gibson, Irene; McCrudden, Zoe; Dunne, Denise; Harris, Aisling; Hynes, Lisa; Murphy, Ella; Murphy, Andrew William; Byrne, Molly; McEvoy, John William	2023	Open Heart	Intervention	Device unspecified	Patients	Steps Heart Rate
Learning in a Virtual Environment to Improve Type 2 Diabetes Outcomes: A Randomized Controlled Trial details	Johnson, Constance; D'Eramo Melkus, Gail; Reagan, Louise; Pan, Wei; Amarasekara, Sathya; Pereira, Katherine; Hassell, Nancy; Nowlin, Sarah; Vorderstrasse, Allison	2023	JMIR formative research	Intervention	Device unspecified	Adults	Steps
Use of Wearable Activity-Monitoring Technologies to Promote Physical Activity in Cancer Survivors: Challenges and Opportunities for Improved Cancer Care details	Keats, Melanie R.; Yu, Xing; Sweeney Magee, Molly; Forbes, Cynthia C.; Grandy, Scott A.; Sweeney, Ellen; Dummer, Trevor J. B.	2023	International Journal of Environmental Research and Public Health	Review	Alta	Adults	Steps
Potential limitations of activity tracking devices in monitoring effects of treatment for sarcoidosis details	Klein, Rebecca; Judson, Marc; Barkes, Briana; Maier, Lisa; Zeigler, Joyce; Culver, Daniel; Sweiss, Nadera; Chen, Edward; Hamzeh, Nabeel; Grutters, Jan; Valeyre, Dominique; Singh, Noopur; Spitzer, Ginger; Shivas, Tricha; Baughman, Robert	2023	Sarcoidosis, vasculitis, and diffuse lung diseases: official journal of WASOG	Usability	One	Patients	User Friendliness
A useful tool or a new challenge? Hand-wrist-worn sleep trackers in patients with insomnia details	Ojalvo, Dávid; Pacheco, André Pekkola; Benedict, Christian	2023	Journal of Sleep Research	Usability	Inspire 2	Patients	Sleep
Real-time Assessment of the Bidirectional Relationship Between Affective States and Glucose: Protocol for a 14-Day Observational Study details	Rethorst, Chad D.; Githinji, Phrashiah; Seguin-Fowler, Rebecca A.; MacMillan Uribe, Alexandra L.; Szeszulski, Jacob; Liao, Yue	2023	JMIR research protocols	Methods	Inspire 2	Adults	Steps Sleep
Feasibility and scalability of a fitness tracker study: Results from a longitudinal analysis of persons with multiple sclerosis details	Sieber, Chloé; Haag, Christina; Polhemus, Ashley; Sylvester, Ramona; Kool, Jan; Gonzenbach, Roman; von Wyl, Viktor	2023	Frontiers in Digital Health	Case Study	Inspire HR	Adults	Steps Intensity Heart Rate
mHealth intervention delivered in general practice to increase physical activity and reduce sedentary behaviour of patients with prediabetes and type 2 diabetes (ENERGISED): rationale and study protocol for a pragmatic randomised controlled trial details	Vetrovsky, Tomas; Kral, Norbert; Pfeiferova, Marketa; Kuhnova, Jitka; Novak, Jan; Wahlich, Charlotte; Jaklova, Andrea; Jurkova, Katerina; Janek, Michael; Omcirk, Dan; Capek, Vaclav; Maes, Iris; Steffl, Michal; Ussher, Michael; Tufano, James J.; Elavsky, Steriani; Van Dyck, Delfien; Cimler, Richard; Yates, Tom; Harris, Tess; Seifert, Bohumil	2023	BMC public health	Methods	Inspire 2	Patients	Steps
Physical Activity and Social Comparison: The Importance of Group Composition in an Employee Fitbit Intervention details	Wentz, Jennifer R.; Wilhelm Stanis, Sonja	2023	Health Promotion Practice	Intervention	Zip Flex 2	Adults	Steps
Daytime Sleep-Tracking Performance of Four Commercial Wearable Devices During Unrestricted Home Sleep details	Chinoy, Evan D.; Cuellar, Joseph A.; Jameson, Jason T.; Markwald, Rachel R.	2023	Nature and Science of Sleep	Usability	Inspire HR	Adults	Sleep	URL
Performance of seven consumer sleep-tracking devices compared with polysomnography details	Chinoy, Evan D.; Cuellar, Joseph A.; Huwa, Kirbie E.; Jameson, Jason T.; Watson, Catherine H.; Bessman, Sara C.; Hirsch, Dale A.; Cooper, Adam D.; Drummond, Sean P. A.; Markwald, Rachel R.	2021	Sleep	Usability Validation	Alta HR	Adults	Sleep
Performance of Four Commercial Wearable Sleep-Tracking Devices Tested Under Unrestricted Conditions at Home in Healthy Young Adults details	Chinoy, Evan D.; Cuellar, Joseph A.; Jameson, Jason T.; Markwald, Rachel R.	2022	Nature and Science of Sleep	Usability Validation	Inspire HR	Adults	Sleep
Fitbit Data to Assess Functional Capacity in Patients Before Elective Surgery: Pilot Prospective Observational Study details	Angelucci, Alessandra; Greco, Massimiliano; Canali, Stefano; Marelli, Giovanni; Avidano, Gaia; Goretti, Giulia; Cecconi, Maurizio; Aliverti, Andrea	2023	Journal of Medical Internet Research	Usability	Inspire 2	Patients	Steps Heart Rate
Influence of sleep duration and sex on age-related differences in heart rate variability: Findings from program 4 of the HAIE study details	Gonzales, Joaquin U.; Elavsky, Steriani; Cipryan, Lukáš; Jandačková, Vera; Burda, Michal; Jandačka, Daniel	2023	Sleep Medicine	Measurement	Charge 3 Charge 4	Adults	Sleep	URL
The Added Value of Remote Technology in Cardiac Rehabilitation on Physical Function, Anthropometrics, and Quality of Life: Cluster Randomized Controlled Trial details	Lahtio, Heli; Heinonen, Ari; Paajanen, Teemu; Sjögren, Tuulikki	2023	Journal of Medical Internet Research	Usability Intervention	Charge HR	Adults	NA
Matched and moving: exploring daily social support among women partnered for exercise after a breast cancer diagnosis details	Murray, Ross M.; Smith-Turchyn, Jenna; Vani, Madison F.; McDonough, Meghan H.; Fong, Angela J.; Mina, Daniel Santa; Arbour-Nicitopoulos, Kelly P.; Trinh, Linda; Jones, Jennifer M.; Bender, Jackie L.; Culos-Reed, S. Nicole; Tomasone, Jennifer R.; Sabiston, Catherine M.	2023	Supportive Care in Cancer: Official Journal of the Multinational Association of Supportive Care in Cancer	Case Study	Inspire HR	Adults	Steps Intensity
Validation of an open-source smartphone step counting algorithm in clinical and non-clinical settings details	Straczkiewicz, Marcin; Keating, Nancy L.; Thompson, Embree; Matulonis, Ursula A.; Campos, Susana M.; Wright, Alexi A.; Onnela, Jukka-Pekka	2023	medRxiv: The Preprint Server for Health Sciences	Validation	Charge 2	Patients	Steps
Wearable Sensor and Mobile App-Based mHealth Approach for Investigating Substance Use and Related Factors in Daily Life: Protocol for an Ecological Momentary Assessment Study details	Takano, Ayumi; Ono, Koki; Nozawa, Kyosuke; Sato, Makito; Onuki, Masaki; Sese, Jun; Yumoto, Yosuke; Matsushita, Sachio; Matsumoto, Toshihiko	2023	JMIR research protocols	Case Study	Inspire 2	Patients	Steps Sleep Heart Rate
Opportune warning of COVID-19 in a Mexican health care worker cohort: Discrete beta distribution entropy of smartwatch physiological records details	Aguado-García, Alejandro; Arroyo-Valerio, América; Escobedo, Galileo; Bueno-Hernández, Nallely; Olguín-Rodríguez, P. V.; Müller, Markus F.; Carrillo-Ruiz, José Damián; Martínez-Mekler, Gustavo	2023	Biomedical Signal Processing and Control	Measurement	Charge 3	Adults	Steps Sleep Heart Rate
Predicting Hospital Readmission among Patients with Sepsis using Clinical and Wearable Data details	Amrollahi, Fatemeh; Shashikumar, Supreeth Prajwal; Yhdego, Haben; Nayebnazar, Arshia; Yung, Nathan; Wardi, Gabriel; Nemati, Shamim	2023	medRxiv: The Preprint Server for Health Sciences	Case Study	Device unspecified	Patients Adults	Energy Expenditure Intensity Heart Rate
A multi-site trial of an electronic health integrated physical activity promotion intervention in breast and endometrial cancers survivors: MyActivity study protocol details	Cadmus-Bertram, Lisa; Solk, Payton; Agnew, Megan; Starikovsky, Julia; Schmidt, Christian; Morelli, Whitney A.; Hodgson, Vanessa; Freeman, Hannah; Muller, Laura; Mishory, Abby; Naxi, Sondra; Carden, Lillian; Tevaarwerk, Amye J.; Wolter, Melanie; Barber, Emma; Spencer, Ryan; Sesto, Mary E.; Gradishar, William; Gangnon, Ronald; Spring, Bonnie; Nahum-Shani, Inbal; Phillips, Siobhan M.	2023	Contemporary Clinical Trials	Intervention	Charge 3	Adults	User Friendliness
Motivational Message Framing Effects on Physical Activity Dynamics in a Digital Messaging Intervention: Secondary Analysis details	Lee, Alexandra M.; Hojjatinia, Sahar; Courtney, Jimikaye B.; Brunke-Reese, Deborah; Hojjatinia, Sarah; Lagoa, Constantino M.; Conroy, David E.	2023	JMIR formative research	Intervention Measurement	Versa Versa Lite	Adults	Steps Heart Rate
Exploratory Validation of Sleep-Tracking Devices in Patients with Psychiatric Disorders details	Ogasawara, Masaya; Takeshima, Masahiro; Kosaka, Shumpei; Imanishi, Aya; Itoh, Yu; Fujiwara, Dai; Yoshizawa, Kazuhisa; Ozaki, Norio; Nakagome, Kazuyuki; Mishima, Kazuo	2023	Nature and Science of Sleep	Usability	Sense	Patients	Sleep
Considering depression as a secondary outcome in the optimization of physical activity interventions for breast cancer survivors in the PACES trial: a factorial randomized controlled trial details	Rethorst, Chad D.; Carmody, Thomas J.; Argenbright, Keith E.; Mayes, Taryn L.; Hamann, Heidi A.; Trivedi, Madhukar H.	2023	The International Journal of Behavioral Nutrition and Physical Activity	Intervention	Alta HR	Patients	Goals User Friendliness
An overview of the together everyone achieves more physical activity (TEAM-PA) trial to increase physical activity among African American women details	Sweeney, Allison M.; Wilson, Dawn K.; Zarrett, Nicole; Martin, Pamela; Hardin, James W.; Fairchild, Amanda; Mitchell, Sheryl; Decker, Lindsay	2023	Contemporary Clinical Trials	Intervention	Device unspecified	Adults	Steps User Friendliness
Efficacy of the Aim2Be Intervention in Changing Lifestyle Behaviors Among Adolescents With Overweight and Obesity: Randomized Controlled Trial details	Tugault-Lafleur, Claire N.; De-Jongh González, Olivia; Macdonald, Janice; Bradbury, Jennifer; Warshawski, Tom; Ball, Geoff D. C.; Morrison, Katherine; Ho, Josephine; Hamilton, Jill; Buchholz, Annick; Mâsse, Louise	2023	Journal of Medical Internet Research	Intervention	Flex 2	Adults Adolescents	Steps
Prediction of Unplanned Hospital Readmission using Clinical and Longitudinal Wearable Sensor Features details	Yhdego, Haben H.; Nayebnazar, Arshia; Amrollahi, Fatemeh; Boussina, Aaron; Shashikumar, Supreeth; Wardi, Gabriel; Nemati, Shamim	2023	medRxiv	Case Study	Device unspecified	Patients Adults	Steps Energy Expenditure Intensity Heart Rate	URL
Health-related impact of illness associated with excessive daytime sleepiness in patients with obstructive sleep apnea details	Grandner, Michael A.; Min, Jae S.; Saad, Ragy; Leary, Eileen B.; Eldemir, Lev; Hyman, Danielle	2023	Postgraduate Medicine	Case Study	Device unspecified	Adults	Steps Intensity Sleep User Friendliness
PCovNet+: A CNN-VAE anomaly detection framework with LSTM embeddings for smartwatch-based COVID-19 detection details	Abir, Farhan Fuad; Chowdhury, Muhammad E. H.; Tapotee, Malisha Islam; Mushtak, Adam; Khandakar, Amith; Mahmud, Sakib; Hasan, Md Anwarul	2023	Engineering Applications of Artificial Intelligence	Review	Device unspecified	NA	Steps Sleep Heart Rate
Gut microbiota composition and functionality are associated with REM sleep duration and continuous glucose levels details	Arnoriaga-Rodríguez, María; Leal, Yenny; Mayneris-Perxachs, Jordi; Pérez-Brocal, Vicente; Moya, Andrés; Ricart, Wifredo; Fernández-Balsells, Mercè; Fernández-Real, José Manuel	2023	The Journal of Clinical Endocrinology and Metabolism	Case Study	Charge 3	Adults	Sleep
Effect of an Automated Advice Algorithm (CloudConnect) on Adolescent-Parent Diabetes-Specific Communication and Glycemic Management: A Randomized Trial details	DeBoer, Mark D.; Valdez, Rupa; Corbett, John P.; Krauthause, Katie; Wakeman, Christian A.; Luke, Alexander S.; Oliveri, Mary C.; Cherñavvsky, Daniel R.; Patek, Stephen D.	2023	Diabetes Therapy: Research, Treatment and Education of Diabetes and Related Disorders	Case Study	Charge 3	Adults Adolescents	NA
Mindfulness Combined with Exercise Online (MOVE) Compared with a Self-Management Guide for Adults with Chronic Pain: A Feasibility Randomised Controlled Trial details	Deegan, Orla; Fullen, Brona M.; Casey, Maire-Brid; Segurado, Ricardo; Hearty, Conor; Doody, Catherine	2023	The Clinical Journal of Pain	Intervention	Device unspecified	Adults	User Friendliness
Clinical validation of an artificial intelligence algorithm offering cross-platform detection of atrial fibrillation using smart device electrocardiograms details	Mannhart, Diego; Lefebvre, Baptiste; Gardella, Christophe; Henry, Christine; Serban, Teodor; Knecht, Sven; Kühne, Michael; Sticherling, Christian; Badertscher, Patrick	2023	Archives of Cardiovascular Diseases	Validation	Sense	Patients	Heart Rate
Machine Learning Approaches to Classify Self-Reported Rheumatoid Arthritis Health Scores Using Activity Tracker Data details	Rao, Kaushal; Speier, William; Meng, Yiwen; Wang, Jinhan; Ramesh, Nidhi; Xie, Fenglong; Su, Yujie; Nowell, Benjamin; Curtis, Jeffrey; Arnold, Corey	2023	JMIR formative research	Intervention	Versa 2	Patients	Steps Energy Expenditure Sleep Heart Rate
The usability of daytime and night-time heart rate dynamics as digital biomarkers of depression severity details	Siddi, S.; Bailon, R.; Giné-Vázquez, I.; Matcham, F.; Lamers, F.; Kontaxis, S.; Laporta, E.; Garcia, E.; Lombardini, F.; Annas, P.; Hotopf, M.; Penninx, B. W. J. H.; Ivan, A.; White, K. M.; Difrancesco, S.; Locatelli, P.; Aguiló, J.; Peñarrubia-Maria, M. T.; Narayan, V. A.; Folarin, A.; Leightley, D.; Cummins, N.; Vairavan, S.; Ranjan, Y.; Rintala, A.; de Girolamo, G.; Simblett, S. K.; Wykes, T.; PAB members; Myin-Germeys, I.; Dobson, R.; Haro, J. M.; RADAR-CNS consortium	2023	Psychological Medicine	Case Study Measurement	Device unspecified	Adults	Heart Rate
A culturally tailored personaliseD nutrition intErvention in South ASIan women at risk of Gestational Diabetes Mellitus (DESI-GDM): a randomised controlled trial protocol details	Stennett, Rosain N.; Adamo, Kristi B.; Anand, Sonia S.; Bajaj, Harpreet S.; Bangdiwala, Shrikant I.; Desai, Dipika; Gerstein, Hertzel C.; Kandasamy, Sujane; Khan, Farah; Lear, Scott A.; McDonald, Sarah D.; Pocsai, Tayler; Ritvo, Paul; Rogge, Andrea; Schulze, Karleen M.; Sherifali, Diana; Stearns, Jennifer C.; Wahi, Gita; Williams, Natalie C.; Zulyniak, Michael A.; de Souza, Russell J.	2023	BMJ open	Intervention	Device unspecified	Pregnant Women	Steps
A feasibility study to understand the components of behavioral sleep extension details	Baron, Kelly G.; Trela-Hoskins, Sarah R.; Duffecy, Jennifer; Allen, Chelsea M.	2022	PEC Innovation	Intervention	Flex 2	Adults	User Friendliness	URL
Feasibility and Utility of a Fitbit Tracker Among Ambulatory Children and Youth With Disabilities details	Bremer, Emily; Arbour-Nicitopoulos, Kelly P.; Tsui, Brianna; Ginis, Kathleen A. Martin; Moore, Sarah A.; Best, Krista L.; Voss, Christine	2023	Pediatric Exercise Science	Usability	Charge HR	Children	Steps User Friendliness
Performance evaluation of Fitbit Charge 3 and actigraphy vs. polysomnography: Sensitivity, specificity, and reliability across participants and nights details	Eylon, Gal; Tikotzky, Liat; Dinstein, Ilan	2023	Sleep Health	Validation	Charge 3	Adults	Sleep
The feasibility of remotely monitoring physical, cognitive, and psychosocial function in individuals with stroke or chronic obstructive pulmonary disease details	French, Margaret A.; Keatley, Eva; Li, Junyao; Balasubramanian, Aparna; Hansel, Nadia N.; Wise, Robert; Searson, Peter; Singh, Anil; Raghavan, Preeti; Wegener, Stephen; Roemmich, Ryan T.; Celnik, Pablo	2023	Digital Health	Usability	Inspire 2	Adults	User Friendliness
Working-from-home persistently influences sleep and physical activity 2 years after the Covid-19 pandemic onset: a longitudinal sleep tracker and electronic diary-based study details	Massar, Stijn A. A.; Ong, Ju Lynn; Lau, TeYang; Ng, Ben K. L.; Chan, Lit Fai; Koek, Daphne; Cheong, Karen; Chee, Michael W. L.	2023	Frontiers in Psychology	Case Study	Versa 2	Adults	Steps Sleep Heart Rate	URL
Data-driven physical actigraphy patterns relate to cognitive and vascular health in older adults details	Paolillo, Emily W.; Lee, Shannon Y.; VandeBunte, Anna; Saloner, Rowan; Gaynor, Leslie S.; Djukic, Nina; Tsuei, Torie; Cobigo, Yann; Kramer, Joel H.; Casaletto, Kaitlin B.	2023	Experimental Gerontology	Case Study	Flex 2	Older Adults	Steps Intensity
Natural patterns of social support for physical activity participation in newly matched breast cancer survivor dyads details	Peck, Serena S.; Vani, Madison F.; Smith-Turchyn, Jenna; Sabiston, Catherine M.	2023	BMC women's health	Case Study	Inspire HR	Adults	Steps Intensity
Development of a Mobile Health Snacktivity App to Promote Physical Activity in Inactive Adults (SnackApp): Intervention Mapping and User Testing Study details	Sanders, James P.; Gokal, Kajal; Thomas, Jonah J. C.; Rawstorn, Jonathan C.; Sherar, Lauren B.; Maddison, Ralph; Greaves, Colin J.; Esliger, Dale; Daley, Amanda J.; Snacktivity Investigators	2023	JMIR formative research	Intervention Measurement	Versa Lite	Adults	User Friendliness
Accuracy of a Standalone Atrial Fibrillation Detection Algorithm Added to a Popular Wristband and Smartwatch: Prospective Diagnostic Accuracy Study details	Selder, Jasper L.; Te Kolste, Henryk Jan; Twisk, Jos; Schijven, Marlies; Gielen, Willem; Allaart, Cornelis P.	2023	Journal of Medical Internet Research	Validation	Ionic	Patients	NA
Sleep Duration on Workdays Is Correlated with Subjective Workload and Subjective Impact of High Workload on Sleep in Young Healthy Adults details	von Gall, Charlotte; Muth, Thomas; Angerer, Peter	2023	Brain Sciences	Case Study	Inspire	Adults	Sleep
Linking Sleep and Aggression: Examining the Role of Response Inhibition and Emotional Processing details	Bozzay, Melanie L.; Verona, Edelyn	2023	Clinical Psychological Science: A Journal of the Association for Psychological Science	Case Study	Flex	Adults	Sleep
Effects of a Mobile-Health Exercise Intervention on Body Composition, Vascular Function, and Autonomic Nervous System Function in Obese Women: A Randomized Controlled Trial details	Choi, Jae-Ho; Kim, Sung-Woo; Seo, Jisoo; Sun, Yerin; Jung, Won-Sang; Park, Hun-Young; Kim, Jisu; Lim, Kiwon	2023	Journal of Multidisciplinary Healthcare	Intervention Measurement	Charge 4	Adults	Steps Energy Expenditure Heart Rate
Assessment of 24-hour physical behaviour in adults via wearables: a systematic review of validation studies under laboratory conditions details	Giurgiu, Marco; Ketelhut, Sascha; Kubica, Claudia; Nissen, Rebecca; Doster, Ann-Kathrin; Thron, Maximiliane; Timm, Irina; Giurgiu, Valeria; Nigg, Claudio R.; Woll, Alexander; Ebner-Priemer, Ulrich W.; Bussmann, Johannes B. J.	2023	The International Journal of Behavioral Nutrition and Physical Activity	Systematic Review	Classic One Flex Charge 2	NA	Steps Energy Expenditure Intensity Sleep
Nonlinear, Multicomponent Physical Exercise with Heart Rate Variability-Guided Prescription in Women with Breast Cancer during Treatment: Feasibility and Preliminary Results (ATOPE Study) details	González-Santos, Á.; Lopez-Garzon, M.; Gil-Gutiérrez, R.; Salinas-Asensio, M. M.; Postigo-Martin, P.; Cantarero-Villanueva, I.	2023	Physical Therapy	Intervention	Inspire	Patients	Steps
Quantifying physical activity needed to mitigate genetic risk for obesity details	Han, Lide; Annis, Jeffrey; Master, Hiral; Hughes, Andrew; Roden, Dan; Harris, Paul; Ruderfer, Douglas; Brittain, Evan	2023	Research Square	Case Study	Device unspecified	Adults	Steps Heart Rate
Heart rate responses in critical care trainees during airway intubation: a comparison between the simulated and clinical environments details	Ji, Jackson; Langley, Bridget; Zordan, Rachel; van Dijk, Julian; Thies, Heidi Helene Graham; Brahmbhatt, Anjalee; Torcasio, Clarissa; Cunningham, Neil	2023	BMC Emergency Medicine	Case Study	Charge 2	Adults	Heart Rate	URL
Validation of the relationship between rapid eye movement sleep and sleep-related erections in healthy adults by a feasible instrument Fitbit Charge2 details	Liu, Guodong; Zhang, Yuyang; Zhang, Wei; Wu, Xu; Jiang, Hui; Zhang, Xiansheng	2023	Andrology	Case Study	Charge 2	Adults	Sleep	URL
Social epidemiology of Fitbit daily steps in early adolescence details	Nagata, Jason M.; Alsamman, Sana; Smith, Natalia; Yu, Jiayue; Ganson, Kyle T.; Dooley, Erin E.; Wing, David; Baker, Fiona C.; Pettee Gabriel, Kelley	2023	Pediatric Research	Case Study	Charge 2	Adolescents	Steps
A Longitudinal Pilot Study of Stress and Sleep in First-Year Osteopathic Medical Students details	Pattanaik, Sambit; Fastring, Danielle; Bateman, Robert C.	2023	Journal of Medical Education and Curricular Development	Case Study	Charge 3	Adults	Sleep
Pain self-management plus activity tracking and nurse-led support in adults with chronic low back pain: feasibility and acceptability of the problem-solving pain to enhance living well (PROPEL) intervention details	Xu, Wanli; Zhang, Yiming; Wang, Zequan; Dorsey, Susan G.; Starkweather, Angela; Kim, Kyounghae	2023	BMC nursing	Usability Intervention	Flex 2	Adults	Intensity User Friendliness
A Series of Remote Melatonin Supplement Interventions for Poor Sleep: Protocol for a Feasibility Pilot Study for a Series of Personalized (N-of-1) Trials details	Butler, Mark; D’Angelo, Stefani; Perrin, Alexandra; Rodillas, Jordyn; Miller, Danielle; Arader, Lindsay; Chandereng, Thevaa; Cheung, Ying Kuen; Shechter, Ari; Davidson, Karina W.	2023	JMIR Research Protocols	Intervention	Charge 5	Adults	Sleep User Friendliness	URL
Ecological Momentary Assessments and Passive Sensing in the Prediction of Short-Term Suicidal Ideation in Young Adults details	Czyz, Ewa K.; King, Cheryl A.; Al-Dajani, Nadia; Zimmermann, Lauren; Hong, Victor; Nahum-Shani, Inbal	2023	JAMA network open	Case Study	Charge 3	Patients Adults	Steps Intensity Heart Rate
Integrating Genome-wide information and Wearable Device Data to Explore the Link of Anxiety and Antidepressants with Heart Rate Variability details	Friligkou, Eleni; Koller, Dora; Pathak, Gita A.; Miller, Edward J.; Lampert, Rachel; Stein, Murray B.; Polimanti, Renato	2023	medRxiv: The Preprint Server for Health Sciences	Case Study Measurement	Device unspecified	Adults	Heart Rate
Health Wearables, Gamification, and Healthful Activity details	Hydari, Muhammad Zia; Adjerid, Idris; Striegel, Aaron D.	2023	Management Science	Usability	Charge HR	Adults	Steps Heart Rate
Online Circuit Training Increases Adherence to Physical Activity: A Randomized Controlled Trial of Men with Obesity details	Keshavarz, Mohammad; Sénéchal, Martin; Bouchard, Danielle R.	2023	Medicine and Science in Sports and Exercise	Intervention	Charge 3	Adults	Heart Rate
The Effects of Auricular Acupressure on Menopausal Symptoms, Stress, and Sleep in Postmenopausal Middle-Aged Women: A Randomized Single-Blind Sham-Controlled Trial details	Kim, Bomi; Park, Hyojung	2023	Journal of Midwifery \& Women's Health	Case Study	Charge 4	Adults	Sleep
Blood glucose variance measured by continuous glucose monitors across the menstrual cycle details	Lin, Georgianna; Siddiqui, Rumsha; Lin, Zixiong; Blodgett, Joanna M.; Patel, Shwetak N.; Truong, Khai N.; Mariakakis, Alex	2023	NPJ digital medicine	Case Study	Sense	Adults	Steps
Consumer Wearables Define a Normative Recovery Trajectory Following the Modified Nuss Procedure details	Carter, M.; Linton, S.; Zeineddin, S.; Jb, Pitt; Figueroa, A.; Perez, A.; Park, E.; DeTella, M.; Kwon, S.; Sd, Goldstein; Ghomrawi, H.; Abdullah, F.	2023	The Journal of surgical research	Measurement	Inspire HR Inspire 2	Patients Children	Steps Heart Rate	URL
Validity of activity wristbands for estimating daily physical activity in primary schoolchildren under free-living conditions: School-Fit study details	Mayorga-Vega, Daniel; Casado-Robles, Carolina; Guijarro-Romero, Santiago; Viciana, Jesús	2023	Frontiers in Public Health	Validation	Garmin Vivofit 2	Children	Steps Intensity
Feasibility of a wrist-worn wearable device for estimating mental health status in patients with mental illness details	Nakagome, Kazuyuki; Makinodan, Manabu; Uratani, Mitsuhiro; Kato, Masaki; Ozaki, Norio; Miyata, Seiko; Iwamoto, Kunihiro; Hashimoto, Naoki; Toyomaki, Atsuhito; Mishima, Kazuo; Ogasawara, Masaya; Takeshima, Masahiro; Minato, Kazumichi; Fukami, Toshikazu; Oba, Mari; Takeda, Kazuyoshi; Oi, Hideki	2023	Frontiers in Psychiatry	Usability	Sense	Patients	Heart Rate
'I saw it as a marriage-You can't have one without the other': A qualitative study of patient and physiotherapist experiences with a therapeutic combined strength and aerobic physical activity exercise programme for hip osteoarthritis details	Allison, Kim; Hall, Michelle; Dobson, Fiona; Lawford, Belinda J.; Bennell, Kim; Spiers, Libby; Hinman, Rana S.	2023	Musculoskeletal Care	Intervention	Device unspecified	Patients	Steps Intensity Heart Rate User Friendliness
A feasibility study to evaluate a purposeful walk intervention with a distance goal using a commercially available activity monitor in elderly people post total hip replacement surgery details	Bahadori, Shayan; Williams, Jonathan Mark; Collard, Sarah; Swain, Ian	2023	Journal of Rehabilitation and Assistive Technologies Engineering	Intervention	Charge 4	Patients	Distance User Friendliness
Applying machine learning to consumer wearable data for the early detection of complications after pediatric appendectomy details	Ghomrawi, Hassan M. K.; O'Brien, Megan K.; Carter, Michela; Macaluso, Rebecca; Khazanchi, Rushmin; Fanton, Michael; DeBoer, Christopher; Linton, Samuel C.; Zeineddin, Suhail; Pitt, J. Benjamin; Bouchard, Megan; Figueroa, Angie; Kwon, Soyang; Holl, Jane L.; Jayaraman, Arun; Abdullah, Fizan	2023	NPJ digital medicine	Usability	Inspire HR Inspire 2	Patients Children	Steps Sleep Heart Rate
Mighty Men: A Pilot Test of the Feasibility and Acceptability of a Faith-Based, Individually Tailored, Cluster-Randomized Weight Loss Trial for Middle-Aged and Older African American Men details	Griffith, Derek M.; Pennings, Jacquelyn S.; Jaeger, Emily C.	2023	American Journal of Men's Health	Usability Intervention	Charge 2	Adults	User Friendliness
Feasibility and preliminary efficacy of a novel web-based physical activity intervention in adults with overweight/obesity: A pilot randomized controlled trial details	Kariuki, Jacob K.; Sereika, Susan; Erickson, Kirk; Burke, Lora E.; Kriska, Andrea; Cheng, Jessica; Milton, Heather; Hirshfield, Sabina; Ogutu, David; Gibbs, Bethany	2023	Contemporary Clinical Trials	Intervention	Charge 2	Adults	Steps
Clinical and Lifestyle Determinants of Continuous Glucose Monitoring Metrics in Insulin-Treated Patients with Type 2 Diabetes Mellitus details	Lee, Da Young; Kim, Namho; Jung, Inha; Park, So Young; Yu, Ji Hee; Seo, Ji A.; Kim, Jihee; Kim, Kyeong Jin; Kim, Nam Hoon; Yoo, Hye Jin; Kim, Sin Gon; Choi, Kyung Mook; Baik, Sei Hyun; Park, Sung-Min; Kim, Nan Hee	2023	Diabetes \& Metabolism Journal	Case Study	Inspire 2	Patients	Steps Energy Expenditure Sleep Heart Rate
Moderating role of physical activity on hippocampal iron deposition and memory outcomes in typically aging older adults details	Lee, Shannon Y.; Paolillo, Emily W.; Saloner, Rowan; Cobigo, Yann; Diaz, Valentina E.; Gontrum, Eva Q.; VandeBunte, Anna; Chatterjee, Ankita; Tucker, Miwa; Kramer, Joel H.; Casaletto, Kaitlin B.	2023	Neurobiology of Aging	Case Study	Flex 2	Older Adults	Steps
Knee Extension Exercise Effects on Physiological and Psychophysical Performance: Normal Weight Versus Obese Office Workers details	Mohammadian, Mostafa; Choobineh, Alireza; Razeghi, Mohsen; Daneshmandi, Hadi; Ghaem, Haleh; Kazemi, Reza; Jahani, Yunes; Hashemi Nejad, Naser	2023	Workplace Health \& Safety	Case Study	Charge HR	Adults	Energy Expenditure Heart Rate
Start-of-day oculomotor screening demonstrates the effects of fatigue and rest during a total immersion training program details	Roberts, Scott; Kufahl, Peter R.; Ryznar, Rebecca J.; Norris, Taylor; Patel, Sagar; Gubler, K. Dean; Paz, Dean; Schwimer, Greg; Besserman, Richard; LaPorta, Anthony J.	2023	Surgery	Case Study	Device unspecified	Adults	Sleep
Association between the use of weight management strategies and weight change among Australian adults over 12 months: an observational study details	Singh, Ben; Olds, Timothy; Curtis, Rachel; Ferguson, Ty; Matricciani, Lisa; Brown, Wendy J.; Dumuid, Dorothea; Esterman, Adrian; Maher, Carol	2023	BMC public health	Case Study	Charge 3 Aria 2	Adults	Weight
Measurement of Physical Activity by Actigraphy in Infants and Young Children with Pulmonary Arterial Hypertension details	Avitabile, Catherine M.; Yung, Delphine; Handler, Stephanie; Hopper, Rachel K.; Fineman, Jeff; Freire, Grace; Varghese, Nidhy; Mullen, Mary P.; Krishnan, Usha S.; Austin, Eric; Silveira, Lori; Ivy, D. Dunbar	2023	The Journal of Pediatrics	Usability	Inspire	Children	Steps Intensity Heart Rate
Evaluation of the effectiveness of sleep hygiene education and FITBIT devices on quality of sleep and psychological worry: a pilot quasi-experimental study among first-year college students details	Bani Issa, Wegdan; Hijazi, Heba; Radwan, Hadia; Saqan, Roba; Al-Sharman, Alham; Samsudin, A. B. Rani; Fakhry, Randa; Al-Yateem, Nabeel; Rossiter, Rachel C.; Ibrahim, Ali; Moustafa, Ibrahim; Naja, Farah; Alameddine, Mohamad; Abbas, Nada; Abdelrahim, Dana N.; Al-Shujairi, Arwa; Awad, Manal	2023	Frontiers in Public Health	Case Study	Flex	Adults	Sleep
Examining changes in vascular function, arterial stiffness and systemic inflammation during hospitalization and recovery from an acute exacerbation of chronic obstructive pulmonary disease details	Fuhr, Desi P.; Brotto, Andrew R.; Rowe, Brian H.; Bhutani, Mohit; Rosychuk, Rhonda J.; Stickland, Michael K.	2023	Scientific Reports	Case Study	Device unspecified	Patients	Steps
The impact of mindfulness training on posttraumatic stress disorder symptoms, subjective sleep quality, and objective sleep outcomes in police officers details	Imhoff-Smith, Theodore P.; Grupe, Daniel W.	2023	Psychological Trauma: Theory, Research, Practice and Policy	Case Study	Charge 2	Adults	Sleep
The Future of Stress Management: Integration of Smartwatches and HRV Technology details	Jerath, Ravinder; Syam, Mohammad; Ahmed, Shajia	2023	Sensors (Basel, Switzerland)	Review	Versa 3 Sense	NA	Heart Rate
Identifying Unique Subgroups of Individuals With Stroke Using Heart Rate and Steps to Characterize Physical Activity details	Koffman, Lily J.; Crainiceanu, Ciprian M.; Roemmich, Ryan T.; French, Margaret A.	2023	Journal of the American Heart Association	Intervention	Inspire 2	Adults	Steps Heart Rate
Promoting participation in physical activity through Snacktivity: A qualitative mixed methods study details	Krouwel, Matthew; Greenfield, Sheila M.; Chalkley, Anna; Sanders, James P.; Parretti, Helen M.; Gokal, Kajal; Jolly, Kate; Skrybant, Magdalena; Biddle, Stuart J. H.; Greaves, Colin; Maddison, Ralph; Mutrie, Nanette; Ives, Natalie; Esliger, Dale W.; Sherar, Lauren; Edwardson, Charlotte L.; Yates, Tom; Frew, Emma; Tearne, Sarah; Daley, Amanda J.	2023	PloS One	Intervention	Versa 2	Adults	Steps User Friendliness
Using mobile health to encourage physical activity in individuals with intellectual disability: a pilot mixed methods feasibility study details	Michalsen, Henriette; Henriksen, André; Pettersen, Gunn; Hartvigsen, Gunnar; Wangberg, Silje; Thrane, Gyrd; Jahnsen, Reidun; Anke, Audny	2023	Frontiers in Rehabilitation Sciences	Intervention	Versa	Adults	Steps
Wellness in Nursing Education to Promote Resilience and Reduce Burnout: Protocol for a Holistic Multidimensional Wellness Intervention and Longitudinal Research Study Design in Nursing Education details	Strout, Kelley; Schwartz-Mette, Rebecca; McNamara, Jade; Parsons, Kayla; Walsh, Dyan; Bonnet, Jen; O'Brien, Liam M.; Robinson, Kathryn; Sibley, Sean; Smith, Annie; Sapp, Maile; Sprague, Lydia; Sabegh, Nima Sajedi; Robinson, Kaitlin; Henderson, Amanda	2023	JMIR research protocols	Intervention	Versa 2	Adults	Steps Sleep
Minute level smartphone derived exposure to greenness and consumer wearable derived physical activity in a cohort of US women details	Wilt, Grete E.; Roscoe, Charlotte J.; Hu, Cindy R.; Mehta, Unnati V.; Coull, Brent A.; Hart, Jaime E.; Gortmaker, Steven; Laden, Francine; James, Peter	2023	Environmental Research	Case Study	Charge HR Charge 2 Charge 3	Adults	Steps
Measurement properties of device-based physical activity instruments in ambulatory adults with physical disabilities and/or chronic diseases: a scoping review details	Brandenbarg, Pim; Hoekstra, Femke; Barakou, Ioulia; Seves, Bregje L.; Hettinga, Florentina J.; Hoekstra, Trynke; van der Woude, Lucas H. V.; Dekker, Rienk; Krops, Leonie A.	2023	BMC sports science, medicine \& rehabilitation	Usability Systematic Review	Classic Ultra One Zip Flex Surge Flex 2 Charge 2 Alta Charge 3 Garmin Vivofit 4 Garmin Vivofit 3 Garmin Vivosmart 4	NA	Steps Energy Expenditure Intensity
Resident Sleep During Traditional Home Call Compared to Night Float details	Chowdhary, Apoorva; Davis, John A.; Ding, Leona; Taravati, Parisa; Feng, Shu	2023	Journal of Academic Ophthalmology (2017)	Case Study	Alta HR	Adults	Sleep
GamerFit-ASD beta test: adapting an evidence-based exergaming and telehealth coaching intervention for autistic youth details	Hatfield, Daniel P.; Must, Aviva; Kennedy, Winston; Staiano, Amanda E.; Slavet, James; Sabelli, Rachael A.; Curtin, Carol; Bandini, Linda G.; Nauta, Phillip; Stuetzle, Christopher; Bowling, April B.	2023	Frontiers in Pediatrics	Intervention	Flex 2	Children	Steps User Friendliness
Unpacking the Drivers of Dissatisfaction and Satisfaction in a Fitness Mobile Application details	Kim, Minseong; Lee, Sae-Mi	2023	Behavioral Sciences (Basel, Switzerland)
Advancing Understanding of Just-in-Time States for Supporting Physical Activity (Project JustWalk JITAI): Protocol for a System ID Study of Just-in-Time Adaptive Interventions details	Park, Junghwan; Kim, Meelim; El Mistiri, Mohamed; Kha, Rachael; Banerjee, Sarasij; Gotzian, Lisa; Chevance, Guillaume; Rivera, Daniel E.; Klasnja, Predrag; Hekler, Eric	2023	JMIR research protocols	Intervention	Versa 3	Adults	Steps Intensity
Moderate and increased physical activity is not detrimental to live birth rates among women with unexplained infertility and obesity details	Vitek, Wendy S.; Sun, Fangbai; Cardozo, Eden; Hoeger, Kathleen M.; Hansen, Karl R.; Santoro, Nanette; Zhang, Heping; Legro, Richard S.	2023	F\&S Reports	Intervention	Device unspecified	Adults	Steps
Feasibility and Preliminary Effectiveness of the INSPIRE Pilot Study for Adolescent Girls details	Voskuil, Vicki R.; Emmanuel, Johanna; Magnuson, Claire; Guidone, Marisa	2023	Journal of Pediatric Health Care: Official Publication of National Association of Pediatric Nurse Associates \& Practitioners	Intervention	Inspire	Adolescents	Heart Rate
Addressing the Chronic Pain-Early Cognitive Decline Comorbidity Among Older Adults: Protocol for the Active Brains Remote Efficacy Trial details	Vranceanu, Ana-Maria; Choukas, Nathaniel R.; Rochon, Elizabeth A.; Duarte, Brooke; Pietrzykowski, Malvina O.; McDermott, Katherine; Hooker, Julia E.; Kulich, Ronald; Quiroz, Yakeel T.; Parker, Robert A.; Macklin, Eric A.; Ritchie, Christine; Active Brains Project; Mace, Ryan A.	2023	JMIR research protocols	Intervention	Inspire	Older Adults	Steps User Friendliness
Accuracy in detecting atrial fibrillation in single-lead ECGs: an online survey comparing the influence of clinical expertise and smart devices details	Weidlich, Simon; Mannhart, Diego; Serban, Teodor; Krisai, Philipp; Knecht, Sven; Du Fay de Lavallaz, Jeanne; Müller, Tatjana; Schaer, Beat; Osswald, Stefan; Kühne, Michael; Sticherling, Christian; Badertscher, Patrick	2023	Swiss Medical Weekly	Case Study	Sense	Adults	Heart Rate
Minimally invasive versus open pancreatoduodenectomy for pancreatic and peri-ampullary neoplasm (DIPLOMA-2): study protocol for an international multicenter patient-blinded randomized controlled trial details	de Graaf, Nine; Emmen, Anouk M. L. H.; Ramera, Marco; Björnsson, Bergthor; Boggi, Ugo; Bruna, Caro L.; Busch, Olivier R.; Daams, Freek; Ferrari, Giovanni; Festen, Sebastiaan; van Hilst, Jony; D'Hondt, Mathieu; Ielpo, Benedetto; Keck, Tobias; Khatkov, Igor E.; Koerkamp, Bas Groot; Lips, Daan J.; Luyer, Misha D. P.; Mieog, J. Sven D.; Morelli, Luca; Molenaar, I. Quintus; van Santvoort, Hjalmar C.; Sprangers, Mirjam A. G.; Ferrari, Clarissa; Berkhof, Johannes; Maisonneuve, Patrick; Abu Hilal, Mohammad; Besselink, Marc G.; European Consortium on Minimally Invasive Pancreatic Surgery (E-MIPS)	2023	Trials	Case Study	Inspire 2	Patients	Steps Heart Rate
Protocol for randomized personalized trial for stress management compared to standard of care details	Goodwin, Ashley M.; Miller, Danielle; D'Angelo, Stefani; Perrin, Alexandra; Wiener, Ruby; Greene, Brittney; Romain, Anne-Marie N.; Arader, Lindsay; Chandereng, Thevaa; Kuen Cheung, Ying; Davidson, Karina W.; Butler, Mark	2023	Frontiers in Psychology	Intervention	Charge 4 Versa 3 Sense Inspire 2 Charge 5	Adults	Steps Sleep
The Hospital to Home study (H2H): smartwatch technology-enabled rehabilitation following hip fracture in older adults, a feasibility non-randomised trial details	Hewage, Kalon; Fosker, Samuel; Leckie, Todd; Venn, Richard; Gonçalves, Anna-Carolina; Koulouglioti, Christina; Hodgson, Luke E.	2023	Future Healthcare Journal	Usability	Charge 4	Older Adults	Steps User Friendliness
Assessment of glycemic variability and lifestyle behaviors in healthy nondiabetic individuals according to the categories of body mass index details	Kashiwagi, Kazuhiro; Inaishi, Jun; Kinoshita, Shotaro; Wada, Yasuyo; Hanashiro, Sayaka; Shiga, Kiko; Kitazawa, Momoko; Tsutsumi, Shiori; Yamakawa, Hiroyuki; Irie, Junichiro; Kishimoto, Taishiro	2023	PloS One	Case Study	Inspire 2	Adults	Steps
Association Between Health Information‒Seeking Behavior on YouTube and Physical Activity Among U.S. Adults: Results From Health Information Trends Survey 2020 details	Lee, Juhan; Turner, Kea; Xie, Zhigang; Kadhim, Bashar; Hong, Young-Rock	2022	AJPM focus	Case Study Survey Research	Device unspecified	Adults	NA
Testing the Effects of App-Based Motivational Messages on Physical Activity and Resting Heart Rate Through Smartphone App Compliance in Patients With Vulnerable Coronary Artery Plaques: Protocol for a Microrandomized Trial details	Mitra, Sayan; Kroeger, Cynthia M.; Xu, Jing; Avery, Leah; Masedunskas, Andrius; Cassidy, Sophie; Wang, Tian; Hunyor, Imre; Wilcox, Ian; Huang, Robin; Chakraborty, Bibhas; Fontana, Luigi	2023	JMIR Research Protocols	Intervention	Inspire 2	Adults	Steps Intensity Heart Rate	URL
Baseline Nutritional Status and In-Hospital Step Count are Associated with Muscle Quantity, Quality, and Function: Results of an Exploratory Study details	Welch, Carly; Greig, Carolyn; Lewis, Danielle; Majid, Zeinab; Masud, Tahir; Moorey, Hannah; Pinkney, Thomas; Stanley, Benjamin; Jackson, Thomas	2023	Journal of Nutrition in Gerontology and Geriatrics	Case Study Methods	Inspire	Older Adults	Steps
Effects of sleep-corrected social jetlag on measures of mental health, cognitive ability, and brain functional connectivity in early adolescence details	Yang, Fan Nils; Picchioni, Dante; Duyn, Jeff H.	2023	Sleep	Case Study	Charge HR	Adolescents	Sleep
Latent Profiles of Sleep Patterns in Early Adolescence: Associations With Behavioral Health Risk details	Zhang, Linhao; Sasser, Jeri; Doane, Leah D.; Peltz, Jack; Oshri, Assaf	2023	The Journal of Adolescent Health: Official Publication of the Society for Adolescent Medicine	Case Study	Charge HR	Adolescents	Sleep
Associations between adverse childhood experiences and early adolescent physical activity in the United States details	Al-Shoaibi, Abubakr A. A.; Iyra, Puja; Raney, Julia H.; Ganson, Kyle T.; Dooley, Erin E.; Testa, Alexander; Jackson, Dylan B.; Gabriel, Kelley Pettee; Baker, Fiona C.; Nagata, Jason M.	2023	Academic Pediatrics	Case Study	Charge HR	Adolescents	Steps
Physical and Mental Health in Adolescence: Novel Insights from a transdiagnostic examination of FitBit data in the ABCD Study details	Damme, Katherine; Vargas, Teresa; Walther, Sebastian; Shankman, Stewart; Mittal, Vijay	2023	Research Square	Case Study	Charge HR	Adolescents	Intensity Heart Rate
A Digital Coach (E-Supporter 1.0) to Support Physical Activity and a Healthy Diet in People With Type 2 Diabetes: Acceptability and Limited Efficacy Testing details	Hietbrink, Eclaire A. G.; Oude Nijeweme-d'Hollosy, Wendy; Middelweerd, Anouk; Konijnendijk, Annemieke A. J.; Schrijver, Laura K.; Ten Voorde, Anouk S.; Fokkema, Elise M. S.; Laverman, Gozewijn D.; Vollenbroek-Hutten, Miriam M. R.	2023	JMIR formative research	Intervention	Charge 2	Adults	Steps
Effects of Sleep on Adolescents' Appetite, Dietary Intake, and Weight details	Juelich, Janell; Owens, Rhoda; Denny, Dawn; Raatz, Susan; Lindseth, Glenda	2023	SAGE open nursing	Case Study	Charge 3	Adolescents	Sleep
Comparing sleep measures in cancer survivors: Self-reported sleep diary versus objective wearable sleep tracker details	Li, Xiaotong; Mao, Jun J.; Garland, Sheila N.; Root, James; Li, Susan Q.; Ahles, Tim; Liou, Kevin T.	2023	Research Square	Case Study	Charge 4	Adults	Steps
Efficacy of a Commercial Physical Activity Monitor in Longitudinal Tracking of Patients With Pulmonary Hypertension: A Pilot Study details	Rosenzweig, Eliana; Villeda, Gerson Antonio Valencia; Crook, Sarah; Koli, Fatima; Rosenzweig, Erika B.; Krishnan, Usha S.	2023	Texas Heart Institute Journal	Validation	Charge HR	Patients Adults	Steps
Postponing sleep after a stressful day: Patterns of stress, bedtime procrastination, and sleep outcomes in a daily diary approach details	Schmidt, Laura I.; Baetzner, Anke S.; Dreisbusch, Marina I.; Mertens, Alica; Sieverding, Monika	2023	Stress and Health: Journal of the International Society for the Investigation of Stress	Case Study	Charge HR	Adults	Sleep
Chronotype-Dependent Sleep Loss Is Associated with a Lower Amplitude in Circadian Rhythm and a Higher Fragmentation of REM Sleep in Young Healthy Adults details	von Gall, Charlotte; Holub, Leon; Pfeffer, Martina; Eickhoff, Simon	2023	Brain Sciences	Case Study	Inspire	Adults	Sleep
The effect of sleep-corrected social jetlag on crystalized intelligence, school performance, and functional connectome in early adolescence details	Yang, Fan Nils; Picchioni, Dante; Duyn, Jeff H.	2023	medRxiv: The Preprint Server for Health Sciences	Case Study	Charge HR	Adolescents	Sleep
Effect of exercise intervention using mobile healthcare on blood lipid level and health-related physical fitness in obese women: a randomized controlled trial details	Choi, Jae-Ho; Park, Hun-Young; Sun, Yerin; Seo, Jisoo; Lee, Eunjoo; Kim, Sung-Woo; Kim, Jisu; Lim, Kiwon	2023	Physical Activity and Nutrition	Intervention	Charge 4	Adults	NA
Reduced Physical Activity During COVID-19 in Children With Congenital Heart Disease: A Longitudinal Analysis details	Kuan, Mimi T. Y.; Hemphill, Nicole M.; Harris, Kevin C.	2022	CJC pediatric and congenital heart disease	Case Study	Charge 2	Children Adolescents	Steps
The Importance of Data Quality Control in Using Fitbit Device Data From the Research Program details	Lederer, Lauren; Breton, Amanda; Jeong, Hayoung; Master, Hiral; Roghanizad, Ali R.; Dunn, Jessilyn	2023	JMIR mHealth and uHealth	Review	Charge 4 Versa 3	NA	Steps Sleep Heart Rate
Accuracy of 11 Wearable, Nearable, and Airable Consumer Sleep Trackers: Prospective Multicenter Validation Study details	Lee, Taeyoung; Cho, Younghoon; Cha, Kwang Su; Jung, Jinhwan; Cho, Jungim; Kim, Hyunggug; Kim, Daewoo; Hong, Joonki; Lee, Dongheon; Keum, Moonsik; Kushida, Clete A.; Yoon, In-Young; Kim, Jeong-Whun	2023	JMIR mHealth and uHealth	Validation Systematic Review	Sense 2	Adults	Sleep
Harnessing technology and gamification to increase adult physical activity: a cluster randomized controlled trial of the Columbia Moves pilot details	Monroe, Courtney M.; Cai, Bo; Edney, Sarah; Jake-Schoffman, Danielle E.; Brazendale, Keith; Bucko, Agnes; Armstrong, Bridget; Yang, Chih-Hsiang; Turner-McGrievy, Gabrielle	2023	The International Journal of Behavioral Nutrition and Physical Activity	Intervention	Alta HR	Adults	Steps Intensity
Functional brain connectivity predicts sleep duration in youth and adults details	Mummaneni, Anurima; Kardan, Omid; Stier, Andrew J.; Chamberlain, Taylor A.; Chao, Alfred F.; Berman, Marc G.; Rosenberg, Monica D.	2023	Human Brain Mapping	Case Study	Device unspecified	Children	Sleep
Design of intelligent monitoring of loneliness in the elderly using a serverless architecture with real-time communication API details	Osa-Sanchez, Ainhoa; Jossa-Bastidas, Oscar; Mendez-Zorrilla, Amaia; Oleagordia-Ruiz, Ibon; Garcia-Zapirain, Begonya	2023	Technology and Health Care: Official Journal of the European Society for Engineering and Medicine	Intervention Methods	Charge 5	Adults	Steps Energy Expenditure Distance Sleep Heart Rate
Ambulation for Latency during Expectant Management of PPROM: A Randomized Controlled Trial (AMBLE) details	Pineles, Beth L.; Vial, Mallory; Castro, Ms Tania; Ghorayeb, Tala; Ajishegiri, Ms Oluwadare; Sadek, Sandra; Pedroza, Claudia; Blackwell, Sean C.; Chauhan, Suneet P.; Sibai, Baha M.	2023	American journal of obstetrics \& gynecology MFM	Case Study	Inspire	Adults	Steps
How shoulder immobilization after surgery influences daily activity - a prospective pedometer-based study details	Rickert, Carolin; Ahlich, Stefanie; Gosheger, Georg; Kalisch, Tobias; Liem, Dennis; Schneider, Kristian Nikolaus; Klingebiel, Sebastian	2023	International Orthopaedics	Case Study	Charge	Patients	Steps
Open-Source, Step-Counting Algorithm for Smartphone Data Collected in Clinical and Nonclinical Settings: Algorithm Development and Validation Study details	Straczkiewicz, Marcin; Keating, Nancy L.; Thompson, Embree; Matulonis, Ursula A.; Campos, Susana M.; Wright, Alexi A.; Onnela, Jukka-Pekka	2023	JMIR cancer	Validation	Charge 2	NA	Steps
Exploring mechanisms of improvement in the Active Brains intervention for older adults with chronic pain and early cognitive decline details	Choukas, Nathaniel R.; Mace, Ryan A.; Rochon, Elizabeth A.; Brewer, Julie R.; Vranceanu, Ana-Maria	2023	Archives of Gerontology and Geriatrics	Intervention	Alta HR	Older Adults	Steps
A wearable device perspective on the standard definitions of disability progression in multiple sclerosis details	Dalla Costa, Gloria; Nos, Carlos; Zabalza, Ana; Buron, Mathias; Magyari, Melinda; Sellebjerg, Finn; Guerrero, Ana Isabel; Roselli, Lucia; La Porta, Maria Libera; Martinis, Matteo; Bailon, Raquel; Kontaxis, Spyridon; Laporta, Estela; Garcia, Esther; Pokorny, Florian B.; Schuller, Björn W.; Folarin, Amos; Stewart, Callum; Leocani, Letizia; Vairavan, Srinivasan; Cummins, Nicholas; Dobson, Richard; Hotopf, Matthew; Narayan, Vaibhav; Montalban, Xavier; Sorensen, Per Soelberg; Comi, Giancarlo	2023	Multiple Sclerosis (Houndmills, Basingstoke, England)	Case Study	Charge 3	Patients Adults	Steps
The Role of Daily Steps in the Treatment of Major Depressive Disorder: Secondary Analysis of a Randomized Controlled Trial of a 6-Month Internet-Based, Mindfulness-Based Cognitive Behavioral Therapy Intervention for Youth details	Dang, Kevin; Ritvo, Paul; Katz, Joel; Gratzer, David; Knyahnytska, Yuliya; Ortiz, Abigail; Walters, Clarice; Attia, Mohamed; Gonzalez-Torres, Christina; Lustig, Andrew; Daskalakis, Zafiris	2023	Interactive Journal of Medical Research	Intervention Case Study	Charge HR 2	Patients Adults	Steps
Comparison of Polysomnography, Single-Channel Electroencephalogram, Fitbit, and Sleep Logs in Patients With Psychiatric Disorders: Cross-Sectional Study details	Kawai, Keita; Iwamoto, Kunihiro; Miyata, Seiko; Okada, Ippei; Fujishiro, Hiroshige; Noda, Akiko; Nakagome, Kazuyuki; Ozaki, Norio; Ikeda, Masashi	2023	Journal of Medical Internet Research	Validation	Sense	Adults	Sleep
Can we let our patients sleep in the hospital? A randomized controlled trial of a pragmatic sleep protocol in surgical oncology patients details	Kelly, Kathrine; Kolbeinsson, Hordur; Blanck, Lauren M.; Khan, Mariam; Kyriakakis, Roxanne; Assifi, M. Mura; Wright, G. Paul; Chung, Mathew	2023	Journal of Surgical Oncology	Clinical Care	Charge 4	Patients	Steps Sleep
Mothers' experiences of a lifestyle intervention for weight reduction 12 months after gestational diabetes mellitus: Qualitative findings from the PAIGE2 study details	Kemp, Bridie J.; Creighton, Rachel M.; Cupples, Gina; Kelly, Bronagh; McAuley, Emma; Fleck, Olwen; Wallace, Helen; Graham, Una; McCance, David R.	2023	Midwifery	Intervention	Device unspecified	Pregnant Women	Steps User Friendliness
Effect of digital monitoring and counselling on self-management ability in patients with rheumatoid arthritis: a randomised controlled trial details	Li, Linda C.; Xie, Hui; Feehan, Lynne M.; Shaw, Chris; Lu, Na; Ramachandran, Smruthi; Wang, Ellen; Therrien, Stephanie; Mucha, Julia; Hoens, Alison M.; English, Kelly; Davidson, Eileen; Liu-Ambrose, Teresa; Backman, Catherine L.; Esdaile, John M.; Miller, Kimberly J.; Lacaille, Diane	2023	Rheumatology (Oxford, England)	Intervention	Inspire	Adults	Intensity Sleep
Association of self-directed walking with toxicity moderation during chemotherapy for the treatment of early breast cancer details	Nyrop, K. A.; Page, A.; Deal, A. M.; Wagoner, C.; Kelly, E. A.; Kimmick, Gretchen G.; Copeland, Anureet; Speca, JoEllen; Wood, William A.; Muss, H. B.	2023	Supportive Care in Cancer: Official Journal of the Multinational Association of Supportive Care in Cancer	Intervention	Device unspecified	Patients Adults	Steps
Selecting a sleep tracker from EEG-based, iteratively improved, low-cost multisensor, and actigraphy-only devices details	Ong, Ju Lynn; Golkashani, Hosein Aghayan; Ghorbani, Shohreh; Wong, Kian F.; Chee, Nicholas I. Y. N.; Willoughby, Adrian R.; Chee, Michael W. L.	2023	Sleep Health	Validation Systematic Review	Sense	Adults	Sleep
Using Consumer Wearable Devices to Profile Postoperative Complications After Pediatric Appendectomy details	Pitt, J. Benjamin; Zeineddin, Suhail; Carter, Michela; Figueroa, Angie; Park, Erica; Kwon, Soyang; Ghomrawi, Hassan; Abdullah, Fizan	2023	The Journal of Surgical Research	Case Study	Inspire HR Inspire 2	Children	Steps
A single-armed proof-of-concept study of Lymfit: A personalized, virtual exercise intervention to improve health outcomes in lymphoma survivors in the pandemic details	Angelillo, Christopher; Tock, Wing Lam; Salaciak, Matthew; Reid, Ryan E. R.; Andersen, Ross E.; Maheu, Christine; Johnson, Nathalie A.	2024	PloS One	Intervention	Device unspecified	Adults	Steps Intensity Sleep
Museum-based art activities to stay young at heart? Results of a randomized controlled trial details	Cami, Margot; Planta, Océane; Matskiv, Jacqueline; Plonka, Alexandra; Gros, Auriane; Beauchet, Olivier	2023	Frontiers in Medicine	Intervention	Alta HR	Older Adults	Heart Rate
Step cadence as a novel objective postoperative recovery metric in children who undergo laparoscopic appendectomy details	Carter, Michela; Zeineddin, Suhail; Bai, Iris; Pitt, J. Benjamin; Hua, Rui; Kwon, Soyang; Ghomrawi, Hassan M. K.; Abdullah, Fizan	2024	Surgery	Case Study	Inspire HR Inspire 2	Patients Children	Steps
Feasibility Test of Personalized (N-of-1) Trials for Increasing Middle-Aged and Older Adults' Physical Activity details	Friel, Ciarán P.; Goodwin, Ashley M.; Robles, Patrick L.; Butler, Mark J.; Pahlevan-Ibrekic, Challace; Duer-Hefele, Joan; Vicari, Frank; Gordon, Samantha; Chandereng, Thevaa; Cheung, Ying Kuen Ken; Davidson, Karina W.	2023	Research Square	Intervention	Charge 4	Older Adults Adults	Steps
Promoting positive physical activity behaviours in children undergoing acute cancer treatment: feasibility of the CanMOVE intervention details	Grimshaw, Sarah L.; Taylor, Nicholas F.; Conyers, Rachel; Shields, Nora	2023	Brazilian Journal of Physical Therapy	Intervention	Inspire	Children Adolescents	Steps User Friendliness
Examining a Remote Group-Based Type 2 Diabetes Self-Management Education Program in the COVID-19 Era Using the ORBIT Model: Small 6-Week Feasibility Study details	Hiemstra, Madison S.; Reichert, Sonja M.; Mitchell, Marc S.	2024	JMIR formative research	Intervention	Inspire 2	Adults	Steps Goals
Improving multimodal physical function in adults with heterogeneous chronic pain; Protocol for a multisite feasibility RCT details	Hooker, Julia E.; Brewer, Julie R.; McDermott, Katherine; Kanaya, Millan; Somers, Tamara J.; Keefe, Francis; Kelleher, Sarah; Fisher, Hannah M.; Burns, John; Wilson, Rebecca; Kulich, Ronald; Polykoff, Gary; Parker, Robert A.; Greenberg, Jonathan; Vranceanu, Ana-Maria; THRIVE Study TeamJennifer Plumb-Vilardaga, PhD. Shannon Miller, Allison Diachina, Sara Hogan, Latrice Yates	2024	Contemporary Clinical Trials	Intervention	Inspire 3	Adults	Steps Goals
Evaluating Accuracy in Five Commercial Sleep-Tracking Devices Compared to Research-Grade Actigraphy and Polysomnography details	Kainec, Kyle A.; Caccavaro, Jamie; Barnes, Morgan; Hoff, Chloe; Berlin, Annika; Spencer, Rebecca M. C.	2024	Sensors (Basel, Switzerland)	Validation Systematic Review	Versa Inspire Garmin Vivosmart	Adults	Sleep
Family-Focused Facilitated Fitness: Feasibility and Acceptability of a Couple-Based Physical Activity Intervention for Hematopoietic Cell Transplant Recipients and their Caregiving Partners details	Langer, Shelby L.; Joseph, Rodney P.; Mistretta, Erin; Tao, Chun; Porter, Laura; Spain, Allison; Khera, Nandita	2024	Transplantation and Cellular Therapy	Intervention	Inspire 2	Adults	Steps
Menstruation-related symptoms are associated with physical activity and midpoint of sleep: a pilot study details	Masuda, Hazuki; Okada, Shima	2023	Frontiers in Global Women's Health	Intervention	Inspire 2	Adults	Energy Expenditure Sleep
Data-efficient machine learning methods in the ME-TIME study: Rationale and design of a longitudinal study to detect atrial fibrillation and heart failure from wearables details	Naseri, Arman; Tax, David; van der Harst, Pim; Reinders, Marcel; van der Bilt, Ivo	2023	Cardiovascular Digital Health Journal	Intervention	Inspire 2 Charge 5	Adults	Heart Rate
Validation of ActiGraph and Fitbit in the assessment of energy expenditure in Huntington's disease details	Simón-Vicente, Lucía; Rodríguez-Fernández, Alejandro; Rivadeneyra-Posadas, Jéssica; Soto-Célix, María; Raya-González, Javier; Castillo-Alvira, Daniel; Calvo, Sara; Mariscal, Natividad; García-Bustillo, Álvaro; Aguado, Laura; Cubo, Esther	2024	Gait \& Posture	Validation	Charge 4	Adults	Energy Expenditure
Engagement With a Remote Symptom-Tracking Platform Among Participants With Major Depressive Disorder: Randomized Controlled Trial details	White, Katie M.; Carr, Ewan; Leightley, Daniel; Matcham, Faith; Conde, Pauline; Ranjan, Yatharth; Simblett, Sara; Dawe-Lane, Erin; Williams, Laura; Henderson, Claire; Hotopf, Matthew	2024	JMIR mHealth and uHealth	Intervention	Charge	Adults	Steps Heart Rate
The experiences of people with chronic obstructive pulmonary disease (COPD) using activity monitors in everyday life: an interpretative phenomenological study details	Wilde, Laura J.; Percy, Carol; Ward, Gillian; Clark, Cain; Wark, Petra A.; Sewell, Louise	2024	Disability and Rehabilitation	Usability	Device unspecified	Adults	User Friendliness
The Use of Wearable Devices in Oncology Patients: A Systematic Review details	Chow, Ronald; Drkulec, Hannah; Im, James H. B.; Tsai, Jane; Nafees, Abdulwadud; Kumar, Swetlana; Hou, Tristan; Fazelzad, Rouhi; Leighl, Natasha B.; Krzyzanowska, Monika; Wong, Philip; Raman, Srinivas	2023	The Oncologist	Systematic Review	Device unspecified	Patients	Steps User Friendliness
Links Between Objectively-Measured Hourly Smartphone Use and Adolescent Wake Events Across Two Weeks details	Garrett, Shedrick L.; Burnell, Kaitlyn; Armstrong-Carter, Emma L.; Nelson, Benjamin W.; Prinstein, Mitchell J.; Telzer, Eva H.	2023	Journal of Clinical Child and Adolescent Psychology: The Official Journal for the Society of Clinical Child and Adolescent Psychology, American Psychological Association, Division 53	Case Study	Inspire 2	Adolescents	Sleep
Tailor Made: A Pilot Virtual Weight Loss Intervention Individually Tailored for African American Men details	Griffith, Derek M.; Jaeger, Emily C.; Pennings, Jacquelyn S.; Semlow, Andrea R.; Ellison, Jennifer M.; Alexander, Leah R.	2023	Health Promotion Practice	Intervention	Charge 2	Adults	Steps User Friendliness
Exploring Variations in Sleep Perception: Comparative Study of Chatbot Sleep Logs and Fitbit Sleep Data details	Jang, Hyunchul; Lee, Siwoo; Son, Yunhee; Seo, Sumin; Baek, Younghwa; Mun, Sujeong; Kim, Hoseok; Kim, Icktae; Kim, Junho	2023	JMIR mHealth and uHealth	Case Study	Inspire 2	Adults	Sleep
From intention to behavior: Within- and between-person moderators of the relationship between intention and physical activity details	Knapova, Lenka; Cho, Young Won; Chow, Sy-Miin; Kuhnova, Jitka; Elavsky, Steriani	2024	Psychology of Sport and Exercise	Case Study	Charge 3 Charge 4	Adults	Steps
Comparing self-reported energy intake using an online dietary tool with energy expenditure by an activity tracker details	Vasileiou, Aikaterina; Searle, Dominique; Larsen, Sofus C.; Magkos, Faidon; Horgan, Graham; Stubbs, R. James; Santos, Inês; Palmeira, António L.; Heitmann, Berit L.	2024	Nutrition (Burbank, Los Angeles County, Calif.)	Validation	Charge 2	Adults	Energy Expenditure Heart Rate
Patient Engagement and Provider Effectiveness of a Novel Sleep Telehealth Platform and Remote Monitoring Assessment in the US Military: Pilot Study Providing Evidence-Based Sleep Treatment Recommendations details	Wickwire, Emerson M.; Collen, Jacob; Capaldi, Vincent F.; Williams, Scott G.; Assefa, Samson Z.; Adornetti, Julianna P.; Huang, Kathleen; Venezia, Janet M.; Jones, Rachell L.; Johnston, Christine W.; Thomas, Connie; Thomas, Mary Ann; Mounts, Charles; Drake, Christopher L.; Businelle, Michael S.; Grandner, Michael A.; Manber, Rachel; Albrecht, Jennifer S.	2023	JMIR formative research	Medical	Inspire 2	Patients	Sleep
Using Wearable Devices to Profile Demographic-Specific Recovery After Pediatric Appendectomy details	Zeineddin, Suhail; Figueroa, Angie; Pitt, J. Benjamin; Carter, Michela; DeTella, Mia; Perez, Amparo; Park, Erica; Kwon, Soyang; Ghomrawi, Hassan; Abdullah, Fizan	2023	The Journal of Surgical Research	Case Study	Inspire HR Inspire 2	Patients Children	Steps
Association between sleep quality and nocturnal erection monitor by RigiScan in erectile dysfunction patients: a prospective study using fitbit charge 2 details	Zhang, Yuyang; Zhang, Wei; Feng, Xingliang; Liu, Guodong; Wu, Xu; Jiang, Hui; Zhang, Xiansheng	2023	Basic and Clinical Andrology	Case Study	Charge 2	Patients	Sleep
Physical activity and menopausal symptoms: evaluating the contribution of obesity, fitness, and ambient air pollution status details	Elavsky, Steriani; Burda, Michal; Cipryan, Lukáš; Kutáč, Petr; Bužga, Marek; Jandačková, Vera; Chow, Sy-Miin; Jandačka, Daniel	2024	Menopause (New York, N.Y.)	Case Study	Charge 3	Older Adults Adults	Intensity
The Annual Rhythms in Sleep, Sedentary Behavior, and Physical Activity of Australian Adults: A Prospective Cohort Study details	Ferguson, Ty; Curtis, Rachel; Fraysse, François; Olds, Timothy; Dumuid, Dorothea; Brown, Wendy; Esterman, Adrian; Maher, Carol	2024	Annals of Behavioral Medicine: A Publication of the Society of Behavioral Medicine	Case Study	Charge 3	Adults	Intensity Sleep
Psychological and behavioral responses to daily weight gain during behavioral weight loss treatment details	Hagerman, Charlotte J.; Onu, Michael C.; Crane, Nicole T.; Butryn, Meghan L.; Forman, Evan M.	2024	Journal of Behavioral Medicine	Case Study	Device unspecified	Adults	Steps Energy Expenditure
Comparison of daily step count between the Fitbit Inspire 3 and the activPAL 3 in adults with transtibial amputation details	Leister, Kyle R.; Burke, Sara E.; Kim, Joon Young; Duenas, Victor H.; Barreira, Tiago V.	2024	Frontiers in Rehabilitation Sciences	Validation	Inspire 3	Adults	Steps
A comparison of the rates of clock-based nocturnal hypoglycemia and hypoglycemia whilst asleep among people living with diabetes: findings from the Hypo-METRICS study details	Martine-Edith, Gilberte; Divilly, Patrick; Zaremba, Natalie; Søholm, Uffe; Broadley, Melanie; Baumann, Petra Martina; Mahmoudi, Zeinab; Gomes, Mikel; Ali, Namam; Abbink, Evertine J.; de Galan, Bastiaan E.; Brøsen, Julie Maria Bøggild; Pedersen-Bjergaard, Ulrik; Vaag, Allan A.; McCrimmon, Rory; Renard, Eric; Heller, Simon; Evans, Mark; Cigler, Monika; Mader, Julia K.; Speight, Jane; Pouwer, Frans; Amiel, Stephanie; Choudhary, Pratik	2024	Diabetes Technology \& Therapeutics	Case Study	Charge 4	Adults	Sleep
Effects of a mobile health intervention on health-related outcomes in Japanese office workers: a pilot study details	Meguro, Takumi; Takayama, Fuminori; Hammarlund, Hampus; Honjo, Masaru	2024	International Journal of Occupational Medicine and Environmental Health	Intervention		Adults	Steps Sleep
Reducing the burden of inconclusive smart device single-lead ECG tracings via a novel artificial intelligence algorithm details	Weidlich, Simon; Mannhart, Diego; Kennedy, Alan; Doggart, Peter; Serban, Teodor; Knecht, Sven; Du Fay de Lavallaz, Jeanne; Kühne, Michael; Sticherling, Christian; Badertscher, Patrick	2024	Cardiovascular Digital Health Journal	Case Study	Sense	Patients Adults	Heart Rate
Missing Data Statistics Provide Causal Insights into Data Loss in Diabetes Health Monitoring by Wearable Sensors details	Braem, Carlijn I. R.; Yavuz, Utku S.; Hermens, Hermie J.; Veltink, Peter H.	2024	Sensors (Basel, Switzerland)	Systematic Review	Charge HR Charge 2	Patients	Steps Heart Rate
Assessment of Fitbit Charge 4 for sleep stage and heart rate monitoring against polysomnography and during home monitoring in Huntington's disease details	Doheny, Emer P.; Renerts, Klavs; Braun, Andreas; Werth, Esther; Baumann, Christian; Baumgartner, Philipp; Morgan-Jones, Philippa; Busse, Monica; Lowery, Madeleine M.; Jung, Hans H.	2024	Journal of clinical sleep medicine: JCSM: official publication of the American Academy of Sleep Medicine	Validation	Charge 4	Adults	Sleep Heart Rate
Imagery rescripting and cognitive restructuring for inpatients with moderate and severe depression - a controlled pilot study details	Kanczok, Jabin; Jauch-Chara, Kamila; Müller, Franz-Josef	2024	BMC psychiatry	Intervention	Charge 3	Patients	Steps
Feasibility Study on Menstrual Cycles With Fitbit Device (FEMFIT): Prospective Observational Cohort Study details	Lang, Anna-Lena; Bruhn, Rosa-Lotta; Fehling, Maya; Heidenreich, Anouk; Reisdorf, Jonathan; Khanyaree, Ifrah; Henningsen, Maike; Remschmidt, Cornelius	2024	JMIR mHealth and uHealth	Case Study	Inspire 2	Adults	Steps Sleep Heart Rate
Optimization of mHealth behavioral interventions for patients with chronic lymphocytic leukemia: the HEALTH4CLL study details	Lee, Che Young; Gordon, Max J.; Markofski, Melissa M.; LaVoy, Emily C.; Peterson, Susan K.; Li, Liang; Fares, Sara; Baum, Miranda; Pace, Margaret; Walsh, Danielle; Ferrajoli, Alessandra; Basen-Engquist, Karen	2024	Journal of Cancer Survivorship: Research and Practice	Intervention	Device unspecified	Patients	Steps Energy Expenditure Goals Food Active Minutes/Active Zone Minutes
Criterion-Related Validity of Consumer-Wearable Activity Trackers for Estimating Steps in Primary Schoolchildren under Controlled Conditions: Fit-Person Study details	Mayorga-Vega, Daniel; Casado-Robles, Carolina; Guijarro-Romero, Santiago; Viciana, Jesús	2024	Journal of Sports Science \& Medicine	Validation	Ace 2	Children	Steps
Assessing the current utilization status of wearable devices in clinical research details	Miyakoshi, Takashi; Ito, Yoichi M.	2024	Clinical Trials (London, England)	Usability Review	Device unspecified	NA	NA
Benefits of physical activity self-monitoring in patients with haemophilia: a prospective study with one-year follow-up details	Pérez-Alenda, Sofía; Carrasco, Juan J.; Querol-Fuentes, Felipe; Megías-Vericat, Juan Eduardo; Querol-Giner, Felipe; Luz Sánchez-Sánchez, María; Bonanad, Santiago; Núñez-Cortés, Rodrigo	2024	Haemophilia: The Official Journal of the World Federation of Hemophilia	Case Study	Charge HR	Adults	Steps Intensity
The moderating effect of social support on the effectiveness of a web-based, computer-tailored physical activity intervention for older adults details	Alley, Stephanie J.; Schoeppe, Stephanie; Moore, Hayley; To, Quyen G.; van Uffelen, Jannique; Parker, Felix; Duncan, Mitch J.; Schneiders, Anthony; Vandelanotte, Corneel	2024	Journal of Health Psychology	Intervention	Device unspecified	Older Adults	Intensity
Actívatexto: Feasibility and Acceptability of a Mobile Intervention That Promotes Smoking Cessation and Physical Activity among Latinos details	Lara, Daimarelys; Alaniz-Cantú, Edgar I.; Siddalingaiaha, Simran; Oliveira, Igor; Chávez-Iñiguez, Arlette; DeJesus, Elisa; Fuller, Daniel; Marquez, David X.; Vásquez, Elizabeth; Li, Dongmei; McIntosh, Scott; Ossip, Deborah J.; Cupertino, Ana Paula; Cartujano-Barrera, Francisco	2024	Cancer Research Communications	Intervention		Adults	Intensity Device Weartime Active Minutes/Active Zone Minutes
Physical activity decreases in patients on the liver transplant waiting list and influences postoperative outcome-a prospective cohort study details	Magyar, Christian T. J.; Bühlmann, Lea; Becchetti, Chiara; Beekman, Lara; Candinas, Daniel; Beldi, Guido; Kim-Fuchs, Corina; Berzigotti, Annalisa; Banz, Vanessa	2024	Hepatobiliary Surgery and Nutrition	Case Study	Charge 2	Patients	Steps Energy Expenditure Intensity Distance
Participatory development of an mHealth intervention delivered in general practice to increase physical activity and reduce sedentary behaviour of patients with prediabetes and type 2 diabetes (ENERGISED) details	Novak, Jan; Jurkova, Katerina; Lojkaskova, Anna; Jaklova, Andrea; Kuhnova, Jitka; Pfeiferova, Marketa; Kral, Norbert; Janek, Michael; Omcirk, Dan; Malisova, Katerina; Maes, Iris; Dyck, Delfien Van; Wahlich, Charlotte; Ussher, Michael; Elavsky, Steriani; Cimler, Richard; Pelclova, Jana; Tufano, James J.; Steffl, Michal; Seifert, Bohumil; Yates, Tom; Harris, Tess; Vetrovsky, Tomas	2024	BMC public health	Intervention	Inspire 2	Patients	Steps
Association between changes in habitual stepping activity and cognition in older adults details	O’Brien, Myles W.; Bray, Nick W.; Quirion, Isadora; Ahmadi, Shirko; Faivre, Pierre; Gallant, Francois; Gagnon, Caroline; Sénéchal, Martin; Dupuy, Olivier; Bélanger, Mathieu; Mekari, Said	2024	Scientific Reports	Intervention		Older Adults	Steps	URL
Accuracy of Fitbit Charge 4, Garmin Vivosmart 4, and WHOOP Versus Polysomnography: Systematic Review details	Schyvens, An-Marie; Van Oost, Nina Catharina; Aerts, Jean-Marie; Masci, Federica; Peters, Brent; Neven, An; Dirix, Hélène; Wets, Geert; Ross, Veerle; Verbraecken, Johan	2024	JMIR mHealth and uHealth	Systematic Review	Charge 4 Garmin Vivosmart 4	NA	Sleep
Exploring the effects of fitbit incentive on treatment outcomes in veterans undergoing intensive pain rehabilitation program details	Toor, Tiffany; Palyo, Sarah; Schopmeyer, Kathryn; Simmons, Alan N.; Strigo, Irina A.	2024	Journal of Patient-Reported Outcomes	Intervention	Device unspecified	Patients	Steps Energy Expenditure Distance Goals
Home-based circuit training and community walking for intermittent claudication details	Waddell, Alexander; Denton, Francesca; Powell, Richard; Broom, David R.; Birkett, Stefan T.; McGregor, Gordon; Harwood, Amy E.	2024	Annals of Vascular Surgery	Intervention	Charge 4	Patients	Steps
Performance of wearable sleep trackers during nocturnal sleep and periods of simulated real-world smartphone use details	Willoughby, Adrian R.; Golkashani, Hosein Aghayan; Ghorbani, Shohreh; Wong, Kian F.; Chee, Nicholas I. Y. N.; Ong, Ju Lynn; Chee, Michael W. L.	2024	Sleep Health	Validation	Sense	Adults	Sleep
Changes in rest-activity rhythms in adolescents as they age: associations with brain changes and behavior in the ABCD study details	Zhang, Rui; Schwandt, Melanie; Vines, Leah; Volkow, Nora D.	2024	medRxiv: The Preprint Server for Health Sciences	Case Study	Charge	Adolescents	Steps Sleep
Using Positive Psychology to Address Emotional Barriers to Physical Activity After Bariatric Surgery: Proof-of-Concept Trial of the Gaining Optimism After Weight Loss Surgery (GOALS) Project details	Feig, Emily H.; Szapary, Claire; Harnedy, Lauren E.; Castillo, Crystal; Thorndike, Anne N.; Psaros, Christina; Millstein, Rachel A.; Huffman, Jeff C.	2024	Cognitive and Behavioral Practice
Testing the Reliability and Validity of the Korean Version of the Pittsburgh Sleep Quality Index Using Fitbit Devices: A Cross-Sectional Analysis details	Lee, Si-Yeon; Choi, Ja-Eun; Lee, Ji-Won; Lee, Yaeji; Park, Jae-Min; Hong, Kyung-Won	2024	Korean Journal of Family Medicine
A pilot randomised controlled trial exploring the feasibility and efficacy of a human-AI sleep coaching model for improving sleep among university students details	Liu, Jintana; Ito, Sakura; Ngo, Tra My; Lawate, Ashwini; Ong, Qi Chwen; Fox, Tatiana Erlikh; Chang, Si Yuan; Phung, Duy; Nair, Elizabeth; Palaiyan, Malar; Joty, Shafiq; Abisheganaden, John; Lee, Chuen Peng; Lwin, May Oo; Theng, Yin Leng; Ho, Moon-Ho Ringo; Chia, Michael; Bojic, Iva; Car, Josip	2024	Digital Health
Associations between sleep health, negative reinforcement learning, and alcohol use among South Florida college students with elevated internalizing symptoms details	Sollenberger, Nathan A.; Cummings, Logan R.; Freitag, Josefina; Trucco, Elisa M.; Gomez, Sthefany; Giraldo, Melanie; Muse, Gabriela; Mattfeld, Aaron T.; McMakin, Dana L.	2024	Alcohol (Fayetteville, N.Y.)
Effectiveness of a Lifestyle Improvement Support App in Combination with a Wearable Device in Japanese People with Type 2 Diabetes Mellitus: STEP-DM Study details	Takahashi, Akiko; Ishii, Manabu; Kino, Yurika; Sasaki, Kazuyo; Matsui, Takahiro; Arakawa, Kenji; Kunisaki, Makoto	2024	Diabetes Therapy: Research, Treatment and Education of Diabetes and Related Disorders
Implementation of a randomized mobile-technology lifestyle program in individuals with nonalcoholic fatty liver disease details	Tincopa, Monica A.; Patel, Nik; Shahab, Areesha; Asefa, Haila; Lok, Anna S.	2024	Scientific Reports
Feasibility of application-based psychomotor vigilance testing to assess fatigue in doctors working night shifts and correlation with smartwatch assessed shift intensity details	Weetman, Stefan; Roche, Matthew; Leckie, Todd; Samuels, Theophilus; Hodgson, Luke	2024	Occupational and Environmental Medicine
Comparison of raw accelerometry data from ActiGraph, Apple Watch, Garmin, and Fitbit using a mechanical shaker table details	White, James W.; Finnegan, Olivia L.; Tindall, Nick; Nelakuditi, Srihari; Brown, David E.; Pate, Russell R.; Welk, Gregory J.; de Zambotti, Massimiliano; Ghosal, Rahul; Wang, Yuan; Burkart, Sarah; Adams, Elizabeth L.; Chandrashekhar, Mvs; Armstrong, Bridget; Beets, Michael W.; Weaver, R. Glenn	2024	PloS One

Movement toward a novel activity monitoring device (2012)

Sleep and Breathing

Abstract

Purpose: Although polysomnography is necessary for diagnosis of most sleep disorders, it is also expensive, time-consuming, intrusive, and interferes with sleep. Field-based activity monitoring is increasingly used as an alternative measure that can be used to answer certain clinical and research questions. The purpose of this study was to evaluate the reliability and validity of a novel activity monitoring device (Fitbit) compared to both polysomnography and standard actigraphy (Actiwatch-64). Methods: To test validity, simultaneous Fitbit and actigraph were worn during standard overnight polysomnography by 24 healthy adults at the West Virginia University sleep research laboratory. To test inter-Fitbit reliability, three participants also wore two of the Fitbit devices overnight at home. Results: Fitbit showed high intradevice reliability =96.5–99.1. Fitbit and actigraph differed significantly on recorded total sleep time and sleep efficiency between each other and polysomnography. Bland–Altman plots indicated that both Fitbit and actigraph overestimated sleep efficiency and total sleep time. Sensitivity of both Fitbit and actigraphy for accurately identifying sleep was high within all sleep stages and during arousals; specificity of both Fitbit and actigraph for accurately identifying wake was poor. Specificity of actigraph was higher except for wake before sleep onset; sensitivity of Fitbit was higher in all sleep stages and during arousals. Conclusions: The web-based Fitbit, available at a markedly reduced price and with several convenience factors compared to standard actigraphy, may be an acceptable activity measurement instrument for use with normative populations. However, Fitbit has the same specificity limitations as actigraphy; both devices consistently misidentify wake as sleep and thus overestimate both sleep time and quality. Use of the Fitbit will also require specific validation before it can be used to assess disordered populations and or different age groups.

Accuracy of the Fitbit pedometer for self-paced and prescribed physical activity (2012)

Medicine & Science in Sports & Exercise

Abstract

PURPOSE: The purpose of this study was to examine the validity of the Fitbit pedometer (FB; Fitbit Inc.) for measuring steps taken during self-paced and prescribed physical activity. METHODS: A total of 17 adults (8 males, 9 females; age = 41.19, BMI = 28.82 kg/m2) participated in this study. Participants completed four to eight bouts of physical activities in a laboratory condition and in a simulated free-living condition. During the laboratory (treadmill) condition participants completed walking bouts at a slow pace (1.5 mph), a normal pace (3.0 mph), a brisk pace (4.0 mph) and one jogging bout (5.5 mph). Each bout lasted three minutes. Participants then completed 400m self-paced walking bouts at slow, normal, and brisk paces as well as one 400m jog on an outdoor track.. Male participants wore one FB and one Omron HJ-720IT (OM) on the right hip attached to an elastic belt and one FB in the right front pocket. Female participants wore the devices in the same configuration but also wore an additional FB attached to the front/center of the sports bra. Absolute and percent differences were calculated for FB vs. directly observed (DO) steps. Linear mixed models were determined effects of body location, speed, condition (laboratory vs. free-living) on FB accuracy. RESULTS:The magnitude of the difference between DO and the FB devices (M(SD); range) across all activities was small for both the FB-waist (2.0%(4.7%); -6 - 23%) and FB-bra (0.8%(4.3%); -11 - 15%) and moderate for the FB-pocket (8.8%(16.8%); 4 - 74%). Model parameter estimates from the full factorial mixed-model indicated the FB under estimated by 24 (SE = 2.83) steps across all conditions compared to direct observation. Significant parameter estimates for body location, speed and the body location × speed interaction (all p textless .001) indicated that the FB in the pocket underestimated steps more than on the waist, both devices underestimated steps more as speed increased, and the magnitude of the underestimation increased more for the pocket compared to the waist as speed increased. CONCLUSIONS:The FB appears to be a valid device for assessing step counts in adults when worn on the waist. Caution should be taken when assessing activities at higher ambulatory speeds. Further research is needed to better understand the validity of the FB in free-living conditions.

A comparison of energy expenditure estimation of several physical activity monitors. (2013)

Medicine and Science in Sports and Exercise

Abstract

NTRODUCTION: Accurately and precisely estimating free-living energy expenditure (EE) is important for monitoring energy balance and quantifying physical activity. Recently, single and multisensor devices have been developed that can classify physical activities, potentially resulting in improved estimates of EE. PURPOSE: This study aimed to determine the validity of EE estimation of a footwear-based physical activity monitor and to compare this validity against a variety of research and consumer physical activity monitors. METHODS: Nineteen healthy young adults (10 men, 9 women) completed a 4-h stay in a room calorimeter. Participants wore a footwear-based physical activity monitor as well as Actical, ActiGraph, IDEEA, DirectLife, and Fitbit devices. Each individual performed a series of postures/activities. We developed models to estimate EE from the footwear-based device, and we used the manufacturer's software to estimate EE for all other devices. RESULTS: Estimated EE using the shoe-based device was not significantly different than measured EE (mean ± SE; 476 ± 20 vs 478 ± 18 kcal, respectively) and had a root-mean-square error of 29.6 kcal (6.2%). The IDEEA and the DirectLlife estimates of EE were not significantly different than the measured EE, but the ActiGraph and the Fitbit devices significantly underestimated EE. Root-mean-square errors were 93.5 (19%), 62.1 kcal (14%), 88.2 kcal (18%), 136.6 kcal (27%), 130.1 kcal (26%), and 143.2 kcal (28%) for Actical, DirectLife, IDEEA, ActiGraph, and Fitbit, respectively. CONCLUSIONS: The shoe-based physical activity monitor provides a valid estimate of EE, whereas the other physical activity monitors tested have a wide range of validity when estimating EE. Our results also demonstrate that estimating EE based on classification of physical activities can be more accurate and precise than estimating EE based on total physical activity.

Live Long and Prosper: Potentials of Low-Cost Consumer Devices for the Prevention of Cardiovascular Diseases (2013)

Medicine 2.0

Abstract

Background: Cardiovascular diseases (CVD) are one of the major causes of death worldwide. Personal behavior such as physical activity considerably influences the risk of incurring a CVD. In the last years numerous products such as pedometers have become available on the mass market that allow monitoring relevant behaviors and vital parameters. These devices are sufficiently precise, affordable, and easy to use. While today they are mostly lifestyle oriented they also have considerable potential for health and prevention. Objective: Our goal is to investigate how recent low-cost devices can be used in real-life settings for the prevention of CVD, and whether using these devices has an advantage over subjective self-assessment. We also examine whether it is feasible to use multiple of such devices in parallel. Methods: We observe whether and how persons are willing and able to use multiple devices in their daily lives. We compare the devices&8217; measurements with subjective self-assessment. We make use of existing low-cost consumer devices to monitor a user's behavior. By mapping the devices' features with pre-defined prevention goals we ensure that the system collects meaningful data that can be used to monitor the individual's behavior. We conducted a user study with 10 healthy adults to measure usability and to identify problems with sensor use in real life. The participants used the devices' original portals to monitor their behavior. The subjects (age range 35-75) used an off-the-shelf pedometer and a sports watch for 4 weeks. Results: The participants responded in principle positively to the use of the devices. Analyzing the sensor data, we found that the users had some difficulties in operating the devices. We also found that the participants' self-assessment of their health behavior was too optimistic compared to the monitored data. They rated the usability of the overall system with 71 of up to 100 points in the &34;System Usability Scale&34;. Conclusions: Our study indicates that today's devices are suitable for a long term monitoring of health for the prevention of CVD. Using the devices provides more precise data than a subjective self-assessment. However usability and acceptance of the systems are still major topics.

Monitoring daily function in persons with transfemoral amputations using a commercial activity monitor: A feasibility study (2014)

PM and R

Abstract

Objective: To assess in a feasibility study the mobility of persons with transfemoral amputations using data collected from a popular, consumer-oriented activity monitor (Fitbit). Design: Observational cohort study. Setting: Research hospital outpatient evaluation. Participants: Nine subjects with transfemoral amputations (4 women and 5 men, ages 21-64 years) and Medicare functional assessments (K level) of K3 (n= 7), K2 (n= 1), and K4 (n= 1). Methods: One-week monitoring of physical activity using the Fitbit One activity monitor. Main Outcome Measures: Daily estimates of step counts, distance walked, floors/stairs climbed, calories burned, and proprietary Fitbit activity scores. For each day, the amount of time in each of the following levels of activity was also reported: sedentary, lightly active, fairly active, and highly active. Results: The percentage of movement time above the fairly active level had a predictable relationship to the designated K level. The average activity measures show decreased levels of activity for obese subjects (body mass index > 30). Estimated step counts were highly predictive/redundant with estimated miles walked without setting individual stride lengths. Using linear regression prediction models, calorie estimates were found to be highly dependent on subject age, height, and weight, whereas the proprietary activity score was independent of all 3 demographic factors. Conclusions: This feasibility study demonstrates that the Fitbit activity monitor estimates the activity of subjects with transfemoral amputations, producing results that correlate with their K-level functional activity classifications. The Fitbit activity score is independent of individual variations in age, weight, and height compared with estimated calories for this small sample size. These tools may provide useful insights into prosthetic use in an at-home environment.

Feasibility of Using Fitbit Flex to Motivate People with Rheumatoid Arthritis to BE Physically Active (2014)

Annals of the Rheumatic Diseases

Abstract

Background: Physical activity (PA) can reduce pain and fatigue and improve quality of life in people with rheumatoid arthritis (RA), but adherence to an active lifestyle is poor. Wearable digital devices, such as Fitbit Flex™, are commercially available for people to track their PA and obtain on-demand feedback, with a goal to motivate users to be physically active. Most devices are designed for the general public. It is unclear whether it is feasible for people with RA, who have unique challenges with physical activity, to use these devices. Objectives: To examine the feasibility for people with RA to use Fitbit Flex for tracking their PA and motivating them to be active. Methods: Participants were recruited from rheumatologists' offices and online in Vancouver, Canada. Individuals were eligible if they were age 19 or older, had physician-diagnosed RA, were available for an orientation session, and had daily access to computer and internet service. Individuals with comorbid conditions that prevented them from being physically active were excluded. Eligible participants were given a Fitbit Flex to wear on the non-dominant wrist for 4 weeks. Instructions were provided to set up the device and their online personal profile, where they could view at real time their step count, caloric output, number of active minutes and sleep quality of the previous night. Participants also wore a SenseWear Mini™ accelerometer, a validated tool for measuring physical activity in RA patients, during the first and last 7 days of the study. Step count, energy expenditure, active time and sedentary time were compared between the two monitors using paired t-test. Participants were also asked to rate on a 5-point scale their experience with Fitbit at the end of 4 weeks. Results: 10 people with RA were recruited in September 2013. The majority were women (n=8) with a median age of 57 years (IQR=17; 67). All participants adhered to the 4-week protocol. The mean rating of the overall experience with the device and the online profile was 4.7 and 4.1, respectively. Compared to Sensewear, Fitbit recorded significantly higher step counts and total sedentary time, and lower total energy expenditure and light activity time. No significant difference was found in the moderate/vigorous physical activity (MVPA) time between the two devices. Conclusions: Participants enjoyed using Fitbit Flex to monitor their physical activity. However, it appeared to overestimate the number of steps and sedentary time, and underestimate light activity time and total energy expenditure. Our findings suggest that Fitbit may be more suitable for motivating patients with RA to increase MVPA than to decrease sedentary behaviours.

Accuracy of 2 activity monitors in detecting steps in people with stroke and traumatic brain injury. (2014)

Physical Therapy

Abstract

BACKGROUND: Advances in sensor technologies and signal processing techniques provide a method to accurately measure walking activity in the home and community. Activity monitors geared toward consumer or patient use may be an alternative to more expensive monitors designed for research to measure stepping activity. OBJECTIVE: The objective of this study was to examine the accuracy of 2 consumer/patient activity monitors, the Fitbit Ultra and the Nike+ Fuelband, in identifying stepping activity in people with stroke and traumatic brain injury (TBI). Secondarily, the study sought to compare the accuracy of these 2 activity monitors with that of the StepWatch Activity Monitor (SAM) and a pedometer, the Yamax Digi-Walker SW-701 pedometer (YDWP). DESIGN: A cross-sectional design was used for this study. METHOD: People with chronic stroke and TBI wore the 4 activity monitors while they performed the Two-Minute Walk Test (2MWT), during which they were videotaped. Activity monitor estimated steps taken were compared with actual steps taken counted from videotape. Accuracy and agreement between activity monitor estimated steps and actual steps were examined using intraclass correlation coefficients (ICC [2,1]) and the Bland-Altman method. RESULTS: The SAM demonstrated the greatest accuracy (ICC [2,1]=.97, mean difference between actual steps and SAM estimated steps=4.7 steps) followed by the Fitbit Ultra (ICC [2,1]=.73, mean difference between actual steps and Fitbit Ultra estimated steps=-9.7 steps), the YDWP (ICC [2,1]=.42, mean difference between actual steps and YDWP estimated steps=-28.8 steps), and the Nike+ Fuelband (ICC [2,1]=.20, mean difference between actual steps and Nike+ Fuelband estimated steps=-66.2 steps). LIMITATIONS: Walking activity was measured over a short distance in a closed environment, and participants were high functioning ambulators, with a mean gait speed of 0.93 m/s. CONCLUSIONS: The Fitbit Ultra may be a low-cost alternative to measure the stepping activity in level, predictable environments of people with stroke and TBI who can walk at speeds =0.58 m/s.

Comparison of FitBit® Ultra to ActiGraph™ GT1M for Assessment of Physical Activity in Young Adults During Treadmill Walking (2014)

The Open Sports Medicine Journal

Abstract

Introduction: Accelerometers are used to quantify energy expenditure in field research. The ActiGraph™ GT1M (ActiGraph™) is a commonly used accelerometer for research. The FitBit® Ultra (FitBit®) is a low-cost alternative to the ActiGraph™; however, there is limited research on the validity of this device. Purpose: The pilot study compares the FitBit® against the ActiGraph™ and metabolic cart for measurement of energy expenditure and step counts during treadmill walking. Methods: Thirty-two (25 female) adults, mean age 22±2 years, performed two thirty-minute phases of walking (slow and brisk) on a treadmill while concurrently wearing the FitBit® and the ActiGraph™. Energy expenditure estimates were compared against energy expenditure measured by a metabolic cart. The Pearson’s correlation and t-tests determine the linear association and similarity between the accelerometers. Results: Energy expenditure estimate is moderately correlated between the two accelerometers during slow walking (r=0.584, p=0.011) and strongly correlated during brisk walking (r=0.910, p<0.001). Step count is strongly correlated between the accelerometers during slow (r=0.974, p<0.001) and brisk (r=0.996, p<0.001) walking. The FitBit® significantly underestimated energy expenditure during brisk walking compared to metabolic cart data. There is no difference between the slow and brisk phases’ step counts using either accelerometer. Conclusion: The results of this pilot study suggest that the FitBit® and the ActiGraph™ can be used interchangeably to measure steps, but not to measure kilocalories. Furthermore, the FitBit® underestimates energy expenditure, compared to a metabolic cart, as exercise intensity increases. This limits its ability to accurately measure energy expenditure in active populations.

A Qualitative Analysis of User Experiences With a Self-Tracker for Activity, Sleep, and Diet (2014)

Interactive Journal of Medical Research

Abstract

Background: The recent increase in chronic diseases and an aging population warrant the necessity of health self-management. As small electronic devices that track one&8217;s activity, sleep, and diet, called self-trackers, are being widely distributed, it is prudent to investigate the user experience and the effectiveness of these devices, and use the information toward engineering better devices that would result in increased efficiency and usability. Objective: The aim of this study was to abstract the constructs that constitute the user experiences of the self-tracker for activity, sleep, and diet. Additionally, we aimed to develop and verify the Health Information Technology Acceptance Model-II (HITAM-II) through a qualitative data analysis approach. Methods: The study group consisted of 18 female college students who participated in an in-depth interview after completing a 3-month study of utilizing a self-tracker designed to monitor activity, sleep, and diet. The steps followed in the analysis were: (1) extraction of constructs from theoretical frameworks, (2) extraction of constructs from interview data using a qualitative methodology, and (3) abstraction of constructs and modeling of the HITAM-II. Results: The constructs that constitute the HITAM-II are information technology factors, personal factors, social factors, attitude, behavioral intention, and behavior. These constructs are further divided into subconstructs to additionally support the HITAM-II. Conclusions: The HITAM-II was found to successfully describe the health consumer&8217;s attitude, behavioral intention, and behavior from another perspective. The result serves as the basis for a unique understanding of the user experiences of HIT.

Validity of consumer-based physical activity monitors. (2014)

Medicine and Science in Sports and Exercise

Abstract

BACKGROUND: Many consumer-based monitors are marketed to provide personal information on the levels of physical activity and daily energy expenditure (EE), but little or no information is available to substantiate their validity. PURPOSE: This study aimed to examine the validity of EE estimates from a variety of consumer-based, physical activity monitors under free-living conditions. METHODS: Sixty (26.4 ± 5.7 yr) healthy males (n = 30) and females (n = 30) wore eight different types of activity monitors simultaneously while completing a 69-min protocol. The monitors included the BodyMedia FIT armband worn on the left arm, the DirectLife monitor around the neck, the Fitbit One, the Fitbit Zip, and the ActiGraph worn on the belt, as well as the Jawbone Up and Basis B1 Band monitor on the wrist. The validity of the EE estimates from each monitor was evaluated relative to criterion values concurrently obtained from a portable metabolic system (i.e., Oxycon Mobile). Differences from criterion measures were expressed as a mean absolute percent error and were evaluated using 95% equivalence testing. RESULTS: For overall group comparisons, the mean absolute percent error values (computed as the average absolute value of the group-level errors) were 9.3%, 10.1%, 10.4%, 12.2%, 12.6%, 12.8%, 13.0%, and 23.5% for the BodyMedia FIT, Fitbit Zip, Fitbit One, Jawbone Up, ActiGraph, DirectLife, NikeFuel Band, and Basis B1 Band, respectively. The results from the equivalence testing showed that the estimates from the BodyMedia FIT, Fitbit Zip, and NikeFuel Band (90% confidence interval = 341.1-359.4) were each within the 10% equivalence zone around the indirect calorimetry estimate. CONCLUSIONS: The indicators of the agreement clearly favored the BodyMedia FIT armband, but promising preliminary findings were also observed with the Fitbit Zip.

Behavior Change Techniques Implemented in Electronic Lifestyle Activity Monitors: A Systematic Content Analysis (2014)

Journal of Medical Internet Research

Abstract

Background: Electronic activity monitors (such as those manufactured by Fitbit, Jawbone, and Nike) improve on standard pedometers by providing automated feedback and interactive behavior change tools via mobile device or personal computer. These monitors are commercially popular and show promise for use in public health interventions. However, little is known about the content of their feedback applications and how individual monitors may differ from one another. Objective: The purpose of this study was to describe the behavior change techniques implemented in commercially available electronic activity monitors. Methods: Electronic activity monitors (N=13) were systematically identified and tested by 3 trained coders for at least 1 week each. All monitors measured lifestyle physical activity and provided feedback via an app (computer or mobile). Coding was based on a hierarchical list of 93 behavior change techniques. Further coding of potentially effective techniques and adherence to theory-based recommendations were based on findings from meta-analyses and meta-regressions in the research literature. Results: All monitors provided tools for self-monitoring, feedback, and environmental change by definition. The next most prevalent techniques (13 out of 13 monitors) were goal-setting and emphasizing discrepancy between current and goal behavior. Review of behavioral goals, social support, social comparison, prompts/cues, rewards, and a focus on past success were found in more than half of the systems. The monitors included a range of 5-10 of 14 total techniques identified from the research literature as potentially effective. Most of the monitors included goal-setting, self-monitoring, and feedback content that closely matched recommendations from social cognitive theory. Conclusions: Electronic activity monitors contain a wide range of behavior change techniques typically used in clinical behavioral interventions. Thus, the monitors may represent a medium by which these interventions could be translated for widespread use. This technology has broad applications for use in clinical, public health, and rehabilitation settings.

An inexpensive device for monitoring patients' weights via automated hovering (2014)

International Journal of Cardiology

Abstract

Daily weight monitoring is integral to the management of heart failure (HF). Indeed, weight gain is a marker of HF decompensation, and daily weight monitoring has been associated with favorable prognosis. Until now, assessment of weight has required patients to attend an in-person visit, which provides only an infrequent snapshot of their weight. Remote hovering practices have gained considerable traction during the past several years, but their use outside of research settings has been limited. We tested the accuracy of remote weight monitoring using the Fitbit® Aria™ weight scale, a commercially available, inexpensive device that communicates data wirelessly, can transmit data remotely to healthcare providers, and yields data that can be banked into medical data repositories. We recruited 33 healthy employees from an academic medical center. Participants were at least 18 years of age, could read or write in English, and were routinely on campus at least 4 days per week. Participants were excluded if they failed to complete their daily weight assessments. Each day participants weighed themselves using both the Fitbit® Aria™ and seca 769 scales, the latter of which served as a gold standard comparator. Participants recorded the date and time of their scale reading and the weights reported by both scales to the nearest 0.1 lb. Participants provided informed consent, and the study protocol was approved by the local institutional review board. 32 participants provided at least 10 weight readings (minimum, 12; maximum, 41; mean, 26; standard deviation [SD], 7.2). Participants' weights ranged from 89.8 lbs to 224.6 lbs, with a mean weight of 150.9 lbs (31.2 lbs) on the Aria™ scale and a mean weight of 150.2 lbs (31.0 lbs) on the seca 769. The Aria™ demonstrated excellent agreement with the seca 769. Multilevel linear regression analysis revealed that the Aria™ systematically provided a weight reading that was 0.6 lbs greater than the seca 769, although the two scales did not differ in their assessment of weight over time. The flood of inexpensive monitoring devices to the consumer market has introduced new possibilities for remote HF management, but little is known about their accuracy. We demonstrated the accuracy of the Fitbit® Aria™ scale compared to the seca 769, a conventional scale designed for use in clinical practice. Daily information about patients' weights can alert clinicians to HF patients who are at high risk for hospitalization. Hovering remotely over patients may also engage patients in their own self-management.

Validation of the Fitbit One activity monitor device during treadmill walking. (2014)

Journal of Science and Medicine in Sport

Abstract

OBJECTIVES: In order to quantify the effects of physical activity such as walking on chronic disease, accurate measurement of physical activity is needed. The objective of this study was to determine the validity and reliability of a new activity monitor, the Fitbit One, in a population of healthy adults. DESIGN: Cross-sectional study. METHODS: Thirty healthy adults ambulated at 5 different speeds (0.90, 1.12, 1.33, 1.54, 1.78 m/s) on a treadmill while wearing three Fitbit One activity monitors (two on the hips and one in the pocket). The order of each speed condition was randomized. Fitbit One step count output was compared to observer counts and distance output was compared to the calibrated treadmill output. Two-way repeated measures ANOVA, concordance correlation coefficients, and Bland and Altman plots were used to assess validity and intra-class correlation coefficients (ICC) were used to assess reliability. RESULTS: No significant differences were noted between Fitbit One step count outputs and observer counts, and concordance was substantial (0.97-1.00). Inter-device reliability of the step count was high for all walking speeds (ICC = 0.95). Percent relative error was less than 1.3%. The distance output of the Fitbit One activity monitors was significantly different from the criterion values for each monitor at all speeds (Ptextless0.001) and exhibited poor concordance (0.0-0.05). Inter-device reliability was excellent for all treadmill speeds (ICC = 0.90). Percent relative error was high (up to 39.6%). CONCLUSIONS: The Fitbit One activity monitors are valid and reliable devices for measuring step counts in healthy young adults. The distance output of the monitors is inaccurate and should be noted with caution.

Activity monitor intervention to promote physical activity of physicians-in-training: randomized controlled trial. (2014)

PloS ONE

Abstract

BACKGROUND: Physicians are expected to serve as role models for healthy lifestyles, but long work hours reduce time for healthy behaviors. A hospital-based physical activity intervention could improve physician health and increase counseling about exercise. METHODS: We conducted a two-phase intervention among 104 medical residents at a large hospital in Boston, Massachusetts. Phase 1 was a 6-week randomized controlled trial comparing daily steps of residents assigned to an activity monitor displaying feedback about steps and energy consumed (intervention) or to a blinded monitor (control). Phase 2 immediately followed and was a 6-week non-randomized team steps competition in which all participants wore monitors with feedback. Phase 1 outcomes were: 1) median steps/day and 2) proportion of days activity monitor worn. The Phase 2 outcome was mean steps/day on days monitor worn (=500 steps/day). Physiologic measurements were collected at baseline and study end. Median steps/day were compared using Wilcoxon rank-sum tests. Mean steps were compared using repeated measures regression analyses. RESULTS: In Phase 1, intervention and control groups had similar activity (6369 vs. 6063 steps/day, p = 0.16) and compliance with wearing the monitor (77% vs. 77% of days, p = 0.73). In Phase 2 (team competition), residents recorded more steps/day than during Phase 1 (CONTROL: 7,971 vs. 7,567, p = 0.002; INTERVENTION: 7,832 vs. 7,739, p = 0.13). Mean compliance with wearing the activity monitor decreased for both groups during Phase 2 compared to Phase 1 (60% vs. 77%, p < 0.001). Mean systolic blood pressure decreased (p = 0.004) and HDL cholesterol increased (p < 0.001) among all participants at end of study compared to baseline. CONCLUSIONS: Although the activity monitor intervention did not have a major impact on activity or health, the high participation rates of busy residents and modest changes in steps, blood pressure, and HDL suggest that more intensive hospital-based wellness programs have potential for promoting healthier lifestyles among physicians.

Validity and usability of low-cost accelerometers for internet-based self-monitoring of physical activity in patients with chronic obstructive pulmonary disease. (2014)

Interactive Journal of Medical Research

Abstract

BACKGROUND: The importance of regular physical activity for patients with chronic obstructive pulmonary disease (COPD) is well-established. However, many patients do not meet the recommended daily amount. Accelerometers might provide patients with the information needed to increase physical activity in daily life. OBJECTIVE: Our objective was to assess the validity and usability of low-cost Internet-connected accelerometers. Furthermore we explored patients' preferences with regards to the presentation of and feedback on monitored physical activity. METHODS: To assess concurrent validity we conducted a field validation study with patients who wore two low-cost accelerometers, Fitbit and Physical Activity Monitor (PAM), at the same time along with a sophisticated multisensor accelerometer (SenseWear Armband) for 48 hours. Data on energy expenditure assessed from registrations from the two low-cost accelerometers were compared to the well validated SenseWear Armband which served as a reference criterion. Usability was examined in a cross-over study with patients who, in succession, wore the Fitbit and the PAM for 7 consecutive days and filled out a 16 item questionnaire with regards to the use of the corresponding device RESULTS: The agreement between energy expenditure (METs) from the SenseWear Armband with METs estimated by the Fitbit and PAM was good (r=.77) and moderate (r=.41), respectively. The regression model that was developed for the Fitbit explained 92% whereas the PAM-model could explain 89% of total variance in METs measured by the SenseWear. With regards to the usability, both the Fitbit and PAM were well rated on all items. There were no significant differences between the two devices. CONCLUSIONS: The low-cost Fitbit and PAM are valid and usable devices to measure physical activity in patients with COPD. These devices may be useful in long-term interventions aiming at increasing physical activity levels in these patients.

Mobile Phone-Connected Wearable Motion Sensors to Assess Postoperative Mobilization (2015)

JMIR mHealth and uHealth

Abstract

Background: Early mobilization after surgery reduces the incidence of a wide range of complications. Wearable motion sensors measure movements over time and transmit this data wirelessly, which has the potential to monitor patient recovery and encourages patients to engage in their own rehabilitation. Objective: We sought to determine the ability of off-the-shelf activity sensors to remotely monitor patient postoperative mobility. Methods: Consecutive subjects were recruited under the Department of Neurosurgery at Columbia University. Patients were enrolled during physical therapy sessions. The total number of steps counted by the two blinded researchers was compared to the steps recorded on four activity sensors positioned at different body locations. Results: A total of 148 motion data points were generated. The start time, end time, and duration of each walking session were accurately recorded by the devices and were remotely available for the researchers to analyze. The sensor accuracy was significantly greater when placed over the ankles than over the hips (P<.001). Our multivariate analysis showed that step length was an independent predictor of sensor accuracy. On linear regression, there was a modest positive correlation between increasing step length and increased ankle sensor accuracy (r=.640, r2=.397) that reached statistical significance on the multivariate model (P=.03). Increased gait speed also correlated with increased ankle sensor accuracy, although less strongly (r=.444, r2=.197). We did not note an effect of unilateral weakness on the accuracy of left- versus right-sided sensors. Accuracy was also affected by several specific measures of a patient’s level of physical assistance, for which we generated a model to mathematically adjust for systematic underestimation as well as disease severity. Conclusions: We provide one of the first assessments of the accuracy and utility of widely available and wirelessly connected activity sensors in a postoperative patient population. Our results show that activity sensors are able to provide invaluable information about a patient’s mobility status and can transmit this data wirelessly, although there is a systematic underestimation bias in more debilitated patients.

Promoting physical activity among women using wearable technology and online social connectivity: a feasibility study (2015)

Health Psychology and Behavioral Medicine

Abstract

Wearable physical activity (PA) tracking technology, which has the capacity to connect users' PA data to online social networks, could address the need for cost-effective, scalable PA programs for women. It is not yet clear how to use wearable devices to maximize the effectiveness of evidence-based PA promotion techniques. There also is need to examine time-sensitive, within-person relationships between aspects of social networking and PA progress. The present study tested the feasibility, acceptability, and effectiveness of an online PA program that employed wearable technology, an associated social network, and the assignment of program partners. Sedentary women (n = 20, MAge = 50, MBMI = 30.9 kg/m2) used wearable devices and attended one face-to-face session that introduced evidence-based PA promotion skills (e.g. goal setting). The remainder of the six-week program emphasized online communication with PA partners and other group members. Participants completed online surveys about their social contact at the end of every week. The recruitment target (set at 20) was reached after three weeks, and retention over six weeks was 100%. Objectively verified daily device wear was high (97%) and participant program ratings were favorable (e.g. M = 3.85 of 5, SD = 1.04 for “effectiveness”). Participants showed large increases in daily steps (from 3796 [SE = 478] to 8190 [SE = 516]) and daily minutes of moderate-to-vigorous PA (from 12.68 [SE = 3.15] to 24.05 [SE = 3.48]). Multilevel models revealed meaningful within-person relationships between PA and social contact (ps textless .02); PA was highest at times when social contact was higher than a participant's average level. Findings support the feasibility, acceptability, and effectiveness of incorporating wearable technology and online social networking into an online PA program for women, and provide time-sensitive evidence that social networking is beneficial for PA. Consequently, larger, randomized trials of this approach are warranted.

Addressing barriers to physical activity among women: A feasibility study using social networking-enabled technology (2015)

Digital Health

Abstract

Objective: Automated physical activity (PA) monitoring technology and associated social networks have potential to address barriers to PA, but have rarely been tested for PA promotion. This technology may be especially beneficial for women, who experience particular barriers to health-based social networking. The present study tested the feasibility and acceptability of pairing women as PA partners via technology-connected social networking. Social comparison (i.e. tendency to make self-evaluations relative to others) was examined as a mechanism of interest. Method: Overweight women (n?=?12, Mage?=?46, MBMI?=?32.60?kg/m2) used a PA sensor (daily wear?=?93%) and communicated with an assigned partner (introduced via technology-connected social networking) for four weeks. Partners did not know one another prior to study enrollment. Results: PA meaningfully increased during the program, and was highest among participants who endorsed stronger (vs. weaker) tendencies toward social comparisons (r?=?0.64). Participants identified several benefits of partner communication; however, some partners had difficulty initiating communication, and direct comparisons with partners were seen as unhelpful in this context. Most participants found the PA sensor beneficial, showed high compliance with daily wear recommendations, and reported an intent to continue using the PA sensor. Participants endorsed satisfaction with the program's approach and confidence in maintaining PA gains. Conclusions: These findings support the use of automated PA sensors and facilitated partner communication via social networking to promote PA among women. Insights from participant feedback identify specific avenues for program improvement; specifically, with respect to the potential difficulties of negative social comparisons.

“Not just another walking program”: Everyday Activity Supports You (EASY) model---a randomized pilot study for a parallel randomized controlled trial (2015)

Pilot and Feasibility Studies

Abstract

Background: Maintaining physical activity is an important goal with positive health benefits, yet many people spend most of their day sitting. Our Everyday Activity Supports You (EASY) model aims to encourage movement through daily activities and utilitarian walking. The primary objective of this phase was to test study feasibility (recruitment and retention rates) for the EASY model. Methods: This 6-month study took place in Vancouver, Canada, from May to December 2013, with data analyses in February 2014. Participants were healthy, inactive, community-dwelling women aged 55--70 years. We recruited through advertisements in local community newspapers and randomized participants using a remote web service. The model included the following: group-based education and social support, individualized physical activity prescription (called Activity 4-1-1), and use of a Fitbit activity monitor. The control group received health-related information only. The main outcome measures were descriptions of study feasibility (recruitment and retention rates). We also collected information on activity patterns (ActiGraph GT3X+ accelerometers) and health-related outcomes such as body composition (height and weight using standard techniques), blood pressure (automatic blood pressure monitor), and psychosocial variables (questionnaires). Results: We advertised in local community newspapers to recruit participants. Over 3 weeks, 82 participants telephoned; following screening, 68% (56/82) met the inclusion criteria and 45% (25/56) were randomized by remote web-based allocation. This included 13 participants in the intervention group and 12 participants in the control group (education). At 6 months, 12/13 (92%) intervention and 8/12 (67%) control participants completed the final assessment. Controlling for baseline values, the intervention group had an average of 2,080 [95% confidence intervals (CIs) 704, 4,918] more steps/day at 6 months compared with the control group. There was an average between group difference in weight loss of -4.3 [95% CI -6.22, -2.40] kg and reduction in diastolic blood pressure of -8.54 [95% CI -16.89, -0.198] mmHg, in favor of EASY. Conclusions: The EASY pilot study was feasible to deliver; there was an increase in physical activity and reduction in weight and blood pressure for intervention participants at 6 months. (Continued

An Evaluation of Commercial Pedometers for Monitoring Slow Walking Speed Populations. (2015)

Telemedicine journal and e-health : the official journal of the American Telemedicine Association

Abstract

BACKGROUND: Pedometers are considered desirable devices for monitoring physical activity. Two population groups of interest include patients having undergone surgery in the lower extremities or who are otherwise weakened through disease, medical treatment, or surgery procedures, as well as the slow walking senior population. For these population groups, pedometers must be able to perform reliably and accurately at slow walking speeds. The objectives of this study were to evaluate the step count accuracy of three commercially available pedometers, the Yamax (Tokyo, Japan) Digi-Walker(®) SW-200 (YM), the Omron (Kyoto, Japan) HJ-720 (OM), and the Fitbit (San Francisco, CA) Zip (FB), at slow walking speeds, specifically at 1, 2, and 3 km/h, and to raise awareness of the necessity of focusing research on step-counting devices and algorithms for slow walking populations. MATERIALS AND METHODS: Fourteen participants 29.93 ±4.93 years of age were requested to walk on a treadmill at the three specified speeds, in four trials of 100 steps each. The devices were worn by the participants on the waist belt. The pedometer counts were recorded, and the error percentage was calculated. RESULTS: The error rate of all three evaluated pedometers decreased with the increase of speed: at 1 km/h the error rates varied from 87.11% (YM) to 95.98% (FB), at 2 km/h the error rates varied from 17.27% (FB) to 46.46% (YM), and at 3 km/h the error rates varied from 22.46% (YM) to a slight overcount of 0.70% (FB). CONCLUSIONS: It was observed that all the evaluated devices have high error rates at 1 km/h and mixed error rates at 2 km/h, and at 3 km/h the error rates are the smallest of the three assessed speeds, with the OM and the FB having a slight overcount. These results show that research on pedometers' software and hardware should focus more on accurate step detection at slow walking speeds.

Randomized Trial of a Fitbit-Based Physical Activity Intervention for Women. (2015)

American Journal of Preventive Medicine

Abstract

INTRODUCTION: Direct-to-consumer mHealth devices are a potential asset to behavioral research but rarely tested as intervention tools. This trial examined the accelerometer-based Fitbit tracker and website as a low-touch physical activity intervention. The purpose of this study is to evaluate, within an RCT, the feasibility and preliminary efficacy of integrating the Fitbit tracker and website into a physical activity intervention for postmenopausal women. METHODS: Fifty-one inactive, postmenopausal women with BMI =25.0 were randomized to a 16-week web-based self-monitoring intervention (n=25) or comparison group (n=26). The Web-Based Tracking Group received a Fitbit, instructional session, and follow-up call at 4 weeks. The comparison group received a standard pedometer. All were asked to perform 150 minutes/week of moderate to vigorous physical activity (MVPA). Physical activity outcomes were measured by the ActiGraph GT3X+ accelerometer. RESULTS: Data were collected and analyzed in 2013-2014. Participants were aged 60 (SD=7) years with BMI of 29.2 (3.5) kg/m(2). Relative to baseline, the Web-Based Tracking Group increased MVPA by 62 (108) minutes/week (ptextless0.01); 10-minute MVPA bouts by 38 (83) minutes/week (p=0.008); and steps by 789 (1,979) (p=0.01), compared to non-significant increases in the Pedometer Group (between-group p=0.11, 0.28, and 0.30, respectively). The Web-Based Tracking Group wore the tracker on 95% of intervention days; 96% reported liking the website and 100% liked the tracker. CONCLUSIONS: The Fitbit was well accepted in this sample of women and associated with increased physical activity at 16 weeks. Leveraging direct-to-consumer mHealth technologies aligned with behavior change theories can strengthen physical activity interventions.

Use of the Fitbit to Measure Adherence to a Physical Activity Intervention Among Overweight or Obese, Postmenopausal Women: Self-Monitoring Trajectory During 16 Weeks. (2015)

JMIR mHealth and uHealth

Abstract

BACKGROUND: Direct-to-consumer trackers and devices have potential to enhance theory-based physical activity interventions by offering a simple and pleasant way to help participants self-monitor their behavior. A secondary benefit of these devices is the opportunity for investigators to objectively track adherence to physical activity goals across weeks or even months, rather than relying on self-report or a small number of accelerometry wear periods. The use of consumer trackers for continuous monitoring of adherence has considerable potential to enhance physical activity research, but few studies have been published in this rapidly developing area. OBJECTIVE: The objective of the study was to assess the trajectory of physical activity adherence across a 16-week self-monitoring intervention, as measured by the Fitbit tracker. METHODS: Participants were 25 overweight or obese, postmenopausal women enrolled in the intervention arm of a randomized controlled physical activity intervention trial. Each participant received a 16-week technology-based intervention that used the Fitbit physical activity tracker and website. The overall study goal was 150 minutes/week of moderate to vigorous intensity physical activity (MVPA) and 10,000 steps/day; however, goals were set individually for each participant and updated at Week 4 based on progress. Adherence data were collected by the Fitbit and aggregated by Fitabase. Participants also wore an ActiGraph GT3X+ accelerometer for 7 days prior to the intervention and again during Week 16. RESULTS: The median participant logged 10 hours or more/day of Fitbit wear on 95% of the 112 intervention days, with no significant decline in wear over the study period. Participants averaged 7540 (SD 2373) steps/day and 82 minutes/week (SD 43) of accumulated "fairly active" and "very active" minutes during the intervention. At Week 4, 80% (20/25) of women chose to maintain/increase their individual MVPA goal and 72% (18/25) of participants chose to maintain/increase their step goal. Physical activity levels were relatively stable after peaking at 3 weeks, with only small declines of 8% for steps (P=.06) and 14% for MVPA (P=.05) by 16 weeks. CONCLUSIONS: These data indicate that a sophisticated, direct-to-consumer activity tracker encouraged high levels of self-monitoring that were sustained over 16 weeks. Further study is needed to determine how to motivate additional gains in physical activity and evaluate the long-term utility of the Fitbit tracker as part of a strategy for chronic disease prevention.

mHealth Physical Activity Intervention: A Randomized Pilot Study in Physically Inactive Pregnant Women. (2015)

Maternal and Child Health Journal

Abstract

Introduction: Physical inactivity is prevalent in pregnant women, and innovative strategies to promote physical activity are strongly needed. The purpose of the study was to test a 12-week mobile health (mHealth) physical activity intervention for feasibility and potential efficacy. Methods: Participants were recruited between December 2012 and February 2014 using diverse recruitment methods. Thirty pregnant women between 10 and 20 weeks of gestation were randomized to an intervention (mobile phone app plus Fitbit) or a control (Fitbit) group. Both conditions targeted gradual increases in physical activity. The mHealth intervention included daily messages and a mobile phone activity diary with automated feedback and self-monitoring systems. Results: On monthly average, 4 women were screened for initial eligibility by telephone and 2.5 were randomized. Intervention participants had a 1096 ± 1898 step increase in daily steps compared to an increase of 259 ± 1604 steps in control participants at 12 weeks. The change between groups in weekly mean steps per day during the 12-week study period was not statistically significant (p = 0.38). The intervention group reported lower perceived barrier to being active, lack of energy, than the control group at 12-week visit (p = 0.02). The rates of responding to daily messages and using the daily diary through the mobile app declined during the 12 week study period. Discussion: It was difficult to recruit and randomize inactive women who wanted to increase physical activity during pregnancy. Pregnant women who were motivated to increase physical activity might find using mobile technologies in assessing and promoting PA acceptable. Possible reasons for the non-significant treatment effect of the mHealth intervention on physical activity are discussed. Public awareness of safety and benefits of physical activity during pregnancy should be promoted.

Are Currently Available Wearable Devices for Activity Tracking and Heart Rate Monitoring Accurate, Precise, and Medically Beneficial? (2015)

Healthcare Informatics Research

Abstract

Objectives: The new wave of wireless technologies, fitness trackers, and body sensor devices can have great impact on healthcare systems and the quality of life. However, there have not been enough studies to prove the accuracy and precision of these trackers. The objective of this study was to evaluate the accuracy, precision, and overall performance of seventeen wearable devices currently available compared with direct observation of step counts and heart rate monitoring. Methods: Each participant in this study used three accelerometers at a time, running the three corresponding applications of each tracker on an Android or iOS device simultaneously. Each participant was instructed to walk 200, 500, and 1,000 steps. Each set was repeated 40 times. Data was recorded after each trial, and the mean step count, standard deviation, accuracy, and precision were estimated for each tracker. Heart rate was measured by all trackers (if applicable), which support heart rate monitoring, and compared to a positive control, the Onyx Vantage 9590 professional clinical pulse oximeter. Results: The accuracy of the tested products ranged between 79.8% and 99.1%, while the coefficient of variation (precision) ranged between 4% and 17.5%. MisFit Shine showed the highest accuracy and precision (along with Qualcomm Toq), while Samsung Gear 2 showed the lowest accuracy, and Jawbone UP showed the lowest precision. However, Xiaomi Mi band showed the best package compared to its price. Conclusions: The accuracy and precision of the selected fitness trackers are reasonable and can indicate the average level of activity and thus average energy expenditure.

Systematic review of the validity and reliability of consumer-wearable activity trackers. (2015)

The International Journal of Behavioral Nutrition and Physical Activity

Abstract

BACKGROUND: Consumer-wearable activity trackers are electronic devices used for monitoring fitness- and other health-related metrics. The purpose of this systematic review was to summarize the evidence for validity and reliability of popular consumer-wearable activity trackers (Fitbit and Jawbone) and their ability to estimate steps, distance, physical activity, energy expenditure, and sleep. METHODS: Searches included only full-length English language studies published in PubMed, Embase, SPORTDiscus, and Google Scholar through July 31, 2015. Two people reviewed and abstracted each included study. RESULTS: In total, 22 studies were included in the review (20 on adults, 2 on youth). For laboratory-based studies using step counting or accelerometer steps, the correlation with tracker-assessed steps was high for both Fitbit and Jawbone (Pearson or intraclass correlation coefficients (CC) textgreater =0.80). Only one study assessed distance for the Fitbit, finding an over-estimate at slower speeds and under-estimate at faster speeds. Two field-based studies compared accelerometry-assessed physical activity to the trackers, with one study finding higher correlation (Spearman CC 0.86, Fitbit) while another study found a wide range in correlation (intraclass CC 0.36-0.70, Fitbit and Jawbone). Using several different comparison measures (indirect and direct calorimetry, accelerometry, self-report), energy expenditure was more often under-estimated by either tracker. Total sleep time and sleep efficiency were over-estimated and wake after sleep onset was under-estimated comparing metrics from polysomnography to either tracker using a normal mode setting. No studies of intradevice reliability were found. Interdevice reliability was reported on seven studies using the Fitbit, but none for the Jawbone. Walking- and running-based Fitbit trials indicated consistently high interdevice reliability for steps (Pearson and intraclass CC 0.76-1.00), distance (intraclass CC 0.90-0.99), and energy expenditure (Pearson and intraclass CC 0.71-0.97). When wearing two Fitbits while sleeping, consistency between the devices was high. CONCLUSION: This systematic review indicated higher validity of steps, few studies on distance and physical activity, and lower validity for energy expenditure and sleep. The evidence reviewed indicated high interdevice reliability for steps, distance, energy expenditure, and sleep for certain Fitbit models. As new activity trackers and features are introduced to the market, documentation of the measurement properties can guide their use in research settings.

The validity of consumer-level, activity monitors in healthy adults worn in free-living conditions: a cross-sectional study. (2015)

The International Journal of Behavioral Nutrition and Physical Activity

Abstract

BACKGROUND: Technological advances have seen a burgeoning industry for accelerometer-based wearable activity monitors targeted at the consumer market. The purpose of this study was to determine the convergent validity of a selection of consumer-level accelerometer-based activity monitors. METHODS: 21 healthy adults wore seven consumer-level activity monitors (Fitbit One, Fitbit Zip, Jawbone UP, Misfit Shine, Nike Fuelband, Striiv Smart Pedometer and Withings Pulse) and two research-grade accelerometers/multi-sensor devices (BodyMedia SenseWear, and ActiGraph GT3X+) for 48-hours. Participants went about their daily life in free-living conditions during data collection. The validity of the consumer-level activity monitors relative to the research devices for step count, moderate to vigorous physical activity (MVPA), sleep and total daily energy expenditure (TDEE) was quantified using Bland-Altman analysis, median absolute difference and Pearson's correlation. RESULTS: All consumer-level activity monitors correlated strongly (r > 0.8) with research-grade devices for step count and sleep time, but only moderately-to-strongly for TDEE (r = 0.74-0.81) and MVPA (r = 0.52-0.91). Median absolute differences were generally modest for sleep and steps (<10% of research device mean values for the majority of devices) moderate for TDEE (<30% of research device mean values), and large for MVPA (26-298%). Across the constructs examined, the Fitbit One, Fitbit Zip and Withings Pulse performed most strongly. CONCLUSIONS: In free-living conditions, the consumer-level activity monitors showed strong validity for the measurement of steps and sleep duration, and moderate valid for measurement of TDEE and MVPA. Validity for each construct ranged widely between devices, with the Fitbit One, Fitbit Zip and Withings Pulse being the strongest performers.

A televideo exercise and nutrition program for children with acute lymphoblastic leukemia in maintenance therapy: design and methods (2015)

Open Access Journal of Clinical Trials

Abstract

Changes in nutrient intake and decreased exercise resulting from cancer therapies as well as their side effects may be contributing factors in the increased body weight and differences in physical fitness observed in survivors of childhood acute lymphoblastic leukemia (ALL). This article will describe the study protocol for an intervention program designed to improve the physical activity and nutrition behaviors of ALL survivors. Twenty-four children aged between 4 years and 12 years with ALL will be randomized to a 6-month technology-based exercise and nutrition program (TLC4ALLKids) or to enhanced usual care (eUC). The participants randomized to the TLC4ALLKids will participate in weekly, 1-hour coaching sessions on nutrition and physical activity and 1-hour physical activity classes delivered by group video conferencing. Participants will be provided with iPad tablets loaded with video conferencing software and the Healthy Lifestyle Tracking calendar to track daily nutrition and physical activity goals and weight. Both groups will be provided with Fitbit™ Zip to monitor physical activity. To assess feasibility, participant recruitment (achievement of proposed sample size), attendance (per weekly online sessions/assessment sessions), and adherence (number of families at 3 and 6 months) will be evaluated. Outcome measures to assess the intervention will include anthropometrics (weight, height, and waist circumference), physical activity (accelerometry), energy and macronutrient intake (food records), sleep habits (Children's Sleep Habits Questionnaire), and quality of life (Pediatric Quality of Life Inventory) will be obtained at baseline, 3 months, and 6 months. Semi-structured interviews will be used to gather information about ways to improve the program and overcome barriers to participation. If successful, the TLC4ALLKids intervention will provide a means to educate and improve the health behaviors of ALL survivors that can be delivered remotely and conveniently to participants.

Impact of increasing physical activity on cognitive functioning in breast cancer survivors: Rationale and study design of Memory & Motion. (2015)

Contemporary Clinical Trials

Abstract

NTRODUCTION: Many breast cancer survivors experience problems with cognitive functioning that can persist years after treatment. Increasing physical activity has been shown to improve cognitive functioning in healthy and cognitively impaired adults, but has not yet been tested in cancer survivors. The primary aim of this randomized controlled trial is to examine the effects of a 3-month physical activity intervention compared to a waitlist Control arm on neuropsychological outcomes and subjective cognitive concerns in breast cancer survivors. METHODS: Eighty sedentary breast cancer survivors, self-reporting difficulties with cognition, will be randomized into an Exercise arm or Control arm. The Exercise arm includes an activity tracker (i.e., a Fitbit), phone calls, plus tailored and non-tailored email content. The Control arm will receive emails on women's health topics on the same schedule as the Exercise arm. Assessments conducted at baseline and 3months include: neuropsychological testing, cognitive concerns and other aspects of quality of life, and 7days of a hip-worn accelerometer. Participants will also provide fasting blood draws to assess brain-derived neurotropic factor, Insulin-like growth factor 1, insulin resistance, and C-reactive protein. Primary and secondary outcomes are changes in neuropsychological testing and cognitive concerns. Biomarkers will be examined to further understand the underlying relationship between physical activity and cognition. CONCLUSION: The Memory & Motion study is designed to test whether increasing physical activity can improve cognitive functioning in breast cancer survivors. Results from this study could be used to guide development of interventions to improve cognitive functioning in breast cancer survivors.

The Walking Interventions Through Texting (WalkIT) Trial: Rationale, Design, and Protocol for a Factorial Randomized Controlled Trial of Adaptive Interventions for Overweight and Obese, Inactive Adults (2015)

JMIR Research Protocols

Abstract

Background: Walking is a widely accepted and frequently targeted health promotion approach to increase physical activity (PA). Interventions to increase PA have produced only small improvements. Stronger and more potent behavioral intervention components are needed to increase time spent in PA, improve cardiometabolic risk markers, and optimize health. Objective: Our aim is to present the rationale and methods from the WalkIT Trial, a 4-month factorial randomized controlled trial (RCT) in inactive, overweight/obese adults. The main purpose of the study was to evaluate whether intensive adaptive components result in greater improvements to adults’ PA compared to the static intervention components. Methods: Participants enrolled in a 2x2 factorial RCT and were assigned to one of four semi-automated, text message–based walking interventions. Experimental components included adaptive versus static steps/day goals, and immediate versus delayed reinforcement. Principles of percentile shaping and behavioral economics were used to operationalize experimental components. A Fitbit Zip measured the main outcome: participants’ daily physical activity (steps and cadence) over the 4-month duration of the study. Secondary outcomes included self-reported PA, psychosocial outcomes, aerobic fitness, and cardiorespiratory risk factors assessed pre/post in a laboratory setting. Participants were recruited through email listservs and websites affiliated with the university campus, community businesses and local government, social groups, and social media advertising. Results: This study has completed data collection as of December 2014, but data cleaning and preliminary analyses are still in progress. We expect to complete analysis of the main outcomes in late 2015 to early 2016. Conclusions: The Walking Interventions through Texting (WalkIT) Trial will further the understanding of theory-based intervention components to increase the PA of men and women who are healthy, insufficiently active and are overweight or obese. WalkIT is one of the first studies focusing on the individual components of combined goal setting and reward structures in a factorial design to increase walking. The trial is expected to produce results useful to future research interventions and perhaps industry initiatives, primarily focused on mHealth, goal setting, and those looking to promote behavior change through performance-based incentives.

Reliability and validity of ten consumer activity trackers. (2015)

BMC Sports Science, Medicine and Rehabilitation

Abstract

BACKGROUND: Activity trackers can potentially stimulate users to increase their physical activity behavior. The aim of this study was to examine the reliability and validity of ten consumer activity trackers for measuring step count in both laboratory and free-living conditions. METHOD: Healthy adult volunteers (n = 33) walked twice on a treadmill (4.8 km/h) for 30 min while wearing ten different activity trackers (i.e. Lumoback, Fitbit Flex, Jawbone Up, Nike+ Fuelband SE, Misfit Shine, Withings Pulse, Fitbit Zip, Omron HJ-203, Yamax Digiwalker SW-200 and Moves mobile application). In free-living conditions, 56 volunteers wore the same activity trackers for one working day. Test-retest reliability was analyzed with the Intraclass Correlation Coefficient (ICC). Validity was evaluated by comparing each tracker with the gold standard (Optogait system for laboratory and ActivPAL for free-living conditions), using paired samples t-tests, mean absolute percentage errors, correlations and Bland-Altman plots. RESULTS: Test-retest analysis revealed high reliability for most trackers except for the Omron (ICC .14), Moves app (ICC .37) and Nike+ Fuelband (ICC .53). The mean absolute percentage errors of the trackers in laboratory and free-living conditions respectively, were: Lumoback (-0.2, -0.4), Fibit Flex (-5.7, 3.7), Jawbone Up (-1.0, 1.4), Nike+ Fuelband (-18, -24), Misfit Shine (0.2, 1.1), Withings Pulse (-0.5, -7.9), Fitbit Zip (-0.3, 1.2), Omron (2.5, -0.4), Digiwalker (-1.2, -5.9), and Moves app (9.6, -37.6). Bland-Altman plots demonstrated that the limits of agreement varied from 46 steps (Fitbit Zip) to 2422 steps (Nike+ Fuelband) in the laboratory condition, and 866 steps (Fitbit Zip) to 5150 steps (Moves app) in the free-living condition. CONCLUSION: The reliability and validity of most trackers for measuring step count is good. The Fitbit Zip is the most valid whereas the reliability and validity of the Nike+ Fuelband is low.

Effects of an Intervention using Movement Technology in a University Physical Activity Class (2015)

Medicine & Science in Sports & Exercise

Abstract

The college-age population is insufficiently active and physical activity declines during the college years. Interventions in university settings are potential avenues for increasing physical activity in this population. PURPOSE: To examine the effect of movement technology on college students' physical activity levels, self-determined motivation for physical activity, physical activity enjoyment, and stage of change. METHODS: Students (N = 75) wore pedometers for one week at baseline and at post-intervention to objectively assess physical activity. Volunteer participants in a university basic instruction course were randomly assigned to condition. The intervention group (n = 34) wore a Fitbit® Flex everyday throughout the 10-week intervention. The comparison group (n = 41) did not wear a Fitbit monitor. The Fitbit is a commercially available monitor that can be used to assess physical activity, provide feedback, self-monitor, and set goals. Intervention effectiveness was evaluated with a series of mixed model analyses of variance and effect size estimates via Cohen's delta (d). RESULTS: Across conditions, students showed decreases in objectively measured steps per day from baseline to post-intervention, possibly due to the time of the semester in which the pedometer assessment was conducted. However, students who wore a Fitbit had a lesser decrease in steps per day (decrease of 104 steps per day, d = -0.05) compared to students who did not wear a Fitbit (decrease of 461 steps per day, d = -0.18). The difference in changes in steps per day between conditions was small and not significant (p = .98; d = 0.16). From baseline to post-intervention, self-reported physical activity increased more in the students who wore a Fitbit than in students who did not (p = .07; d = 0.32). Changes in self-determined motivation for physical activity, physical activity enjoyment, and stage of change were similar for both groups (p textgreater .05, d textless 0.16). Intrinsic regulation was the only motivational variable that increased more among students who wore a Fitbit compared to students who did not wear a Fitbit (d = 0.33), although this difference was not significant (p = .11). CONCLUSIONS: Commercially available activity monitors, such as the Fitbit, can have a small positive impact on physical activity and intrinsic regulation for physical activity.

Comparison of a Commercial Accelerometer with Polysomnography and Actigraphy in Children and Adolescents (2015)

Sleep

Abstract

Study Objectives: To evaluate the reliability and validity of the commercially available Fitbit Ultra (2012) accelerometer compared to polysomnography (PSG) and two different actigraphs in a pediatric sample. Design and Setting: All subjects wore the Fitbit Ultra while undergoing overnight clinical polysomnography in a sleep laboratory; a randomly selected subset of participants also wore either the Ambulatory Monitoring Inc. Motionlogger Sleep Watch (AMI) or Phillips-Respironics Mini-Mitter Spectrum (PRMM). Participants: 63 youth (32 females, 31 males), ages 3–17 years (mean 9.7 years, SD 4.6 years). Measurements: Both “Normal” and “Sensitive” sleep-recording Fitbit Ultra modes were examined. Outcome variables included total sleep time (TST), wake after sleep onset (WASO), and sleep efficiency (SE). Primary analyses examined the differences between Fitbit Ultra and PSG using repeated-measures ANCOVA, with epoch-by-epoch comparisons between Fitbit Ultra and PSG used to determine sensitivity, specificity, and accuracy. Intra-device reliability, differences between Fitbit Ultra and actigraphy, and differences by both developmental age group and sleep disordered breathing (SDB) status were also examined. Results: Compared to PSG, the Normal Fitbit Ultra mode demonstrated good sensitivity (0.86) and accuracy (0.84), but poor specificity (0.52); conversely, the Sensitive Fitbit Ultra mode demonstrated adequate specificity (0.79), but inadequate sensitivity (0.70) and accuracy (0.71). Compared to PSG, the Fitbit Ultra significantly overestimated TST (41 min) and SE (8%) in Normal mode, and underestimated TST (105 min) and SE (21%) in Sensitive mode. Similar differences were found between Fitbit Ultra (both modes) and both brands of actigraphs. Conclusions: Despite its low cost and ease of use for consumers, neither sleep-recording mode of the Fitbit Ultra accelerometer provided clinically comparable results to PSG. Further, pediatric sleep researchers and clinicians should be cautious about substituting these devices for validated actigraphs, with a significant risk of either overestimating or underestimating outcome data including total sleep time and sleep efficiency.

Feasibility of popular m-health technologies for activity tracking among individuals with serious mental illness. (2015)

Telemedicine journal and e-health: the official journal of the American Telemedicine Association

Abstract

Obesity prevalence is nearly double among individuals with serious mental illness (SMI), including schizophrenia spectrum disorders, bipolar disorder, or major depressive disorder, compared with the general population. Emerging mobile health (m-health) technologies are increasingly available and offer the potential to support lifestyle interventions targeting weight loss, yet the practical feasibility of using these technologies in this high-risk group has not been established. We evaluated the feasibility and acceptability of popular m-health technologies for activity tracking among overweight and obese individuals with SMI. We provided wearable activity monitoring devices (FitBit [San Francisco, CA] Zip™ or Nike Inc. [Beaverton, OR] FuelBand) and smartphones (Apple [Cupertino, CA] iPhone(®) 4S) for accessing the smartphone application for each device to participants with SMI enrolled in a weight loss program. Feasibility of these devices was measured by the frequency of use over time. Acceptability was measured through qualitative follow-up interviews with participants. Ten participants with SMI wore the devices for a mean of 89% (standard deviation=13%) of the days in the study. Five participants wore the devices 100% of the time. Participants reported high satisfaction, stating the devices were easy to use, helpful for setting goals, motivational, and useful for self-monitoring. Several participants liked the social connectivity feature of the devices where they could see each other's progress on the smartphone application, noting that "friendly" competition increased motivation to be more physically active. This study supports using popular m-health technologies for activity tracking among individuals with SMI. These findings can inform the design of weight loss interventions targeting this vulnerable patient population.

Text Messaging to Reduce Inactivity Using Real-Time Step Count Monitoring in Sedentary Overweight Females (2015)

Circulation

Abstract

Introduction: Prolonged periods of of inactivity are associated with higher CV morbidity independent of level of activity. Work and lifestyle choices today are associated with long periods of sitting, e.g. computer or screen watching. Wearable activity monitors such as pedometers have focused on increasing daily activity but not on reducing inactivity. Hypothesis: Sedentary women will decrease inactivity if given a real time reminder of inactivity . Method: We developed a program to monitor real-time activity levels generated by a wearable activity monitor (Fitbit). Fitbit communicated step counts to a smartphone which sent this information to a database every 15 minutes. When 10 or fewer steps had been recorded for a 60 min period of time, an inactivity reminder text message was sent to the participant to encourage them to move about. 30 obese women with self-reported > 3 hours of inactivity per day, were enrolled, provided with a Fitbit and smartphone, and monitored for 8 weeks. Women were randomized to two groups. Gp A received inactivity reminders (IR) for the first 4 weeks followed by 4 weeks without IR. Gp B received no IR during the first 4 weeks and IR was given during the second 4 weeks. We defined inactivity as = 20 steps in a 2 hr block. We compared the percent of 2-hr blocks between 8:00 and 24:00 hr where women were inactive during the 4 weeks with IR-on vs IR-off, for each of the 2 groups. The mean number of steps per day over each 4 week block was also compared. Results: Gps A and B were similar in age (51.8 + 12.2 y), BMI (37.1 + 6.9), self-reported hypertension (27% ), diabetes (23 %), and hyperlipidemia (23%). Fitbit data was available from 27 women. Gp A (n=15) self-reported 5.2 + 2.5 hr sitting/day at work during IR-off vs 3.9 + 2.7 with IR-on (p<0.004). Gp B (n=12), who had IR turned on in the second 4 week block, self-reported no change in hours sitting with or without IR. Activity monitoring data showed that Gp A were inactive for 32 + 23% of the 2-hr periods during the 4 weeks with IR-off vs 21 + 14% with IR-on (p < 0.004). However, Gp B had no difference in inactivity periods (28 + 12% with IR-off vs 28 + 14 % with IR-on). There were no differences in mean total daily steps walked during either 4 week block with IR-on or IR-off in either Gp. Conclusion: The use of real-time text messaging to inform women that they had been inactive for more than 60 mins was associated with a reduction in number of inactive periods independent of the number of steps taken a day. However, this effect was seen only when the reminder was implemented soon after enrollment. The behavior change did not persist after the reminders stopped. This study demonstrates that automated real time reminders can reduce periods of inactivity though the timing of the intervention affected the results. Further work will explore the behavioral factors that could improve the effectiveness and sustainability of the intervention.

Comparison of Three Inexpensive Commercially Available Activity Monitors to a Previously Validated Tri-Axial Accelerometer (2015)

Medicine & Science in Sports & Exercise

Abstract

Inexpensive personal activity monitors have recently gained popularity with the general public. Given the current wide-spread use of these devices, it is important to ensure that users are attaining accurate information compared to previously validated measures. PURPOSE: To compare the accuracy of the Fitbit Force™ and One™ activity monitors in measuring steps per day and time spent in different intensities of activity with the ActiGraph GT3X™tri-axial accelerometer. METHODS: Twenty-two women, 18-26y.o. with a BMI of 23.24 ± 3.26kg/m2 wore two Fitbit Ones TM (Bra and Waist respectively), a Fitbit Force TM (Wrist), and an ActiGraph GT3X™(Waist) for 7-consecutive days. ActiGraph™ data was processed using Actilife™ V5.1.1 and the Freedson equation activity cut points. Fitbit™ data was processed using their proprietary online software. A multivariate analysis of variance (MANOVA) was used to compare steps per day and time spent in light, moderate and vigorous intensity activities between the four devices. A post-hoc test of the least squared difference was used to determine where any differences occurred. RESULTS: The activity monitors differed in time spent in light F(3,19)= 3.16, ptextgreater.05) and vigorous (F(3,19)= 3.16, ptextgreater.05) intensity activities. Post-hoc comparisons revealed that the GT3X™ differed from all three Fitbit™ devices in the measurement of time spent in light (GT3X= 89.92±36 min, Force=419.97±129.22 min, Bra=379.93±122.2 min, Waist=392.75±114.78 min) and vigorous activities (GT3X=7.05±9.63 min, Force=36.91±27.7 min, Bra=31.29±18.53 min, Waist=30.45±18.3 min). There was no difference found in the number of steps per day or minutes of moderate intensity activity recorded between the four devices. CONCLUSIONS: Fitbit™ activity monitors compare well to other previously validated objective activity monitors in the measurement of steps per day and time spent in moderate intensity activities. However, Fitbit™ devices may not accurately quantify the time spent in light and vigorous intensity activities, and therefore may not be the ideal solution for individuals' personal activity monitoring needs.

Intergenerational activity tracker program: Impact with health related outcomes on older adults (2015)

2015 Long Island Systems, Applications and Technology

Abstract

As resilient as the human body may appear to be, the aging process is inevitable and takes a physical toll on all bodies, but getting older does not equate to neglecting healthy habits. If anything, as people age it is more important to be vigilant and lead proactive lifestyles. In an effort to increase health awareness and promote a healthier lifestyle Pace University conducted a 7 week pilot study using activity trackers and Telemedical Bluetooth devices. Most Senior citizens are intimidated and unaccepting when it comes to relying on technology but through the use of activity trackers senior citizens are able to measure their progress towards a healthier lifestyle. With the data gained from the activity trackers students will be able to monitor daily activity and assess whether or not it has encouraged a positive effect on senior vital data. This paper analyzes the impact of activity trackers on senior citizen biometric data including weight, blood pressure and pulse. By tracking the amount of steps the seniors have taken over the 7 week study. We will be able to determine if an increase in physical activity has a positive effect on the vital signs of older adults. Through the use of wireless technology, senior vital sings and step tracking data are synchronized wirelessly once a week by students at Pace University and measured for any significant changes. Prior to distributing activity trackers seniors were administered Pre assessment surveys and Post surveys, after receiving the trackers. Some of the collected data included preexisting health conditions, activity habits, and willingness to try new technology. By assessing the results, this paper explores whether the activity tracker had a significant effect on senior vital signs.

A Mobile Health Driven Walking Program Improves Quality of Life but not Fitness or Fatness in Obese Sedentary Women (2015)

Circulation

Abstract

Background: Many mHealth programs exist for increasing physical activity (PA) yet few have been tested for their effects on fitness, fatness, and health related quality of life (HRQOL). Methods: We conducted a 3-month randomized trial that examined 2 message formats that promoted the 10,000 steps/day walking program. Women (n=70), aged 48+/-12 years, BMI 35.0 +/-3.8, with self-report of no regular (PA), were given a FitBit and smartphone, which automatically sent daily step data to us daily. Daily messages with feedback about meeting step goals and motivation to add 500 more daily steps/week were sent to the subject's smartphone. We compared the Multimedia Messaging Service (MMS) consisting of audio, video and graphs versus the Short Message Service (SMS), or plain text messages. Message content was the same for both groups. Outcomes included the 6-minute walk test, weight, waist and hip circumferences, BMI, SF-36, and Profile of Mood States (POMS). We hypothesized that MMS subjects would be more engaged, walk more, and attain greater gains in study outcomes. Results: We delayed messaging for the first 2 weeks to establish a daily step baseline, which was 6665 +/-2270. There were no group differences in daily steps at baseline or at any time point in the study. Daily steps in Month 1 was 6926+/-2465; Month 2, 7327+/- 3310; and Month 3, 6642+/-2914. The increases from baseline to the full Month 1 (p=0.004) and Month 1 to 2 (p=0.025) were significant but not the decrease from Month 2 to 3. The FitBit was worn 91% of study days in both groups. There were no changes or group differences in any fitness or fatness measure. Both groups similarly improved SF-36 General Health (p=0.02) and Vitality (p=0.01); and POMS Fatigue (p=0.01), Vigor (p=0.001), Tension-Anxiety (p=0.03), and Total Mood Score, (p=0.02). Conclusions: Baseline steps were about 2-3 times more than expected for sedentary women. It seems that the novelty of using a tracking device accelerated the planned progression for walking, with a much higher than expected step count initially and then a slight drop over time. An average of 10,000 steps/day was not met. There were no changes in physical measures though indicators of HRQOL improved. Contrary to our hypothesis, the different messaging formats did not affect outcomes.

Step Detection and Activity Recognition Accuracy of Seven Physical Activity Monitors (2015)

PLOS ONE

Abstract

The aim of this study was to compare the seven following commercially available activity monitors in terms of step count detection accuracy: Movemonitor (Mc Roberts), Up (Jawbone), One (Fitbit), ActivPAL (PAL Technologies Ltd.), Nike+ Fuelband (Nike Inc.), Tractivity (Kineteks Corp.) and Sensewear Armband Mini (Bodymedia). Sixteen healthy adults consented to take part in the study. The experimental protocol included walking along an indoor straight walkway, descending and ascending 24 steps, free outdoor walking and free indoor walking. These tasks were repeated at three self-selected walking speeds. Angular velocity signals collected at both shanks using two wireless inertial measurement units (OPAL, ADPM Inc) were used as a reference for the step count, computed using previously validated algorithms. Step detection accuracy was assessed using the mean absolute percentage error computed for each sensor. The Movemonitor and the ActivPAL were also tested within a nine-minute activity recognition protocol, during which the participants performed a set of complex tasks. Posture classifications were obtained from the two monitors and expressed as a percentage of the total task duration. The Movemonitor, One, ActivPAL, Nike+ Fuelband and Sensewear Armband Mini underestimated the number of steps in all the observed walking speeds, whereas the Tractivity significantly overestimated step count. The Movemonitor was the best performing sensor, with an error lower than 2% at all speeds and the smallest error obtained in the outdoor walking. The activity recognition protocol showed that the Movemonitor performed best in the walking recognition, but had difficulty in discriminating between standing and sitting. Results of this study can be used to inform choice of a monitor for specific applications.

The Effect of Fitbit Technology on Physical Activity in Inactive Adults (50+ Years of Age) (2015)

Medicine & Science in Sports & Exercise

Abstract

The Fitbit tracker is a wearable technology designed to motivate individuals to become physically active by increasing awareness of physical activity (PA) levels. The Fitbit tracks activity, sleep, nutrition, uploads wirelessly, and is tiny and discreet. Published data is limited concerning the effectiveness of Fitbit technology on increasing PA levels in inactive adult populations. PURPOSE: To study changes in PA as a result of wearing the Fitbit for 8 weeks in individuals 50+ years of age. METHODS: Sixty university employees were stratified into two groups, intervention (n=29) and comparison (n=31), based on meeting PA recommendations of 150 minutes per week. Of these employees, 18 were ages 50 and over (7 intervention; 11 comparison). Intervention group participants (not meeting ACSM PA recommendations) were given a Fitbit to wear for 8 weeks, while comparison group participants were not and were encouraged to maintain their current PA habits. Demographic, anthropometric, and biometric data, body composition, flexibility, muscular strength/endurance, and cardiorespiratory fitness were assessed prior to and following the intervention. Self-reported PA was assessed via online survey. Independent sample T-tests were used to analyze differences between intervention and control groups. Paired sample T-tests were used to analyze differences pre- and post-Fitbit intervention. RESULTS: Mean age, weight, and BMI for the adult (50+) cohort of the sample was 58±5 years, 172±27 pounds, and 28.8±3.8 kg/m2, respectively. There were no significant differences between intervention and control groups at pre-test despite a large difference in self-reported PA (intervention: 70 minutes, comparison: 399 minutes). There was a positive change in self-reported PA in the intervention group from pre- to post-intervention that trended towards significance (p=0.08). Six out of seven of the intervention group participants were meeting PA recommendations post-intervention. CONCLUSIONS: Based on these results, wearing the Fitbit for 8 weeks increased PA in inactive adults (50+) to the point of meeting/exceeding PA recommendations. Further research is needed to determine the levels of improvement for this older adult population using this technology.

Wearable Sensor/Device (Fitbit One) and SMS Text-Messaging Prompts to Increase Physical Activity in Overweight and Obese Adults: A Randomized Controlled Trial. (2015)

Telemedicine and e-Health

Abstract

BACKGROUND: Studies have shown self-monitoring can modify health behaviors, including physical activity (PA). This study tested the utility of a wearable sensor/device (Fitbit(®) One™; Fitbit Inc., San Francisco, CA) and short message service (SMS) text-messaging prompts to increase PA in overweight and obese adults. MATERIALS AND METHODS: Sixty-seven adults wore a Fitbit One tracker for 6 weeks; half were randomized to also receive three daily SMS-based PA prompts. The Fitbit One consisted of a wearable tracker for instant feedback on performance and a Web site/mobile application (app) for detailed summaries. Outcome measures were objectively measured steps and minutes of PA by intensity using two accelerometers: Actigraph™ (Pensacola, FL) GT3X+ (primary measure) at baseline and Week 6 and Fitbit One (secondary measure) at baseline and Weeks 1, 2, 3, 4, 5, and 6. RESULTS: Mixed-model repeated-measures analysis of primary measures indicated a significant within-group increase of +4.3 (standard error [SE]=2.0) min/week of moderate- to vigorous-intensity PA (MVPA) at 6-week follow-up (p=0.04) in the comparison group (Fitbit only), but no study group differences across PA levels. Secondary measures indicated the SMS text-messaging effect lasted for only 1 week: the intervention group increased by +1,266 steps (SE=491; p=0.01), +17.8 min/week MVPA (SE=8.5; p=0.04), and +38.3 min/week total PA (SE=15.9; p=0.02) compared with no changes in the comparison group, and these between-group differences were significant for steps (p=0.01), fairly/very active minutes (ptextless0.01), and total active minutes (p=0.02). CONCLUSIONS: These data suggest that the Fitbit One achieved a small increase in MVPA at follow-up and that the SMS-based PA prompts were insufficient in increasing PA beyond 1 week. Future studies can test this intervention in those requiring less help and/or test strategies to increase participants' engagement levels.

Validation of Fitbit-Flex as a measure of free-living physical activity in a community-based phase III cardiac rehabilitation population (2016)

European Journal of Preventive Cardiology

Abstract

Background: Accurate physical activity monitoring is important for cardiac patients. Novel activity monitoring devices may enable precise measurement of physical activity. This study aimed to validate Fitbit-Flex against Actigraph accelerometer for monitoring physical activity. Design: A validation study with a comparative design. Methods: Cardiac patients and family members participating in community-based exercise programs wore Fitbit-Flex and Actigraph simultaneously over four days to monitor daily step counts and minutes of moderate to vigorous physical activity (MVPA). Results: Participants (N?=?48) comprised 52.1% males, with a mean age of 65.6?±?6.9 years and 58.9% had a cardiac diagnosis. Fitbit-Flex and Actigraph were significantly correlated in males, females, total participants and cardiac patients for step counts (r?=?.96; r?=?.95; r?=?.95; r?=?.95), though less so for MVPA (r?=?.81; r?=?.65, r?=?.74; r?=?.71). As step counts increased the differences between Fitbit-Flex and Actigraph also increased. Fitbit-Flex over-estimated step counts in females (556 steps/day), males (1462 steps/day) and total participants (1038 steps/day) as well as for minutes of MVPA in females (4?min/day), males (15?min/day) and total participants (10?min/day). Fitbit-Flex had high sensitivity and specificity in classifying participants who achieved the recommended physical activity guidelines. Conclusion: Fitbit-Flex is accurate in assessing attainment of physical activity guideline recommendations and is useful for monitoring physical activity in cardiac patients. The device does, however, slightly over-estimate step counts and MVPA.

Integrating Mobile health and Physical Activity to reduce the burden of Chronic low back pain Trial (IMPACT): a pilot trial protocol (2016)

BMC Musculoskeletal Disorders

Abstract

It is well recognised that low back pain is a significant public health problem and engagement in moderate levels of physical activity is associated with positive outcomes. Conservative active care, such as exercise, is effective in reducing pain and disability associated with chronic low back pain. However, a rapid decline in clinical outcomes is commonly seen after discharge from treatment. We will conduct a randomised controlled trial to investigate the effectiveness of a mobile health supported physical activity intervention (compared to standard care) in care-seeking, pain and disability in people with chronic low back pain after discharge from treatment. We will recruit 68 patients with chronic low back pain following discharge from an outpatient hospital program, who will be randomly allocated to the physical activity intervention (n?=?34) or the standard care group (n?=?34) and monitored for 6 months. The physical activity intervention will involve a physical activity advice booklet, a face-to-face health coaching session and 12 fortnightly follow-up telephone-based health coaching sessions. This intervention will be supported by provision of a specifically designed web app and a physical activity monitoring device (FitBit). The standard care group will receive the physical activity advice booklet only. This pilot trial will investigate a new model to prevent clinical decline in people following conservative treatment for chronic low back pain. If proven to be effective, this approach will constitute a major advance in the management of low back pain. Chronic patients who experience recurrent pain and disability after treatment are prone to seek additional care in the form of physiotherapy, medication, emergency department attendance, specialist consultation or spinal surgery. This model aims to maintain functional levels and reduce care-seeking empowering patients to self-manage their low back pain by offering them a contemporary patient-centered physical activity program with the support of mobile health technology. The outcomes of this trial will have immediate implications for clinical practice.

Real-World Walking Performance of Individuals with Lower-Limb Amputation Classified as Medicare Functional Classification Level 2 and 3 (2016)

Journal of Prosthetics and Orthotics

Abstract

Introduction: Lower-limb prostheses are prescribed based on each patient's individual needs. However, there is no standardized method to identify a patient's needs, which results in difficulty and variability in making prosthetic recommendations. Using measures of real-world walking performance in conjunction with clinical measures and the clinician's expertise may be an ideal way to ensure that patients receive the optimal prosthetic treatment. Currently, there is only limited understanding of the relationship among in-clinic performance-based measures, real-world walking performance, and K-level. Therefore, the purpose of this study was to investigate outcomes of in-clinic performance-based evaluations and real-world walking performance measures for individuals with unilateral lower-limb amputation classified as K2 and K3. Materials and Methods: Twenty-seven individuals with unilateral lower-limb amputation classified as K2 or K3 functional levels underwent a clinical assessment visit that included a 10-m walk test and a 6-minute walk test. After the assessment, the individuals were equipped with an activity monitor, which they wore for a 7-day observation period. From these data, participants' self-selected walking speed, total distance walked in 6 minutes, total number of steps taken during the observation period, total number of active minutes during the observation period, and percentage of time spent in low-, moderate-, and high-intensity activity during the observation period were calculated. Results: Individuals classified as K2 had, on average, a significantly slower self-selected walking speed, significantly shorter distance walked in 6 minutes, significantly lower total step count, and a significantly fewer number of active minutes when compared with those classified as K3. Although not significant, trends in activity intensity were seen with individuals classified as K2, spending a larger percentage of time in low-intensity activity and less percentage of time in high-intensity activity when compared with those classified as K3. Conclusions: This study quantified real-world walking performance, in conjunction with in-clinic performance-based data, for individuals classified as K2 and K3 and showed significant differences between the 2 groups. This study is a step forward toward developing an objective and standardized method to classify an individual's functional ability and make prosthetic recommendations.

The Electronic CardioMetabolic Program (eCMP) for Patients With Cardiometabolic Risk: A Randomized Controlled Trial. (2016)

Journal of Medical Internet Research

Abstract

BACKGROUND Effective lifestyle interventions targeting high-risk adults that are both practical for use in ambulatory care settings and scalable at a population management level are needed. OBJECTIVE Our aim was to examine the potential effectiveness, feasibility, and acceptability of delivering an evidence-based Electronic Cardio-Metabolic Program (eCMP) for improving health-related quality of life, improving health behaviors, and reducing cardiometabolic risk factors in ambulatory care high-risk adults. METHODS We conducted a randomized, wait-list controlled trial with 74 adults aged =18 years recruited from a large multispecialty health care organization. Inclusion criteria were (1) BMI =35 kg/m(2) and prediabetes, previous gestational diabetes and/or metabolic syndrome, or (2) BMI =30 kg/m(2) and type 2 diabetes and/or cardiovascular disease. Participants had a mean age of 59.7 years (SD 11.2), BMI 37.1 kg/m(2) (SD 5.4) and were 59.5% female, 82.4% white. Participants were randomized to participate in eCMP immediately (n=37) or 3 months later (n=37). eCMP is a 6-month program utilizing video conferencing, online tools, and pre-recorded didactic videos to deliver evidence-based curricula. Blinded outcome assessments were conducted at 3 and 6 months postbaseline. Data were collected and analyzed between 2014 and 2015. The primary outcome was health-related quality of life. Secondary outcomes included biometric cardiometabolic risk factors (eg, body weight), self-reported diet and physical activity, mental health status, retention, session attendance, and participant satisfaction. RESULTS Change in quality of life was not significant in both immediate and delayed participants. Both groups significantly lost weight and reduced waist circumference at 6 months, with some cardiometabolic factors trending accordingly. Significant reduction in self-reported anxiety and perceived stress was seen in the immediate intervention group at 6 months. Retention rate was 93% at 3 months and 86% at 6 months post-baseline. Overall eCMP attendance was high with 59.5-83.8% of immediate and delayed intervention participants attending 50% of the virtual stress management and behavioral lifestyle sessions and 37.8-62.2% attending at least 4 out of 7 in-person physical activity sessions. The intervention received high ratings for satisfaction. CONCLUSIONS The technology-assisted eCMP is a feasible and well-accepted intervention and may significantly decrease cardiometabolic risk among high-risk individuals.

Comparison of consumer and research monitors under semistructured settings (2016)

Medicine and Science in Sports and Exercise

Abstract

PURPOSE: This study evaluated the relative validity of different consumer and research activity monitors during semi-structured periods of sedentary activity, aerobic exercise and resistance exercise. METHODS: A total of 52 participants (28 males) ages 18-65 performed 20-minutes of self-selected sedentary activities, 25-minutes of aerobic exercise, and 25-minutes of resistance exercise, with 5-minutes rest between each activity. Each participant wore five wrist-worn consumer monitors [Fitbit Flex (FBF), Jawbone UP 24 (JU24), Misfit Shine (MS), Nike+Fuelband SE (NFS), Polar Loop (PL)] and two research monitors [Actigraph GT3X+ (GT3X+) on the waist, and BodyMedia Core (BMC) on the arm] while being concurrently monitored with the Oxycon Mobile (OM), a portable metabolic system. The energy expenditure (EE) from different activity sessions were measured by OM and estimated by all monitors. RESULTS: Mean absolute percent error (MAPE) values for the full 80-minute protocol ranged from 15.3% (BMC) to 30.4% (MS). The EE estimates from the GT3X+ was found to be equivalent to those from the OM (±10% equivalence zone: 285.1, 348.5). Correlations between OM and the various monitors were generally high (ranges between 0.71 and 0.90). Three monitors had MAPE values less than 20% for sedentary activity: BMC (15.7%), MS (18.2%), and NFS (20.0%). Two monitors had MAPE values less than 20% for aerobic: BMC (17.2%) and NFS (18.5%). None of the monitors had MAPE values less than 25% for resistance exercise. CONCLUSION: Overall, the research monitors and the FBF, JU24, and NFS provided reasonably accurate total EE estimates at the individual level. However, larger error was evident for individual activities, especially resistance exercise. Further research is needed to examine these monitors across various activities and intensities, and under real-world conditions.

Impact of Digital Health Methods for Weight Management on Atherosclerotic Cardiovascular Disease Risk in “at-risk” Women (2016)

Canadian Journal of Cardiology

Abstract

BACKGROUND: The LEARN (Lifestyle-Exercise-AttitudesRelationships-Nutrition) program is an established weight loss program that may have positive impact on atherosclerotic cardiovascular disease (ASCVD) risk, but can be limited in application due to requirement of weekly meetings. We examine the changes in lipid profiles and 10-year ASCVD risk score in a prospective study of a novel format of the LEARN program utilizing digital health (DH) tools compared to a traditional meeting format. METHODS: Eighty-nine women (age: 55.59.3 years, BMI: 34.35.2 kg/m2) with 2 CVD risk factors were prospectively randomized to a 3-month duration LEARN program delivered either through a DH approach using internet-based podcasts with an online interactive message board and FitBit (San Francisco, CA) activity trackers (DH group), or traditional weekly meetings (Control group¼C). Lipids (total cholesterol, LDL, HDL, triglycerides, NMR lipoprofile patterns) were analyzed at baseline and 3 months (LabCorp, Burlington, NC). Statistical analysis was performed (ANOVA and matched pairs). S9 CWHHS Abstracts RESULTS: Fifty-eight women (DH¼30, C¼28) completed baseline and 3-month testing. At baseline there were no differences in hypertension (52%), fasting glucose impairment (29%), or smoking history (31%). However, DH women were younger than C (52 vs 59 years, p¼0.007), and had lower 10-year ASCVD risk scores (6.25.1% vs 3.73.7, p¼0.042). Upon LEARN program completion, mean weight loss (2.94.1 kg), was similar in both groups (p¼0.773), and there was no change in SBP or ASCVD risk scores for either group. Lipid profile parameters showed favorable trends for both groups. A significant reduction was seen only for triglycerides in DH group (11940 at baseline vs 9826 at 3 months, p¼0.0009). Similarly, NMR lipoprofile analysis showed a significant reduction in large VLDL particles for DH group (5.33.6 at baseline vs 3.42.2 at 3 months, p¼0.0188). CONCLUSION: In women with ASCVD risk completing a 3 month LEARN program, Digital Health administration resulted in similar decreases in weight loss as traditional methods, but more favorable cardiometabolic results. This study demonstrates the feasibility and effectiveness of novel Digital Health methods for a weight loss program with immediate beneficial impact on weight loss and triglyceride levels. 19 C

Using a Community Wellness Program Pairing People with Shelter Dogs to Increase Physical Activity (2016)

Medicine & Science in Sports & Exercise

Abstract

FosterFit™ is a community wellness program centered on the pairing of people with shelter dogs (Savannah, GA). Participants are matched with a shelter dog for 10 weeks and encouraged to 1) walk with their dog for at least 30 min/d and 2) consume a plant-based diet. Participants meet regularly for group sessions and events. PURPOSE: We examined whether this community program led to change in objectively measured physical activity (PA). We also examined program acceptability and change in psychosocial outcomes. METHODS: Steps/d and moderate-to-vigorous PA min/d were assessed via the FitBit Flex™ pre- and post-program. Participants rated their satisfaction (‘Overall, how would you rate the FosterFit wellness program?') and success with the exercise and diet components (‘Over the past 10 weeks, how much success have you had in following the exercise plan [healthy eating plan] set out by FosterFit?'). Surveys (stress, depressive symptoms, exercise self-efficacy) were also collected pre- and post-program. RESULTS: Nine participants (6 female, mean age 37 yrs, all non-Hispanic white or mixed race) joined the program and completed a baseline assessment. Program completion rate was 78% (7/9). Despite a small sample size, the program led to medium effect sizes for change in steps (d=0.41) and exercise self-efficacy (d=0.48), Average daily steps increased by 1,494, with increases in four of six participants with complete PA data. There were also small effect sizes for change in depressive symptoms (d=-0.20) and perceived stress (d=-0.19) and five of seven participants saw improvements in at least one psychosocial outcome. Of participants completing a program evaluation (n=8), 75% rated the program as ‘good' or ‘excellent', 86% reported ‘some success' or ‘a lot of success' following the exercise plan, and 75% reported ‘some success' or ‘a lot of success' following the healthy eating plan. and. Additionally, 6 participants permanently adopted their foster dog. CONCLUSION: These data demonstrate the feasibility of pairing people with shelter dogs for 10 weeks, and provide preliminary evidence that fostering a dog increases PA and mental wellbeing. Community-based interventions centered on dog fostering could be a novel approach to wellness promotion with added humanitarian benefits.

Development of a Mobile Phone-Based Weight Loss Lifestyle Intervention for Filipino Americans with Type 2 Diabetes: Protocol and Early Results From the PilAm Go4Health Randomized Controlled Trial (2016)

JMIR Research Protocols

Abstract

Background: Filipino Americans are the second largest Asian subgroup in the United States, and were found to have the highest prevalence of obesity and type 2 diabetes (T2D) compared to all Asian subgroups and non-Hispanic whites. In addition to genetic factors, risk factors for Filipinos that contribute to this health disparity include high sedentary rates and high fat diets. However, Filipinos are seriously underrepresented in preventive health research. Research is needed to identify effective interventions to reduce Filipino diabetes risks, subsequent comorbidities, and premature death. Objective: The overall goal of this project is to assess the feasibility and potential efficacy of the Filipino Americans Go4Health Weight Loss Program (PilAm Go4Health). This program is a culturally adapted weight loss lifestyle intervention, using digital technology for Filipinos with T2D, to reduce their risk for metabolic syndrome. Methods: This study was a 3-month mobile phone-based pilot randomized controlled trial (RCT) weight loss intervention with a wait list active control, followed by a 3-month maintenance phase design for 45 overweight Filipinos with T2D. Participants were randomized to an intervention group (n=22) or active control group (n=23), and analyses of the results are underway. The primary outcome will be percent weight change of the participants, and secondary outcomes will include changes in waist circumference, fasting plasma glucose, glycated hemoglobin A1c, physical activity, fat intake, and sugar-sweetened beverage intake. Data analyses will include descriptive statistics to describe sample characteristics and a feasibility assessment based on recruitment, adherence, and retention. Chi-square, Fisher's exact tests, t-tests, and nonparametric rank tests will be used to assess characteristics of randomized groups. Primary analyses will use analysis of covariance and linear mixed models to compare primary and secondary outcomes at 3 months, compared by arm and controlled for baseline levels. Results: Recruitment was completed in January, 2016, and participant follow-up continued through June, 2016. At baseline, mean age was 57 years, 100% (45/45) of participants self-identified as Filipinos, and the cohort was comprised of 17 males and 28 females. Overall, participants were obese with a baseline mean body mass index of 30.2 kg/m2 (standard deviation 4.9). The majority of participants were immigrants (84%, 38/45), with 47% (21/45) living in the United States for more than 10 years. One third of all participants (33%, 15/45) had previously used a pedometer. Conclusions: This study will provide preliminary evidence to determine if the PilAm Go4Health weight loss lifestyle intervention is feasible, and if the program demonstrates potential efficacy to reduce risks for metabolic syndrome in Filipinos with T2D. Positive results will lend support for a larger RCT to evaluate the effectiveness of the PilAm Go4Health intervention for Filipinos.

Evaluation of pedometry as a patient-centered outcome in patients undergoing hematopoietic cell transplant (HCT): a comparison of pedometry and patient reports of symptoms, health, and quality of life (2016)

Quality of Life Research

Abstract

Aims: We evaluated pedometry as a novel patient-centered outcome because it enables passive continuous assessment of activity and may provide information about the consequences of symptomatic toxicity complementary to self-report. Methods: Adult patients undergoing hematopoietic cell transplant (HCT) wore pedometers and completed PRO assessments during transplant hospitalization (4 weeks) and 4 weeks post-discharge. Patient reports of symptomatic treatment toxicities (single items from PRO-CTCAE, http://healthcaredelivery.cancer.gov/pro-ctcae) and symptoms, physical health, mental health, and quality of life (PROMIS® Global-10, http://nih.promis.org), assessed weekly with 7-day recall on Likert scales, were compared individually with pedometry data, summarized as average daily steps per week, using linear mixed models. Results: Thirty-two patients [mean age 55 (SD = 14), 63 % male, 84 % white, 56 % autologous, 43 % allogeneic] completed a mean 4.6 (SD = 1.5, range 1–8) evaluable assessments. Regression model coefficients (ß) indicated within-person decrements in average daily steps were associated with increases in pain (ß = -852; 852 fewer steps per unit increase in pain score, p < 0.001), fatigue (ß = -886, p < 0.001), vomiting (ß = -518, p < 0.01), shaking/chills (ß = -587, p < 0.01), diarrhea (ß = -719, p < 0.001), shortness of breath (ß = -1018, p < 0.05), reduction in carrying out social activities (ß = 705, p < 0.01) or physical activities (ß = 618, p < 0.01), and global physical health (ß = 101, p < 0.001), but not global mental health or quality of life. Conclusions: In this small sample of HCT recipients, more severe symptoms, impaired physical health, and restrictions in the performance of usual daily activities were associated with statistically significant decrements in objectively measured daily steps. Pedometry may be a valuable outcome measure and validation anchor in clinical research.

Video and computer-based interactive exercises are safe and improve task-specific balance in geriatric and neurological rehabilitation: a randomised trial (2016)

Journal of Physiotherapy

Abstract

Question: Does adding video/computer-based interactive exercises to inpatient geriatric and neurological rehabilitation improve mobility outcomes? Is it feasible and safe? Design: Randomised trial. Participants: Fifty-eight rehabilitation inpatients. Intervention: Physiotherapist-prescribed, tailored, video/computer-based interactive exercises for 1 hour on weekdays, mainly involving stepping and weight-shifting exercises. Outcome measures: The primary outcome was the Short Physical Performance Battery (0 to 3) at 2 weeks. Secondary outcomes were: Maximal Balance Range (mm); Step Test (step count); Rivermead Mobility Index (0 to 15); activity levels; Activity Measure for Post Acute Care Basic Mobility (18 to 72) and Daily Activity (15 to 60); Falls Efficacy Scale (10 to 40), ED5D utility score (0 to 1); Reintegration to Normal Living Index (0 to 100); System Usability Scale (0 to 100) and Physical Activity Enjoyment Scale (0 to 126). Safety was determined from adverse events during intervention. Results: At 2 weeks the between-group difference in the primary outcome (0.1, 95% CI –0.2 to 0.3) was not statistically significant. The intervention group performed significantly better than usual care for Maximal Balance Range (38mm difference after baseline adjustment, 95% CI 6 to 69). Other secondary outcomes were not statistically significant. Fifty-eight (55%) of the eligible patients agreed to participate, 25/29 (86%) completed the intervention and 10 (39%) attended textgreater 70% of sessions, with a mean of 5.6 sessions (SD 3.3) attended and overall average duration of 4.5hours (SD 3.1). Average scores were 62 (SD 21) for the System Usability Scale and 62 (SD 8) for the Physical Activity Enjoyment Scale. There were no adverse events. Conclusion: The addition of video/computer-based interactive exercises to usual rehabilitation is a safe and feasible way to increase exercise dose, but is not suitable for all. Adding the exercises to usual rehabilitation resulted in task-specific improvements in balance but not overall mobility. Registration: ACTRN12613000610730. [van den Berg M, Sherrington C, Killington M, Smith S, Bongers B, Hassett L, Crotty M (2016) Video and computer-based interactive exercises are safe and improve task-specific balance in geriatric and neurological rehabilitation: a randomised trial. Journal of Physiotherapy 62: 20–28]

Accuracy of the Fitbit for Measuring Preschoolers' Physical Activity (2016)

Medicine & Science in Sports & Exercise

Abstract

Identifying an accurate and feasible measurement tool for assessing physical activity (PA) is a priority in epidemiologic research. Although wearable activity monitors yields high potentials for being utilized in research, little information is available on the accuracy of those monitors in young children. PURPOSE: To evaluate the accuracy of the Fitbit Flex (FF) activity monitor for assessing preschooler' PA and sedentary behavior (SED) in free-living conditions, using the previously validated accelerometer-based monitor, ActiGraph GT3X+ (AG), as a criterion measure. METHODS: 27 preschool age children (Girl: 41%, Age: 4.9 ± 1.1 yrs, BMI: 16.5 ± 1.6 kgtextperiodcenteredm2) wore AG (on dominant hip) and FF (on the non-dominant wrist) simultaneously for 24 hours. Using age-appropriate cutpoints for AG (Pate's 2006) and manufacturer-specific algorithms for FF, data from AG (15-sec epoch) and FF (60-sec epoch) were reduced in terms of time spent (min/day) in SED, moderate-to-vigorous PA (MVPA), and Total PA (TPA). Pearson correlations was used examine agreement between the estimates from AG and FF. Mean absolute percent errors (MAPEs) were computed as measurement errors. Equivalence test using SAS PROC MIXED procedure was used to compare the 85% confidence intervals (CI) of the estimates from the FF with the respective equivalence zone (EZ; ± 15% of the mean estimates) from the AG. RESULTS: The FF yielded significantly equivalent estimates of SED (FF: Mean (M) = 673 min, 85% CI: 632-714 min vs. AG: M = 631, EZ: 537-726 min) and TPA (FF: M = 337 min, 85% CI: 309-365 min vs. AG: M = 379 min, EZ: 303-435 min) as the AG. However, the estimate of MVPA from the FF was not equivalent to that from the AG. Correlations between FF and AG were consistently high for SED (r = 0.86, P textless.01) and TPA (r = 0.70, P textless.01), but moderate for MVPA (r = 0.59, P textless.01). MAPEs were 9.2, 70.1, and 14.5% for SED, MVPA, and TPA, respectively. CONCLUSIONS: The accuracy of the FF for estimating SED and TPA was supported by the high correlations and significant equivalence to AG. However, relatively large MAPEs and results from equivalence test suggest that the MVPA estimates from the FF were not equivalent to those from AG. Future studies utilizing FF in preschool age children should be aware of these findings. Efforts to replicate our findings with longer monitoring in larger samples are warranted

Fitbit Provides Valid Measures of Sedentary and Physical Activity in Preschool Children (2016)

Circulation

Abstract

Introduction: Both monitoring and promoting PA in children, two important areas in epidemiologic research, require the use of accurate and feasible measurement tools. The Fitbit activity monitor is one of the most widely commercialized, consumer-based activity monitors and its validity and reliability has been determined in adults; however, little research has determined the validity of Fitbit activity monitor in measuring sedentary behavior and PA in preschool-age children. Hypothesis: We hypothesized that, when comparing to the direct observation (DO), the Fitbit Flex (FF) activity monitor would accurately measure the time spent in sedentary, moderate-to-vigorous PA (MVPA), and Total (light-to-vigorous) PA in preschool-age children. Methods: A total of 28 preschool age children (Female: 46%, Age: 4.6 ± 1.0 yrs, BMI: 16.4 ± 1.5 kg·m2) wore the FF (on the wrist) and were directly observed while performing a set of unstructured and structured activities with varying intensity levels such as watching TV, drawing, playing with toys, playing soccer, and shooting basket. Breath-by-breath oxygen consumption were also measured using Oxycon Mobile indirect calorimetry (OM). Data from the FF (PA counts) and the OM (VO2 ml·kg·min-1) were summarized in terms of minute-by-minute basis. The validity of FF for measuring the time spent in sedentary behavior, MVPA, and Total PA against DO was examined using three ways: (1) Pearson correlation examining the relatively agreement between FF and DO; (2) Mean absolute percent error (MAPE) examining the measurement errors of FF; and (3) One-sided equivalence test examining the absolute agreement between FF and DO by comparing the 90% confidence intervals (CI) of the estimates from the FF with the respective equivalence zone (EZ; ± 10% of the mean estimates from the DO). Results: On average, oxygen consumptions for sedentary, light, MVPA were 9.0 ± 1.6, 14.9 ± 3.9, 23.5 ± 5.5, and 33.8 ± 5.6 ml·kg·min-1, respectively. Correlations between FF and DO were consistently high for sedentary (r = 0.81, P <.001), MVPA (r = 0.62, P <.001), and Total PA (r = 0.81, P <.001). MAPEs were 8.2%, 21.1%, and 8.2% for sedentary, MVPA, and Total PA, respectively. The estimates from the FF were not significantly equivalent to those from the DO; sedentary (FF: Mean (M) = 7.8 min, 90% CI: 7.3 - 8.3 min vs. DO: M = 6 min, EZ: 5.4 - 6.6 min), MVPA (FF: M = 7.2 min, 90% CI: 6.2 - 8.2 min vs. DO: M = 12 min, EZ: 10.8 - 13.2 min), and Total PA (FF: M = 14.0 min, 90% CI: 13.6 - 14.4 min vs. DO: M = 16 min, EZ: 14.4 - 17.6 min). Conclusions: The validity of the FF was supported by the consistently high correlations with the criterion measure. However, inconsistent results from equivalence testing warrants subsequent assessment of the validity of the FF as it has a high potential for replacing research-based PA monitors in preschool-age children.

A Study to Determine the Most Popular Lifestyle Smartphone Applications and Willingness of the Public to Share Their Personal Data for Health Research. (2016)

Telemedicine and e-Health

Abstract

INTRODUCTION: Smartphone lifestyle applications (apps) and wearable fitness-tracking devices collect a wealth of data that could provide research insights to support prevention and treatment of obesity and chronic diseases. The aim of this study was to pilot a survey to explore patterns of behavioral tracking using smartphone lifestyle apps and individuals' willingness to share their app-generated data. METHODS: A cross-sectional Web-based survey was conducted within a university setting. The 35-item survey asked participants about their self-tracking patterns; use of lifestyle apps and wearable devices; how their self-tracked health data could be useful to them; and any restrictions they would impose on sharing personal data. Responses were tabulated and analyzed for trends. RESULTS: The survey was completed by 101 participants. On average, 3.1 (standard deviation [SD] ±1.9) health and fitness apps were installed by current app users (n?=?85), with MyFitnessPal, MapMyRun, Nike+, and Fitbit being most popular. Most participants were willing to share their personal health data for research (77%). Those who did not normally share their health-tracking data were more likely than sharers to be concerned about privacy (odds ratio [OR]?=?5.93; 95% confidence interval [95% CI]?=?2.09-16.78), as were those not identifying with the quantified-self movement compared with those who were (OR?=?5.04; 95% CI?=?1.64-15.50). DISCUSSION: Participants were generally willing to share personal data, thus increasing the potential for these data to inform public health research and for use in targeted personalized program and intervention development. CONCLUSIONS: Opportunities for partnerships between researchers and commercial app developers or industry could improve public health research and practice.

Accuracy of Wristband Activity Monitors during Ambulation and Activities. (2016)

Medicine & Science in Sports & Exercise

Abstract

PURPOSE: The main purpose of this study was to examine the accuracy of wristband activity monitors on measuring step counts at prescribed speeds on a treadmill and under short-bouts of common daily activities. METHODS: Thirty healthy young adults wore three wristband activity monitors on both wrists while walking or jogging on a treadmill at different speeds (54, 80, 107, and 134 m·min) and performing six different common daily activities for 5 minutes each. The monitors included the Fitbit Flex, Garmin Vivofit, and Jawbone UP. The common daily activity conditions included 2 sitting activities (playing a tablet computer game, folding laundry), 2 walking activities (pushing a stroller, carrying a bag), and 2 stair climbing activities (down, up). Absolute percentage error (APE) scores were computed to examine the accuracy between actual observed steps and monitor-detected steps. RESULTS: Under the treadmill condition, the APE ranged between 1.5% and 9.6%. Accuracy was improved at faster speeds (134 m·min) for all the monitors (APE < 2.5%). In the common daily activity conditions, substantial step counts were registered when folding laundry. All monitors significantly underestimated actual steps (all APE >33%) when pushing a stroller. Higher APE was observed when wore on the dominant wrist under the common daily activity conditions. CONCLUSIONS: The wristband activity monitors examined were more accurate for measuring step counts between 80 m·min to 134 m·min as compared to a slower speed. Accuracy under each common daily activity condition ranged widely between monitors and activity, with less error when worn on the nondominant wrist. These results will help to inform researchers on the use and accuracy of wristband activity monitors for future studies.

Validating The Latest Commercial Physical Activity Monitors (2016)

Medicine & Science in Sports & Exercise

Abstract

PURPOSE: Completing 10,000 steps per day is recommended by the World Health Organisation, the US Centre for Disease Control and the Australian National Heart Foundation. Physical activity monitors (PAM) are popular and have become increasingly available, marketed for their ability to count steps. However, limited data is available on the newer monitors (Fitbits/Jawbone) in comparison to the Actigraph, a validated research-based accelerometer, or a visual step counts, at multiple speeds and placement sites. This study aimed to compare these newer PAMs to the Actigraph and a visual count 1) across different speeds; and 2) at different placement sites in a lab based treadmill test. METHODS: Participants wore an Actigraph wGT3X-BT and Fitbit One around the waist and each wrist, as well as a Fitbit Flex, Fitbit Charge HR and Jawbone UP24 on each wrist while performing 5 x 3 minute bouts on a treadmill at multiple speeds (5.0, 6.5, 8.0, 10.0 and 12.0km/h). A video of their lower limbs was recorded for a visual count (criterion measure) to be completed in duplicate. Paired sample t-tests to test for any significant differences and Spearman's correlations for associations, between the device and visual count were completed. RESULTS: 31 participants (61% male) aged 24.3±5.2yrs ranging in BMI (18.6kg/m2 to 29.9kg/m2) were recruited. Across all speeds the Actigraph (waist) and Fitbit One (waist) were accurate when compared to visual counts (p,0.001), with the Fitbit One (waist) being the most accurate PAM at slower speeds (5.0, 6.5km/h). All PAMs worn around the wrist became more accurate at the faster speeds with the strongest associations at 8.0km/h=Fitbit Charge (r=0.87, p,0.01); 10.0km/h=Fitbit One (r=0.91, p<0.01) and 12.0km/h=Jawbone (r=0.90, p<0.01). Most PAMS, excluding the Fitbit One significantly undercounted at 5.0 and 6.5km/h (p>0.05). At the higher speeds (10.0, 12.0kph) there were no significant differences (p<0.05) between step counts for all devices. CONCLUSION: The step count accuracy of PAMs was affected by speed and placement site. The Fitbit One (waist) was the most accurate PAM across all speeds. Other PAMs worn on the wrist were accurate at the faster speeds only. Future studies should look at the effect of body mass index and outdoor free living environments on the accuracy of these newer PAMs.

Gender Differences in Regional Brain Activity in Patients with Chronic Primary Insomnia: Evidence from a Resting-State fMRI Study (2016)

Journal of Clinical Sleep Medicine

Abstract

Study Objectives: To explore the regional brain activities in patients with chronic primary insomnia (PCPIs) and their sex differences. Methods: Forty-two PCPIs (27 females, 15 males) and 42 good sleepers (GSs; 24 females, 18 males) were recruited. Six PCPIs (3 males, 3 females) were scanned twice by MRI to examine the test-retest reliability. Amplitude of low frequency fluctuation (ALFF) method was used to assess the local brain features. The mean signal values of the different ALFF areas were analyzed with a receiver operating characteristic (ROC) curve. Simple linear regression analysis was performed to investigate the relationships between clinical features and different brain areas. Results: Both female and male PCPIs showed higher ALFF in the temporal lobe and occipital lobe, especially in female PCPIs. Female PCPIs had lower ALFF in the bilateral cerebellum posterior lobe, left dorsolateral prefrontal cortex, and bilateral limbic lobe; however, male PCPIs showed lower ALFF in the left occipital gyrus. The mean signal value of the cerebellum in female PCPIs showed negative correlations with negative emotions. Compared with male PCPIs, female PCPIs showed higher ALFF in the bilateral middle temporal gyrus and lower ALFF in the left limbic lobe. The different areas showed high test-retest stability (Clusters of contiguous volumes = 1080 mm3 with an intraclass correlation coefficient = 0.80) and high degree of sensitivity and specificity. Conclusions: Female PCPIs showed more regional brain differences with higher and lower ALFF responses than male PCPIs. However, they shared analogous excessive hyperarousal mechanism and wide variations in aberrant brain areas.

Men on the Move-Nashville: Feasibility and Acceptability of a Technology-Enhanced Physical Activity Pilot Intervention for Overweight and Obese Middle and Older Age African American Men. (2016)

American Journal of Men's Health

Abstract

Men on the Move-Nashvillewas a quasi-experimental, 10-week pilot physical activity intervention. A total of 40 overweight or obese African American men ages 30 to 70 (mean age = 47) enrolled in the intervention. Participants attended 8 weekly, 90-minute small group sessions with a certified personal trainer. Each session consisted of discussions aimed to educate and motivate men to be more physically active, and an exercise component aimed to increase endurance, strength, and flexibility. Throughout each week, men used wearable activity trackers to promote self-monitoring and received informational and motivational SMS text messages. Of the 40 enrolled men, 85% completed the intervention, and 80% attended four or more small group sessions. Additionally, 70% of participants successfully used the activity tracker, but only 30% of men utilized their gym memberships. Participants benefited from both the small group discussions and activities through increasing social connection and guidance from their trainer and group members. These African American men reported being motivated to engage in physical activity through each of these technologies. Men reported that the activity trackers provided an important extension to their social network of physically active people. The intervention resulted in significant increases in men's self-reported levels of light, moderate, vigorous, and sports-related physical activities, and high-density lipoprotein cholesterol levels, and significant decreases in weight and body fat percentage with small, moderate and large effects shown. Including technology and didactic components in small group-based interventions holds promise in motivating African American men to increase their physical activity.

Assessment, Assurance, and Actuation: The Use of Activity Trackers to Monitor Physical Activity in a Pediatric Population with Congenital Heart Disease (2016)

Canadian Journal of Cardiology

Abstract

BACKGROUND: Physical activity (PA) trackers have demonstrated the potential to improve the PA habits of pediatric patients with congenital heart disease (CHD). This study was designed to reach patients across the province of British Columbia in order to assess levels of PA in a pediatric CHD population, provide assurance to patients and families regarding exercise capacity, and actuate an increase in PA. METHODS: Eligible patients (9-18 years old with a diagnosis of CHD) were recruited during their regular clinic appointments. Baseline testing involved PA measurement using an accelerometer (GT3X+ or GT9X ActiGraph LLC). Participants were then given an activity tracker (FitBit Charge HRTM) to wear for 16 weeks (late October-early February). Their activity was monitored during this intervention by an exercise physiologist who would interact with participants on a bi-weekly basis. Introduction to the exercise physiologist was carried out over SkypeTM; subsequent interaction was conducted by email in order to eliminate geography as a barrier to participation. PA was re-assessed after 16 weeks with an accelerometer. RESULTS: Sixty-six subjects are currently enrolled and 21 subjects (8M, 13F) have completed the intervention thus far; recruitment success rate is 97%. There were no significant changes between baseline and follow-up daily step count (8384`2544 vs. 7524`1908, respectively) or moderate-to- vigorous PA (54`21 vs. 48`15 mins respectively; all p>0.05). The mean temperature ranged from 6 C at the beginning of the study period to 0 C at the end of the study period; mean snow cover ranged from 0.0cm to 9.0cm over the same time points. Participant compliance with the activity tracker remained high throughout the intervention; wear-time averaged 5.7 days per week. Of those (n1/416) who completed a voluntary feedback survey 63% found the activity tracker useful as a motivational tool. CONCLUSION: It is difficult to comment on the motivational power of the intervention because changes in weather likely acted as a confounding variable in this cohort. However, use of an activity tracker inspired interest in PA over the winter months and may have blunted the seasonal decline in PA that has been previously established in this age group. Subjects currently enrolled will provide the opportunity to compare the efficacy of the intervention across various seasons. The success of recruitment and high rates of compliance indicate that pediatric patients (and their families) are aware of the importance of PA and are seeking assurance regarding current activity habits and, for some, actuation to improve upon them.

Accuracy of Wrist and Hip-worn Commercial Physical Activity Monitors In Free Living Conditions (2016)

Medicine & Science in Sports & Exercise

Abstract

It is recommended that individuals participate in 150 minutes of moderate intensity physical activity or 75 minutes of vigorous physical activity each week. Commercially-available PA monitors are becoming popular for everyday use. These PA monitors provide feedback on steps, energy expenditure, moderate-to-vigorous intensity physical activity (MVPA), and sleep quality, with new models introduced to the public at an increasing rate. Whether these devices provide the consumer with accurate information is not well understood. PURPOSE: To examine the accuracy of a wrist worn commercial device (Fitbit Charge) and a hip worn device (Fitbit One) in regard to steps and MVPA under free living conditions, compared to a research-grade accelerometer (Actigraph GT3X). METHODS: Participants (n=17, Mean SD, Age: 27.8 9.0; BMI: 24.7 4.2) wore 3 devices (Actigraph GT3X, Fitbit Charge and Fitbit One) for 7 consecutive days. Participants were told to go about their regular daily activities while wearing all 3 devices at the same time. The GT3X was worn on the right hip. RESULTS: Paired t-tests showed that mean steps per day between the Actigraph (10510.6 2443.7) and the Fitbit Charge (12537.7 3936.2) differed significantly (P = 0.016). Mean steps per day between the Actigraph and the Fitbit One (11726.7 11726.7) also differed (P = 0.001). Steps between the Fitbit Charge and Fitbit One did not differ (P = 0.195). No significant differences were found in active minutes recorded between any of the devices (53.0 15.2, 52.0 42.9 and 52.8 28.9) for the Actigraph, Charge and One, respectively). Bland-Altman analysis concluded that the Actigraph and Charge (P = 0.02) and the Actigraph and One (P textless 0.01) did not agree for steps, whereas the Charge and the One did agree (P = 0.2). All three devices showed good agreement with regard to MVPA. CONCLUSION: Results showed that the 2 commercial devices did not agree with the Actigraph, a widely used PA monitor for research purposes, in mean step data. However, the devices did show good agreement with regard to MVPA, suggesting that these commercial devices may provide useful feedback for individuals seeking to achieve the current public health PA guidelines for MVPA.

Shaffer, J.A., Ma, Y., Goldsmith, J., Schwartz, J.E. and Davidson, K.W. Validation of the Fitbit One® for physical activity measurement at an upper torso attachment site (2016)

BMC Research Notes

Abstract

Background: The upper torso is recommended as an attachment site for the Fitbit One®, one of the most common wireless physical activity trackers in the consumer market, and could represent a viable alternative to wrist- and hip-attachment sites. The objective of this study was to provide evidence concerning the validity of the Fitbit One® attached to the upper torso for measuring step counts and energy expenditure among female adults. Results: Thirteen female adults completed a four-phase treadmill exercise protocol (1.9, 3.0, 4.0, and 5.2 mph). Participants were fitted with three Fitbit® trackers (two Fitbit One® trackers: one on the upper torso, one on the hip; and a wrist-based Fitbit Flex®). Steps were assessed by manual counting of a video recording. Energy expenditure was measured by gas exchange indirect calorimetry. Concordance correlation coefficients of Fitbit-estimated step counts to observed step counts for the upper torso-attached Fitbit One®, hip-attached Fitbit One® and wrist-attached Fitbit Flex® were 0.98 (95 % CI 0.97–0.99), 0.99 (95 % CI 0.99–0.99), and 0.75 (95 % CI 0.70–0.79), respectively. The percent error for step count estimates from the upper torso attachment site was =3 % for all walking and running speeds. Upper torso step count estimates showed similar accuracy relative to hip attachment of the Fitbit One® and were more accurate than the wrist-based Fitbit Flex®. Similar results were obtained for energy expenditure estimates. Energy expenditure estimates for the upper torso attachment site yielded relative percent errors that ranged from 9 to 19 % and were more accurate than the wrist-based Fitbit Flex®, but less accurate than hip attachment of the Fitbit One®. Conclusions: Our study shows that physical activity measures obtained from the upper torso attachment site of the Fitbit One® are accurate across different walking and running speeds in female adults. The upper torso attachment site of the Fitbit One® outperformed the wrist-based Fitbit Flex® and yielded similar step count estimates to hip-attachment. These data support the upper torso as an alternative attachment site for the Fitbit One®.

Physical Activity Assessment Between Consumer- and Research-Grade Accelerometers: A Comparative Study in Free-Living Conditions (2016)

JMIR Mhealth Uhealth

Abstract

Background: Wearable activity monitors such as Fitbit enable users to track various attributes of their physical activity (PA) over time and have the potential to be used in research to promote and measure PA behavior. However, the measurement accuracy of Fitbit in absolute free-living conditions is largely unknown. Objective: To examine the measurement congruence between Fitbit Flex and ActiGraph GT3X for quantifying steps, metabolic equivalent tasks (METs), and proportion of time in sedentary activity and light-, moderate-, and vigorous-intensity PA in healthy adults in free-living conditions. Methods: A convenience sample of 19 participants (4 men and 15 women), aged 18-37 years, concurrently wore the Fitbit Flex (wrist) and ActiGraph GT3X (waist) for 1- or 2-week observation periods (n=3 and n=16, respectively) that included self-reported bouts of daily exercise. Data were examined for daily activity, averaged over 14 days and for minutes of reported exercise. Average day-level data included steps, METs, and proportion of time in different intensity levels. Minute-level data included steps, METs, and mean intensity score (0 = sedentary, 3 = vigorous) for overall reported exercise bouts (N=120) and by exercise type (walking, n=16; run or sports, n=44; cardio machine, n=20). Results: Measures of steps were similar between devices for average day- and minute-level observations (all P values > .05). Fitbit significantly overestimated METs for average daily activity, for overall minutes of reported exercise bouts, and for walking and run or sports exercises (mean difference 0.70, 1.80, 3.16, and 2.00 METs, respectively; all P values < .001). For average daily activity, Fitbit significantly underestimated the proportion of time in sedentary and light intensity by 20% and 34 respectively, and overestimated time by 3% in both moderate and vigorous intensity (all P values < .001). Mean intensity scores were not different for overall minutes of exercise or for run or sports and cardio-machine exercises (all P values > .05). Conclusions: Fitbit Flex provides accurate measures of steps for daily activity and minutes of reported exercise, regardless of exercise type. Although the proportion of time in different intensity levels varied between devices, examining the mean intensity score for minute-level bouts across different exercise types enabled interdevice comparisons that revealed similar measures of exercise intensity. Fitbit Flex is shown to have measurement limitations that may affect its potential utility and validity for measuring PA attributes in free-living conditions.

Weight management for adolescents with intellectual and developmental disabilities: Rationale and design for an 18month randomized trial (2016)

Contemporary Clinical Trials

Abstract

Adolescents with intellectual and developmental disabilities (IDD) are an underserved group in need of weight management. However, information regarding effective weight management for this group is limited, and is based primarily on results from small, non-powered, non-randomized trials that were not conducted in accordance with current weight management guidelines. Additionally, the comparative effectiveness of emerging dietary approaches, such as portion-controlled meals (PCMs) or program delivery strategies such as video chat using tablet computers have not been evaluated. Therefore, we will conduct an 18month trial to compare weight loss (6months) and maintenance (7–18months) in 123 overweight/obese adolescents with mild to moderate IDD, and a parent, randomized to a weight management intervention delivered remotely using FaceTime™ on an iPad using either a conventional meal plan diet (RD/CD) or a Stop Light diet enhanced with PCMs (RD/eSLD), or conventional diet delivered during face-to-face home visits (FTF/CD). This design will provide an adequately powered comparison of both diet (CD vs. eSLD) and delivery strategy (FTF vs. RD). Exploratory analyses will examine the influence of behavioral session attendance, compliance with recommendations for diet (energy intake), physical activity (min/day), self-monitoring of diet and physical activity, medications, and parental variables including diet quality, physical activity, baseline weight, weight change, and beliefs and attitudes regarding diet and physical activity on both weight loss and maintenance. We will also complete a cost and contingent valuation analysis to compare costs between RD and FTF delivery.

Balanced: a randomised trial examining the efficacy of two self-monitoring methods for an app-based multi-behaviour intervention to improve physical activity, sitting and sleep in adults. (2016)

BMC Public Health

Abstract

BACKGROUND: Many adults are insufficiently physically active, have prolonged sedentary behaviour and report poor sleep. These behaviours can be improved by interventions that include education, goal setting, self-monitoring, and feedback strategies. Few interventions have explicitly targeted these behaviours simultaneously or examined the relative efficacy of different self-monitoring methods. METHODS/DESIGN: This study aims to compare the efficacy of two self-monitoring methods in an app-based multi-behaviour intervention to improve objectively measured physical activity, sedentary, and sleep behaviours, in a 9 week 2-arm randomised trial. Participants will be adults (n?=?64) who report being physically inactive, sitting textgreater8 h/day and frequent insufficient sleep (=14 days out of last 30). The "Balanced" intervention is delivered via a smartphone 'app', and includes education materials (guidelines, strategies to promote change in behaviour), goal setting, self-monitoring and feedback support. Participants will be randomly allocated to either a device-entered or user-entered self-monitoring method. The device-entered group will be provided with a activity tracker to self-monitor behaviours. The user-entered group will recall and manually record behaviours. Assessments will be conducted at 0, 3, 6, and 9 weeks. Physical activity, sedentary behaviour and sleep-wake behaviours will be measured using the wrist worn Geneactiv accelerometer. Linear mixed models will be used to examine differences between groups and over time using an alpha of 0.01. DISCUSSION: This study will evaluate an app-based multi-behavioural intervention to improve physical activity, sedentary behaviour and sleep; and the relative efficacy of two different approaches to self-monitoring these behaviours. Outcomes will provide information to inform future interventions and self-monitoring targeting these behaviours.

Effectiveness of activity trackers with and without incentives to increase physical activity (TRIPPA): a randomised controlled trial (2016)

The Lancet Diabetes & Endocrinology

Abstract

Background: Despite the increasing popularity of activity trackers, little evidence exists that they can improve health outcomes. We aimed to investigate whether use of activity trackers, alone or in combination with cash incentives or charitable donations, lead to increases in physical activity and improvements in health outcomes. Methods: In this randomised controlled trial, employees from 13 organisations in Singapore were randomly assigned (1:1:1:1) with a computer generated assignment schedule to control (no tracker or incentives), Fitbit Zip activity tracker, tracker plus charity incentives, or tracker plus cash incentives. Participants had to be English speaking, full-time employees, aged 21–65 years, able to walk at least ten steps continuously, and non-pregnant. Incentives were tied to weekly steps, and the primary outcome, moderate-to-vigorous physical activity (MVPA) bout min per week, was measured via a sealed accelerometer and assessed on an intention-to-treat basis at 6 months (end of intervention) and 12 months (after a 6 month post-intervention follow-up period). Other outcome measures included steps, participants meeting 70?000 steps per week target, and health-related outcomes including weight, blood pressure, and quality-of-life measures. This trial is registered at ClinicalTrials.gov, number NCT01855776. Findings: Between June 13, 2013, and Aug 15, 2014, 800 participants were recruited and randomly assigned to the control (n=201), Fitbit (n=203), charity (n=199), and cash (n=197) groups. At 6 months, compared with control, the cash group logged an additional 29 MVPA bout min per week (95% CI 10–47; p=0·0024) and the charity group an additional 21 MVPA bout min per week (2–39; p=0·0310); the difference between Fitbit only and control was not significant (16 MVPA bout min per week [–2 to 35; p=0·0854]). Increases in MVPA bout min per week in the cash and charity groups were not significantly greater than that of the Fitbit group. At 12 months, the Fitbit group logged an additional 37 MVPA bout min per week (19–56; p=0·0001) and the charity group an additional 32 MVPA bout min per week (12–51; p=0·0013) compared with control; the difference between cash and control was not significant (15 MVPA bout min per week [–5 to 34; p=0·1363]). A decrease in physical activity of -23 MVPA bout min per week (95% CI -42 to -4; p=0·0184) was seen when comparing the cash group with the Fitbit group. There were no improvements in any health outcomes (weight, blood pressure, etc) at either assessment. Interpretation: The cash incentive was most effective at increasing MVPA bout min per week at 6 months, but this effect was not sustained 6 months after the incentives were discontinued. At 12 months, the activity tracker with or without charity incentives were effective at stemming the reduction in MVPA bout min per week seen in the control group, but we identified no evidence of improvements in health outcomes, either with or without incentives, calling into question the value of these devices for health promotion. Although other incentive strategies might generate greater increases in step activity and improvements in health outcomes, incentives would probably need to be in place long term to avoid any potential decrease in physical activity resulting from discontinuation.

Validation of Consumer-Based Hip and Wrist Activity Monitors in Older Adults With Varied Ambulatory Abilities. (2016)

The Journals of Gerontology. Series A, Biological Sciences and Medical Sciences

Abstract

BACKGROUND: The accuracy of step detection in consumer-based wearable activity monitors in older adults with varied ambulatory abilities is not known. METHODS: We assessed the validity of two hip-worn (Fitbit One and Omron HJ-112) and two wrist-worn (Fitbit Flex and Jawbone UP) activity monitors in 99 older adults of varying ambulatory abilities and also included the validity results from the ankle-worn StepWatch as a comparison device. Nonimpaired, impaired (Short Physical Performance Battery Score < 9), cane-using, or walker-using older adults (62 and older) ambulated at a self-selected pace for 100 m wearing all activity monitors simultaneously. The criterion measure was directly observed steps. Intraclass correlation coefficients (ICC), mean percent error and mean absolute percent error, equivalency, and Bland-Altman plots were used to assess accuracy. RESULTS: Nonimpaired adults steps were underestimated by 4.4% for StepWatch (ICC = 0.87), 2.6% for Fitbit One (ICC = 0.80), 4.5% for Omron HJ-112 (ICC = 0.72), 26.9% for Fitbit Flex (ICC = 0.15), and 2.9% for Jawbone UP (ICC = 0.55). Impaired adults steps were underestimated by 3.5% for StepWatch (ICC = 0.91), 1.7% for Fitbit One (ICC = 0.96), 3.2% for Omron HJ-112 (ICC = 0.89), 16.3% for Fitbit Flex (ICC = 0.25), and 8.4% for Jawbone UP (ICC = 0.50). Cane-user and walker-user steps were underestimated by StepWatch by 1.8% (ICC = 0.98) and 1.3% (ICC = 0.99), respectively, where all other monitors underestimated steps by >11.5% (ICCs < 0.05). CONCLUSIONS: StepWatch, Omron HJ-112, Fitbit One, and Jawbone UP appeared accurate at measuring steps in older adults with nonimpaired and impaired ambulation during a self-paced walking test. StepWatch also appeared accurate at measuring steps in cane-users.

Program Design for Healthy Weight in Lesbian and Bisexual Women: A Ten-City Prevention Initiative (2016)

Women's Health Issues

Abstract

PURPOSE Adult lesbian and bisexual (LB) women are more likely to be obese than adult heterosexual women. To address weight- and fitness-related health disparities among older LB women using culturally appropriate interventions, the Office on Women's Health (OWH) provided funding for the program, Healthy Weight in Lesbian and Bisexual Women (HWLB): Striving for a Healthy Community. This paper provides a description of the interventions that were implemented. METHODS Five research organizations partnered with lesbian, gay, bisexual, and transgender community organizations to implement healthy weight interventions addressing the needs of LB women 40 years and older. The interventions incorporated evidence-based recommendations related to physical activity and nutrition. Each group intervention developed site-specific primary objectives related to the overall goal of improving the health of LB women and included weight and waist circumference reduction as secondary objectives. A 57-item core health survey was administered across the five sites. At a minimum, each program obtained pre- and post-program assessments. RESULTS Each program included the OWH-required common elements of exercise, social support, and education on nutrition and physical activity, but adopted a unique approach to deliver intervention content. CONCLUSION This is the first time a multisite intervention has been conducted to promote healthy weight in older LB women. Core measurements across the HWLB programs will allow for pooled analyses, and differences in study design will permit analysis of site-specific elements. The documentation and analysis of the effectiveness of these five projects will provide guidance for model programs and future research on LB populations.

Reliability and Validity of Ten Consumer Activity Trackers Depend on Walking Speed (2016)

Medicine & Science in Sports & Exercise

Abstract

Purpose: To examine the test-retest reliability and validity of ten activity trackers for step counting at three different walking speeds. Methods: Thirty-one healthy participants walked twice on a treadmill for 30 minutes while wearing ten activity trackers (Polar Loop, Garmin Vivosmart, Fitbit Charge HR, Apple Watch Sport, Pebble Smartwatch, Samsung Gear S, Misfit Flash, Jawbone Up Move, Flyfit and Moves). Participants walked three walking speeds for ten minutes each; slow (3.2 km[middle dot]h-1), average (4.8 km[middle dot]h-1), and vigorous (6.4 km[middle dot]h-1). To measure test-retest reliability, intraclass correlations (ICCs) were determined between the first and second treadmill test. Validity was determined by comparing the trackers with the gold standard (hand counting), using mean differences, mean absolute percentage errors, and ICCs. Statistical differences were calculated by paired-sample t-tests, Wilcoxon signed-rank tests, and by constructing Bland-Altman plots. Results: Test-retest reliability varied with ICCs ranging from -0.02 to 0.97. Validity varied between trackers and different walking speeds with mean differences between the gold standard and activity trackers ranging from 0.0 to 26.4%. Most trackers showed relatively low ICCs and broad limits of agreement of the Bland-Altman plots at the different speeds. For the slow walking speed, the Garmin Vivosmart and Fitbit Charge HR showed the most accurate results. The Garmin Vivosmart and Apple Watch Sport demonstrated the best accuracy at an average walking speed. For vigorous walking, the Apple Watch Sport, Pebble Smartwatch, and Samsung Gear S exhibited the most accurate results. Conclusion: Test-retest reliability and validity of activity trackers depends on walking speed. In general, consumer activity trackers perform better at an average and vigorous walking speed than at a slower walking speed.

Validation of the Fitbit Charge Heart Rate Device to Monitor Physical Activity in Children with Congenital Heart Disease (2016)

Canadian Journal of Cardiology

Abstract

Background: Commercial wearable activity trackers, like the FitBit™, may be useful to monitor and promote activity in children. However, these trackers are designed for adults and it is unclear whether they are valid for use in children. The aim of this study was to determine whether the FitBit Charge HR™ accurately estimates daily step counts in a pediatric population. Methods: Eligible participants (10-18yrs) were recruited from the Children's Heart Centre at BC Children's Hospital and partnership clinics across the province of British Columbia. Participants were fitted with an accelerometer positioned over the right hip (Actigraph LLC; ‘criterion') and a wrist-worn FitBit Charge HR™ (‘measure'). Participants were asked to wear both devices simultaneously for 7 consecutive days. We retrieved daily sums of step counts from valid accelerometry days, which was defined as monitor wear time of =600 minutes/day (Actilife LLC). We paired these days with corresponding daily step counts from the FitBit™ for analyses. A subset of participants underwent exercise tolerance testing while wearing the FitBit™ to validate its heart rate tracking feature. We calculated intra-class correlation coefficients (ICC) and mean bias (criterion-measure) with 95% limits of agreement (95%LoA) to estimate agreement between devices. Results: Twenty-one participants (13.4±2.0yrs, 57% female) provided 97 person-days of data. Overall, there was a strong association between the two devices for daily step counts across person-days (ICC=0.852, ptextless0.001). However, FitBit™ devices systematically recorded more steps than accelerometers (9,914±5,255 vs. 7,637±3,953 steps/day) with a mean bias of -2,277 steps/day (95%LoA= -7,587-3,034). We were unable to assess the validity of the FitBit™ heart rate tracking feature during exercise tolerance testing because of electromagnetic interference from clinical equipment, which temporarily impacted the data recording ability of the FitBit™. Conclusion: FitBit Charge HR™ devices are a valid tool to assess patterns in daily step count in a pediatric population; however, caution is required when comparing FitBit Charge HR™ step counts to physical activity studies that are based on the more conservative criterion measure.

Efficacy, feasibility, and acceptability of a novel technology-based intervention to support physical activity in cancer survivors (2016)

Supportive Care in Cancer

Abstract

Purpose: Physical activity is known to minimize the long-term side effects of cancer treatment. Yet, rates of physical activity participation by cancer survivors are significantly lower compared to the general population. The purpose of this pilot study was to examine the efficacy, feasibility, and acceptability of a technology-based intervention to promote maintenance of physical activity after completing an exercise-based oncology rehabilitation program. Methods: The pre-post 4-week intervention included support delivered through tailored text messages, Fitbit® self-monitoring, and brief health coaching sessions. The primary outcome measure was accelerometer-assessed physical activity levels. Self-efficacy, self-regulation, social support, fatigue, sleep disturbance, depression, and Fitbit® step counts were assessed as secondary outcomes. Results: Twenty-four participants (20 females; mean age = 57.9 years ± 10.4) completed the intervention. Mean daily step counts and weekly minutes of moderate-to-vigorous intensity physical activity were maintained after the intervention, as compared to baseline levels achieved at the end of 12 weeks of exercise-based oncology rehabilitation. Both self-regulation (goal setting, relapse prevention) and fatigue severity increased significantly post intervention as compared to baseline (p = 0.05 and p = 0.02, respectively). Qualitative responses demonstrated overall satisfaction with intervention components. Conclusions: Results demonstrate efficacy of the intervention for maintenance of physical activity levels achieved during exercise-based oncology rehabilitation. Low attrition and high satisfaction provide evidence for both the acceptability and feasibility of the intervention components. Exercise interventions post oncology treatment provide multiple benefits for cancer survivors, yet continued maintenance after program completion is challenging. Technological options offer low-cost, accessible modes to deliver continued monitoring and support beyond traditional facility-based programs.

Impact of a Digital Activity Tracker-Based Workplace Activity Program on Health and Wellbeing (2016)

Proceedings of the 6th International Conference on Digital Health Conference

Abstract

Chronic disease is endemic within the Australian community. 3.6 million Australians have diabetes or pre-diabetes with the number increasing by 7% each year. Fifty three percent of Australians have one or more chronic diseases. Increasing levels of activity has proved relatively straightforward, especially through workplace physical activity interventions. What is still not certain are the short, and long-term, health benefits arising from these workplace activity challenges. Research into workplace activity challenges is beset with a number of methodological obstacles that may, in part, explain why consistent outcomes have not been found from studies of this type. The aim of this study was to assess whether participation in a 16-week activity challenge would result in measurable changes in lipid profile, blood glucose, renal function, blood pressure, weight and health and well being as measured using a health and wellbeing assessment. The study demonstrated that participants could increase their levels of activity and maintain at least 10,000 steps a day for a period of 16 weeks. The study also identified that participants in teams were significantly more active than those participating as individuals. Furthermore, attrition from the activity challenge was greater amongst participants not in a team. This demonstrated the importance of social interactions, support and possibly other factors that being part of a group brought to the experience of participating in the activity challenge. In addition to the above, the challenge resulted in reductions in non-HDL cholesterol, and triglyceride concentrations and health and well being score.

Estimating Physical Activity and Sedentary Behavior in a Free-Living Context: A Pragmatic Comparison of Consumer-Based Activity Trackers and ActiGraph Accelerometry (2016)

Journal of Medical Internet Research

Abstract

Background: Activity trackers are increasingly popular with both consumers and researchers for monitoring activity and for promoting positive behavior change. However, there is a lack of research investigating the performance of these devices in free-living contexts, for which findings are likely to vary from studies conducted in well-controlled laboratory settings. Objective: The aim was to compare Fitbit One and Jawbone UP estimates of steps, moderate-to-vigorous physical activity (MVPA), and sedentary behavior with data from the ActiGraph GT3X+ accelerometer in a free-living context. Methods: Thirty-two participants were recruited using convenience sampling; 29 provided valid data for this study (female: 90%, 26/29; age: mean 39.6, SD 11.0 years). On two occasions for 7 days each, participants wore an ActiGraph GT3X+ accelerometer on their right hip and either a hip-worn Fitbit One (n=14) or wrist-worn Jawbone UP (n=15) activity tracker. Daily estimates of steps and very active minutes were derived from the Fitbit One (n=135 days) and steps, active time, and longest idle time from the Jawbone UP (n=154 days). Daily estimates of steps, MVPA, and longest sedentary bout were derived from the corresponding days of ActiGraph data. Correlation coefficients and Bland-Altman plots with examination of systematic bias were used to assess convergent validity and agreement between the devices and the ActiGraph. Cohen's kappa was used to assess the agreement between each device and the ActiGraph for classification of active versus inactive (=10,000 steps per day and =30 min/day of MVPA) comparable with public health guidelines. Results: Correlations with ActiGraph estimates of steps and MVPA ranged between .72 and .90 for Fitbit One and .56 and .75 for Jawbone UP. Compared with ActiGraph estimates, both devices overestimated daily steps by 8% (Fitbit One) and 14% (Jawbone UP). However, mean differences were larger for daily MVPA (Fitbit One: underestimated by 46%; Jawbone UP: overestimated by 50%). There was systematic bias across all outcomes for both devices. Correlations with ActiGraph data for longest idle time (Jawbone UP) ranged from .08 to .19. Agreement for classifying days as active or inactive using the =10,000 steps/day criterion was substantial (Fitbit One: ?=.68; Jawbone UP: ?=.52) and slight-fair using the criterion of =30 min/day of MVPA (Fitbit One: ?=.40; Jawbone UP: ?=.14). Conclusions: There was moderate-strong agreement between the ActiGraph and both Fitbit One and Jawbone UP for the estimation of daily steps. However, due to modest accuracy and systematic bias, they are better suited for consumer-based self-monitoring (eg, for the public consumer or in behavior change interventions) rather than to evaluate research outcomes. The outcomes that relate to health-enhancing MVPA (eg, “very active minutes” for Fitbit One or “active time” for Jawbone UP) and sedentary behavior (“idle time” for Jawbone UP) should be used with caution by consumers and researchers alike.

Implementation of the physical activity guidelines in lung cancer: The CAPACITY study (2016)

European Respiratory Journal

Abstract

Introduction: Physical activity (PA) is important in lung cancer yet the majority of patients do not meet minimum recommended levels. Aims: To determine if a PA self-management program is feasible and effective at preventing decline in PA, which commonly occurs after surgery. Method: Prospective case series. 40 patients (55% male, mean age 66±10years) with newly diagnosed operable lung cancer. The intervention was a PA program based on the cancer PA guidelines, commenced pre-surgery (if recruitment occurred textgreater7days prior to surgery) or post-surgery if not and continued to 8weeks post-surgery. It included prescription of an unsupervised home exercise program, provision of a Fitbit (activity monitor), education and weekly telephone support. Outcomes: feasibility, self-reported PA, function, symptoms, self-efficacy and health-related quality of life (HRQoL) assessed pre-surgery and 2weeks, 8weeks and 6months post-surgery. Results: 16% of patients commenced the intervention pre-surgery and 84% commenced post-surgery. Median[IQR] number of intervention sessions (face-to-face or telephone) delivered was 4[3-6] per patient. There was no change in PA levels (total estimated mets/week pre-op=1543±1501, 8weeks=1748±2581, p=0.72), sedentary time (sitting hours/day pre-op= 6.1±3.6, 8-weeks=5.6±2.7, p=0.59), function (p=0.39) or dyspnoea (p=0.51) from pre to 8weeks post-surgery and global HRQoL improved (p=0.005) over this time. 63% and 67% met PA guidelines pre-op and 8weeks respectively. Conclusion: The PA program was feasible when implemented in the post-surgical setting. Compared with published literature that shows decline, patients maintained their PA levels after surgery.

Does Non-Running Physical Activity Contribute to the Risk of Developing a Running Related Overuse Injury? (2016)

Medicine & Science in Sports & Exercise

Abstract

Running related overuse injury (RROI) affects a significant proportion of runners annually. To date, only running mileage has been considered when quantifying the amount of loading that may elicit tissue micro-damage and injury, while the loading that occurs from activities of daily living and non-running physical activity has been ignored. By ignoring non-running physical activity (PA), we may be severely underestimating the amount of loading that leads to RROI. PURPOSE: To determine the role of daily non-running PA on the risk of RROI development among recreational and competitive runners. METHODS: As part of a larger prospective running injury pilot study, 50 recreational and competitive male and female runners will be enrolled and complete questionnaires regarding running history, injury history, and all forms of non-running daily PA. Participants will receive a FitBit Charge activity monitor to track PA for 24 hrs/day over 12 months. To date, questionnaires and a typical running gait analysis had been completed by 21 participants (9 male, 12 female; 27.5±6.3 years; BMI = 22.6±2.0 kg/mˆ2). RESULTS: Participants had mean±SD running experience of 8.6±5.6 years and run 24.1±15.2 mi/wk. Among n = 21 participants, RROI occurred within the past year in n = 4, within the past two years in n = 11, and in their lifetime in n = 16. Current mi/wk was 54% greater in those that had sustained a RROI in their lifetime compared with those that had not (No RROI = 15.3±4.5 mi/wk; Yes RROI 26.9±16.4 mi/wk, P=0.02). Total PA was not different between those who had and had not sustained a RROI in their lifetime or in the past 1-2 years (Ptextgreater0.05). Non-running PA was greater in those that did not sustain a RROI in each time period, but the differences were not significant with this initial sample size (No RROI vs Yes RROI: lifetime 11.8±8.3 vs 7.1±7.2 hrs/wk, Ptextgreater0.05; 1 yr: 8.7±8.2 vs 6.1±3.9 hrs/wk, Ptextgreater0.05; 2 yr: 9.2±8.6 vs 7.3±6.8 hrs/wk, Ptextgreater0.05). CONCLUSIONS: Results from the first 21 participants enrolled in this study support previous reports that running mi/wk may increase RROI risk. Given that total PA was the same between injured and non-injured runners in this sample, more hrs/wk spent in non-running PA vs running PA may reduce RROI risk. Continuous monitoring of PA collected in this 12 month study will provide evidence to support or reject this hypothesis.

A Leisure-Time Physical Activity Intervention for Adolescents With Visual Impairments (2016)

Research Quarterly for Exercise and Sport

Abstract

Background/Purpose: School-aged individuals with visual impairments tend to be less physically active than their non-visually impaired peers (Kozub & Oh, 2004). However, a recent literature review revealed only 7 physical activity intervention studies targeting this population during a recent 30-year period (Haegele & Porretta, 2015). Of those studies, 1 utilized a theoretical framework. Therefore, the purpose of this study was to examine the effects of a social-cognitive theory-based physical education intervention on leisure-time physical activity of adolescents with visual impairments. Method: Six adolescents with visual impairments attending a Midwestern residential school for the blind received a 9-lesson physical activity intervention during in-school physical education. Lessons were designed to assist participants in developing individual activity programs while addressing social-cognitive theory constructs. Each lesson included curricular concepts, in-class activities, and homework. A multiple-baseline across-participants design was used, and physical activity behavior was measured by steps taken using Fitbit Zips. Questionnaires were used to collect data (i.e., change scores) on the social-cognitive theory constructs of self-efficacy, self-regulation, social support, and outcome expectancy. Data were analyzed through visual analysis, effect size estimates (percentage of nonoverlapping data [PND]), and descriptive statistics. A treatment integrity checklist was developed, and 2 trained observers completed them independently (99.8% agreement). Analysis/Results: Results did not demonstrate a functional relation between physical activity (i.e., number of steps taken) and the intervention. PND scores ranged from 0% to 48%. However, positive changes in mean steps per day for all participants were found from baseline to final intervention (Participant 1, 2,225–4,178 [87%]; Participant 2, 1,328–2,632 [98%]; Participant 3, 1,137– 2,111 [86%]; Participant 4, 1,151–1,740 [51%]; Participant 5, 521–588 [13%]; and Participant 6, 2,751–3,858 [40%]). Relative to the social-cognitive theory constructs, all 6 participants demonstrated positive changes in selfregulation (37%–203%), 5 demonstrated positive changes in outcome expectancy (6%–69%), and 4 demonstrated positive changes in social support (15%–42%). Three of 6 participants demonstrated increases in each of 2 selfefficacy questionnaires. Conclusion: The intervention did not exert functional control of the participants' physical activity (i.e., steps taken). Results may have been directly influenced by several study limitations, such as seasonality and program truncation for 3 participants. Therefore, the results may not represent the full intervention effect. With the lack of physical activity intervention research related to adolescent-aged individuals with visual impairments (Haegele & Porretta, 2015) and the influence physical activity has on health-related outcomes (e.g., obesity), further research is needed.

Accuracy of Commercial Activity Trackers to Measure Energy Expenditure During a Controlled Exercise Trial (2016)

Medicine & Science in Sports & Exercise

Abstract

Commercially available activity trackers are increasingly popular for the general public in measuring physical activity. Daily energy expenditure (EE) is one of the primary measures provided to the public by these devices. Whether these devices can accurately estimate EE is not clear. PURPOSE: To compare the EE values obtained from two commercially available devices (Fitbit Charge and Fitbit One) to that measured by the Actigraph GT3X and indirect calorimetry (IC). METHODS: Seventeen men (n = 4) and women (n = 13) (Age: Mean ± SD = 27.8 ± 9.0; BMI = 24.7 ± 4.2 kg/m2) completed a submaximal treadmill exercise trial while wearing a Fitbit Charge, a Fitbit One, and an Actigraph GT3X. Oxygen consumption and EE (in kcals) was measured via breath-by-breath IC system. The exercise trial consisted of four, 5-minute stages (2.0 mph/0% grade; 2.0 mph/5%; 3.0 mph/0%; 3.0 mph/5%). To approximate the EE reported by the Fitbit devices, an estimated resting metabolic rate was added to the EE reported by the Actigraph. Repeated measures ANOVA was used to asses the main effects for measurement device and stage. RESULTS: There was a significant main effect for measurement device (P < 0.01) and stage (P < 0.01). Post-hoc analysis determined that the Fitbit Charge (P < 0.01), the Fitbit One (P textless 0.01), and the Actigraph (P < 0.01), all differed from IC in measuring EE. The Fitbit Charge (P < 0.01) overestimated EE across all stages (25.3 ± 4.7, 31.0 ± 3.7, 33.0 ± 5.8 and 34.2 ± 6.1 vs 13.2 ± 2.3, 18.8 ± 3.0, 20.5 ± 2.8 and 26.9 ± 5.0 for Fitbit One and IC, respectively). The Fitbit One (14.4 ± 2.7, 15.3 ± 2.7, 19.9 ± 3.4 and 21.1 ± 4.6; P < 0.01) and the Actigraph (6.7 ± 3.8, 7.4 ± 25.4 ± 8.5 and 26.4 ± 7.9; P textless 0.01) underestimated EE compared to IC across all stages. Significant interactions between IC and the Fitbit One (P < 0.01), and IC and the Actigraph (P < 0.01), however, indicate that they are less sensitive to grade changes than IC. CONCLUSION: Results show that the wrist-worn Fitbit Charge overestimates EE, which may negatively impact the fitness goals of the wearer, particularly as it relates to energy balance and weight loss. The hip worn devices appear to be closer to IC in EE measures, but do not accurately reflect changes in treadmill grade, impacting those who use this exercise mode.

Technology- and Phone-Based Weight Loss Intervention (2016)

American Journal of Preventive Medicine

Abstract

Introduction: For women with an increased breast cancer risk, reducing excess weight and increasing physical activity are believed to be important approaches for reducing their risk. This study tested a weight loss intervention that combined commercially available technology-based self-monitoring tools with individualized phone calls. Design: Women were randomized to a weight loss intervention arm (n=36) or a usual care arm (n=18). Setting/Participants: Participants were women with a BMI = 27.5 kg/m2 and elevated breast cancer risk recruited from the mammography clinic at the Moores Cancer Center at the University of California San Diego. Intervention; Intervention participants used the MyFitnessPal website and phone app to monitor diet and a Fitbit to monitor physical activity. Participants received 12 standardized coaching calls with trained counselors over 6 months. Usual care participants received the U.S. Dietary Guidelines for Americans at baseline and two brief calls over the 6 months. Main outcome measures: Weight and accelerometer-measured physical activity were assessed at baseline and 6 months. Data were collected in San Diego, CA, from 2012 to 2014 and analyzed in 2015. Results: Participants (n=54) had a mean age of 59.5 (SD=5.6) years, BMI of 31.9 (SD=3.5), and a mean Gail Model score of 2.5 (SD=1.4). At 6 months, intervention participants had lost significantly more weight (4.4 kg vs 0.8 kg, p=0.004) and a greater percentage of starting weight (5.3% vs 1.0%, p=0.005) than usual care participants. Across arms, greater increases in moderate-to-vigorous physical activity resulted in greater weight loss (p=0.01). Conclusions: Combining technology-based self-monitoring tools with phone counseling supported weight loss over 6 months in women at increased risk for breast cancer.

Effect of affordable technology on physical activity levels and mobility outcomes in rehabilitation: a protocol for the Activity and MObility UsiNg Technology (AMOUNT) rehabilitation trial. (2016)

BMJ Open

Abstract

NTRODUCTION People with mobility limitations can benefit from rehabilitation programmes that provide a high dose of exercise. However, since providing a high dose of exercise is logistically challenging and resource-intensive, people in rehabilitation spend most of the day inactive. This trial aims to evaluate the effect of the addition of affordable technology to usual care on physical activity and mobility in people with mobility limitations admitted to inpatient aged and neurological rehabilitation units compared to usual care alone. METHODS AND ANALYSIS A pragmatic, assessor blinded, parallel-group randomised trial recruiting 300 consenting rehabilitation patients with reduced mobility will be conducted. Participants will be individually randomised to intervention or control groups. The intervention group will receive technology-based exercise to target mobility and physical activity problems for 6 months. The technology will include the use of video and computer games/exercises and tablet applications as well as activity monitors. The control group will not receive any additional intervention and both groups will receive usual inpatient and outpatient rehabilitation care over the 6-month study period. The coprimary outcomes will be objectively assessed physical activity (proportion of the day spent upright) and mobility (Short Physical Performance Battery) at 6 months after randomisation. Secondary outcomes will include: self-reported and objectively assessed physical activity, mobility, cognition, activity performance and participation, utility-based quality of life, balance confidence, technology self-efficacy, falls and service utilisation. Linear models will assess the effect of group allocation for each continuously scored outcome measure with baseline scores entered as a covariate. Fall rates between groups will be compared using negative binomial regression. Primary analyses will be preplanned, conducted while masked to group allocation and use an intention-to-treat approach.

Individual Differences in Diurnal Preference and Time-of-Exercise Interact to Predict Exercise Frequency (2016)

Annals of Behavioral Medicine

Abstract

Background Diurnal preference (and chronotype more generally) has been implicated in exercise behavior, but this relation has not been examined using objective exercise measurements nor have potential psychosocial mediators been examined. Furthermore, time-of-day often moderates diurnal preference’s influence on outcomes, and it is unknown whether time-of-exercise may influence the relation between chronotype and exercise frequency. Purpose: The current study examined whether individual differences in diurnal preference (“morningness-eveningness”) predict unique variance in exercise frequency and if commonly studied psychosocial variables mediate this relation (i.e., behavioral intentions, internal exercise control, external exercise control, and conscientiousness). Moreover, the study sought to test whether individuals’ typical time-of-exercise moderated the impact of diurnal preference on exercise frequency. Methods: One hundred twelve healthy adults (mean age = 25.4; SD = 11.6 years) completed baseline demographics and then wore Fitbit Zips® for 4 weeks to objectively measure exercise frequency and typical time-of-exercise. At the end of the study, participants also self-reported recent exercise. Results: Diurnal preference predicted both self-reported exercise and Fitbit-recorded exercise frequency. When evaluating mediators, only conscientiousness emerged as a partial mediator of the relation between diurnal preference and self-reported exercise. In addition, time-of-exercise moderated diurnal preference’s relation to both self-reported exercise and Fitbit-recorded exercise frequency such that diurnal preference predicted higher exercise frequency when exercise occurred at a time that was congruent with one’s diurnal preference. Conclusion: Based on these findings, diurnal preference is valuable, above and beyond other psychological constructs, in predicting exercise frequency and represents an important variable to incorporate into interventions seeking to increase exercise.

Trajectories of objectively-measured physical activity and sedentary time over the course of pregnancy in women self-identified as inactive (2016)

Preventive Medicine Reports

Abstract

There is a need for investigations that document the daily course of pregnancy-related changes in PA and sedentary behavior. The purpose of this study was to describe the trajectory of PA and sedentary behavior and whether they differ among weight status in pregnant women self-identified as inactive. Eighty inactive pregnant women (8–16 weeks) were recruited from a nationwide text-message intervention. PA was measured using a Fitbit. Chi-square analyses and t-tests were used to analyze univariate demographic and PA variables. Mixed model-repeated measures ANOVA was used to analyze trajectory changes in daily PA and sedentary behavior. Light activity (beta [SE] = 2.79 [0.30], p < .001), active time (b [SE] = 1.62 [0.16], p < .001), and steps (b [SE] = 112.21 [10.66], p < .001) increased during the second trimester followed by a precipitous decline during the third trimester. Sedentary behavior followed an opposite pattern (b = - 9.88 [1.07], p < .001). Overweight and obese women took significantly fewer steps/day (b [SE] = - 742.37 [362.57], p < .05 and - 855.94 [381.25], p < .05, respectively) than normal weight women, and obese women had less “active” minutes/day (~> 3.0 metabolic equivalents; b [SE] = - 12.99 [5.89], p < .05) than normal weight women (P's < 0.05). Women who self-identify as inactive, become more sedentary and less physically active as pregnancy progresses. This study was among the first to describe the trajectory of daily PA and sedentary behavior throughout pregnancy. This study may help inform health care provider and patient communication related to PA, sedentary behavior, and the time in which to communicate about these behaviors.

Can a Home-based Cardiac Rehabilitation Program Improve the Physical Function Quality of Life in Children With Fontan Circulation? (2016)

Congenital Heart Disease

Abstract

Objective: Patients after Fontan operation for complex congenital heart disease (CHD) have decreased exercise capacity and report reduced health-related quality of life (HRQOL). Studies suggest hospital-based cardiac physical activity programs can improve HRQOL and exercise capacity in patients with CHD; however, these programs have variable adherence rates. The impact of a home-based cardiac physical activity program in Fontan survivors is unclear. This pilot study evaluated the safety, feasibility, and benefits of an innovative home-based physical activity program on HRQOL in Fontan patients. Methods: A total of 14 children, 8–12 years, with Fontan circulation enrolled in a 12-week moderate/high intensity home-based cardiac physical activity program, which included a home exercise routine and 3 formalized in-person exercise sessions at 0, 6, and 12 weeks. Subjects and parents completed validated questionnaires to assess HRQOL. The Shuttle Test Run was used to measure exercise capacity. A Fitbit Flex Activity Monitor was used to assess adherence to the home activity program. Results: Of the 14 patients, 57% were male and 36% had a dominant left ventricle. Overall, 93% completed the program. There were no adverse events. Parents reported significant improvement in their child's overall HRQOL (P?<?.01), physical function (P?<?.01), school function (P?=?.01), and psychosocial function (P?<?.01). Patients reported no improvement in HRQOL. Exercise capacity, measured by total shuttles and exercise time in the Shuttle Test Run and calculated VO2max, improved progressively from baseline to the 6 and 12 week follow up sessions. Monthly Fitbit data suggested adherence to the program. Conclusion: This 12-week home-based cardiac physical activity program is safe and feasible in preteen Fontan patients. Parent proxy-reported HRQOL and objective measures of exercise capacity significantly improved. A 6-month follow up session is scheduled to assess sustainability. A larger study is needed to determine the applicability and reproducibility of these findings in other age groups and forms of complex CHD.

Effects of an mHealth Adaptive Intervention on Vascular Health and Cardiorespiratory Fitness (2016)

Circulation

Abstract

ntroduction: Behavioral economics-based interventions for increasing steps/day may improve cardiorespiratory fitness (CRF; Peak O2 uptake - VO2peak), central/aortic blood pressure, and vascular stiffness. The purpose of this study was to examine the effects of adaptive (Ad) physical activity (PA) goals (daily step goals based on prior 10-day window and a percentile algorithm) vs. static (St) PA goals (10,000 steps/day) combined with either immediate (Im) financial micro-incentives or delayed (De) incentives (Groups: Ad+Im; Ad+De; St+Im; St+De) on VO2peak, blood pressure and vascular stiffness. We hypothesized that participants randomized to adaptive goals with immediate reinforcement (Ad+Im) would have greater improvements in cardiovascular health measures than participants assigned to static goals and/or delayed reinforcement conditions (active controls). Methods: Inactive adults (Age = 41.8 ± 6.7yrs; BMI = 34.5 ± 6 kg/m2) were randomized to an adaptive/static goal intervention with either immediate micro-incentives or delayed incentives in a 4-month factorial RCT to improve steps/day. Additionally, all four groups received daily text-message prompts-to-action and Fitbit ZipTM activity monitors. Pre and post VO2peak (n=81) were estimated using a modified, sub-maximal Balke treadmill protocol while central blood pressures, augmentation index (Aix@75) (n=70) and carotid-femoral pulse wave velocity (cf-PWV) (n=64) were assessed with the Sphygmocor XCELTM device. Between- and within-group differences were examined using linear mixed models, adjusted for sex, age, race/ethnicity, and smoking history. Alpha was set at 0.05, and data are presented as estimated marginal means ± standard errors (SE). Results: All groups improved from pre to post, but we found no significant between-group differences in the degree of improvement. Across all groups, we found the following: VO2peak and all vascular markers improved significantly pre to post intervention (ptextless0.05). VO2peak improved by ˜5% (pre = 24.9 ± 0.65 vs. post = 26.3 ± 0.66 ml/kg/min). Central systolic blood pressures (cSBP) decreased (cSBP pre = 117.5 ± 2.1 vs. post = 113.1 ± 2.2 mmHg). Cf-PWV (pre = 7.5 ± 0.18 vs. post = 7.3 ± 0.18 m/sec) and AIX@75 (pre =14.1 ± 2.2 vs. post = 11.4 ± 2.2) decreased. Conclusion: Cardiorespiratory fitness and markers of vascular health improved when participants were simply provided a Fitbit and prompted daily to be more physically active, regardless of incentive or goal-setting approach. Implications for designing behavioral interventions for cardiovascular risk reduction will be discussed.

Feasibility and preliminary results from utilizing a Fitbit device in a pediatric pain rehabilitation program (2016)

The Journal of Pain

Abstract

Personal informatics technologies including mobile electronic devices (e.g., Fitbit) facilitate objective measurement, communication between patients and medical teams, and sustainable personal management of physical activity (PA) and sleep. As PA and sleep are often problematic in youth with chronic pain, the current study presents feasibility and preliminary data from use of Fitbit devices in an interdisciplinary pediatric pain rehabilitation clinic (PPRC). Participants are youth with chronic pain, between the ages of 12 and 18, who were scheduled to participate in a 3-week PPRC. 64 youth were identified as eligible and contacted regarding participation in the current study. Youth were asked to wear a Fitbit for 3 weeks prior to and during the PPRC. 47% agreed to participate. 25% of those contacted decided not to attend the PPRC. 22% did not respond and missed their window of eligibility. 6% were sent Fitbits but did not participate in the PPRC and did not return devices. Restructuring of staff responsibilities was helpful early in recruitment. Significant difficulties arose in collecting sleep data. Preliminary PA data shows significant increases in both steps taken and distance walked from pre-PPRC to during PRC (p < .001). Multiple challenges exist when initiating use of a Fitbit device in an interdisciplinary rehabilitation setting. Youth were more compliant with regular use during, as opposed to before, programming and when multiple disciplines were involved in checking compliance. Despite the difficulties, recruiting adjustments have improved feasibility and PA changes are significant and promising. Additional recruitment, program design, and implications for future research, as well as preliminary physical activity data, will be presented.

A comparison of wearable fitness devices. (2016)

BMC Public Health

Abstract

BACKGROUND: Wearable trackers can help motivate you during workouts and provide information about your daily routine or fitness in combination with your smartphone without requiring potentially disruptive manual calculations or records. This paper summarizes and compares wearable fitness devices, also called "fitness trackers" or "activity trackers." These devices are becoming increasingly popular in personal healthcare, motivating people to exercise more throughout the day without the need for lifestyle changes. The various choices in the market for wearable devices are also increasing, with customers searching for products that best suit their personal needs. Further, using a wearable device or fitness tracker can help people reach a fitness goal or finish line. Generally, companies display advertising for these kinds of products and depict them as beneficial, user friendly, and accurate. However, there are no objective research results to prove the veracity of their words. This research features subjective and objective experimental results, which reveal that some devices perform better than others. METHODS: The four most popular wristband style wearable devices currently on the market (Withings Pulse, Misfit Shine, Jawbone Up24, and Fitbit Flex) are selected and compared. The accuracy of fitness tracking is one of the key components for fitness tracking, and some devices perform better than others. This research shows subjective and objective experimental results that are used to compare the accuracy of four wearable devices in conjunction with user friendliness and satisfaction of 7 real users. In addition, this research matches the opinions between reviewers on an Internet site and those of subjects when using the device. RESULTS: Withings Pulse is the most friendly and satisfactory from the users' viewpoint. It is the most accurate and repeatable for step and distance tracking, which is the most important measurement of fitness tracking, followed by Fitbit Flex, Jawbone Up24, and Misfit Shine. In contrast, Misfit Shine has the highest score for design and hardware, which is also appreciated by users. CONCLUSIONS: From the results of experiments on four wearable devices, it is determined that the most acceptable in terms of price and satisfaction levels is the Withings Pulse, followed by the Fitbit Flex, Jawbone Up24, and Misfit Shine.

An Exercise Intervention for South Asian Mothers with Risk Factors for Diabetes (2016)

Translational Journal of the American College of Sports Medicine

Abstract

South Asians (Indians, Pakistanis, Bangladeshis, Sri Lankans, Nepalis, and Bhutanese) in the United States have a very high prevalence of type 2 diabetes mellitus (DM). This pilot study evaluated a culturally tailored exercise intervention among South Asian immigrant mothers with DM risk factors. Through an academic–community partnership, South Asian women with risk factors for DM and who had at least one child between 6 and 14 yr were enrolled into this single-arm study. The intervention for the mothers included 16 wk of twice weekly exercise classes, self-monitoring with activity trackers, goal setting, and classes on healthy eating. Based on prior community-based participatory research, children were offered exercise classes during the mothers' classes. The primary efficacy outcomes were change in mothers' moderate/vigorous physical activity and body weight pre- and postintervention (16 wk). Program adherence, clinical, and psychosocial outcomes were measured. A qualitative process evaluation was conducted to understand participant perspectives. Participants' (n = 30) average age was 40 yr (SD, 5), 57% had a high school education or less, and all were overweight/obese. At baseline, women were not meeting the recommended physical activity guidelines. Overall, participants attended 75% of exercise classes. Compared with baseline, participants' weight decreased by 3.2 lb (95% confidence interval, -5.5 to -1.0) postintervention. Among women who attended at least 80% of classes (n = 17), weight change was -4.8 lb (95% confidence interval, -7.7 to -1.9). Change in accelerometer-measured physical activity was not significant; however, exercise-related confidence increased from baseline (P textless 0.01). Women described multiple physical and psychosocial benefits from the intervention. In conclusion, this pilot study suggests that a culturally tailored exercise intervention that included exercise classes for children was feasible and had physical and psychosocial benefits in South Asian mothers with risk factors for DM.

Wellbeing in the Making: Peoples' Experiences with Wearable Activity Trackers. (2016)

Psychology of Well-being

Abstract

BACKGROUND Wearable activity trackers have become a viable business opportunity. Nevertheless, research has raised concerns over their potentially detrimental effects on wellbeing. For example, a recent study found that while counting steps with a pedometer increased steps taken throughout the day, at the same time it decreased the enjoyment people derived from walking. This poses a serious threat to the incorporation of healthy routines into everyday life. Most studies aim at proving the effectiveness of activity trackers. In contrast, a wellbeing-oriented perspective calls for a deeper understanding of how trackers create and mediate meaningful experiences in everyday life. METHODS We present a study of real life experiences with three wearable activity trackers: Fitbit, Jawbone Up and Nike + Fuelband. Using need fulfillment as a theoretical lens, we study recent, memorable experiences submitted by 133 users of activity trackers. RESULTS We reveal a two-dimensional structure of users' experience driven by the needs of physical thriving or relatedness. Our qualitative findings further show a nuanced picture of the adoption of activity trackers and their impact on wellbeing. For instance, while reflection about own exercising practices lost its relevance over time, users continued to wear the tracker to document and collect their runs. More than just supporting behavioral change, we find trackers to provide multiple psychological benefits. For instance, they enhance feelings of autonomy as people gain more control about their exercising regime. Others experience relatedness, when family members purchase a tracker for relatives and join them in their efforts towards a better, healthier self. CONCLUSIONS The study highlights that activity trackers can be more than "tools" to change behavior. Through incorporation in daily life, they offer new social experiences, new ways of boosting our self-esteem and getting closer to our ideal selves.

Validity of Wearable Fitness Trackers on Sleep Measure (2016)

Medicine & Science in Sports & Exercise

Abstract

PURPOSE: To examine the validity of wearable fitness trackers for estimating total sleep time (TST) with respect to a sleep log as a reference measure. METHODS: Nineteen healthy individuals (mean ± SD; age = 29.5 ± 13.4 years; body mass index = 25.87 ± 5.03 kg textperiodcentered m2) participated in the study. Participants randomly assigned to one of two groups. Group 1 (n = 10) wore the BodyMedia SenseWear Mini Armband (SWA), Basis Peak (BP), and Fitbit Charge HR (FB). Group 2 (n = 9) wore the ActiGraph Sleep (AG), Jawbone UP3 (JU), and Garmin Vivosmart (GV). Trackers were worn on the non-dominant wrist for one night and a sleep log was completed. Two existing sleep algorithms for the AG (Sadeh and Cole-Kripke) and Fitbit sleep sensitive algorithm were also included for comparison. Pearson correlation was used to examine the linearity of mean TST minutes (TSTM) from each tracker compared to the log TSTM. Mean absolute percentage errors (MAPE) of TSTM from each tracker were calculated against the log TSTM. Lastly, mean differences in average TST between the trackers were examined by a general linear model for repeated measures. RESULTS: Pearson correlation coefficients were .32, .69, .24, and -.26 for the SWA, FB, and FBs with regard to log TSTM, respectively. Group 2 correlations between the log TSTM and Sadeh, Cole-Kripke, JU, and GV were .34, .65, .54, and .92, respectively. MAPE were 17.1%, 16.3%, 40.2%, and 32.9% for SWA, FB, FBs, and BP, respectively. MAPE of 17.0%, 11.5%, 14.9%, and 10% were observed for Sadeh, Cole-Kripke, JU, and GV, respectively. Bland-Altman Plots showed no systematic bias between all variables for TST compared with log TSTM. ANOVA and post-hoc analysis revealed a significant difference in the Fitbit sensitive TST (p = .001) in Group 1 (F (5, 51) = 8.06, p = .00) and no significant difference between Group 2 (F (4, 40) = 1.27, p = .296). CONCLUSION: The FB, Cole-Kripke, JU, and GV display the closest estimation of TST compared with log TST minutes. However, further research is needed to validate these monitors with polysomnography.

Effects of Charitable Versus Monetary Incentives on the Acceptance of and Adherence to a Pedometer-Based Health Intervention: Study Protocol and Baseline Characteristics of a Cluster-Randomized Controlled Trial (2016)

JMIR Research Protocols

Abstract

Background: Research has so far benefited from the use of pedometers in physical activity interventions. However, when public health institutions (eg, insurance companies) implement pedometer-based interventions in practice, people may refrain from participating due to privacy concerns. This might greatly limit the applicability of such interventions. Financial incentives have been successfully used to influence both health behavior and privacy concerns, and may thus have a beneficial effect on the acceptance of pedometer-based interventions. Objective: This paper presents the design and baseline characteristics of a cluster-randomized controlled trial that seeks to examine the effect of financial incentives on the acceptance of and adherence to a pedometer-based physical activity intervention offered by a health insurance company. Methods: More than 18,000 customers of a large Swiss health insurance company were allocated to a financial incentive, a charitable incentive, or a control group and invited to participate in a health prevention program. Participants used a pedometer to track their daily physical activity over the course of 6 months. A Web-based questionnaire was administered at the beginning and at the end of the intervention and additional data was provided by the insurance company. The primary outcome of the study will be the participation rate, secondary outcomes will be adherence to the prevention program, physical activity, and health status of the participants among others. Results: Baseline characteristics indicate that residence of participants, baseline physical activity, and subjective health should be used as covariates in the statistical analysis of the secondary outcomes of the study. Conclusions: This is the first study in western cultures testing the effectiveness of financial incentives with regard to a pedometer-based health intervention offered by a large health insurer to their customers. Given that the incentives prove to be effective, this study provides the basis for powerful health prevention programs of public health institutions that are easy to implement and can reach large numbers of people in need.

Accuracy of a Wrist-Worn Wearable Device for Monitoring Heart Rates in Hospital Inpatients: A Prospective Observational Study (2016)

Journal of Medical Internet Research

Abstract

Background: As the sensing capabilities of wearable devices improve, there is increasing interest in their application in medical settings. Capabilities such as heart rate monitoring may be useful in hospitalized patients as a means of enhancing routine monitoring or as part of an early warning system to detect clinical deterioration. Objective: To evaluate the accuracy of heart rate monitoring by a personal fitness tracker (PFT) among hospital inpatients. Methods: We conducted a prospective observational study of 50 stable patients in the intensive care unit who each completed 24 hours of heart rate monitoring using a wrist-worn PFT. Accuracy of heart rate recordings was compared with gold standard measurements derived from continuous electrocardiographic (cECG) monitoring. The accuracy of heart rates measured by pulse oximetry (Spo2.R) was also measured as a positive control. Results: On a per-patient basis, PFT-derived heart rate values were slightly lower than those derived from cECG monitoring (average bias of ?1.14 beats per minute [bpm], with limits of agreement of 24 bpm). By comparison, Spo2.R recordings produced more accurate values (average bias of +0.15 bpm, limits of agreement of 13 bpm, P<.001 as compared with PFT). Personal fitness tracker device performance was significantly better in patients in sinus rhythm than in those who were not (average bias ?0.99 bpm vs ?5.02 bpm, P=.02). Conclusions: Personal fitness tracker--derived heart rates were slightly lower than those derived from cECG monitoring in real-world testing and not as accurate as Spo2.R-derived heart rates. Performance was worse among patients who were not in sinus rhythm. Further clinical evaluation is indicated to see if PFTs can augment early warning systems in hospitals.

Sedentary Behavior in the Workplace: A Potential Occupational Hazard for Radiologists (2016)

Current Problems in Diagnostic Radiology

Abstract

In this study, we sought to quantify the sedentary worklife of the radiologist, a potential health risk. Radiologists of all training levels at our academic institution were surveyed to estimate the levels of at-work and out-of-work sitting. Fitbit One activity monitors were used to measure the at-work activity levels of radiology, pediatric, and internal medicine (IM) residents. Correlation between awareness and utilization of dynamic (sitting or standing, walking, or biking) picture archiving and communication system (PACS) workstations among radiology residents was assessed. Among surveyed radiologists (n = 89), 78% estimated sitting for at least 6 hours per workday. Estimated workplace sitting accounted for most of the total sitting for 81% of respondents. As measured by activity monitors, radiology residents (n = 27) took fewer steps per day (2683 vs 4602 vs 4967) and per hour (294 vs 419 vs 444) and experienced more sedentary time per hour (40.3 vs 36.2 vs 34.9min/h) than IM (n = 15) and pediatric (n = 9) residents. Activity experienced during reading room–based work and interventional work was compared by studying 4 additional radiology residents during both types of rotations. Reading-room activity was low, whereas activity on interventional rotations surpassed average levels for the pediatric and IM residents in our study. Radiology residents' (n = 28) awareness and utilization of dynamic PACS workstations varied among reading rooms, but were generally low—75% reported never or rarely using them. Resident utilization correlated with awareness of dynamic workstations available at our institution (R2 = 0.64; P = 0.013). In conclusion, radiology residents in our study led more sedentary worklives compared with residents from other specialties and took minimal advantage of available tools to mitigate this. Potential health risks of inactivity justify individual and departmental efforts to limit workplace inactivity among radiologists.

A home-based physical activity intervention using activity trackers in survivors of childhood cancer: A pilot study (2016)

Pediatric Blood & Cancer

Abstract

Background: Over 70% of childhood cancer survivors develop late complications from therapy, many of which can be mitigated by physical activity. Survivors engage in exercise at similar or lower rates than their sedentary healthy peers. We piloted a novel home-based exercise intervention with a motivational activity tracker. We evaluated (i) feasibility, (ii) impact on activity levels and physical fitness, and (iii) barriers, preferences, and beliefs regarding physical activity. Methods: Childhood cancer survivors currently 15 years or older and not meeting the Centers for Disease Control and Prevention physical activity guidelines were enrolled and instructed to wear the Fitbit One, a 4.8 cm × 1.8 cm motivational activity tracker, daily for 6 months. Baseline and follow-up evaluations included self-report surveys, an Actigraph accelerometer for 7 days, and a VO2 maximum test by cardiac stress test. Results: Nineteen participants were enrolled (13.4% participation rate) with a mean age of 24.3 ± 5.8 years (range 15–35). Four participants withdrew with a 79% retention rate. Participants wore the Fitbit an average of 19.0 ± 4.7 days per month during months 1–3 and 15.0 ± 7.9 days per month during months 4–6. Total weekly moderate to vigorous physical activity increased from 265.6 ± 117.0 to 301.4 ± 135.4 min and VO2 maximum increased from 25.7 ± 7.7 to 27.2 ± 7.4 ml/kg/min. These changes were not statistically significant (P = 0.47 and 0.30, respectively). Survey responses indicated no change in barriers, preferences, and beliefs regarding physical activity. Conclusions: This pilot study of a motivational activity tracker demonstrated feasibility as measured by participant retention, receptivity, and belief of utility. Future studies with a large sample size are needed to demonstrate the efficacy and sustainability of this intervention.

Examining The Validity Of Fitbit Charge HR For Measuring Heart Rate In Free-living Conditions (2016)

Medicine & Science in Sports & Exercise

Abstract

Optical blood flow sensors (i.e. photoplethysmographic techniques) have recently been utilized in wearable activity trackers. The Fitbit Charge HRTM (FBHR) is one of the widely recognized wearable activity trackers that utilizes Fitbit's proprietary PurePulse optical heart rate (HR) technology to automatically measure wrist-based HR. Despite its increasing popularity, however, no study to date has addressed the validity of FBHR for measuring HR in free-living conditions. PURPOSE: The purpose of this study was to examine the validity of FBHR for measuring HR using a chest strap Polar HR monitor (PHR) as a reference measure in free-living conditions. METHODS: Ten healthy college students (8 males; mean age = 26.5 ± 5.4 years; mean body mass index (BMI) = 24.5 ± 3.23 kgtextperiodcenteredm2) participated in the study. The participants were asked to perform normal daily activities for 8 hours in a day while wearing the PHR (model RS400) on their chest and two FBHRs on their dominant and non-dominant wrists, respectively. HR was recorded every minute and the minute-by-minute HR data from each monitor were synchronized by time of day. Pearson correlation was used to examine the linearity of average beats-per-minute (bpm) estimated from FBHRs with respect to the PHR. Mean differences in average bpm between the monitors were examined by a general linear model for repeated measures. Lastly, mean absolute percentage error (MAPE) of minute-by-minute bpm estimated from the FBHRs were calculated against the PHR. RESULTS: Average HRs (mean ± SD) for PHR, FBHR non-dominant, and FBHR dominant were 75.6 ± 18.5 bpm, 72.8 ± 16.7 bpm, and 73.9 ± 17.06 bpm, respectively. Pearson correlation coefficients (r) between the PHR and FBHR non-dominant and dominant were r=.805 and r=.793, respectively. MAPE were 9.17 ± 10.9% for FBHR non-dominant and 9.71 ± 12.4% for FBHR HR dominant. ANOVA and post-hoc analyses with Bonferroni revealed significant differences in estimating HR from FBHR non-dominant wrist (p=.001) and FBHR dominant wrist (p=.001) compared to PHR monitor. CONCLUSION: The results indicated that the wrist-oriented Fitbit Charge HRTM device does not provide an accurate measurement of HR during free-living condition in this study. However, further research is needed to validate these monitors with a larger sample with different population groups.

Evidence of Convergent Validity for Measuring Free-Living Walking Using Wearable Devices (2016)

Medicine & Science in Sports & Exercise

Abstract

PURPOSE: To determine the convergent validity of consumer-based wearable devices for assessing walking during free-living activities. METHODS: A total of 48 healthy adults (10 males, 50.3±10.6 yrs & BMI 24.9±2.5; 38 females, 53.3±7.4 yrs & BMI 24.4±2.9) participated. The consumer-based wearable devices included Actigraph GT3X+(AT), BodyMedia Armband (BM), Fitbit Charge (FC), Fitbit One (FO), Jawbone up (JB), Misfit (MF), and the Nike Fuelband (NF) worn on wrist, waist, or upper arm. The Omron HJ720IT pedometer served as the reference measure. While simultaneously wearing all devices, walking time (6.64±.80min), steps (13.28±1.22), and speed (1.43±.20m/sec) were assessed (10 m walk). Participants also completed selected free-living activities including moving boxes, cleaning desks, mopping, walking up & down stairs, and outdoor walking on hills. On average these activities took 40min to complete. Descriptive statistics, correlation coefficients (r) and paired t-test among the wearable devices and the Omron pedometer were calculated using SPSS 20. RESULTS: It was unable to examine the accuracy of the JB due to constant synchronizing errors. Walking Time, Steps, and Speed showed moderate correlations with the Omron (r = .39, .55, & .43, respectively); but low correlations with wearable devices r = .11 to .23(Time), -.10 to .37(Steps), and -.08 to -.23(Speed). Step counts measured by the devices during free-living were as follows: FC=178±238, NF=1595±156, MF=1628±283, BM=1667±191, FO=1735±174, and AT=1778±180. The Omron (reference) recorded 1749±169 steps. The correlation coefficients between the step counts from the wearable devices and the Omron during free-living were AT=.91, FO=.91, NF =.61, FC=.59, BM=.44, and MF=-.15. All wearable devices showed statistically significant differences when compared to the Omron (t = -2.2 to -14.3, p textless0.05). CONCLUSIONS: : Of the devices examined, AT and FO were the most valid for measuring walking steps during free-living activities. On the other hand, quality control and measurement error associated with JB and MF suggest they should not be used.

Validation and Accuracy of FITBIT Charge: A Pilot Study in a University Worksite Walking Program (2016)

Journal of Fitness Research

Abstract

PURPOSE: Wearable activity monitors (e.g., Fitbit®) have become increasingly popular for both researchers and lay people alike. Recent studies have reported several Fitbit® models correlate with research grade monitors; however none to date have examined the Fitbit® Charge model. Furthermore, none have reported on the accuracy of Fitbit® devices. The primary purpose of this study was to compare step counts assessed by Fitbit® Charge and a research grade accelerometer (i.e., Actigraph®). The secondary purpose of this study was to compare the Fitbit® Charge algorithms for physical activity intensity with an algorithm validated for research purposes. METHODS: Participants (n=8) wore Fitbit® Charge and Actigraph® monitors for 7 days. First, correlations were run to examine the concordance of step counts and physical activity intensity levels derived from Fitbit® Charge and Actigraph® algorithms. Next, accuracy was investigated by paired sample t-tests comparing each device’s assessment of step counts and minutes spent in light, moderate and vigorous intensity activity. RESULTS: Significant correlations were found for all study outcomes (p’s < .05). Significant differences were observed for step counts on all days and weekly total step count (p’s < .05). Analyses of each intensity level revealed significant differences on most days for light and moderate intensity (p’s <.05). For vigorous intensity, only day 6 (p = .007) and accumulated weekly total of minutes were different (p = .014). CONCLUSIONS: Our correlation results were similar to previous research examining validity of other Fitbit® models. However, Fitbit® Charge devices may overestimate step counts. Moreover, the algorithm used by Fitbit® may overestimate light and vigorous physical activity, while underestimating minutes in moderate physical activity. Further research is needed to determine reliability of Fitbit® Charge to assess activity and intensity that reflects current health recommendations. Therefore, researchers should use discretion if considering use of Fitbit® Charge for research purposes.

Validation And Accuracy Of Fitbit Charge (2016)

Medicine & Science in Sports & Exercise

Abstract

URPOSE: Wearable activity monitors (e.g., Fitbit®) have become increasingly popular for both researchers and lay people alike. Recent studies have reported several Fitbit® models correlate with research grade monitors; however none to date have examined the Fitbit® Charge model. Furthermore, none have reported on the accuracy of Fitbit® devices. The primary purpose of this study was to compare step counts assessed by Fitbit® Charge and a research grade accelerometer (i.e., Actigraph®). The secondary purpose of this study was to compare the Fitbit® Charge algorithms for physical activity intensity with an algorithm validated for research purposes. METHODS: Participants (n=8) wore Fitbit® Charge and Actigraph® monitors for 7 days. First, correlations were run to examine the concordance of step counts and physical activity intensity levels derived from Fitbit® Charge and Actigraph® algorithms. Next, accuracy was investigated by paired sample t-tests comparing each device's assessment of step counts and minutes spent in light, moderate and vigorous intensity activity. RESULTS: Significant correlations were found for all study outcomes (p's textless .05). Significant differences were observed for step counts on all days and weekly total step count (p's textless .05). Analyses of each intensity level revealed significant differences on most days for light and moderate intensity (p's textless.05). For vigorous intensity, only day 6 (p = .007) and accumulated weekly total of minutes were different (p = .014). CONCLUSIONS: Our correlation results were similar to previous research examining validity of other Fitbit® models. However, Fitbit® Charge devices may overestimate step counts. Moreover, the algorithm used by Fitbit® may overestimate light and vigorous physical activity, while underestimating minutes in moderate physical activity. Further research is needed to determine reliability of Fitbit® Charge to assess activity and intensity that reflects current health recommendations. Therefore, researchers should use discretion if considering use of Fitbit® Charge for research purposes.

Use of Fitbit Charge HR for Management of Post-Concussive Syndrome: A Case Report (2016)

PM&R

Abstract

Case/Program Description: A 43-year-old woman with Hashimoto’s thyroiditis presented to outpatient clinic with diagnosis of post-concussive syndrome for the past 1.5 years. She had had a short course of physical and cognitive therapy but returned to work and had full decompensation and resumption of concussive symptoms. She continued to experience significant headaches, sleep disturbance, decreased processing speed, and concentration deficits. Additionally, she had labile blood pressures, tachycardia, and intermittent sweating episodes. Despite normal TSH, free T4 levels, and a negative tilt table test, these symptoms persisted. She purchased a Fitbit Charge HR and began a sub-aerobic workout regimen. Clonidine 0.1 mg was prescribed to be taken twice a day to treat suspected dysautonomia and results were obtained via the Fitbit software. Setting: Academic outpatient practice. Results Using the Fitbit Charge HR we were able to visualize and quantify the effect of clonidine on her suspected dysautonomia symptoms. The heart rate and heart rate variability response were graphically represented. Additionally, we objectively measured hours of sleep increase, and noted fewer episodes of restlessness. Discussion: This is the first documented use of the Fitbit Charge HR in this capacity. This technology allows us to collect larger amounts of data that helps us more objectively measure response to interventions. While Fitbit data is not the gold standard, the information gathered is likely more detailed and objective than the self reporting. Conclusions: Fitbit Charge HR is a novel and affordable technology that may have a significant role in informing patient-physician interaction. More specifically, the technology transforms what was originally a patient’s subjective and static measurement of response to an objective and dynamic assessment, empowering both patients and physicians with immediate and easily accessible data.

Mission Possible: Using Ubiquitous Social Goal Sharing Technology to Promote Physical Activity in Children (2016)

Movement, Health & Exercise

Abstract

The present study investigated the acceptability of a novel ubiquitous social goal-sharing intervention aimed at promoting physical activity (PA) in schoolchildren. Methods: Thirty children (18 boys; 10.1±0.3 years; 1.39±0.06 m; 19.85±4.03 kgtextperiodcenteredm-2) were randomly assigned to ten groups and provided with Fitbit monitors. Video-clips describing mission-based activities were shown on iPads each week, for four consecutive weeks. An LED lighting-strip provided visual feedback on daily group PA levels. Three semistructured group interviews were conducted with 10 children (4 boys, 6 girls; n=2) and two teachers (n=1). Additionally, at baseline and post-intervention, seven-day accelerometry, cardiorespiratory fitness (CRF; 20m shuttle run test), anthropometrics and physical selfperceptions were assessed. Data were analysed using a mixed “between-within" analysis of variance (ANOVA). Results: Children stated that peers were positive role models and provided encouragement to accomplish the missions. Teachers felt that children's fitness, teamwork and problem-solving skills considerably improved. Statistical analyses revealed no significant intervention effect (ptextgreater0.05), though BMI and waist circumference, and CRF, decreased and increased, respectively. Conclusion: The integration of ubiquitous social goal-sharing technology in schools was well received by teachers and pupils. However, further studies integrating a larger sample size encompassing numerous schools, comparison groups and a longer intervention period with associated follow-up measurements, are warranted to ascertain the feasibility as a low-cost intervention to promote children's PA levels.

Older Adults' Experiences Using a Commercially Available Monitor to Self-Track Their Physical Activity. (2016)

JMIR mHealth and uHealth

Abstract

BACKGROUND: Physical activity contributes to older adults' autonomy, mobility, and quality of life as they age, yet fewer than 1 in 5 engage in activities as recommended. Many older adults track their exercise using pencil and paper, or their memory. Commercially available physical activity monitors (PAM) have the potential to facilitate these tracking practices and, in turn, physical activity. An assessment of older adults' long-term experiences with PAM is needed to understand this potential. OBJECTIVE: To assess short and long-term experiences of adults textgreater70 years old using a PAM (Fitbit One) in terms of acceptance, ease-of-use, and usefulness: domains in the technology acceptance model. METHODS: This prospective study included 95 community-dwelling older adults, all of whom received a PAM as part of randomized controlled trial piloting a fall-reducing physical activity promotion intervention. Ten-item surveys were administered 10 weeks and 8 months after the study started. Survey ratings are described and analyzed over time, and compared by sex, education, and age. RESULTS: Participants were mostly women (71/95, 75%), 70 to 96 years old, and had some college education (68/95, 72%). Most participants (86/95, 91%) agreed or strongly agreed that the PAM was easy to use, useful, and acceptable both 10 weeks and 8 months after enrolling in the study. Ratings dropped between these time points in all survey domains: ease-of-use (median difference 0.66 points, P=.001); usefulness (median difference 0.16 points, P=.193); and acceptance (median difference 0.17 points, P=.032). Differences in ratings by sex or educational attainment were not statistically significant at either time point. Most participants 80+ years of age (28/37, 76%) agreed or strongly agreed with survey items at long-term follow-up, however their ratings were significantly lower than participants in younger age groups at both time points. CONCLUSIONS: Study results indicate it is feasible for older adults (70-90+ years of age) to use PAMs when self-tracking their physical activity, and provide a basis for developing recommendations to integrate PAMs into promotional efforts.

Acceptance of Commercially Available Wearable Activity Trackers Among Adults Aged Over 50 and With Chronic Illness: A Mixed-Methods Evaluation. (2016)

JMIR mHealth and uHealth

Abstract

BACKGROUND: Physical inactivity and sedentary behavior increase the risk of chronic illness and death. The newest generation of "wearable" activity trackers offers potential as a multifaceted intervention to help people become more active. OBJECTIVE: To examine the usability and usefulness of wearable activity trackers for older adults living with chronic illness. METHODS: We recruited a purposive sample of 32 participants over the age of 50, who had been previously diagnosed with a chronic illness, including vascular disease, diabetes, arthritis, and osteoporosis. Participants were between 52 and 84 years of age (mean 64); among the study participants, 23 (72%) were women and the mean body mass index was 31 kg/m(2). Participants tested 5 trackers, including a simple pedometer (Sportline or Mio) followed by 4 wearable activity trackers (Fitbit Zip, Misfit Shine, Jawbone Up 24, and Withings Pulse) in random order. Selected devices represented the range of wearable products and features available on the Canadian market in 2014. Participants wore each device for at least 3 days and evaluated it using a questionnaire developed from the Technology Acceptance Model. We used focus groups to explore participant experiences and a thematic analysis approach to data collection and analysis. RESULTS: Our study resulted in 4 themes: (1) adoption within a comfort zone; (2) self-awareness and goal setting; (3) purposes of data tracking; and (4) future of wearable activity trackers as health care devices. Prior to enrolling, few participants were aware of wearable activity trackers. Most also had been asked by a physician to exercise more and cited this as a motivation for testing the devices. None of the participants planned to purchase the simple pedometer after the study, citing poor accuracy and data loss, whereas 73% (N=32) planned to purchase a wearable activity tracker. Preferences varied but 50% felt they would buy a Fitbit and 42% felt they would buy a Misfit, Jawbone, or Withings. The simple pedometer had a mean acceptance score of 56/95 compared with 63 for the Withings, 65 for the Misfit and Jawbone, and 68 for the Fitbit. To improve usability, older users may benefit from devices that have better compatibility with personal computers or less-expensive Android mobile phones and tablets, and have comprehensive paper-based user manuals and apps that interpret user data. CONCLUSIONS: For older adults living with chronic illness, wearable activity trackers are perceived as useful and acceptable. New users may need support to both set up the device and learn how to interpret their data.

Behavior Change Techniques Present in Wearable Activity Trackers: A Critical Analysis (2016)

JMIR mHealth and uHealth

Abstract

Background: Wearable activity trackers are promising as interventions that offer guidance and support for increasing physical activity and health-focused tracking. Most adults do not meet their recommended daily activity guidelines, and wearable fitness trackers are increasingly cited as having great potential to improve the physical activity levels of adults. Objective: The objective of this study was to use the Coventry, Aberdeen, and London-Refined (CALO-RE) taxonomy to examine if the design of wearable activity trackers incorporates behavior change techniques (BCTs). A secondary objective was to critically analyze whether the BCTs present relate to known drivers of behavior change, such as self-efficacy, with the intention of extending applicability to older adults in addition to the overall population. Methods: Wearing each device for a period of 1 week, two independent raters used CALO-RE taxonomy to code the BCTs of the seven wearable activity trackers available in Canada as of March 2014. These included Fitbit Flex, Misfit Shine, Withings Pulse, Jawbone UP24, Spark Activity Tracker by SparkPeople, Nike+ FuelBand SE, and Polar Loop. We calculated interrater reliability using Cohen's kappa. Results: The average number of BCTs identified was 16.3/40. Withings Pulse had the highest number of BCTs and Misfit Shine had the lowest. Most techniques centered around self-monitoring and self-regulation, all of which have been associated with improved physical activity in older adults. Techniques related to planning and providing instructions were scarce. Conclusions: Overall, wearable activity trackers contain several BCTs that have been shown to increase physical activity in older adults. Although more research and development must be done to fully understand the potential of wearables as health interventions, the current wearable trackers offer significant potential with regard to BCTs relevant to uptake by all populations, including older adults.

Physical Activity Measured with Accelerometer and Self-Rated Disability in Lumbar Spine Surgery: A Prospective Study. (2016)

Global Spine Journal

Abstract

OBJECTIVE: Patient-based subjective ratings of symptoms and function have traditionally been used to gauge the success and extent of recovery following spine surgery. The main drawback of this type of assessment is the inherent subjectivity involved in patient scoring. We aimed to objectively measure functional outcome in patients having lumbar spine surgery using quantitative physical activity measurements derived from accelerometers. METHODS: A prospective study of 30 patients undergoing spine surgery was conducted with subjective outcome scores (visual analog scale [VAS], Oswestry Disability Index [ODI] and Short Form 12 [SF-12]) recorded; patients were given a Fitbit accelerometer (Fitbit Inc., San Francisco, California, United States) at least 7 days in advance of surgery to record physical activity (step count, distance traveled, calories burned) per day. Following surgery, postoperative activity levels were reported at 1-, 2-, and 3-month follow-up. RESULTS: Of the 28 compliant patients who completed the full trial period, mean steps taken per day increased 58.2% (p?=?0.008) and mean distance traveled per day increased 63% (p?=?0.0004) at 3-month follow-up. Significant improvements were noted for mean changes in VAS back pain, VAS leg pain, ODI, and SF-12 Physical Component Summary (PCS) scores. There was no significant correlation between the improvement in steps or distance traveled per day with improvements in VAS back or leg pain, ODI, or PCS scores at follow-up. CONCLUSIONS: High compliance and statistically significant improvement in physical activity were demonstrated in patients who had lumbar decompression and lumbar fusion. There was no significant correlation between improvements in subjective clinical outcome scores with changes in physical activity measurements at follow-up. Limitations of the present study include its small sample size, and the validity of objective physical activity measurements should be assessed in future larger, prospective studies.

Consumer-based Physical Activity Monitors and Research-grade Accelerometers Underestimate Physical Activity in a Semi-structured Setting (2016)

Circulation

Abstract

Introduction: Consumer-based physical activity (PA) monitors have become popular tools to track PA (steps and energy expenditure [EE]), but little is known about their accuracy and comparability to research-grade PA monitors. Hypothesis: We hypothesized that consumer-based PA monitors would have lower PA measurement accuracy than a research-grade PA monitor. Methods: Healthy adults (n=30 [n=15 male], aged 18-79 years) were fitted with three Fitbit PA monitors (One and Zip, left hip; Flex, non-dominant wrist), one Jawbone PA monitor (UP24, non-dominant wrist), and one research-grade, accelerometer-based PA monitor (ActiGraph Link; right hip). Subjects performed =12 activities from a list of 21 sedentary, household, and ambulatory activities during an 80-minute, semi-structured protocol while wearing all five monitors and a portable metabolic analyzer. Steps and EE were recorded from PA monitors at the beginning and end of the protocol. A previously validated EE prediction equation (Freedson) was used to estimate EE from PA (PAEE) using ActiGraph data. A previous lab visit by subjects was used to estimate resting EE; resting EE was added to PAEE to estimate total EE from the ActiGraph. Criterion measures included a trained research assistant counting total steps (with a hand tally counter) and the metabolic analyzer for measurement of total EE and PAEE (total EE - resting EE). Repeated measures analysis of variance was used to assess step, total EE, and PAEE measures among the consumer-based PA monitors, ActiGraph, and criterion methods. Results: Participants averaged 3,231±104 steps, 294±10 kcals for total EE, and 235±9 kcals for PAEE according to criterion measures. Consumer-based PA monitors and the ActiGraph significantly underestimated steps (mean difference [MD]: 714-1,047 steps [22.1-32.4%], p<0.01), total EE (MD: 37-109 kcals [12.6-37.1%], p<0.05), and PAEE (MD: 35-107 kcals [14.9-45.5%], p<0.05) compared to criterion measures. The Fitbit One, Zip, and Flex monitors estimated significantly higher steps than the ActiGraph (MD: 199-302 steps [9.1-13.8%], p<0.05), but there was no significant difference in steps estimated by the UP24 compared to the ActiGraph (MD: 144 steps [6.6%], p=0.21). All consumer-based monitors estimated significantly higher total EE and PAEE than the ActiGraph (MD: 23-60 kcals [12.6-32.7%] and 22-53 kcals [17.3.0-41.2%], respectively; p<0.05). Conclusions: All PA monitors underestimated steps, total EE, and PAEE compared to the criterion measures. Large variability existed in the step and EE estimates from the PA monitors tested. Contrary to our hypothesis, the research-grade ActiGraph monitor (using Freedson EE prediction equation) had more severe PA underestimates than the consumer-based PA monitors. In conclusion, estimates of PA between studies using consumer-based vs. research-grade PA monitors should be interpreted with caution.

Wearable Devices and Smartphones for Activity Tracking Among People with Serious Mental Illness. (2016)

Mental Health and Physical Activity

Abstract

INTRODUCTION: People with serious mental illness, including schizophrenia spectrum and mood disorders, are more physically inactive than people from the general population. Emerging wearable devices and smartphone applications afford opportunities for promoting physical activity in this group. This exploratory mixed methods study obtained feedback from participants with serious mental illness to assess the acceptability of using wearable devices and smartphones to support a lifestyle intervention targeting weight loss. METHODS: Participants with serious mental illness and obesity enrolled in a 6-month lifestyle intervention were given Fitbit activity tracking devices and smartphones to use for the study. Participants completed quantitative post-intervention usability and satisfaction surveys, and provided qualitative feedback regarding acceptability of using these devices and recommendations for improvement through in-depth interviews. RESULTS: Eleven participants wore Fitbits for an average of 84.7% (SD=18.1%) of the days enrolled in the study (median=93.8% of the days enrolled, interquartile range=83.6-94.3%). Participants were highly satisfied, stating that the devices encouraged them to be more physically active and were useful for self-monitoring physical activity and reaching daily step goals. Some participants experienced challenges using the companion mobile application on the smartphone, and recommended greater technical support, more detailed training, and group tutorials prior to using the devices. DISCUSSION: Participants' perspectives highlight the feasibility and acceptability of using commercially available mHealth technologies to support health promotion efforts targeting people with serious mental illness. This study offers valuable insights for informing future research to assess the effectiveness of these devices for improving health outcomes in this high-risk group.

Validity of Consumer-Based Physical Activity Monitors for Specific Activity Types. (2016)

Medicine and Science in Sports and Exercise

Abstract

PURPOSE: Consumer-based physical activity (PA) monitors are popular for individual tracking of PA variables. However, current research has not examined how these monitors track energy expenditure (EE) and steps in distinct activities. This study examined the accuracy of the Fitbits One, Zip, and Flex and Jawbone UP24 for estimating EE and steps for specific activities and activity categories. METHODS: Thirty subjects completed a structured protocol consisting of three sedentary, four household, and four ambulatory/exercise activities. All subjects began by lying on a bed for 10 min; 10 other activities were performed for 5 min each. Indirect calorimetry (COSMED) and researcher-counted steps were criterion measures for EE and step counts, respectively. The Omron HJ-720IT pedometer was used as a comparison of step count accuracy. EE and steps were compared with criterion measures using the Friedman repeated-measures nonparametric test and mean absolute percent error (MAPE). RESULTS: All PA monitors predicted EE within 8% of COSMED for sedentary activity but overestimated EE by 16%-40% during ambulatory activity. All monitors except the Fitbit Flex (within 8% of criterion) underestimated EE by 27%-34% during household activity. EE predictions were accompanied with MAPE textgreater10%. For household activity, the Fitbit Flex estimated steps within 10% of researcher-counted steps; all other monitors underestimated steps by 35%-64%. All monitors estimated steps within 4% of researcher-counted steps and displayed MAPE textless10% during ambulatory activity. The Omron underestimated household steps by 74% but was within 1% for ambulatory steps. All monitors severely underestimated EE and steps during cycling. CONCLUSION: Consumer-based PA monitors should be used cautiously for estimating EE, although they provide accurate measures of steps for structured ambulatory activity, similar to validated pedometers.

Evaluating a community-based exercise intervention with adults living with HIV: protocol for an interrupted time series study (2016)

BMJ Open

Abstract

Introduction: Our aim was to evaluate a community-based exercise (CBE) intervention with the goal of reducing disability and enhancing health for community-dwelling people living with HIV (PLWH). Methods and analysis: We will use a mixed-methods implementation science study design, including a prospective longitudinal interrupted time series study, to evaluate a CBE intervention with PLWH in Toronto, Canada. We will recruit PLWH who consider themselves medically stable and safe to participate in exercise. In the baseline phase (0-8 months), participants will be monitored bimonthly. In the intervention phase (8-14 months), participants will take part in a 24-week CBE intervention that includes aerobic, resistance, balance and flexibility exercise at the YMCA 3 times per week, with weekly supervision by a fitness instructor, and monthly educational sessions. In the follow-up phase (14-22 months), participants will be encouraged to continue to engage in unsupervised exercise 3 times per week. Quantitative assessment: We will assess cardiopulmonary fitness, strength, weight, body composition and flexibility outcomes followed by the administration of self-reported questionnaires to assess disability and contextual factor outcomes (coping, mastery, stigma, social support) bimonthly. We will use time series regression analysis to determine the level and trend of outcomes across each phase in relation to the intervention. Qualitative assessment: We will conduct a series of face-to-face interviews with a subsample of participants and recreation providers at initiation, midpoint and completion of the 24-week CBE intervention. We will explore experiences and anticipated benefits with exercise, perceived impact of CBE for PLWH and the strengths and challenges of implementing a CBE intervention. Interviews will be audio recorded and analysed thematically.

These Shoes Are Made for Walking: Sensitivity Performance Evaluation of Commercial Activity Monitors under the Expected Conditions and Circumstances Required to Achieve the International Daily Step Goal of 10,000 Steps. (2016)

PLOS ONE

Abstract

INTRODUCTION: Physical activity is a vitally important part of a healthy lifestyle, and is of major benefit to both physical and mental health. A daily step count of 10,000 steps is recommended globally to achieve an appropriate level of physical activity. Accurate quantification of physical activity during conditions reflecting those needed to achieve the recommended daily step count of 10,000 steps is essential. As such, we aimed to assess four commercial activity monitors for their sensitivity/accuracy in a prescribed walking route that reflects a range of surfaces that would typically be used to achieve the recommended daily step count, in two types of footwear expected to be used throughout the day when aiming to achieve the recommended daily step count, and in a timeframe required to do so. METHODS: Four commercial activity monitors were worn simultaneously by participants (n = 15) during a prescribed walking route reflective of surfaces typically encountered while achieving the daily recommended 10,000 steps. Activity monitors tested were the Garmin Vivofit ™, New Lifestyles' NL-2000 ™ pedometer, Withings Smart Activity Monitor Tracker (Pulse O2) ™, and Fitbit One ™. RESULTS: All activity monitors tested were accurate in their step detection over the variety of different surfaces tested (natural lawn grass, gravel, ceramic tile, tarmacadam/asphalt, linoleum), when wearing both running shoes and hard-soled dress shoes. CONCLUSION: All activity monitors tested were accurate in their step detection sensitivity and are valid monitors for physical activity quantification over the variety of different surfaces tested, when wearing both running shoes and hard-soled dress shoes, and over a timeframe necessary for accumulating the recommended daily step count of 10,000 steps. However, it is important to consider the accuracy of activity monitors, particularly when physical activity in the form of stepping activities is prescribed as an intervention in the treatment or prevention of a disease state.

Workstations To Increase Workplace Physical Activity And Reduce Sitting Time (2016)

Medicine & Science in Sports & Exercise

Abstract

There is growing evidence prolonged workplace sitting is associated with multiple health risks. Utilizing static and active workstations may increase daily workplace physical activity and reduce workplace-sitting time. PURPOSE: The purpose of study was to determine if access to both static and active workstations could increase workplace physical activity and decrease sitting time. METHODS: Participants (N=15) were assigned to an experimental (sit-stand workstation and shared treadmill desk; n=8) or control group (n=7) for seven weeks. The International Physical Activity Questionnaire long form was administered at baseline and at the end of the study to assess workplace physical activity and sitting time. Participants wore an activity tracker (Fitbit One) to objectively assess daily footsteps and physically active hours. A mixed between-within subject analysis of variance was used to compare the groups (alpha: p textless .05). RESULTS: There were no statistically significant between group differences in the dependent variables. CONCLUSIONS: This is the first study to examine the use of static and active workstations to increase workplace physical activity and reduce sitting time. Workday sitting decreased 61% in the experimental group, while increasing 8.3% in the control group. Although only half of the experimental group participants used the shared treadmill desk, the mean number of footsteps taken (8897.25) was well above the recommended 5000 steps/day. Because no attempt was made to motivate or promote use of the equipment in any way, a health education component may be needed, along with a larger sample size to attain more significant increases in workplace physical activity and reductions in sitting time.

Using High-Tech and High-Touch Methods for Effective Population Health Management (2016)

Iproceedings

Abstract

Background: Healthcare consumers today are more engaged and empowered than ever before. Social, mobile, and in-home self-monitoring tools now enable health consumers to manage their health conditions and receive advice, support, and care without leaving home. Consumers are increasingly buying biometric devices such the Fitbit and Apple Watch to monitor their health. Many payers and employers are providing fitness devices to encourage consumers to be healthy, often offering discounts to their healthcare premiums. These devices create a sense of euphoria initially, but they do not yield sustainable health results and necessary behavioral changes in consumers to sustain it in the long-term. According to BJ Fogg, Stanford professor, sustainable behavior change (B) is a function of the motivation of the individual (M) × ability of the individual to do the task (A) × Trigger (T); B=MAT. Objective: Our hypothesis was that self-managed digital triggers alone would not yield sustainable change, but a combination of digital biometrics devices that automatically monitor health supplemented by weekly contact with a health coach would deliver sustainable changes to consumer behavior. Methods: During the 6-month pilot in 2014 and 12-month program in 2015, Cognizant wanted to test this hypothesis by providing appropriate triggers to motivate individuals to achieve their goals and ensure sustainable change in behavior. We provided Fitbit devices to participants and shared their digital information with our health coach. The coach worked with the individuals in setting individual goals, reviewed their progress, and helped with better food and exercise choices. The coach also used gamification to motivate the group into friendly competitions. Results: At the end of the pilot, participants averaged 70,000 steps per week. This was well above the average US baseline of 36,000 steps and Fitbit average of 42,000 steps. Pilot participants also lost over an average of 10 pounds each. However, during the pilot we determined coaching needed more focus on incremental, sustainable goals to lead toward long-term behavior changes. During a 6 month period between the pilot ending and the 2015 program initiation where pilot participants did not have access to a coach, participants' activity level decreased, and weight lost during the pilot was gained back. During the 2015 program, participants sustained on average 66,000 steps per week (well above both the US and Fitbit average). Obese 1 participants normalized steps at 70,000 per week and increased activity from baseline almost 30%. This group started at the very bottom of “Somewhat Active” and transitioned into the “Active” category. Obese 2 participants normalized steps at 60,000 per week and increased activity from baseline almost 33%, which transitioned them from “Low Active” to “Somewhat Active.” This was the largest increase by any group from baseline. Our control group, who did not have access to coaches, averaged only 43,000 steps per week. Conclusions: Coach influence and participant change is most effective in the first 6 months. After that, coaches concentrate on incrementally increasing activity in a sustainable manner. Effective population health management and high value personalized services can be achieved by weight loss from increasing physical activity and better food choices brought about by direct consumer engagement that combines “high tech” with “high touch.” [iproc 2016;2(1):e17]

Retrofit Weight-Loss Outcomes at 6, 12, and 24 Months and Characteristics of 12-Month High Performers: A Retrospective Analysis (2016)

JMIR mHealth and uHealth

Abstract

Background: Obesity is the leading cause of preventable death costing the health care system billions of dollars. Combining self-monitoring technology with personalized behavior change strategies results in clinically significant weight loss. However, there is a lack of real-world outcomes in commercial weight-loss program research. Objective: Retrofit is a personalized weight management and disease-prevention solution. This study aimed to report Retrofit’s weight-loss outcomes at 6, 12, and 24 months and characterize behaviors, age, and sex of high-performing participants who achieved weight loss of 10% or greater at 12 months. Methods: A retrospective analysis was performed from 2011 to 2014 using 2720 participants enrolled in a Retrofit weight-loss program. Participants had a starting body mass index (BMI) of >25 kg/m² and were at least 18 years of age. Weight measurements were assessed at 6, 12, and 24 months in the program to evaluate change in body weight, BMI, and percentage of participants who achieved 5% or greater weight loss. A secondary analysis characterized high-performing participants who lost =10% of their starting weight (n=238). Characterized behaviors were evaluated, including self-monitoring through weigh-ins, number of days wearing an activity tracker, daily step count average, and engagement through coaching conversations via Web-based messages, and number of coaching sessions attended. Results: Average weight loss at 6 months was -5.55% for male and -4.86% for female participants. Male and female participants had an average weight loss of -6.28% and -5.37% at 12 months, respectively. Average weight loss at 24 months was -5.03% and -3.15% for males and females, respectively. Behaviors of high-performing participants were assessed at 12 months. Number of weigh-ins were greater in high-performing male (197.3 times vs 165.4 times, P=.001) and female participants (222 times vs 167 times, P<.001) compared with remaining participants. Total activity tracker days and average steps per day were greater in high-performing females (304.7 vs 266.6 days, P<.001; 8380.9 vs 7059.7 steps, P<.001, respectively) and males (297.1 vs 255.3 days, P<.001; 9099.3 vs 8251.4 steps, P=.008, respectively). High-performing female participants had significantly more coaching conversations via Web-based messages than remaining female participants (341.4 vs 301.1, P=.03), as well as more days with at least one such electronic message (118 vs 108 days, P=.03). High-performing male participants displayed similar behavior. Conclusions: Participants on the Retrofit program lost an average of -5.21% at 6 months, -5.83% at 12 months, and -4.09% at 24 months. High-performing participants show greater adherence to self-monitoring behaviors of weighing in, number of days wearing an activity tracker, and average number of steps per day. Female high performers have higher coaching engagement through conversation days and total number of coaching conversations.

Smartwomantm: Feasibility of a Mobile Application to Improve Cardiovascular Risk Factors in Indigent Diabetic Women (2016)

Circulation: Cardiovascular Quality and Outcomes

Abstract

Introduction: Cardiovascular disease disparately affects women with diabetes mellitus. Among women with diabetes, cardiovascular disease prevention, including risk factor monitoring and lifestyle modification is important but underutilized. Mobile health strategies are emerging communication technologies to improve self management and health behaviours. However, the feasibility of mobile health technology (m-health) to enhance health behaviors in an indigent population is unknown. We developed and examined the feasibility of m-health to improve health behaviors and self-management regarding coronary risk factors in indigent women with diabetes. Methods: We developed a smartphone application based on the AHA's “Life's Simple 7” prevention module. Mobile technology was integrated into the overall program, with communication enabled devices (weight scales, glucometers, and sphygmomanometers) synchronized to the smartphone. FitBit was used to monitor activity level. We tested the feasibility of the mobile application in the SMARTWOMANTM project which enrolled women with diabetes, without known CVD at an inner city hospital;,these women had suboptimal access to care, limited transportation and resources, and limited health literacy. Results: Fourteen women, all African-American were enrolled in SMARTWOMANTM (mean age 52 yrs, 86% with hypertension, hypercholesterolemia and obesity). The overall weekly mobile-application reporting was 85% for blood glucose, 83% for daily steps, and 77% for systolic blood pressure. The majority reported the text messages to be useful (92%) and appropriate in frequency (92%). Participants found positive text messages as a strong reinforcement to healthy behaviors. They indicated that study participation increased their motivation in taking charge of their health, and increased understanding of their health condition and self-management. Conclusions: Among indigent African-American women with diabetes and no known CVD, the development and use of mobile health application was feasible and resulted in increased patient satisfaction and positive reinforcement to healthy behaviors.

Intrinsic rewards predict exercise via behavioral intentions for initiators but via habit strength for maintainers (2016)

Sport, Exercise, and Performance Psychology

Abstract

Regular exercise is thought to involve both reflective (e.g., intention) and automatic (e.g., habit) mechanisms. Intrinsic motivation is a reflective factor in exercise initiation; we propose that the experience of intrinsic exercise rewards (enjoyment; stress reduction) may come to function as a factor in exercise automaticity, or habit, and therefore of exercise maintenance. The current studies evaluate whether the relationship between intrinsic exercise rewards and exercise is mediated by behavioral intention for those newer to exercise (initiators) but mediated by behavioral habit strength for longer term exercisers (maintainers). In 2 studies, self-reported exercise stage (initiation vs. maintenance), intrinsic exercise rewards, intentions, and habit strength were measured at baseline. For outcomes, Study 1 concurrently assessed self-reported exercise in a large sample of U.S. college students (n = 463), and Study 2 prospectively assessed objective activity using accelerometers for 1 month in a U.S. college student and staff population (n = 114). Moderated mediation analyses resulted in support of the hypotheses: Habit strength significantly mediated the relationship between intrinsic rewards and exercise for maintainers in Studies 1 and 2 (unstandardized indirect effect = 7.66 and 0.04, respectively; p < .05) but less strongly for initiators in Study 1 and not at all for initiators in Study 2. Intentions mediated the relationship for initiators (unstandardized indirect effect = 0.94 and 0.02, respectively; p < .05) but not for maintainers, as expected. We concluded that intrinsic rewards may promote exercise repetition via intentional or reflective means in initiation but via habit strength in maintenance. Interventions that foster intrinsic exercise rewards may promote exercise maintenance through habitual action. (PsycINFO Database Record (c) 2016 APA, all rights reserved)

Self-management of chronic illness: the role of 'habit' versus reflective factors in exercise and medication adherence. (2016)

Journal of Behavioral Medicine

Abstract

Non-adherence to health behaviors required for chronic illness self-management is pervasive. Advancing health-behavior theory to include behavioral initiation and maintenance factors, including reflective (e.g., belief- and feedback-based) and automatic (e.g., habit-based) mechanisms of adherence to different treatment-related behaviors could improve non-adherence prediction and intervention efforts. To test behavioral initiation and maintenance factors from an extended common sense self-regulation theoretical framework for predicting medication adherence and physical activity among patients with Type 2 diabetes. Patients (n = 133) in an in-person (n = 80) or online (n = 53) version of the study reported treatment-related (1) barriers, (2) beliefs and experiential feedback (reflective mechanisms of treatment-initiation and short-term repetition), and (3) habit strength (automatic mechanism of treatment-maintenance) for taking medication and engaging in regular physical activity at baseline. Behaviors were assessed via self-reports (n = 133) and objectively (electronic monitoring pill bottles, accelerometers; n = 80) in the subsequent month. Treatment-specific barriers and habit strength predicted self-reported and objective adherence for both behaviors. Beliefs were inconsistently related to behavior, even when habits were "weak". Experiential feedback from behavior was not related to adherence. Among patients with Type 2 diabetes diagnosis, medication and physical activity adherence were better predicted by their degree of automatic behavioral repetition than their beliefs/experiences with the treatment-actions. Habit strength should be an intervention target for chronic illness self-management; assessing it in practice settings may effectively detect non-adherence to existing treatment-regimens. However, future research and further refining of CS-SRM theory regarding the processes required for such habit development are needed.

Validation of the Fitbit One, Garmin Vivofit and Jawbone UP activity tracker in estimation of energy expenditure during treadmill walking and running (2016)

Journal of Medical Engineering & Technology

Abstract

Objectives: To determine the validity of energy expenditure estimation made by the Fitbit One, Garmin Vivofit and Jawbone UP activity trackers during treadmill walking and running. Determining validity of such trackers will inform the interpretation of the data they generate. Design: Cross-sectional study. Method: Fourteen adults walked at 0.70, 1.25, 1.80?ms-1 and ran at 2.22, 2.78, 3.33?ms-1 on a treadmill wearing a Fitbit One, Garmin Vivofit and Jawbone UP. Estimation of energy expenditure from each tracker was compared to measurement from indirect calorimetry (criterion). Paired t-tests, correlation coefficients and Bland–Altman plots assessed agreement and proportional bias. Mean percentage difference assessed magnitude of difference between estimated and criterion energy expenditure for each speed. Results: Energy expenditure estimates from the Fitbit One and Garmin Vivofit correlated significantly (p<?0.01; r=?0.702; 0.854) with criterion across all gait speeds (0.70–3.33?ms-1). Fitbit One, Garmin Vivofit and Jawbone UP correlated significantly (p?<?0.05; r?=?0.729; 0.711; 0.591) with criterion across all walking speeds (0.70–1.80?ms-1). However, only the Garmin Vivofit correlated significantly (p<?0.05; r?=?0.346) with energy expenditure estimations from criterion across running speeds (2.22–3.33?ms-1). Bland–Altman plots showed proportional bias for the Fitbit One and Garmin Vivofit. Energy expenditure estimations of single speeds were overestimated by the Fitbit One and underestimated by the Garmin Vivofit. Conclusions: Energy expenditure reported by the devices distinguished between walking and running, with a general increase as exercise intensity increased. However, the reported energy expenditure from these devices should be interpreted with caution, given their potential bias and error.

Can Commercially Available Pedometers Be Used For Physical Activity Monitoring In Patients With COPD Following Exacerbations? (2016)

Chronic Obstructive Pulmonary Diseases: Journal of the COPD Foundation

Abstract

BACKGROUND Commercially available pedometers have been used as tools to measure endpoints in studies evaluating physical activity promotion programs. However, their accuracy in patients recovering from COPD exacerbations is unknown. The objectives of this study were to 1) assess the relative accuracy of different commercially available pedometers in healthy volunteers and 2) evaluate the accuracy of the top-performing commercially available pedometer in patients recovering from COPD exacerbations following hospital discharge. METHODS: Twelve healthy volunteers wore 2 pedometers, 2 smartphones with pedometer apps and an accelerometer for 15 minutes of indoor activity. The top-performing device in healthy volunteers was evaluated in 4 patients recovering from COPD exacerbations following hospital discharge during 6 minutes of walking performed at home. Bland-Altman plots were employed to evaluate accuracy of each device compared with direct observation (the reference standard). RESULTS: In healthy volunteers, the mean percent error compared to direct observation of the various devices ranged from -49% to +1%. The mean percent error [95% confidence interval (CI)] of the top-performing device in healthy volunteers, the Fitbit Zip(®), was +1% [-33 to +35%], significantly lower than that of the accelerometer (-13% [-56 to +29%], p=0.01). The mean percent error [95% CI] for the Fitbit Zip(®) in patients recovering from COPD exacerbations was -3% [-7 to +12%]. CONCLUSIONS: The accuracy of commercially available pedometers in healthy volunteers is highly variable. The top-performing pedometer in our study, the Fitbit Zip,(®) accurately measures step counts in both healthy volunteers and patients recovering from COPD exacerbations.

Pilot and Feasibility Test of a Mobile Health-Supported Behavioral Counseling Intervention for Weight Management Among Breast Cancer Survivors (2016)

JMIR Cancer

Abstract

Background: Health behavior and weight management interventions for cancer survivors have the potential to prevent future cancer recurrence and improve long-term health; however, their translation can be limited if the intervention is complex and involves high participant burden. Mobile health (mHealth) offers a delivery modality to integrate interventions into daily life routines. Objective: The objective of this study was to evaluate the effects of a one-group trial with a pre-post evaluation design on engagement (use and acceptability), physiological (weight), behavioral (diet and physical activity), and other secondary outcomes. Methods: The 10-week intervention consisted of mHealth components (self-monitoring of selected diet behaviors via daily text messages, wireless devices to automatically track weight and steps) and 4 motivational interviewing–based technology-assisted phone sessions with a nonprofessionally trained counselor. Participants were overweight breast cancer survivors who had completed treatment and owned a smartphone. Weight was measured objectively; diet and physical activity were measured with brief self-reported questionnaires. Results: Ten women participated; they had a mean age of 59 years (SD 6), 50% belonged to a racial or ethnic minority group, 50% had some college or less, and 40% reported using Medicaid health insurance. Engagement was high: out of 70 days in total, the mean number of days recording steps via the wristband pedometer was 64 (SD 7), recording a weight via the scale was 45 (SD 24), and responding to text messages was 60 (SD 13); 100% of participants completed all 4 calls with the counselor. Most (90%) were very likely to participate again and recommend the program to others. Mean weight in pounds decreased (182.5 to 179.1, mean change -3.38 [SD 7.67]), fruit and vegetable daily servings increased (2.89 to 4.42, mean change 1.53 [SD 2.82]), and self-reported moderate physical activity increased in metabolic equivalent of task (MET) minutes per week (2791 to 3336, mean change 545 [SD 1694]). Conclusions: Findings support the conduct of a fully powered trial to evaluate the efficacy of mHealth as a feasible intervention modality for breast cancer survivors. Future research should employ accelerometer-based physical activity assessment and consider development of an all-in-one app to integrate devices, messaging, and educational content and other mHealth approaches to support behavioral counselors conducting weight management interventions.

Physical activity levels and perceived benefits and barriers to physical activity in HIV-infected women living in the deep south of the United States. (2016)

AIDS Care

Abstract

Engaging in regular physical activity (PA) is important in maintaining health and increasing the overall quality of life of people living with HIV (PLWH). The deep south of the USA is known for its high rate of sedentary behavior although data on the activity levels and perceptions of the benefits and barriers to exercise in women living with HIV in the deep south are lacking. Understanding the perceived benefits and barriers to exercise can guide the development of PA interventions. We conducted a cross-sectional study to determine the PA levels and perceived benefits and barriers to exercise associated with both age and depression level in a group of HIV+ women living in the deep south. We recruited a total of 50 participants from a cohort site for the Women's Interagency HIV Study. Depression was assessed using the Center for Epidemiological Studies Depression Scale (CES-D) and benefits/barriers to exercise were measured using the Exercise Benefits and Barriers Scale (EBBS). We measured PA both subjectively and objectively using the International Physical Activity Questionnaire (IPAQ) and a Fitbit PA monitor, respectively. Our sample was predominantly African-American (96%) and the mean ±SD age, body mass index, and CES-D score were 42?±?8.8 years, 36.6?±?11.5 kg/m(2), and 15.6?±?11.4, respectively. Both subjective and objective measures of PA indicated that our participants were sedentary. The greatest perceived benefit to exercise was physical performance and the greatest barrier to exercise was physical exertion. Higher overall perceived benefits were reported by women =43 years and women reporting higher levels of depression. There was no difference in overall barriers associated with age and depression level, but women with depression felt more fatigued by exercise. The results of this study can be helpful when designing and implementing PA interventions in women living with HIV in the deep south.

Unravelling the potential mechanisms behind hospitalization-associated disability in older patients; the Hospital-Associated Disability and impact on daily Life (Hospital-ADL) cohort study protocol. (2016)

BMC Geriatrics

Abstract

BACKGROUND: Over 30 % of older patients experience hospitalization-associated disability (HAD) (i.e., loss of independence in Activities of Daily Living (ADLs)) after an acute hospitalization. Despite its high prevalence, the mechanisms that underlie HAD remain elusive. This paper describes the protocol for the Hospital-Associated Disability and impact on daily Life (Hospital-ADL) study, which aims to unravel the potential mechanisms behind HAD from admission to three months post-discharge. METHODS/DESIGN: The Hospital-ADL study is a multicenter, observational, prospective cohort study aiming to recruit 400 patients aged =70 years that are acutely hospitalized at departments of Internal Medicine, Cardiology or Geriatrics, involving six hospitals in the Netherlands. Eligible are patients hospitalized for at least 48 h, without major cognitive impairment (Mini Mental State Examination score =15), who have a life expectancy of more than three months, and without disablement in all six ADLs. The study will assess possible cognitive, behavioral, psychosocial, physical, and biological factors of HAD. Data will be collected through: 1] medical and demographical data; 2] personal interviews, which includes assessment of cognitive impairment, behavioral and psychosocial functioning, physical functioning, and health care utilization; 3] physical performance tests, which includes gait speed, hand grip strength, balance, bioelectrical impedance analysis (BIA), and an activity tracker (Fitbit Flex), and; 4] analyses of blood samples to assess inflammatory and metabolic markers. The primary endpoint is additional disabilities in ADLs three months post-hospital discharge compared to ADL function two weeks prior to hospital admission. Secondary outcomes are health care utilization, health-related quality of life (HRQoL), physical performance tests, and mortality. There will be at least five data collection points; within 48 h after admission (H1), at discharge (H3), and at one (P1; home visit), two (P2; by telephone) and three months (P3; home visit) post-discharge. If the patient is admitted for more than five days, additional measurements will be planned during hospitalization on Monday, Wednesday, and Friday (H2). DISCUSSION: The Hospital-ADL study will provide information on cognitive, behavioral, psychosocial, physical, and biological factors associated with HAD and will be collected during and following hospitalization. These data may inform new interventions to prevent or restore hospitalization-associated disability.

Validity and reliability of Fitbit activity monitors compared to ActiGraph GT3X+ with female adults in a free-living environment (2016)

Journal of Science and Medicine in Sport

Abstract

OBJECTIVES Inexpensive activity monitors have recently gained popularity with the general public. Researchers have evaluated these consumer-based monitors in laboratory-conditions. Given the current wide-spread consumer use of these devices, it is important to ensure users are attaining accurate information compared to previously validated measures. This study investigates the accuracy of Fitbit One and Flex activity monitors in measuring steps, sedentary time, and time spent in light, moderate, and vigorous intensity activities with ActiGraph GT3X+ with female adults in free-living conditions. DESIGN Cross-sectional study METHODS Twenty-two women, 21.23±1.63 yrs, BMI: 22.35±2.34kg/m2 wore two Fitbit Ones (bra and waist), one Fitbit Flex on the wrist, and one ActiGraph GT3X+ on the waist for seven-consecutive days. Repeated measures ANOVA was used to explore differences in steps, sedentary time, and time spent in light, moderate and vigorous intensity activities among the four devices. RESULTS No differences were found in number of steps recorded across the four devices. Fitbit One, waist and bra, overestimated time spent in light intensity activities. Fitbit One (waist) and Fitbit Flex overestimated time spent in moderate intensity activities. Fitbit One, waist and bra, and Fitbit Flex overestimated time spent in vigorous intensity activities. All Fitbit activity monitors overestimated MVPA and underestimated sedentary time compared to the ActiGraph. CONCLUSIONS Regardless of wear-location all Fitbit devices provide similar activity monitoring and users can wear the devices wherever best accommodates their lifestyle or needs. Users should not rely solely on these monitors when tracking vigorous and MVPA activities.

Feasibility and Effectiveness of Using Wearable Activity Trackers in Youth: A Systematic Review (2016)

JMIR mHealth and uHealth

Abstract

Background: The proliferation and popularity of wearable activity trackers (eg, Fitbit, Jawbone, Misfit) may present an opportunity to integrate such technology into physical activity interventions. While several systematic reviews have reported intervention effects of using wearable activity trackers on adults' physical activity levels, none to date have focused specifically on children and adolescents. Objective: The aim of this review was to examine the effectiveness of wearable activity trackers as a tool for increasing children's and adolescents' physical activity levels. We also examined the feasibility of using such technology in younger populations (age range 5-19 years). Methods: We conducted a systematic search of 5 electronic databases, reference lists, and personal archives to identify articles published up until August 2016 that met the inclusion criteria. Articles were included if they (1) specifically examined the use of a wearable device within an intervention or a feasibility study; (2) included participants aged 5-19 years old; (3) had a measure of physical activity as an outcome variable for intervention studies; (4) reported process data concerning the feasibility of the device in feasibility studies; and (5) were published in English. Data were analyzed in August 2016. Results: In total, we identified and analyzed 5 studies (3 intervention, 2 feasibility). Intervention delivery ranged from 19 days to 3 months, with only 1 study using a randomized controlled trial design. Wearable activity trackers were typically combined with other intervention approaches such as goal setting and researcher feedback. While intervention effects were generally positive, the reported differences were largely nonsignificant. The feasibility studies indicated that monitor comfort and design and feedback features were important factors to children and adolescents. Conclusions: There is a paucity of research concerning the effectiveness and feasibility of wearable activity trackers as a tool for increasing children's and adolescents' physical activity levels. While there are some preliminary data to suggest these devices may have the potential to increase activity levels through self-monitoring and goal setting in the short term, more research is needed to establish longer-term effects on behavior.

Objective Measure of Activity Level After Total Knee Arthoplasty with The Use of the 'Fitbit Device (2016)

Orthopaedic Journal of Sports Medicine

Abstract

Objectives: The commercial release of non invasive, light weight, low cost accelerometers has great potential to more accurately assess activity level before and after knee arthroplasty from a small light weight wrist strap. These devices have been shown to be valid and reliable assessment tools for activity levels in normal subjects. This study examines activity levels before and after knee arthroplasty using a modern commercially available accelerometer known as a “FitBit”. Methods: Observational Series. Subjects undergoing knee arthroplasty under the care of the senior surgeon were invited to participate and wear the accelerometer for 5-7 days at 4 times periods: before surgery, day 1-3, 6 weeks and 12 month after arthroplasty. Preoperatively and at 12 months after TKR subjects completed the Knee injury and Osteoarthritis Outcome Score (KOOS), WOMAC Assessment, and British Orthopaedic Association Patient Satisfaction Scale. Results: 54 subjects were recruited to the study over a 12 month period. Prior to total knee arthroplasty, the mean number of recorded steps per day was 6600. Post arthroplasty the mean number of steps per day was 1200 in the first 3 days, 5700 at 6 weeks and 7200 at 1 year. Preoperative activity level was significantly correlated with preoperative Pain Score (p=0.003). Activity level within the first 3 days after surgery was correlated with 6 weeks activity level (0.473, p=0.004), and 1 year activity level (0.593, p=0.02), but not 1 year KOOS scores. Conclusion: Modern accelerometers present a novel way to objectively measure activity level before and after knee arthroplasty. Preoperative activity level is significantly associated with post operative activity level. One year after knee arthroplasty the mean number of steps per day is 7200, and 50% of subjects taking more than 7000 steps per day, which is the recommended daily average for those age 65+ years.

Evaluation of a culturally-adapted lifestyle intervention to treat elevated cardiometabolic risk of Latino adults in primary care (Vida Sana): A randomized controlled trial (2016)

Contemporary Clinical Trials

Abstract

Latinos bear a disproportionate burden of the dual pandemic of obesity and diabetes. However, successful interventions addressing this disparity through primary care are lacking. To address this gap, the 5-year Vida Sana (Healthy Life) study tests a culturally adapted and technology-enhanced group-based Diabetes Prevention Program intervention in a randomized controlled trial with overweight/obese Latino adults who have metabolic syndrome and/or pre-diabetes. Eligible, consenting patients (n=186) from a large community-based multispecialty group practice in Northern California will be randomly assigned to receive the culturally-adapted intervention or usual care. The RE-AIM (Reach, Effectiveness, Adoption, Implementation, and Maintenance) framework guided the planned evaluations. The primary aim is to determine the effectiveness of the intervention (the “E” in RE-AIM). We hypothesize that the intervention will lead to a greater mean reduction in weight at 24months (primary endpoint) vs. usual care. Secondary outcomes will include measures of cardiometabolic risk factors (e.g., blood pressure), psychosocial well-being (e.g., health-related quality of life), and behavior change (e.g., physical activity). The secondary aim is to evaluate the other RE-AIM dimensions using mixed methods: reach (e.g., participation rate of the target population), adoption (e.g., participating clinic and provider characteristics), implementation (e.g., intervention fidelity), and maintenance (e.g., sustainability in the practice setting). These findings have real word applicability with value to clinicians, patients, and other decision makers considering effective diabetes prevention programs for primary care that would support the millions of Latino adults who experience a disproportionate burden of diabetes. Trial registration: NCT02459691.

Physical activity intervention using Fitbits in an introductory college health course (2016)

Health Education Journal

Abstract

Objective: This study took the form of an intervention examining change in physical activity and quality of experience among students in an introductory health course who were asked to wear a Fitbit activity monitor throughout the semester. Method: College students (N = 56) took part in this controlled trial. Students enrolled in an introductory health course (Education + Fitbit; n = 24) were asked to purchase a Fitbit and wear it throughout the semester. This activity monitor purchase replaced the textbook requirement to reduce the financial burden for students. Change in objectively measured physical activity within this group was compared to students enrolled in a traditional introductory health course (Education Only; n = 14) and students enrolled in an introductory humanities course (Control; n = 18). To assess objectively measured physical activity, all participants wore a sealed pedometer for one week at the beginning and end of the semester. Students in the Education + Fitbit group also provided written feedback on their experience with the Fitbit. Results: A 2 × 3 repeated-measures analysis of variance (ANOVA) revealed a significant interaction between time and group, F(2, 53) = 3.957, p = .025. Post hoc analysis of this interaction indicated that students in the Education + Fitbit group significantly increased (p = .014) objectively measured physical activity by 1,078 steps/day, whereas physical activity in Education Only and Control groups did not significantly change. Qualitative data demonstrated that student experiences with the Fitbit were resoundingly positive. Conclusion: Replacing a textbook requirement with requiring a commercially available activity monitor in an introductory health course may be an effective and enjoyable strategy to increase physical activity among US college students.

Devices for Self-Monitoring Sedentary Time or Physical Activity: A Scoping Review. (2016)

Journal of Medical Internet Research

Abstract

BACKGROUND: It is well documented that meeting the guideline levels (150 minutes per week) of moderate-to-vigorous physical activity (PA) is protective against chronic disease. Conversely, emerging evidence indicates the deleterious effects of prolonged sitting. Therefore, there is a need to change both behaviors. Self-monitoring of behavior is one of the most robust behavior-change techniques available. The growing number of technologies in the consumer electronics sector provides a unique opportunity for individuals to self-monitor their behavior. OBJECTIVE: The aim of this study is to review the characteristics and measurement properties of currently available self-monitoring devices for sedentary time and/or PA. METHODS: To identify technologies, four scientific databases were systematically searched using key terms related to behavior, measurement, and population. Articles published through October 2015 were identified. To identify technologies from the consumer electronic sector, systematic searches of three Internet search engines were also performed through to October 1, 2015. RESULTS: The initial database searches identified 46 devices and the Internet search engines identified 100 devices yielding a total of 146 technologies. Of these, 64 were further removed because they were currently unavailable for purchase or there was no evidence that they were designed for, had been used in, or could readily be modified for self-monitoring purposes. The remaining 82 technologies were included in this review (73 devices self-monitored PA, 9 devices self-monitored sedentary time). Of the 82 devices included, this review identified no published articles in which these devices were used for the purpose of self-monitoring PA and/or sedentary behavior; however, a number of technologies were found via Internet searches that matched the criteria for self-monitoring and provided immediate feedback on PA (ActiGraph Link, Microsoft Band, and Garmin Vivofit) and sedentary time (activPAL VT, the Lumo Back, and Darma). CONCLUSIONS: There are a large number of devices that self-monitor PA; however, there is a greater need for the development of tools to self-monitor sedentary time. The novelty of these devices means they have yet to be used in behavior change interventions, although the growing field of wearable technology may facilitate this to change.

Strength in numbers: group fitness programming for children with cerebral palsy (2016)

Developmental Medicine & Child Neurology

Abstract

Background and Objective(s) Children with cerebral palsy should engage, to the extent they are able, in aerobic, anaerobic, and muscle-strengthening activities (Maltais, 2014). This is a particularly challenging professional charge for physical therapists; across ages and levels of motor function, young people with CP participated in 13% to 53% less habitual physical activity than their peers (Carlon, 2013). These levels of physical activity are approximately 30% lower than public health guidelines and double the maximum recommended sedentary time. Fortunately, studies by Duncan and colleagues (2005) indicate that positive correlates of physical activity in youth do exist. Peers appear to serve as a particularly important social and emotional support for increased physical activity. The purpose of this multicenter pilot effort was to examine the effect of a group fitness program on changes in functional strength, activity, and participation in adolescents with cerebral palsy. Study Design: Cross-sectional, multicenter study. Study Participants & Setting: A total of 8 males and 3 females with Cerebral Palsy (age range 10–22 y; GMFCS levels I–III) who were currently receiving outpatient therapy services for gross motor deficits were recruited for participation in Fitness programming. Materials/Methods: We implemented an 8 week program that was piloted in two outpatient pediatric physical therapy clinics. Participants completed weight training, cardiovascular exercises, stretching, balance, and coordination activities at each 90 minute session. Physical assessments were conducted at the beginning and completion of the program. Outcome measures included Timed Up and Go (TUG), Pediatric Berg Balance Scale (PBS), and 6 Minute Walk Test (6MWT). A subset of participants were equipped with accelerometry in order to monitor physical activity change over the course of the 8 week period. A questionnaire addressing satisfaction with care was also administered to this participant subset. Results: All participants demonstrated improvements in exercise tolerance, including increased repetition and weight, and tolerance to longer periods of cardiovascular activity. Subjects also demonstrated significant improvement on the PBS (p textless 0.05). TUG times reflected a trend toward improvement in functional strength. Submaximal aerobic capacity, measured by the 6MWT, was also enhanced following intervention. Although not statistically significant, TUG and 6MWT changes did exceed thresholds for clinically important differences. Data retrieved from FitBit Charge HR devices was widely variable, but seem to reflect an increase in daily step count over the 8 week period. All of children surveyed at program completion indicated that they enjoyed the group class and that they “got better at working out”; 2/3rds of responders noted that they felt capable of continuing to exercise independently. Conclusions/Significance: We propose that a group-based fitness program may be a pragmatic approach to targeted gains in cardiorespiratory function and strength. Given the critical need for increased physical activity in this at risk-population, we feel encouraged by the feasibility of this initial effort and remarkably positive responses collected in semi-structured interviews following the intervention period. We will continue to study aspects of group-based fitness in future work, including identification of best responders and types of social support that are most associated with improvement, and the inclusion of measures that characterize changes in participation.

Biosensors to assess performance status in cancer (BioAPS Study). (2016)

Journal of Clinical Oncology

Abstract

Background: Oncologists assess patient (Pt) performance status (PS) to inform appropriate chemotherapy (Ct) decisions. Eastern Cooperative Oncology Group PS (ECOG-PS, range 0-5) and Karnofsky PS (KPS; range 0-100%), are subjective scales of function and ability for self-care, and are the primary scales used in oncologic research and practice. Higher ECOG-PS/Lower KPS scores are associated with poorer survival outcomes and Ct tolerance. Despite its prognostic importance, ECOG-PS/KPS have been criticized for being biased and incomplete resulting in inappropriate Ct decisions that are costly and potentially harmful. They also do not capture a Pts ?free range? activity outside of the clinic. Wearable biometric sensors, such as Fitbit Charge HR (FC), can provide objective data regarding activity (number of steps, stairs climbed, heart rate (HR)) outside the the four walls of the clinic. Longitudinally collected patient reported outcomes (PROs) around function and activity may provide a better understanding of Pt PS. We hypothesize that combining objective data from FC with PROs could provide a more accurate assessment of patient PS. Furthermore, longitudinally collected biometric data may allow for real-time detection of Pt distress. Methods: We will recruit 30 Pts with stage 3/4 cancer, = 18yrs, English speaking, ambulatory, with a smartphone, and prognosis of textgreater 3 months. Subjects will wear a FC for 2 weeks, and complete online NIH PROMIS tools (Physical Function, Pain, Sleep, Fatigue, and Emotional Distress) on D1, 8, and 15. At Pt visits, MDs/RNs will score ECOG-PS/KPS History, physical exam and assessment for frailty phenotype will be completed at baseline. An exit interview will capture Pt experience with FC. Ct toxicity and survival will be recorded at 3- and 6-month follow-up. Primary Objective: Assess the association between FC data, and PROMIS PROs versus validated ECOG-PS/KPS scales. Secondary Objectives: Assess 1) the association between FC data and PROMIS indicators of distress, 2) the correlation of frailty phenotype with biosensor activity data, 3) the feasibility of collecting biometric data and PROMIS tools in cancer pts, and 4) the relationship between FC data and Ct toxicity and survival outcomes. Clinical trial information: NCT02659358.

Effects of Playing a Serious Computer Game on Body Mass Index and Nutrition Knowledge in Women (2016)

JMIR Serious Games

Abstract

Background: Obesity and weight gain is a critical public health concern. Serious digital games are gaining popularity in the context of health interventions. They use persuasive and fun design features to engage users in health-related behaviors in a non-game context. As a young field, research about effectiveness and acceptability of such games for weight loss is sparse. Objective: The goal of this study was to evaluate real-world play patterns of SpaPlay and its impact on body mass index (BMI) and nutritional knowledge. SpaPlay is a computer game designed to help women adopt healthier dietary and exercise behaviors, developed based on Self-Determination theory and the Player Experience of Need Satisfaction (PENS) model. Progress in the game is tied to real-life activities (e.g., eating a healthy snack, taking a flight of stairs). Methods: We recruited 47 women to partake in a within-subject 90-day longitudinal study, with assessments taken at baseline, 1-, 2-, and 3- months. Women were on average, 29.8 years old (±7.3), highly educated (80.9% had BA or higher), 39% non-White, baseline BMI 26.98 (±5.6), who reported at least contemplating making changes in their diet and exercise routine based on the Stages of Change Model. We computed 9 indices from game utilization data to evaluate game play. We used general linear models to examine inter-individual differences between levels of play, and multilevel models to assess temporal changes in BMI and nutritional knowledge. Results: Patterns of game play were mixed. Participants who reported being in the preparation or action stages of behavior change exhibited more days of play and more play regularity compared to those who were in the contemplation stage. Additionally, women who reported playing video games 1-2 hours per session demonstrated more sparse game play. Brief activities, such as one-time actions related to physical activity or healthy food, were preferred over activities that require a longer commitment (e.g., taking stairs every day for a week). BMI decreased significantly (P<.001) from baseline to 3-month follow-up, yielding a large effect size of 1.28. Nutritional knowledge increased significantly (P<.001) from first to third month follow-ups, with an effect size of .86. The degree of change in both outcomes was related to game play, baseline readiness to change, and the extent of video game play in general. Conclusions: This work demonstrates initial evidence of success for using a serious game as an intervention for health behavior change in real world settings. Our findings also highlight the need to understand not only game effectiveness but also inter-individual differences. Individualizing content and the intervention medium appears to be necessary for a more personalized and long-lasting impact.

‘Well, if the kids can do it, I can do it': older rehabilitation patients' experiences of telerehabilitation (2016)

Health Expectations

Abstract

BACKGROUND AND OBJECTIVE Although trials continue to emerge supporting the role of telerehabilitation, implementation has been slow. Key users include older people living with disabilities who are frequent users of hospital rehabilitation services but whose voices are rarely heard. It is unclear whether the use of technologies and reduced face-to-face contact is acceptable to these people. We report on a qualitative study of community dwelling participants who had received a home telerehabilitation programme as an alternative to conventional rehabilitation. DESIGN Thirteen older participants, three spouses and one carer were interviewed. All had participated in an individualized therapy programme, using a combination of face-to-face and video consults with therapists. The programme used 'off-the-shelf' technologies including iPads for videoconferencing and electronic FitBit(R) devices. Interviews were recorded, transcribed verbatim and analysed using NVivo software. RESULTS Thematic analysis resulted in five emergent themes: (i) telerehabilitation is convenient; (ii) telerehabilitation promotes motivation and self-awareness; (iii) telerehabilitation fosters positive therapeutic relationships; (iv) mastering technologies used by younger relatives is a valued aspect of telerehabilitation; and (v) Telerehabilitation does not replace traditional face-to-face rehabilitation therapies. CONCLUSIONS Participants found telerehabilitation convenient and motivating, coped well with the technology and developed positive therapeutic relationships. The learning and practice aspects sat well in the context of a rehabilitation programme. The use of commercially available technologies may have contributed to respondents' high levels of acceptability. The perception of telerehabilitation as complementary to in-person care and the expectation of technological support have implications for the implementation and delivery of telerehabilitation services to older people.

Accuracy Of Wrist-worn Activity Monitors At Three Walking Speeds On The Treadmill (2016)

Medicine & Science in Sports & Exercise

Abstract

Wrist-worn activity monitors are becoming increasingly popular with many manufacturers offering such devices. PURPOSE: To assess the accuracy of wrist-worn activity monitors at three walking speeds on a treadmill. METHODS: Seventy-seven participants (age = 24.9±3.5 yrs, ht = 170.0±8.9 cm, wt = 78.6±19.9 kg) were fitted with a Fitbit Flex (FF) on the right wrist, a Movband Model 2 (MV), Lifetrak C200 (LT), Garmin Vivofit (GV), Polar Loop (PL) on the left wrist, and an Omron HJ-113 (HJ) on the right waist. Each participant walked for two minutes at 53.6 m/min (2.0 mph), 80.5 m/min (3.0 mph), and 107.3 m/min (4.0 mph). At the end of each trial monitor counts and actual step counts from a hand tally (AC) were recorded. A repeated measures ANOVA was used to determine significant differences between the counts. Single measure intraclass correlation (ICC) from a two-way random effects ANOVA was used to assess the agreement between AC and monitor counts. Pedometer error was calculated as [(monitor steps-actual steps)/actual steps] * 100. RESULTS: For all speeds, monitor counts were significantly lower than AC (ptextless.05) except for GV and HJ at 53.6 m/min and 80.5 m/min, and for LT and HJ at 107.3 m/min, none of which were significantly different than AC (ptextgreater.05). Agreement according to ICC was highest with HJ for all speeds (0.68, 0.97, and 0.82 during 53.6 m/min, 80.5 m/min, and 107.3 m/min, respectively). Agreement among the wrist worn monitors was highest with GV (0.71) and lowest with FF (0.18) at the slowest speed, highest with GV (0.67) and lowest with LT (0.03) at 80.7 m/min, and highest with PL (0.28) and lowest with LT (0.0) at the fastest speed. Error was greatest with LT at 40.6% while walking at 53.6 m/min, 19.6% with PL at 80.5 m/min, and 19.0% with PL at 107.3 m/min. Error was least with GV at 3.2% while walking at 53.6 m/min. Although error was least with HJ during 53.6 m/min and 107.3 m/min (1.2% and 1.2%, respectively), GV was 3.2% and LT was 7.7% during 80.5 m/min and 107.3 m/min, respectively. CONCLUSION: The hip-worn pedometer (HJ) provides the most accurate step count across all speeds. Among the wrist-worn monitors, GV seems to provide the most accurate measure of step count in this laboratory-controlled study. Weaver, A.L., Stutzman, S.E., Supnet, C. and Olson, D.M.

Incorporation of a Physical Education and Nutrition Program Into Neurosurgery: A Proof of Concept Pilot Program. (2016)

Neurosurgery

Abstract

BACKGROUND: Balancing the demands of a busy medical career with personal wellness can be daunting, and there is little education on these principles available to physicians in training. OBJECTIVE: To implement a voluntary wellness initiative in our neurosurgery department to promote healthy lifestyle choices. This report details the baseline data collected as part of this quality improvement initiative. METHODS: The wellness initiative was implemented in July 2015 and available to all faculty and resident physicians in the Department of Neurological Surgery in collaboration with the Medical University of South Carolina Wellness Center. All participants were provided a Fitbit Surge HR wrist monitor (Fitbit, Boston, Massachusetts) and underwent baseline physical and psychological testing. RESULTS: Six faculty physicians and 9 residents participated. Overall physical fitness levels varied widely between subjects. Health screening demonstrated abnormalities in 80% of participants (elevated systolic blood pressure in 60%, elevated diastolic in 47%, elevated serum low-density lipoprotein in 53%). Body composition analysis demonstrated body weight higher than ideal in 69% (47% overweight; 13% obese). Recommended average body fat mass reduction was 25.4 pounds. Seventy-nine percent reported below-average quality of life compared with the average healthy adult. All subjects reported wanting more time for personal health. CONCLUSION: Baseline health and psychological screenings in our department demonstrated alarmingly prevalent, previously undiagnosed abnormalities on cardiovascular and body weight screenings. Obstacles to leading a healthier lifestyle have been identified and solutions have been incorporated into the program. This quality improvement initiative may serve as a template for other programs seeking to improve physician physical and mental well-being.

Piece it Together: Comprehensive Wellness for Teens and Young Adults with Autism and Neurodevelopment Disorders (2016)

Journal of the American Academy of Child & Adolescent Psychiatry

Abstract

Objectives: Adults and youth with neurodevelopmental disabilities are at increased risk of poor health and obesity as a result of restricted diets, sensory deficits, limited interests, sedentary lifestyles, and medications. Traditional fitness programs do not address socialization and stress reduction strategies that help to promote the likelihood of success with individualized goals for this unique population. Methods: A 6-week summer Piece it Together (PIT) Wellness program (2015) brought together teenagers and young adults with ASD and other mild neurodevelopmental disabilities for a comprehensive wellness program to improve fitness, nutritional habits, and socialization; reduce stress; and encourage individuals to get out of their comfort zone. Classes included opportunities for socialization, fitness activities, group exercise, stress reduction, yoga, mindfulness, and discussion groups on lessons learned. Assessments were completed before and after session for 12 youth (ages 15–27) via Patient Health Questionnaire-9 depression scale, nutrition and lifestyle questionnaires, a self-satisfaction scale, and InBody 570 Analyzer©2014. Results: Appropriate individualized goals were made, new friendships were made, a Facebook group was established, and most became online Fitbit buddies. Average PHQ-9 depression scores decreased from 7.67 to 3.42 (from mild to minimal depression; P = 0.000063). Self-report data revealed more fruits and veggies in the diet at time 2 (t = 2.39, P = 0.038) and modest improvement in self-efficacy-related questions (e.g., I like the way I look/I am a good worker/I have good friends; t = 2.46, P = 0.032). There was a small decrease in body fat, an increase in skeletal muscle mass, and a decrease in carbonated drink consumption (P = not significant). Volunteers have included health professional students, and this has enhanced their knowledge about youth with ASD. Results of the 2016 summer program, which includes twice the number of participants, pre- and post behavioral markers, and biological markers, as well as the success of technology supports, will be shared. Conclusions: Comprehensive wellness programs with a multidisciplinary team can help to address the unique fitness, nutrition, socialization, and stress reduction needs of this population and are needed to help prevent obesity. Components of this program have been individualized and adapted as a model to promote overall brain health.

Validity and Reliability of Fitbit Flex for Step Count, Moderate to Vigorous Physical Activity and Activity Energy Expenditure (2016)

PLOS ONE

Abstract

Objectives: To examine the validity and reliability of the Fitbit Flex against direct observation for measuring steps in the laboratory and against the Actigraph for step counts in free-living conditions and for moderate-to-vigorous physical activity (MVPA) and activity energy expenditure (AEE) overall. Methods: Twenty-five adults (12 females, 13 males) wore a Fitbit Flex and an Actigraph GT3X+ during a laboratory based protocol (including walking, incline walking, running and stepping) and free-living conditions during a single day period to examine measurement of steps, AEE and MVPA. Twenty-four of the participants attended a second session using the same protocol. Results: Intraclass correlations (ICC) for test-retest reliability of the Fitbit Flex were strong for walking (ICC = 0.57), moderate for stair stepping (ICC = 0.34), and weak for incline walking (ICC = 0.22) and jogging (ICC = 0.26). The Fitbit significantly undercounted walking steps in the laboratory (absolute proportional difference: 21.2%, 95%CI 13.0–29.4%), but it was more accurate, despite slightly over counting, for both jogging (6.4%, 95%CI 3.7–9.0%) and stair stepping (15.5%, 95%CI 10.1–20.9%). The Fitbit had higher coefficients of variation (Cv) for step counts compared to direct observation and the Actigraph. In free-living conditions, the average MVPA minutes were lower in the Fitbit (35.4 minutes) compared to the Actigraph (54.6 minutes), but AEE was greater from the Fitbit (808.1 calories) versus the Actigraph (538.9 calories). The coefficients of variation were similar for AEE for the Actigraph (Cv = 36.0) and Fitbit (Cv = 35.0), but lower in the Actigraph (Cv = 25.5) for MVPA against the Fitbit (Cv = 32.7). Conclusion: The Fitbit Flex has moderate validity for measuring physical activity relative to direct observation and the Actigraph. Test-rest reliability of the Fitbit was dependant on activity type and had greater variation between sessions compared to the Actigraph. Physical activity surveillance studies using the Fitbit Flex should consider the potential effect of measurement reactivity and undercounting of steps.

Cardiac Patients' Walking Activity Determined by a Step Counter in Cardiac Telerehabilitation: Data From the Intervention Arm of a Randomized Controlled Trial. (2016)

Journal of Medical Internet Research

Abstract

BACKGROUND: Walking represents a large part of daily physical activity. It reduces both overall and cardiovascular diseases and mortality and is suitable for cardiac patients. A step counter measures walking activity and might be a motivational tool to increase and maintain physical activity. There is a lack of knowledge about both cardiac patients' adherence to step counter use in a cardiac telerehabilitation program and how many steps cardiac patients walk up to 1 year after a cardiac event. OBJECTIVE: The purpose of this substudy was to explore cardiac patients' walking activity. The walking activity was analyzed in relation to duration of pedometer use to determine correlations between walking activity, demographics, and medical and rehabilitation data. METHODS: A total of 64 patients from a randomized controlled telerehabilitation trial (Teledi@log) from Aalborg University Hospital and Hjoerring Hospital, Denmark, from December 2012 to March 2014 were included in this study. Inclusion criteria were patients hospitalized with acute coronary syndrome, heart failure, and coronary artery bypass grafting or valve surgery. In Teledi@log, the patients received telerehabilitation technology and selected one of three telerehabilitation settings: a call center, a community health care center, or a hospital. Monitoring of steps continued for 12 months and a step counter (Fitbit Zip) was used to monitor daily steps. RESULTS: Cardiac patients walked a mean 5899 (SD 3274) steps per day, increasing from mean 5191 (SD 3198) steps per day in the first week to mean 7890 (SD 2629) steps per day after 1 year. Adherence to step counter use lasted for a mean 160 (SD 100) days. The patients who walked significantly more were younger (P=.01) and continued to use the pedometer for a longer period (P=.04). Furthermore, less physically active patients weighed more. There were no significant differences in mean steps per day for patients in the three rehabilitation settings or in the disease groups. CONCLUSIONS: This study indicates that cardiac telerehabilitation at a call center can support walking activity just as effectively as telerehabilitation at either a hospital or a health care center. In this study, the patients tended to walk fewer steps per day than cardiac patients in comparable studies, but our study may represent a more realistic picture of walking activity due to the continuation of step counter use. Qualitative studies on patients' behavior and motivation regarding step counter use are needed to shed light on adherence to and motivation to use step counters.

Pedometer use and self-determined motivation for walking in a cardiac telerehabilitation program: a qualitative study (2016)

BMC Sports Science, Medicine and Rehabilitation

Abstract

Exercise-based cardiac rehabilitation reduces morbidity and mortality. Walking is a convenient activity suitable for people with cardiac disease. Pedometers count steps, measure walking activity and motivate people to increase physical activity. In this study, patients participating in cardiac telerehabilitation were provided with a pedometer to support motivation for physical activity with the purpose of exploring pedometer use and self-determined motivation for walking experienced by patients and health professionals during a cardiac telerehabilitation program. A qualitative research design consisting of observations, individual interviews and patient documents made the basis for a content analysis. Data was analysed deductively using Self Determination Theory as a frame for analysis and discussion, focusing on the psychological needs of autonomy, competence and relatedness. Twelve cardiac patients, 11 health professionals, 6 physiotherapists and 5 registered nurses were included. The pedometer offered independence from standardised rehabilitation since the pedometer supported tailoring, individualised walking activity based on the patient's choice. This led to an increased autonomy. The patients felt consciously aware of health benefits of walking, and the pedometer provided feedback on walking activity leading to an increased competence to achieve goals for steps. Finally, the pedometer supported relatedness with others. The health professionals' surveillance of patients' steps, made the patients feel observed, yet supported, furthermore, their next of kin appeared to be supportive as walking partners. Cardiac patients' motivation for walking was evident due to pedometer use. Even though not all aspects of motivation were autonomous and self determined, the patients felt motivated for walking. The visible steps and continuous monitoring of own walking activity made it possible for each individual patient to choose their desired kind of activity and perform ongoing adjustments of walking activity. The immediate feedback on step activity and the expectations of health benefits resulted in motivation for walking. Finally, pedometer supported walking made surveillance possible, giving the patients a feeling of being looked after and supported. Current study is a part of The Teledi@log project.

Health coaching and pedometers to enhance physical activity and prevent falls in community-dwelling people aged 60 years and over: study protocol for the Coaching for Healthy AGEing (CHAnGE) cluster randomised controlled trial (2016)

BMJ Open

Abstract

INTRODUCTION Prevention of falls and promotion of physical activity are essential for maximising well-being in older age. However, there is evidence that promoting physical activity among older people without providing fall prevention advice may increase fall rates. This trial aims to establish the impact of a physical activity and fall prevention programme compared with a healthy eating programme on physical activity and falls among people aged 60+ years. METHODS AND ANALYSIS This cluster randomised controlled trial will involve 60 groups of community-dwelling people aged 60+ years. Participating groups will be randomised to: (1) a physical activity and fall prevention intervention (30 groups), involving written information, fall risk assessment and prevention advice, a pedometer-based physical activity tracker and telephone-based health coaching; or (2) a healthy eating intervention (30 groups) involving written information and telephone-based dietary coaching. Primary outcomes will be objectively measured physical activity at 12 months post-randomisation and self-reported falls throughout the 12-month trial period. Secondary outcomes include: the proportion of fallers, the proportion of people meeting the Australian physical activity guidelines, body mass index, eating habits, mobility goal attainment, mobility-related confidence, quality of life, fear of falling, risk-taking behaviour, mood, well-being, self-reported physical activity, disability, and health and community service use. The between-group difference in the number of falls per person-year will be analysed using negative binomial regression models. For the continuously scored primary and secondary outcome measures, linear regression adjusted for corresponding baseline scores will assess the effect of group allocation. Analyses will be preplanned, conducted while masked to group allocation, will take into account cluster randomisation, and will use an intention-to-treat approach.

The monitoring of activity at home after total hip arthroplasty (2016)

The Bone & Joint Journal

Abstract

Aims: Total hip arthroplasty (THA) has well known subjective benefits, but little is known objectively about the recovery of mobility in the early post-operative period. Patients and Methods: A total of 33 patients aged textgreater 60 years who underwent elective primary THA had their activity monitored for 30 days post-operatively using an at-home (Fitbit) ankle accelerometer. Their mean age was 70.7 years (61 to 86); 15 (45.5%) were female. The rate of compliance and the mean level of activity were determined. Comparisons between subgroups based on age, body mass index (BMI), surgical approach, and the destination of the patients when discharged were also performed. Results: The mean compliance over the 30 days was 26.7 days (16 to 30; 89%) of use. The mean number of steps increased from 235 (5 to 1152) to 2563 (87 to 7280) (p textless 0.001) between the first and the 30th post-operative day. Age textless 70 years and an anterior surgical approach were significantly associated with higher levels of activity (1600 to 2400 (p = 0.016 to 0.031) and 1000 to 1800 (p = 0.017 to 0.037) more steps per day, respectively) between the second and the fourth week post-operatively. There was also a trend towards higher levels of activity in those who were discharged to their home rather than to a nursing facility (a mean of 1500 more steps per day, p = 0.02). BMI greater or less than 30 kg/m2 was not predictive of activity (p = 0.45 to 0.98). Conclusion: At-home remote mobility monitoring using existing commercially available technology is feasible in patients who have undergone THA. It showed a clear trend towards increased activity with the passage of time. Additionally, the remote device was able to detect differences in levels of activity clearly between patients in relation to variables of interest including age, BMI, surgical approach, and the destination of the patient at the time of discharge from hospital. Such monitoring may allow for the early identification and targeted intervention in patients who recover slowly.

Health Outcomes of Information System Use Lifestyles among Adolescents: Videogame Addiction, Sleep Curtailment and Cardio-Metabolic Deficiencies. (2016)

PLOS ONE

Abstract

BACKGROUND AND OBJECTIVE: Obesity is a rising problem among adolescents in modern societies; it results in long-term cardio-metabolic problems. Possible overlooked drivers of obesity and its consequent cardio-metabolic deficits include videogame addiction and the resulting curtailed sleep; both are growing problems among adolescents. The objective of this study is to examine possible associations among these concepts in adolescents, as a means to point to plausible interventions. METHODS: Data were collected from 94 adolescents who play videogames and are enrolled in outpatient clinics, using surveys, wearable sleep monitors (FitBit), physical exams, and blood tests at three points in time. These data were subjected to structural equation modeling (SEM) analyses and bootstrapping-based mediation testing procedures. RESULTS: Videogame addiction among adolescents was negatively associated with sleep duration (ß = -0.24). Sleep duration was negatively associated with obesity (ß = -0.30), which in turn was associated with elevated blood pressure (ß = 0.26), low high-density lipoprotein cholesterol (ß = -0.18), high triglycerides (ß = 0.61), and high insulin resistance (ß = 0.39). The model explained 36.2% of the variation in sleep duration, 32.7% of the variation in obesity, and between 12.8% and 28.1% of the variation in cardio-metabolic indicators. Post-hoc analyses indicated that curtailed sleep is a possible full mediator of the association between videogame addiction, abdominal obesity and the associated cardio-metabolic deficits. CONCLUSION: The findings point to possible information systems use lifestyle-health links, which behooves researchers and practitioners to pay closer attention to possible adverse health outcomes of technology-related addictions. Interventions that target problematic video-gaming and sleep should be devised as a possible means for improving adolescents' long-term cardio-metabolic health.

Association between activity limitations and pain in patients scheduled for total knee arthroplasty (2016)

BMC Musculoskeletal Disorders

Abstract

Background: Historically, persons scheduled for total knee arthroplasty (TKA) have reported severe pain with low demand activities such as walking, but recent data suggests that TKA recipients may have less preoperative pain. Little is known about people who elect TKA with low levels of preoperative pain. To better understand current TKA utilization, we evaluated the association between preoperative pain and difficulty performing high demand activities, such as kneeling and squatting, among TKA recipients. Methods: We used baseline data from a randomized control trial designed to improve physical activity following TKA. Prior to TKA, participants were categorized according to Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) Pain scores: Low (0–25), Medium (26–40), and High (41–100). Within each group, limitations in both low demand and high demand activities were assessed. Results: The sample consisted of 202 persons with a mean age of 65 (SD 8) years; 21 %, 34 %, and 45 % were categorized in the Low, Medium, and High Pain groups, respectively. Of the Low Pain group, 60 % reported at least one of the following functional limitations: limited flexion, limp, limited walking distance, and limitations in work or housework. While only 12 % of the Low Pain group reported at least moderate pain with walking on a flat surface, nearly all endorsed at least moderate difficulty with squatting and kneeling. Conclusions: A substantial number of persons scheduled for TKA report Low WOMAC Pain (=25) prior to surgery. Persons with Low WOMAC Pain scheduled for TKA frequently report substantial difficulty with high demand activities such as kneeling and squatting. Studies of TKA appropriateness and effectiveness for patients with low WOMAC Pain should include measures of these activities.

Feasibility of a Memory Clinic-Based Physical Activity Prescription Program (2016)

Journal of Alzheimer's Disease

Abstract

Background: Effective programs for promoting physical activity are needed for those with cognitive impairment. Objective: To test the feasibility of mobile Health (mHealth) technology-supported physical activity prescription from a tertiary care memory clinic. Methods: This feasibility study was designed as a 16-week randomized, crossover trial of a physical activity prescription: 8 weeks of intervention, 8 weeks of baseline or maintenance phase data collection. We recruited 2 cohorts: 21 individuals with Alzheimer-related cognitive impairment (mean age 72.3 (5.2), 9 females), and 9 individuals with normal cognition (mean age 69.6 (5.8), 8 females). We gave each cohort an mHealth accelerometer-based physical activity prescription to double number of steps taken. Our primary outcomes were feasibility and safety. Our secondary outcomes were change in weekly steps taken, Dementia Quality of Life Scale, Self-efficacy Scale, 6-minute Walk, and mini-Physical Performance Test. Results: Set-up and use of the device was not a barrier to participation. However, only 62% of participants with cognitive impairment completed the intervention. The cohort with cognitive impairment did not change their weekly step count above Week 1. All participants in the cohort with normal cognition were able to set up and use their device and increased their weekly step count above Week 1. There were no differences between Week 1 and Week 8 for any secondary measures in either cohort. Conclusions: Setup and daily use of mHealth technology appears to be feasible for a person with cognitive impairment with the help of a partner, but increasing daily step counts over 8 weeks was not achieved. Future work needs to assess alternative activity prescription goals or additional support for patients and their partners.

Validity of commercial activity trackers in children with congenital heart disease (2016)

Canadian Journal of Cardiology

Abstract

BACKGROUND Increasing physical activity levels is a high priority to optimize long-term health in children with congenital heart disease (CHD). Commercial activity trackers have been validated in adults and are increasingly used to measure and promote physical activity in pediatric populations, but they have not been validated in children. METHODS In 30 children with CHD aged 10-18yrs, we assessed the validity of physical activity form the wrist-based Fitbit Charge HRTM against hip-based ActiGraph accelerometers under free living conditions for 7 days. We assessed the association between devices by intra-class correlation coefficients and Bland-Altman plots. Receiver-operating curves were used to identify Fitbit step cut-points. RESULTS There was a strong association between the two devices for daily steps across 138 analysed person-days (ICC=0.855, ptextless0.001), but poorer agreement for time spent in physical activity intensities (ICCstextless0.7). Daily Fitbit steps of =12,500 identified meeting physical activity guidelines defined as =60 min MVPA/day. Fitbit devices recorded more steps than accelerometers (-2,242 steps/d, 95%LoA of -7,738 to 3,253). Between-device differences were greater in boys vs. girls. Fitbit devices were worn for longer than accelerometers (-36 min/d, 95%LoA -334 to 261), but overall differences in wear time explained little of the variance in step differences (7%, p=0.048). CONCLUSIONS Commercial activity trackers provide opportunities to remotely monitor physical activity in children with CHD, but absolute values may differ from accelerometers. These findings are important given the increasing emphasis on physical activity promotion and monitoring in children with cardiovascular risk factors.

Is There an Association of Physical Activity with Brain Volume, Behavior, and Day-to-day Functioning? A Cross Sectional Design in Prodromal and Early Huntington Disease. (2016)

PLoS Currents

Abstract

BACKGROUND: Huntington disease (HD) is a genetic neurodegenerative disease leading to progressive motor, cognitive, and behavioral decline. Subtle changes in these domains are detectable up to 15 years before a definitive motor diagnosis is made. This period, called prodromal HD, provides an opportunity to examine lifestyle behaviors that may impact disease progression. THEORETICAL FRAMEWORK: Physical activity relates to decreased rates of brain atrophy and improved cognitive and day-to-day functioning in Alzheimer disease and healthy aging populations. Previous research has yielded mixed results regarding the impact of physical activity on disease progression in HD and paid little attention to the prodromal phase. METHODS: We conducted analyses of associations among current physical activity level, current and retrospective rate of change for hippocampus and striatum volume, and cognitive, motor, and day-to-day functioning variables. Participants were 48 gene-expanded cases with prodromal and early-diagnosed HD and 27 nongene-expanded control participants. Participants wore Fitbit Ultra activity monitors for three days and completed the self-reported International Physical Activity Questionnaire (IPAQ). Hippocampal and striatal white matter volumes were measured using magnetic resonance imaging. Cognitive tests included the Stroop Color and Word Test, and the Symbol Digit Modalities Test (SDMT). Motor function was assessed using the Unified Huntington's Disease Rating Scale total motor score (TMS). Day-to-day functioning was measured using the World Health Organization Disability Assessment Schedule (WHODAS) version 2.0. RESULTS: Higher Fitbit activity scores were significantly related to better scores on the SDMT and WHODAS in case participants but not in controls. Fitbit activity scores tracked better with TMS scores in the group as a whole, though the association did not reach statistical significance in the case participants. Higher Fitbit activity scores related to less day-to-day functioning decline in retrospective slope analyses. Fitbit activity scores did not differ significantly between cases and controls. CONCLUSIONS: This is the first known study examining the associations between activity level and imaging, motor, cognitive, and day-to-day functioning outcomes in prodromal and early HD. Preliminary results suggest physical activity positively correlates with improved cognitive and day-to-day functioning and possibly motor function in individuals in the prodromal and early phase of the condition.

Accuracy of Heart Rate Watches: Implications for Weight Management (2016)

PLOS ONE

Abstract

Background: Wrist-worn monitors claim to provide accurate measures of heart rate and energy expenditure. People wishing to lose weight use these devices to monitor energy balance, however the accuracy of these devices to measure such parameters has not been established. Aim: To determine the accuracy of four wrist-worn devices (Apple Watch, Fitbit Charge HR, Samsung Gear S and Mio Alpha) to measure heart rate and energy expenditure at rest and during exercise. Methods: Twenty-two healthy volunteers (50% female; aged 24 ± 5.6 years) completed 1-hr protocols involving supine and seated rest, walking and running on a treadmill and cycling on an ergometer. Data from the devices collected during the protocol were compared with reference methods: electrocardiography (heart rate) and indirect calorimetry (energy expenditure). Results: None of the devices performed significantly better overall, however heart rate was consistently more accurate than energy expenditure across all four devices. Correlations between the devices and reference methods were moderate to strong for heart rate (0.67–0.95 [0.35 to 0.98]) and weak to strong for energy expenditure (0.16–0.86 [-0.25 to 0.95]). All devices underestimated both outcomes compared to reference methods. The percentage error for heart rate was small across the devices (range: 1–9%) but greater for energy expenditure (9–43%). Similarly, limits of agreement were considerably narrower for heart rate (ranging from -27.3 to 13.1 bpm) than energy expenditure (ranging from -266.7 to 65.7 kcals) across devices. Conclusion: These devices accurately measure heart rate. However, estimates of energy expenditure are poor and would have implications for people using these devices for weight loss.

A Technology-Mediated Behavioral Weight Gain Prevention Intervention for College Students: Controlled, Quasi-Experimental Study. (2016)

Journal of Medical Internet Research

Abstract

BACKGROUND: Both men and women are vulnerable to weight gain during the college years, and this phenomenon is linked to an increased risk of several chronic diseases and mortality. Technology represents an attractive medium for the delivery of weight control interventions focused on college students, given its reach and appeal among this population. However, few technology-mediated weight gain prevention interventions have been evaluated for college students. OBJECTIVE: This study examined a new technology-based, social media-facilitated weight gain prevention intervention for college students. METHODS: Undergraduates (n =58) in two sections of a public university course were allocated to either a behavioral weight gain prevention intervention (Healthy Weight, HW; N=29) or a human papillomavirus (HPV) vaccination awareness intervention (control; N=29). All students were enrolled, regardless of initial body weight or expressed interest in weight management. The interventions delivered 8 lessons via electronic newsletters and Facebook postings over 9 weeks, which were designed to foster social support and introduce relevant educational content. The HW intervention targeted behavioral strategies to prevent weight gain and provided participants with a Wi-Fi-enabled scale and an electronic physical activity tracker to facilitate weight regulation. A repeated-measures analysis of variance was conducted to examine within- and between-group differences in measures of self-reported weight control practices and objectively measured weight. Use of each intervention medium and device was objectively tracked, and intervention satisfaction measures were obtained. RESULTS: Students remained weight stable (HW: -0.48+1.9 kg; control: -0.45+1.4 kg), with no significant difference between groups over 9 weeks (P =.94). However, HW students reported a significantly greater increase in the number of appropriate weight control strategies than did controls (2.1+4.5 vs -1.1+3.4, respectively; P =.003) and there was no increase in inappropriate weight control behaviors (P =.11). More than 90% of students in the HW arm opened the electronic newsletters each week, and the average number of Facebook interactions (comments and likes) per student each week was 3.3+1.4. Each self-monitoring device was initialized by 90% of HW students. On average, they used their physical activity tracker for 23.7+15.2 days and their Wi-Fi scale for 14.1+13.1 days over the 9 weeks. HW students rated the intervention favorably. CONCLUSIONS: The short-term effect of this technology-based weight gain prevention intervention for college students is promising and merits evaluation over a longer duration to determine whether engagement and behavioral improvements positively affect weight outcomes and can be maintained.

A hospital and home-based exercise program to address functional decline in people following allogeneic stem cell transplantation (2017)

Supportive Care in Cancer

Abstract

Purpose The aims of this study are to investigate the feasibility of an exercise program commencing 60 days following allogeneic stem cell transplantation (alloSCT), to investigate changes in physical function and health-related quality of life (HRQoL) in patients from pre- to post-alloSCT and to explore changes in patient outcomes before and after the program. Methods This study is a single site, prospective case series including 43 adults undergoing alloSCT. The intervention was an 8-week outpatient and home-based exercise and education program. Outcomes included feasibility (consent, attendance, compliance and completion rates), functional exercise capacity (incremental shuttle walk test), muscle strength (hand-held dynamometry), self-efficacy for physical activity (Physical Activity Assessment Inventory) and HRQoL (Functional Assessment of Cancer Therapy-Bone Marrow Transplant). Outcomes were measured pre-alloSCT, 60 days post-alloSCT (pre-intervention) and 100 days post-alloSCT (post-intervention). Results The consent rate was 93%. From baseline to 60 days post-alloSCT, there was significant decline in functional exercise capacity (mean difference 224 m, 95% CI 153–295, p < 0.0005), self-efficacy for physical activity (294 points, 95% CI 136–452, p = 0.001) and HRQoL (15 points, 95% CI 8–21, p < 0.0005). Ten participants did not commence the exercise program due to death (n = 5), illness (n = 1) or cancellation of alloSCT (n = 4). The intervention was feasible in those not affected by major medical complications or death. No adverse events occurred. From pre- to post-intervention, there was significant improvement in functional exercise capacity (p = 0.001) and HRQoL (p = 0.001). Conclusions AlloSCT results in significant decline in functional exercise capacity, self-efficacy for physical activity and HRQoL, which may be improved through an exercise program. This pilot demonstrated safety, feasibility and high patient interest. Further randomised research is required.

Developing a Fitbit-supported lifestyle physical activity intervention for depressed alcohol dependent women (2017)

Journal of Substance Abuse Treatment

Abstract

Purpose Women in alcohol treatment are more likely to relapse when in unpleasant, negative emotional states. Given the demonstrated benefits of exercise for decreasing depression, negative affect, and urges to drink, helping women engage in a lifestyle physical activity (LPA) intervention in early recovery may provide them a tool they can utilize “in the moment” in order to cope with negative emotional states and alcohol craving when relapse risk is highest. New digital fitness technologies (e.g., Fitbit activity monitor with web and mobile applications) may facilitate increases in physical activity (PA) through goal setting and self-monitoring. Method We piloted a 12-week LPA + Fitbit intervention focused on strategically using bouts of PA to cope with affect and alcohol cravings to prevent relapse in 20 depressed women (mean age = 39.5 years) in alcohol treatment. Results Participants wore their Fitbit on 73% of days during the intervention period. An average of 9174 steps/day were taken on the days the Fitbit was worn. Participants completed 4.7 of the 6 scheduled phone PA counseling sessions (78%). Among women who completed the intervention (n = 15), 44% remained abstinent throughout the entire course of treatment. On average, women were abstinent on 95% of days during the 12-week intervention. Participants reported an increase in using PA to cope with either negative affect or urges to drink from baseline to end of treatment (p textless 0.05). Further, participants reported high satisfaction with the LPA + Fitbit intervention and with the Fitbit tracker. Conclusions Further research is needed to evaluate the LPA + Fitbit intervention in a more rigorous randomized controlled trial. If the LPA + Fitbit intervention proves to be helpful during early recovery, this simple, low-cost and easily transported intervention can provide a much-needed alternate coping strategy to help reduce relapse risk among women in alcohol treatment.

Adaptive goal setting and financial incentives: a 2 × 2 factorial randomized controlled trial to increase adults' physical activity (2017)

BMC Public Health

Abstract

Background: Emerging interventions that rely on and harness variability in behavior to adapt to individual performance over time may outperform interventions that prescribe static goals (e.g., 10,000 steps/day). The purpose of this factorial trial was to compare adaptive vs. static goal setting and immediate vs. delayed, non-contingent financial rewards for increasing free-living physical activity (PA). Methods: A 4-month 2 × 2 factorial randomized controlled trial tested main effects for goal setting (adaptive vs. static goals) and rewards (immediate vs. delayed) and interactions between factors to increase steps/day as measured by a Fitbit Zip. Moderate-to-vigorous PA (MVPA) minutes/day was examined as a secondary outcome. Results: Participants (N = 96) were mainly female (77%), aged 41 ± 9.5 years, and all were insufficiently active and overweight/obese (mean BMI = 34.1 ± 6.2). Participants across all groups increased by 2389 steps/day on average from baseline to intervention phase (p textless .001). Participants receiving static goals showed a stronger increase in steps per day from baseline phase to intervention phase (2630 steps/day) than those receiving adaptive goals (2149 steps/day; difference = 482 steps/day, p = .095). Participants receiving immediate rewards showed stronger improvement (2762 step/day increase) from baseline to intervention phase than those receiving delayed rewards (2016 steps/day increase; difference = 746 steps/day, p = .009). However, the adaptive goals group showed a slower decrease in steps/day from the beginning of the intervention phase to the end of the intervention phase (i.e. less than half the rate) compared to the static goals group (-7.7 steps vs. -18.3 steps each day; difference = 10.7 steps/day, p textless .001) resulting in better improvements for the adaptive goals group by study end. Rate of change over the intervention phase did not differ between reward groups. Significant goal phase x goal setting x reward interactions were observed. Conclusions: Adaptive goals outperformed static goals (i.e., 10,000 steps) over a 4-month period. Small immediate rewards outperformed larger, delayed rewards. Adaptive goals with either immediate or delayed rewards should be preferred for promoting PA.

The Physical Activity Patterns of Cardiac Rehabilitation Program Participants Using Three Different Measures: Exercise Capacity, Functional Activity, and Habitual Physical Activity (2017)

Heart, Lung and Circulation

Abstract

Background: Understanding physical activity (PA) patterns and precise PA assessments are essential in cardiac rehabilitation (CR) settings to promote PA and to evaluate interventions. Purpose: Report the comprehensive patterns of PA and determine whether Fitbit-Flex, Physical Activity Scale for the Elderly (PASE), and six-minute walk test (6MWT) provided similar PA outcomes. Methods: 40 patients attending CR programs completed PASE and 6MWT following assessment at baseline and 8 weeks. Participants also wore Fitbit-Flex for four consecutive days including two weekend days. Results: Participants' mean age was 66 years, 67% were male. Significant increases occurred from baseline to 8 weeks in: PASE score (114–158; p = .04); mean 6MWT-distance (477–516; p = .01) and average steps (8024–9818; p = .01). A greater increase in average weekday steps (8923 steps/day) than weekend steps (7317 steps/day; p = .01) occurred at baseline. Non-significant increase in average moderate-to-vigorous-physical-activity (MVPA) occurred from baseline to 8 weeks measured by Fitbit-Flex and PASE (33–41; p = .14, and 49–64; p = .15, respectively). Average Fitbit-Flex steps at baseline and 8 weeks was weakly correlated with PASE score (r = .24; r = 16, respectively); and moderately-weakly correlated with 6MWT (r = .33; r = .23, respectively). Significant moderate correlations occurred at baseline between 6MWT and Fitbit-Flex for weekend steps, weekend MVPA, and overall average MVPA, with coefficients between .35 and .42. 6MWT correlated weakly with PASE score (r = .30) at week 8, but reasonably well (r = .57) at baseline. Conclusion: CR patients' PA patterns changed over time. Fitbit-Flex is the best at capturing and differentiating habitual PA from exercise capacity and functional activity.

How Accurate Is Your Activity Tracker? A Comparative Study of Step Counts in Low-Intensity Physical Activities (2017)

JMIR Mhealth and Uhealth

Abstract

Background: As commercially available activity trackers are being utilized in clinical trials, the research community remains uncertain about reliability of the trackers, particularly in studies that involve walking aids and low-intensity activities. While these trackers have been tested for reliability during walking and running activities, there has been limited research on validating them during low-intensity activities and walking with assistive tools. Objective: The aim of this study was to (1) determine the accuracy of 3 Fitbit devices (ie, Zip, One, and Flex) at different wearing positions (ie, pants pocket, chest, and wrist) during walking at 3 different speeds, 2.5, 5, and 8 km/h, performed by healthy adults on a treadmill; (2) determine the accuracy of the mentioned trackers worn at different sites during activities of daily living; and (3) examine whether intensity of physical activity (PA) impacts the choice of optimal wearing site of the tracker. Methods: We recruited 15 healthy young adults to perform 6 PAs while wearing 3 Fitbit devices (ie, Zip, One, and Flex) on their chest, pants pocket, and wrist. The activities include walking at 2.5, 5, and 8 km/h, pushing a shopping cart, walking with aid of a walker, and eating while sitting. We compared the number of steps counted by each tracker with gold standard numbers. We performed multiple statistical analyses to compute descriptive statistics (ie, ANOVA test), intraclass correlation coefficient (ICC), mean absolute error rate, and correlation by comparing the tracker-recorded data with that of the gold standard. Results: All the 3 trackers demonstrated good-to-excellent (ICC>0.75) correlation with the gold standard step counts during treadmill experiments. The correlation was poor (ICC<0.60), and the error rate was significantly higher in walker experiment compared to other activities. There was no significant difference between the trackers and the gold standard in the shopping cart experiment. The wrist worn tracker, Flex, counted several steps when eating (P<.01). The chest tracker was identified as the most promising site to capture steps in more intense activities, while the wrist was the optimal wearing site in less intense activities. Conclusions: This feasibility study focused on 6 PAs and demonstrated that Fitbit trackers were most accurate when walking on a treadmill and least accurate during walking with a walking aid and for low-intensity activities. This may suggest excluding participants with assistive devices from studies that focus on PA interventions using commercially available trackers. This study also indicates that the wearing site of the tracker is an important factor impacting the accuracy performance. A larger scale study with a more diverse population, various activity tracker vendors, and a larger activity set are warranted to generalize our results.

Assessing the Benefits and Use of Gain-Framed Text Messaging to Improve Physical Activity in Advanced Stage Lung Cancer (2017)

Chest

Abstract

PURPOSE: Physical activity (PA) is a potential therapy to improve quality of life (QoL) in patients with advanced stage lung cancer (LC). Many regimens have been studied, but no PA regimen has been shown to be both beneficial and sustainable. We have previously used weekly phone calls to deliver PA goals. We hypothesize that a walking-based PA regimen using gain-framed text messaging will improve PA levels more effectively than weekly phone calls. METHODS: In patients with advanced stage LC, we implemented a Patient-Centered Activity Regimen (PCAR) utilizing a FitBit Flex® (San Francisco, CA) accelerometer, an educational session at enrollment, individualized walking goals, and twice daily gain-framed text messages. Feedback surveys were collected at the end of the intervention. Over a 4 week period, activity levels were compared between the PCAR group (n=15) and prior efforts using weekly phone calls (n=29). Data collection and likelihood of increasing PA were compared, and a repeated measures, mixed-effects model was constructed. RESULTS: Compared to those receiving weekly phone calls, subjects receiving twice daily gain-framed text messages more frequently used the device (100% vs 76%), increased their step count (67% vs 34%), and less frequently had missing data (12% vs 37%). Scatterplots revealed “more active” and “less active” groups. Participants were grouped into “more active” and “less active” groups based on above- or below-average step counts at baseline. The “less active” group improved their PA level whereas the “more active” group became less active. Most subjects (92%) found the intervention helpful and would participate in another activity study. Most subjects would prefer individualized (rather than group) PA interventions (83%) and to be motivated via daily text messages (55%) that are sent at noon (55%). CONCLUSIONS: In this small cohort, compared to weekly phone calls, twice daily gain-framed text messaging (1) improves data collection, (2) is more likely than weekly phone calls to improve PA (especially in “less active” subjects), and (3) has high patient satisfaction scores. Gain-framed text messaging appears to be an effective way to motivate patients with advanced stage LC to exercise. Future studies should further evaluate the benefits of walking-based PA in this group as well as the differences between the “more active” and “less active” groups. CLINICAL IMPLICATIONS: This study will help inform future efforts to motivate patients with advanced stage lung cancer toward exercise.

The Validity of Fitbit Charge in Free Living Conditions (2017)

Medicine & Science in Sports & Exercise

Abstract

PURPOSE: Research is accumulating regarding the accuracy of wrist-worn consumer activity monitors in controlled lab settings. However, there is a lack of evidence of validity under free living conditions. The study investigated the accuracy of Fitbit Charge (FBC) for estimating minutes of moderate and vigorous physical activity (MVPA) compared to research grade accelerometers. A secondary purpose was to evaluate the accuracy of steps/day. METHODS: Ninety-four healthy men and women (mean age 41 ± 9 years) wore a FBC as part of a 12-week intervention. The participants were also asked to wear research grade accelerometers concurrently as the criterion physical activity measure during the last week of intervention. A minimal of 10 hours/day wear-time was applied to both methods. Estimated daily MVPA and steps taken from the FBC were compared against the criterion using indicators of Pearson correlation, mean absolute percent errors, mean percent errors, and equivalence testing. The activity classification agreement of MVPA at the minute level was evaluated with Kappa, sensitivity and specificity. RESULTS: Complete data were collected on 61 individuals with an average of 5.5 days of wear-time. The average daily MVPA measured by accelerometer was 76.3 minutes compared to a higher value, 118.7 minutes, estimated by the FBC. The correlation between the two methods was 0.8 (ptextless0.0001). The mean absolute percent errors and mean percent errors were 68.2% and -64.2% indicating a consistent overestimating MVPA by the FBC. The average daily steps were 8,897 and 7,716 measured by FBC and accelerometer, respectively, with a correlation of 0.76 (ptextless0.0001). The mean absolute percent errors and mean percent errors of steps estimated by FBC were 30.0% and -20.1%. Neither of the MVPA and steps measured by FBC fell into the ±10% equivalence zone set up by the accelerometer. The Kappa statistics of the classification agreement between the two methods was 0.32 with a low the sensitivity of 30.1% but a high specificity of 96.7%. CONCLUSIONS: This FBC estimated substantially higher minutes of MVPA in free living conditions among healthy adults and significantly higher steps compared to research grade accelerometer. The researchers who use FBC to track physical activity need to interpret their results with caution.

Does supplementation with carnosine improve cardiometabolic health and cognitive function in patients with pre-diabetes and type 2 diabetes? study protocol for a randomised, double-blind, placebo-controlled trial (2017)

BMJ Open

Abstract

Introduction: Carnosine, an over-the-counter food supplement, has a promising potential for the prevention and treatment of chronic diseases such as type 2 diabetes (T2DM), cardiovascular and neurodegenerative diseases through its anti-inflammatory, antiglycation, antioxidative and chelating effects. We have previously shown that supplementation with carnosine preserves insulin sensitivity and secretion in non-diabetic overweight and obese individuals. The effect of carnosine on cardiometabolic risk and related cognitive outcomes in patients with pre-diabetes and T2DM has thus far not been studied. We therefore aim to investigate whether supplementation with carnosine improves cardiometabolic health and cognitive function in patients with pre-diabetes and T2DM. Methods and analysis: We will employ a parallel design randomised controlled trial. Fifty participants with pre-diabetes (impaired fasting glycaemia and impaired glucose tolerance) and T2DM (with HbA1c level textless 8%) aged between 18 to 70 years will be randomly assigned to the intervention or control group. At baseline, participants will undergo a medical review and series of tests including anthropometric measurements (body mass index, a dual X-ray absorptiometry and peripheral quantitative computed tomography scan), an oral glucose tolerance test, cardiovascular measurements (central blood pressure, endothelial function and arterial stiffness), cognitive function, physical activity measurement, heart rate variability and liver fibroscan as well as questionnaires to assess dietary habits, sleep quality, depression and quality of life. The intervention group will receive 2?g of carnosine daily in two divided doses while the control group will receive identical placebo capsules for 14 weeks. All baseline measurements will be repeated at the end of the intervention. The change in glycaemic, cardiovascular and cognitive parameters as well as other measures will be compared between the groups. Ethics and dissemination: This study is approved by the Human Research Ethics Committee of Monash Health and Monash University, Australia. The findings will be disseminated via peer-reviewed publications and conference presentations.

Estimation of Sleep Stages Using Cardias and Accelerometer Data from a Wrist-Worn Device (2017)

Sleep

Abstract

Introduction: We investigated the ability of a wrist-worn tracker to estimate sleep stages in normal adult sleepers. Such a device could be useful in simplifying sleep research and in increasing public knowledge of sleep. Methods: Movement and cardiac data was collected from 60 adult subjects (36 M: 24 F, ages 34?±?10 yrs) wearing two wrist worn devices (left and right hand) containing a 3D-accelerometer and an optical photoplethysmogram (PPG), while undergoing a sleep stage study using a Type III home sleep testing device. The accelerometer was used to generate various features of movement; the PPG records cardiac peaks generated by each heartbeat, and can be used to determine heart rate and heart rate variability metrics. The sleep study was scored independently by two registered PSG technicians, using consensus AASM scoring rules. Using these labels, an automated classifier and post-processing rule was developed to label 30-second epochs as one of Wake/Light/Deep/REM (note that Stages N1 and N2 were combined into a single “Light” classification). The estimated performance of this automated classifier system was calculated using a leave-one out validation method. The performance metrics were Cohen's kappa (measures the level of agreement greater than chance) and per-epoch accuracy (percent of epochs correctly labeled). Results: The estimated Cohen's kappa was 0.52?±?0.14 for left hand wear, and 0.53?±?0.14 (right hand). The per-epoch accuracy was 69%. Across the population, there was no statistically significant bias in the estimated durations of the wake, light, deep and REM stages versus the gold standard measurements. Conclusion: These results suggest that a wrist worn device with movement and cardiac sensors can be used to determine sleep stages with a reasonable degree of accuracy in normal adult sleepers, but without the cost and artificial sleep environment of a sleep laboratory. The reported performance figures are similar or better than previously reported results from non-EEG based sleep staging using combinations of cardiac, respiratory and movement information.

Validity, reliability and feasibility of commercially available activity trackers in physical therapy for people with a chronic disease: a study protocol of a mixed methods research (2017)

Pilot and Feasibility Studies

Abstract

For older people and people with a chronic disease, physical activity provides health benefits. Patients and healthcare professionals can use commercially available activity trackers to objectively monitor (alterations in) activity levels and patterns and to support physical activity. However, insight in the validity, reliability, and feasibility of these trackers in people with a chronic disease is needed. In this article, a study protocol is described in which the validity, reliability (part A), and feasibility from a patient and therapist's point of view (part B) of commercially available activity trackers in daily life and health care is investigated. In part A, a quantitative cross-sectional study, an activity protocol that simulates everyday life activities will be used to determine the validity and reliability of nine commercially available activity trackers. Video recordings will act as the gold standard. In part B, a qualitative participatory action research study will be performed to gain insight in the use of activity trackers in peoples' daily life and therapy settings. Objective feasibility of the activity trackers will be measured with questionnaires, and subjective feasibility (experiences) will be explored in a community of practice. Physical therapists (n = 8) will regularly meet during 6 months to learn from each other regarding the actual use of activity trackers in therapy. Therapists and patients (n = 48) will decide together which tracker will be used in therapy and for which purpose (e.g., monitoring, goal setting). Data from the therapist' and patients' experiences will be collected by interviews (individual and focus groups) and analyzed by a directed content analysis. At the time of submission, selection of activity trackers, development of the activity protocol, and the ethical approval process are finished. Data collection and data processing are ongoing. The relevance of the study as well as the advantages and disadvantages of several aspects of the chosen design are discussed. The results acquired from both study parts can be used to create decision aids that may assist therapists and people with a chronic disease in choosing a suitable activity tracker, and to facilitate use of these activity trackers in health care settings. Ethical approval has been obtained from two medical-ethical committees (nr. 15-N-109, 15-N-48 and MEC-15-07).

A Feasible and Efficacious Mobile-Phone Based Lifestyle Intervention for Filipino Americans with Type 2 Diabetes: Randomized Controlled Trial (2017)

JMIR Diabetes

Abstract

Background: Filipino Americans have a high prevalence of obesity, type 2 diabetes (T2D), and cardiovascular disease compared with other Asian American subgroups and non-Hispanic whites. Mobile health (mHealth) weight loss interventions can reduce chronic disease risks, but these are untested in Filipino Americans with T2D. Objective: The objective of this study was to assess feasibility and potential efficacy of a pilot, randomized controlled trial (RCT) of a culturally adapted mHealth weight loss lifestyle intervention (Pilipino Americans Go4Health [PilAm Go4Health]) for overweight Filipino Americans with T2D. Methods: This was a 2-arm pilot RCT of the 3-month PilAm Go4Health intervention (phase 1) with an active waitlist control and 3-month follow-up (phase 2). The waitlist control received the PilAm Go4Health in phase 2, whereas the intervention group transitioned to the 3-month follow-up. PilAm Go4Health incorporated a Fitbit accelerometer, mobile app with diary for health behavior tracking (steps, food/calories, and weight), and social media (Facebook) for virtual social support, including 7 in-person monthly meetings. Filipino American adults ≥18 years with T2D were recruited from Northern California. Feasibility was measured by rates of recruitment, engagement, and retention. Multilevel regression analyses assessed within and between group differences for the secondary outcome of percent weight change and other outcomes of weight (kg), body mass index (BMI), waist circumference, fasting plasma glucose, HbA1c, and steps. Results: A total of 45 Filipino American adults were enrolled and randomized. Mean age was 58 (SD 10) years, 62% (28/45) were women, and mean BMI was 30.1 (SD 4.6). Participant retention and study completion were 100%, with both the intervention and waitlist group achieving near-perfect attendance at all 7 intervention office visits. Groups receiving the PilAm Go4Health in phase 1 (intervention group) and phase 2 (waitlist group) had significantly greater weight loss, −2.6% (−3.9 to −1.4) and −3.3% (−1.8 to −4.8), respectively, compared with the nonintervention group, resulting in a moderate to small effect sizes (d=0.53 and 0.37, respectively). In phase 1, 18% (4/22) of the intervention group achieved a 5% weight loss, whereas 82% (18/22) maintained or lost 2% to 5% of their weight and continued to maintain this weight loss in the 3-month follow-up. Other health outcomes, including waist circumference, BMI, and step counts, improved when each arm received the PilAm Go4Health, but the fasting glucose and HbA1c outcomes were mixed. Conclusions: The PilAm Go4Health was feasible and demonstrated potential efficacy in reducing diabetes risks in overweight Filipino Americans with T2D. This study supports the use of mHealth and other promising intervention strategies to reduce obesity and diabetes risks in Filipino Americans. Further testing in a full-scale RCT is warranted. These findings may support intervention translation to reduce diabetes risks in other at-risk diverse populations. Trial Registration: Clinicaltrials.gov NCT02290184; https://clinicaltrials.gov/ct2/show/NCT02290184 (Archived by WebCite at http://www.webcitation.org/6vDfrvIPp) [JMIR Diabetes 2017;2(2):e30]

A Feasible and Effective Mobile Health Weight Loss Lifestyle Intervention for Filipinos With Type 2 Diabetes (2017)

Circulation

Abstract

Introduction: Filipino Americans have the highest prevalence of obesity and type 2 diabetes (T2D) compared to Asian American subgroups placing them at high risk for cardiometabolic disease. Effective interventions are needed to reduce these health disparities. Mobile health (mHealth) weight loss lifestyle interventions have been effective in reducing cardiometabolic risks, but are untested among Filipinos, particularly with T2D. As prolific users of digital technology, Filipinos are ideal candidates for mHealth lifestyle interventions. Therefore, we conducted the PilAm Go4Health intervention study - a culturally adapted weight loss lifestyle intervention using mobile technology to reduce cardiometabolic risks among Filipinos with T2D. Objective: To demonstrate intervention feasibility and potential efficacy. Hypothesis: 1) participant retention rate will be greater than 80%; 2) Compared to the control, intervention group will have significantly greater reduction in: % weight loss, waist circumference, fasting plasma glucose, and HbA1; and greater increase in step-counts. Methods: Two-arm (intervention +active control groups) RCT compared a 3-month intervention (Fitbit accelerometer +mHealth app/diary +Facebook group) and control (Fitbit accelerometer). N=45 overweight Filipino adults with T2D were recruited from Northern California communities. Between group differences from baseline to 3-months were analyzed using: 1) multilevel regression for within-person change in weight and step-counts using a nonparametric bias-corrected bootstrapped 95% CI for the multilevel models, and 2) T-tests, ANOVA for waist circumference, fasting plasma glucose, and HbA1c (significance =ptextless0.05, 2-sided). Cohen's d was used for effect size analyses. Results: Randomized N=45 Filipinos (intervention =22 and control =23). Mean age was 58±10 years, 62% women, and retention rate=100%. There was significantly greater reduction in the intervention group compared to the control for: % weight (2.3% greater decrease, d=0.46); waist circumference (-2.68cm; d= 0.88); and fasting plasma glucose (-18.52mg/dl; d= -0.86). HbA1c group difference was not significant (-0.34%; ptextless 0.19). Step-counts significantly increased in the intervention group compared to control (3432 steps at endpoint; d=1.44). Conclusion: PilAm Go4Health intervention demonstrated excellent feasibility in recruitment and retention, and potential efficacy for reducing cardiometabolic risks in Filipinos with T2D. Results warrant further testing of this lifestyle intervention that may support translation to other at-risk diverse populations living with T2D.

Exploring the Association Between Self-Reported Asthma Impact and Fitbit-Derived Sleep Quality and Physical Activity Measures in Adolescents (2017)

JMIR mHealth and uHealth

Abstract

BACKGROUND Smart wearables such as the Fitbit wristband provide the opportunity to monitor patients more comprehensively, to track patients in a fashion that more closely follows the contours of their lives, and to derive a more complete dataset that enables precision medicine. However, the utility and efficacy of using wearable devices to monitor adolescent patients' asthma outcomes have not been established. OBJECTIVE The objective of this study was to explore the association between self-reported sleep data, Fitbit sleep and physical activity data, and pediatric asthma impact (PAI). METHODS We conducted an 8-week pilot study with 22 adolescent asthma patients to collect: (1) weekly or biweekly patient-reported data using the Patient-Reported Outcomes Measurement Information System (PROMIS) measures of PAI, sleep disturbance (SD), and sleep-related impairment (SRI) and (2) real-time Fitbit (ie, Fitbit Charge HR) data on physical activity (F-AM) and sleep quality (F-SQ). To explore the relationship among the self-reported and Fitbit measures, we computed weekly Pearson correlations among these variables of interest. RESULTS We have shown that the Fitbit-derived sleep quality F-SQ measure has a moderate correlation with the PROMIS SD score (average r=-.31, P=.01) and a weak but significant correlation with the PROMIS PAI score (average r=-.18, P=.02). The Fitbit physical activity measure has a negligible correlation with PAI (average r=.04, P=.62). CONCLUSIONS Our findings support the potential of using wrist-worn devices to continuously monitor two important factors-physical activity and sleep-associated with patients' asthma outcomes and to develop a personalized asthma management platform.

Trial to Incentivise Adherence for Diabetes (TRIAD): study protocol for a randomised controlled trial (2017)

Trials

Abstract

Many people with diabetes have suboptimal glycaemic control due to not being adherent to their treatment regimen. Behavioural economic theory suggests that the lack of adherence results from the disconnect between the timing of when costs and benefits accrue. One strategy to address this discontinuity is to offer patients a near-term benefit, such as a financial reward. Whereas there is evidence that rewards can improve treatment adherence and sometimes health outcomes, further research is needed to determine whether rewards are more effective when targeting processes or intermediary health outcomes. In the Trial to Incentivise Adherence for Diabetes (TRIAD) we test whether adding financial incentives to usual care can improve HbA1c levels among people with diabetes and whether the financial incentives work better when targeting processes (adherence to blood glucose testing, medication, and daily physical activity) or the primary intermediary health outcome of self-monitored blood glucose within an acceptable range. TRIAD is a randomised, controlled, open-label, single-centre superiority trial with three parallel arms. A total of 240 patients with suboptimally controlled diabetes (HbA1c ≥ 8%) from a polyclinic in Singapore are block-randomised (blocking factor: current vs. new glucometer users) into three arms, namely (1) usual care (UC) only, (2) UC with process incentive and (3) UC with outcome incentive, in a 2:3:3 ratio. Masking the arm allocation will be precluded by the behavioural nature of the intervention but blocking size will not be disclosed to protect concealment. The primary outcome (change in HbA1c level at month 6) will be measured by a laboratory that is independent from the study team. Secondary outcomes (at month 6) include the number of blood glucose testing days, glucose readings within the normal range (between 4 to 7 mmol/L), medication-adherent days, physically active days, and average incentives earned and time spent administrating the incentives. This study will provide evidence on whether financial incentives can cost-effectively improve glycaemic control. It will also provide evidence on the benefit incidence of interventions involving financial incentives. By comparing process to outcome incentives, this study will inform the design of future incentive strategies in chronic disease management and beyond.

Daily Activity Measured With Wearable Technology as a Novel Measurement of Treatment Effect in Patients With Coronary Microvascular Dysfunction: Substudy of a Randomized Controlled Crossover Trial (2017)

JMIR Research Protocols

Abstract

Background: Digital wearable devices provide a “real-world” assessment of physical activity and quantify intervention-related changes in clinical trials. However, the value of digital wearable device-recorded physical activity as a clinical trial outcome is unknown. Objective: Because late sodium channel inhibition (ranolazine) improves stress laboratory exercise duration among angina patients, we proposed that this benefit could be quantified and translated during daily life by measuring digital wearable device-determined step count in a clinical trial. Methods: We conducted a substudy in a randomized, double-blinded, placebo-controlled, crossover trial of participants with angina and coronary microvascular dysfunction (CMD) with no obstructive coronary artery disease to evaluate the value of digital wearable device monitoring. Ranolazine or placebo were administered (500-1000 mg twice a day) for 2 weeks with a subsequent 2-week washout followed by crossover to ranolazine or placebo (500-1000 mg twice a day) for an additional 2 weeks. The outcome of interest was within-subject difference in Fitbit Flex daily step count during week 2 of ranolazine versus placebo during each treatment period. Secondary outcomes included within-subject differences in angina, quality of life, myocardial perfusion reserve, and diastolic function. Results: A total of 43 participants were enrolled in the substudy and 30 successfully completed the substudy for analysis. Overall, late sodium channel inhibition reduced within-subject daily step count versus placebo (mean 5757 [SD 3076] vs mean 6593 [SD 339], P=.01) but did not improve angina (Seattle Angina Questionnaire-7 [SAQ-7]) (P=.83). Among the subgroup with improved angina (SAQ-7), a direct correlation with increased step count (r=.42, P=.02) was observed. Conclusions: We report one of the first studies to use digital wearable device-determined step count as an outcome variable in a placebo-controlled crossover trial of late sodium channel inhibition in participants with CMD. Our substudy demonstrates that late sodium channel inhibition was associated with a decreased step count overall, although the subgroup with angina improvement had a step count increase. Our findings suggest digital wearable device technology may provide new insights in clinical trial research. Trial Registration: Clinicaltrials.gov NCT01342029; https://clinicaltrials.gov/ct2/show/NCT01342029 (Archived by WebCite at http://www.webcitation.org/6uyd6B2PO) [JMIR Res Protoc 2017;6(12):e255]

Accuracy of Wearable Devices for Determining Physiological Measures during Different Physical Activities (2017)

Medicine & Science in Sports & Exercise

Abstract

PURPOSE: Wearable technology is ACSM's number one 2016-2017 fitness trend. The accuracy of these devices has yet to be firmly established. The objective of the study was to determine the validity of wearable devices' assessment of step count, heart rate (HR) and caloric expenditure (KCAL) during various physical activities. METHODS: Thirty college students (19 female, 11 male) engaged in 3 activity sessions (sweeping with a broom and dustpan, climbing stairs, and walking 1/4 mile) while wearing an Apple Watch Sport, Fitbit Charge HR, Accusplit Hip Pedometer, and iPhone 6 Plus. Step counts from devices were compared to those objectively recorded using a tally counter. Subjects also completed a treadmill graded exercise test during which HR and KCAL were reported by an Apple Watch Sport, Fitbit Charge HR, and Polar T-31. Values were compared to those from a six-lead ECG and metabolic analyzer. HR was recorded at rest and during each stage. KCAL was determined at the end of the protocol. RESULTS: Correlations between objective step counts and from the devices were: walking (.08 to .84), stair climbing (.12 to .90), and sweeping (.12 to .70). No device was accurate across all activities. The most accurate devices for activities were: walking (Apple Watch, r=.84); stair climbing (iPhone, r=.90); sweeping (Fitbit, r=.70). During the treadmill test, correlations between HR assessed via ECG and devices were: Apple Watch (.76 to .99), Polar T-31 (.72 to .94), and Fitbit (.19 to .98). Heart rate accuracy across the session was highest in the Apple Watch. KCAL from neither the Apple Watch (r=.63) nor Fitbit (r=.48) had a high correlational value to that from the metabolic analyzer. CONCLUSIONS: Fitness-related values provided by wearable devices had varying levels of accuracy when compared to objective step counts, HR and KCAL assessed by calibrated scientific equipment. Accuracy of step counts varied by activity and was higher across activities in the iPhone. HR reported from wearable devices, similarly, had varying levels of accuracy, with the Apple Watch being most accurate across a graded exercise test. Fitness-related information from wearables that are not medical devices and should be considered as estimates and used for motivation. Further validation of these devices should include a variety of physical activity modalities.

Validity of Wearable Activity Monitors during Cycling and Resistance Exercise (2017)

Medicine & Science in Sports & Exercise

Abstract

The use of wearable activity monitors has seen rapid growth; however, the mode and intensity of exercise could affect validity of heart rate (HR) and caloric (energy) expenditure (EE) readings. There is a lack of data regarding the validity of wearable activity monitors during graded cycling regimen and a standard resistance exercise. The present study determined the validity of eight monitors for HR compared to an ECG and seven monitors for EE compared to a metabolic analyzer during graded cycling and resistance exercise. Fifty subjects (28 women, 22 men) completed separate trials of graded cycling and three sets of four resistance exercises at a 10-repetition maximum (RM) load. Monitors included: Apple Watch Series 2 (AWS2), Fitbit Blaze, Fitbit Charge 2, Polar H7 (PH7), Polar A360, Garmin Vivosmart HR, TomTom Touch, and Bose SoundSport Headphones (BSP). HR was recorded after each cycling intensity and following each resistance exercise set. EE was recorded following both protocols. Validity was established as having a mean absolute percent error (MAPE) value of textless10%. The PH7 and BSP were valid during both exercise modes (Cycling: MAPE=6.87%, R=0.79; Resistance Exercise: MAPE=6.31%, R=0.83). During cycling, the AWS2 revealed the greatest HR validity (MAPE=4.14%, R=0.80). The BSP revealed the greatest HR accuracy during resistance exercise (MAPE=6.24%, R=0.86). Across all devices, as exercise intensity increased, there was greater underestimation of HR. No device was valid for EE during cycling or resistance exercise. HR from wearable devices differed at different exercise intensities; EE estimates from wearable devices were inaccurate. Wearable devices are not medical devices and users should employ caution when utilizing these devices for monitoring physiological responses to exercise.

Establishing Linkages Between Distributed Survey Responses and Consumer Wearable Device Datasets: A Pilot Protocol (2017)

JMIR Research Protocols

Abstract

Background: As technology increasingly becomes an integral part of everyday life, many individuals are choosing to use wearable technology such as activity trackers to monitor their daily physical activity and other health-related goals. Researchers would benefit from learning more about the health of these individuals remotely, without meeting face-to-face with participants and avoiding the high cost of providing consumer wearables to participants for the study duration. Objective: The present study seeks to develop the methods to collect data remotely and establish a linkage between self-reported survey responses and consumer wearable device biometric data, ultimately producing a de-identified and linked dataset. Establishing an effective protocol will allow for future studies of large-scale deployment and participant management. Methods: A total of 30 participants who use a Fitbit will be recruited on Mechanical Turk Prime and asked to complete a short online self-administered questionnaire. They will also be asked to connect their personal Fitbit activity tracker to an online third-party software system, called Fitabase, which will allow access to 1 month's retrospective data and 1 month's prospective data, both from the date of consent. Results: The protocol will be used to create and refine methods to establish linkages between remotely sourced and de-identified survey responses on health status and consumer wearable device data. Conclusions: The refinement of the protocol will inform collection and linkage of similar datasets at scale, enabling the integration of consumer wearable device data collection in cross-sectional and prospective cohort studies.

Randomized trial of a clinic-based weight loss intervention in cancer survivors (2017)

Journal of Cancer Survivorship

Abstract

Background This trial examined the efficacy of a clinic-based weight loss intervention in cancer survivors. Methods This single-center phase II trial randomized survivors of solid tumors and hematologic malignancies to a 15-week group-based weight loss intervention that included caloric restriction and physical activity (n = 30) or a wait-list control intervention (n = 30). The primary study outcome was body mass. Secondary study outcomes included body composition using dual-energy X-ray absorptiometry, physical fitness using the 6-min walk test (6MWT), and concentrations of serum biomarkers. Results Participants in the intervention group lost 5.6 ± 4.4% of baseline weight (4.6 ± 3.9 kg), whereas participants in the control group gained 0.2 ± 2.4% of baseline weight (0.2 ± 2.0 kg); intervention effect − 5.8% (95% CI − 7.8, − 3.8); − 4.8 kg (95% CI − 6.6, − 3.0); P = 0.0001. A larger proportion of participants in the intervention group lost ≥ 5% of baseline weight compared to the control group (43 vs 0%; P textless 0.0001). The intervention led to reductions in fat mass (− 3.2 ± 0.7 kg; P textless 0.0001), improvements in physical fitness (an increase of 22.6 ± 10.8 m on 6MWT; P = 0.03), and reductions in concentrations of insulin (− 7.7 ± 3.5 μU/mL; P = 0.004) and leptin (− 7.3 ± 4.0 ng/mL; P = 0.04). Conclusion A 15-week clinic-based weight loss intervention resulted in significant weight loss and improvements in body composition, physical fitness, and concentrations of serum biomarkers in cancer survivors. Implications for cancer survivors Weight loss programs provide a number of benefits for cancer survivors; survivors should inquire about the availability of lifestyle programs offered at their cancer center and within their local communities.

Does Stress Result in You Exercising Less? Or Does Exercising Result in You Being Less Stressed? Or Is It Both? Testing the Bi-directional Stress-Exercise Association at the Group and Person (N of 1) Level (2017)

Annals of Behavioral Medicine

Abstract

Background: Psychosocial stress contributes to heart disease in part by adversely affecting maintenance of health behaviors, while exercise can reduce stress. Assessing the bi-directional relationship between stress and exercise has been limited by lack of real-time data and theoretical and statistical models. This lack may hinder efforts to promote exercise maintenance. Purpose: We test the bi-directional relationship between stress and exercise using real-time data for the average person and the variability—individual differences—in this relationship. Methods: An observational study was conducted within a single cohort randomized controlled experiment. Healthy young adults, (n = 79) who reported only intermittent exercise, completed 12 months of stress monitoring by ecological momentary assessment (at the beginning of, end of, and during the day) and continuous activity monitoring by Fitbit. A random coefficients linear mixed model was used to predict end-of-day stress from the occurrence/non-occurrence of exercise that day; a logistic mixed model was used to predict the occurrence/non-occurrence of exercise from ratings of anticipated stress. Separate regression analyses were also performed for each participant. Sensitivity analysis tested all models, restricted to the first 180 days of observation (prior to randomization). Results: We found a significant average inverse (i.e., negative) effect of exercise on stress and of stress on exercise. There was significant between-person variability. Of N = 69, exercise was associated with a stress reduction for 15, a stress increase for 2, and no change for the remainder. We also found that an increase in anticipated stress reported the previous night or that morning was associated with a significant 20–22% decrease (OR = 0.78–0.80) in the odds of exercising that day. Of N = 69, this increase in stress reduced the likelihood of exercise for 17, increased the odds for 1, and had no effect for the remainder. We were unable to identify psychosocial factors that moderate the individual differences in these effects. Conclusions: The relationship of stress to exercise can be uni- or bi-directional and varies from person to person. A precision medicine approach may improve exercise uptake.

Short-Term Efficacy of an Innovative Mobile Phone Technology-Based Intervention for Weight Management for Overweight and Obese Adolescents: Pilot Study. (2017)

Interactive Journal of Medical Research

Abstract

BACKGROUND In the United States, approximately one-third of adolescents are now overweight or obese, and one in six is obese. This financial cost and the larger nonfinancial costs of obesity make obesity prevention and management for adolescents imperative for the health of the nation. However, primary care visits are typically brief, and primary care providers may lack adequate resources to help overweight or obese adolescents to manage weight issues. To augment the efficacy of primary care visits for adolescent weight management, mobile phone technology can be used as an adjunct treatment that provides additional opportunities for encouraging improvement in lifestyle, attainment, and maintenance of healthy weight. OBJECTIVE The purposes of this study were to (1) measure effects of an innovative mobile phone technology-based intervention for overweight and obese adolescents and to (2) examine the intervention's feasibility for use in primary care clinics. METHODS The mobile phone-based intervention had three components: use of the Fitbit Flex, participation in an online educational program, and receipt of biweekly text messages during the maintenance phase. A randomized controlled study design was utilized. Data regarding anthropometrics (body mass index [BMI] and waist-to-hip ratio), blood pressure, levels of physical and sedentary activity, diet, and self-efficacy regarding physical activity and diet were collected at baseline and at 3 and 6 months after the baseline assessment. RESULTS A total of 40 adolescents participated in the study. At the 6-month follow-up visit, compared to participants in the control group, the mobile phone-based intervention participants had significant improvement in BMI (z=-4.37, P=.001), diastolic blood pressure (z=-3.23, P=.001), physical activity days per week (z=2.58, P=.01), TV and computer time (z=-3.34, P=.001), servings of fruits and vegetables per day (z=2.74, P=.006), servings of soda and sweetened drinks (z=-3.19, P=.001), physical activity self-efficacy (z=2.75, P=.006), and dietary self-efficacy (z=5.05, P=.001). Medium to large effect sizes were found in these outcome variables. CONCLUSIONS The use of mobile technologies may offer a practical, reliable adjunct to weight management for overweight and obese adolescents in busy primary care clinics serving adolescents.

Accuracy of Accelerometer (Fitbit® ChargeHR™) Measured Distance and Heart Rate in Patients with Pulmonary Arterial Hypertension (2017)

American Journal of Respiratory and Critical Care Medicine

Abstract

Journal of Respiratory and Critical Care MedicineRationale: Functional capacity assessment in patients with pulmonary arterial hypertension (PAH) is used to establish treatment goals and prognoses. Higher resting heart rate and poor heart rate recovery after activity has been associated with worse outcomes. Devices that can accurately monitor heart rate and activity can thus be valuable to monitor patients with PAH. Accelerometers measure movement using a piezo-electric crystal and can quantify activity as steps. Several accelerometers can monitor heart rate using optical blood flow sensing by photoplethysmography. Accelerometers have been validated to measure step count in healthy adults and patients with COPD. Studies validating distance and heart rate (HR) measured by activity monitors are discrepant. Validity in patients with PAH has not been studied. The Fitbit® ChargeHR™ is a “watch” type activity monitor that can continuously measure step count and heart rate. It uses height to estimate gait length and calculates distance walked. We examined the accuracy of this device in measuring distance walked and HR at rest and after exercise in patients with PAH. Methods: Thirty patients with WHO Group I PAH were enrolled. Subjects wore a Fitbit® ChargeHR™ during a 6 minute walk test (6MWT). Distance and HR as determined by the Fitbit® and 6MWT were recorded. HR measured using pulse oximetry was used at the reference. The Fitbit® failed to record steps if patients used a walker (n = 6), and these data were not analyzed. The concordance correlation coefficient (CCC) was used to compare the accelerometer with reference values. Results: 30 patients with WHO Group I PAH (23 idiopathic and 7 connective tissue disease), with a mean age 50.49 ± 12.97 were enrolled in the study. Mean six minute walk distance was 368.6 ± 144 m, WHO functional class was I - 0%, II - 56%, III - 34.9 %, and IV - 8.7% The distance walked as determined by the accelerometer did not correlate with distance measured in the 6MWT (CCC 0.52, 95% CI [0.19, 0.74]). Heart rate measured by the Fitbit® did not correlate with HR measured by pulse oximetry at rest or after exertion (CCC and [95% CI] were 0.57 [0.28, 0.76] and 0.24 [-0.03, 0.47]. Conclusions: We demonstrate that the Fitbit® ChargeHR™ did not accurately determine walk distance or resting heart rate in a controlled test environment in patients with WHO Group I PAH. Reported HR and distance walked should be interpreted with caution in patients with PAH.

Assessment of laboratory and daily energy expenditure estimates from consumer multi-sensor physical activity monitors (2017)

PLOS ONE

Abstract

Wearable physical activity monitors are growing in popularity and provide the opportunity for large numbers of the public to self-monitor physical activity behaviours. The latest generation of these devices feature multiple sensors, ostensibly similar or even superior to advanced research instruments. However, little is known about the accuracy of their energy expenditure estimates. Here, we assessed their performance against criterion measurements in both controlled laboratory conditions (simulated activities of daily living and structured exercise) and over a 24 hour period in free-living conditions. Thirty men (n = 15) and women (n = 15) wore three multi-sensor consumer monitors (Microsoft Band, Apple Watch and Fitbit Charge HR), an accelerometry-only device as a comparison (Jawbone UP24) and validated research-grade multi-sensor devices (BodyMedia Core and individually calibrated Actiheart™). During discrete laboratory activities when compared against indirect calorimetry, the Apple Watch performed similarly to criterion measures. The Fitbit Charge HR was less consistent at measurement of discrete activities, but produced similar free-living estimates to the Apple Watch. Both these devices underestimated free-living energy expenditure (-394 kcal/d and -405 kcal/d, respectively; Ptextless0.01). The multi-sensor Microsoft Band and accelerometry-only Jawbone UP24 devices underestimated most laboratory activities and substantially underestimated free-living expenditure (-1128 kcal/d and -998 kcal/d, respectively; Ptextless0.01). None of the consumer devices were deemed equivalent to the reference method for daily energy expenditure. For all devices, there was a tendency for negative bias with greater daily energy expenditure. No consumer monitors performed as well as the research-grade devices although in some (but not all) cases, estimates were close to criterion measurements. Thus, whilst industry-led innovation has improved the accuracy of consumer monitors, these devices are not yet equivalent to the best research-grade devices or indeed equivalent to each other. We propose independent quality standards and/or accuracy ratings for consumer devices are required.

Comparison of wrist-worn Fitbit Flex and waist-worn ActiGraph for measuring steps in free-living adults (2017)

PLOS ONE

Abstract

Introduction: Accelerometers are commonly used to assess physical activity. Consumer activity trackers have become increasingly popular today, such as the Fitbit. This study aimed to compare the average number of steps per day using the wrist-worn Fitbit Flex and waist-worn ActiGraph (wGT3X-BT) in free-living conditions. Methods: 104 adult participants (n = 35 males; n = 69 females) were asked to wear a Fitbit Flex and an ActiGraph concurrently for 7 days. Daily step counts were used to classify inactive (textless10,000 steps) and active (=10,000 steps) days, which is one of the commonly used physical activity guidelines to maintain health. Proportion of agreement between physical activity categorizations from ActiGraph and Fitbit Flex was assessed. Statistical analyses included Spearman's rho, intraclass correlation (ICC), median absolute percentage error (MAPE), Kappa statistics, and Bland-Altman plots. Analyses were performed among all participants, by each step-defined daily physical activity category and gender. Results: The median average steps/day recorded by Fitbit Flex and ActiGraph were 10193 and 8812, respectively. Strong positive correlations and agreement were found for all participants, both genders, as well as daily physical activity categories (Spearman's rho: 0.76–0.91; ICC: 0.73–0.87). The MAPE was: 15.5% (95% confidence interval [CI]: 5.8–28.1%) for overall steps, 16.9% (6.8–30.3%) vs. 15.1% (4.5–27.3%) in males and females, and 20.4% (8.7–35.9%) vs. 9.6% (1.0–18.4%) during inactive days and active days. Bland-Altman plot indicated a median overestimation of 1300 steps/day by the Fitbit Flex in all participants. Fitbit Flex and ActiGraph respectively classified 51.5% and 37.5% of the days as active (Kappa: 0.66). Conclusions: There were high correlations and agreement in steps between Fitbit Flex and ActiGraph. However, findings suggested discrepancies in steps between devices. This imposed a challenge that needs to be considered when using Fitbit Flex in research and health promotion programs.

Acceptability of the Fitbit in behavioural activation therapy for depression: a qualitative study. (2017)

Evidence-based Mental Health

Abstract

INTRODUCTION Major depressive disorder is characterised by low mood and poor motivation. Literature suggests that increased physical activity has positive effects on alleviating depression. Fitness-tracking devices may complement behavioural activation (BA) therapy to improve physical activity and mental health in patients with depression. OBJECTIVES To understand patients' perceived benefit from the Fitbit and explore themes associated with patient experiences. To compare perceived benefit, patient factors, Fitbit usage and Beck's Depression Inventory (BDI) scores. METHODS Semistructured interviews were conducted with patients (n=36) who completed a 28-week BA group programme in a mood disorders outpatient clinic. All patients were asked to carry a Fitbit One device. We conducted thematic analyses on the interviews and exploratory quantitative analyses on patient characteristics, Fitbit usage, steps recorded, perceived benefit and BDI scores. FINDINGS Twenty-three patients found the Fitbit helpful for their physical activity. Themes of positive experiences included self-awareness, peer motivation and goal-setting opportunities. Negative themes included inconvenience, inaccuracies and disinterest. Age, baseline and change in BDI scores, prior physical activity goals and familiarity with technology were not associated with perceived benefit from the Fitbit or usage. Perceived benefit was significantly (p<0.01) associated with usage. CONCLUSIONS Overall, the Fitbit is an acceptable tool to complement BA therapy for patients with depression. Many positive themes were concordant with current literature; however, patients also reported negative aspects that may affect use. CLINICAL IMPLICATIONS Clinicians and researchers should consider both strengths and limitations of activity trackers when implementing them to motivate patients with depression. TRIAL REGISTRATION NUMBER NCT02045771; Pre-results.

Utility of the Fitbit Flex to Evaluate Sleep in Major Depressive Disorder: A comparison against polysomnography and wrist-worn actigraphy (2017)

Journal of Affective Disorders

Abstract

Background Sleep disturbance is a common and important component of affective illness. Fitness activity trackers are emerging as alternative means to estimate sleep in psychiatric patients; however, their ability to quantify sleep in mood disorders has not been empirically evaluated. Thus, this study sought to evaluate the utility of the Fitbit Flex (FBF) to estimate sleep in patients with major depressive disorder (MDD) relative to gold standard polysomnography (PSG) and a widely-used actigraph (Actiwatch-2; AW-2). Methods Twenty-one patients with unipolar MDD wore the FBF and AW-2 during in-laboratory PSG. Bland-Altman analysis compared sleep variables among devices. Epoch-by-epoch analysis further evaluated sensitivity, specificity, and accuracy for the FBF and AW-2 relative to PSG. Results The FBF demonstrated significant limitations in quantifying sleep and wake, relative to PSG. In the normal setting, the FBF significantly overestimated sleep time and efficiency, and displayed poor ability to correctly identify wake epochs (i.e. low specificity). In the sensitive setting, the FBF significantly underestimated sleep time and efficiency relative to PSG. Performance characteristics of the FBF were more similar to the AW-2 in the normal compared to sensitive setting. Limitations Participants were young to middle aged and predominantly female, which may limit generalizability of findings. Study design also precluded ability to assess longitudinal performance of FBF. Conclusions The FBF is not an adequate substitute for PSG when quantifying sleep in MDD, and the settings of the device sizably impact its performance relative to PSG and other standard actigraphs. The limitations and capabilities of the FBF should be carefully considered prior to clinical and research implementation.

Development of the Physical activity and Your Nutrition for Cancer (PYNC) smartphone app for preventing breast cancer in women (2017)

mHealth

Abstract

Background: In the U.S., breast cancer accounts for more cancer deaths in women than any site other than lung cancer. Based upon attributable risks, about 30–35% of breast cancers could potentially be prevented by addressing obesity, physical inactivity, increased alcohol consumption, and carcinogenic exposures such as hormone replacement therapy (HRT). We need methods of reducing women's risks of this disease that are attractive and easy to use, widely accessible to diverse women, and able to be easily amended to account for new research. Methods: The overall objective of this 12-month project is to develop and test a smartphone app to provide women with information about how they can reduce their risk of breast cancer through healthy behaviors such as physical activity, weight management, restricting caloric intake, consuming a healthy diet and proper nutrition, engaging in regular physical activity, and avoiding carcinogenic exposures such as HRT and alcohol. The specific aims are: (I) to develop a smartphone app for breast cancer prevention using a behavioral framework; (II) to ensure interconnectivity with commercially available products (Fitbit device for monitoring physical activity and the LoseIt! smartphone app for monitoring and tracking diet and nutrition); and (III) to ensure that the mHealth intervention is suitable for women with varying levels of health literacy and eHealth literacy. Results: The app, referred to as Physical activity and Your Nutrition for Cancer (PYNC), is being coded on an iOS platform. Users will be able to access the breast cancer prevention app using their smartphone or tablet. The app's design will ensure interconnectivity with commercially available products for monitoring and tracking physical activity, caloric intake, diet and nutrition. Using the app, it will be feasible for users to connect and sync their Fitbit and LoseIt! accounts so that information collected about physical activity, caloric intake, diet, and nutrition can be conveniently assessed from one portal. The Fitbit device and app provides reminders and allows users to set physical activity goals. Users will be able to access health education information about breast cancer risk-reduction with attractive graphics and visual displays. Conclusions: Future directions will include testing the efficacy of the mHealth intervention in increasing physical activity, improving diet and nutrition, and weight management through a randomized controlled trial, and widespread dissemination and implementation research.

Healthy lifestyle intervention for adult clinic patients with type 2 diabetes mellitus (2017)

Diabetes Management

Abstract

Background: Diet and exercise therapy have been reported to be effective in improving blood glucose control and are an important part of treatment of type 2 diabetes mellitus. Objective: The goal of this study is to examine the efficacy of a healthy lifestyle intervention for adult clinic patients with type 2 diabetes mellitus, as measured by Hgb-A1c, cardiovascular indicators, physical activity, weight, and BMI. Also of interest are optimal strategies for subject recruitment, the number of intervention sessions attended, and participant use of the Fitbit watch to monitor their physical activity and track food and beverage consumption. Methods: A pre/post-test design will be used in this pilot study. Non-institutionalized adult patients (n=50) aged 18-65 years who have been seen at the Augusta Health outpatient clinics (General Internal Medicine or Family Medicine) for type 2 diabetes in the past 12 months, and who are interested in reducing their risk of disease recurrence through healthy lifestyle behaviors, will be eligible to participate. At orientation visit, eligible individuals will be asked to provide written informed consent. Consenting volunteers (n=50) will be asked to complete the baseline and 6-month follow-up questionnaire and to participate in 12 weekly group sessions of 90 min duration, involving physical activity and to meet with a dietitian (baseline, one month, 90 days) to receive individualized advice on diet and nutrition. The technology–based intervention will use wrist-worn Fitbit Blaze physical activity monitoring devices. Conclusions: This pilot study will provide important information about the feasibility and preliminary efficacy of a healthy lifestyle intervention for adult clinic patients with type 2 diabetes mellitus. The use of consumer-facing devices such as the Fitbit watch has the potential advantage over the use of research accelerometers, pedometers, or actigraphs in increasing the likelihood that the intervention will be sustainable after the study ends.

A pragmatic, phase III, multisite, double-blind, placebo-controlled, parallel-arm, dose increment randomised trial of regular, low-dose extended-release morphine for chronic breathlessness: Breathlessness, Exertion And Morphine Sulfate (BEAMS) study protocol (2017)

BMJ Open

Abstract

Introduction: Chronic breathlessness is highly prevalent and distressing to patients and families. No medication is registered for its symptomatic reduction. The strongest evidence is for regular, low-dose, extended- release (ER) oral morphine. A recent large phase III study suggests the subgroup most likely to benefit have chronic obstructive pulmonary disease (COPD) and modified Medical Research Council breathlessness scores of 3 or 4. This protocol is for an adequately powered, parallel-arm, placebo-controlled, multisite, factorial, block-randomised study evaluating regular ER morphine for chronic breathlessness in people with COPD. Methods and analysis: The primary question is what effect regular ER morphine has on worst breathlessness, measured daily on a 0–10 numerical rating scale. Uniquely, the coprimary outcome will use a FitBit to measure habitual physical activity. Secondary questions include safety and, whether upward titration after initial benefit delivers greater net symptom reduction. Substudies include longitudinal driving simulation, sleep, caregiver, health economic and pharmacogenetic studies. Seventeen centres will recruit 171 participants from respiratory and palliative care. The study has five phases including three randomisation phases to increasing doses of ER morphine. All participants will receive placebo or active laxatives as appropriate. Appropriate statistical analysis of primary and secondary outcomes will be used. Ethics and dissemination: Ethics approval has been obtained. Results of the study will be submitted for publication in peer-reviewed journals, findings presented at relevant conferences and potentially used to inform registration of ER morphine for chronic breathlessness.

Using Wrist-Worn Actigraphic Devices to Measure Sleep in People with Huntington's Disease (2017)

Sleep

Abstract

Introduction: Sleep disturbances are an early symptom of Huntington's disease (HD), and may be linked to the cognitive, motor and psychiatric symptoms of HD. Long-term collection of HD ambulatory sleep data can show the impact of sleep disturbances as HD symptoms progress. Actigraphy provides an objective measure of sleep well suited to long-term ambulatory data collection, but is cost prohibitive for large-scale studies. Commercial actigraphy devices may be a good alternative. This study aimed to validate research and commercial actigrapy in people with HD. Methods: Seven people with the gene for Huntington's disease (Age=54.1?±?6.4, 6F) completed an overnight sleep study using polysomnography, research-grade (Actiwatch Spectrum Pro), and consumer-grade (Fitbit One) actigraphy to measure sleep. For each actigraph, sensitivity and specificity compared to polysomnography were calculated. Intraclass correlations (ICCs) and Bland-Altman analyses (BAA) were used to compare the actigraphs to polysomnography on total sleep time (TST), sleep efficiency (SE), sleep latency (SL), and wake after sleep onset (WASO). Results: The sensitivity of the research-grade and consumer-grade actigraphs were 97.13% and 98.90% respectively, and specificities were 31.32% and 27.05% respectively. The only significant ICC was the correspondence between the consumer-grade actigraph and polysomnography for TST (r=0.52, p=.03). Using BAA, both actigraphs overestimated TST (Research-grade - Avg.dif=74.3?±?54.1min; Consumer-grade - Avg.dif=94.3?±?50.2min) and SE (Research-grade - Avg.dif=14.8?±?11.0%; Consumer-grade - Avg.dif=17.5?±?9.8%), and showed good agreement for SL (Research-grade - Avg.dif=-27.9?±?28.0min; Consumer-grade - Avg.dif=-22.0?±?24.7?min). The research-grade actigraph showed good agreement with polysomnography on WASO (Avg.dif=-20.0?±?32.2min), whilst the consumer-grade actigraph underestimated WASO (Avg.dif=-39.0?±?29.1min). Conclusion: In people with HD, neither actigraph showed sufficient agreement with polysomnography to be a suitable replacement for ambulatory sleep measurement, particularly considering the very low specificity values. Our research-grade actigraphy performed notably worse than what is typically seen in patient populations. The development of a HD specific actigraphy algorithm would greatly improve the agreement between actigraphy and polysomnography.

Nurses’ Physical Activity Study: Caring For You So You Can Care For Others (2017)

Medicine & Science in Sports & Exercise

Abstract

Nurses face multiple negative stressors and report the greatest stress of all health care workers. The stress can have a negative effect on the health of nurses (e.g., obesity, fatigue, decrease in quality of life and satisfaction with life, and burnout). Stress is common among nurses with negative impacts on a nurse’s health as well as the quality of care patients receive. It is important for nurses to implement self-care strategies techniques (e.g., physical activity) to lower feelings of stress. PURPOSE: To examine the relationship between physical activity levels and measures of health among nurses in eastern NC. METHODS: Nurses (n = 62, 94% females; 85% White; age = 42.2 ± 11.7) were assessed for physical activity via the Fitbit Flex activity tracker over a 7 day period and the International Physical Activity Questionnaire. Nurses reported burnout, stress, and fatigue using self-reported questionnaires. Body mass index was measured by dividing weight (kg) by the participant’s squared height (m). RESULTS: BMI was significantly correlated with moderate-to-vigorous physical activity (r = -.28), steps (r = -0.29), and vigorous intensity physical activity (r = -.32). Fatigue was significantly correlated with moderate-to-vigorous physical activity (r = -.27). Stress was significantly correlated with steps (r = -.27), moderate intensity physical activity (r = -.25), and light intensity physical activity (r = -.40). Significant associations were also found between quality of life and light intensity physical activity (r = .40), moderate intensity physical activity (r = .30), and steps (r = .29). A relationship was found between high intensity physical activity and burnout (r = .23, p < .001) and between steps and compassion fatigue (r = -.26, p < .05). CONCLUSIONS: Findings demonstrated that stress, fatigue, and burnout were lowly correlated with physical activity among nurses. Thus, a physical activity intervention among nurses may be needed to fully see the effects of physical activity on nurses’ health, and ultimately patient health and safety.

Does Wearable Technology Provide Accurate Heart Rate Measures While Playing Pickleball Doubles? (2017)

Medicine & Science in Sports & Exercise

Abstract

The sport of pickleball is increasing in popularity throughout the United States, especially in middle-aged, and older adults. Research investigating the physiological demands of pickleball is limited. The use of wearable technology is also gaining popularity, however, the accuracy of such devices is under question. PURPOSE: The purpose of this study was to determine if wearable technology provide valid heart rate (HR) measures during pickleball doubles in middle aged adults. METHODS: 8 female, intermediate level pickleball players (IFP = 3.0 ± 0.8; age = 47 ± 11 years; mass = 72.5 ± 12.8 kg; height = 1.70 ± 0.08 m) participated in this study. All subjects played pickleball doubles for 30 minutes. HR was measured using two devices (Fitbit HR, worn on the dominate writs, and Polar HR monitor, worn at the xiphoid process level). Peak and mean HR were determined for each device. A paired sample t-tests was used to determine differences in HR between devices for each dependent variable (HR max and HR mean). A Pearson product-moment correlation coefficient was used to evaluate the concurrent validity between the gold standard (Polar HR system) and the wearable technology (Fitbit HR). RESULTS: Peak HR measured by the Polar system (151.9 ± 15.9 beats/min) was not significantly different from the peak HR measured by the Fitbit HR (149.6 ± 18.0 beats/min; p = 0.69). Similarly, mean HR measured by the Polar system (127.9 ± 17.9 beats/min) was not significantly different from the mean HR measured by the Fitbit HR (121.0 ± 18.2 beats/min; p = 0.23). Concurrent validity between the Polar HR system and the Fitbit HR for both peak (r = 0.6) and mean HR (r = 0.66) was also nonsignificant (p textgreater 0.05). CONCLUSIONS: On average, peak and mean HR was similar between the two devices. These results support the validity of wearable technology; the Fitbit HR was moderately valid in peak and mean HR compared to the Polar HR system. These results are promising for those who own wearable technology and are using it to monitor HR during physical activities such as pickleball doubles. We acknowledge our small sample size and admit that further investigation of the validity of wearable technology on physiological measures during pickleball and other racket sports is warranted.

An Examination of Body Mass Index Influence on Activity Tracker Accuracy (2017)

Medicine & Science in Sports & Exercise

Abstract

A variety of activity trackers with evolving technology are commercially available, yet it remains uncertain the influence body size has on these devices' accuracy (as previous activity trackers have been), which would compromise their generalizability. PURPOSE: To examine the accuracy of consumer-grade activity trackers in quantifying steps and moderate-vigorous physical activity (MVPA) engagement in free-living conditions in a diverse sample of body mass index (BMI) categories. METHODS: Sixty individuals (21.1 ± 1.5 years, 2.3 ± 0.2 ft stride length) across three BMI categories (“Normal” [n=25]: 20-24.9 kg/m2; “Overweight” [n=22]: 25-29.9 kg/m2; “Obese” [n=13]: =30 kg/m2) wore four activity trackers during one 24-hour day (wear time 12.7 ± 1.9 hours). On the non-dominant side of the body, the Fitbit Charge HR (Charge) and Jawbone UP3 (UP3) were worn on the wrist and the Fitbit One (ONE) on the waist, with the NL1000 activity tracker (NL; serving as the criterion device) worn on the dominant side of the waist. Mixed between-within ANOVA analyses were performed to examine differences in steps and MVPA for the activity trackers across three BMI categories. RESULTS: There were no differences in steps or MVPA for the individual devices across BMI categories. In the Normal group, the UP3 (6667 ± 3366 steps) and ONE (7400 ± 8135 steps) underestimated steps (p=.000), compared to the NL (8135 ± 3562 steps). Similarly, in the Overweight group, the UP3 (8799 ± 3986 steps) and ONE (9019 ± 3841 steps) underestimated steps (NL 9312 ± 3986 steps, both < .05). In the Obese group the Charge overestimated steps (8847 ± 4168, < .05) and the ONE underestimated steps (7726 ± 3791, < .05), compared to the NL (8224 ± 4264 steps). The UP3 overestimated MVPA in the Normal group (67.7 ± 44.3 minutes), compared to the NL (27.6 ± 16.3, p=.000). In both the Overweight and Obese groups, the UP3 overestimated MVPA (Overweight: 81.5 ± 44.3 minutes, Obese: 77.5 ± 39.4 minutes, both p=.000), whereas the ONE underestimated MVPA (Overweight: 25.9 ± 26.5 minutes, Obese: 19.5 ± 25.3 minutes, both < .05), compared to the NL (Overweight: 36.6 ± 19.2 minutes, Obese: 31.6 ± 27.3 minutes). CONCLUSION: Within BMI groups, The UP3 underestimated steps and overestimated MVPA, the ONE underestimated steps and MVPA, whereas the Charge performed relatively well for both measures.

Estimating Accuracy at Exercise Intensities: A Comparative Study of Self-Monitoring Heart Rate and Physical Activity Wearable Devices (2017)

JMIR mHealth and uHealth

Abstract

Background: Physical activity tracking wearable devices have emerged as an increasingly popular method for consumers to assess their daily activity and calories expended. However, whether these wearable devices are valid at different levels of exercise intensity is unknown. Objective: The objective of this study was to examine heart rate (HR) and energy expenditure (EE) validity of 3 popular wrist-worn activity monitors at different exercise intensities. Methods: A total of 62 participants (females: 58%, 36/62; nonwhite: 47% [13/62 Hispanic, 8/62 Asian, 7/62 black/ African American, 1/62 other]) wore the Apple Watch, Fitbit Charge HR, and Garmin Forerunner 225. Validity was assessed using 2 criterion devices: HR chest strap and a metabolic cart. Participants completed a 10-minute seated baseline assessment; separate 4-minute stages of light-, moderate-, and vigorous-intensity treadmill exercises; and a 10-minute seated recovery period. Data from devices were compared with each criterion via two-way repeated-measures analysis of variance and Bland-Altman analysis. Differences are expressed in mean absolute percentage error (MAPE). Results: For the Apple Watch, HR MAPE was between 1.14% and 6.70%. HR was not significantly different at the start (P=.78), during baseline (P=.76), or vigorous intensity (P=.84); lower HR readings were measured during light intensity (P=.03), moderate intensity (P=.001), and recovery (P=.004). EE MAPE was between 14.07% and 210.84%. The device measured higher EE at all stages (Ptextless.01). For the Fitbit device, the HR MAPE was between 2.38% and 16.99%. HR was not significantly different at the start (P=.67) or during moderate intensity (P=.34); lower HR readings were measured during baseline, vigorous intensity, and recovery (Ptextless.001) and higher HR during light intensity (Ptextless.001). EE MAPE was between 16.85% and 84.98%. The device measured higher EE at baseline (P=.003), light intensity (Ptextless.001), and moderate intensity (P=.001). EE was not significantly different at vigorous (P=.70) or recovery (P=.10). For Garmin Forerunner 225, HR MAPE was between 7.87% and 24.38%. HR was not significantly different at vigorous intensity (P=.35). The device measured higher HR readings at start, baseline, light intensity, moderate intensity (Ptextless.001), and recovery (P=.04). EE MAPE was between 30.77% and 155.05%. The device measured higher EE at all stages (Ptextless.001). Conclusions: This study provides one of the first validation assessments for the Fitbit Charge HR, Apple Watch, and Garmin Forerunner 225. An advantage and novel approach of the study is the examination of HR and EE at specific physical activity intensities. Establishing validity of wearable devices is of particular interest as these devices are being used in weight loss interventions and could impact findings. Future research should investigate why differences between exercise intensities and the devices exist. [JMIR Mhealth Uhealth 2017;5(3):e34]

Varying Exercise Intensities: The Accuracy of Three Self-Monitoring Heart Rate and Physical Activity Wearable Devices (2017)

Medicine & Science in Sports & Exercise

Abstract

Physical activity tracking wearables have emerged as a popular method for consumers to assess their daily activity, calories expended and heart rate. However, less is known if these health measures are valid at various levels of exercise intensity. PURPOSE: To examine heart rate (HR) and energy expenditure (EE) validity of three popular wrist-worn activity monitors at different exercise intensities. METHODS: 62 participants (36 females, 46.8% non-white) wore the Apple Watch (AW), Fitbit Charge HR (FCHR) and Garmin Forerunner 225 (GF). Validity was assessed by 2 criterion devices: HR chest strap and a metabolic cart. Participants completed a 10-min seated baseline; separate 4-min stages of light, moderate and vigorous-intensity treadmill exercises; and a 10-min seated recovery. Data from devices were compared to each criterion via two-way RM-ANOVA and Bland-Altman analysis. Differences were expressed in mean absolute percentage error values (MAPE). RESULTS: AW - HR MAPE between 1.14-6.70%. HR not significantly different at the start (p = .13), baseline (p = .76) or vigorous intensity (p = .84). Lower HR readings during light (p textless .05), moderate (p < .01) and recovery (p textless .01). EE MAPE between 14.07-210.84%. Measured higher EE at all stages (p textless .01). FCHR - HR MAPE between 2.38-16.99%. HR not significantly different at start (p = .43) or moderate intensity (p = .34). Lower HR readings during baseline, vigorous and recovery (p textless .001) and higher HR in light (p textless .001). EE MAPE between 16.85-84.98%. Measured higher EE at baseline (p textless .05), light (p textless .001) and moderate (p < .001). GF - HR MAPE between 7.87-24.38%. HR not significantly different at vigorous intensity (p = .35). Measured higher HR readings start, baseline, light, moderate (p textless .001) and recovery (p < .05). EE MAPE between 30.77-155.05%. Measured higher EE at all stages (p < .001). CONCLUSION: This study provides one of the first validation assessments for the Fitbit Charge HR, Apple Watch and Garmin Forerunner 225. An advantage and novel approach of the study is the examination of HR and EE at specific PA intensities. Establishing validity of wearables is of particular interest as these devices are being used in weight loss interventions and could impact findings. Future research should investigate why differences between exercise intensities and the devices exist.

Quantitative and qualitative measures of poststroke walking activity with inertial technologies (2017)

Annals of Physical and Rehabilitation Medicine

Abstract

OBJECTIVE Inertial technologies (activity monitors, inertial sensors) allow the evaluation of the number of steps and spatial-temporal parameters (STP) in daily life environments. However, activity monitors, generally worn at the hip, are not accurate for quantifying steps at slow walking speed (textless0.8m/s). Moreover, current models behind inertial sensors measurements estimate STP supposing symmetry between limbs. The current use of these tools decreases the accuracy of outdoor walking assessment in individuals poststroke since they generally walk slowly and have an asymmetrical gait pattern. The aim of this study was to assess the accuracy of: (1) an activity monitor placed at the ankle to count the number of steps and (2) an algorithm developed to estimate asymmetric STP in individuals poststroke. MATERIAL/PATIENTS AND METHODS Up to now, eight poststroke individuals who were independent walkers (with or without walking aid) were recruited. Study 1: an activity monitor (Fitbit® One) placed at the non-paretic ankle (not at the hip as recommended) was tested during a walk in a commercial center. The number of steps measured was compared to the one measured by video recording. Study 2: four inertial sensors (OPAL, APDM inc.) were placed bilaterally at the ankle and thigh of the participants and used to assess a new algorithm developed to estimate the right and left STP independently. The estimated STP was compared to those measured simultaneously by a GAITRite® walking mat. For each study, the agreement between data provided by the tools was estimated using Altman-Bland charts. RESULTS Among participants, the range of walking speed (0.4–1.2m/s) and STP asymmetry was large. In study 1, the Fitbit® One error for the number of steps was acceptable (0.14–4.60%), for participants walking faster than 0.4m/s. In study 2, the stride times were precisely estimated but the support times were underestimated (−13 [6.2]%) and the oscillation times overestimated (+30.1 [11.3]%). Spatial parameter estimation was inconsistent. DISCUSSION, CONCLUSION Placing the Fitbit® One at the non-paretic ankle is a suitable method for evaluating the amount of poststroke walking, except for very slow walkers. The algorithm for estimating STP from inertial sensors needs further work before being used in the community.

Activity Trackers Implement Different Behavior Change Techniques for Activity, Sleep, and Sedentary Behaviors. (2017)

Interactive Journal of Medical Research

Abstract

BACKGROUND: Several studies have examined how the implementation of behavior change techniques (BCTs) varies between different activity trackers. However, activity trackers frequently allow tracking of activity, sleep, and sedentary behaviors; yet, it is unknown how the implementation of BCTs differs between these behaviors. OBJECTIVE: The aim of this study was to assess the number and type of BCTs that are implemented by wearable activity trackers (self-monitoring systems) in relation to activity, sleep, and sedentary behaviors and to determine whether the number and type of BCTs differ between behaviors. METHODS: Three self-monitoring systems (Fitbit [Charge HR], Garmin [Vivosmart], and Jawbone [UP3]) were each used for a 1-week period in August 2015. Each self-monitoring system was used by two of the authors (MJD and BM) concurrently. The Coventry, Aberdeen, and London-Refined (CALO-RE) taxonomy was used to assess the implementation of 40 BCTs in relation to activity, sleep, and sedentary behaviors. Discrepancies in ratings were resolved by discussion, and interrater agreement in the number of BCTs implemented was assessed using kappa statistics. RESULTS: Interrater agreement ranged from 0.64 to 1.00. From a possible range of 40 BCTs, the number of BCTs present for activity ranged from 19 (Garmin) to 33 (Jawbone), from 4 (Garmin) to 29 (Jawbone) for sleep, and 0 (Fitbit) to 10 (Garmin) for sedentary behavior. The average number of BCTs implemented was greatest for activity (n=26) and smaller for sleep (n=14) and sedentary behavior (n=6). CONCLUSIONS: The number and type of BCTs implemented varied between each of the systems and between activity, sleep, and sedentary behaviors. This provides an indication of the potential of these systems to change these behaviors, but the long-term effectiveness of these systems to change activity, sleep, and sedentary behaviors remains unknown.

Feasibility study protocol to examine the role of mantra meditation at reducing psychological distress in emergency department staff (2017)

International Journal of Clinical Trials

Abstract

Background: Work in a healthcare setting can affect the psychological wellbeing of healthcare professionals (HCPs). Emotional exhaustion among HCPs can have a significant negative impact on the quality of healthcare provided to patients in terms of increased medical errors and decreased patient satisfaction. There is a need for an effective stress-reducing intervention, such as mantra meditation. This feasibility study will examine the suitability of random controlled trial (RCT) methodology to assess the efficacy of mantra meditation at reducing emotional exhaustion among emergency department (ED) staff. Methods: This is a mixed methods, stratified feasibility study with intent-to-treat protocol, using two study arms (passive control and intervention), the purpose of which is to examine (1) recruitment, retention, and adherence; (2) outcome measures (psychological wellbeing and stress-related biological parameters such as blood pressure, heart rate variability and salivary cortisol); and (3) data management, control, and dissemination prior to conducting a full RCT. Results Eligible ED staff allocated to the intervention group (n = 30) will be taught mantra meditation and discuss prescribed texts (4 x 4 hour session over 6 weeks), as well as engage in 20 minutes of twice-daily mantra meditation practice. Participants in the passive control group (n = 30) will work as usual. Data will be collected pre (T1), post (week 11; T2) and at follow-up (week 19; T3). Conclusions: This study will pave the way for a larger RCT that will investigate mantra meditation as a definitive intervention to reduce emotional exhaustion among ED staff.

Use of a Fitness Tracking Device to Monitor Physical Activity and Sleep Patterns during Hospitalization for Hematopoietic Stem Cell Transplant. Results of a Prospective Observational Study (2017)

Blood

Abstract

Background The prolonged hospitalization often associated with hematopoietic stem cell transplant (HSCT) increases risk of immobility and inactivity-associated complications. There is limited information on the daily activity and sleep patterns of patients undergoing HSCT and their effect on transplant related outcomes, functional recovery and length of hospital stay. We conducted a prospective observational study to determine the feasibility of using a fitness tracking device to monitor the physical activity and sleep patterns of patients undergoing HSCT. Methods Patients undergoing autologous (autoHSCT) or allogeneic (alloHSCT) were consented to wear a Fitbit HR ® activity tracking device. The device was placed on the patients' wrist within 48 hours of admission and was removed on discharge. Patients were followed for 100 days post-HCT. Daily activity parameters measured by the Fitbit HR® device included steps, distance, time spent in physical activity, active calories burned, minimum and maximum heart rate. Sleep parameters included: total sleep duration and number of times patients were awake or restless. All patients were evaluated by a Physical Therapist upon admission. Comparisons between groups were done using the Mann-Whitney test for continuous variables and Fisher's exact test for qualitative variables; correlations were evaluated with Pearson's and Spearman's correlation tests; modeling was done with linear regression for quantitative variable pairs and one-way ANOVA for qualitative and quantitative variable pairs. Results 31 patients were consented, 4 were removed from the study (2 patients withdrew consent; 2 patients were not compliant). The baseline characteristics of 27 subjects with available activity and sleep data are summarized in table 1. There were no statistically significant differences in baseline characteristics between subjects receiving autoHSCT and alloHSCT. All patients undergoing alloHSCT received reduced intensity conditioning regimens. Transplant outcomes and results from activity and sleep pattern measurements are presented in table 2. As expected, longer length of hospitalization, longer time to neutrophil engraftment, infectious complications and readmissions were more common in subjects undergoing alloHSCT. While the number of steps at admission was comparable between both groups, alloHCT subjects had a more intense decrease in their physical activity, manifested by a lower number of steps at nadir (249 vs 697, p=0.03) as well as by a larger percentage decrease in physical activity from admission (89% vs 68%, p=0.02) and a decrease compared to their peak performance during admission (92% vs 82%, p =0.02). Among baseline characteristics, age and presence of cardiovascular risk factors correlated with number of steps at admission (Pearson R =-0.43, p=0.03 and biserial correlation R=0.39, p=0.04, respectively); BMI showed a positive correlation with the decrease in activity from peak to nadir (Pearson R=0.54, p=0.005) and ECOG PS of 1 (vs 0) correlated with the absolute decrease in steps from admission to discharge (Spearman R -0.47, p=0.02). ANOVA showed that infectious complications were associated with changes in activity from peak to nadir (R2=0.18, p=0.03), while linear regression identified an association between time from transplant to discharge and both the decrease in activity from admission and peak to nadir (R2=0.15, p=0.05 and R2=0.16,p=0.04). Conclusions This study demonstrates the feasibility of monitoring activity and sleep patterns in HSCT patients with a commercially available fitness tracking device. Approximately 90% of patients remained compliant with the use of the device. The fitness data obtained showed correlation with known transplant outcomes, and the patterns observed highlighted differences within the patient population. Use of a fitness tracking device in patients undergoing HCT represents an objective method for measuring physical activity during hospitalization, and potentially identifies patients in which interventions such as intensified physical therapy could improve outcomes. Studies comparing the quality of data of Fitbit HR® devices with other instruments such as Actigraph® are underway at our institution. Future studies evaluating the effect physical activity devices have on patient's activity during hospitalization and in the post-transplant recovery period are planned.

Effect of personal activity trackers on weight loss in families enrolled in a comprehensive behavioral family-lifestyle intervention program in the federally qualified health center setting: A randomized controlled trial (2017)

Contemporary Clinical Trials Communications

Abstract

BACKGROUND Childhood obesity continues to be a substantial problem despite major public health efforts, and disproportionately impacts children from low-income families. Digital health tools and consumer technology offer promising opportunities for interventions, but few studies have evaluated how they might be incorporated into existing interventions or used to create new types of interventions. It remains unclear which approaches would be most beneficial for underserved pediatric populations. PURPOSE To describe the design and rationale of a single-center randomized, controlled trial evaluating the effects of personal activity tracker (PAT) use by parents on weight-status improvement in both parents and overweight children enrolled in BodyWorks (BW), a comprehensive behavioral family-lifestyle intervention program (CBFLI), in a primary-care clinic serving a predominantly low-income Latino population. METHODS This study is being conducted in the AltaMed general pediatrics clinic at Children's Hospital Los Angeles. Eligible participants are families (child and adult caregiver) in which the child is between 7 and 18 years of age, has a BMI =85th percentile for age and sex, and has been referred to BW by their AltaMed pediatrician. BW consists of one weekly, two-hour session for 7 weeks. In a given cycle, the program is offered on two separate nights: Monday (Spanish) and Wednesday (English). Families self sort into one of two groups based on language preference. To ensure balanced allocation of language preference groups and prevent in-group cross contamination, block randomization is used to assign whole groups to either the intervention or control arms of the study. The control arm consists of usual care, while the intervention arm adds assigning a Fitbit PAT to the parents and training them in its proper use. Study personnel are blinded to group assignment during the analysis phase. Study outcomes include attendance rate, program completion rate, and changes in weight-status improvement, defined as change in weight and BMI for adults and change in BMI z-score for children. We hypothesize that the intervention arm will have better weight-related outcomes than the control arm. Study completion is anticipated in 2017, after the enrollment of approximately 150 families. CONCLUSIONS The study aim is to evaluate the effects of PATs on weight-related outcomes in overweight children and parents participating in a CBFLI. The results will be important for determining whether wearable devices are an effective addition to weight loss interventions for overweight and obese children.

Virtual weight loss program for African-American breast cancer survivors: Preliminary results. (2017)

Journal of Clinical Oncology

Abstract

Background: Obesity is associated with higher breast cancer recurrence and death, and poorer health and quality of life. African-American (AA) women have the highest prevalence of obesity, obesity-related comorbidities, and breast cancer mortality compared with other racial/ethnic groups. Weight loss after breast cancer diagnosis may lower rates of recurrence and improve fitness, fatigue, and quality of life. Methods: This 6-month randomized controlled trial pilot-tests the use of a Fitbit activity tracker (Fitbit only group) versus Fitbit plus SparkPeople, a free web-based weight loss program (combined group) among 70 AA breast cancer survivors. Paired t-tests assess changes from baseline to 6-months among each participant in primary (weight, body mass index [BMI], percent body fat) and secondary (24-hour caloric intake, daily number of steps, quality of life, self-monitoring strategies, self-efficacy) outcomes. Two-group t-tests assess differences in outcomes between the two groups. Results: Currently, 36 of 46 (78.3%) eligible participants have enrolled and completed baseline assessments. Mean age of participants is 61.7 years (SD 8.7) and mean BMI is 36.9 (SD 7.0). Analyses of the first 25 participants who completed 3-month assessments (Fitbit only N = 12; combined group N = 13) show significant weight loss in both groups; Fitbit only: mean weight change -6.73 pounds, SD 4.61, p textless 0.001; mean BMI change -0.96 kg/m2, SD 0.84, p = 0.002; combined group: mean weight change -5.95 pounds, SD 5.84, p = 0.003; mean BMI change -1.03 kg/m2, SD 0.77, p textless 0.001. All participants significantly increased tracking of diet (Fitbit only p = 0.016; combined group p textless 0.001) and physical activity (Fitbit only p textless 0.001; combined group p = 0.001). Though not significant, combined group participants showed greater increases in self-efficacy for eating healthy and reducing fat and calories, and increases in daily steps (+1308 vs. +285 for Fitbit only group). Preliminary analyses show no statistically significant difference in changes in outcomes from baseline to 3 months between the two groups. Conclusions: Both programs show potential as convenient and efficient weight loss methods for African-American breast cancer survivors. Clinical trial information: NCT02699983.

Comparison Between SenseWear and FitBit Physical Activity Monitors in Chronic Obstructive Pulmonary Disease (2017)

American Journal of Respiratory and Critical Care Medicine

Abstract

Introduction: Higher physical activity levels have been shown to reduce the likelihood of hospital readmission in chronic obstructive pulmonary disease (COPD). Improving physical activity levels may be an effective disease management strategy, thus accurate physical activity monitors (PAMs) are needed.The Sensewear Mini armband (SWM), a relatively expensive PAM, has been shown to provide accurate estimations of energy expenditure,but underestimate step count in patients with COPD. The Fitbit Flex (FB), a relatively inexpensive and more accessible PAM, has been shown to give accurate estimations of step count at a variety of walking speeds, but underestimate energy expenditure in other clinical conditions. The aim of this study was to compare the step count and energy expenditure calculated for FB and SWM in individuals with COPD. Methods: 39 individuals with COPD (mean±SD; Age (yrs): 68±10; FEV (% predicted): 63.2±28.2) wore SWM and FB devices during 1 unrestricted physical activity over a period of five days and an average five day step count and energy expenditure was determined. Results: While a significant correlation between FB and SWM daily step count was observed (R =0.89, p<0.05), SWM daily step count was 2 significantly lower than FB step count (FB (steps/day): 3704±2325 vs. SWM (steps/day): 2486±1895; p<0.01). In spite of this, SWM calculated a significantly higher daily energy expenditure as compared to the FB (FB (kcal): 919±229 vs. SWM (kcal): 1220±471; p<0.01). Conclusions: A strong correlation between FB and SWM devices was found in individuals with COPD, however, caution must be taken when interpreting absolute results for step count and energy expenditure. Slower walking speeds in patients with COPD may affect the algorithms used to calculate step count and energy expenditure in both PAMs. Future work should include a criterion measure for step count and energy expenditure between these PAMs in order to more accurately assess their validity.

Accuracy of Fitbit Activity Trackers During Walking in a Controlled Setting (2017)

Medicine & Science in Sports & Exercise

Abstract

BACKGROUND: Activity trackers are widely used to measure daily physical activity. Many devices have been shown to measure steps more accurately at higher intensities, however, it is also important to determine accuracy at a walking pace. PURPOSE: To assess 6 popular activity trackers at measuring steps while walking on a treadmill. METHODS: Twenty-six college students (Mean±SD; 22.1±3.7yrs; 25.1±4.0kg/m2; 13 male) walked 500 steps at 3mph on a treadmill while wearing 6 different activity trackers (Pedometer, Fitbit Blaze, Charge HR, Alta, Flex, Zip, One). The Charge HR was placed two fingers above the right wrist while the Flex was next to the wrist bone. The Blaze was placed two fingers above the left wrist while the Alta was next to the wrist bone. The Fitbit Zip and the One were aligned with the hip bone on the left and right waistband respectively. A trained researcher using a hand tally counter counted the steps. Missing values were replaced with the mean value for that device. Step counts were correlated between Fitbit devices and the pedometer and tally counter using Pearson correlations. Significance was set at ptextless0.05. Mean bias scores were calculated between the step counts for each device and the tally counter. Mean Absolute Percent Error (MAPE) values were also calculated for each device relative to the tally counter. RESULTS: Fitbit Zip and One were significantly correlated with the tally counter (r=0.50, ptextless0.05; r=0.68, ptextless0.01, respectively) while the other devices were not significantly correlated. Mean bias and MAPE values were as follows: Fitbit Zip and One were within one half percent of actual steps while wrist-worn Fitbits ranged from 7.0-19.7% from actual step counts. CONCLUSION: Consistent with previous research, activity trackers worn at the waist provide the most accurate step counts compared to wrist-worn models. Differences found in wrist-worn models may result in significant over- or underestimation of activity levels when worn for long periods of time.

Accuracy of Smartwatches in Assessing College Students’ Energy Expenditure in Exercise with Different Intensities (2017)

Medicine & Science in Sports & Exercise

Abstract

PURPOSE: Despite the recent prevalence of health wearable devices (e.g., smartwatches), the validity and reliability of smartwatches’ physiological outcomes remain largely unexplored. This study was designed to examine the accuracy of the Microsoft Band (MS), Fitbit Surge HR (FB), TomTom Cardio Watch (TT) and Apple Watch (AW) in assessing energy expenditure (EE) during three exercise sessions with different intensities. METHODS: Participants were 25 college students (12 males; Mage= 23.52, SD = ± 1.04) from Southcenter region in China. They completed three separate 10-minute exercise sessions at light physical activity (LPA, walking at 3.0 km/h), moderate PA (MPA, running at 5.0 km/h), and vigorous PA (VPA, running at 7.0 km/h) conditions on the Hpcosmos treadmill at a highly controlled laboratory. In this study, the AW and TT were placed on the right wrist while the FB and MS were worn on the left wrist. All demographic information (i.e., height, weight, age, gender) was loaded onto each smartwatch prior to testing. EEs of smartwatches were then validated against EE data from a Actigraph wGT3X accelerometer worn on the right side of waist. RESULTS: ANOVAs with repeated measures revealed significant differences between smartwatches for EE, F(9, 16)=45.73, p<0.01, ?2=0.98. When validated against accelerometer, post-hoc comparisons suggested significantly different EE assessments for the FB at LPA and MPA conditions (p<0.01), and for MS and FB at the VPA (p<0.01). Interclass correlations between the accelerometer and smartwatches revealed reliability for EE for the TT at LPA (r=0.65, p<0.01), for the FB, TT and AW at MPA (r=0.53-0.59, p<0.01), and for MS, FB and TT at VPA (r=0.59-0.65, p<0.01). However, only the TT demonstrated excellent agreement with the accelerometer at LPA with coefficient of variation of 2.0%. Additionally, Bland-Altman plots yielded satisfactory precision/no bias for FB and TT measurements against the accelerometers at all conditions (p<0.05), for MS at LPA and VPA (p<0.05), and for AW at VPA (p < 0.05). CONCLUSIONS: Findings suggested EE measurements of smartwatches are far from ideal in terms of validity and reliability. In this study the most valid and reliable measurements were from the TT. Future study may investigate the validity and reliability of smartwatch measurements in everyday life.

A Bit of Fit: Minimalist Intervention in Adolescents Based on a Physical Activity Tracker. (2017)

JMIR mHealth and uHealth

Abstract

BACKGROUND Only 5% of Canadian youth meet the recommended 60 minutes of moderate to vigorous physical activity (MVPA) per day, with leisure time being increasingly allocated to technology usage. Direct-to-consumer mHealth devices that promote physical activity, such as wrist-worn physical activity trackers, have features with potential appeal to youth. OBJECTIVE The primary purpose of this study was to determine whether a minimalist physical activity tracker-based intervention would lead to an increase in physical activity in young adolescents. A secondary aim of this study was to assess change in physical activity across a 7-week intervention, as measured by the tracker. METHODS Using a quasi-experimental crossover design, two groups of 23 young adolescents (aged 13-14 years) were randomly assigned to immediate intervention or delayed intervention. The intervention consisted of wearing a Fitbit-Charge-HR physical activity tracker over a 7-week period. Actical accelerometers were used to measure participants' levels of MVPA before and at the end of intervention periods for each group. Covariates such as age, sex, stage of change for physical activity behavior, and goal commitment were also measured. RESULTS There was an increase in physical activity over the course of the study period, though it was not related to overall physical activity tracker use. An intervention response did, however, occur in a subset of participants. Specifically, exposure to the physical activity tracker was associated with an average daily increase in MVPA by more than 15 minutes (P=.01) among participants who reported being in the action and maintenance stages of behavior change in relation to participation in physical activity. Participants in the precontemplation, contemplation, and preparation stages of behavior change had no change in their level of MVPA (P=.81). CONCLUSIONS These results suggest that physical activity trackers may elicit improved physical activity related behavior in young adolescents demonstrating a readiness to be active. Future studies should seek to investigate if integrating physical activity trackers as part of more intensive interventions leads to greater increases in physical activity across different levels of stages of behavior change and if these changes can be sustained over longer periods of time.

Variable Accuracy of Wearable Heart Rate Monitors during Aerobic Exercise (2017)

Medicine & Science in Sports & Exercise

Abstract

Purpose: Athletes and members of the public increasingly rely upon wearable heart rate (HR) monitors to guide physical activity and training. The accuracy of newer, optically based monitors is unconfirmed. We sought to assess the accuracy of 5 optically based HR monitors during various types of aerobic exercise. Methods: Fifty healthy adult volunteers (mean age 38+12 years, 54% female) completed exercise protocols on a treadmill, stationary bicycle and an elliptical trainer (+/- arm movement). Each participant underwent HR monitoring with an electrocardiogaphic, chest strap monitor (Polar H7), forearm monitor (Scosche Rhythm+), and two randomly assigned wrist-worn HR monitors (Apple Watch, Fitbit Blaze, Garmin Forerunner 235, TomTom Spark Cardio), one on each wrist. For each exercise type, HR was recorded at rest, light, moderate and vigorous intensity. Agreement between HR measurements was assessed using Lin's concordance correlation coefficient (rc). Results: Across all exercise conditions, the chest strap monitor (Polar H7) had the best agreement with ECG (rc=.996) followed by the Apple Watch (rc=.92), the TomTom Spark (rc=.83) and Garmin Forerunner (rc=.81). The Scosche Rhythm+ and Fitbit Blaze were less accurate (rc=.75 and rc=.67, respectively). On treadmill, all devices performed well (rc=.88-.93) except the Fitbit Blaze (rc=.76). While bicycling, only the Garmin, Apple Watch and Scosche Rhythm+ had acceptable agreement (rctextgreater.80). On the elliptical trainer without arm levers, only the Apple Watch was accurate (rc=.94). None of the devices was accurate during elliptical trainer use with arm levers (all rc textless .80). Conclusion: The accuracy of wearable, optically based HR monitors varies with exercise type and is greatest on the treadmill and lowest on elliptical trainer. Electrode-containing chest monitors should be used when accurate HR measurement is imperative.

Personalized Mobile Health Intervention for Health and Weight Loss in Postpartum Women Receiving Women, Infants, and Children Benefit: A Randomized Controlled Pilot Study (2017)

Journal of Women's Health

Abstract

Background: Prepregnancy maternal obesity and excessive weight gain during pregnancy lead to significant morbidities in mothers and their children. Mothers who never return to their prepregnancy weight begin subsequent pregnancies at a greater weight and have a larger propensity for excess gestational weight gain and postpartum weight retention. Methods: In this pilot study, 40 postpartum women credentialed to receive postpartum women, infants, and children (WIC) service were randomized to usual care (“WIC Moms”) or a personalized health intervention delivered via a SmartPhone (“E-Moms”). Assessments, including body weight, vital signs, circumferences, and body composition, were completed at week 0 (6–8 weeks postpartum), week 8, and week 16. Results: Results are presented as change from week 0 at 16. As per the completers analysis, body weight change was not different between the groups (WIC Moms vs. E-Moms; 1.8?±?0.9 vs. -0.1?±?0.9?kg; p?=?0.10), neither was the change in percent body fat (1.7?±?0.6 vs. 0.1%?±?0.6%; p?=?0.90) or waist/hip ratio (-0.01?±?0.01 vs. -0.02?±?0.01?cm; p?=?0.60). However, due to notable variability in intervention adherence as the study progressed, participants were classified post hoc as having low (<40% adherence), medium (40%–70% adherence), or high adherence (>70% adherence). Participants with high intervention adherence (n?=?5) had a significant reduction in body weight (-3.6?±?1.6 vs. 1.8?±?0.9?kg; p?=?0.005) and percent body fat (-2.5?±?1.0 vs. 1.7%?±?0.6%; p?=?0.001) when compared to WIC Moms. Conclusions: Overall, the E-Moms intervention was not able to decrease postpartum weight retention in women receiving WIC benefits compared to usual care received through the current WIC program. However, there is some evidence to suggest improved adherence to the intervention would improve weight management.

Enhancing mHealth Technology in the Patient-Centered Medical Home Environment to Activate Patients With Type 2 Diabetes: A Multisite Feasibility Study Protocol. (2017)

JMIR Research Protocols

Abstract

BACKGROUND The potential of mHealth technologies in the care of patients with diabetes and other chronic conditions has captured the attention of clinicians and researchers. Efforts to date have incorporated a variety of tools and techniques, including Web-based portals, short message service (SMS) text messaging, remote collection of biometric data, electronic coaching, electronic-based health education, secure email communication between visits, and electronic collection of lifestyle and quality-of-life surveys. Each of these tools, used alone or in combination, have demonstrated varying degrees of effectiveness. Some of the more promising results have been demonstrated using regular collection of biometric devices, SMS text messaging, secure email communication with clinical teams, and regular reporting of quality-of-life variables. In this study, we seek to incorporate several of the most promising mHealth capabilities in a patient-centered medical home (PCMH) workflow. OBJECTIVE We aim to address underlying technology needs and gaps related to the use of mHealth technology and the activation of patients living with type 2 diabetes. Stated differently, we enable supporting technologies while seeking to influence patient activation and self-care activities. METHODS This is a multisite phased study, conducted within the US Military Health System, that includes a user-centered design phase and a PCMH-based feasibility trial. In phase 1, we will assess both patient and provider preferences regarding the enhancement of the enabling technology capabilities for type 2 diabetes chronic care management. Phase 2 research will be a single-blinded 12-month feasibility study that incorporates randomization principles. Phase 2 research will seek to improve patient activation and self-care activities through the use of the Mobile Health Care Environment with tailored behavioral messaging. The primary outcome measure is the Patient Activation Measure scores. Secondary outcome measures are Summary of Diabetes Self-care Activities Measure scores, clinical measures, comorbid conditions, health services resource consumption, and technology system usage statistics. RESULTS We have completed phase 1 data collection. Formal analysis of phase 1 data has not been completed. We have obtained institutional review board approval and began phase 1 research in late fall 2016. CONCLUSIONS The study hypotheses suggest that patients can, and will, improve their activation in chronic care management. Improved activation should translate into improved diabetes self-care. Expected benefits of this research to the scientific community and health care services include improved understanding of how to leverage mHealth technology to activate patients living with type 2 diabetes in self-management behaviors. The research will shed light on implementation strategies in integrating mHealth into the clinical workflow of the PCMH setting. TRIAL REGISTRATION ClinicalTrials.gov NCT02949037. https://clinicaltrials.gov/ct2/show/NCT02949037.

OPREVENT2: Design of a multi-institutional intervention for obesity control and prevention for American Indian adults. (2017)

BMC Public Health

Abstract

BACKGROUND: Obesity and other nutrition-related chronic disease rates are high in American Indian (AI) populations, and an urgent need exists to identify evidence-based strategies for prevention and treatment. Multi-level, multi-component (MLMC) interventions are needed, but there are significant knowledge gaps on how to deliver these types of interventions in low-income rural AI communities. METHODS: OPREVENT2 is a MLMC intervention targeting AI adults living in six rural reservations in New Mexico and Wisconsin. Aiming to prevent and reduce obesity in adults by working at multiple levels of the food and physical activity (PA) environments, OPREVENT2 focuses on evidence-based strategies known to increase access to, demand for, and consumption of healthier foods and beverages, and increase worksite and home-based opportunities for PA. OPREVENT2 works to create systems-level change by partnering with tribal stakeholders, multiple levels of the food and PA environment (food stores, worksites, schools), and the social environment (children as change agents, families, social media). Extensive evaluation will be conducted at each level of the intervention to assess effectiveness via process and impact measures. DISCUSSION: Novel aspects of OPREVENT2 include: active engagement with stakeholders at many levels (policy, institutional, and at multiple levels of the food and PA system); use of community-based strategies to engage policymakers and other key stakeholders (community workshops, action committees); emphasis on both the built environment (intervening with retail food sources) and the social environment. This paper describes the design of the intervention and the evaluation plan of the OPREVENT2.

Fitbit Charge HR Wireless Heart Rate Monitor: Validation Study Conducted Under Free-Living Conditions (2017)

JMIR mHealth and uHealth

Abstract

Background: Many modern smart watches and activity trackers feature an optical sensor that estimates the wearer's heart rate. Recent studies have evaluated the performance of these consumer devices in the laboratory. Objective: The objective of our study was to examine the accuracy and sensitivity of a common wrist-worn tracker device in measuring heart rates and detecting 1-min bouts of moderate to vigorous physical activity (MVPA) under free-living conditions. Methods: Ten healthy volunteers were recruited from a large university in Singapore to participate in a limited field test, followed by a month of continuous data collection. During the field test, each participant would wear one Fitbit Charge HR activity tracker and one Polar H6 heart rate monitor. Fitbit measures were accessed at 1-min intervals, while Polar readings were available for 10-s intervals. We derived intraclass correlation coefficients (ICCs) for individual participants comparing heart rate estimates. We applied Centers for Disease Control and Prevention heart rate zone cut-offs to ascertain the sensitivity and specificity of Fitbit in identifying 1-min epochs falling into MVPA heart rate zone. Results: We collected paired heart rate data for 2509 1-min epochs in 10 individuals under free-living conditions of 3 to 6 hours. The overall ICC comparing 1-min Fitbit measures with average 10-s Polar H6 measures for the same epoch was .83 (95% CI .63-.91). On average, the Fitbit tracker underestimated heart rate measures by −5.96 bpm (standard error, SE=0.18). At the low intensity heart rate zone, the underestimate was smaller at −4.22 bpm (SE=0.15). This underestimate grew to −16.2 bpm (SE=0.74) in the MVPA heart rate zone. Fitbit devices detected 52.9% (192/363) of MVPA heart rate zone epochs correctly. Positive and negative predictive values were 86.1% (192/223) and 92.52% (2115/2286), respectively. During subsequent 1 month of continuous data collection (270 person-days), only 3.9% of 1-min epochs could be categorized as MVPA according to heart rate zones. This measure was affected by decreasing wear time and adherence over the period of follow-up. Conclusions: Under free-living conditions, Fitbit trackers are affected by significant systematic errors. Improvements in tracker accuracy and sensitivity when measuring MVPA are required before they can be considered for use in the context of exercise prescription to promote better health. [JMIR Mhealth Uhealth 2017;5(10):e157]

Reversal of type 2 diabetes in youth who adhere to a very-low-energy diet: a pilot study (2017)

Diabetologia

Abstract

Aims/hypothesis: The aim of the study was to investigate whether a very-low-energy diet (VLED) is a feasible and acceptable treatment option for type 2 diabetes in children and adolescents, and whether adherence can lead to rapid weight loss, reversal of type 2 diabetes and reduced liver fat as seen in adult studies. Methods: Eight participants with type 2 diabetes and obesity, aged 7–16 years, non-medicated (n?=?1) or treated with metformin (n?=?7) and in some cases insulin (n?=?3), followed a VLED (textless3360 kJ/day) for 8 weeks, then transitioned to a hypocaloric diet (~6300 kJ/day) that they followed to 34 weeks. HbA1c, fasting glucose and 2 h post-glucose load plasma glucose (2hG) were determined from fasting blood and an OGTT. Liver fat concentration was quantified using proton magnetic resonance spectroscopy. Adherence was defined as =5% weight loss during the 8 week VLED. Results: Adherers (n?=?5) and non-adherers (n?=?3) had median weight loss of 7.5% and 0.5%, respectively, at 8 weeks. Overall, HbA1c (mean [SE] 8.1% [0.7%] to 6.6% [0.5%]; p?=?0.004) and 2hG (15.6 [1.6] mmol/l to 11.3 [1.0] mmol/l; p?=?0.009) were significantly reduced at 8 weeks compared with baseline. Liver fat was also significantly reduced from baseline (14.7% [2.2%]) to 8 weeks (5.8% [1.7%]; p?=?0.001). Only three out of eight participants met non-alcoholic fatty liver disease (NAFLD) criteria (=5.5%) at 8 weeks, compared with eight out of eight at baseline. The three participants on insulin therapy at baseline were able to cease therapy during the 8 week VLED. At 34 weeks, adherers (n?=?5) achieved 12.3% weight loss, none met NAFLD criteria and four did not meet American Diabetes Association criteria for type 2 diabetes. Conclusions/interpretation: A VLED appears to be a feasible treatment option for some youth with type 2 diabetes on metformin therapy. Youth who agree to participate and adhere to a VLED achieve rapid weight loss, dramatic reductions in liver fat and reversal of type 2 diabetes. This highlights the capacity of a VLED to be used as a first-line treatment option in newly diagnosed youth. A larger trial with a control group and longer follow-up will be required to encourage a change in standard treatment.

Meaningful weight loss in obese postmenopausal women: A pilot study of high-intensity interval training and wearable technology. (2017)

Menopause

Abstract

OBJECTIVE Postmenopausal women cite lack of time as their primary barrier to exercise and related weight control behaviors. The purpose of this study was to address this barrier via testing the feasibility and initial outcomes of a short-duration/high-intensity interval training (HIIT)-based weight loss program among obese postmenopausal women, with descriptive comparison to an endurance-focused exercise program. METHODS A 16-week behavioral program incorporated (1) HIIT to limit time necessary to produce health benefits of exercise, and (2) wearable activity sensors to facilitate self-monitoring exercise. Participants (n = 11; MAge = 59 ± 5.33; MBMI = 32.0 ± 2.53 kg/m) were randomly assigned to HIIT or endurance exercise. Both groups followed a calorie-restricted diet, attended monthly in-person meetings, weekly weigh-ins and electronic check-ins to review behavioral skills, and monitored their exercise with a Fitbit ChargeHR. Adherence to exercise programs, assessed with the Fitbit sensor, was used to determine feasibility. RESULTS Participants in the HIIT group (6/6) adhered to their program, whereas 3 of 5 adhered to the Endurance program. Participants in the HIIT group lost twice as much weight as those in the Endurance group (8.7% vs 4.3% of initial body weight), and lost an additional 6 in. of body mass. In addition, only the HIIT group significantly changed fat mass, body mass index, and fat-free mass (Ps textless 0.04). CONCLUSIONS These findings support the feasibility and potential effectiveness of HIIT for weight loss and body composition changes in obese postmenopausal women, and indicate that additional investigation of this approach is warranted to reduce postmenopausal chronic disease risk.

Regular exercise improves the well-being of parents of children with cancer (2017)

Pediatric Blood & Cancer

Abstract

Aim: Parents of children with cancer describe impaired physical and social functioning, sleep disturbance and poor mental health. Exercise-related interventions impact positively on these quality of life domains, but have not been examined in this population. The aim of this longitudinal pilot study was to explore the feasibility of a 12-week pedometer-monitored walking intervention among parents of children with cancer, assessing adherence to a set activity target of 70,000 steps per week, and to explore the benefits of physical activity on mental and physical health. Methods: Parents were provided with a pedometer and requested to achieve a daily step count of 10,000 steps per day for 12 weeks. Mood, well-being and psychological distress were examined using validated questionnaires (Profile of Mood States 2nd edition [POMS-2], Distress Thermometer for Parents [DT-P] and Depression Anxiety Stress Scales [DASS-42]) at baseline, midpoint (6 weeks) and endpoint (12 weeks) to identify changes in these domains with increased activity. Results: Fifteen parents were recruited. The majority increased their counts during the first 4 weeks of the study and maintained this to week 8 (n = 12). Time-dependent improvements were identified in the following psychometric test outcomes at week 12: DT-P score (likelihood ratio test [LRT] P = 0.02), POMS-2 total mood disturbance (LRT P = 0.03), fatigue inertia (LRT P = 0.009), tension anxiety (LRT P = 0.007) and vigour activity (LRT P = 0.001). Conclusions: Mental health benefits of a pedometer-based exercise intervention for parents of children with cancer were identified. Such programs should be included in a holistic approach to improve the psychological outcomes of parents whose children are receiving treatment for cancer.

Physical activity among children: objective measurements using Fitbit One® and ActiGraph (2017)

BMC Research Notes

Abstract

BACKGROUND Self-quantification of health parameters is becoming more popular; thus, the validity of the devices requires assessments. The aim of this study was to evaluate the validity of Fitbit One step counts (Fitbit Inc., San Francisco, CA, USA) against Actigraph wActisleep-BT step counts (ActiGraph, LLC, Pensacola, FL, USA) for measuring habitual physical activity among children. DESIGN The study was implemented as a cross-sectional experimental design in which participants carried two waist-worn activity monitors for five consecutive days. METHODS The participants were chosen with a purposive sampling from three fourth grade classes (9-10 year olds) in two comprehensive schools. Altogether, there were 34 participants in the study. From these, eight participants were excluded from the analysis due to erroneous data. Primary outcome measures for step counts were Fitbit One and Actigraph wActisleep-BT. The supporting outcome measures were based on activity diaries and initial information sheets. Classical Bland-Altman plots were used for reporting the results. RESULTS The average per-participant daily difference between the step counts from the two devices was 1937. The range was [116, 5052]. Fitbit One gave higher step counts for all but the least active participant. According to a Bland-Altman plot, the hourly step counts had a relative large mean bias across participants (161 step counts). The differences were partially explained by activity intensity: higher intensity denoted higher differences, and light intensity denoted lower differences. CONCLUSIONS Fitbit One step counts are comparable to Actigraph step counts in a sample of 9-10-year-old children engaged in habitual physical activity in sedentary and light physical activity intensities. However, in moderate-to-vigorous physical activity, Fitbit One gives higher step counts when compared to Actigraph.

Zero Cadence as a Proxy Indicator of Sitting Behaviors in Objective Monitoring (2017)

Medicine & Science in Sports & Exercise

Abstract

Consistent evidence has been presented regarding the potentially deleterious health consequences of prolonged sitting time independent of physical activity level. Zero cadence (0 steps/min) determined using accelerometers may be a suitable proxy measure of sitting behaviors since theoretically no steps should be registered in this position. PURPOSE: The purpose of the current study was to determine the capability of accelerometer-determined zero cadence to predict seated activities of daily living. METHODS: A total of 80 young adults (mean age 29.8±6.7 years), 10 men and 10 women representing each 5-year age-group category between 21-40 years (21-25, 26-30, 31-35, 36-40) performed lab-based simulated daily living activities (i.e., seated rest, watching a movie, computer work, folding laundry while standing, vacuuming, stair stepping and a self-paced over-ground walk) while concurrently wearing multiple (n=7) research-grade (e.g., ActiGraph, ActivPAL, ActiCal and StepWatch) and consumer-grade (e.g., Fitbit One and Vivofit2) accelerometers on manufacturer-indicated locations (i.e., hip, wrist, thigh or ankle). Receiver Operating Characteristic (ROC) analyses were conducted to examine the accuracy of zero cadence for classifying sitting behaviors (e.g., seated rest, watching a movie and computer work) among the performed activities. Sensitivity, specificity, accuracy and area under the ROC curve (AUC) values were compared for each device. RESULTS: All devices demonstrated the capability of using zero cadence to determine sitting activities with high combined sensitivity and specificity (e.g., ranges between 75-98%). AUC values of all devices were above 0.8 (p<.001) indicating good or excellent discrimination ability of zero cadence. Vivofit2 and wrist-worn ActiGraph provided the highest (93%) and lowest (75%) accuracy, respectively. CONCLUSION: In agreement with zero cadence, both research- and consumer-grade activity monitors provided valid capacity for discriminating seated activities from common daily living activities. Based on this preliminary analysis it appears that accumulated time spent at zero cadence obtained by activity monitors can be used as a proxy indicator of time spent sitting.

Validity Of The Diagnostix 2100 And Fitbit Charge HR In Assessing Heart Rate (2017)

Medicine & Science in Sports & Exercise

Abstract

The importance of exercise intensity is an important factor when maximizing the health benefits that result from exercise. New innovations in consumer based activity trackers and electronic healthcare monitoring devices have been able to quantify the level of exercise intensity based on heart rate (HR) values. However, there is limited research assessing the validity of these devices. PURPOSE: To assess the validity of the Fitbit Charge HR (FB) and ACSC Diagnostix 2100 Fingertip Pulse Oximeter (PO) in assessing heart rate during exercise in comparison to the heart rate from an electrocardiogram (EKG). METHODS: Healthy college students (n=30, 18 females) performed 1 min of standing rest, 10 min of the standard Bruce Protocol test on a treadmill, and a 2 min cool-down walk. Each participant simultaneously wore the FB on the right wrist, the PO on the right index finger, and had 10 electrodes placed on their chest for the 12-lead EKG. HR was recorded from each device every minute. Absolute differences between the 3 HR monitors were compared using repeated measures ANOVA. Pearson r correlation coefficients and Standard Error of Estimate (SEE) were calculated to determine the relationships between each HR monitor vs. EKG. RESULTS: Repeated measures ANOVA indicated a significant difference in HR between the 3 monitors, F(2,58)=16.876, p<0.001. Post hoc tests indicated a significant difference between EKG and FB (132.1 ± 13 vs. 120.5 ± 13.7 bpm, p<0.001), and EKG and PO (132.1 ± 13 vs. 121.9 ± 15.5 bpm, p<0.001). The correlation between the EKG and FB was r=0.64, p<0.001, SEE 10.2 bpm, whereas the correlation between EKG and PO was r=0.63, p<0.001, SEE 10.27 bpm. CONCLUSIONS: Large absolute differences and modest correlation values do not indicate a strong agreement between the FB or PO vs. EKG. Therefore, the extent of each device’s validity to monitor HR is questionable when compared to the gold standard EKG.

A Trial of Financial and Social Incentives to Increase Older Adults' Walking (2017)

American Journal of Preventive Medicine

Abstract

INTRODUCTION Despite evidence that regular physical activity confers health benefits, physical activity rates among older adults remain low. Both personal and social goals may enhance older adults' motivation to become active. This study tested the effects of financial incentives, donations to charity, and the combined effects of both interventions on older adults' uptake and retention of increased levels of walking. STUDY DESIGN RCT comparing three interventions to control. Data collection occurred from 2012 to 2013. Analyses were conducted in 2013–2016. PARTICIPANTS Ninety-four adults aged =65 years from Philadelphia-area retirement communities. INTERVENTION All participants received digital pedometers, walking goals of a 50% increase in daily steps, and weekly feedback on goal attainment. Participants were randomized to one of four groups: (1) Control: received weekly feedback only; (2) Financial Incentives: received payment of $20 each week walking goals were met; (3) Social Goals: received donation of $20 to a charity of choice each week walking goals were met; and (4) Combined: received $20 each week walking goals were met that could be received by participant, donated to a charity of choice, or divided between the participant and charity. MAIN OUTCOME MEASURES Mean proportion of days walking goals were met during the 16-week intervention and 4-week follow-up period. RESULTS After adjusting for baseline walking, the proportion of days step goals were met during the 16-week intervention period was higher in all intervention groups versus controls (relative risk, 3.71; 95% CI=1.37, 10.01). During the 4-week follow up period, the proportion of days step goals were met did not differ in intervention groups compared to control (relative risk, 2.91; 95% CI=0.62, 13.64). CONCLUSIONS Incentive schemes that use donations to a charity of choice, personal financial incentives, or a combination of the two can each increase older adults' initial uptake of increased levels of walking.

Randomized controlled trial of increasing physical activity on objectively measured and self-reported cognitive functioning among breast cancer survivors: The memory & motion study (2017)

Cancer

Abstract

BACKGROUND Increasing physical activity can improve cognition in healthy and cognitively impaired adults; however, the benefits for cancer survivors are unknown. The current study examined a 12-week physical activity intervention, compared with a control condition, on objective and self-reported cognition among breast cancer survivors. METHODS Sedentary breast cancer survivors were randomized to an exercise arm (n = 43) or a control arm (n = 44). At baseline and at 12 weeks, objective cognition was measured with the National Institutes of Health Cognitive Toolbox, and self-reported cognition using the Patient-Reported Outcomes Measurement Information System scales. Linear mixed-effects regression models tested intervention effects for changes in cognition scores. RESULTS On average, participants (n = 87) were aged 57 years (standard deviation, 10.4 years) and were 2.5 years (standard deviation, 1.3 years) post surgery. Scores on the Oral Symbol Digit subscale (a measure of processing speed) evidenced differential improvement in the exercise arm versus the control arm (b = 2.01; P < .05). The between-group differences in improvement on self-reported cognition were not statistically significant but were suggestive of potential group differences. Time since surgery moderated the correlation, and participants who were ≤2 years post surgery had a significantly greater improvement in Oral Symbol Digit score (exercise vs control (b = 4.00; P < .01), but no significant improvement was observed in patients who were >2 years postsurgery (b = −1.19; P = .40). A significant dose response was observed with greater increased physical activity associated with objective and self-reported cognition in the exercise arm. CONCLUSIONS The exercise intervention significantly improved processing speed, but only among those who had been diagnosed with breast cancer within the past 2 years. Slowed processing speed can have substantial implications for independent functioning, supporting the potential importance of early implementation of an exercise intervention among patients with breast cancer. Cancer 2017. © 2017 American Cancer Society.

Tea, talk and technology: patient and public involvement to improve connected health ‘wearables' research in dementia (2017)

Research Involvement and Engagement

Abstract

Background Increasingly, researchers are recognising the potential for connected health devices, including smartphones and smartwatches, to generate high resolution data about patterns of daily activity and health outcomes. One aim of the Dementias Platform UK (DPUK) project is to provide researchers with a secure means to collect, collate and link data generated by such devices, thereby accelerating this type of research in the field of dementia. We aimed to involve members of the public in discussions about the acceptability and feasibility of different devices and research designs to inform the development of a device pool, software platform and written guidance to support future studies. Methods Over 30 people attended a series of interactive workshops, drop-in sessions and meetings in Greater Manchester. This included people living with dementia and cognitive impairments, carers and people without memory problems. Discussions were tailored to suit different audiences and focused on the feasibility and acceptability of a range of different wearable devices and research designs. We also invited volunteers to borrow a device to test at home, enabling further insights from hands-on interactions with devices. Results Discussions revealed that people were supportive of connected health dementia research in principle, provided they gave informed consent and that devices were discreet, comfortable and easy to use. Moreover, they recommended technical support and regular feedback on study progress to encourage ongoing participation. Conclusion By using a range of discussion-based and practical activities, we found it was feasible to involve people affected by dementia and use their insights to shape the development of a software platform and device pool to support future connected health dementia research. We recommend that researchers planning such studies in future pay adequate attention to designing suitable participant information, technical support and mechanisms of providing study progress updates to support sustained engagement from participants.

Determinants for Sustained Use of an Activity Tracker: Observational Study (2017)

JMIR mHealth and uHealth

Abstract

Background: A lack of physical activity is considered to cause 6% of deaths globally. Feedback from wearables such as activity trackers has the potential to encourage daily physical activity. To date, little research is available on the natural development of adherence to activity trackers or on potential factors that predict which users manage to keep using their activity tracker during the first year (and thereby increasing the chance of healthy behavior change) and which users discontinue using their trackers after a short time. Objective: The aim of this study was to identify the determinants for sustained use in the first year after purchase. Specifically, we look at the relative importance of demographic and socioeconomic, psychological, health-related, goal-related, technological, user experience–related, and social predictors of feedback device use. Furthermore, this study tests the effect of these predictors on physical activity. Methods: A total of 711 participants from four urban areas in France received an activity tracker (Fitbit Zip) and gave permission to use their logged data. Participants filled out three Web-based questionnaires: at start, after 98 days, and after 232 days to measure the aforementioned determinants. Furthermore, for each participant, we collected activity data tracked by their Fitbit tracker for 320 days. We determined the relative importance of all included predictors by using Random Forest, a machine learning analysis technique. Results: The data showed a slow exponential decay in Fitbit use, with 73.9% (526/711) of participants still tracking after 100 days and 16.0% (114/711) of participants tracking after 320 days. On average, participants used the tracker for 129 days. Most important reasons to quit tracking were technical issues such as empty batteries and broken trackers or lost trackers (21.5% of all Q3 respondents, 130/601). Random Forest analysis of predictors revealed that the most influential determinants were age, user experience–related factors, mobile phone type, household type, perceived effect of the Fitbit tracker, and goal-related factors. We explore the role of those predictors that show meaningful differences in the number of days the tracker was worn. Conclusions: This study offers an overview of the natural development of the use of an activity tracker, as well as the relative importance of a range of determinants from literature. Decay is exponential but slower than may be expected from existing literature. Many factors have a small contribution to sustained use. The most important determinants are technical condition, age, user experience, and goal-related factors. This finding suggests that activity tracking is potentially beneficial for a broad range of target groups, but more attention should be paid to technical and user experience–related aspects of activity trackers. [JMIR Mhealth Uhealth 2017;5(10):e164]

A Multi-Level, Mobile-Enabled Intervention to Promote Physical Activity in Older Adults in the Primary Care Setting (iCanFit 2.0): Protocol for a Cluster Randomized Controlled Trial (2017)

JMIR Research Protocols

Abstract

Background: Most older adults do not adhere to the US Centers for Disease Control physical activity guidelines; their physical inactivity contributes to overweight and multiple chronic conditions. An urgent need exists for effective physical activity-promotion programs for the large number of older adults in the United States. Objective: This study presents the development of the intervention and trial protocol of iCanFit 2.0, a multi-level, mobile-enabled, physical activity-promotion program developed for overweight older adults in primary care settings. Methods: The iCanFit 2.0 program was developed based on our prior mHealth intervention programs, qualitative interviews with older patients in a primary care clinic, and iterative discussions with key stakeholders. We will test the efficacy of iCanFit 2.0 through a cluster randomized controlled trial in six pairs of primary care clinics. Results: The proposed protocol received a high score in a National Institutes of Health review, but was not funded due to limited funding sources. We are seeking other funding sources to conduct the project. Conclusions: The iCanFit 2.0 program is one of the first multi-level, mobile-enabled, physical activity-promotion programs for older adults in a primary care setting. The development process has actively involved older patients and other key stakeholders. The patients, primary care providers, health coaches, and family and friends were engaged in the program using a low-cost, off-the-shelf mobile tool. Such low-cost, multi-level programs can potentially address the high prevalence of physical inactivity in older adults. [JMIR Res Protoc 2017;6(9):e183]

MyAirCoach: the use of home-monitoring and mHealth systems to predict deterioration in asthma control and the occurrence of asthma exacerbations; study protocol of an observational study. (2017)

BMJ Open

Abstract

INTRODUCTION Asthma is a variable lung condition whereby patients experience periods of controlled and uncontrolled asthma symptoms. Patients who experience prolonged periods of uncontrolled asthma have a higher incidence of exacerbations and increased morbidity and mortality rates. The ability to determine and to predict levels of asthma control and the occurrence of exacerbations is crucial in asthma management. Therefore, we aimed to determine to what extent physiological, behavioural and environmental data, obtained by mobile healthcare (mHealth) and home-monitoring sensors, as well as patient characteristics, can be used to predict episodes of uncontrolled asthma and the onset of asthma exacerbations. METHODS AND ANALYSIS In an 1-year observational study, patients will be provided with mHealth and home-monitoring systems to record daily measurements for the first-month (phase I) and weekly measurements during a follow-up period of 11 months (phase II). Our study population consists of 150 patients, aged =18 years, with a clinician's diagnosis of asthma, currently on controller medication, with uncontrolled asthma and/or minimally one exacerbation in the past 12 months. They will be enrolled over three participating centres, including Leiden, London and Manchester. Our main outcomes are the association between physiological, behavioural and environmental data and (1) the loss of asthma control and (2) the occurrence of asthma exacerbations. ETHICS This study was approved by the Medical Ethics Committee of the Leiden University Medical Center in the Netherlands and by the NHS ethics service in the UK. TRIAL REGISTRATION NUMBER NCT02774772.

Comparison of four Fitbit and Jawbone activity monitors with a research-grade ActiGraph accelerometer for estimating physical activity and energy expenditure (2017)

British Journal of Sports Medicine

Abstract

Background/aim Consumer-based physical activity (PA) monitors have become popular tools to track PA behaviours. Currently, little is known about the validity of the measurements provided by consumer monitors. We aimed to compare measures of steps, energy expenditure (EE) and active minutes of four consumer monitors with one research-grade accelerometer within a semistructured protocol. Methods Thirty men and women (18–80 years old) wore Fitbit One (worn at the waist), Fitbit Zip (waist), Fitbit Flex (wrist), Jawbone UP24 (wrist) and one waist-worn research-grade accelerometer (ActiGraph) while participating in an 80?min protocol. A validated EE prediction equation and active minute cut-points were applied to ActiGraph data. Criterion measures were assessed using direct observation (step count) and portable metabolic analyser (EE, active minutes). A repeated measures analysis of variance (ANOVA) was used to compare differences between consumer monitors, ActiGraph, and criterion measures. Similarly, a repeated measures ANOVA was applied to a subgroup of subjects who didn't cycle. Results Participants took 3321±571 steps, had 28±6?active min and expended 294±56 kcal based on criterion measures. Comparatively, all monitors underestimated steps and EE by 13%–32% (ptextless0.01); additionally the Fitbit Flex, UP24, and ActiGraph underestimated active minutes by 35%–65% (ptextless0.05). Underestimations of PA and EE variables were found to be similar in the subgroup analysis. Conclusion Consumer monitors had similar accuracy for PA assessment as the ActiGraph, which suggests that consumer monitors may serve to track personal PA behaviours and EE. However, due to discrepancies among monitors, individuals should be cautious when comparing relative and absolute differences in PA values obtained using different monitors.

Wrist-Worn Activity Monitoring Devices Overestimate Sleep Duration and Efficiency in Healthy Adults (2017)

Sleep

Abstract

ntroduction: The use of activity monitors for sleep measurement purposes has increased in research and consumer settings. However, validation of such monitors is lacking. This study examined agreement on total sleep time (TST) and sleep efficiency (SE) between polysomnography and three activity monitors - Actiwatch Spectrum Pro (ACT), Fitbit One (FB) and Jawbone UP2 (JB). Differences between polysomnography and each activity monitor, and differences between research-grade ACT and commercial devices FB and JB, were examined. Methods: Twenty-two healthy adults (Mage =29.3, SDage=11.4) had one night of sleep measured by polysomnography and each activity monitor simultaneously in a laboratory. Minute-by-minute data were extracted and compared. Wilcoxon signed-ranks tests assessed statistical differences between measures, and Bland-Altman analyses examined clinically meaningful differences between measures, using cut-offs of ±30 minutes for TST and ±5% for SE. Results: Compared to polysomnography, all activity monitors significantly overestimated TST and SE. Differences between polysomnography and each monitor were also clinically meaningful, as Bland-Altman upper and lower limits of agreement for TST exceeded clinical cut-offs for ACT (-64.7min-+166.1min), FB (-66.1min-+189.2min), and JB (-103.6min-+186.2min). Similarly, upper and lower limits of agreement for SE exceeded clinical cut-offs for ACT (-13.0%-+34.0%), FB (-13.1%-+38.5%), and JB (-20.7%-+37.9%). Compared to ACT, only FB significantly overestimated TST and SE. However, differences between ACT and each of FB and JB were clinically meaningful. For FB, Bland-Altman upper limits of agreement exceeded clinical cut-offs for TST (-18.7min-+40.2min) and SE (-3.8%-+8.1%). For JB, upper and lower limits of agreement exceeded clinical cut-offs for TST (-100.4min-+79.3min) and SE (-20.1%-+16.0%). Agreement between devices decreased as TST and SE decreased. All monitors demonstrated poor specificity (18.8–35.6%), but high sensitivity (94.2–99.2%). Conclusion: Results suggest these models of ACT, FB, and JB cannot be used interchangeably with polysomnography. When activity monitors must be used, such as in field settings, FB and JB cannot replace research-grade ACT. Overall, users should account for each monitor's potential to overestimate or underestimate TST and SE to an unacceptable degree. Future research should examine within-subject variability over time to determine whether monitors can be used to track long-term sleep patterns.

Ka-HOLO Project: a protocol for a randomized controlled trial of a native cultural dance program for cardiovascular disease prevention in Native Hawaiians (2017)

BMC Public Health

Abstract

Background As a major risk factor for cardiovascular and cerebrovascular disease (CVD), hypertension affects 33% of U.S. adults. Relative to other US races and ethnicities, Native Hawaiians have a high prevalence of hypertension and are 3 to 4 times more likely to have CVD. Effective, culturally-relevant interventions are needed to address CVD risk in this population. Investigators of the Ka-HOLO Project developed a study design to test the efficacy of an intervention that uses hula, a traditional Hawaiian dance, to increase physical activity and reduce CVD risk. Methods A 2-arm randomized controlled trial with a wait-list control design will be implemented to test a 6-month intervention based on hula to manage blood pressure and reduce CVD risk in 250 adult Native Hawaiians with diagnosed hypertension. Half of the sample will be randomized to each arm, stratified across multiple study sites. Primary outcomes are reduction in systolic blood pressure and improvement in CVD risk as measured by the Framingham Risk Score. Other psychosocial and sociocultural measures will be included to determine mediators of intervention effects on primary outcomes. Assessments will be conducted at baseline, 3 months, and 6 months for all participants, and at 12 months for intervention participants only. Discussion This trial will elucidate the efficacy of a novel hypertension management program designed to reduce CVD risk in an indigenous population by using a cultural dance form as its physical activity component. The results of this culturally-based intervention will have implications for other indigenous populations globally and will offer a sustainable, culturally-relevant means of addressing CVD disparities.

Examination of a Wearable Activity Tracker to Assess Children’s Physical Activity (2017)

Medicine & Science in Sports & Exercise

Abstract

ewer wearable activity monitors have the capability to measure heart rate (HR) from the user’s wrist using an optical blood flow sensor (i.e., photoplethysmography techniques), and none of the previous studies examining children’s free-living activity utilizing a newly developed wearable activity tracker which includes built-in HR sensor. PURPOSE: To examine PA intensity using the wearable HR monitor. METHODS: a total of 43 children (girls = 18, boys = 25), aged 8 - 12 years, participated in the study. Participants were asked to wear the Fitbit Charge HR (FHR) on their left (FHRL), right wrist (FHRR) and Polar HR (PHR) monitor on their chest, while completing 11 different activities lasted 48 mins, monitoring their HR. Oxygen consumption was measured throughout the routine with the Cosmed K4B2 metabolic analyzer. 10 min average values of the resting metabolic rate were used as one metabolic equivalent of task (MET) to categorize children’s PA intensity. Activity intensities using HR from the FHR were classified by calculating children’s target HR between 50 and 75% of their maximum HR as moderate intensity activity and greater than 75% of their maximum HR as a vigorous intensity activity. An estimate of children’s maximum age-related HR was obtained from the 208 - 0.7 (age) equation. Each intensity classified by the MET determined by HR was compared to the intensity classified by the MET determined by resting metabolic rate (i.e., criterion measure) to examine the measurement agreement. McNemar’s test was used to examine the measurement agreement for paired intensity frequency. RESULTS: After setting PA intensity classification with 50% and 75% of HR, the frequency determined by measured MET from the Cosmed K4B2 were 1002, 407, and 120 for light, moderate, and vigorous intensity. The frequency determined by HR monitor and FHR were as follow: 1076, 394, and 58 by PHR, 1393, 127, and 9 by FHRL, 1393, 129, and 7 by FHRR. The values of weighted Kappa statistics from the McNemar’s test were 0.40 (95% CL: 0.36-0.44) for the intensity by PHR, 0.21 (95% CL: 0.18-0.25) for the intensity by FHRL, 0.19 (95% CL: 0.16-0.23) for the intensity by FHRR. CONCLUSIONS: The agreement of PA intensity classified by the wearable activity tracker to the intensity classified the metabolic analyzer showed fair agreement.

Physical Activities in Patients with β-Thalassemia As Measured By Fitbit in Association with Patient-Reported Outcomes (2017)

Blood

Abstract

Background: Tiredness, weakness, and shortness of breath are the most commonly reported symptoms in patients with β-thalassemia, and they may also adversely affect the quality of life (QoL) of patients. We aimed to explore the associations between β-thalassemia symptom severity and QoL in relation to physical activities, as tracked by Fitbit wearable electronic devices (Fitbit, Inc., San Francisco, CA). Method: Adult patients with β-thalassemia under routine clinical care were enrolled in a prospective observational study. All patients completed Short Form 36 Health Survey version 2 (SF-36v2) and Functional Assessment of Cancer Therapy (FACT)-Anemia (An) questionnaires at baseline and then once every 3 weeks using a hand-held electronic device. Patients with transfusion-dependent (TD) β-thalassemia also completed the Transfusion-dependent QoL (TranQoL) questionnaire, a disease-specific QoL instrument developed for TD patients, with the same scheduling. Transfusion dependent was defined as receiving ≥ 6 red blood cell units in the 24 weeks prior to study entry and no transfusion-free period for ≥ 35 days during that period. Patients with non-transfusion-dependent (NTD) β-thalassemia were instructed to complete the NTDT-PROtextcopyright daily, a symptom diary developed specifically for patients with NTD β-thalassemia with 2 domains: tiredness/weakness (T/W; score range: 0-40) and shortness of breath (SoB; score range: 0-20) with higher scores indicating more severe symptoms. Physical activities were passively tracked by a Fitbit wearable electronic device in terms of steps taken and floors climbed. This analysis examined the association between physical activities at week 1 of the study with QoL measurements and β-thalassemia symptom severity at baseline. For this analysis, average daily steps taken and floors climbed were grouped into quartile categories for those patients who had 4 or more days of Fitbit data during week 1. Results: A total of 102 patients with β-thalassemia (52 TD and 50 NTD) were enrolled; the mean age of patients was 31.2 years and 70 (68.6%) were females. A total of 91 patients had Fitbit data and, on average, they took 10,811 steps (median: 9,656) and climbed 16.0 floors (median: 10.0) daily. There were no associations observed between the number of steps taken and QoL measurements and β-thalassemia symptom severity. The floors climbed, however, were associated with SF-36v2 Physical Functioning and General Health domains. Patients in the lowest quartile for floors climbed had statistically significant lower Physical Functioning and General Health scores compared with patients in the highest quartile for floors climbed (47.0 vs 52.3; P textless 0.01; and 42.1 vs 46.3; P= 0.01, respectively). Floors climbed were also associated with the NTDT-PROtextcopyright symptom severity scores for T/W and SoB domains. Patients in the lowest quartile for floors climbed had T/W and SoB domain scores of 12.8 and 4.6, respectively, compared with those patients in the highest quartile for floors climbed who had scores of 4.9 and 0.8 (P textless 0.01 and P= 0.02). Conclusions: Steps taken tracked by Fitbit did not appear to be associated with QoL and symptom severity in patients with β-thalassemia. Floors climbed, however, were associated with SF-36v2 Physical Functioning and General Health domains, and β-thalassemia symptom severity. Floors climbed is likely to be a more sensitive endpoint as a measurement for physical activities in clinical studies.

Physical Activity to Reduce Fatigue in Rheumatoid Arthritis: A Randomized, Controlled Trial (2017)

Arthritis Care & Research

Abstract

Objective: Effective treatments for rheumatoid arthritis (RA) fatigue are limited. We tested the effect of a pedometer-based intervention on increasing physical activity and decreasing fatigue among individuals with RA. Methods: Participants completed baseline questionnaires; had one week of activity monitoring; were randomized to control (education; EDUC), pedometer and step-monitoring diary (PED), or pedometer and diary plus step targets (PED+) groups; and followed for 21 weeks. At week 10, questionnaires were administered by phone to all participants. During the final week, all participants again had one week of activity monitoring. Primary outcomes were changes in average weekly steps and fatigue (PROMIS 7-item questionnaire) from baseline to week 21. Secondary outcomes were self-reported disease activity, physical function, pain interference, and depressive symptoms. Changes in steps were tested using a linear mixed model. Changes in fatigue were tested with repeated measures models including baseline, week-10, and week-21 scores. Results: 96 individuals participated. Eight did not complete 21-week assessments. Both intervention groups significantly increased steps (PED: +1441, p=0.004; PED+: +1656, p=0.001); EDUC decreased steps (-747, p=0.14) (Group-by-time interaction p=0.0025). Between group changes in fatigue were not significantly different (interaction p=.21). Mean changes in fatigue scores from baseline to week 21 were -1.6 (with-group p=.26), -3.2 (p=.02), and -4.8 (p=.0002) for EDUC, PED, and PED+ groups, respectively. Function and self-reported disease activity also improved in PED and PED+. Conclusion: Provision of pedometers, with and without providing step targets, was successful in increasing activity levels and decreasing fatigue in this sample of individuals with RA.

Consumer-Based Physical Activity Monitor as a Practical Way to Measure Walking Intensity During Inpatient Stroke Rehabilitation (2017)

Stroke

Abstract

BACKGROUND AND PURPOSE Identifying practical ways to accurately measure exercise intensity and dose in clinical environments is essential to advancing stroke rehabilitation. This is especially relevant in monitoring walking activity during inpatient rehabilitation where recovery is greatest. This study evaluated the accuracy of a readily available consumer-based physical activity monitor during daily inpatient stroke rehabilitation physical therapy sessions. METHODS Twenty-one individuals admitted to inpatient rehabilitation were monitored for a total of 471 one-hour physical therapy sessions which consisted of walking and nonwalking therapeutic activities. Participants wore a consumer-based physical activity monitor (Fitbit One) and the gold standard for assessing step count (StepWatch Activity Monitor) during physical therapy sessions. Linear mixed modeling was used to assess the relationship of the step count of the Fitbit to the StepWatch Activity Monitor. Device accuracy is reported as the percent error of the Fitbit compared with the StepWatch Activity Monitor. RESULTS A strong relationship (slope=0.99; 95% confidence interval, 0.97-1.01) was found between the number of steps captured by the Fitbit One and the StepWatch Activity Monitor. The Fitbit One had a mean error of 10.9% (5.3) for participants with walking velocities textless0.4 m/s, 6.8% (3.0) for walking velocities between 0.4 and 0.8 m/s, and 4.4% (2.8) for walking velocities textgreater0.8 m/s. CONCLUSIONS This study provides preliminary evidence that the Fitbit One, when positioned on the nonparetic ankle, can accurately measure walking steps early after stroke during inpatient rehabilitation physical therapy sessions. CLINICAL TRIAL REGISTRATION URL: https://www.clinicaltrials.gov. Unique identifier: NCT01915368.

The Volunteering-in-Place Program: Meaningful Volunteer Activity for Assisted Living Residents (2017)

Innovation in Aging

Abstract

The Volunteering-in-Place (VIP) Program was developed to provide individualized meaningful volunteer activities matched to interests and capabilities for older adults in assisted living (AL). The purposes of this study were to (1) establish feasibility of the VIP Program based on treatment fidelity (design, treatment, delivery, enactment); and (2) evaluate preliminary efficacy via improvement in psychological health (depressive symptoms, usefulness, purpose, resilience, and life satisfaction) and decreased sedentary activity (survey and Fitbit) at 3 and 6 months. Guided by the Social Ecological Model and Social Cognitive Theory, the VIP Program addressed barriers to volunteering through innovative motivational techniques and making volunteer opportunities accessible to residents. A Volunteer Coordinator facilitated the VIP Program using a four step approach: 1) Environmental Scan and Staff Education; 2) Assessment of Residents Abilities and Interests; 3) Ongoing Implementation of the VIP Program; and 4) Individualized Reassessment of the Volunteer Activity. This was a single group, single-site, repeated measures study. Residents were eligible if they were = 65years old, scored = 2 on the three-item recall, and could sign consent. Survey and Fitbit data were collected at baseline, three and six months post volunteering. Ten residents participated. The majority was white, female, educated, and on average 88 years. The VIP Program was feasible and most participants continued to volunteer at 6 months. There were non-significant improvements in depressive symptoms, usefulness, purpose, resilience and recreational physical activity. The results of this study provide support for the feasibility of the VIP Program. Further study is necessary to examine efficacy.

Implementation of a Home Monitoring System for Heart Failure Patients: A Feasibility Study. (2017)

JMIR Research Protocols

Abstract

BACKGROUND Improving the management of patients with complex chronic disease is a substantial undertaking with the simultaneous goals of improving patient outcomes and controlling costs. Reducing avoidable hospitalization for such patients is a step toward both objectives. Some of the deterioration experienced in chronic disease patients occurs outside the view of their clinicians, and before the patient becomes overtly symptomatic. Home monitoring has been used for more than 20 years to detect deterioration earlier so that the patients could be treated before they became ill enough to require hospitalization. Patient participation is an important requirement for successful home monitoring. There has been some concern that patients would be unwilling or unable to engage in a program that collected multiple measurements. The Cedars-Sinai Cardiology Center provides a high-touch, intense management program for patients with congestive heart failure (CHF). A group of their patients were chosen to join a complex, multidevice home monitoring system to see whether such patients would find value in the additional effort. OBJECTIVE The objective of our study was to determine whether patients already actively engaged in a high-touch intensive management program for CHF would take on the additional burden of a complex home monitoring effort. METHODS A total of 20 patients from the Cedars-Sinai group were enrolled in a monitoring program utilizing 5 different devices. Anonymous surveys were collected from the patients to assess their satisfaction with the program. RESULTS In total, 90% (18/20) completed the program, and 61% (11/20) submitted the survey. Among the 18 patients, overall compliance with the requested measurements was 70%. It was found that 73% (8/11) felt better about their health as a result of the program, whereas another 73% (8/11) believed that the care team now had a better picture of their health. CONCLUSIONS Substantial patient compliance and satisfaction can be achieved in a sophisticated home monitoring program.

Wearable technology to reduce sedentary behavior and CVD risk in older adults: Design of a randomized controlled trial (2017)

Contemporary Clinical Trials Communications

Abstract

Persons aged over 65 years account for over the vast majority of healthcare expenditures and deaths attributable to cardiovascular disease (CVD). Accordingly, reducing CVD risk among older adults is an important public health priority. Structured physical activity (i.e. exercise) is a well-documented method of decreasing CVD risk, but recent large-scale trials suggest that exercise alone is insufficient to reduce CVD events in high-risk populations of older adults. Thus adjuvant strategies appear necessary to reduce CVD risk. Accumulating evidence indicates that prolonged sedentary behavior (e.g. sitting) has detrimental health effects that are independent of engagement in recommended levels of moderate-intensity exercise. Yet clinical trials in this area are lacking. We hypothesize that exercise, when combined with a novel technology based intervention specifically designed to reduce sedentary behavior will reduce CVD risk among sedentary older adults. The purpose of this study is to evaluate the feasibility and efficacy of combining a traditional, structured exercise intervention with an innovative intervention designed to decrease sedentary behavior and increase non-exercise physical activity (NEPA). This study will provide us with critical data necessary to design and implement a full-scale trial to test our central hypothesis. Participants aged =60 years with moderate to high risk of coronary heart disease (CHD) events are randomly assigned to either the exercise and technology intervention (EX + NEPA) or exercise alone (EX) groups. Study dependent outcomes include changes in 1) daily activity patterns, 2) blood pressure, 3) exercise capacity, 4) waist circumference, and 5) circulating indices of cardiovascular function. This study will provide critical information for designing a fully-powered clinical trial, which could have health implications for the ever increasing population of older adults.

Use of a mobile social networking intervention for weight management: a mixed-methods study protocol (2017)

BMJ Open

Abstract

Introduction Obesity and physical inactivity are major societal challenges and significant contributors to the global burden of disease and healthcare costs. Information and communication technologies are increasingly being used in interventions to promote behaviour change in diet and physical activity. In particular, social networking platforms seem promising for the delivery of weight control interventions. We intend to pilot test an intervention involving the use of a social networking mobile application and tracking devices (Fitbit Flex 2 and Fitbit Aria scale) to promote the social comparison of weight and physical activity, in order to evaluate whether mechanisms of social influence lead to changes in those outcomes over the course of the study. Methods and analysis Mixed-methods study involving semi-structured interviews and a pre–post quasi-experimental pilot with one arm, where healthy participants in different body mass index (BMI) categories, aged between 19 and 35 years old, will be subjected to a social networking intervention over a 6-month period. The primary outcome is the average difference in weight before and after the intervention. Secondary outcomes include BMI, number of steps per day, engagement with the intervention, social support and system usability. Semi-structured interviews will assess participants' expectations and perceptions regarding the intervention. Ethics and dissemination Ethics approval was granted by Macquarie University's Human Research Ethics Committee for Medical Sciences on 3 November 2016 (ethics reference number 5201600716). The social network will be moderated by a researcher with clinical expertise, who will monitor and respond to concerns raised by participants. Monitoring will involve daily observation of measures collected by the fitness tracker and the wireless scale, as well as continuous supervision of forum interactions and posts. Additionally, a protocol is in place to monitor for participant misbehaviour and direct participants-in-need to appropriate sources of help.

Use It And Lose It: Fitbit Use, Daily Steps, And Weight Change Among Overweight Adults (2017)

Medicine & Science in Sports & Exercise

Abstract

Despite the increasing proliferation of advanced wearable physical activity tracking devices (e.g., Fitbit Zip), their value as tools for physical activity promotion and weight loss remains unclear. PURPOSE: As part of a pilot intervention trial that examined the efficacy of a novel social support approach for enhancing weight loss, participants were provided with a Fitbit Zip to monitor their physical activity. The current study examined the relationships between Fitbit use and change in weight and daily steps over 4 months. METHODS: Overweight adults (N=36) were randomized to either a standard or social support-enhanced, 16-week behavioral weight loss intervention. In addition to a Fitbit Zip, both groups received weekly, in-person group counseling sessions and digital body weight scales. Participants in the social-support enhanced group received two extra Fitbits and scales to share with up to two persons in their social circle. There were no significant differences between conditions, so analyses collapsed groups and examined the full sample. Paired t-tests were used to evaluate changes in weight and daily steps from baseline to post-treatment. Spearman rank correlation coefficients were calculated to test the associations between the total number of days the Fitbit was used (out of 112 days) and changes in daily steps and weight. Fitbit use was objectively established by weekly monitoring of synced data from the Fitbit website. RESULTS: At baseline, participants were obese (M BMI= 36.1 + 7.3 kg/m2) and low active (M=5546 + 2390 steps/d). Weight losses averaged -3.5+4.3 kg (p<.0001) and daily steps increased an average of 1101+2395 (p=.009) over baseline. Participants used the Fitbit an average of 5.9+2.5 days/wk. A significant correlation between total number of days the Fitbit was used and weight loss such that there was greater use of Fitbit was associated with greater weight loss (rs = -.60, p<.0001). A significant, positive correlation was found between the change in daily steps and the number of days the Fitbit was used (rs=.43, p=.008). CONCLUSION: These preliminary findings suggest that advanced wearable physical activity trackers hold promise as tools for assisting with physical activity promotion and weight loss in adults within the context of a multi-component behavioral weight loss program.

A technology-enabled Counselling program versus a delayed treatment control to support physical activity participation in people with inflammatory arthritis: study protocol for the OPAM-IA randomized controlled trial (2017)

BMC Rheumatology

Abstract

Being physically active is an essential component of successful self-management for people with inflammatory arthritis; however, the vast majority of patients are inactive. This study aims to determine whether a technology-enabled counselling intervention can improve physical activity participation and patient outcomes. The Effectiveness of Online Physical Activity Monitoring in Inflammatory Arthritis (OPAM-IA) project is a community-based randomized controlled trial with a delayed control design. We will recruit 130 people with rheumatoid arthritis or systemic lupus erythematosus, who can be physically active without health professional supervision. Randomization will be stratified by diagnosis. In Weeks 1–8, participants in the Immediate Group will: 1) receive education and counselling by a physical therapist (PT), 2) use a Fitbit and a new web-based application, FitViz, to track and obtain feedback about their physical activity, 3) receive 4 biweekly follow-up calls from the PT. Those in the Delayed Group will receive the same program in Week 10. We will interview a sample of participants about their experiences with the intervention. Participants will be assessed at baseline, and Weeks 9, 18 and 27. The primary outcome measure is time spent in moderate/vigorous physical activity in bouts of ≥ 10 min, measured with a portable multi-sensor device in the free-living environment. Secondary outcomes include step count, time in sedentary behaviour, pain, fatigue, mood, self-management capacity, and habitual behaviour. A limitation of this study is that participants, who also administer the outcome measures, will not be blinded. Nonetheless, by customizing existing self-monitoring technologies in a patient-centered manner, individuals can be coached to engage in an active lifestyle and monitor their performance. The results will determine if this intervention improves physical activity participation. The qualitative interviews will also provide insight into a paradigm to integrate this program to support self-management. Date of last update in ClinicalTrials.gov: September 18, 2015. ClinicalTrials.gov Identifier: NCT02554474 .

A Community-Based Physical Activity Counselling Program for People With Knee Osteoarthritis: Feasibility and Preliminary Efficacy of the Track-OA Study (2017)

JMIR mHealth and uHealth

Abstract

Background: Physical activity can improve health outcomes in people with knee osteoarthritis (OA); however, participation in physical activity is very low in this population. Objective: The objective of our study was to assess the feasibility and preliminary efficacy of the use of wearables (Fitbit Flex) and telephone counselling by a physical therapist (PT) for improving physical activity in people with a physician-confirmed diagnosis of knee OA, or who have passed 2 validated criteria for early OA. Methods: We conducted a community-based feasibility randomized controlled trial. The immediate group (n=17) received a brief education session by a physical therapist, a Fitbit Flex activity tracker, and a weekly telephone call for activity counselling with the physical therapist. The delayed group (n=17) received the same intervention 1 month later. All participants were assessed at baseline (T0), and the end of 1 month (T1) and 2 months (T2). Outcomes were (1) mean moderate to vigorous physical activity time, (2) mean time spent on sedentary behavior, (3) Knee Injury and Osteoarthritis Outcome Score (KOOS), and (4) Partners in Health Scale. Feasibility data were summarized with descriptive statistics. We used analysis of covariance to evaluate the effect of the group type on the outcome measures at T1 and T2, after adjusting for blocking and T0. We assessed planned contrasts of changes in outcome measures over measurement periods. Results: We identified 46 eligible individuals; of those, 34 (74%) enrolled and no one dropped out. All but 1 participant adhered to the intervention protocol. We found a significant effect, with the immediate intervention group having improved in the moderate to vigorous physical activity time and in the Partners in Health Scale at T0 to T1 compared with the delayed intervention group. The planned contrast of the immediate intervention group at T0 to T1 versus the delayed group at T1 to T2 showed a significant effect in the sedentary time and the KOOS symptoms subscale, favoring the delayed group. Conclusions: This study demonstrated the feasibility of a behavioral intervention, supported by the use of a wearable device, to promote physical activity among people with knee OA.

Metformin to Augment Strength Training Effective Response in Seniors (MASTERS): study protocol for a randomized controlled trial (2017)

Trials

Abstract

Background Muscle mass and strength are strong determinants of a person's quality of life and functional independence with advancing age. While resistance training is the most effective intervention to combat age-associated muscle atrophy (sarcopenia), the ability of older adults to increase muscle mass and strength in response to training is blunted and highly variable. Thus, finding novel ways to complement resistance training to improve muscle response and ultimately quality of life among older individuals is critical. The purpose of this study is to determine whether a commonly prescribed medication called metformin can be repurposed to improve the response to resistance exercise training by altering the muscle tissue inflammatory environment. Methods/design Individuals aged 65 and older are participating in a two-site, randomized, double-blind, placebo-controlled trial testing the effects of metformin or placebo on muscle size, strength, and physical function when combined with a progressive resistance training program. Participants consume 1700 mg of metformin per day or placebo for 2 weeks before engaging in a 14-week progressive resistance training regimen, with continued metformin or placebo. Participants are then monitored post-training to determine if the group taking metformin derived greater overall benefit from training in terms of muscle mass and strength gains than those on placebo. Muscle biopsies are taken from the vastus lateralis at three time points to assess individual cellular and molecular adaptations to resistance training and also changes in response to metformin. Discussion The response of aged muscles to a resistance training program does not always result in a positive outcome; some individuals even experience a loss in muscle mass following resistance training. Thus, adjuvant therapies, including pharmacological ones, are required to optimize response to training in those who do not respond and may be at increased risk of frailty. This is the first known metformin repurposing trial in non-diseased individuals, aimed specifically at the resistance exercise “non-responder” phenotype present in the aging population. The overall goal of this trial is to determine if combined exercise-metformin intervention therapy will benefit older individuals by promoting muscle hypertrophy and strength gains, thereby maintaining functional independence.

Implementation of a workplace intervention using financial rewards to promote adherence to physical activity guidelines: a feasibility study (2017)

BMC Public Health

Abstract

We designed and implemented the Brigham and Women's Wellness Initiative (B-Well), a single-arm study to examine the feasibility of a workplace program that used individual and team-based financial incentives to increase physical activity among sedentary hospital employees. We enrolled sedentary, non-clinician employees of a tertiary medical center who self-reported low physical activity. Eligible participants formed or joined teams of three members and wore Fitbit Flex activity monitors for two pre-intervention weeks followed by 24 weeks during which they could earn monetary rewards. Participants were rewarded for increasing their moderate-to-vigorous physical activity (MVPA) by 10% from the previous week or for meeting the Centers for Disease Control and Prevention (CDC) physical activity guidelines (150 min of MVPA per week). Our primary outcome was the proportion of participants meeting weekly MVPA goals and CDC physical activity guidelines. Secondary outcomes included Fitbit-wear adherence and factors associated with meeting CDC guidelines more consistently. B-Well included 292 hospital employees. Participants had a mean age of 38 years (SD 11), 83% were female, 38% were obese, and 62% were non-Hispanic White. Sixty-three percent of participants wore the Fitbit ≥4 days per week for ≥20 weeks. Two-thirds were satisfied with the B-Well program, with 79% indicating that they would participate again. Eighty-six percent met either their personal weekly goal or CDC physical activity guidelines for at least 6 out of 24 weeks, and 52% met their goals or CDC physical activity guidelines for at least 12 weeks. African Americans, non-obese subjects, and those with lower impulsivity scores reached CDC guidelines more consistently. Our data suggest that a financial incentives-based workplace wellness program can increase MVPA among sedentary employees. These results should be reproduced in a randomized controlled trial. Clinicaltrials.gov, NCT02850094 . Registered July 27, 2016 [retrospectively registered].

Estimation of Symptom Severity During Chemotherapy From Passively Sensed Data: Exploratory Study. (2017)

Journal of Medical Internet Research

Abstract

BACKGROUND Physical and psychological symptoms are common during chemotherapy in cancer patients, and real-time monitoring of these symptoms can improve patient outcomes. Sensors embedded in mobile phones and wearable activity trackers could be potentially useful in monitoring symptoms passively, with minimal patient burden. OBJECTIVE The aim of this study was to explore whether passively sensed mobile phone and Fitbit data could be used to estimate daily symptom burden during chemotherapy. METHODS A total of 14 patients undergoing chemotherapy for gastrointestinal cancer participated in the 4-week study. Participants carried an Android phone and wore a Fitbit device for the duration of the study and also completed daily severity ratings of 12 common symptoms. Symptom severity ratings were summed to create a total symptom burden score for each day, and ratings were centered on individual patient means and categorized into low, average, and high symptom burden days. Day-level features were extracted from raw mobile phone sensor and Fitbit data and included features reflecting mobility and activity, sleep, phone usage (eg, duration of interaction with phone and apps), and communication (eg, number of incoming and outgoing calls and messages). We used a rotation random forests classifier with cross-validation and resampling with replacement to evaluate population and individual model performance and correlation-based feature subset selection to select nonredundant features with the best predictive ability. RESULTS Across 295 days of data with both symptom and sensor data, a number of mobile phone and Fitbit features were correlated with patient-reported symptom burden scores. We achieved an accuracy of 88.1% for our population model. The subset of features with the best accuracy included sedentary behavior as the most frequent activity, fewer minutes in light physical activity, less variable and average acceleration of the phone, and longer screen-on time and interactions with apps on the phone. Mobile phone features had better predictive ability than Fitbit features. Accuracy of individual models ranged from 78.1% to 100% (mean 88.4%), and subsets of relevant features varied across participants. CONCLUSIONS Passive sensor data, including mobile phone accelerometer and usage and Fitbit-assessed activity and sleep, were related to daily symptom burden during chemotherapy. These findings highlight opportunities for long-term monitoring of cancer patients during chemotherapy with minimal patient burden as well as real-time adaptive interventions aimed at early management of worsening or severe symptoms.

Step Count Accuracy and Reliability from Activity Monitors Worn by Mobility-Intact Older Adults (2017)

Innovation in Aging

Abstract

Accurate and reliable measurement of physical activity is necessary to assess adherence to and effects of physical activity interventions within a growing population of physically inactive older adults. The STRIDES Study was designed to determine accuracy (criterion=tally counter) and reliability (test-retest) of step counting in mobility-intact older adults from six wearable activity monitors (Fitbit Charge, Fitbit One, Garmin Vivofit2, Jawbone UP2, Misfit Shine, and New-Lifestyles 1000 pedometer). Participants (n=10, 69–80 years) completed a continuous 400-meter walk (average speed 1.3m/s), a non-continuous 400-meter walk (1.1m/s) designed to mimic daily walking patterns incorporating small disturbances, and two 100-step reliability walks. For the continuous walk, five monitors had small mean errors less than +/-5% (One -0.3%,95%CI=-0.4to-0.2%; Vivofit2 -0.6%,CI=-1.4to0.2%; UP2 -3.2%,CI=-5.7to-0.6%; Shine 0.5%,CI=-6.3to7.4%; New-Lifestyles -0.7%,CI=-1.1to-0.3%) while error for the Charge was larger (-7.7%;CI=-17.1to1.6%). Step counts from the One, Vivofit2 and New-Lifestyles were strongly correlated with criterion (r=0.95). For the non-continuous walk, the One, Vivofit2, and Shine had small errors less than +/-5% (-4.4%,CI=-5.4to-3.5%; -4.2%,CI=-8.1to-0.2%; -1.4%,CI=-5.6to2.8%), while errors for the Charge, UP2 and New-Lifestyles were larger (-8.1%,CI=-15.5to-0.6%; -6.3%,CI=-10.6to-2.1%; -6.2%,CI=-11.4to-0.9%). Only step counts from the One were strongly correlated with criterion (r=0.95). The Charge, One, Vivofit2, and New-Lifestyles were reliable with no significant differences in step counts between trials, while the Shine and UP2 were unreliable (mean differences between trials of 25.6 and 14.6%, p<0.05). In summary, we found that most activity monitors were reliable and accurate for step counting in older adults during continuous walking, while accuracy of some monitors was compromised during non-continuous walking.

Aerobic exercise training in children and adolescents with inflammatory bowel disease: Influence on psychological functioning, sleep and physical performance – An exploratory trial (2017)

Mental Health and Physical Activity

Abstract

People with Inflammatory Bowel Disease (IBD) are at risk for mental health issues, poor sleep quality and limited engagement in physical activity (PA). Standard treatment consists of immune modulating pharmaceuticals though evidence is growing that aerobic exercise training (AET) could serve as an adjuvant option to reduce disease symptoms and improve mental health. The aim of the present exploratory trial was to investigate possible AET effects on psychological functioning, symptoms of depression, sleep and physical activity in paediatric patients with IBD. Twenty-one paediatric patients with IBD and 23 gender- and age-matched healthy controls (HC) were assessed. The IBD group was further split into a “remission-group” (IBD-RE; n = 14) and an “active disease group” (IBD-AD; n = 7). All participants completed an 8-week AET exergame intervention reaching 60–80% of maximum heart rate for 5 days per week. At baseline and after 8 weeks, psychological functioning, depressive symptoms, objective sleep EEG, subjective sleep and objective and subjective PA were assessed. AET improved the exercise capacity of all participants. Self-reported fitness and daily physical activity increased in IBD-AD, but not in IBD-RE and HC. No improvements were observed for psychological functioning, depressive symptoms or subjective sleep. Objective sleep improved, deep sleep increased, light sleep decreased, and in the IBD-AD group the number of arousals after sleep onset decreased. Descriptively, the IBD-AD group reported lower psychological functioning and poorer subjective sleep quality. Among paediatric patients with IBD and healthy controls, an exergaming intervention has the potential to improve exercise capacity, self-reported fitness, daily physical activity, and aspects of objective sleep. Results suggest that children and adolescents in an active state of IBD are at increased risk of poor psychological functioning and poor sleep quality.

Promoting Optimal Physical Exercise for Life (PROPEL): aerobic exercise and self-management early after stroke to increase daily physical activity-study protocol for a stepped-wedge randomised trial. (2017)

BMJ Open

Abstract

INTRODUCTION Physical exercise after stroke is essential for improving recovery and general health, and reducing future stroke risk. However, people with stroke are not sufficiently active on return to the community after rehabilitation. We developed the Promoting Optimal Physical Exercise for Life (PROPEL) programme, which combines exercise with self-management strategies within rehabilitation to promote ongoing physical activity in the community after rehabilitation. This study aims to evaluate the effect of PROPEL on long-term participation in exercise after discharge from stroke rehabilitation. We hypothesise that individuals who complete PROPEL will be more likely to meet recommended frequency, duration and intensity of exercise compared with individuals who do not complete the programme up to 6 months post discharge from stroke rehabilitation. METHODS AND ANALYSIS Individuals undergoing outpatient stroke rehabilitation at one of six hospitals will be recruited (target n=192 total). A stepped-wedge design will be employed; that is, the PROPEL intervention (group exercise plus self-management) will be 'rolled out' to each site at a random time within the study period. Prior to roll-out of the PROPEL intervention, sites will complete the control intervention (group aerobic exercise only). Participation in physical activity for 6 months post discharge will be measured via activity and heart rate monitors, and standardised physical activity questionnaire. Adherence to exercise guidelines will be evaluated by (1) number of 'active minutes' per week (from the activity monitor), (2) amount of time per week when heart rate is within a target range (ie, 55%-80% of age-predicted maximum) and (3) amount of time per week completing 'moderate' or 'strenuous' physical activities (from the questionnaire). We will compare the proportion of active and inactive individuals at 6 months post intervention using mixed-model logistic regression, with fixed effects of time and phase and random effect of cluster (site). ETHICS AND DISSEMINATION To date, research ethics approval has been received from five of the six sites, with conditional approval granted by the sixth site. Results will be disseminated directly to study participants at the end of the trial, and to other stake holders via publication in a peer-reviewed journal. TRIAL REGISTRATION NUMBER NCT02951338; Pre-results.

Validity of the Fitbit? Distance Traveled Feature Among Multiple Speed Trials (2017)

Medicine & Science in Sports & Exercise

Abstract

A science review by Delago (2014) suggested that the distance-traveled feature on Fitbit® was an accurate measurement. However, as speed increased the accuracy of the Fitbit® decreased, meaning the faster the pace, the greater the error for distance measured. Whereas a systematic review conducted by Evenson et al. (2015) reported only one study that measured the distance-traveled feature. This study concluded that the Fitbit® over-estimated at lower speeds and underestimated at faster speeds. PURPOSE: To examine whether the fitness device, Fitbit® Charge, provides an accurate measurement of the distance-traveled feature at various speeds and to expand on the research regarding the distance-traveled feature. METHODS: Twenty-eight healthy students from a Division III college participated in this study. A repeated measure ANVOA experimental design compared the Fitbit® distance-traveled output under three dependent variable speeds of 2.5mph (76.1m/min), 4.5mph (120.7m/min), and 6mph (160.9m/min) to a Quinton MedTrack ST55 treadmill distance-traveled measurement. All participants completed a six-minute trial for each of three different speeds on a treadmill while wearing the Fitbit® Charge. Following each six-minute trial, the distance-traveled was compared between the treadmill and the Fitbit® output. A repeated measure ANOVA analysis was used to test for significant differences (p < .05) among the three speeds between the Fitbit® and the treadmill. RESULTS: Results did not detect significant differences in distance-traveled between the Fitbit® Charge compared to the treadmill at 2.5mph (F(1,27) = 0.67, p = .42), 4.5mph (F(1,27) = 2.45, p = .13), or 6mph (F(1,24) = .66, p = .43) speeds. CONCULSION: It seems reasonable to conclude that the distance-traveled feature on the Fitbit® Charge is valid when compared to treadmill output at these three speeds. Future research could look at a wider range of speeds to ensure further accuracy.

Assessing the Effects of Interpersonal and Intrapersonal Behavior Change Strategies on Physical Activity in Older Adults: a Factorial Experiment (2017)

Annals of Behavioral Medicine

Abstract

Background: Little is known about which behavior change strategies motivate older adults to increase their physical activity. Purpose: The purpose of this study was to assess the relative effects of two sets of behavior change strategies to motivate increased physical activity among older adults: interpersonal and intrapersonal. Methods: Community-dwelling older adults (N = 102, mean age = 79) were randomized in a 2 × 2 factorial experiment to receive interpersonal (e.g., social support, friendly social comparison; no, yes) and /or intrapersonal (e.g., goal setting, barriers management; no, yes) behavior change strategies, combined with an evidence-based, physical activity protocol (Otago exercise program) and a physical activity monitor (Fitbit One™). Results: Based on monitor data, participants who received interpersonal strategies, compared to those who did not, increased their average minutes of total physical activity (light, moderate, vigorous) per week, immediately (p = .006) and 6 months (p = .048) post-intervention. Similar, increases were observed on measures of functional strength and balance, immediately (p = .012) and 6 months (p = .003) post-intervention. The intrapersonal strategies did not elicit a significant increase in physical activity or functional strength and balance. Conclusions: Findings suggest a set of interpersonally oriented behavior change strategies combined with an evidencebased physical activity protocol can elicit modest, but statistically and clinically significant, increases in older adults' physical activity and functional strength and balance. Future research should replicate these findings and investigate the sustained quantity of physical activity elicited by these strategies and their impact on older adults' quality of life and falls.

A Fitbit and Facebook mHealth intervention for promoting physical activity among adolescent and young adult childhood cancer survivors: A pilot study (2017)

Pediatric Blood & Cancer

Abstract

Background Physical activity (PA) may be important for preventing chronic diseases for adolescent and young adult (AYA) childhood cancer survivors. Randomized controlled trials (RCTs) of PA interventions for AYA survivors are sparse, but necessary to determine effective programs for increasing PA among this population. Thus, we conducted a pilot RCT, testing the feasibility of a mobile health (mHealth) intervention to promote PA among AYA survivors. Procedure We recruited 14– to 18–year–olds who were =1-year post cancer therapy from Seattle Children's Hospital. The 10-week intervention consisted of a wearable PA-tracking device (Fitbit Flex) and a peer-based virtual support group (Facebook group). Research staff helped set step goals and awarded badges weekly. Controls received usual care. Baseline assessments occurred before randomization and follow-up assessments occurred during weeks 8–10 of the intervention period. Feasibility criteria are defined below. Qualitative interviews assessed acceptability. Exploratory outcomes included PA, quality of life, and motivation for PA. Results All feasibility criteria were met: we recruited 60 survivors, intervention participants wore the Fitbit on the majority (71.5%) of intervention days, and =90% of all participants completed questionnaires. Qualitative data confirmed intervention acceptability. Exploratory analyses found no significant adjusted group differences for change in moderate-to-vigorous PA (4.4 vs. 5.0 min/day; P = 0.92) or sedentary time (-4.5 vs. 1.0 min/day; P = 0.73), comparing intervention subjects to controls. Some modest differences were found for select subscales of quality of life and motivation for PA. Conclusions This mHealth PA intervention was feasible and acceptable to AYA childhood cancer survivors and warrants a fully powered RCT.

A Validation Study of the Fitbit One in Daily Life Using Different Time Intervals (2017)

Medicine & Science in Sports & Exercise

Abstract

Purpose: Accelerometer-based wearables can provide the user with real-time feedback through the device's interface and the mobile platforms. Few studies have focused on the minute-by-minute validity of wearables, which is essential for high-quality real-time feedback. This study aims to assess the validity of the Fitbit One compared with the ActiGraph GT3x + for assessing physical activity (i.e., steps, time spent in moderate, vigorous, and moderate–vigorous physical activity) in young adults using traditional time intervals (i.e., days) and smaller time intervals (i.e., minutes and hours). Methods: Healthy young adults (N = 34) wore the ActiGraph GT3x+ and a Fitbit One for 1 wk. Three aggregation levels were used: minute, hour, and day. Mixed models analyses, intraclass correlation coefficients, Bland–Altman analyses, and absolute error percentage for steps per day were conducted to analyze the validity for steps and minutes spent in moderate, vigorous, and moderate–vigorous physical activity. Results: As compared with ActiGraph (mean = 9 steps per minute, 509 steps per hour and 7636 steps per day), the Fitbit One systematically overestimated physical activity for all aggregation levels: on average 0.82 steps per minute, 45 steps per hour, and 677 steps per day. Strong and significant associations were found between ActiGraph and Fitbit results for steps taken (B = 0.72–0.89). Weaker but statistically significant associations were found for minutes spent in moderate, vigorous, and moderate–vigorous physical activity for all time intervals (B = 0.39–0.57). Conclusions: Although the Fitbit One overestimates the step activity compared with the ActiGraph, it can be considered a valid device to assess step activity, including for real-time minute-by-minute self-monitoring. However, agreement and correlation between ActiGraph and Fitbit One regarding time spent in moderate, vigorous, and moderate–vigorous physical activity were lower.

Mobile Device Accuracy for Step Counting Across Age Groups. (2017)

JMIR mHealth and uHealth

Abstract

BACKGROUND Only one in five American meets the physical activity recommendations of the Department of Health and Human Services. The proliferation of wearable devices and smartphones for physical activity tracking has led to an increasing number of interventions designed to facilitate regular physical activity, in particular to address the obesity epidemic, but also for cardiovascular disease patients, cancer survivors, and older adults. However, the inconsistent findings pertaining to the accuracy of wearable devices for step counting needs to be addressed, as well as factors known to affect gait (and thus potentially impact accuracy) such as age, body mass index (BMI), or leading arm. OBJECTIVE We aim to assess the accuracy of recent mobile devices for counting steps, across three different age groups. METHODS We recruited 60 participants in three age groups: 18-39 years, 40-64 years, and 65-84 years, who completed two separate 1000 step walks on a treadmill at a self-selected speed between 2 and 3 miles per hour. We tested two smartphones attached on each side of the waist, and five wrist-based devices worn on both wrists (2 devices on one wrist and 3 devices on the other), as well as the Actigraph wGT3X-BT, and swapped sides between each walk. All devices were swapped dominant-to-nondominant side and vice-versa between the two 1000 step walks. The number of steps was recorded with a tally counter. Age, sex, height, weight, and dominant hand were self-reported by each participant. RESULTS Among the 60 participants, 36 were female (60%) and 54 were right-handed (90%). Median age was 53 years (min=19, max=83), median BMI was 24.1 (min=18.4, max=39.6). There was no significant difference in left- and right-hand step counts by device. Our analyses show that the Fitbit Surge significantly undercounted steps across all age groups. Samsung Gear S2 significantly undercounted steps only for participants among the 40-64 year age group. Finally, the Nexus 6P significantly undercounted steps for the group ranging from 65-84 years. CONCLUSIONS Our analysis shows that apart from the Fitbit Surge, most of the recent mobile devices we tested do not overcount or undercount steps in the 18-39-year-old age group, however some devices undercount steps in older age groups. This finding suggests that accuracy in step counting may be an issue with some popular wearable devices, and that age may be a factor in undercounting. These results are particularly important for clinical interventions using such devices and other activity trackers, in particular to balance energy requirements with energy expenditure in the context of a weight loss intervention program.

Types of Social Support and Weight Change among Overweight Adults (2017)

Medicine & Science in Sports & Exercise

Abstract

Engaging social support has been found to be an effective strategy to promote weight loss. Yet, little is known about the specific types of social support that may be the most influential. PURPOSE: The aim of this study was to examine the association between weight change and the types of social support adults participating in a behavioral weight loss intervention identified as the most helpful for their weight loss efforts. METHODS: Overweight adults (N=36) were randomized to one of two, 16-week behavioral weight loss interventions. Both treatments consisted of weekly, in-person group counseling sessions, Fitbit Zips for monitoring physical activity, and scales for monitoring body weight. One group also received two extra Fitbit Zips and scales to share with up to two persons in their social circle. There were no significant differences between conditions, so analyses collapsed groups and examined those who started the intervention (N=35). Participants completed an online survey at the end of each intervention week, which asked them to select the most helpful type of social support they received (informational; tangible; network; emotional; esteem). The support type identified the most frequently as the most helpful type over time was determined for each participant and used in Fisher’s exact tests to measure the frequencies with which each type of support was reported between those who lost > 5% of their initial body weight and those who did not. RESULTS: Participants were obese at baseline (M BMI = 36.1 + 7.3 kg/m2), and 43% lost > 5% of their initial weight by 16 weeks. Only emotional, esteem, and tangible support emerged as the most frequently reported most helpful types of support among all participants across the intervention period. A greater proportion of those who lost > 5% of their baseline weight identified esteem support as the most helpful type of support versus those who lost < 5% of their initial weight (47% vs 10%, p = .02). No significant differences between weight change groups were found for other support types (ps > .05). CONCLUSION: These findings suggest that esteem support may be especially influential for fostering weight loss. However, future studies should employ designs that allow for the direct comparison and more robust evaluation of the effectiveness of different types of support on weight loss in adults.

Reduction of Health Disparities in Appalachians With Multiple Cardiovascular Disease Risk Factors: A Randomized Controlled Trial (2017)

Circulation

Abstract

Introduction: Appalachia is a socioeconomically austere environment with marked health disparities. People living in Appalachian Kentucky represent the top 1% for cardiovascular disease (CVD) risk, morbidity and mortality in the US. Hypothesis: We hypothesized that compared to control (referral of patients to a primary care provider), a culturally appropriate, self-care CVD risk reduction intervention (HeartHealth) would be superior in promoting improvement of these risk factors: tobacco use (urinary cotinine), blood pressure (BP), lipid profile (point-of-care device), body mass index, depressive symptoms (PHQ-9), and physical activity levels (Fitbit). Methods: The study was developed with a community advisory board of lay community members, business owners, church leaders, and healthcare providers. In this randomized trial, we enrolled 352 individuals living in Appalachian Kentucky who were at risk for CVD by virtue of having two or more CVD risk factors. The intervention was delivered in person to groups of 10 or less over 6 weeks. The HeartHealth intervention was designed to provide participants with self-care skills appropriate to CVD risk reduction while reducing barriers to risk reduction found in austere environments. The targeted CVD risk factors were measured at baseline, then 4 and 12 months post-intervention. Linear mixed models analysis was used to compare outcomes between the groups across time. Results: The intervention produced improvement in systolic and diastolic BP (p=0.002 and p=0.001 for group X time interaction [GXT])), total cholesterol (p=0.026 GXT), high density lipoprotein (p=0.026 GXT), body mass index (p=0.017 GXT), smoking status (p=0.001 GXT), physical activity (p=0.001 GXT) and depressive symptoms (p=0.019 GTX) compared to the control group, and these improvements were maintained through 12 months. There were no differences seen across time by group in low density lipoprotein or triglyceride levels.Conclusion: Interventions like HeartHealth that focus on self-care of CVD risk factors and that are derived from collaboration with the community of interest are highly appropriate and effective in underserved, socioeconomically distressed areas.

Development and evaluation of two web-based interventions for the promotion of physical activity in older adults: study protocol for a community-based controlled intervention trial (2017)

BMC Public Health

Abstract

BACKGROUND Regular physical activity (PA) is a key contributor to healthy ageing. However, despite known health benefits, only one third of older adults in Germany reach the PA levels recommended for persons aged 65 years and above by the World Health Organization. The aim of the current study is to evaluate the effectiveness of two web-based interventions for the initiation and maintenance of regular PA (i.e., intervention groups 1 and 2) compared to a delayed intervention control group of older adults aged 65 to 75 years. METHODS/DESIGN Study participants will be randomly assigned to one of three study arms in five communities in the Bremen-Oldenburg metropolitan region: a) Participants in the first arm will receive access to a web-based intervention for 10 weeks allowing them to track their weekly PA (subjective self-monitoring, intervention group 1); b) participants in the second arm will receive access to the web-based intervention for 10 weeks and, in addition, track PA using Fitbit Zips (objective self-monitoring, intervention group 2); c) participants in the delayed intervention control group will receive access to the intervention implemented in the first study arm after completion of the 12-week follow-up in the other two groups within each community. In addition, weekly group meetings in the communities will be offered to study participants in the intervention groups providing the opportunity to address questions related to the use of the website and to practice PA in groups (e.g., neighborhood walks, strength and balance exercises). To evaluate short-term effects of the intervention on physical and psychological health, PA, physical fitness, and cognitive and psychological variables will be assessed at baseline and 12-week follow-up. DISCUSSION This study will provide answers regarding acceptance and effectiveness of web-based interventions promoting uptake and maintenance of regular PA in persons aged 65-75 years. Study findings will contribute to a growing body of evidence in Germany concerning the role of community-based interventions for the promotion of PA and healthy ageing in older adults. T

Minimal Detectable Change Thresholds and Responsiveness of Zephyr Bioharness & Fitbit Charge Devices. (2017)

Journal of Strength and Conditioning Research

Abstract

The aim of the current study was to define the MDC thresholds for Zephyr Bioharness (ZB) and Fitbit Charge (FC-HR) heart rate variables, and to establish the internal responsiveness of ZB and FC-HR devices in terms of their heart rate, respiratory rate variables and number of steps taken variables, in a healthy sample of males and females at resting, sub-maximal activity and throughout recovery phases. A total of sixty participants (30 females, 48 ± 15) and (30 males, 48 ± 15) were recruited using stratified convenience and snowball sampling approaches from our university student, staff and faculty population. At rest, MDC90 values of ± 4.89 and ± 5.15 were noted for ZB and FC-HR devices respectively. Throughout the recovery, thresholds of ±8.14 for ZB and ± 8.74 for FC-HR were reported. During the sub-maximal phases, scores of ± 8.18 and ± 9.35 were established for ZB and FC-HR respectively. From a resting to a sub-maximal state, the Standardized Response Means for ZB and FC-HR heart rate variables exhibited large responsiveness of SRMZB-HR=4.42; SRMFC-HR=4.04. In addition, the ZB respiratory rate variable showed the largest effect size of SRMZB-RR= 2.55. Similarly, large effect size of SRMFC-ST=1.76 were reported for the FC-HR steps taken variable. From a sub-maximal to a recovery state, he Standardized Response Means for ZB and FC-HR heart rate variables demonstrated effect sizes of SRMZBHR= -3.10; SRMFC-HR= -3.12). Furthermore, the ZB respiratory rate variable displayed similar internal responsiveness measures of SRMZB-RR= -2.45. To conclude, Similar MDC heart rate thresholds can be expected using the ZB and FC-HR devices, and that the two devices demonstrated large internal responsiveness in our healthy male and female cohort when transitioning from resting to submaximal states and vice-versa. Our findings suggest that the ZB and FC-HR devices can be used in monitoring performance improvements or deterioration over time, and would allow qualified personnel in assessing the effectiveness of exercise programs in healthy individuals.

A qualitative evaluation of breast cancer survivors' acceptance of and preferences for consumer wearable technology activity trackers (2017)

Supportive Care in Cancer

Abstract

Background: Physical inactivity and sedentary behaviour are common amongst breast cancer survivors. These behaviours are associated with an increased risk of comorbidities such as heart disease, diabetes and other cancers. Commercially available, wearable activity trackers (WATs) have potential utility as behavioural interventions to increase physical activity and reduce sedentary behaviour within this population. Purpose: The purpose of the study is to explore the acceptability and usability of consumer WAT amongst postmenopausal breast cancer survivors. Methods: Fourteen participants tested two to three randomly assigned trackers from six available models (Fitbit One, Jawbone Up 24, Garmin Vivofit 2, Garmin Vivosmart, Garmin Vivoactive and Polar A300). Participants wore each device for 2 weeks, followed by a 1-week washout period before wearing the next device. Four focus groups employing a semi-structured interview guide explored user perceptions and experiences. We used a thematic analysis approach to analyse focus group transcripts. Results: Five themes emerged from our data: (1) trackers' increased self-awareness and motivation, (2) breast cancer survivors' confidence and comfort with wearable technology, (3) preferred and disliked features of WAT, (4) concerns related to the disease and (5) peer support and doctor monitoring were possible strategies for WAT application. Conclusions: WATs are perceived as useful and acceptable interventions by postmenopausal breast cancer survivors. Effective WAT interventions may benefit from taking advantage of the simple features of the trackers paired with other behavioural change techniques, such as specialist counselling, doctor monitoring and peer support, along with simple manual instructions.

Determining the role of Maternal Leptin and Activity in Pregnancy Outcomes (2017)

The FASEB Journal

Abstract

Leptin, an adipocytokine with an important role in energy homeostasis and other neuroendocrine functions, is found in higher levels in obese individuals. Maternal obesity is associated with numerous adverse pregnancy outcomes, such as miscarriage, gestational diabetes, gestational hypertension, and preeclampsia. However, the relationship between leptin, activity, and pregnancy outcomes is unclear. Some studies have found reduced leptin with physical activity and leptin is known to have direct effects on the cardiovascular system. Therefore, we hypothesized that women who have higher activity during pregnancy will have lower plasma leptin levels and experience fewer adverse outcomes during their pregnancy. In a cross-sectional study, 100 pregnant women were co-enrolled into the Maternal Fetal Tissue Bank (IRB200901784) and this study (IRB201502767). Pregnant women received triaxial accelerometers (Fitbit Zip) to wear throughout gestation. Maternal blood samples from each trimester were assayed in duplicate for plasma leptin concentration using an enzyme-linked immunosorbent assay (ELISA). Pregnancy outcomes including maternal development of gestational diseases such as preeclampsia as well as mode of delivery, birthweight, fetal gender, and APGAR scores were extracted from the electronic health record (EPIC). Additionally, maternal demographics (age, race, and ethnicity) and health characteristics such as body mass index and any chronic health conditions at the beginning of pregnancy were recorded. Average step counts were obtained for the first (5523.77 ± 3720.33), second (5667.62 ± 4029.63), and third (4979.32 ± 3138.37) trimesters. In addition, no significant differences in average plasma leptin concentrations were obtained for the first (19839.8 ± 2390.9 pg/mL), second (11319.0 ± 2000.9 pg/mL), and third (26468.3 ± 5918.5 pg/mL) trimesters (P = 0.08). Leptin was only inversely correlated with activity in the first trimester (R2 =-0.32, P = 0.02), whereas no associations were detected in the 2nd and 3rd trimester. As an adverse outcome, we examined activity and leptin in patients with and without preeclampsia. The preeclamptic cohort took significantly fewer steps than the control group (5656.59 vs. 3586.72, P textless 0.05) during the first trimester. The preeclamptic cohort also had a significantly higher average BMI (39 ± 6 vs. 27 ± 7, P = 0.02) and rates of chronic hypertension (67% vs. 6%, P textless 0.001) than controls. Leptin was also significantly higher in the preeclamptic cohort than controls (52476.1 ± 10456.0 pg/mL vs. 16777.1 ± 1395.0 pg/mL, P textless0.001). In conclusion, significant differences exist between the activity levels, leptin, and pregnancy outcomes of the preeclamptic and control groups.

Accuracy of Consumer Monitors for Measuring Sleep Across Seven Nights (2017)

Sleep

Abstract

Introduction: Sleep and fitness trackers available to the consumer market show promise for measuring sleep. However, there are few published studies examining their accuracy compared with well-validated actigraphy devices for multiple nights outside the sleep laboratory. The purpose of this study was to evaluate the reliability and validity of several commercially-available devices compared to a well-validated actigraphy device. Methods: Healthy adults (N=40, mean age= 42?±?14, 50% female) wore a Fitbit Flex, Jawbone Up24, Misfit Shine, and Actiwatch-2 (AW-2) on the same wrist for seven consecutive 24-hour periods. Outcome variables included total sleep time (TST), wake after sleep onset (WASO), and sleep efficiency (SE) averaged over 7 nights. Both “Normal” and “Sensitive” sleep-recording Fitbit modes were examined. Each commercial monitor was compared with AW-2 using paired t-test and Bland-Altman concordance analyses. Clinical acceptance was defined a priori as =60 minutes difference between devices for TST and WASO, and =10% difference for SE. Results: Fitbit (normal), Jawbone, and Misfit were in the clinically acceptable range for TST, with Fitbit (normal) and Misfit overestimating by 2 minutes and 60 minutes, respectively; and Jawbone underestimating by 15 minutes compared with AW-2 (m=406?±?47 minutes). Fitbit (sensitive) overestimated by 160 minutes. All devices met clinically acceptable criteria but underestimated WASO by 13 minutes (Jawbone), 21 minutes (Fitbit sensitive), 23 minutes (Fitbit normal), and 25 minutes (Misfit) compared to AW-2 (m=38?±?15 minutes). Jawbone and Fitbit (normal) met clinically acceptable criteria for SE, with both devices overestimating by 4% and 5%, respectively compared to AW-2 (m=85?±?6%). Misfit overestimated by 12% and Fitbit (sensitive) underestimated by 30%. Conclusion: Fitbit (normal) and Jawbone accelerometers provided clinically comparable result for TST, WASO, and SE when compared to AW-2. Misfit met criteria for TST and WASO but not SE. Fitbit (sensitive) performed poorest. Further research is needed to reevaluate these findings with newer models.

Measuring and understanding adherence in a home-based exercise intervention during chemotherapy for early breast cancer (2017)

Breast Cancer Research and Treatment

Abstract

PURPOSE Ensuring and measuring adherence to prescribed exercise regimens are fundamental challenges in intervention studies to promote exercise in adults with cancer. This study reports exercise adherence in women who were asked to walk 150 min/week throughout chemotherapy treatment for early breast cancer. Participants were asked to wear a Fitbit(TM) throughout their waking hours, and Fitbit steps were uploaded directly into study computers. METHODS Descriptive statistics are reported, and both unadjusted and multivariable linear regression models were used to assess associations between participant characteristics, breast cancer diagnosis, treatment, chemotherapy toxicities, and patient-reported symptoms with average Fitbit steps/week. RESULTS Of 127 women consented to the study, 100 had analyzable Fitbit data (79%); mean age was 48 and 31% were non-white. Mean walking steps were 3956 per day. Nineteen percent were fully adherent with the target of 6686 steps/day and an additional 24% were moderately adherent. In unadjusted analysis, baseline variables associated with fewer Fitbit steps were: non-white race (p = 0.012), high school education or less (p = 0.0005), higher body mass index (p = 0.0024), and never/almost never drinking alcohol (p = 0.0048). Physical activity variables associated with greater Fitbit steps were: pre-chemotherapy history of vigorous physical activity (p = 0.0091) and higher self-reported walking minutes/week (p textless 0.001), and higher outcome expectations from exercise (p = 0.014). Higher baseline anxiety (p = 0.03) and higher number of chemotherapy-related symptoms rates "severe/very severe" (p = 0.012) were associated with fewer steps. In multivariable analysis, white race was associated with 12,146 greater Fitbit steps per week (p = 0.004), as was self-reported walking minutes prior to start of chemotherapy (p < 0.0001). CONCLUSIONS Inexpensive commercial-grade activity trackers, with data uploaded directly into research computers, enable objective monitoring of home-based exercise interventions in adults diagnosed with cancer. Analysis of the association of walking steps with participant characteristics at baseline and toxicities during chemotherapy can identify reasons for low/non-adherence with prescribed exercise regimens.

When a Step Is Not a Step! Specificity Analysis of Five Physical Activity Monitors (2017)

PLOS ONE

Abstract

Introduction: Physical activity is an essential aspect of a healthy lifestyle for both physical and mental health states. As step count is one of the most utilized measures for quantifying physical activity it is important that activity-monitoring devices be both sensitive and specific in recording actual steps taken and disregard non-stepping body movements. The objective of this study was to assess the specificity of five activity monitors during a variety of prescribed non-stepping activities. Methods: Participants wore five activity monitors simultaneously for a variety of prescribed activities including deskwork, taking an elevator, taking a bus journey, automobile driving, washing and drying dishes; functional reaching task; indoor cycling; outdoor cycling; and indoor rowing. Each task was carried out for either a specific duration of time or over a specific distance. Activity monitors tested were the ActivPAL micro™, NL-2000™ pedometer, Withings Smart Activity Monitor Tracker (Pulse O2)™, Fitbit One™ and Jawbone UP™. Participants were video-recorded while carrying out the prescribed activities and the false positive step count registered on each activity monitor was obtained and compared to the video. Results: All activity monitors registered a significant number of false positive steps per minute during one or more of the prescribed activities. The Withings™ activity performed best, registering a significant number of false positive steps per minute during the outdoor cycling activity only (P = 0.025). The Jawbone™ registered a significant number of false positive steps during the functional reaching task and while washing and drying dishes, which involved arm and hand movement (P < 0.01 for both). The ActivPAL™ registered a significant number of false positive steps during the cycling exercises (P < 0.001 for both). Conclusion: As a number of false positive steps were registered on the activity monitors during the non-stepping activities, the authors conclude that non-stepping physical activities can result in the false detection of steps. This can negatively affect the quantification of physical activity with regard to step count as an output. The Withings™ activity monitor performed best with regard to specificity during the activities of daily living tested.

What Matters in Weight Loss? An In-Depth Analysis of Self-Monitoring. (2017)

Journal of Medical Internet Research

Abstract

BACKGROUND Using technology to self-monitor body weight, dietary intake, and physical activity is a common practice used by consumers and health companies to increase awareness of current and desired behaviors in weight loss. Understanding how to best use the information gathered by these relatively new methods needs to be further explored. OBJECTIVE The purpose of this study was to analyze the contribution of self-monitoring to weight loss in participants in a 6-month commercial weight-loss intervention administered by Retrofit and to specifically identify the significant contributors to weight loss that are associated with behavior and outcomes. METHODS A retrospective analysis was performed using 2113 participants enrolled from 2011 to 2015 in a Retrofit weight-loss program. Participants were males and females aged 18 years or older with a starting body mass index of =25 kg/m2, who also provided a weight measurement at the sixth month of the program. Multiple regression analysis was performed using all measures of self-monitoring behaviors involving weight measurements, dietary intake, and physical activity to predict weight loss at 6 months. Each significant predictor was analyzed in depth to reveal the impact on outcome. RESULTS Participants in the Retrofit Program lost a mean -5.58% (SE 0.12) of their baseline weight with 51.87% (1096/2113) of participants losing at least 5% of their baseline weight. Multiple regression model (R(2)=.197, P<0.001) identified the following measures as significant predictors of weight loss at 6 months: number of weigh-ins per week (P<.001), number of steps per day (P=.02), highly active minutes per week (P,.001), number of food log days per week (P<.001), and the percentage of weeks with five or more food logs (P<.001). Weighing in at least three times per week, having a minimum of 60 highly active minutes per week, food logging at least three days per week, and having 64% (16.6/26) or more weeks with at least five food logs were associated with clinically significant weight loss for both male and female participants. CONCLUSIONS The self-monitoring behaviors of self-weigh-in, daily steps, high-intensity activity, and persistent food logging were significant predictors of weight loss during a 6-month intervention.

Screening of Atrial Fibrillation Using Wrist Photoplethysmography from a Fitbit Tracker (2017)

Iproceedings

Abstract

Background: Atrial Fibrillation is the most common clinically significant cardiac arrhythmia, estimated to affect between 2% ( 65 years of age) of the U.S. population. Screening and early detection of AF can help prevent associated stroke and cardiovascular disease. Commonly used monitoring devices are limited to short periods (textless 4 weeks) and are usually employed in symptomatic subjects. Wrist-worn wearables equipped with photoplethysmography (PPG) could potentially aid in AF screening, as they can be easily worn by most people for extended periods of time. Objective: This study aimed to investigate the ability of wrist-based PPG to detect the presence of AF in subjects while at rest. Methods: This study was conducted with the approval of local institutional review boards, and all patients provided written, informed consent. The first dataset consisted of 9 patients with persistent AF and 13 subjects with normal sinus rhythm. For these 22 subjects, PPG and ECG data were collected while sitting stationary for 15 minutes (awake dataset). A second dataset consisted of 10 patients with persistent AF and 27 subjects with no known diagnosis of AF. These subjects were asked to wear a Fitbit wrist-band which recorded PPG and accelerometry data during sleep (sleep dataset, 73 total nights). There was no overlap between subjects in the awake and sleep datasets. Results: Data were analyzed in overlapping 1 or 5-minute windows. Pulse Rate Variability (PRV) features, PPG morphology features and accelerometer features were extracted for each window. An algorithm was trained on the awake dataset and was validated on the sleep dataset. The performance was 95.7% (98%) sensitivity (Se) and 0.8% (0.8%) False Positive Rate (FPR) for 1 min (5 min) windows. We also investigated the shortest duration of an AF episode that could be detected by using synthesized data. With 1 minute analysis windows, the algorithm was sensitive down to 40-second episodes. Using 5-minute intervals, episodes down to 3 minutes could be reliably detected. Conclusions: Wrist-bands equipped with PPG sensors can be used to detect atrial fibrillation during sleep or during awake, stationary periods. While reliable detection of short AF episodes can be challenging due to the noisy nature of PPG signals, we have demonstrated that select PPG features can enable accurate detection of AF down to 40 second periods. These findings suggest that wrist-worn devices equipped with a PPG sensor could be used to screen for AF in high-risk subjects or monitor patients post treatment, but are limited to cases where users are not moving or exercising. Additional, larger clinical studies are planned for the future to validate these promising early results and to validate the algorithm's specificity against other forms of arrhythmia. [iproc 2017;3(1):e17]

Sources of Error with Wearable Step Counters (2017)

Medicine & Science in Sports & Exercise

Abstract

PURPOSE: To investigate sources of error with 11 wearable step counting devices, during common types of physical activities. METHODS: 20 participants performed 15 activities for 2 min each, while wearing 11 step counters on the waist, ankle, or non-dominant wrist. Arm activities included: snacking, brushing hair, folding laundry, sweeping, brushing teeth, and meal preparation. Overground activities included: walking holding onto backpack strap, walking with umbrella, walking with hands in pockets, and pushing stroller. Treadmill activities included: walking at 1 mph, walking at 2 mph, walking at 3 mph, walking at 3 mph holding onto bars, and jogging at 6 mph. Wrist-worn devices included: Garmin Vivofit 2, Fitbit Charge, Polar A360, Withings Pulse Ox, and ActiGraph GT3X. Waist-worn devices included: Yamax Digi-Walker SW-200, Fitbit Zip, Omron HJ-322U, and ActiGraph GT3X (without low-frequency extension). Ankle-worn devices included: two StepWatch 3 devices, one with preprogrammed, default settings and one with modified cadence and sensitivity settings. A researcher hand-counted steps during each activity; this served as the criterion. The step counts reported from each device compared to the hand count using a 1-way (1x12) repeated measured ANOVA. If the overall effect for an activity was significant, the outputs from individual devices and the criterion were analyzed using planned contrasts. Devices with significant contrasts (p = 0.05) and observed power greater than 0.8 were considered to be significantly different than the criterion. RESULTS: During arm activities, the wrist-worn devices overcounted steps while hip-worn devices slightly undercounted steps. The ActiGraph GT3X on the wrist greatly overcounted steps during arm activities, while other devices had smaller errors. During treadmill walking at 1 mph, all wrist and hip-worn devices undercounted steps. The ankle-worn device (StepWatch 3) had the smallest error across all activities, especially when programmed with the modified setting. CONCLUSIONS: Individuals using step counting devices should be aware of sources of error in step counts. Contributing factors to error are the wear location, the algorithms used to count steps, and the activities performed.

Effect of a Game-Based Intervention Designed to Enhance Social Incentives to Increase Physical Activity Among Families: The BE FIT Randomized Clinical Trial (2017)

AMA Internal Medicine

Abstract

mportance Gamification, the application of game design elements such as points and levels in nongame contexts, is often used in digital health interventions, but evidence on its effectiveness is limited. Objective To test the effectiveness of a gamification intervention designed using insights from behavioral economics to enhance social incentives within families to increase physical activity. Design, Setting, and Participants The Behavioral Economics Framingham Incentive Trial (BE FIT) was a randomized clinical trial with a 12-week intervention period and a 12-week follow-up period. The investigation was a community-based study between December 7, 2015, and August 14, 2016. Participants in the modified intent-to-treat analysis were adults enrolled in the Framingham Heart Study, a long-standing cohort of families. Interventions All participants tracked daily step counts using a wearable device or a smartphone, established a baseline, selected a step goal increase, and received daily individual feedback on goal performance by text message or email for 24 weeks. Families in the gamification arm could earn points and progress through levels based on physical activity goal achievement during the 12-week intervention. The game design was meant to enhance collaboration, accountability, and peer support. Main Outcomes and Measures The primary outcome was the proportion of participant-days that step goals were achieved during the intervention period. Secondary outcomes included the proportion of participant-days that step goals were achieved during the follow-up period and the change in the mean daily steps during the intervention and follow-up periods. Results Among 200 adults comprising 94 families, the mean age was 55.4 years, and 56.0% (n = 112) were female. During the intervention period, participants in the gamification arm achieved step goals on a significantly greater proportion of participant-days (0.53 vs 0.32; adjusted difference, 0.27; 95% CI, 0.20-0.33; P < .001) and had a significantly greater increase in the mean daily steps compared with baseline (1661 vs 636; adjusted difference, 953; 95% CI, 505-1401; P < .001) than the control arm. During the follow-up period, physical activity in the gamification arm declined but remained significantly greater than that in the control arm for the proportion of participant-days achieving step goals (0.44 vs 0.33; adjusted difference, 0.12; 95% CI, 0.05-0.19; P < .001) and the mean daily steps compared with baseline (1385 vs 798; adjusted difference, 494; 95% CI, 170-818; P < .01). Conclusions and Relevance Gamification designed to leverage insights from behavioral economics to enhance social incentives significantly increased physical activity among families in the community.

Physical Activity Trackers in Combination with Motivational Interviewing to Increase Activity (2017)

Medicine & Science in Sports & Exercise

Abstract

Evidence is equivocal regarding the benefits of wearable technology for increasing physical activity. Use of these devices in combination with health coaching strategies like motivational interviewing (MI) may be more effective. PURPOSE: The study examined if physical activity trackers increase activity levels in healthy adults and if the addition of MI results in greater benefits. A secondary purpose was to examine characteristics of those who were successful in increasing physical activity versus those who were not in order to determine who is more likely to benefit from this type of intervention. METHODS: Ninety-four healthy men and women (mean age 41 ± 9 years) were randomly assigned to one of two groups for a 12-week intervention. Groups received either 1) a physical activity tracker (PAT) alone, or 2) a physical activity tracker and three sessions of MI (PAT+MI). Physical activity was assessed pre and post-intervention with accelerometers. Average steps per day were compared within and between groups pre- and post-intervention using paired and independent sample t-tests. Participants were then split into two groups based on whether they increased their mean daily step count from baseline. These post-hoc groups were then compared on demographic and baseline physical activity characteristics. RESULTS: Complete data were collected on 84 individuals. Physical activity measured in average steps per day did not increase significantly for either group (PAT+MI – pre: 7496 ± 2895 steps/day, post: 7624 ± 3557 steps/day; PAT – pre: 7519 ± 2259 steps/day, post: 7097 ± 2179 steps/day; p>0.05); further, no group differences were observed (p>0.05). When comparing those who improved over the intervention to those who did not, there were no differences in demographic characteristics including age, gender, income, or education level. However, those who improved over the intervention accumulated significantly fewer steps at baseline (6650 ± 2056 vs. 8522 ± 2871, p < 0.0001). CONCLUSION: The provision of a physical activity tracker (with or without brief MI sessions) was not sufficient to increase physical activity in this sample; however, individuals with low baseline activity achieved more significant benefits.

Wellness, sleep and exercise in emergency medicine residents: an observational study (2017)

Canadian Journal of Emergency Medicine

Abstract

Introduction: Burnout is well documented in residents and emergency physicians. Wellness initiatives are becoming increasingly prevalent, but there is a lack of data supporting their efficacy. In some populations, a relationship between sleep, exercise and wellness has been documented, but this relationship has not been established in emergency medicine (EM) residents or physicians. We aim to determine whether exercise and sleep quality and quantity as measured by a Fitbit are associated with greater perceived wellness in EM residents. Methods: Fifteen EM residents from two training sites wore a Fitbit during a 4-week EM rotation. The Fitbit recorded data on sleep quantity (minutes sleeping)/quality (sleep disruptions) and exercise quantity (daily step count)/quality (daily active minutes performing activity of 3-6 and >6 metabolic equivalents). Participants completed an end-of-rotation Perceived Wellness Survey (PWS) which provided information on six domains of personal wellness (psychological, emotional, social, physical, spiritual and intellectual). Associations between PWS scores and the Fitbit markers were evaluated using a Mann-Whitney-U statistical analysis. Results: Preliminary results indicate that residents who scored =50th percentile for sleep quantity had significantly higher PWS scores than those who scored =50th percentile (median PWS 17.0 vs 13.0 respectively, p=0.04). There was no significant correlation between PWS scores, sleep interruptions, daily step count and average daily active minutes. Postgraduate Year PGY1 and PGY2-5 report median PWS scores of 13.9 and 17.2 respectively. Conclusion: To our knowledge, this is the first study to objectively measure the quality and quantity of sleep as well as exercise habits of EM residents using a Fitbit device. Our data indicates a significant relationship between better sleep quantity and higher wellness scores in this population. We aim to enroll 30 residents in order to obtain a more robust data set. A larger sample size will increase statistical power and allow us to more extensively evaluate the use of exercise and sleep monitoring devices in the efficacy assessment of wellness initiatives.

Step It Up: Higher Step Count Is Significantly Correlated with Better Exercise Capacity in Individuals with Cystic Fibrosis (2017)

American Journal of Respiratory and Critical Care Medicine

Abstract

Rationale: The positive health benefits of physical activity (PA) have been extensively studied and commonly accepted. The CDC and the American College of Sports Medicine have recommended regular PA to reduce risk factors for chronic disease including physical functioning, aerobic fitness and quality of life. PA in individuals with cystic fibrosis (CF) improves exercise capacity, slows decline in lung function, increases mucus clearance and improves health-related quality of life. Hypothesis: Increased daily PA as measured by step count in participants with CF will be associated with greater exercise capacity as measured by the Modified Shuttle Walk Test (MSWT). Methods: We completed the MSWT and spirometry in 22 participants at enrollment into the Do More, B’More, Live Fit trial. This randomized control trial is a 6-month fitness training program designed to optimize exercise habits of 12-21 year-olds. Participants received a FitBit Flex (Fitbit© Boston, MA). Daily step count was assessed over 7 consecutive days within the enrollment period prior to any intervention. The step count variables assessed included mean, median and highest daily step count. Lung function was measured via spirometry and was compared to both FitBit step count and the number of MSWT shuttles completed using multiple linear regression analysis and Spearman Rho correlation. Results: MWST shuttles were significantly correlated with better lung function as measured by FEV1pp, Rho=0.58 (p=0.005). FitBit step count (mean and maximum) was significantly correlated to performance on MSWT, Rho=0.53 (p=0.01) and Rho=0.58 (p=0.005) respectively. The association between mean FitBit step count with MSWT performance remained significant after controlling for FEV1pp, [overall model R2=0.67; step count B=0.006 (p=0.001)]. Similar results were found using the median step count and highest daily value step count values. Conclusions: Measuring FitBit daily step count provides a novel approach to assessing effects of PA on exercise capacity in individuals with CF. More daily steps significantly correlated with better MSWT performance. Based on these data, it is suggested that daily fitness as reflected in FitBit step count may be a significant predictor for exercise capacity even after accounting for disease severity as measured by lung function. Within the context of the Do More, B’More, Live Fit trial, the value of step counts as a real-world measure of changes in exercise capacity may be further elucidated.

Accuracy And Reliability Of The Fitbit Charge? Activity Tracker Among Older Adults (2017)

Medicine & Science in Sports & Exercise

Abstract

Wearable technology, including activity trackers, remains a top fitness trend world-wide; however, the ability of individual activity trackers to accurately and consistently record physical activity remains unknown. PURPOSE: To evaluate the accuracy and reliability of step counts from the Fitbit Charge™ among older adults. METHODS: Thirteen participants with a mean age of 70.38 ±4.27 years volunteered to participate and completed all testing. Participants completed a 96 meter walk around a gymnasium while wearing the Fitbit Charge™ activity tracker. Step counts on the activity tracker were recorded before and after the walk to determine the step count. The walk was also video recorded to determine the actual number of steps taken during the walk. Step counts from video analysis were confirmed by two researchers. The same procedures were repeated on a second, non-consecutive day of testing to determine the reliability of the activity tracker. Accuracy of the activity tracker was determined by comparing step counts to the observed step counts from each testing session. The reliability of the activity tracker was determined by correlation analysis and comparison of step counts from the first testing session to the second testing session. RESULTS: The activity tracker significantly underestimated observed steps at both testing sessions by 21.31 steps and 22.62 steps, respectively (p < .05). No difference in step count from the activity tracker was seen from session one to session two (151.85 steps vs. 152.54 steps, p > .05); however, the correlation between the two sessions was only moderate, r = .55, p = .05. CONCLUSION: Among older adults, the Fitbit Charge™ appears to underestimate steps taken even over a short distance. The reliability of the Fitbit Charge™ is questionable given only a moderate correlation between sessions. While preliminary, these results call into question the accuracy and reliability of daily step counts from the Fitbit Charge™. It is suggested that all new activity trackers to hit the market are given careful study to determine their ability to accurately measure daily activity.

Validation of a self-monitoring tool for use in exercise therapy. (2017)

PM&R

Abstract

BACKGROUND: Aerobic exercise at a sub-symptom heart rate has been recommended as therapy for Post-Concussion Syndrome (PCS). Assessing adherence with an accurate heart rate monitoring instrument is difficult limiting the proliferation of large-scale randomized controlled trials. OBJECTIVE: To evaluate the validity of the Fitbit Charge HR against electrocardiogram (EKG) to monitor heart rate during a treadmill-based exercise protocol. DESIGN: A methods comparison study. SETTING: Sports medicine research center within a tertiary care institution. PARTICIPANTS: A convenience sample of 22 healthy participants (12 female) aged 18-26 years (mean age: 22 ± 2 years). METHODS: Fitbit Charge HR heart rate measurements were compared to EKG data concurrently collected while participants completed the Buffalo Concussion Treadmill Test (BCTT). MAIN OUTCOME MEASURES: Agreement between Fitbit Charge HR and EKG was assessed by intraclass correlation coefficients (ICC3,1), Bland-Altman limits of agreement, and percent error. RESULTS: We observed a strong single-measure absolute agreement between Fitbit Charge HR and EKG (ICC3,1 = 0.83; 95% CI: 0.67 - 0.90). Fitbit Charge HR underestimated heart rate compared to EKG (mean difference = -6.04 beats per min (bpm); SD = 10.40 bpm; Bland-Altman 95% limits of agreement = -26.42 to 14.35 bpm). 69.9% of Fitbit heart rate measurements were within 10% error compared to EKG, and 91.5% of all heart rate measurements were within 20% error. CONCLUSIONS: While the mean bias in measuring heart rate was relatively small, the limits of agreement between the Fitbit Charge HR and EKG were broad. Thus, the Fitbit Charge HR would not be a suitable option for monitoring heart rate within a narrow range. For the purposes of post-concussion exercise therapy, the relatively inexpensive cost, easy implementation, and low maintenance make Fitbit Charge HR a viable option for assessing adherence to an exercise program when expensive clinical equipment is unavailable.

A Six-Week Supervised Exercise and Educational Intervention After Total Hip Arthroplasty: A Case Series (2017)

The International Journal of Sports Physical Therapy

Abstract

Background and Purpose: Most rehabilitation interventions after total hip arthroplasty (THA) are not designed to return patients to high-levels of physical activity and, thus, low levels of physical activity and residual weakness are common. The purpose of this case series was to describe the feasibility and preliminary efficacy of an exercise and education intervention for patients after THA who have already completed formal outpatient physical therapy. Study Design: Case series Case Description: Two participants underwent unilateral THA seven (case A) or eight (case B) months prior to the intervention. Individuals participated in 18 treatment sessions that included progressive aerobic and strengthening exercises and meetings with a health coach. Change in function, strength, and self-reported physical activity were measured. Outcomes 12 months after surgery were compared to a historical cohort of patients after THA. Outcome: There were no adverse events during the intervention. At the end of the intervention, hip and knee strength on the surgical side increased approximately 30% compared to baseline in both cases. Activity level, and recreational performance, including walking up stairs and hiking uphill (case A), and running and golfing (case B), improved by the end of the intervention. Most changes were maintained at follow-up, although hip strength for case B decreased 27% after discharge from the intervention. Outcomes for both cases exceeded historical averages for patients 12 months after THA, with the exception of strength in case B. Discussion: The exercise intervention was well tolerated and without negative effects in two participants. Both par-ticipants increased their ability to complete demanding recreational and sports-related activities, physical activity, and demonstrated improved hip abductor and knee extensor strength. Further research is needed to evaluate the implementation and effectiveness of similar interventions after THA. Acknowledgements: We would like to acknowledge Alan Jette, PT, PhD, FAPTA and Stefano Zaffagnini, MD for critically reviewing our work; Tara Leonard, MS who carried out the health coach portion of the intervention; and Michael Brian, PhD for performing the graded exercise tests.

Effectiveness of SmartMoms, a Novel eHealth Intervention for Management of Gestational Weight Gain: Randomized Controlled Pilot Trial (2017)

JMIR mHealth and uHealth

Abstract

Background: Two-thirds of pregnant women exceed gestational weight gain (GWG) recommendations. Because excess GWG is associated with adverse outcomes for mother and child, development of scalable and cost-effective approaches to deliver intensive lifestyle programs during pregnancy is urgent. Objective: The aim of this study was to decrease the proportion of women who exceed the Institute of Medicine (IOM) 2009 GWG guidelines. Methods: In a parallel-arm randomized controlled trial, 54 pregnant women (age 18-40 years) who were overweight (n=25) or obese (n=29) were enrolled to test whether an intensive lifestyle intervention (called SmartMoms) decreased the proportion of women with excess GWG, defined as exceeding the 2009 IOM guidelines, compared to no intervention (usual care group). The SmartMoms intervention was delivered through mobile phone (remote group) or in a traditional in-person, clinic-based setting (in-person group), and included a personalized dietary intake prescription, self-monitoring weight against a personalized weight graph, activity tracking with a pedometer, receipt of health information, and continuous personalized feedback from counselors. Results: A significantly smaller proportion of women exceeded the IOM 2009 GWG guidelines in the SmartMoms intervention groups (in-person: 56%, 10/18; remote: 58%, 11/19) compared to usual care (85%, 11/13; P=.02). The remote intervention was a lower cost to participants (mean US $97, SD $6 vs mean US $347, SD $40 per participant; Ptextless.001) and clinics (US $215 vs US $419 per participant) and with increased intervention adherence (76.5% vs 60.8%; P=.049). Conclusions: An intensive lifestyle intervention for GWG can be effectively delivered via a mobile phone, which is both cost-effective and scalable. Trial Registration: Clinicaltrials.gov NCT01610752; https://clinicaltrials.gov/ct2/show/NCT01610752 (Archived by WebCite at http://www.webcitation.org/6sarNB4iW) [JMIR Mhealth Uhealth 2017;5(9):e133]

A cluster-randomised controlled trial to promote physical activity in adolescents: the Raising Awareness of Physical Activity (RAW-PA) Study. (2017)

BMC Public Health

Abstract

BACKGROUND Recent technological advances provide an alternative yet underutilised opportunity for promoting physical activity in youth. The primary aim of the Raising Awareness of Physical Activity (RAW-PA) Study is to examine the short- and longer-term impact of a wearable activity monitor combined with digital behaviour change resources on adolescents' daily physical activity levels. METHODS/DESIGN RAW-PA is a 12 week, multicomponent physical activity intervention that utilises a popular activity tracker (Fitbit® Flex) and supporting digital materials that will be delivered online via social media. The resources target key behaviour change techniques. The intervention structure and components have been informed by participatory research principles. RAW-PA will be evaluated using a cluster randomised controlled trial design with schools as the unit of randomisation. Twelve schools located in Melbourne, Australia, will allocated to either the intervention or wait-list control group. The target sample size is 300 Year 8 adolescents (aged 13-14 years). Participants' moderate- to vigorous-intensity physical activity will be the primary outcome. Survey measures will be completed. Process factors (e.g. feasibility, acceptability/appeal, fidelity) will also be collected. DISCUSSION To our knowledge, this study will provide some of the first evidence concerning the effect of wearable activity trackers and digital behaviour change resources on adolescents' physical activity levels. This study will provide insights into the use of such technologies for physical activity promotion, which may have a significant impact on health education, promotion, practice and policy. Tregistration: July 7, 2016.

Smartphone-Enabled Health Coaching Intervention (iMOVE) to Promote Long-Term Maintenance of Physical Activity in Breast Cancer Survivors: Protocol for a Feasibility Pilot Randomized Controlled Trial (2017)

JMIR Research Protocols

Abstract

Background: Although physical activity has been shown to contribute to long-term disease control and health in breast cancer survivors, a majority of breast cancer survivors do not meet physical activity guidelines. Past research has focused on promoting physical activity components for short-term breast cancer survivor benefits, but insufficient attention has been devoted to long-term outcomes and sustained exercise adherence. We are assessing a health coach intervention (iMOVE) that uses mobile technology to increase and sustain physical activity maintenance in initially inactive breast cancer survivors. Objective: This pilot randomized controlled trial (RCT) is an initial step in evaluating the iMOVE intervention and will inform development of a full-scale pragmatic RCT. Methods: We will enroll 107 physically inactive breast cancer survivors and randomly assign them to intervention or control groups at the University Health Network, a tertiary cancer care center in Toronto, Canada. Participants will be women (age 18 to 74 years) stratified by age (55 years and older/younger than 55 years) and adjuvant hormone therapy (AHT) exposure (AHT vs no AHT) following breast cancer treatment with no metastases or recurrence who report less than 60 minutes of preplanned physical activity per week. Both intervention and control groups receive the 12-week physical activity program with weekly group sessions and an individualized, progressive, home-based exercise program. The intervention group will additionally receive (1) 10 telephone-based health coaching sessions, (2) smartphone with data plan, if needed, (3) supportive health tracking software (Connected Wellness, NexJ Health Inc), and (4) a wearable step-counting device linked to a smartphone program. Results: We will be assessing recruitment rates; acceptability reflected in selective, semistructured interviews; and enrollment, retention, and adherence quantitative intervention markers as pilot outcome measures. The primary clinical outcome will be directly measured peak oxygen consumption. Secondary clinical outcomes include health-related quality of life and anthropometric measures. All outcome measures are administered at baseline, after exercise program (month 3), and 6 months after program (month 9). Conclusions: This pilot RCT will inform full-scale RCT planning. We will assess pilot procedures and interventions and collect preliminary effect estimates. Clinical Trial: ClinicalTrials.gov NCT02620735; https://clinicaltrials.gov/ct2/show/NCT02620735 (Archived by WebCite at https://clinicaltrials.gov/ct2/show/NCT02620735) [JMIR Res Protoc 2017;6(8):e165]

Accuracy of Fitbit Charge 2 Worn at Different Wrist Locations During Exercise (2017)

Medicine & Science in Sports & Exercise

Abstract

Many newly released activity monitors use heart rate measured at the wrist to estimate exercise intensity, however, where the device is placed on the wrist may affect accuracy of the measurement. PURPOSE: To determine whether the Pure Pulse technology on the Fitbit Charge 2 will show different heart rate readings when placed on the recommended exercise position compared to the all-day wear position at various exercise intensities. METHODS: Thirty five participants (MEAN ± SD; 22.0 ± 2.9yrs; 23.9 ± 2.6kg/m2; 18 male) consented to participate in a single visit where two Fitbit Charge 2 devices were placed on the non-dominant wrist. Fitbit A was placed 2-3 fingers above the wrist bone. Fitbit B was placed directly above the wrist bone. The treadmill was set at 3 mph with 0% grade. Participants remained at this speed for 4 minutes. Heart rate measurements were taken at the last 10 seconds of each stage from both Fitbits and a polar heart rate monitor (chest strap). The same procedure was followed for 5 and 6 mph. Statistical analyses were performed using IBM SPSS 23.0. A Two-way (speed x location) Repeated Measures ANOVA was used to examine mean differences. Pairwise comparisons with Bonferroni correction were used in post-hoc analysis. Pearson correlations and mean bias between polar heart rate monitor and activity monitors were also calculated for each speed. RESULTS: Repeated Measures ANOVA found significant differences between speeds (ptextless0.01) and location (ptextless0.01), but not for the interaction (p=0.234). Pairwise comparisons indicated significant differences between each speed (ptextless0.01) and between the polar monitor and Fitbit B (ptextless0.05), but not between the polar monitor and Fitbit A (p=0.608). Pearson correlations indicated strong correlations between each Fitbit and the polar monitor (r= .58-.91; all ptextless0.01). Mean bias decreased as speed increased for Fitbit A (mean bias bpm ± SD; -1.1 ± 5.4; -1.9 ± 9.5; -0.4 ± 6.9; -0.3 ± 7.3 for resting, 3mph, 5mph, 6mph respectively) while mean bias for Fitbit B increased as speed increased (-2.8 ± 8.8; -3.1 ± 11.1; -3.9 ± 14.6; -6.7 ± 14.3 for resting, 3mph, 5mph, 6mph respectively). CONCLUSION: Wrist-worn heart rate monitors appear to provide values adequate for recreational use, however, following recommended guidelines on wear-position may impact heart rate readings.

Piloting a mobile health intervention to increase physical activity for adolescents with ADHD (2017)

Preventive Medicine Reports

Abstract

Physical activity (PA) reduces symptoms of Attention Deficit Hyperactivity Disorder (ADHD); interventions to increase PA may improve functioning and health for adolescents with ADHD. Mobile health (mHealth) technology and social media constitute promising interactive modalities for engaging adolescents—who are at highest risk for ADHD treatment drop-out—in interventions to increase PA. The current pilot study evaluated feasibility and acceptability of an innovative intervention incorporating an mHealth-linked wearable activity tracker (Fitbit Flex) and a Facebook group to increase PA among adolescents with ADHD. 11 adolescents diagnosed with ADHD (age 14–18, m=15.5; 54% female) participated in a 4-week trial utilizing the Fitbit Flex in conjunction with (1) weekly personalized step count goals (2) social support through a Facebook group and (3) daily text messages about PA. The study took place in the greater Seattle, Washington area in the fall of 2015. Adolescents completed online surveys twice per week to rate their ADHD symptoms and positive and negative mood states, and parents rated adolescent ADHD symptoms weekly. Participants were adherent to the study protocol and acceptability of the intervention was high. Linear mixed models indicated that participants significantly increased their average weekly steps over the course of the study and demonstrated improvements in both adolescent and parent-reported ADHD Inattentive symptoms. Results indicate that this mHealth intervention is engaging and promising for increasing PA among adolescents with ADHD, and warrant further study. Implications for improving ADHD symptoms and overall functioning for this undertreated population are discussed.

What factors predict Fitbit adherence in Stroke and Parkinson disease? (2017)

Neurology

Abstract

Objective: To investigate predictors of adherence to wearing the Fitbit activity monitor in patients with Parkinson disease (PD) and stroke. Background: Exercise and activity improve symptoms of PD and stroke, and aid in primary and secondary stroke prevention. However, adherence to physical activity programs is low and little is known about adherence to daily activity monitoring. Design/Methods: Patients (n=19, age=58.8+/-11, range 37–83Y) with PD (n=12) and stroke (n=8) participated in a pilot study to investigate feasibility and adherence of Fitbit monitoring to track activity between clinical office visits. Results: Comparing PD and stroke, PD patients were more disabled (Rankin; p= .004) but more likely to be employed (p= .03). Stroke patients were more depressed (PROMIS; p=.045), more cognitively impaired (MoCA; p=.02) and had lower numeracy (Subjective Numeracy Scale; p=.03). Fitbit data was collected for 28–53 days (mean=32.9). Patients used the Fitbit for 86% of days and 13.5 hours/day on average. Average steps/day were 6041+/-2797. The only significant predictor of adherence to the Fitbit (% of days used) was medical co-morbidity (r=-.46, p=.047) such that those with greater comorbidity (Cumulative Illness Rating Scale) wore the Fitbit less. Other high correlations with adherence (but nonsignificant) were: PROMIS Self-Efficacy for Managing Medications (r=.57), Self-Efficacy for Managing Social Interactions (r=.46), greater e-Health Literacy (eHEALs; r=.39), and less pain (r=-.37). Adherence showed low correlation (rtextless.2) with disease severity, disability, cognition, depression, age and socioeconomic status. Conclusions: The strongest predictor of adherence to daily activity monitoring was level of medical co-morbidity. Other determinants of adherence were self-efficacy for managing conditions, e-Health literacy and pain. Age and level of disability did not affect daily use of small wireless monitors to track activity in stroke and PD.

Assessing the Influence of a Fitbit Physical Activity Monitor on the Exercise Practices of Emergency Medicine Residents: A Pilot Study (2017)

JMIR mHealth and uHealth

Abstract

Background: Targeted interventions have improved physical activity and wellness of medical residents. However, no exercise interventions have focused on emergency medicine residents. Objective: This study aimed to measure the effectiveness of a wearable device for tracking physical activity on the exercise habits and wellness of this population, while also measuring barriers to adoption and continued use. Methods: This pre-post cohort study enrolled 30 emergency medicine residents. Study duration was 6 months. Statistical comparisons were conducted for the primary end point and secondary exercise end points with nonparametric tests. Descriptive statistics were provided for subjective responses. Results: The physical activity tracker did not increase the overall self-reported median number of days of physical activity per week within this population: baseline 2.5 days (interquartile range, IQR, 1.9) versus 2.8 days (IQR 1.5) at 1 month (P=.36). There was a significant increase in physical activity from baseline to 1 month among residents with median weekly physical activity level below that recommended by the Centers for Disease Control and Prevention at study start, that is, 1.5 days (IQR 0.9) versus 2.4 days (IQR 1.2; P=.04), to 2.0 days (IQR 2.0; P=.04) at 6 months. More than half (60%, 18/30) of participants reported a benefit to their overall wellness, and 53% (16/30) reported a benefit to their physical activity. Overall continued use of the device was 67% (20/30) at 1 month and 33% (10/30) at 6 months. Conclusions: The wearable physical activity tracker did not change the overall physical activity levels among this population of emergency medicine residents. However, there was an improvement in physical activity among the residents with the lowest preintervention physical activity. Subjective improvements in overall wellness and physical activity were noted among the entire study population. [JMIR Mhealth Uhealth 2017;5(1):e2]

Fitbit Vs Six Minute Walk in PAH (2017)

American Journal of Respiratory and Critical Care Medicine

Abstract

Rationale: The six minute walk test is a widely used instrument for monitoring and prognostication in patients with pulmonary arterial hypertension (PAH). It is a valid measure of exercise capacity and has been used as an endpoint in clinical trials. Limitations include a ‘ceiling effect’ in patients with minimal functional limitation and confounding factors affecting mobility. Accelerometers measure an individual's activity in step counts. A piezo-electric crystal measures acceleration, which is quantified as steps. Accelerometers have been validated as a measure of step count in patients with chronic lung disease. In patients with PAH, activity measured by an accelerometer has correlated with WHO functional class, six minute walk distance and survival. We prospectively studied the use of a wrist worn accelerometer (Fitbit ChargeHR) as a monitoring tool in patients with PAH. We present initial data from the first two weeks of patient monitoring. Methods: 30 ambulatory patients with WHO group one PAH were enrolled. A Fitbit ChargeHR was worn by the patient on the wrist until their next follow up visit. Six minute walk test, physical exam and WHO functional class stratification were done during the visits. Serum biomarkers and ECHO were done if clinically indicated. Pearson correlation test was used for continuous variables. ANOVA was performed for categorical variables. Results: 30 patients with WHO group one PAH (23- idiopathic, 7- connective tissue disease) with a mean age 50.49 ± 12.97 were enrolled. Mean six minute walk distance (6MWD) was 368.6 ± 144 m, WHO functional class classification was FC I- 10%, II-46%, III-36 %, IV- 2% The mean daily step count (r 0.55, p 0.004) and peak daily six minute step counts (r 0.44, p 0.01) in the first two weeks, correlated with the baseline 6MWD. Mean daily step count correlated with ability to self-care, but not with mobility, usual care, anxiety or pain. Both the daily step count and 6MWD did not correlate with NTproBNP levels and ECHO parameters (RA dilatation, LA dilatation, RV function, RVSP). Correlation of Step count with six minute walk distance – Pearson correlation Mean step count (two weeks) R=0.55 P=0.004 Peak daily six minute walk step count R=0.46 P=0.01 Conclusions: We demonstrate that daily step count measured by a wrist worn accelerometer (Fitbit ChargeHR) correlates with the 6MWD and ability to self-care in patients with PAH. 6MWD and daily step counts in our patients did not correlate with other PAH parameters measured. The Fitbit ChargeHR can be used to measure activity and a change over time may be useful monitoring tool.

"Pre-Habilitation" of Frail Candidates for Lung Transplantation Using a Mobile Health Enabled Home-Based Intervention Is Feasible and Safe (2017)

American Journal of Respiratory and Critical Care Medicine

Abstract

Rationale: Frailty is prevalent in lung transplant candidates and is associated delisting or death on the waiting list. Studies in community dwelling older adults suggest that frailty may be reversible through exercise-based intervention. It is unknown whether such an intervention in advanced lung disease is safe. Herein, we report early feasibility and safety data on a pilot intervention to treat frailty in lung transplant candidates using novel mobile health technologies. Methods: In March 2016, we launched a pilot, home-based combined exercise and nutrition intervention in lung transplant candidates diagnosed with COPD or pulmonary fibrosis. We customized a commercially available telehealth platform (AidCube) originally designed for home-based pulmonary rehabilitation (PR). The platform features a participant-facing app and a web-based provider interface, allowing for real-time interaction and communication between participants and study personnel. In our protocol, an in-person assessment and training is performed and measures of frailty (Short Physical Performance Battery), disability, and health-related quality of life collected. Subjects are provided tablets loaded with the customized app and can choose from a tailored menu of exercises based on ATS/ERS PR recommendations. Subjects are taught how to safely perform the exercises in person and are provided Fitbit activity trackers. Once home, adherence, safety, and progress is assessed in real-time through AidCube, Fitbit and weekly phone check-ins. Based on participant feedback, exercises are progressed via the provider interface. After 8-weeks, measures of frailty, disability, HRQL, are reassessed. Results: To date, we have enrolled 15 participants (target n=24). Participants to date are 31% female with a mean age of 62 years (standard deviation [SD]=5), FEV1% predicted 48 (SD=27), and median LAS 35 (25, 75%: 33, 37). Over 209 participant-weeks of the intervention, we have observed no adverse events (i.e., falls/injuries, desaturations <85%, respiratory complications). Qualitatively, participants report the “app” platform is easy to use and that the embedded gamification features and Fitbit trackers help in motivation. Early adherence appears reasonable. Participants have completed a median of 44 exercises per week (95% confidence interval: 27, 55). Of the eight subjects who have completed the intervention, six adhered; of these, four demonstrated improvement in SPPB frailty by at least 1 point on a 12-point scale. Conclusions: Preliminarily, a home-based pre-habilitation program to treat frailty in lung transplant candidates using a novel mobile health technology appears feasible and safe. Also, our very early data support that frailty is modifiable in a population of adults with end-stage lung disease.

Accuracy Of Steps, Energy Expenditure, And Distance In Nine Activity Trackers (2017)

Medicine & Science in Sports & Exercise

Abstract

PURPOSE: To evaluate the accuracy of the step counts, energy expenditure (EE), and distance measured from 9 consumer grade activity trackers. METHODS: Twenty participants completed 1 mile of walking followed by 1 mile of running on a treadmill in the lab. Participants completed 3 sessions of exercise while wearing as many as 4 devices set up for their height, weight, and age in addition to an Actigraph GT3X (GT3X) accelerometer. Devices included in the study were (1) the Fitbit Surge (FBS), Charge (FBC), and Charge HR (FBH); (2) the Garmin Vivoactive (GVA) and Vivosmart HR (GVS); (3) the Jawbone UP2 and UP3; (4) the Polar Loop and; (5) the Microsoft Band 2 (MSB). Data from the devices were compared to the GT3X for steps and American College of Sports Medicine metabolic equations (ACSMME) for estimated EE. Distance recorded by the devices was compared to the 1-mile treadmill completed distance. RESULTS: The GVA and GVS performed best with accurate measures for running steps counted, walking EE, running EE, and walking distance (p < .05). The FBH and UP3 performed worst with only accurate step counts for running (p < .05). The FBH and FBS underestimated walking steps by 370 steps (p < .000) and 318 steps (p = .002) respectively. Only the PL and UP2 were accurate for steps counted at a walking pace; however, both devices underestimated steps at 48 steps (p = .227) and 86 steps (p = .06) respectively. CONCLUSION: Few devices accurately measured steps at speeds lower than 4.5mph when compared to a validated accelerometer; however, most devices may accurately measure steps taken at speeds greater than 4.5mph. Few devices accurately measured EE for a 1-mile walk or run when compared to ACSMME. Few devices accurately measured distance for a walk, and none of the devices accurately measured distance for a run. It is recommended that accuracy should be considered when using activity tracking devices for the measurement of daily physical activity.

Healthy lifestyle intervention for African American uterine cancer survivors: Study protocol (2017)

Contemporary Clinical Trials Communications

Abstract

BACKGROUND Cancer of the uterine corpus is the most common gynecologic malignancy and the fourth most common cancer in U.S. women. There is a racial disparity in the survival from endometrial cancer and this may be addressed by culturally-tailored lifestyle interventions to help African American (AA) endometrial cancer survivors lose weight or maintain a healthy weight. OBJECTIVE The overall purpose of this pilot study is to develop and evaluate a culturally-tailored lifestyle intervention to help AA uterine cancer survivors reduce their risk of cancer recurrence and improve their quality of life through healthy eating, physical activity, and weight management. While many interventions have been evaluated to assist cancer survivors through diet and physical activity, few have focused on AA women with a uterine cancer diagnosis. METHODS Community-engaged research principles are being followed. This study was developed with input from the Augusta University (AU) College of Nursing Community Advisory Board (CAB) and the Division of Gynecologic Oncology at the Georgia Cancer Center at AU. Weekly sessions throughout a 12-week intervention will include physical activity and lectures on improving nutritional status. The pre/post-test design includes baseline and 6-month follow-up, where participants will complete a questionnaire that assesses knowledge and attitudes about physical activity, nutrition, uterine cancer, social support, and quality of life. CONCLUSIONS From this pilot study, we will learn more about the feasibility and integration of healthy lifestyle interventions in this patient population, and the results can provide an opportunity for a larger-scale, multi-center study with a randomized controlled design.

A multicenter controlled study for dementia prevention through physical, cognitive and social activities - GESTALT-kompakt. (2017)

Clinical Interventions in Aging

Abstract

Prevention of dementia is a public health priority. Physical activity (PA) can reduce the risk of dementia, but the majority of people remain sedentary. We conducted a multicenter controlled study with older adults (60+ years). We hypothesized that an evidence-based PA intervention - GEhen, Spielen und Tanzen Als Lebenslange Tätigkeiten - kompakt [walking, playing and dancing as lifelong activities-compact] (GESTALT-kompakt) - would lead to significantly larger improvements in PA levels (step counts/Fitbit Zip™), cognitive functions (DemTect) and social activities (Social Activity Log), compared to an active control group. Data were collected at baseline and after 3 and 12 months. The intervention group received a 12-week (1/week) multimodal and multicomponent PA program, which combined PA with cognitive and social activities. The control group received either regular gymnastics or cognitive training (1/week). A mixed linear model was chosen for analysis. A total of 87 older individuals were recruited in the GESTALT-kompakt study (68 females, average age =76.0 years, SD ±9.2, range 52-95 years). Marginally significant differences were observed in the intervention group (n=57) in comparison to the control group (n=30), regarding improvements in PA (difference of mean changes =866.4 steps,p=0.055) after 3 months. However, their PA decreased to the baseline score value after 12 months (-866.0 steps,p=0.061). GESTALT-kompakt did not cause significant differences in cognitive functioning (-0.8620,p=0.074) and social activities (-0.2428,p=0.288) in comparison to the control intervention from T0 to T1. Sixteen (24.2%) study participants who finished T2 reported a negative life event during the follow-up period, which severely influenced their PA behavior. GESTALT-kompakt might be effective in increasing PA in the short term, but did not have a long-term impact on the PA levels, cognitive functions or social activities of the participants. We recommend PA programs with longer duration to change behavior in the long term.

The Spread of Physical Activity Through Social Networks (2017)

Proceedings of the 26th International Conference on World Wide Web - WWW '17

Abstract

Many behaviors that lead to worsened health outcomes are modifiable, social, and visible. Social influence has thus the potential to foster adoption of habits that promote health and improve disease management. In this study, we consider the evolution of the physical activity of 44.5 thousand Fitbit users as they interact on the Fitbit social network, in relation to their health status. The users collectively recorded 9.3 million days of steps over the period of a year through a Fitbit device. 7,515 of the users also self-reported whether they were diagnosed with a major chronic condition. A time-aggregated analysis shows that ego net size, average alter physical activity, gender, and body mass index (BMI) are significantly predictive of ego physical activity. For users who self-reported chronic conditions, the direction and effect size of associations varied depending on the condition, with diabetic users specifically showing almost a 6-fold increase in additional daily steps for each additional social tie. Subsequently, we consider the co-evolution of activity and friendship longitudinally on a month by month basis. We show that the fluctuations in average alter activity significantly predict fluctuations in ego activity. By leveraging a class of novel non-parametric statistical tests we investigate the causal factors in these fluctuations. We find that under certain stationarity assumptions, non-null causal dependence exists between ego and alter's activity, even in the presence of unobserved stationary individual traits. We believe that our findings provide evidence that the study of online social networks have the potential to improve our understanding of factors affecting adoption of positive habits, especially in the context of chronic condition management.

Fitbit And Actigraph: A Comparison Of Physical Activity And Sedentary Time In Overweight/obese Adults (2017)

Medicine & Science in Sports & Exercise

Abstract

here is limited information regarding the validity of consumer targeted wearable physical activity (PA) trackers, such as the Fitbit, to assess PA in free-living adults. PURPOSE: To compare daily minutes of sedentary time, and moderate-to-vigorous (MVPA) colleted using two types of activity trackers, in a sample of overweight and obese adults (n = 12, BMI= 37±4 kg/m2, age = 48±12 yrs.) who completed a 6-mo. weight loss intervention (diet + self-directed PA) delivered using Facebook. METHODS: Participants wore a Fitbit FlexTM on their non-dominant wrist continuously over 6 mos. They were also asked to wear an ActiGraph GTX1TM on their non-dominant hip for 7 consecutive days at both baseline and 6 mos. Fitbit and ActiGraph data for a minimum of one 10-hr. day at baseline and 6 mos. was required for inclusion in this analysis. Categorization of sedentary time and MVPA was derived from the Fitbit algorithm or using NHANES Actigraph cut-points (Troiano, 2008). RESULTS: Sedentary time was significantly lower and MVPA was significantly higher when assessed with the Fitbit compared with the ActiGraph at both baseline (sedentary time: Fitbit = 427 ±168, ActiGraph = 639 ±132 min/d, p< 0.001; MPVA: Fitbit= 128 ±49, ActiGraph = 18 ±19 min/d, p <0.0001) or 6 mos. (sedentary time: Fitbit = 459 ±168, ActiGraph = 613 ±103 min/d, p= 0.001; MPVA: Fitbit= 123±53, ActiGraph= 14 ±10 min/d, p<0.0001). Change in both sedentary time and MVPA from baseline to 6 mos. was not statistically significant measured either with the Fitbit (sedentary time = +63 ±46, p =0.16; MVPA = -8±13 min/d, p = 0.72) or the ActiGraph (sedentary time = - 49±52, p =0.39; MVPA = -2±13 min/d, p = 0.48). CONCLUSIONS: The Fitbit significantly underestimated sedentary time and overestimated MVPA when compared with the ActiGraph in a small sample. Changes in sedentary time and MVPA over 6 mos. were non-significant when assessed by either the Fitbit or ActiGraph. These observations suggest that the Fitbit, which is relatively inexpensive when compared with the ActiGraph, may be useful for assessing changes in sedentary time and MVPA in response to an intervention. However, the absolute values for sedentary time and MVPA assessed by the Fitbit are questionable, and worthy of additional investigation in larger samples of free-living adults.Funded by Kansas City Life Sciences Institute

Effect of sleep curtailment on dietary behavior and physical activity: A randomized crossover trial (2017)

Physiology & Behavior

Abstract

Our objective was to clarify the effect of sleep curtailment on energy intake (EI) and physical activity under free-living conditions. Participants were 16 healthy women aged 21–22 years. A randomized crossover trial design was used to compare a short sleep condition (SS): 4 h/night (2:00–6:00) and a control sleep condition (CS): 7 h/night (23:00–6:00). Each condition comprised 3 consecutive nights. Sleep duration was assessed using a wristwatch-type accelerometer at home. All living activities except sleeping were free-living. Physical activity was assessed using a tri-axial accelerometer, and was categorized by intensity level (sedentary; sedentary to light; moderate to vigorous). Participants were asked to purchase and consume meals with visible nutrient information. EI was evaluated by adding values from these food labels. Mean sleep duration in the two conditions was significantly different (4.3 ± 0.3 vs. 7.1 ± 0.4 h, p < 0.01). For the shared wakefulness period in the two conditions (6:00–23:00), step counts and physical activity were not significantly different. Sedentary time (878 ± 61 vs. 727 ± 40 min, p < 0.01), and sedentary to light-intensity activity time (1122 ± 18 vs. 932 ± 63 min, p textless 0.01) were significantly increased in SS (waking time, 06:00–02:00) compared with CS (waking time, 06:00–23:00). However, these significant effects were clearly attenuated after adjustment for awake time (p ≥ 0.05). Total EI was not significantly different between conditions (8.64 ± 0.82 vs. 8.46 ± 1.28 MJ, p = 0.05), nor were leptin levels (p > 0.05), but insulin and cortisol levels after SS were significantly higher than after CS (p < 0.05). In this study, physical activity was increased in the SS condition and attributed to differences in awake time between conditions. However, there were no differences in EI. Further studies to investigate the effect of sleep curtailment on weight gain through stress and insulin resistance are necessary.

Study protocol for the FITR Heart Study: Feasibility, safety, adherence, and efficacy of high intensity interval training in a hospital-initiated rehabilitation program for coronary heart disease (2017)

Contemporary Clinical Trials Communications

Abstract

BACKGROUND For decades, moderate intensity continuous training (MICT) has been the cornerstone of exercise prescription for cardiac rehabilitation (CR). High intensity interval training (HIIT) is now recognized in CR exercise guidelines as an appropriate and efficient modality for improving cardiorespiratory fitness, a strong predictor of mortality. However, the clinical application of HIIT in a real world CR setting, in terms of feasibility, safety, and long-term adherence, needs further investigation to address ongoing reservations. Furthermore, studies using objective measures of exercise intensity (such as heart rate; HR) have produced variable outcomes. Therefore we propose investigating the use of subjective measures (such as rating of perceived exertion (RPE)) for prescribing exercise intensity. METHODS One hundred adults with coronary artery disease (CAD) attending a hospital-initiated CR program will be randomized to 1) HIIT: 4 × 4 min high intensity intervals at 15–18 RPE interspersed with 3-min active recovery periods or 2) MICT: usual care exercise including 40 min continuous exercise at a moderate intensity corresponding to 11–13 RPE. Primary outcome is change in exercise capacity (peak VO2) following 4 weeks of exercise training. Secondary outcome measures are: feasibility, safety, exercise adherence, body composition, vascular function, inflammatory markers, intrahepatic lipid, energy intake, and dietary behavior over 12-months; and visceral adipose tissue (VAT) following 12 weeks of exercise training. CONCLUSIONS This study aims to address the ongoing concerns regarding the practicality and safety of HIIT in CR programs. We anticipate study findings will lead to the development of a standardized protocol to facilitate CR programs to incorporate HIIT as a standard exercise option for appropriate patients.

Accuracy of a step counter during treadmill and daily life walking by healthy adults and patients with cardiac disease (2017)

BMJ Open

Abstract

BACKGROUND Step counters have been used to observe activity and support physical activity, but there is limited evidence on their accuracy. OBJECTIVE The purpose was to investigate the step accuracy of the Fitbit Zip (Zip) in healthy adults during treadmill walking and in patients with cardiac disease while hospitalised at home. METHODS Twenty healthy adults aged 39±13.79 (mean ±SD) wore four Zips while walking on a treadmill at different speeds (1.7-6.1 km/hour), and 24 patients with cardiac disease (age 67±10.03) wore a Zip for 24 hours during hospitalisation and for 4 weeks thereafter at home. A Shimmer3 device was used as a criterion standard. RESULTS At a treadmill speed of 3.6 km/hour, the relative error (±SD) for the Zips on the upper body was -0.02±0.67 on the right side and -0.09 (0.67) on the left side. For the Zips on the waist, this was 0.08±0.71 for the right side and -0.08 (0.47) on the left side. At a treadmill speed of 3.6 km/hour and higher, the average per cent of relative error was textless3%. The 24-hour test for the hospitalised patients showed a relative error of -47.15±24.11 (interclass correlation coefficient (ICC): 0.60), and for the 24-hour test at home, the relative error was -27.51±28.78 (ICC: 0.87). Thus, none of the 24-hour tests had less than the expected 20% error. In time periods of evident walking during the 24 h test, the Zip had an average per cent relative error of textless3% at 3.6 km/hour and higher speeds. CONCLUSIONS A speed of 3.6 km/hour or higher is required to expect acceptable accuracy in step measurement using a Zip, on a treadmill and in real life. Inaccuracies are directly related to slow speeds, which might be a problem for patients with cardiac disease who walk at a slow pace.

Step Count Filters in Wearable Step Counters (2017)

Medicine & Science in Sports & Exercise

Abstract

Manufacturers of step counting devices apply filters to their step counting algorithms to prevent accumulation of steps when none are taken (i.e. false positives). However because filters prevent steps from being recorded during short, intermittent walking bouts, it is possible that these filters may be a source of error. Since few manufacturers disclose the type of filter they use, we decided to investigate this topic. PURPOSE: To determine whether the devices used in this study have a filter, and to describe the effects of the filter on short, intermittent walking bouts with varied walk and pause durations. METHODS: In Parts A and B, 20 participants performed intermittent walking bouts for 2 min, at a cadence of 100 steps/min. In Part A participants were instructed to walk a certain number of steps (i.e. 4, 6, 8, 10, and 12) followed by a 10-sec pause and repeat this until the trial ended. In Part B participants were instructed to walk four steps followed by various pause intervals (i.e. 8, 6, 4, 2, and 1 sec) and repeat this. A researcher counted steps using a hand-tally device (criterion). “Percent of actual steps taken” was used for statistical analysis. A one-way repeated measures ANOVA was completed for both parts. In the case of significant overall effects (p textless 0.05), the results were further examined using planned contrasts to see which conditions differed from the criterion. RESULTS: In Parts A and B the multivariate results for ActiGraph GT3X (AG) (without low frequency extension) worn at the wrist, StepWatch 3, and Yamax Digi-Walker SW-200 were not significantly different from the criterion, indicating absence of a step count filter. Walking bouts shorter than 4 steps (AG at the hip), 6 steps (Withings), 8 steps (Omron and Garmin Vivofit 2), and 12 steps (Polar A360), resulted in a significant decrease in the number of steps counted, indicating presence of a filter. The minimum pause needed to break up a walking bout was 1 sec (Fitbit Charge, Fitbit Zip, and Withings), and textless 1 sec (Omron HJ-322U). For both the Polar and Garmin, the longer the pause, the less likely they were to record steps. CONCLUSIONS: Devices with step count filters will contribute to error in daily step counts because steps taken during short, intermittent walking bouts (e.g., meal preparation, and housework) are not registered.

Validity of Different Activity Monitors to Count Steps in an Inpatient Rehabilitation Setting. (2017)

Physical Therapy

Abstract

Background Commonly used activity monitors have been shown to be accurate in counting steps in active people, however, further validation is needed in slower walking populations. Objectives To determine the validity of activity monitors for measuring step counts in rehabilitation inpatients compared to visually-observed step counts. To explore the influence of gait parameters, activity monitor position and use of walkers on activity monitor accuracy. Methods 166 inpatients admitted to a rehabilitation unit with an average walking speed of 0.4 m/s (SD 0.2) wore 16 activity monitors (seven different devices in different positions) simultaneously during 6-minute and 6-metre walks. The number of steps taken during the tests was also counted by a physiotherapist. Gait parameters were assessed using the GAITRite system. To analyse the influence of different gait parameters, the percentage accuracy for each monitor was graphed against various gait parameters for each activity monitor. Results The StepWatch , Fitbit One worn on the ankle and the ActivPAL showed excellent agreement with observed step count (ICC 2,1 0.98; 0.92; 0.78 respectively). Other devices ( Fitbit Charge , Fitbit One worn on hip, G-Sensor , Garmin Vivofit , Actigraph ) showed poor agreement with the observed step count (ICC 2,1 0.12-0.40). Percentage agreement with observed step count was highest for the StepWatch (mean 98%). The StepWatch and the Fitbit One worn on the ankle maintained accuracy in individuals who walked more slowly and with shorter strides but other devices were less accurate in these individuals. Limitations There were small numbers of participants for some gait parameters. Conclusions The StepWatch showed the highest accuracy and closest agreement with observed step count. This device can be confidently used by researchers for accurate measurement of step counts in inpatient rehabilitation in individuals who walk slowly. If immediate feedback is desired, the Fitbit One when worn on the ankle would be the best choice for this population.

Patient-Specific Activity Level Benchmarks During Recovery for Total Knee Arthoplasty (2017)

Orthopaedic Proceedings

Abstract

Introduction: Ambulation in the postoperative period following TKR is a marker of speed of recovery and, potentially longer term outcomes. However, patient lifestyle factors are a major confounder. This study sought to develop a model of expected patient step count taking into account preoperative condition and demographics in order to benchmark recovery at a patient specific level. Method: 94 patients were recruited to the study. BMI, demographics, the Short Form 12 (SF-12) and the Knee injury and Osteoarthritis Outcome Score (KOOS) were all captured preoperatively. Step count was measured using commercially available Fitbit devices preoperatively, immediately postoperatively and at 6 weeks postoperatively. Stepwise multiple linear regression models were developed using the preoperative information to define a predictive model of the postoperative step count levels. Spearman's Rho correlations for all relevant data series were also calculated. Results: Of the personal and clinical characteristics, BMI and the SF-12 physical component score had the strongest correlations with outcome. Prior step count periods all had significant correlations with later step count periods. The most significant correlations occurred between the 6 week postoperative step count period and the preoperative period (0.709), while correlations with the period immediately following surgery were weaker (0.389 and 0.536 for preoperative and 6 week postoperative step counts respectively.) All are significantly different from 0 (to p textless 0.01.) Likewise, BMI had a significantly negative relationship with step count (-0.526, -0.346 and -0.553 for the preoperative, immediate postoperative and 6 week postoperative periods, see Figure 2), as did the KOOS activities of daily living score and the SF-12 physical health component score. Males were significantly less mobile than females during recovery. A multiple linear regression model of 6 week step count using prior data had an adjusted R2 of 0.754, explaining much of the variation, but the immediate postoperative period performed poorly. Predictors in the 6 week model were gender, preoperative SF-12 score, preoperative and immediate postoperative step count. Conclusions: Patient specific factors, including but not limited to that from prior step count periods need to be considered if using step count as a means of benchmarking patient recovery after surgery. The variation in recovery at 6 weeks is more readily explained with the data collected than in the immediate postoperative period, where variations in specific care received, anaesthetic response or surgical outcomes might be more expected to have an impact. Reporting patient performance against customised goals on an individual patient basis could provide a means to drive greater patient mobility and appropriate activity levels during postoperative recovery.

Self-monitoring and reminder texts to increase physical activity after colorectal cancer (Smart Pace): A pilot trial. (2017)

Journal of Clinical Oncology

Abstract

Background: Colorectal cancer (CRC) survivors can improve their quality-of-life (QOL), and potentially survival, by engaging in physical activity. The aim of this pilot randomized controlled trial (RCT) is to determine the feasibility of a technology-based physical activity intervention for CRC survivors. Methods: 40 CRC survivors will be randomized (1:1) to a 12-week physical activity intervention (Fitbit Flex, daily text messages) or usual care. Eligible individuals must: 1) have stage I-III colon or rectal adenocarcinoma; 2) have completed therapy; 3) be considered cancer-free; 4) be English speaking; 5) have no contraindications to moderate physical activity; 6) engage in < 150 min/week of moderate physical activity; and 7) have Internet and a phone that can receive text messages. Our primary outcomes are adherence (e.g., Fitbit wear time, text response rate) and acceptability assessed via survey. Secondary outcomes include change in physical activity via 7-days of ActiGraph GT3X+ accelerometers and QOL. Results: We have screened 350 individuals with stage I-III CRC at the University of California, San Francisco (UCSF) for eligibility. Of these, 181 (52%) were not eligible [non-English speaking (46%), = 150 min/week of physical activity (22%), contraindications to physical activity (22%), not cancer-free (7%), did not own a mobile phone (2%)]. We invited the remaining 169 eligible CRC survivors to participate by mail, e-mail, phone, or in clinic; 76 (45%) actively declined and 59 (35%) did not respond after up to 3 contact efforts. As of October 2016, 34 CRC survivors have been randomized to intervention (n = 16) or control (n = 18). These individuals are: 55% female; 64% Non-Hispanic White; 73% have a 4-y college degree; and 64% work full-time. The median (IQR) age and BMI are: 56 (50, 65) y and 26.4 (23.2, 32.4) kg/m2, respectively. Conclusions: The results of this pilot study will inform a definitive RCT to determine whether a technology-based physical activity intervention improves QOL and survival after CRC diagnosis. Funding: This research was supported by the NIH (K07CA197077, KL2TR000143), Mt. Zion Health Fund and UCSF Helen Diller Family Comprehensive Cancer Center.

The Fitbit One Physical Activity Tracker in Men With Prostate Cancer: Validation Study (2017)

JMIR Cancer

Abstract

BACKGROUND Physical activity after cancer diagnosis improves quality of life and may lengthen survival. However, objective data in cancer survivors are limited and no physical activity tracker has been validated for use in this population. OBJECTIVE The aim of this study was to validate the Fitbit One's measures of physical activity over 7 days in free-living men with localized prostate cancer. METHODS We validated the Fitbit One against the gold-standard ActiGraph GT3X+ accelerometer in 22 prostate cancer survivors under free-living conditions for 7 days. We also compared these devices with the HJ-322U Tri-axis USB Omron pedometer and a physical activity diary. We used descriptive statistics (eg, mean, standard deviation, median, interquartile range) and boxplots to examine the distribution of average daily light, moderate, and vigorous physical activity and steps measured by each device and the diary. We used Pearson and Spearman rank correlation coefficients to compare measures of physical activity and steps between the devices and the diary. RESULTS On average, the men wore the devices for 5.8 days. The mean (SD) moderate-to-vigorous physical activity (MVPA; minutes/day) measured was 100 (48) via Fitbit, 51 (29) via ActiGraph, and 110 (78) via diary. The mean (SD) steps/day was 8724 (3535) via Fitbit, 8024 (3231) via ActiGraph, and 6399 (3476) via pedometer. Activity measures were well correlated between the Fitbit and ActiGraph: 0.85 for MPVA and 0.94 for steps (all p < 001). The Fitbit's step measurements were well correlated with the pedometer (0.67, P=.001), and the Fitbit's measure of MVPA was well correlated with self-reported activity in the diary (0.84; p < .001). CONCLUSIONS Among prostate cancer survivors, the Fitbit One's activity and step measurements were well correlated with the ActiGraph GT3X+ and Omron pedometer. However, the Fitbit One measured two times more MVPA on average compared with the ActiGraph.

Virtual Environment for Diabetes Self-Management Education and Support: Preliminary RCT Outcomes (2017)

Circulation

Abstract

Introduction: Internet interventions are vital to reaching the growing population with type 2 diabetes. Methods: Based on our promising pilot study, we are conducting an RCT to test the efficacy of this virtual environment (LIVE) as compared to a traditional website format for providing education and support. The LIVE community is a gaming platform for adults with type 2 diabetes. Platform functionality allows for group information sharing and social networking, individual learning and behavior modification strategies. Participants attend synchronous diabetes education classes with educators and peers in the site; play games based on diabetes knowledge and scenarios; or visit locations (i.e., grocery store, bookstore) for advice and information to support adult learning and health behaviors. Results: 208 participants enrolled with mean age of 59 and diabetes duration of 10.9 years; 38% male; 56.5% White. Outcomes at six months reveal significant improvement in self-report physical activity (est 0.28;p=0.046) for those with the highest engagement in the LIVE site; and decrease in sedentary time (Fitbit™ data) for the LIVE group as compared to control (est -12.17;p=0.048). Those in the top quartile of time spent in the sites also had a significantly greater decrease in fat intake over 6 months in the LIVE group versus control (est -0.65;p=0.025). A1c did not decrease significantly overall; however, among those with A1c ≥10% at baseline there was a significant decrease (mean 11.42 [1.53] to 8.77 [1.49]; p=0.018). Conclusions: These early RCT outcomes indicate that among those who are active participants, a virtual environment creates more positive behavioral changes than a traditional website for DSMT/S.

Criterion-Validity of Commercially Available Physical Activity Tracker to Estimate Step Count, Covered Distance and Energy Expenditure during Sports Conditions (2017)

Frontiers in Physiology

Abstract

Background: In the past years, there was an increasing development of physical activity tracker (Wearables). For recreational people, testing of these devices under walking or light jogging conditions might be sufficient. For (elite) athletes, however, scientific trustworthiness needs to be given for a broad spectrum of velocities or even fast changes in velocities reflecting the demands of the sport. Therefore, the aim was to evaluate the validity of eleven Wearables for monitoring step count, covered distance and energy expenditure (EE) under laboratory conditions with different constant and varying velocities. Methods: Twenty healthy sport students (10 men, 10 women) performed a running protocol consisting of four 5 min stages of different constant velocities (4.3; 7.2; 10.1; 13.0 km·h−1), a 5 min period of intermittent velocity, and a 2.4 km outdoor run (10.1 km·h−1) while wearing eleven different Wearables (Bodymedia Sensewear, Beurer AS 80, Polar Loop, Garmin Vivofit, Garmin Vivosmart, Garmin Vivoactive, Garmin Forerunner 920XT, Fitbit Charge, Fitbit Charge HR, Xaomi MiBand, Withings Pulse Ox). Step count, covered distance, and EE were evaluated by comparing each Wearable with a criterion method (Optogait system and manual counting for step count, treadmill for covered distance and indirect calorimetry for EE). Results: All Wearables, except Bodymedia Sensewear, Polar Loop, and Beurer AS80, revealed good validity (small MAPE, good ICC) for all constant and varying velocities for monitoring step count. For covered distance, all Wearables showed a very low ICC (<0.1) and high MAPE (up to 50%), revealing no good validity. The measurement of EE was acceptable for the Garmin, Fitbit and Withings Wearables (small to moderate MAPE), while Bodymedia Sensewear, Polar Loop, and Beurer AS80 showed a high MAPE up to 56% for all test conditions. Conclusion: In our study, most Wearables provide an acceptable level of validity for step counts at different constant and intermittent running velocities reflecting sports conditions. However, the covered distance, as well as the EE could not be assessed validly with the investigated Wearables. Consequently, covered distance and EE should not be monitored with the presented Wearables, in sport specific conditions.

Polysomnography vs Consumer Devices: Comparing Common Sleep Indices (2017)

Sleep

Abstract

Introduction: As public interest in health tracking technology increases, so too does the prevalence of commercially available consumer activity devices and apps utilising actigraphy. With the increase of the population's ability to access this technology, and limited research available on the concordance of these devices with polysomnography (PSG), the accuracy, specificity and sensitivity of the FitbitCharge HR device's common sleep indices were compared and evaluated against those of Level 1 PSG in participants with a diagnosis of respiration within normal limits and those with varying degrees of sleep-disordered breathing (SDB). Methods: Analysis was performed on 41 participants (age, mean ± SD: 55?±?14 years; BMI ± SD: 32?±?6?kg/m2). The different categories of SDB were classified upon AASM PSG guidelines and consequently broken up into normal respiration (AHI = 5); mild OSA (AHI = 5 but = 14.99); moderate OSA (AHI = 15 but = 29.99); severe OSA (AHI = 30.0). Full overnight PSG was performed (in accordance with AASM guidelines) in a laboratory setting and recorded concurrently with the consumer device. Primary outcome measures were limited to sleep-onset latency (SOL), total sleep time (TST), wake after sleep-onset (WASO) and sleep-efficiency (SE). Exclusion criteria were applied. Results: These common sleep indices of Fitbit Charge HR were compared to similar indices of PSG using paired t-tests and Bland-Altman plots. Significant differences were seen in SOL for the participants within the category of respiration within normal limits, moderate OSA and severe OSA; WASO was significantly different in all categories; SE was significantly different in the category of moderate OSA; there were no significant differences in TST at any category. Conclusion: Previous research has shown that actigraphy has high sensitivity in detecting sleep, but poor specificity in detecting wake in persons with respiration within normal limits. This poster shows that there was a significant difference in the participants with SDB in their SOL, SE and WASO but no difference in TST when comparing the FitbitCharge HR to PSG.

Evaluating the Consistency of Current Mainstream Wearable Devices in Health Monitoring: A Comparison Under Free-Living Conditions (2017)

Journal of Medical Internet Research

Abstract

Background: Wearable devices are gaining increasing market attention; however, the monitoring accuracy and consistency of the devices remains unknown. Objective: The purpose of this study was to assess the consistency of the monitoring measurements of the latest wearable devices in the state of normal activities to provide advice to the industry and support to consumers in making purchasing choices. Methods: Ten pieces of representative wearable devices (2 smart watches, 4 smart bracelets of Chinese brands or foreign brands, and 4 mobile phone apps) were selected, and 5 subjects were employed to simultaneously use all the devices and the apps. From these devices, intact health monitoring data were acquired for 5 consecutive days and analyzed on the degree of differences and the relationships of the monitoring measurements ??by the different devices. Results: The daily measurements by the different devices fluctuated greatly, and the coefficient of variation (CV) fluctuated in the range of 2-38% for the number of steps, 5-30% for distance, 19-112% for activity duration, .1-17% for total energy expenditure (EE), 22-100% for activity EE, 2-44% for sleep duration, and 35-117% for deep sleep duration. After integrating the measurement data of 25 days among the devices, the measurements of the number of steps (intraclass correlation coefficient, ICC=.89) and distance (ICC=.84) displayed excellent consistencies, followed by those of activity duration (ICC=.59) and the total EE (ICC=.59) and activity EE (ICC=.57). However, the measurements for sleep duration (ICC=.30) and deep sleep duration (ICC=.27) were poor. For most devices, there was a strong correlation between the number of steps and distance measurements (R2 >.95), and for some devices, there was a strong correlation between activity duration measurements and EE measurements (R2 > .7). A strong correlation was observed in the measurements of steps, distance and EE from smart watches and mobile phones of the same brand, Apple or Samsung (R2 > 88). Conclusions: Although wearable devices are developing rapidly, the current mainstream devices are only reliable in measuring the number of steps and distance, which can be used as health assessment indicators. However, the measurement consistencies of activity duration, EE, sleep quality, and so on, are still inadequate, which require further investigation and improved algorithms. [J Med Internet Res 2017;19(3):e68]

Sensing interstitial glucose to nudge active lifestyles (SIGNAL): feasibility of combining novel self-monitoring technologies for persuasive behaviour change. (2017)

BMJ Open

Abstract

INTRODUCTION Increasing physical activity (PA) reduces the risk of developing diabetes, highlighting the role of preventive medicine approaches. Changing lifestyle behaviours is difficult and is often predicated on the assumption that individuals are willing to change their lifestyles today to reduce the risk of developing disease years or even decades later. The self-monitoring technologies tested in this study will present PA feedback in real time, parallel with acute physiological data. Presenting the immediate health benefits of being more physically active may help enact change by observing the immediate consequences of that behaviour. The present study aims to assess user engagement with the self-monitoring technologies in individuals at moderate-to-high risk of developing type 2 diabetes. METHODS AND ANALYSIS 45 individuals with a moderate-to-high risk, aged ≥40 years old and using a compatible smartphone, will be invited to take part in a 7-week protocol. Following 1 week of baseline measurements, participants will be randomised into one of three groups: group 1- glucose feedback followed by biobehavioural feedback (glucose plus PA); group 2-PA feedback followed by biobehavioural feedback; group 3-biobehavioural feedback. A PA monitor and a flash glucose monitor will be deployed during the intervention. Participants will wear both devices throughout the intervention but blinded to feedback depending on group allocation. The primary outcome is the level of participant engagement and will be assessed by device use and smartphone usage. Feasibility will be assessed by the practicality of the technology and screening for diabetes risk. Semistructured interviews will be conducted to explore participant experiences using the technologies. TRIAL REGISTRATION NUMBER ISRCTN17545949. Registered on 15/05/2017.

Improved Mental Acuity Forecasting with an Individualized Quantitative Sleep Model. (2017)

Frontiers in Neurology

Abstract

Sleep impairment significantly alters human brain structure and cognitive function, but available evidence suggests that adults in developed nations are sleeping less. A growing body of research has sought to use sleep to forecast cognitive performance by modeling the relationship between the two, but has generally focused on vigilance rather than other cognitive constructs affected by sleep, such as reaction time, executive function, and working memory. Previous modeling efforts have also utilized subjective, self-reported sleep durations and were restricted to laboratory environments. In the current effort, we addressed these limitations by employing wearable systems and mobile applications to gather objective sleep information, assess multi-construct cognitive performance, and model/predict changes to mental acuity. Thirty participants were recruited for participation in the study, which lasted 1?week. Using the Fitbit Charge HR and a mobile version of the automated neuropsychological assessment metric called CogGauge, we gathered a series of features and utilized the unified model of performance to predict mental acuity based on sleep records. Our results suggest that individuals poorly rate their sleep duration, supporting the need for objective sleep metrics to model circadian changes to mental acuity. Participant compliance in using the wearable throughout the week and responding to the CogGauge assessments was 80%. Specific biases were identified in temporal metrics across mobile devices and operating systems and were excluded from the mental acuity metric development. Individualized prediction of mental acuity consistently outperformed group modeling. This effort indicates the feasibility of creating an individualized, mobile assessment and prediction of mental acuity, compatible with the majority of current mobile devices.

Validation of Three Wearable Fitness Trackers for Measuring Physical Activity in College Students (2017)

Medicine & Science in Sports & Exercise

Abstract

Many wearable fitness trackers have a function measuring physical activity such as energy expenditure but the accuracy of the measures is unclear. PURPOSE: The aim of the study was to validate the energy expenditure estimation of three fitness trackers: LT, FB, and AW for treadmill walking and running. METHODS: The participants of the study were 30 college students (17 males and 13 females) from a public university in Pennsylvania. All participants completed six trials of 10-minute walking and running activities on a treadmill at speeds of 54, 80, 107, 134, 161, & 188 m•min-. Participants were wearing all the fitness trackers and connected to indirect calorimetry during the exercise protocol. All devices provided caloric expenditure while the indirect calorimetry was used as a criterion measure. Resting Metabolic Rate was collected along with a familiarization trial prior to the execution of the exercise protocol. Two-way ANOVA with repeated measures and Pearson Correlation were used to compare the caloric expenditure estimates between the criterion and three fitness trackers. RESULTS: There were no significant differences in energy expenditure estimates between the criterion and AW (mean difference 4.6 kcal, p textgreater 0.05), nor criterion and LT (mean difference 0.8 kcal, p textgreater 0.05). However there was a significant difference between the criterion measure and FB (mean difference 18.2 kcal, p = 0.001). Pearson correlation coefficients (r): the criterion of indirect calorimetry-derived activity energy expenditure yielded the strongest positive correlations with activity energy expenditure estimated from the AW at all speed levels (range from .55 to .85, all ps textless0.01). FB was positively correlated to the criterion measure at five speed levels (range from .46 to .67, ps textless0.05). No positive correlation was found between the criterion and LT measures (range from -.40 to .26). CONCLUSION: AW and FB demonstrated a moderate to high level of validity on measuring physical activity while LT had a low level of validity.

An analysis of how consumer physical activity monitors are used in biomedical research (2017)

The FASEB Journal

Abstract

BACKGROUND Biomedical researchers are very interested in measuring physical activity because sedentary lifestyle and lack of physical activity are well-established risk factors for chronic disease and adverse health outcomes. Many consumer physical activity monitors have accuracies similar to research-grade monitors for measuring steps and offer a relatively new tool for measuring physical activity in an ecological setting. AIM To analyze how consumer physical activity monitors are currently used in biomedical research. METHOD Searches were conducted in Ovid Medline, PubMed Medline, clinicaltrials.gov, and NIH RePorter using search terms including Fitbit, Jawbone, Apple watch, Garmin, Polar, Microsoftband, Misfit, Nike, Withings, and Xiaomi. Results were quantitated by category: condition/topic, intervention, enrollment status, study type and design, participant age, funding mechanism, and primary outcome. RESULTS Fitbit devices were the most common consumer physical activity monitors used in published work (89%), clinical trials (83%), and NIH-funded research (95%). Validity studies represented the plurality (19/40) of published studies involving consumer physical activity monitors. The design of the majority of clinical trials (116/127) and unique NIH-funded grants (20/25) involving Fitbit are interventional. The most common primary outcome measure reported in clinical trials using Fitbit is physical activity (50/127), with biomarkers of health (e.g., blood pressure, HbA1c, fasting glucose) being the second most prominent. The most common clinical conditions studied in clinical trials are mental and brain related (74/127), second most being body weight related (62/127). CONCLUSION The current state and potential growth of the consumer monitor technology is transforming biomedical research, and is enabling us to ask new and more granular questions about physical activity in health and disease.

Habitual sleep duration and sleep duration variation are independently associated with body mass index (2017)

International Journal of Obesity

Abstract

BACKGROUND: Sleep plays a vital role in maintaining homeostasis and promoting health. Previous studies show that shorter sleep duration is associated with elevated body mass index (BMI) and other cardiovascular risk factors. The goal of this study was to investigate the effects of habitual sleep duration and nightly sleep duration variation based on daily device-recorded data on BMI and obesity-related biomarkers. METHOD: 748 individuals (50.6% females, 85.4% European-Americans, average age: 49.7 years old) participated in a commercial lifestyle coaching program beginning in July 2015. Daily sleep data were recorded by Fitbit Charge HRTM wristbands. Clinical laboratory blood tests were measured up to 3 times over a 12-month period. Linear regression models were used for cross-sectional analyses, and generalized estimating equations for longitudinal analyses. All models were adjusted for age, sex, geographic location, season, genetic ancestry inferred from whole genome sequencing data, and BMI (if applicable). Multiple testing issues were corrected by False Discovery Rate (FDR). RESULTS: We calculated habitual sleep duration and nightly sleep duration variation. In general, females slept 15-minutes longer on average than males. A negative correlation was found between habitual sleep duration and BMI (ß=-1.12, standard error=0.25, Ptextless0.001). Moreover, we identified a positive correlation between sleep duration variation and BMI (ß=2.97, standard error=0.79, Ptextless0.001) while controlling for sleep duration, indicating that larger sleep duration variation is significantly and independently associated with increased BMI. CONCLUSION: We explored the impact of habitual sleep duration and sleep duration variation, and identified that shorter habitual sleep duration and larger duration variation were independently associated with increased BMI.

Stress moderation impacting lupus with exercise (S.M.I.L.E.): influence of daily moderate exercise and stress modification on autoimmune-mediated inflammation in mice and humans with lupus (2017)

Annals of the Rheumatic Diseases

Abstract

Background Despite numerous studies indicating the positive effects of exercise and psychological stress reduction in patients with autoimmune disease, these therapeutic modalities are currently underemphasized due to the absence of comprehensive immunological characterization and regimen standardization. Objectives In order to examine the influence on the immune system at the cellular and tissue level, disease pathology was analyzed in the NZM2410 mouse model of lupus nephritis. To translate these results and begin to characterize a consensus treatment regimen, a pilot cohort of systemic lupus erythematosus (SLE) patients with active disease was enrolled into a daily Tai Chi program, which emphasized moderate exercise levels with meditative breathing to provide daily physical activity and stress reduction. Methods Mice were exercised daily by treadmill walking at moderate intensity. Social disruption stress was induced in mice by disturbing the social order within an established hierarchy. All mice were removed from the study when experimental removal criteria was reached [blood urea nitrogen (BUN)textgreater50 mg/dL; weight losstextgreater20%]. Kidney tissue and serum were collected from mice at experimental endpoint. SLE patients completed daily Tai Chi exercises and data was collected at baseline and throughout the study via questionnaires to access physical activity and stress levels, activity trackers (Fitbit), and serum sample analysis. Results Histopathological analysis of NZM2410 mice demonstrated that psychosocial stress induction significantly exacerbated and daily moderate exercise significantly reduced lupus nephritis disease pathology, as measured by BUN levels, complement component 3 and IgG complex deposition in glomeruli, pathological grading of H&E-stained kidney sections, and renal macrophage infiltration. Furthermore, stressors induced levels of IL-6, TNF-a, and IL-1ß, while exercise suppressed IL-6, TNF-a, IL-10, and CXCL1 in mice. Compared to baseline data, questionnaires confirmed a significant reduction in perceived social stress and an increase in combined metabolic equivalent of task (MET) and overall physical activity in SLE patients. Moreover, fitness activity tracker data showed a significant increase in steps, distance, and activity calories with no changes in body mass index or vigorous activity levels. Interestingly, this correlated with an increased average time in bed each night. Analysis of pro-inflammatory serum cytokine expression revealed suppression in the relative fold change of IL-6 by 23%, IL-8 by 30%, TNF-a by 11%, and IFN-? by 21% with Tai Chi. Conclusions These data suggest that moderate exercise and stress management can have potent immunoregulatory effects on the chronic, systemic inflammation associated with SLE and identify daily Tai Chi exercise as a viable adjunct therapy to compliment current pharmacological interventions.

A validation study of Fitbit Charge 2™ compared with polysomnography in adults (2017)

Chronobiology International

Abstract

We evaluated the performance of a consumer multi-sensory wristband (Fitbit Charge 2™), against polysomnography (PSG) in measuring sleep/wake state and sleep stage composition in healthy adults. In-lab PSG and Fitbit Charge 2™ data were obtained from a single overnight recording at the SRI Human Sleep Research Laboratory in 44 adults (19—61 years; 26 women; 25 Caucasian). Participants were screened to be free from mental and medical conditions. Presence of sleep disorders was evaluated with clinical PSG. PSG findings indicated periodic limb movement of sleep (PLMS, > 15/h) in nine participants, who were analyzed separately from the main group (n = 35). PSG and Fitbit Charge 2™ sleep data were compared using paired t-tests, Bland–Altman plots, and epoch-by-epoch (EBE) analysis. In the main group, Fitbit Charge 2™ showed 0.96 sensitivity (accuracy to detect sleep), 0.61 specificity (accuracy to detect wake), 0.81 accuracy in detecting N1+N2 sleep (“light sleep”), 0.49 accuracy in detecting N3 sleep (“deep sleep”), and 0.74 accuracy in detecting rapid-eye-movement (REM) sleep. Fitbit Charge 2™ significantly (p < 0.05) overestimated PSG TST by 9 min, N1+N2 sleep by 34 min, and underestimated PSG SOL by 4 min and N3 sleep by 24 min. PSG and Fitbit Charge 2™ outcomes did not differ for WASO and time spent in REM sleep. No more than two participants fell outside the Bland–Altman agreement limits for all sleep measures. Fitbit Charge 2™ correctly identified 82% of PSG-defined non-REM–REM sleep cycles across the night. Similar outcomes were found for the PLMS group. Fitbit Charge 2™ shows promise in detecting sleep-wake states and sleep stage composition relative to gold standard PSG, particularly in the estimation of REM sleep, but with limitations in N3 detection. Fitbit Charge 2™ accuracy and reliability need to be further investigated in different settings (at-home, multiple nights) and in different populations in which sleep composition is known to vary (adolescents, elderly, patients with sleep disorders).

A Pilot Study to Assess the Feasibility and Impact of a Home Exercise Program on Heart Rate and Heart Rate Variability in Children with Muscular Dystrophy (P4.163) (2017)

Neurology

Abstract

Objective: To determine if a home exercise program is feasible and beneficial in a pediatric muscular dystrophy (MD) population. Background: Research has shown that children with MD exhibit tachycardia and decreased heart rate variability (HRV), sometimes reversible with aerobic exercise. Whether aerobic exercise will improve long-term clinical outcomes in children with MD is unknown. Feasibility and barriers with respect to a voluntary, aerobic home exercise program are poorly studied. Design/Methods: A 4-week voluntary game-based home exercise program was created and trialed by 12 children (3–11 yrs) with MD. Fitbit Charge HR bands were worn to track heart rate (HR). Satisfaction surveys and Pediatric Quality of Life Neuromuscular Module (Peds QOL:NM) were submitted weekly by parents. 24-hour Holter monitoring was performed concurrently with Fitbit in a subset of subjects. Results: 12 games were created to provide exercise at a mean of 25 minutes three times weekly (with 95% rate of positive feedback). 75% of the participants completed participation with 82% of total surveys returned. Peds QOL: NM demonstrated a high baseline quality of life without interval change. 60% of the HR recordings during exercise were adequate. These demonstrated tachycardia for age at baseline (mean 113 BPM) with a mean increase during exercise of 7 BPM. 24-hour Holter monitoring was performed on 3 subjects simultaneously wearing a Fitbit. Holter HR and Fitbit HR were comparable with Holter mean HR consistently higher than Fitbit mean HR (+2.0% to +8.4%). The average cost per exercise kit was $3.88. Conclusions: A novel, inexpensive aerobic exercise program was trialed allowing children with MD to exercise at home in an enjoyable, cost-effective manner. Fitbit Charge HR is an inexpensive, long term HR monitor that recorded values comparable to Holter HR monitoring. Fitbit HR averages are slightly lower than Holter monitoring likely due to inability to capture short bursts of tachycardia.

Exercise Among Women With Ovarian Cancer: A Feasibility and Pre-/Post-Test Exploratory Pilot Study. (2017)

Oncology Nursing Forum

Abstract

PURPOSE/OBJECTIVES: To establish the feasibility and acceptability of completing a higher dose of the planned physical activity volume among women with ovarian cancer, including those undergoing active treatment.  . DESIGN: A pre-/post-test exercise intervention. All participants were asked to complete 225 minutes per week of physical activity for 26 weeks. Multiple supports were provided, including exercise DVDs, self-reported logs, and an objective physical activity tracker (Fitbit®). . SETTING: Home-based exercise intervention with in-person training and telephone follow-ups. . SAMPLE: 10 women with ovarian cancer who were treated within Penn Medicine in Philadelphia, Pennsylvania. . METHODS: Home-based, in-person exercise counseling was provided by an exercise trainer weekly for the first six weeks and then monthly for a total of 26 weeks. Weekly follow-up telephone calls were used to assess exercise adherence and barriers to completing exercise, review symptom changes, and provide behavioral support.  . MAIN RESEARCH VARIABLES: Feasibility and acceptability. . FINDINGS: Eight participants completed the study and achieved at least 80% of the prescribed exercise dose. Five participants were undergoing chemotherapy simultaneously. Participants experienced no adverse events during the 26-week intervention. Compared to baseline, average steps increased by 1,593 per day and moderate-intensity physical activity increased by 15 minutes per day.  . CONCLUSIONS: A 225-minutes-per-week exercise program is feasible and acceptable in a population of patients with ovarian cancer. Participants significantly improved their physical activity during the 26-week intervention. . IMPLICATIONS FOR NURSING: The findings suggest that nursing professionals could recommend that women with ovarian cancer exercise 225 minutes per week regardless of cancer and/or treatment trajectory. For those experiencing aches and pains, behavioral supports and suggestions of a lower exercise dose are needed to maintain physical activity.

Assessing personal exposure to PM using data from an integrated indoor-outdoor experiment in Athens-Greece (2018)

Science of The Total Environment

Abstract

An integrated indoor-outdoor 15-day PM sampling campaign in a general area close to the centre of Athens, targeted to examine personal exposure. All microenvironments (MEs) (second and fourth floor flats, cafes, cars, restaurants, underground metro, outdoor etc.) frequented by the residents were included in the study. The instrumentation used was both stationary (low volume samplers) and portable/wearable to be able to measure continuously PM10, PM2.5, PM1 and analyze chemically PM2.5 and PM1 samples. The study showed that the residences' air quality was determined by the type and intensity of outdoor sources and their vertical distance from the street. Indoor activities such as cooking, cleaning further increased PM levels and formulated the air quality, while particulate accumulation was evident. In general, PM2.5 concentrations were higher outdoors, 11-43 μg/m3, than in the second floor flat as well as on days within different MEs, 13-33 μg/m3 and 8-35 μg/m3, respectively and finally in the fourth floor 10-18 μg/m3. PM2.5 chemical composition was typical of a Mediterranean urban area predominantly composed on average of OC/EC (33%), sulfate (13%), ammonium (9%), nitrate (5%) and crustal material (Cl−, Na+, K+, Mg2+ and Ca2+) (5%). On days when other MEs were visited crustal material increased on average to 16%. The PM levels measured with the portable instrumentation at all mEs showed that the persons were exposed to higher PM10 concentrations in the subway (avg. 218 μg/m3) due to the resuspension of crustal material, while maximum PM2.5 and PM1 were experienced in cafes where smoking was allowed (avg. 126 and 108 μg/m3, respectively). Using the car resulted to the lowest PM10, PM2.5 and PM1 exposure (58, 10 and 6 μg/m3, respectively). Total exposure to particulates depended both on the time spent in each ME and on the mixture of MEs visited in 24 h.

Technology-Assisted Behavioral Intervention to Extend Sleep Duration: Development and Design of the Sleep Bunny Mobile App. (2018)

JMIR Mental Health

Abstract

BACKGROUND Despite the high prevalence of short sleep duration (29.2% of adults sleep textless6 hours on weekdays), there are no existing theory-based behavioral interventions to extend sleep duration. The popularity of wearable sleep trackers provides an opportunity to engage users in interventions. OBJECTIVE The objective of this study was to outline the theoretical foundation and iterative process of designing the "Sleep Bunny," a technology-assisted sleep extension intervention including a mobile phone app, wearable sleep tracker, and brief telephone coaching. We conducted a two-step process in the development of this intervention, which was as follows: (1) user testing of the app and (2) a field trial that was completed by 2 participants with short sleep duration and a cardiovascular disease risk factor linked to short sleep duration (body mass index [BMI] textgreater25). METHODS All participants had habitual sleep duration textless6.5 hours verified by 7 days of actigraphy. A total of 6 individuals completed initial user testing in the development phase, and 2 participants completed field testing. Participants in the user testing and field testing responded to open-ended surveys about the design and utility of the app. Participants in the field testing completed the Epworth Sleepiness Scale and also wore an actigraph for a 1-week baseline period and during the 4-week intervention period. RESULTS The feedback suggests that users enjoyed the wearable sleep tracker and found the app visually pleasing, but they suggested improvements to the notification and reminder features of the app. The 2 participants who completed the field test demonstrated significant improvements in sleep duration and daytime sleepiness. CONCLUSIONS Further testing is needed to determine effects of this intervention in populations at risk for the mental and physical consequences of sleep loss.

We12BFit!-Improving lifestyle physical activity in children aged 7-12 years with developmental coordination disorder: protocol of a multicentre single-arm mixed-method study. (2018)

BMJ open

Abstract

INTRODUCTION Children with developmental coordination disorder (DCD) are less physically active than their typically developing peers. No substantiated interventions are available to address this issue. Therefore, this study aims to describe the design and rationale of (1) a family-focused intervention to increase motivation for physical activity (PA) and, indirectly, lifestyle PA in children aged 7-12 years with DCD and (2) the methods to examine its preliminary effectiveness and feasibility. METHODS AND ANALYSIS This intervention is the second part of a more comprehensive, multidisciplinary treatment called We12BFit! The intervention was developed using the steps of treatment theory which includes the concept of targets, mechanism of action and essential ingredients. The content of the intervention is based on the transtheoretical model of change (TTM). In the intervention, the motivation for PA will be targeted through application of behaviour change strategies that fit the stages of the TTM. The modes of delivery include: pedometer, poster, parent meeting, booklet and coaching. At least 19 children with DCD, aged 7-12 years, will be included from two schools for special education and two rehabilitation centres. The intervention will be evaluated using a single-arm mixed-method design. Effectiveness will be assessed at three instances by using ActiGraph accelerometers accompanied by an activity log. Feasibility will be assessed using interviews with the participants and coaches. This evaluation may add to our understanding of motivation for PA in children with DCD and may eventually improve the rehabilitation programme of children with DCD. ETHICS AND DISSEMINATION The study has been approved by the Medical Ethics Committee of the University Medical Center of Groningen (METc 2015.216). We will disseminate the final results to the public through journal publications and presentations for practice providers and scientists. A final study report will also be provided to funding organisations. PROTOCOL VERSION 4, 12 April 2018. TRIAL REGISTRATION NUMBER NTR6334; Pre-results.

Exercise combined with Acceptance and Commitment Therapy (ExACT) compared to a supervised exercise programme for adults with chronic pain: study protocol for a randomised controlled trial (2018)

Trials

Abstract

Acceptance and Commitment Therapy (ACT) is a form of cognitive behavioural therapy, which may be beneficial for people with chronic pain. The approach aims to enhance daily functioning through increased psychological flexibility. Whilst the therapeutic model behind ACT appears well suited to chronic pain, there is a need for further research to test its effectiveness in clinical practice, particularly with regards to combining ACT with physical exercise. This prospective, two-armed, parallel-group, single-centre randomised controlled trial (RCT) will assess the effectiveness of a combined Exercise and ACT programme, in comparison to supervised exercise for chronic pain. One hundred and sixty patients, aged 18 years and over, who have been diagnosed with a chronic pain condition by a physician will be recruited to the trial. Participants will be individually randomised to one of two 8-week, group interventions. The combined group will take part in weekly psychology sessions based on the ACT approach, in addition to supervised exercise classes led by a physiotherapist. The control group will attend weekly supervised exercise classes but will not take part in an ACT programme. The primary outcome will be pain interference at 12-week follow-up, measured using the Brief Pain Inventory-Interference Scale. Secondary outcomes will include self-reported pain severity, self-perception of change, patient satisfaction, quality of life, depression, anxiety and healthcare utilisation. Treatment process measures will include self-efficacy, pain catastrophising, fear avoidance, pain acceptance and committed action. Physical activity will be measured using Fitbit ZipTM activity trackers. Both groups will be followed up post intervention and again after 12 weeks. Estimates of treatment effects at follow-up will be based on an intention-to-treat framework, implemented using a linear mixed-effects model. Individual and focus group qualitative interviews will be undertaken with a purposeful sample of participants to explore patient experiences of both treatments. To our knowledge, this will be the first RCT to examine whether combining exercise with ACT produces greater benefit for patients with chronic pain, compared to a standalone supervised exercise programme.

Effect of physical activity trackers and daily text messages on quality-of-life in colorectal survivors (Smart Pace): A pilot randomized controlled trial. (2018)

JCO

Abstract

559Background: There are over 1.3 million colorectal cancer (CRC) survivors in the United States, and many of whom suffer from lower health-related quality-of-life (HRQoL) years after diagnosis and treatment. Physical activity may improve survival outcomes and HRQoL for CRC survivors. Feasible interventions to support physical activity after CRC diagnosis are needed. Methods: We conducted a two-arm randomized controlled trial of 41 men and women who had completed treatment for CRC. Participants in the intervention arm were given a Fitbit Flex? and received daily text messages for 12 weeks. HRQoL was assessed in both arms at baseline and 12 weeks using the RAND Short Form Survey (SF-36) and the Functional Assessment of Cancer Therapy ? Colorectal (FACT-C). Survey score changes from baseline to 12 weeks were compared between the two arms using independent t-tests, and scores at baseline and 12 weeks were compared using paired t-tests. SAS was used for analysis, and statistical significance was declared at p < 0.05. Results: We observed a statistically significant increase in the FACT-C functional well-being sub-scale in individuals in the intervention arm pre- and post- intervention (mean ? 1.81 ± 2.76; p: 0.02). There was no change in functional well-being in the control arm (mean ? -0.35 ± 4.12; p: 0.71). The between-arm comparison of change from baseline to 12 weeks was not statistically significantly (p: 0.08). There was a statistically significant increase in the FACT-C emotional well-being sub-scale in the control arm (mean ? 1.20 ± 2.48; p: 0.04) and in the SF-36 role physical sub-scale in the control arm (mean ? 22.5 ± 38.8; p: 0.02). No other measures of HRQoL were statistically significantly different within groups, across time points, or between groups. Conclusions: A 12-week physical activity intervention using a Fitbit Flex? and daily text messages may improve functional well-being among CRC survivors. Larger randomized studies are needed to definitively determine if a digital physical activity intervention improves functional well-being among CRC survivors, and if the improvement can be sustained over time. Clinical trial information: NCT02966054. 559Background: There are over 1.3 million colorectal cancer (CRC) survivors in the United States, and many of whom suffer from lower health-related quality-of-life (HRQoL) years after diagnosis and treatment. Physical activity may improve survival outcomes and HRQoL for CRC survivors. Feasible interventions to support physical activity after CRC diagnosis are needed. Methods: We conducted a two-arm randomized controlled trial of 41 men and women who had completed treatment for CRC. Participants in the intervention arm were given a Fitbit Flex? and received daily text messages for 12 weeks. HRQoL was assessed in both arms at baseline and 12 weeks using the RAND Short Form Survey (SF-36) and the Functional Assessment of Cancer Therapy ? Colorectal (FACT-C). Survey score changes from baseline to 12 weeks were compared between the two arms using independent t-tests, and scores at baseline and 12 weeks were compared using paired t-tests. SAS was used for analysis, and statistical significance was declared at p < 0.05. Results: We observed a statistically significant increase in the FACT-C functional well-being sub-scale in individuals in the intervention arm pre- and post- intervention (mean ? 1.81 ± 2.76; p: 0.02). There was no change in functional well-being in the control arm (mean ? -0.35 ± 4.12; p: 0.71). The between-arm comparison of change from baseline to 12 weeks was not statistically significantly (p: 0.08). There was a statistically significant increase in the FACT-C emotional well-being sub-scale in the control arm (mean ? 1.20 ± 2.48; p: 0.04) and in the SF-36 role physical sub-scale in the control arm (mean ? 22.5 ± 38.8; p: 0.02). No other measures of HRQoL were statistically significantly different within groups, across time points, or between groups. Conclusions: A 12-week physical activity intervention using a Fitbit Flex? and daily text messages may improve functional well-being among CRC survivors. Larger randomized studies are needed to definitively determine if a digital physical activity intervention improves functional well-being among CRC survivors, and if the improvement can be sustained over time. Clinical trial information: NCT02966054.

Preliminary concurrent validity of the Fitbit-Zip and ActiGraph activity monitors for measuring steps in people with polymyalgia rheumatica. (2018)

Gait & posture

Abstract

BACKGROUND Activity monitors provide objective measurements of physical activity, however, the accuracy of these devices in people with polymyalgia rheumatica (PMR) is unknown. Therefore, this study aimed to obtain preliminary evidence of the accuracy of two activity monitors and explore if clinical and gait-related factors altered device accuracy in people with PMR. METHODS The ActiGraph with low frequency extension (+LFE) and standard (-LFE) algorithms, Fitbit-Zip (waist) and Fitbit-Zip (shirt) were concurrently tested using a two-minute walk test (2MWT) and stairs test in 27 people with PMR currently treated with prednisolone. To determine accuracy, activity monitor step-count was compared to a gold-standard step-count (GSSC; calculated from video recording) using Bland-Altman plots. RESULTS The Fitbit-Zip (waist) achieved closest agreement to the GSSC for the 2MWT (mean bias (95%CI): 10 (-3, 23); 95%LOA: -55, 74). The ActiGraph (+LFE) achieved closest agreement to the GSSC for the stairs test (mean bias (95%CI): 0 (-1, 1); 95%LOA: -5, 5). The ActiGraph (-LFE) performed poorly in both tests. All devices demonstrated reduced accuracy in participants with lower gait velocity, reduced stride length, longer double-limb support phase and greater self-reported functional impairment. CONCLUSION Our preliminary results suggest that in controlled conditions, the Fitbit-Zip fairly accurately measures step-count during walking in people with PMR receiving treatment. However, device error was greater than data published in healthy people. The ActiGraph may not be recommended without activation of the LFE. We identified clinical and gait-related factors associated with higher levels of functional impairment that reduced device accuracy. Further work is required to evaluate the validity of the activity monitors in field conditions.

A Physical Therapist–Administered Physical Activity Intervention After Total Knee Replacement: Protocol for a Randomized Controlled Trial (2018)

Physical Therapy

Abstract

Background The definitive treatment for knee osteoarthritis is a total knee replacement, which results in a clinically meaningful improvement in pain and physical function. However, evidence suggests that physical activity (PA) remains unchanged after total knee replacement (TKR). Objective The objective of this study is to investigate the efficacy, fidelity, and safety of a physical therapist–administered PA intervention for people after TKR. Design This study is a protocol for a randomized controlled trial. Setting The setting is an outpatient physical therapy clinic. Participants The participants are 125 individuals who are over the age of 45 and are seeking outpatient physical therapy following a unilateral TKR. Intervention In addition to standardized physical therapy after TKR, the intervention group will receive, during physical therapy, a weekly PA intervention that includes a wearable activity tracking device, individualized step goals, and face-to-face feedback provided by a physical therapist. Control The control group will receive standardized physical therapy alone after TKR. Measurements The efficacy of the intervention will be measured as minutes per week spent in moderate to vigorous PA at enrollment, at discharge, and at 6 months and 12 months after discharge from physical therapy. The fidelity and safety of the intervention will be assessed throughout the study. Limitations Participants will not be masked, PA data will be collected after randomization, and the trial will be conducted at a single site. Conclusions The goal of this study is to increase PA after TKR. A protocol for investigating the efficacy, fidelity, and safety of a physical therapist–administered PA intervention for people after total knee replacement is presented. The findings will be used to support a large multisite clinical trial to test the effectiveness, implementation, and cost of this intervention.

Engagement Strategies for Self-Monitoring Symptoms of Bipolar Disorder With Mobile and Wearable Technology: Protocol for a Randomized Controlled Trial (2018)

JMIR Research Protocols

Abstract

Background: Monitoring signs and symptoms in bipolar disorder (BP) is typically based on regular assessments from patient-clinician interactions. Mobile and wearable technology promises to make monitoring symptoms in BP easier, but little is known about how best to engage individuals with BP in monitoring symptoms. Objective: The objective of this study was to provide the rationale and protocol for a randomized controlled trial that investigates engagement strategies for monitoring symptoms of BP, including the strategies of using activity trackers compared with self-reports and reviewing recorded symptoms weekly with an interviewer. Methods: A total of 50 individuals with BP will be recruited from the Prechter Longitudinal Study of Bipolar Disorder at the University of Michigan to participate in a 6-week study. Participants will monitor their symptoms through an activity tracker (Fitbit Alta HR) and a mobile phone app designed for this study. In addition to monitoring symptoms, participants have a 50-50 chance of being assigned to an arm that reviews self-reports and activity information weekly. Statistical tests will be performed to test hypotheses that participants adhere to activity tracking significantly more than self-reporting, prefer activity tracking significantly more than self-reporting, and better adhere to both activity tracking and self-reporting when reviewing collected information weekly. Results: Recruitment commenced in November 2017. The first group of participants began the study in January 2018. Conclusions: This study aims to establish strategies to engage individuals with BP in monitoring their symptoms with mobile and wearable technology. Better engagement strategies are expected to aid current efforts in bipolar research and clinical care, from the development of new mobile phone apps to providing the right intervention to the right individual at the right moment. Trial Registration: ClinicalTrials.gov NCT03358238; https://clinicaltrials.gov/ct2/show/NCT03358238 (Archived by WebCite at http://www.webcitation.org/6yebuNfz5) Registered Report Identifier: RR1-10.2196/9899 [JMIR Res Protoc 2018;7(5):e130]

Peer Coaching Through mHealth Targeting Physical Activity in People With Parkinson Disease: Feasibility Study. (2018)

JMIR mHealth and uHealth

Abstract

BACKGROUND Long-term engagement in exercise and physical activity mitigates the progression of disability and increases quality of life in people with Parkinson disease (PD). Despite this, the vast majority of individuals with PD are sedentary. There is a critical need for a feasible, safe, acceptable, and effective method to assist those with PD to engage in active lifestyles. Peer coaching through mobile health (mHealth) may be a viable approach. OBJECTIVE The purpose of this study was to develop a PD-specific peer coach training program and a remote peer-mentored walking program using mHealth technology with the goal of increasing physical activity in persons with PD. We set out to examine the feasibility, safety, and acceptability of the programs along with preliminary evidence of individual-level changes in walking activity, self-efficacy, and disability in the peer mentees. METHODS A peer coach training program and a remote peer-mentored walking program using mHealth was developed and tested in 10 individuals with PD. We matched physically active persons with PD (peer coaches) with sedentary persons with PD (peer mentees), resulting in 5 dyads. Using both Web-based and in-person delivery methods, we trained the peer coaches in basic knowledge of PD, exercise, active listening, and motivational interviewing. Peer coaches and mentees wore FitBit Zip activity trackers and participated in daily walking over 8 weeks. Peer dyads interacted daily via the FitBit friends mobile app and weekly via telephone calls. Feasibility was determined by examining recruitment, participation, and retention rates. Safety was assessed by monitoring adverse events during the study period. Acceptability was assessed via satisfaction surveys. Individual-level changes in physical activity were examined relative to clinically important differences. RESULTS Four out of the 5 peer pairs used the FitBit activity tracker and friends function without difficulty. A total of 4 of the 5 pairs completed the 8 weekly phone conversations. There were no adverse events over the course of the study. All peer coaches were "satisfied" or "very satisfied" with the training program, and all participants were "satisfied" or "very satisfied" with the peer-mentored walking program. All participants would recommend this program to others with PD. Increases in average steps per day exceeding the clinically important difference occurred in 4 out of the 5 mentees. CONCLUSIONS Remote peer coaching using mHealth is feasible, safe, and acceptable for persons with PD. Peer coaching using mHealth technology may be a viable method to increase physical activity in individuals with PD. Larger controlled trials are necessary to examine the effectiveness of this approach.

Free-Living Physical Activity Monitoring in Adult US Patients with Multiple Sclerosis Using a Consumer Wearable Device (2018)

Digital Biomarkers

Abstract

Introduction: Wearable devices have been used to characterize physical activity in multiple sclerosis (MS). The objectives of this study were to advance the literature on the utility of free-living physical activity tracking from secondary analyses of a pilot study in MS patients. Method: The original observational study was conducted in participants with MS at PatientsLikeMe (www.PatientsLikeMe.com), an online network of patients with chronic diseases. Participants completed a baseline self-assessment, and received a Fitbit OneTM wearable device with instructions to upload data. Eligible participants (1) self-reported MS, (2) logged on to the PatientsLikeMe website 90 days prior to enrollment, and (3) consented to participate electronically. Participants (1) textless 18 years, (2) living outside the United States, and (3) requiring wheelchair assistance for most daily activities were excluded. The secondary analyses were limited to participants with complete data on MS type, disease duration, and Multiple Sclerosis Rating Scale (MSRS) and at least 7 days of wearable data. Step count was used as a measure of physical activity. Results: The analysis cohort of 114 participants uploaded a mean of 20.1 days of wearable data over the 23-day study (87% adherence); participants averaged 4,393 steps per day. The mean age of participants was 52 years, predominantly female (75%), relapsing-remitting type (79%), with mean disease duration of 16 years. Mean MSRS score within 30-day of baseline was 32; 72% reported mild-moderate walking disability. The reliability of step count measured by intraclass correlation was 0.55 for a single day, ≥0.7 for 2-day average, and ≥0.9 for 7-day average. After controlling for covariates, self-reported disease severity (MSRS quartile) was an independent predictor of step count (p textless 0.001). Least square means (LS means) for participants that were least disabled (lowest quartile) was 5,937 steps, which was significantly higher than participants in the second, third, and fourth quartiles (4,570, 3,490, and 3,272, respectively). Similarly, LS means of participants with no ambulatory disability (measured by MSRS walk component) was 6,931 steps, significantly higher than participants with greater disability (4,743, 4,394, 2,727 steps for symptomatic, mild, and moderate disability, respectively, p textless 0.001). Discussion: Using an interactive platform, this study captured free-living mobility data in MS patients. Important metrics such as the use of a minimum of 2-day estimates and self-reported disability were found to be robust indicators and correlates, respectively, of participant activity levels. Further triangulation of such metrics may reduce the burden on patients, clinicians, and researchers when monitoring clinical status.

Buying time: a proof-of-concept randomized controlled trial to improve sleep quality and cognitive function among older adults with mild cognitive impairment (2018)

Trials

Abstract

Current evidence suggests that good quality sleep is associated with preserved cognitive function and reduced dementia risk in older adults. Sleep complaints are especially common among older adults with mild cognitive impairment (MCI), and this may contribute to their increased risk for progression to dementia. Thus, improving their sleep may be important for maintaining their cognitive health. Chronotherapy is a set of intervention strategies that can improve sleep quality through strengthening the entrainment of the biological clock to the solar light-dark cycle, and includes strategies such as (1) bright light therapy (BLT); (2) physical activity (PA); and (3) good sleep hygiene. Of these strategies, BLT is the most potent and is based on providing individualized timing to entrain circadian rhythms. Thus, a personalized chronotherapy intervention of individually timed BLT and individually tailored PA promotion, in conjunction with general sleep hygiene education may promote older adult sleep quality. We therefore aim to carry out a proof-of-concept randomized controlled trial (RCT) to examine the efficacy of such a personalized chronotherapy intervention to improve sleep quality among older adults with MCI. This was a 24-week RCT of a personalized chronotherapy intervention aimed to primarily improve sleep quality as measured by the MotionWatch8©. Participants in the personalized chronotherapy group (INT) will receive four once-weekly, general sleep hygiene education classes, followed by 20 weeks of (1) individually timed BLT and (2) bi-weekly, individually tailored PA counseling phone calls in conjunction with receiving a consumer-available PA tracker--the Fitbit® Flex™. Ninety-six adults (aged 65-85 years) classified as having MCI (i.e., Mini-Mental State Exam (MMSE) ≥ 24; Montreal Cognitive Assessment (MoCA) ≤ 26; without dementia or significant functional impairment) will be randomized to either INT or a waitlist control group (CON). The results of this trial will help determine if a personalized chronotherapy intervention that includes individually timed BLT and individually tailored PA promotion, along with general sleep hygiene education can promote sleep quality among older adults at increased risk for dementia. Our results will help inform best practices for promoting sleep quality among older adults with MCI. ClinicalTrials.gov , NCT02926157 . Registered on 6 October 2016.

A Mobile Health Intervention to Reduce Pain and Improve Health (MORPH) in Older Adults With Obesity: Protocol for the MORPH Trial. (2018)

JMIR research protocols

Abstract

BACKGROUND Chronic pain is a complex, age-related health issue that affects both physical functioning and quality of life. Because the impact of chronic pain is worsened by obesity and inactivity, nonpharmacological interventions that promote movement, reduce sitting, and aid in weight loss are needed to help manage pain symptoms among older adults with chronic pain. OBJECTIVE The Mobile Intervention to Reduce Pain and Improve Health (MORPH) pilot trial aims to develop and test the feasibility and acceptability of a novel, patient-centered intervention to reduce chronic pain and improve physical functioning in older adults, leveraging the combination of telecoaching and individually adaptive mHealth tools to decrease both body mass and sedentary behavior. METHODS MORPH comprises 2 phases, including a 1-year iterative development phase, and a 1-year pilot randomized controlled trial (RCT). During the development phase, representative participants will engage in one-on-one structured interviews and a 1-week field test. The resulting feedback will be used to guide the development of the finalized MORPH intervention package. During the second phase, the finalized intervention will be tested in a pilot RCT (N=30) in which older adult participants with chronic pain and obesity will be assigned to receive the 12-week MORPH intervention or to a waitlist control. Primary outcomes include self-reported pain symptoms and physical function. RESULTS Phase 1 recruitment is ongoing as of December 2017. CONCLUSIONS The MORPH intervention brings together a strong body of evidence using group-based behavioral intervention designs with contemporary mHealth principles, allowing for intervention when and where it matters the most. Given the ubiquity of smartphone devices and the popularity of consumer activity and weight monitors, the results of this study may serve to inform the development of scalable, socially driven behavioral pain management interventions. TRIAL REGISTRATION ClinicalTrials.gov NCT03377634; https://clinicaltrials.gov/ct2/show/NCT03377634 (Archived by WebCite at http://www.webcitation.org/6yj0J5Pan). REGISTERED REPORT IDENTIFIER RR1-10.2196/9712.

The Feasibility of a Gender- and Culturally-Sensitive Weight Loss Intervention Plus Mobile Health Technology for Hispanic Males (2018)

Medicine & Science in Sports & Exercise

Abstract

BACKGROUND: Hispanic males suffer disproportionate rates of overweight and obesity compared to other racial/ethnic groups. However, few weight loss interventions have been developed for this high-risk group. Further, the use of mobile health (mHealth) technologies to support lifestyle behavior changes in weight loss interventions for Hispanic men are untested. PURPOSE: This pilot study examined the feasibility and acceptability of integrating mHealth technology into a 12-week gender- and culturally- sensitive weight loss intervention (GCSWLI) for sedentary, overweight/obese Hispanic males. METHODS: Eighteen Hispanic males (age: 38 ± 11 years; BMI: 34 ± 5 kg/m2; 56% Spanish monolingual) received a GCSWLI including weekly in-person individual sessions, a daily calorie goal, and prescription of ≥225 minutes of moderate-intensity physical activity/week. mHealth technology support included tailored text messaging and behavior and weight self-monitoring support using the Fitbit Charge 2, a consumer- wearable physical activity tracker, and a Fitbit Aria Wi-Fi Smart Scale. Participants were encouraged to use the Fitbit tracker during all waking hours and weigh themselves daily using the provided scale. Participants received biweekly SMS customized to address self-reported barriers to behavior change and in response to objective data collected via tracking tools. Changes in weight from baseline to 12-weeks were estimated using a paired t-test. Descriptive analyses characterized use of the Fitbit and the smart scale. RESULTS: Sixteen of 18 participants completed 12-week assessments; overall attrition rate was 11.1%. Mean weight loss at week 12 was 4.7 kg (95% CI [-7.1, -2.3], p < 0.001). Participants wore the Fitbit 71.6% of intervention days and logged body weight using the smart scale 30.5% of intervention days. Participants identified barriers for use of the technology, including strenuous work activities prohibiting them from wearing the Fitbit. In addition, reliable internet access was cited as a barrier to using the smart scale. CONCLUSIONS: While significant weight loss was achieved integrating mHealth technology into a GCSWLI, the use of technology was modest. Addressing barriers identified in our work may help to refine an mHealth intervention approach for Hispanic men.

A mobile phone-based program to promote healthy behaviors among adults with prediabetes: study protocol for a pilot randomized controlled trial (2018)

Pilot and Feasibility Studies

Abstract

Rates of participation in Diabetes Prevention Programs (DPPs) are low. This may be due, in part, to low levels of autonomous motivation (i.e., motivation that arises from internal sources and sustains healthy behaviors over time) to prevent type 2 diabetes (T2DM) among many individuals with prediabetes. Mobile health (mHealth) technologies that incorporate principles from the Self-Determination Theory offer an effective and scalable approach to increase autonomous motivation levels. One promising mobile phone-based application is JOOL Health, which aims to help users connect certain health behaviors (e.g., sleep and diet) with personal values in specific life domains (e.g., family and work). The first aim of this study is to estimate whether JOOL Health can increase autonomous motivation to prevent T2DM among individuals with prediabetes who declined DPP participation. The second aim of this pilot study is to examine the intervention's feasibility and acceptability. This is a 12-week, three-arm pilot randomized controlled trial. We will recruit 105 individuals with prediabetes who did not engage in a DPP despite invitation from their health plan to participate in face-to-face or web-based programs at no out-of-pocket-cost. Participants will be randomized to one of three study arms: (1) a group that receives information on prediabetes, evidence-based strategies to decrease progression to T2DM, and a list of resources for mHealth tools for monitoring diet, physical activity, and weight (comparison group); (2) a group that receives the JOOL Health application; and (3) a group that receives the JOOL Health application as well as a Fitbit activity tracker and wireless-enabled scale. Our primary outcome is change in autonomous motivation to prevent T2DM (measured using the Treatment Self-Regulation Questionnaire). We will also collect data related to the intervention's feasibility (recruitment and retention rates) and acceptability (adherence and qualitative experience) as well as changes in psychosocial outcomes, hemoglobin A1c, and weight. To our knowledge, this is the first study that aims to promote positive health behaviors among individuals with prediabetes who previously declined to participate in a DPP. Our results will inform a larger trial to test the effect of JOOL Health on clinically relevant outcomes, including weight loss, physical activity, and DPP engagement.

Assessment of the accuracy of nutrient calculations of five popular nutrition tracking applications (2018)

Public Health Nutrition

Abstract

Objective To assess the accuracy of nutrient intake calculations from leading nutrition tracking applications (apps). Design Nutrient intake estimates from thirty 24 h dietary recalls collected using Nutrition Data System for Research (NDSR) were compared with intake calculations from these recalls entered by the researcher into five free nutrition tracking apps. Apps were selected from the Apple App Store based on consumer popularity from the list of free ‘Health and Fitness' apps classified as a nutrition tracking apps. Subjects Dietary recall data collected from thirty lower-income adults. Results Correlations between nutrient intake calculations from NDSR and the nutrition tracking apps ranged from 0textperiodcentered73 to 0textperiodcentered96 for energy and macronutrients. Correlations for the other nutrients examined (Na, total sugars, fibre, cholesterol, saturated fat) ranged from 0textperiodcentered57 to 0textperiodcentered93. For each app, one or more mean nutrient intake calculations were significantly lower than those from NDSR. These differences included total protein (P=0textperiodcentered03), total fat (P=0textperiodcentered005), Na (P=0textperiodcentered02) and cholesterol (P=0textperiodcentered005) for MyFitnessPal; dietary fibre (P=0textperiodcentered04) for Fitbit; total protein (P=0textperiodcentered0004), total fat (P=0textperiodcentered008), Na (P=0textperiodcentered002), sugars (P=0textperiodcentered007), cholesterol (P=0textperiodcentered0006) and saturated fat (P=0textperiodcentered005) for Lose It!; Na (P=0textperiodcentered03) and dietary fibre (P=0textperiodcentered005) for MyPlate; and total fat (P=0textperiodcentered03) for Lifesum. Conclusions Findings suggest that nutrient calculations from leading nutrition tracking apps tend to be lower than those from NDSR, a dietary analysis software developed for research purposes. Further research is needed to evaluate the validity of the apps when foods consumed are entered by consumers.

Enhancing Lifestyle Change in Cardiac Patients Through the Do CHANGE System ("Do Cardiac Health: Advanced New Generation Ecosystem"): Randomized Controlled Trial Protocol. (2018)

JMIR Research Protocols

Abstract

BACKGROUND Promoting a healthy lifestyle (eg, physical activity, healthy diet) is crucial for the primary and secondary prevention of cardiac disease in order to decrease disease burden and mortality. OBJECTIVE The current trial aims to evaluate the effectiveness of the Do Cardiac Health: Advanced New Generation Ecosystem (Do CHANGE) service, which is developed to assist cardiac patients in adopting a healthy lifestyle and improving their quality of life. METHODS Cardiac patients (ie, people who have been diagnosed with heart failure, coronary artery disease, and/or hypertension) will be recruited at three pilot sites (Badalona Serveis Assistencials, Badalona, Spain [N=75]; Buddhist Tzu Chi Dalin General Hospital, Dalin, Taiwan [N=100] and Elisabeth-TweeSteden Hospital, Tilburg, The Netherlands [N=75]). Patients will be assisted by the Do Something Different (DSD) program to change their unhealthy habits and/or lifestyle. DSD has been developed to increase behavioral flexibility and subsequently adopt new (healthier) habits. In addition, patients' progress will be monitored with a number of (newly developed) devices (eg, Fitbit, Beddit, COOKiT, FLUiT), which will be integrated in one application. RESULTS The Do CHANGE trial will provide us with new insights regarding the effectiveness of the proposed intervention in different cultural settings. In addition, it will give insight into what works for whom and why. CONCLUSIONS The Do CHANGE service integrates new technologies into a behavior change intervention in order to change the unhealthy lifestyles of cardiac patients. The program is expected to facilitate long-term, sustainable behavioral change. TRIAL REGISTRATION Clinicaltrials.gov NCT03178305; https://clinicaltrials.gov/ct2/show/NCT03178305 (Archived by WebCite at http://www.webcitation.org/6wfWHvuyU).

Patterns of Fitbit Use and Activity Levels Throughout a Physical Activity Intervention: Exploratory Analysis from a Randomized Controlled Trial (2018)

JMIR mHealth and uHealth

Abstract

BACKGROUND There has been a rapid increase in the use of technology-based activity trackers to promote behavior change. However, little is known about how individuals use these trackers on a day-to-day basis or how tracker use relates to increasing physical activity. OBJECTIVE The aims were to use minute level data collected from a Fitbit tracker throughout a physical activity intervention to examine patterns of Fitbit use and activity and their relationships with success in the intervention based on ActiGraph-measured moderate to vigorous physical activity (MVPA). METHODS Participants included 42 female breast cancer survivors randomized to the physical activity intervention arm of a 12-week randomized controlled trial. The Fitbit One was worn daily throughout the 12-week intervention. ActiGraph GT3X+ accelerometer was worn for 7 days at baseline (prerandomization) and end of intervention (week 12). Self-reported frequency of looking at activity data on the Fitbit tracker and app or website was collected at week 12. RESULTS Adherence to wearing the Fitbit was high and stable, with a mean of 88.13% of valid days over 12 weeks (SD 14.49%). Greater adherence to wearing the Fitbit was associated with greater increases in ActiGraph-measured MVPA (binteraction=0.35, Ptextless.001). Participants averaged 182.6 minutes/week (SD 143.9) of MVPA on the Fitbit, with significant variation in MVPA over the 12 weeks (F=1.91, P=.04). The majority (68%, 27/40) of participants reported looking at their tracker or looking at the Fitbit app or website once a day or more. Changes in Actigraph-measured MVPA were associated with frequency of looking at one's data on the tracker (b=-1.36, P=.07) but not significantly associated with frequency of looking at one's data on the app or website (P=.36). CONCLUSIONS This is one of the first studies to explore the relationship between use of a commercially available activity tracker and success in a physical activity intervention. A deeper understanding of how individuals engage with technology-based trackers may enable us to more effectively use these types of trackers to promote behavior change. TRIAL REGISTRATION ClinicalTrials.gov NCT02332876; https://clinicaltrials.gov/ct2/show/NCT02332876?term=NCT02332876 &rank=1 (Archived by WebCite at http://www.webcitation.org/6wplEeg8i).

Using Fitness Trackers and Smartwatches to Measure Physical Activity in Research: Analysis of Consumer Wrist-Worn Wearables. (2018)

Journal of Medical Internet Research

Abstract

BACKGROUND New fitness trackers and smartwatches are released to the consumer market every year. These devices are equipped with different sensors, algorithms, and accompanying mobile apps. With recent advances in mobile sensor technology, privately collected physical activity data can be used as an addition to existing methods for health data collection in research. Furthermore, data collected from these devices have possible applications in patient diagnostics and treatment. With an increasing number of diverse brands, there is a need for an overview of device sensor support, as well as device applicability in research projects. OBJECTIVE The objective of this study was to examine the availability of wrist-worn fitness wearables and analyze availability of relevant fitness sensors from 2011 to 2017. Furthermore, the study was designed to assess brand usage in research projects, compare common brands in terms of developer access to collected health data, and features to consider when deciding which brand to use in future research. METHODS We searched for devices and brand names in six wearable device databases. For each brand, we identified additional devices on official brand websites. The search was limited to wrist-worn fitness wearables with accelerometers, for which we mapped brand, release year, and supported sensors relevant for fitness tracking. In addition, we conducted a Medical Literature Analysis and Retrieval System Online (MEDLINE) and ClinicalTrials search to determine brand usage in research projects. Finally, we investigated developer accessibility to the health data collected by identified brands. RESULTS We identified 423 unique devices from 132 different brands. Forty-seven percent of brands released only one device. Introduction of new brands peaked in 2014, and the highest number of new devices was introduced in 2015. Sensor support increased every year, and in addition to the accelerometer, a photoplethysmograph, for estimating heart rate, was the most common sensor. Out of the brands currently available, the five most often used in research projects are Fitbit, Garmin, Misfit, Apple, and Polar. Fitbit is used in twice as many validation studies as any other brands and is registered in ClinicalTrials studies 10 times as often as other brands. CONCLUSIONS The wearable landscape is in constant change. New devices and brands are released every year, promising improved measurements and user experience. At the same time, other brands disappear from the consumer market for various reasons. Advances in device quality offer new opportunities for research. However, only a few well-established brands are frequently used in research projects, and even less are thoroughly validated.

A Reimbursable Counseling Intervention Improves Physical Activity and Function for Primary Care Patients with Type 2 Diabetes (2018)

Diabetes

Abstract

Physical activity (PA) is a cornerstone of treatment for type 2 diabetes mellitus (T2D). Among other benefits, regular PA improves physical function which can prevent institutionalization. However, effective PA interventions are not widely adopted in primary care. Our recent systematic review identified an effective PA intervention for adults with T2D that was nearly pragmatic enough for use in primary care. However, it still required adaptation to fit clinic work flows and to be reimbursed by insurance. After adapting the intervention (Be ACTIVE) to address these issues, we hypothesized Be ACTIVE would improve PA and physical function relative to randomized controls. This pragmatic pilot trial randomized adults with T2D to 12 weeks of Be ACTIVE (6 telephone behavioral counseling sessions + PA tracking (FitBit©); 3 primary care visits to teach strength exercises + monitor safety) vs. enhanced usual care (3 self-management mailings). At baseline and post-intervention, we assessed PA with Actigraph© accelerometers (steps/week) and physical function by a timed 400-meter walk (sec) and the Short Physical Performance Battery (SPPB, 0-12 scale). To date, we have completed assessments for 15 participants (n=8 Be ACTIVE, n=7 controls). Pre-post PA increased by 19.8% in the Be ACTIVE group (+5122 ± 10steps/week; data shown as mean ± SE ) and decreased by 15.9% in controls (-3122 ± 1233 steps/week). SPPB scores increased by a clinically important >1-point difference in the Be ACTIVE group (+1.1 ± 0.2), and were stable in controls (+0.3 ± 0.1). The 400-meter walk time improved by 10.3% in Be ACTIVE participants (-38.3 ± 6.6 sec) and declined by 5.4% in controls (+17.3 ± 6.4 sec). Be ACTIVE is a reimbursable intervention delivered by primary care staff. Strong pilot data support that Be ACTIVE improves PA/physical function. If our data remain promising at trial completion, the next step towards broader dissemination will be to test Be ACTIVE in multiple clinical settings.

VALIDITY AND RELIABILITY OF THE FITBIT FLEX™ AND ACTIGRAPH GT3X+ AT JOGGING AND RUNNING SPEEDS. (2018)

International journal of sports physical therapy

Abstract

Background Monitoring levels of physical activity, as an outcome or in guiding rehabilitation, is challenging for clinicians. Personal activity monitors are increasing in popularity and provide potential to enhance rehabilitation protocols. However, research to support the validity and reliability of these devices at jogging and running speeds is limited. Purpose The purpose of this study was to evaluate the validity of the Fitbit Flex™ and ActiGraph GT3X + for measuring step count at jogging and running speeds. A secondary purpose was to examine inter-device reliability of the Fitbit Flex™. Study Design Cross-sectional study. Methods Thirty healthy participants aged between 19 and 50 years, completed a treadmill protocol at jogging and running speeds (8 km/h to 16 km/h). Treadmill speed was progressively increased by intervals of 2 km/h. Each interval was four minutes in duration with a two minute rest period between stages. Participants were encouraged to continue through the graded exercise test until they reached the maximum running speed that they felt they could maintain for four minutes. Step count data was collected for Fitbit Flex™ devices and the ActiGraph GT3X+. Video analysis of step count was used as the criterion measure. Results At speeds of 8 to 14 km/h Mean Absolute Percentage Errors were ≤1% for the Fitbit Flex™ and the ActiGraph GT3X + when compared to step count via video analysis. Standard Error of Measurement between the three Fitbit Flex™ devices was ≤ 7 steps for speeds of 8 to 14 km/h and varied between 9 to 19 steps at 16 km/h. Fitbit Flex™ devices showed good to excellent between device reliability at speeds of 8 to14 km/h (ICC 0.723 to 0.999; p ≤ 0.001). Greater variability was evident with the low participant numbers at 16 km/h (ICC 0.527 to 0.896; p ≥ 0.02). Conclusion Both the Fitbit Flex™ and the ActiGraph GT3X + provide a valid account of steps taken at jogging and running speeds up to 14 km/hr, attainable by non-elite runners on a treadmill. Fitbit Flex™ devices provide equivalent step count output to each other, enabling comparison between devices during treadmill jogging and running. Level of evidence 2b.

Impact Of Placement Of Wrist-worn Activity Monitors During The Lab And Free-living Settings (2018)

Medicine & Science in Sports & Exercise

Abstract

Wrist-worn activity monitors have been widely used to measure heart rates, step counts, and/or calories. While the versatility of altering the wearing locations (i.e., left vs. right; distal vs. proximal) may prove appealing, the influence of monitor placement on the outcome measurements needs to be examined. PURPOSE: This study was to examine the difference in measurements of heart rates, step counts, and calories estimated from the Fitbit monitors worn at different locations. METHODS: Thirty-two healthy male and female, aged 26.03 ± 6.59 years, participated in this study. Participants wore Fitbit monitors at four different locations [Right Proximal (RP), Right Distal (RD), Left Proximal (LP), Left Distal (LD)]. Lab testing consisted of four 5-min phases: slow and brisk walking and jogging at 53.6, 107.3, 160.9 m/min on a treadmill and a recovery. Free living activities involved ten 5-min activities with different intensity levels (e.g., stretching, climbing stairs, jogging). Heart rates, step counts, and calories were recorded during various activities. Repeated measures ANOVAs were performed with a monitor placement as a within-subjects factor on 14 lab and free-living activities for each outcome measurement. Bonferroni technique was used to adjust the alpha level of .004 (.05/14). The Greenhouse-Geisser (G-G) adjusted F and degrees of freedom were reported. RESULTS: Overall, there were no significant differences in measurements of heart rates, step counts, and calories estimated from the four Fitbit monitors during the lab activities. In free-living activities, step counts were significantly different during climbing stairs, F(2.86, 88.76) = 5.16, G-G p = .003 and sports, F(1.24, 38.40) = 27.93, G-G p < .001. Step counts estimated from LD (446 ± 49) were significantly higher than Fitbit monitors worn at RP (413 ± 61) and RD (417 ± 49) when climbing stairs. Fitbit monitors worn at LP (649 ± 64) and LD (642 ± 70) estimated significantly higher step counts than Fitbit monitors worn at RP (587 ± 75) and RD (565 ± 73) as participated in sports. CONCLUSION: This study revealed that monitor placement does not make a significant influence on the measurements of heart rates and calories during the lab and free- living settings. Further studies on the impact of monitor placement against criterion measures are warranted.

Use of Consumer Monitors for Estimating Energy Expenditure in Youth (2018)

Medicine & Science in Sports & Exercise

Abstract

Consumer-grade physical activity monitors (PAMs) have been extensively examined for estimating energy expenditure (EE) in adults; however, few studies have examined their ability to estimate EE in youth. PURPOSE: The purpose of this study was to examine equivalence between predicted EE (consumer-grade PAMs) and measured EE (indirect calorimetry) in youth. METHODS: Ninety-five youth (mean(SD); age, 12.2(3.5) yr; 49% male) performed 30 min of supine rest and 16 structured activities ranging from sedentary behaviors to vigorous intensities. Each structured activity was performed twice: once for 60-90 s and once for 4-5 min. During all testing, participants wore an Apple Watch 2 (AW, left wrist) and Mymo Activity Tracker (MT, right hip). Subsamples wore two Misfit Shine 2 devices (MSH, right hip; MSS, right shoe, n = 27), a Samsung Gear Fit 2 (SG, right wrist, n = 44), and/or a Fitbit Charge 2 (FC, right wrist, n = 53). A Cosmed K4b2 was used as the criterion measure of EE. Oxygen consumption was converted to EE in kilocalories (kcal), then summed over the whole trial to obtain gross EE. Net EE was calculated by subtracting the estimated basal EE (Schofield’s equation) from the measured gross EE. For all PAMS, EE was recorded at the start and end of the trial, and the difference was used for analysis. 95% equivalence testing with ±10% equivalence zone was used to assess equivalence between the estimated (PAM) and measured (K4b2) EE. All PAMs estimated gross EE, except for AW which estimated net EE, thus separate equivalence test was performed for the AW. Mean absolute percent error (MAPE) was used to assess individual-level error. RESULTS: Equivalence testing results and MAPE are summarized in Table 1. CONCLUSION: Only the MT was equivalent to the K4b2, however it also had the largest individual error. Caution is warranted when using consumer-grade PAMs in youth for tracking EE as there is a high degree of variability in device output.

Commonly available activity tracker apps and wearables as a mental health outcome indicator: A prospective observational cohort study among young adults with psychological distress (2018)

Journal of Affective Disorders

Abstract

BACKGROUND Monitoring is integral to adequately recognise and track mental health indicators of symptoms and functioning. Early identification of warning signs from digital footprints could facilitate adaptive and dynamic just in-time monitoring and care for individuals with common mental disorders. METHODS Self-report data on mental health and lifestyle behaviour from 120 male and female Australian young adults experiencing psychological distress were collected online. API software was used to download participant's daily activity duration measurements over eight months from linked commercial activity tracker apps and wearables in real time. An independent samples t-test was conducted to compare the differences in daily durations of recorded physical activity between wearable devises and smartphone apps. Entropy techniques using R interpol package were used to analyse volatility in daily activity duration. RESULTS DASS-21 depression, stress and anxiety sub-scale scores indicated the study sample on average, had a moderate level of psychological distress. Daily activity duration was significantly greater from wearable devices when compared with smartphone apps (t-test = 25.4, p textless 0.001). Entropy indices were not related with any of the DASS-21 measures. However, significant correlation between DASS-21 anxiety subscale scores and entropy of those with over 45 days measurements (r = 0.58, p = 0.02) was observed. LIMITATIONS The observational nature of this study prohibits causal inference. As a convenience sample was used, the results may lack generalisability to the wider population. CONCLUSIONS Continuous monitoring using commercial apps and wearables as a resource to help clinicians augment clinical care for common mental disorders appears viable.

Clinical Feasibility of Continuously Monitored Data for Heart Rate, Physical Activity, and Sleeping by Wearable Activity Trackers in Patients with Thyrotoxicosis: Protocol for a Prospective Longitudinal Observational Study. (2018)

JMIR Research Protocols

Abstract

BACKGROUND Thyrotoxicosis is a common disease caused by an excess of thyroid hormones. The prevalence of thyrotoxicosis about 2% and 70-90% of thyrotoxicosis cases are caused by Graves' disease, an autoimmune disease, which has a high recurrence rate when treated with antithyroid drugs such as methimazole or propylthiouracil. The clinical symptoms and signs of thyrotoxicosis include palpitation, weight loss, restlessness, and difficulty sleeping. Although these clinical changes in thyrotoxicosis can be detected by currently available wearable activity trackers, there have been few trials of the clinical application of wearable devices in patients with thyrotoxicosis. OBJECTIVE The aim of this study is to investigate the clinical applicability of wearable device-generated data to the management of thyrotoxicosis. We are analyzing continuously monitored data for heart rate, physical activity, and sleep in patients with thyrotoxicosis during their clinical course after treatment. METHODS Thirty thyrotoxic patients and 10 control subjects were enrolled in this study at Seoul National University Bundang Hospital. Heart rate, physical activity, and sleep are being monitored using a Fitbit Charge HR or Fitbit Charge 2. Clinical data including anthropometric measures, thyroid function test, and hyperthyroidism symptom scale are recorded. RESULTS Study enrollment began in December 2016, and the intervention and follow-up phases are ongoing. The results of the data analysis are expected to be available by September 2017. CONCLUSIONS This study will provide a foundational feasibility trial of the clinical applications of biosignal measurements to the differential diagnosis, prediction of clinical course, early detection of recurrence, and treatment in patients with thyrotoxicosis.

Efficacy of a Community-Based Technology-Enabled Physical Activity Counseling Program for People With Knee Osteoarthritis: Proof-of-Concept Study (2018)

Journal of Medical Internet Research

Abstract

Background: Current practice guidelines emphasize the use of physical activity as the first-line treatment of knee osteoarthritis; however, up to 90% of people with osteoarthritis are inactive. Objective: We aimed to assess the efficacy of a technology-enabled counseling intervention for improving physical activity in people with either a physician-confirmed diagnosis of knee osteoarthritis or having passed two validated criteria for early osteoarthritis. Methods: We conducted a proof-of-concept randomized controlled trial. The immediate group received a brief education session by a physical therapist, a Fitbit Flex, and four biweekly phone calls for activity counseling. The delayed group received the same intervention 2 months later. Participants were assessed at baseline (T0) and at the end of 2 months (T1), 4 months (T2), and 6 months (T3). Outcomes included (1) mean time on moderate-to-vigorous physical activity (MVPA ≥3 metabolic equivalents [METs], primary outcome), (2) mean time on MVPA ≥4 METs, (3) mean daily steps, (4) mean time on sedentary activities, (5) Knee Injury and Osteoarthritis Outcome Score (KOOS), and (6) Partners in Health scale. Mixed-effects repeated measures analysis of variance was used to assess five planned contrasts of changes in outcome measures over measurement periods. The five contrasts were (1) immediate T1-T0 vs delayed T1-T0, (2) delayed T2-T1 vs delayed T1-T0, (3) mean of contrast 1 and contrast 2, (4) immediate T1-T0 vs delayed T2-T1, and (5) mean of immediate T2-T1 and delayed T3-T2. The first three contrasts estimate the between-group effects. The latter two contrasts estimate the effect of the 2-month intervention delay on outcomes. Results: We recruited 61 participants (immediate: n=30; delayed: n=31). Both groups were similar in age (immediate: mean 61.3, SD 9.4 years; delayed: mean 62.1, SD 8.5 years) and body mass index (immediate: mean 29.2, SD 5.5 kg/m2; delayed: mean 29.2, SD 4.8 kg/m2). Contrast analyses revealed significant between-group effects in MVPA ≥3 METs (contrast 1 coefficient: 26.6, 95% CI 4.0-49.1, P=.02; contrast 3 coefficient: 26.0, 95% CI 3.1-49.0, P=.03), daily steps (contrast 1 coefficient: 1699.2, 95% CI 349.0-3049.4, P=.02; contrast 2 coefficient: 1601.8, 95% CI 38.7-3164.9, P=.045; contrast 3 coefficient: 1650.5, 95% CI 332.3-2968.7; P=.02), KOOS activity of daily living subscale (contrast 1 coefficient: 6.9, 95% CI 0.1-13.7, P=.047; contrast 3 coefficient: 7.2, 95% CI 0.8-13.6, P=.03), and KOOS quality of life subscale (contrast 1 coefficient: 7.4, 95% CI 0.0-14.7, P=.049; contrast 3 coefficient: 7.3, 95% CI 0.1-14.6, P=.048). We found no significant effect in any outcome measures due to the 2-month delay of the intervention. Conclusions: Our counseling program improved MVPA ≥3 METs, daily steps, activity of daily living, and quality of life in people with knee osteoarthritis. These findings are important because an active lifestyle is an important component of successful self-management. Trial Registration: ClinicalTrials.gov NCT02315664; https://clinicaltrials.gov/ct2/show/NCT02315664 (Archived by WebCite at http://www.webcitation.org/6ynSgUyUC) [J Med Internet Res 2018;20(4):e159]

Joint Modeling of Heterogeneous Sensing Data for Depression Assessment via Multi-task Learning (2018)

Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies

Abstract

Depression is a common mood disorder that causes severe medical problems and interferes negatively with daily life. Identifying human behavior patterns that are predictive or indicative of depressive disorder is important. Clinical diagnosis of depression relies on costly clinician assessment using survey instruments which may not objectively reflect the fluctuation of daily behavior. Self-administered surveys, such as the Quick Inventory of Depressive Symptomatology (QIDS) commonly used to monitor depression, may show disparities from clinical decision. Smartphones provide easy access to many behavioral parameters, and Fitbit wrist bands are becoming another important tool to assess variables such as heart rates and sleep efficiency that are complementary to smartphone sensors. However, data used to identify depression indicators have been limited to a single platform either iPhone, or Android, or Fitbit alone due to the variation in their methods of data collection. The present work represents a large-scale effort to collect and integrate data from mobile phones, wearable devices, and self reports in depression analysis by designing a new machine learning approach. This approach constructs sparse mappings from sensing variables collected by various tools to two separate targets: self-reported QIDS scores and clinical assessment of depression severity. We propose a so-called heterogeneous multi-task feature learning method that jointly builds inference models for related tasks but of different types including classification and regression tasks. The proposed method was evaluated using data collected from 103 college students and could predict the QIDS score with an R2 reaching 0.44 and depression severity with an F1-score as high as 0.77. By imposing appropriate regularizers, our approach identified strong depression indicators such as time staying at home and total time asleep.

Effect of a Home-Based Exercise Intervention of Wearable Technology and Telephone Coaching on Walking Performance in Peripheral Artery Disease (2018)

JAMA

Abstract

Importance Clinical practice guidelines support home-based exercise for patients with peripheral artery disease (PAD), but no randomized trials have tested whether an exercise intervention without periodic medical center visits improves walking performance. Objective To determine whether a home-based exercise intervention consisting of a wearable activity monitor and telephone coaching improves walking ability over 9 months in patients with PAD. Design, Setting, and Participants Randomized clinical trial conducted at 3 US medical centers. Patients with PAD were randomized between June 18, 2015, and April 4, 2017, to home-based exercise vs usual care for 9 months. Final follow-up was on December 5, 2017. Interventions The exercise intervention group (n = 99) received 4 weekly medical center visits during the first month followed by 8 months of a wearable activity monitor and telephone coaching. The usual care group (n = 101) received no onsite sessions, active exercise, or coaching intervention. Main Outcomes and Measures The primary outcome was change in 6-minute walk distance at 9-month follow-up (minimal clinically important difference [MCID], 20 m). Secondary outcomes included 9-month change in subcomponents of the Walking Impairment Questionnaire (WIQ) (0-100 score; 100, best), SF-36 physical functioning score, Patient-Reported Outcomes Measurement Information System (PROMIS) mobility questionnaire (higher = better; MCID, 2 points), PROMIS satisfaction with social roles questionnaire, PROMIS pain interference questionnaire (lower = better; MCID range, 3.5-4.5 points), and objectively measured physical activity. Results Among 200 randomized participants (mean [SD] age, 70.2 [10.4] years; 105 [52.5%] women), 182 (91%) completed 9-month follow-up. The mean change from baseline to 9-month follow-up in the 6-minute walk distance was 5.5 m in the intervention group vs 14.4 m in the usual care group (difference, −8.9 m; 95% CI, −26.0 to 8.2 m; P = .31). The exercise intervention worsened the PROMIS pain interference score, mean change from baseline to 9 months was 0.7 in the intervention group vs −2.8 in the usual care group (difference, 3.5; 95% CI, 1.3 to 5.8; P = .002). There were no significant between-group differences in the WIQ score, the SF-36 physical functioning score, or the PROMIS mobility or satisfaction with social roles scores. Conclusions and Relevance Among patients with PAD, a home-based exercise intervention consisting of a wearable activity monitor and telephone coaching, compared with usual care, did not improve walking performance at 9-month follow-up. These results do not support home-based exercise interventions of wearable devices and telephone counseling without periodic onsite visits to improve walking performance in patients with PAD. Trial Registration clinicaltrials.gov Identifier: NCT02462824

Simultaneous Aerobic Exercise and Memory Training Program in Older Adults with Subjective Memory Impairments (2018)

Journal of Alzheimer's Disease

Abstract

Abstract: Background:Several modifiable lifestyle factors have been shown to have potential beneficial effects in slowing cognitive decline. Two such factors that may affect cognitive performance and slow the progression of memory loss into dementia in older adults are cognitive training and physical activity. There are currently no effective treatments for dementia; therefore, preventative strategies to delay or prevent the onset of dementia are of critical importance. Objective:The aim of this study was to determine the relative effectiveness of simultaneous performance of memory training and aerobic exercise to a sequential performance intervention on memory functioning in older adults. Methods:55 older adults (aged 60– 75) with subjective memory impairments (non-demented and non-MCI) completed the intervention that consisted of 90-minute small group classes held twice weekly. Participants were randomized to either 4-weeks of supervised strategy-based memory training done simultaneously while stationary cycling (SIM) or sequentially after the stationary cycling (SEQ). Standardized neurocognitive measures of memory, executive functioning, speed of processing, attention, and cognitive flexibility were assessed at baseline and post-intervention. Results:The SIM group, but not the SEQ group, had a significant improvement on composite memory following the intervention (t(51) = 2.7, p = 0.01, effect size (ES) = 0.42) and transfer to non-trained reasoning abilities (t(51) = 6.0, ES = 0.49) and complex attention (t(51) = 3.1, p = 0.003, ES = 0.70). Conversely, the SEQ group, but not the SIM, showed significant improvement in executive functioning (t(51) = 5.0, p = 0.0001, ES = 0.96). Conclusion:These findings indicate that a 4-week simultaneous memory training and aerobic exercise program is sufficient to improve memory, attention, and reasoning abilities in older adults.

Validating Fitbit Zip for monitoring physical activity of children in school: a cross-sectional study (2018)

BMC Public Health

Abstract

BACKGROUND Modern activity trackers, including the Fitbit Zip, enable the measurement of both the step count as well as physical activity (PA) intensities. However, there is a need for field-based validation studies in a variety of populations before using trackers for research. Therefore, the purpose of the current study was to investigate the validity of Fitbit Zip step count, moderate to vigorous physical activity (MVPA) and sedentary minutes, in different school segments in 3rd grade students. METHODS Third grade students (N = 147, aged 9-10 years) wore a Fitbit Zip and an ActiGraph GT3x-BT accelerometer simultaneously on a belt for five days during school hours. The number of steps, minutes of MVPA and sedentary time during class time, physical education lessons and recess were extracted from both devices using time filters, based on the information from school time tables obtained from class teachers. The validity of the Fitbit Zip in different school segments was assessed using Bland-Altman analysis and Spearman's correlation. RESULTS There was a strong correlation in the number of steps in all in-school segments between the two devices (r = 0.85-0.96, P < 0.001). The Fitbit Zip overestimated the number of steps in all segments, with the greatest overestimation being present in physical education lessons (345 steps). As for PA intensities, the agreement between the two devices in physical education and recess was moderate for MVPA minutes (r = 0.56 and r = 0.72, P < 0.001, respectively) and strong for sedentary time (r = 0.85 and r = 0.87, P < 0.001, respectively). During class time, the correlation was weak for MVPA minutes (r = 0.24, P < 0.001) and moderate for sedentary time (r = 0.57, P < 0.001). For total in-school time, the correlation between the two devices was strong for steps (r = 0.98, P < 0.001), MVPA (r = 0.80, P < 0.001) and sedentary time (r = 0.94, P < 0.001). CONCLUSION In general, the Fitbit Zip can be considered a relatively accurate device for measuring the number of steps, MVPA and sedentary time in students in a school-setting. However, in segments where sedentary time dominates (e.g. academic classes), a research-grade accelerometer should be preferred.

Reliability of Zephyr Bioharness and Fitbit Charge Measures of Heart Rate and Activity at Rest, During the Modified Canadian Aerobic Fitness Test and Recovery (2018)

Journal of Strength and Conditioning Research

Abstract

The purpose of current study was to determine the intra-session and inter-session reliability of Zephyr Bioharness and Fitbit Charge variables in both healthy men and women at rest, during the Modified Canadian Aerobic Fitness Test (mCAFT) and throughout recovery. Stratified convenience and snowball sampling were used to recruit sixty participants (30 females, 48 ± 15) and (30 males, 48 ± 15) from McMaster University student, staff and faculty population. At rest, intra-session average heart rate (beats/min.) Intra-class correlation coefficients (ICCs) and Standard Error of Measurement [SEM] for Zephyr ranged from (0.94 to 0.97) [1.17 - 1.70] and (0.92 - 0.97) [1.45 - 2.10] for Fitbit Charge. During the mCAFT, the Zephyr ICCs and (SEM) ranged from 0.31 - 0.99 (1.28 - 8.10) and 0.45 - 0.99 (1.45 - 8.71) for the Fitbit Charge. Throughout the recovery, the ICCs and (SEM) ranged from 0.44 - 0.98 (1.26 - 10.47) and 0.45 - 0.98 (1.15 - 11.90) for Zephyr and Fitbit devices respectively. At rest, intersession ICCs (SEM) for Zephyr and Fitbit ranged from 0.90 - 0.94 (1.73 - 2.37) and 0.88 - 0.94 (1.83 - 2.67) respectively. At mCAFT, the Zephyr ICCs (SEM) ranged from 0.91 - 0.97 (3.12 - 4.64) and 0.85 - 0.98 (3.28 - 4.88) for the Fitbit. Throughout the recovery, the ICCs (SEM) ranged from 0.93 - 0.97 (2.65 - 4.66) and 0.76 -0.91 (3.17 - 4.67) for Zephyr and Fitbit devices respectively. To conclude both the Zephyr Bioharness and Fitbit Charge devices demonstrated excellent reliability measures throughout the three phases. The findings from our study adds to the existing pool of literature regarding the reliability parameters of wearable devices and suggests that stable and consistent measures of heart rate and physical activity can be obtained using Zephyr Bioharness and Fitbit Charge devices among healthy male and female participants at rest, during a standardized sub-maximal fitness test (mCAFT) and throughout recovery.

Veterans’ Perspectives on Fitbit Use in Treatment for Post-Traumatic Stress Disorder: An Interview Study (2018)

JMIR Mental Health

Abstract

Background: The increase in availability of patient data through consumer health wearable devices and mobile phone sensors provides opportunities for mental health treatment beyond traditional self-report measurements. Previous studies have suggested that wearables can be effectively used to benefit the physical health of people with mental health issues, but little research has explored the integration of wearable devices into mental health care. As such, early research is still necessary to address factors that might impact integration including patients' motivations to use wearables and their subsequent data. Objective: The aim of this study was to gain an understanding of patients’ motivations to use or not to use wearables devices during an intensive treatment program for post-traumatic stress disorder (PTSD). During this treatment, they received a complementary Fitbit. We investigated the following research questions: How did the veterans in the intensive treatment program use their Fitbit? What are contributing motivators for the use and nonuse of the Fitbit? Methods: We conducted semistructured interviews with 13 veterans who completed an intensive treatment program for PTSD. We transcribed and analyzed interviews using thematic analysis. Results: We identified three major motivations for veterans to use the Fitbit during their time in the program: increase self-awareness, support social interactions, and give back to other veterans. We also identified three major reasons certain features of the Fitbit were not used: lack of clarity around the purpose of the Fitbit, lack of meaning in the Fitbit data, and challenges in the veteran-provider relationship. Conclusions: To integrate wearable data into mental health treatment programs, it is important to understand the patient’s perspectives and motivations in using wearables. We also discuss how the military culture and PTSD may have contributed to our participants' behaviors and attitudes toward Fitbit usage. We conclude with possible approaches for integrating patient-generated data into mental health treatment settings that may address the challenges we identified. [JMIR Ment Health 2018;5(2):e10415]

The efficacy of a multimodal physical activity intervention with supervised exercises, health coaching and an activity monitor on physical activity levels of patients with chronic, nonspecific low back pain (Physical Activity for Back Pain (PAyBACK) trial): study protocol for a randomised controlled trial (2018)

Trials

Abstract

Physical activity plays an important role in the management of chronic low back pain (LBP). Engaging in an active lifestyle is associated with a better prognosis. Nevertheless, there is evidence to suggest that patients with chronic LBP are less likely to meet recommended physical activity levels. Furthermore, while exercise therapy has been endorsed by recent clinical practice guidelines, evidence from systematic reviews suggests that its effect on pain and disability are at best moderate and not sustained over time. A limitation of current exercises programmes for chronic LBP is that these programmes are not designed to change patients' behaviour toward an active lifestyle. Therefore, we will investigate the short- and long-term efficacy of a multimodal intervention, consisting of supervised exercises, health coaching and use of an activity monitor (i.e. Fitbit Flex) compared to supervised exercises plus sham coaching and a sham activity monitor on physical activity levels, pain intensity and disability, in patients with chronic, nonspecific LBP. This study will be a two-group, single-blind, randomised controlled trial. One hundred and sixty adults with chronic, nonspecific LBP will be recruited. Participants allocated to both groups will receive a group exercise programme. In addition, the intervention group will receive health coaching sessions (i.e. assisting the participants to achieve their physical activity goals) and an activity monitor (i.e. Fitbit Flex). The participants allocated to the control group will receive sham health coaching (i.e. encouraged to talk about their LBP or other problems, but without any therapeutic advice from the physiotherapist) and a sham activity monitor. Outcome measures will be assessed at baseline and at 3, 6 and 12 months post randomisation. The primary outcomes will be physical activity, measured objectively with an accelerometer, as well as pain intensity and disability at 3 months post randomisation. Secondary outcomes will be physical activity, pain intensity and disability at 6 and 12 months post randomisation as well as other self-report measures of physical activity and sedentary behaviour, depression, quality of life, pain self-efficacy and weight-related outcomes at 3, 6, and 12 months post randomisation. This study is significant as it will be the first study to investigate whether a multimodal intervention designed to increase physical activity levels reduces pain and disability, and increases physical activity levels compared to a control intervention in patients with chronic LBP.

Physical Activity in Patients With Type 2 Diabetes: The Case for Objective Measurement in Routine Clinical Care. (2018)

Diabetes Care

Abstract

To perform at least 150 min of moderate-to-vigorous physical activity (MVPA) per week is a major aim in type 2 diabetes treatment (1), but actual measurements are not routinely performed in clinical practice. We questioned whether subjective assessment of physical activity is accurate to guide lifestyle advice. We compared the results of the Short Questionnaire to Assess Health–Enhancing Physical Activity (SQUASH) (2) and the Fitbit Flex accelerometer (3) in 50 patients with type 2 diabetes included in the DIAbetes and LifEstyle Cohort Twente (DIALECT) trial, which was approved by the local institutional review board METC-Twente (NL57219.044.16) (4). Patients wore the Fitbit for 7 consecutive days and were instructed to maintain their usual activities. Raw Fitbit data (steps/min) were organized into ready variables by an algorithm written in MATLAB (MathWorks, Natick, MA). MVPA was defined as ≥95 steps/min (5). Patients maintained a diary regarding activities not detected by the Fitbit (i.e., cycling, swimming, and fitness). Data are presented as median (interquartile range). The association between log-transformed measured minutes of MVPA was tested using linear regression analyses and the difference between MVPA with the Wilcoxon test for paired nonparametric data. Median age was 70 (63–76) years, median diabetes duration was 16 (10–21) years, 74% of the patients were male, 82% used insulin, and 84% had microvascular and 42% had macrovascular complications. According to SQUASH, patients had 165 (0–645) min of MVPA/week, and 20 participants (40%) adhered to the American Diabetes Association (ADA) recommendation of ≥150 min of MVPA/week (1) (Fig. 1). Fitbit data of >5 days were available in all patients. Median total steps per day were 4,277 (2,588–6,407), and 86% were able to reach ≥95 steps/min at some point during the measurement period. Based on the Fitbit data, patients had 23 (5–41) min of MVPA/week, and 1 (2%) participant adhered to the ADA guideline. When nonregistered activity was added, the figures increased to 31 (5–72) min of MVPA/week and 7 (14%) patients fulfilling the guideline. There was an association between SQUASH-assessed minutes of MVPA/week and Fitbit-assessed minutes of MVPA/week (β = 0.54, P < 0.001) (Fig. 1); however, the number of SQUASH-assessed minutes of MVPA/week was significantly and substantially higher (P < 0.001). Minutes of MVPA measured with SQUASH vs. minutes of MVPA measured with Fitbit. There was a correlation between measured minutes of MVPA between the two methods. However, in the majority of patients, the absolute number of weekly minutes of MVPA was substantially higher using the SQUASH results. Subjective assessment grossly overestimated weekly MVPA compared with objective assessment. When self-reporting, roughly half of the patients seem to meet the MVPA recommendations, whereas objective measurements indicate this number is 14%, at best. A limitation is that the Fitbit Flex, while validated for measuring steps, does not register other activities. Currently, several types of activity trackers with additional functionalities have become available, and future research should evaluate which tracker is most appropriate for clinical use. Although it has not been tested as to whether overestimation of activity occurs in a wider array of patients with diabetes, the findings presented here clearly point out the importance of using objective measurements of activity. We propose to incorporate objective measurements of physical activity in the standard care for patients with type 2 diabetes. Such measurements can not only identify individuals at risk but also increase patient awareness of physical inactivity and help to evaluate interventions.

Using the Habit App for Weight Loss Problem Solving: Development and Feasibility Study (2018)

JMIR mHealth and uHealth

Abstract

Background: Reviews of weight loss mobile apps have revealed they include very few evidence-based features, relying mostly on self-monitoring. Unfortunately, adherence to self-monitoring is often low, especially among patients with motivational challenges. One behavioral strategy that is leveraged in virtually every visit of behavioral weight loss interventions and is specifically used to deal with adherence and motivational issues is problem solving. Problem solving has been successfully implemented in depression mobile apps, but not yet in weight loss apps. Objective: This study describes the development and feasibility testing of the Habit app, which was designed to automate problem-solving therapy for weight loss. Methods: Two iterative single-arm pilot studies were conducted to evaluate the feasibility and acceptability of the Habit app. In each pilot study, adults who were overweight or obese were enrolled in an 8-week intervention that included the Habit app plus support via a private Facebook group. Feasibility outcomes included retention, app usage, usability, and acceptability. Changes in problem-solving skills and weight over 8 weeks are described, as well as app usage and weight change at 16 weeks. Results: Results from both pilots show acceptable use of the Habit app over 8 weeks with on average two to three uses per week, the recommended rate of use. Acceptability ratings were mixed such that 54% (13/24) and 73% (11/15) of participants found the diet solutions helpful and 71% (17/24) and 80% (12/15) found setting reminders for habits helpful in pilots 1 and 2, respectively. In both pilots, participants lost significant weight (P=.005 and P=.03, respectively). In neither pilot was an effect on problem-solving skills observed (P=.62 and P=.27, respectively). Conclusions: Problem-solving therapy for weight loss is feasible to implement in a mobile app environment; however, automated delivery may not impact problem-solving skills as has been observed previously via human delivery. Trial Registration: ClinicalTrials.gov NCT02192905; https://clinicaltrials.gov/ct2/show/NCT02192905 (Archived by WebCite at http://www.webcitation.org/6zPQmvOF2) [JMIR Mhealth Uhealth 2018;6(6):e145]

Changes in Physical Activity during a Pilot Weight Loss Program Before and After Knee Replacement (2018)

Medicine & Science in Sports & Exercise

Abstract

PURPOSE: Although knee replacement (KR) surgery typically results in pain reductions and functional improvement, most patients do not increase their physical activity. This study examined changes in objectively measured physical activity in KR patients who were participating in a weight loss program that started either before or after surgery. METHODS: Consented patients scheduled for KR were randomized to a 14 session pilot weight loss program starting ≤6 weeks before surgery (PACE) or 12 weeks post-op (Delayed PACE). Participants were encouraged to increase activity, set weekly activity goals, and self-monitor using paper, website, app, or Fitbit. Coaching sessions took place weekly or biweekly based on patient preference. Activity (moderate-to-vigorous activity [MVPA] bouts of ≥10 min of ≥2020 cpm and daily steps) was assessed using Actigraph GT3X monitors. PROMIS was used to assess pain intensity and function. Assessments were completed at baseline (pre-op), 12, and 26 weeks after surgery. Intent-to-treat was used with the last observation carried forward. Repeated measures ANOVAs examined changes in activity across time and group. RESULTS: Thirteen participants (mean±SD 63.5±7.9 years, 69% female, 69% White, BMI 35.7±5.1 kg/m2) provided physical activity data at baseline. Physical activity data was obtained for 77% of the sample at 12 and 26 weeks. Pain intensity decreased (P≤0.001) and function improved (P≤0.001) significantly, but no significant changes were observed in physical activity (Table 1). CONCLUSIONS: On average, KR patients participating in a weight loss program did not increase physical activity (weekly bouted MVPA and daily steps) after surgery. The lack of changes in activity, even in the presence of an intensive behavioral intervention and improvements in pain and function, highlight the challenges of altering behavior in this population. Future studies are needed to explore methods to increase activity after knee replacement.

Modeling Individual Differences: A Case Study of the Application of System Identification for Personalizing a Physical Activity Intervention (2018)

Journal of Biomedical Informatics

Abstract

BACKGROUND Control systems engineering methods, particularly, system identification (system ID), offer an idiographic (i.e., person-specific) approach to develop dynamic models of physical activity (PA) that can be used to personalize interventions in a systematic, scalable way. The purpose of this work is to: 1) apply system ID to develop individual dynamical models of PA (steps/day measured using Fitbit Zip) in the context of a goal setting and positive reinforcement intervention informed by Social Cognitive Theory; and 2) compare insights on potential tailoring variables (i.e., predictors expected to influence steps and thus moderate the suggested step goal and points for goal achievement) selected using the idiographic models to those selected via a nomothetic (i.e., aggregated across individuals) approach. METHOD A personalized goal setting and positive reinforcement intervention was deployed for 14 weeks. Baseline PA measured in weeks 1-2 was used to inform personalized daily step goals delivered in weeks 3-14. Goals and expected reward points (granted upon goal achievement) were pseudo-randomly assigned using techniques from system ID, with goals ranging from their baseline median steps/day up to 2.5x baseline median steps/day, and points ranging from 100-500 (i.e., $0.20 - $1.00). Participants completed a series of daily self-report measures. Auto Regressive with eXogenous Input (ARX) modeling and multilevel modeling (MLM) were used as the idiographic and nomothetic approaches, respectively. RESULTS Participants (N=20, mean age = 47.25 ± 6.16 years, 90% female) were insufficiently active, overweight (mean BMI = 33.79 ± 6.82 kg/m2) adults. Results from ARX modeling suggest that individuals differ in the factors (e.g., perceived stress, weekday/weekend) that influence their observed steps/day. In contrast, the nomothetic model from MLM suggested that goals and weekday/weekend were the key variables that were predictive of steps. Assuming the ARX models are more personalized, the obtained nomothetic model would have led to the identification of the same predictors for 5 of the 20 participants, suggesting a mismatch of plausible tailoring variables to use for 75% of the sample. CONCLUSION The idiographic approach revealed person-specific predictors beyond traditional MLM analyses and unpacked the inherent complexity of PA; namely that people are different and context matters. System ID provides a feasible approach to develop personalized dynamical models of PA and inform person-specific tailoring variable selection for use in adaptive behavioral interventions.

Comparison Of Daily Energy Expenditure And Weekly Physical Activity Exposure Estimated Using Consumer And Research-grade Physical Activity Monitors During Officer Cadet Initial Military Training (2018)

Medicine & Science in Sports & Exercise

Abstract

PURPOSE: Wearable physical activity monitors provide the capability to estimate the physical demands of military training and to potentially inform training practices. This study aimed to compare both the daily energy expenditure (EE) and weekly physical activity (PA) measured from two wrist-worn activity monitoring devices in Officer Cadets (OC) during initial military training. METHODS: Forty (26 male, 14 female) OC’s (mean ± SD: age 24 ± 2, height 1.76 ± 0.08 m, body mass 79.21 ± 9.97 kg) wore a consumer (“CN”; Fitbit Surge, San Francisco, USA) and a research-grade (“RG”; GENEActiv Original, Activinsights, Cambridge, UK) activity monitor during the second 14-week term of British Army OC training. A filtering process was implemented to exclude training days that did not fulfil wear-time criteria. Bivariate Pearsons correlations and limits of agreement (LoA) were used to compare EE measurement and duration of sedentary, light, moderate and vigorous PA between devices. RESULTS: Mean daily estimated EE from the CN and RG were 13.9 ± 2.5 and 15.7 ± 1.8 MJ.day-1, respectively. Estimated daily EE ranged from 8.6 ± 2.8 (day 35) to 22.4 ± 6.7 MJ.day-1 (day 49) in CN and from 10.7 ± 4.5 (day 55) to 21.3 ± 8.1 MJ.day-1 (day 49) in RG. There was a strong correlation between EE in CN and RG over 14 weeks (r = 0.761, p<0.001). However, the LoA indicated that CN underestimated EE (mean bias [95% CI] by -1.4 [-16 - 3.2] MJ.day-1, p<0.001) compared to the RG. Mean daily estimates from CN were 1069 ± 86 sedentary minutes, 221 ± 44 light minutes, 40 ± 14 moderate minutes and 56 ± 16 vigorous minutes. RG estimated 543 ± 53 sedentary minutes, 90 ± 16 light minutes, 249 ± 52 moderate minutes and 33 ± 14 vigorous minutes. All intensities were significantly different between CN and RG (p<0.05). The CN overestimated on sedentary minutes (526 mins.day-1 [319 - 734] mins.day-1), light minutes (131 [39 - 224] mins.day-1) and vigorous minutes (24 [-15 - 62] mins.day-1), but underestimated moderate minutes -208 [-295 - -122] mins.day-1). CONCLUSION: Consumer grade physical activity monitors provide an easily accessible tool for monitoring military training but more research is required to improve their accuracy before they can be used to inform training practices.

Remote Exercise for Adults with Down Syndrome (2018)

Translational Journal of the American College of Sports Medicine

Abstract

Adults with Down syndrome are less physically active than their typically developed peers. The purpose of this study was to assess the feasibility of delivering moderate-to-vigorous exercise sessions, led by a trained health educator using real-time video conferencing, to groups of young adults with Down syndrome in their homes. Participants were randomized to 30-min group exercise sessions either one or two times a week delivered on an iPad mini tablet computer using the Zoom video conferencing application, and were asked to attend individual support/education sessions once a week using FaceTime® on the iPad, for 12 wk. Minutes of moderate-to-vigorous physical activity (MVPA) during all group sessions were assessed using a Fitbit Charge HR activity/heart rate monitor. Participants were also asked to complete weekly homework assignments involving MVPA. Twenty-seven participants (n = 14, one session per week; n = 13, two sessions per week) with a mean age of 27.9 ± 7.1 yr ( 41% female) enrolled in and completed the 12-wk intervention. Attendance at group exercise and individual support/education sessions did not differ significantly between those randomized to one session per week (exercise sessions, 89.9% ± 8.8%; support/education sessions, 81.2% ± 18.7%) or two sessions per week (exercise sessions, 88.8% ± 7.7% (P = 0.79); support/education sessions, 86.0% ± 20.9% (P = 0.87)). Participants averaged 27.7 ± 5.7 min per session of MVPA with no significant difference between the one- (26.6 ± 3.0 min per session) and two-session-per-week groups (28.8 ± 7.7 min per session, P = 0.16). The completion rate for homework assignments did not differ significantly between the one- (21.4% ± 26.3%) and two-session-per-week groups (37.7% ± 21.7%, P = 0.28). Exercise delivered by group video conferencing may be a feasible and potentially effective approach for increasing MVPA in adults with Down syndrome.

Development of a Just-in-Time Adaptive mHealth Intervention for Insomnia: Usability Study (2018)

JMIR Human Factors

Abstract

Background: Healthy sleep is a fundamental component of physical and brain health. Insomnia, however, is a prevalent sleep disorder that compromises functioning, productivity, and health. Therefore, developing efficient treatment delivery methods for insomnia can have significant societal and personal health impacts. Cognitive behavioral therapy for insomnia (CBTI) is the recommended first-line treatment of insomnia but access is currently limited for patients, since treatment must occur in specialty sleep clinics, which suffer from an insufficient number of trained clinicians. Smartphone-based interventions offer a promising means for improving the delivery of CBTI. Furthermore, novel features such as real-time monitoring and assessment, personalization, dynamic adaptations of the intervention, and context awareness can enhance treatment personalization and effectiveness, and reduce associated costs. Ultimately, this “Just in Time Adaptive Intervention” for insomnia--an intervention approach that is acceptable to patients and clinicians, and is based on mobile health (mHealth) platform and tools--can significantly improve patient access and clinician delivery of evidence-based insomnia treatments. Objective: This study aims to develop and assess the usability of a Just in Time Adaptive Intervention application platform called iREST (“interactive Resilience Enhancing Sleep Tactics”) for use in behavioral insomnia interventions. iREST can be used by both patients and clinicians. Methods: The development of iREST was based on the Iterative and Incremental Development software development model. Requirement analysis was based on the case study’s description, workflow and needs, clinician inputs, and a previously conducted BBTI military study/implementation of the Just in Time Adaptive Intervention architecture. To evaluate the usability of the iREST mHealth tool, a pilot usability study was conducted. Additionally, this study explores the feasibility of using an off-the-shelf wearable device to supplement the subjective assessment of patient sleep patterns. Results: The iREST app was developed from the mobile logical architecture of Just in Time Adaptive Intervention. It consists of a cross-platform smartphone app, a clinician portal, and secure 2-way communications platform between the app and the portal. The usability study comprised 19 Active Duty Service Members and Veterans between the ages of 18 and 60. Descriptive statistics based on in-app questionnaires indicate that on average, 12 (mean 12.23, SD 8.96) unique devices accessed the clinician portal per day for more than two years, while the app was rated as “highly usable”, achieving a mean System Usability Score score of 85.74 (SD 12.37), which translates to an adjective rating of “Excellent”. The participants also gave high scores on “ease of use and learnability” with an average score of 4.33 (SD 0.65) on a scale of 1 to 5. Conclusions: iREST provides a feasible platform for the implementation of Just in Time Adaptive Intervention in mHealth-based and remote intervention settings. The system was rated highly usable and its cross-platformness made it readily implemented within the heavily segregated smartphone market. The use of wearables to track sleep is promising; yet the accuracy of this technology needs further improvement. Ultimately, iREST demonstrates that mHealth-based Just in Time Adaptive Intervention is not only feasible, but also works effectively. [JMIR Hum Factors 2018;5(2):e21]

Physical Activity during Pregnancy Alters Gene Expression in Neonatal Tissue (2018)

Medicine & Science in Sports & Exercise

Abstract

Offspring born to mothers who exercise during pregnancy have been shown to have reduced birth weight and body weight during adolescence; however, mechanisms to explain this phenomenon are lacking. PURPOSE: This study examined whether male infants born to mothers with low levels of naturally occurring physical activity during pregnancy (<6,000 steps/day; <6K) had altered expression of genes related to glucose metabolism and adipogenesis compared to infants born to mothers with greater than 6,000 steps/day (>6K). METHODS: Physical activity levels were assessed via Fitbit Flex during the second and third trimester of pregnancy. The dartos layer of the foreskin was collected following circumcision in full term, singleton, male neonates (n = 6 per group). Tissue was homogenized, RNA isolated, and a NanoString code set run to quantify a panel of genes related to glucose metabolism and adipogenesis, as well as housekeeping genes. RESULTS: Peroxisome proliferator-activated receptor alpha (<6K: 86.4±9.2 vs. >6K: 59.4±5.0) and cAMP responsive element binding protein 1 (<6K: 285.7±36.8 vs. >6K: 197.4±12.1), genes encoding proteins that are involved in partially regulating adipogenesis, and DNA methyltransferase 1 (DNMT1) (<6K: 132.2±6.4 vs. >6K: 112.5±3.7), a gene coding an enzyme involved in the regulation of DNA methylation patterns, were significantly decreased in the infants born to mothers with higher levels of physical activity during pregnancy (p<0.05). Adiponectin receptor 1 (<6K: 490.1±38.1 vs. >6K: 743.3±99.7), a receptor that is downregulated in obese, insulin resistant populations, and glucose transporter 1 (<6K: 88.0±15.7 vs. >6K: 153.3±13.0), a glucose transporter responsible for basal levels of glucose uptake in most cells, were significantly increased in the tissue of neonates born to mothers with higher levels of physical activity (p<0.05). CONCLUSION: These preliminary data suggest that low levels of physical activity during pregnancy are associated with increased gene expression of markers of adipogenesis and decreased markers involved in insulin sensitivity and glucose uptake. Further, these adaptations or other gene expression changes may be epigenetically regulated as DNMT1 levels were significantly lower in the lower physical activity group.

Wearable Activity Tracker Use Among Australian Adolescents: Usability and Acceptability Study. (2018)

JMIR mHealth and uHealth

Abstract

BACKGROUND Wearable activity trackers have the potential to be integrated into physical activity interventions, yet little is known about how adolescents use these devices or perceive their acceptability. OBJECTIVE The aim of this study was to examine the usability and acceptability of a wearable activity tracker among adolescents. A secondary aim was to determine adolescents' awareness and use of the different functions and features in the wearable activity tracker and accompanying app. METHODS Sixty adolescents (aged 13-14 years) in year 8 from 3 secondary schools in Melbourne, Australia, were provided with a wrist-worn Fitbit Flex and accompanying app, and were asked to use it for 6 weeks. Demographic data (age, sex) were collected via a Web-based survey completed during week 1 of the study. At the conclusion of the 6-week period, all adolescents participated in focus groups that explored their perceptions of the usability and acceptability of the Fitbit Flex, accompanying app, and Web-based Fitbit profile. Qualitative data were analyzed using pen profiles, which were constructed from verbatim transcripts. RESULTS Adolescents typically found the Fitbit Flex easy to use for activity tracking, though greater difficulties were reported for monitoring sleep. The Fitbit Flex was perceived to be useful for tracking daily activities, and adolescents used a range of features and functions available through the device and the app. Barriers to use included the comfort and design of the Fitbit Flex, a lack of specific feedback about activity levels, and the inability to wear the wearable activity tracker for water-based sports. CONCLUSIONS Adolescents reported that the Fitbit Flex was easy to use and that it was a useful tool for tracking daily activities. A number of functions and features were used, including the device's visual display to track and self-monitor activity, goal-setting in the accompanying app, and undertaking challenges against friends. However, several barriers to use were identified, which may impact on sustained use over time. Overall, wearable activity trackers have the potential to be integrated into physical activity interventions targeted at adolescents, but both the functionality and wearability of the monitor should be considered.

A Latino Patient-Centered, Evidence-Based Approach to Diabetes Prevention. (2018)

Journal of the American Board of Family Medicine : JABFM

Abstract

INTRODUCTION Cultural tailoring of evidence-based diabetes prevention program (DPP) interventions is needed to effectively address obesity and its related chronic diseases among Latinos in primary care. This article describes the patient-centered process used to adapt the DPP and reports cultural adaptations. METHODS We used a 2-stage formative research process to culturally adapt an evidence-based DPP intervention in the context of primary care. The first stage involved 5 focus groups of Latino patients and interviews with 5 stakeholders (3 with primary care physicians and 2 with medical directors) to inform a first round of adaptations. The second stage included pretesting the stage I-adapted intervention with a Latino patient advisory board to complete a second round of adaptations. RESULTS Key stakeholders involved in this 2-stage adaptation process included 34 Latino patients who participated in 5 focus groups and 5 physicians and medical directors who participated in key informant interviews during stage I and 11 patients who attended the 16 advisory board meetings and their family members who attended 1 of the meetings during stage II. Using this patient-centered stakeholder-engaged approach, we found the original intervention was largely congruent with the cultural values of the study population. To further strengthen the cultural relevance of the intervention, salient cultural values emphasized by patients and stakeholders underscored the importance of family and community support for behavior change. Accordingly, key adaptations were made to (1) invite family members to the orientation session and at 2 other key timepoints to facilitate family support, (2) provide participants support from the coach and each other via smartphone applications, and (3) provide healthy, easy, low-cost culturally appropriate meals at each group session. CONCLUSIONS The 2-stage approach actively engaging patients, family members, providers, and health care system leaders reinforced the cultural congruence of the existing intervention while further strengthening it with adaptations promoting Latino family and community support.

Acceptability and feasibility of a Fitbit physical activity monitor for endometrial cancer survivors. (2018)

Gynecologic Oncology

Abstract

OBJECTIVE Endometrial cancer survivors are the least physically active of all cancer survivor groups and exhibit up to 70% obesity. While studies suggest lifestyle interventions result in improved health outcomes, recruitment and availability of these programs are limited. The purpose was to evaluate the acceptability and validity of the Fitbit Alta™ physical activity monitor (Fitbit) for socioculturally diverse endometrial cancer survivors. METHODS Thirty endometrial cancer survivors were given wrist-worn Fitbits to wear for 30 days. Participants then returned the Fitbits, completed the Godin Leisure-Time Exercise Questionnaire (GLTEQ), Technology Acceptance Questionnaire, and answered qualitative prompts. Correlations between daily Fitbit step counts, demographic factors, body mass index (BMI), and GLTEQ Index, were analyzed using Stata 13.0. Concordance Correlation Coefficient using U statistics was used to examine convergent validity. RESULTS Twenty-five participants completed the study. Mean age was 62 ± 9 years. Mean BMI was 32 ± 9 kgtextperiodcenteredm-2. Self-identified race/ethnicity was 36% Hispanic, 36% non-Hispanic white, 16% non-Hispanic black and 12% Asian. Participants wore the Fitbits a median of 93% of possible days. Median daily Fitbit step count was 5325 (IQR: 3761-8753). Mean Technology Acceptance score was 2.8 ± 0.5 out of 4.0. Younger (textless65 years) and employed participants were more likely to achieve at least 6000 daily steps (p textless 0.05). There was no correlation (CCC = 0.00, p = 0.99) between step count and GLTEQ Index. Most free responses reflected positive experiences. CONCLUSIONS The Fitbits were well accepted in this sample. Self-reported physical activity was not associated with steps recorded. The physical activity data indicate an insufficiently active population.

Consumer Wearable Devices for Activity Monitoring Among Individuals After a Stroke: A Prospective Comparison (2018)

JMIR Cardio

Abstract

Background: Activity monitoring is necessary to investigate sedentary behavior after a stroke. Consumer wearable devices are an attractive alternative to research-grade technology, but measurement properties have not been established. Objective: The purpose of this study was to determine the accuracy of 2 wrist-worn fitness trackers: Fitbit Charge HR (FBT) and Garmin Vivosmart (GAR). Methods: Adults attending in- or outpatient therapy for stroke (n=37) wore FBT and GAR each on 2 separate days, in addition to an X6 accelerometer and Actigraph chest strap monitor. Step counts and heart rate data were extracted, and the agreement between devices was determined using Pearson or Spearman correlation and paired t or Wilcoxon signed rank tests (one- and two-sided). Subgroup analyses were conducted. Results: Step counts from FBT and GAR positively correlated with the X6 accelerometer (ρ=.78 and ρ=.65, Ptextless.001, respectively) but were significantly lower (Ptextless.01). For individuals using a rollator, there was no significant correlation between step counts from the X6 accelerometer and either FBT (ρ=.42, P=.12) or GAR (ρ=.30, P=.27). Heart rate from Actigraph, FBT, and GAR demonstrated responsiveness to changes in activity. Both FBT and GAR positively correlated with Actigraph for average heart rate (r=.53 and .75, Ptextless.01, respectively) and time in target zone (ρ=.49 and .74, Ptextless.01, respectively); these measures were not significantly different, but nonequivalence was found. Conclusions: FBT and GAR had moderate to strong correlation with best available reference measures of walking activity in individuals with subacute stroke. Accuracy appears to be lower among rollator users and varies according to heart rhythm. Consumer wearables may be a viable option for large-scale studies of physical activity. [JMIR Cardio 2018;2(1):e1]

Validation of Activity Tracking Procedures in Elderly Patients after Operative Treatment of Proximal Femur Fractures (2018)

Rehabilitation Research and Practice

Abstract

Background. Early postoperative physical activity in elderly patients suffering from proximal femoral fractures may reduce mortality. We hypothesized that activity trackers can reliably and objectively monitor the in-hospital mobilization, correlating with functional independence and quality of life. Methods. Three different tracker types (Fitbit™ flex, Misfit™ Shine, and Axivity AX3) at three locations (wrist, ankle, and femur) recorded steps and signal vector magnitudes (SVM) in 22 patients. They were years old, were equally distributed between the sexes, and had an ASA score of . Single protocoled activity events () were clinically categorized into 4 levels and correlated with the monitored signals. Additionally, and days after the operation, the EuroQol-5D and the Barthel-20 index supplemented this data. Results. All measurements at the wrist (Fitbit, Misfit) resulted in unacceptable accuracy; however, sensitivity and specificity reached around 90% using the Misfit at the ankle. Applying this combination, the correlation between real and measured steps ( = 0.99) and the category discrimination were statistically significant (). A discriminant analysis featured the calculation of four activity levels based on SVM measurements using the Axivity tracker at the femur. A cluster analysis showed a 100% agreement between the clinically observed and the calculated activity levels. The amount of active minutes or periods and both the EuroQol-5D and the Barthel-20 indices significantly increased between the analyzed time points after the operation. However, only the Barthel-20 was associated with the measured activity levels (). Conclusion. The Misfit and the Axivity trackers can reliably monitor activity in elderly patients after operative treatment of proximal femur fractures. However, the wear location is decisive. Objectively measured activity correlated with functional independence and quality of life.

Using financial incentives to promote physical activity in American Indian adolescents: A randomized controlled trial (2018)

PLOS ONE

Abstract

American Indians (AI) have high prevalence of diabetes in youth and may benefit from increasing physical activity as a strategy to improve metabolic health. We tested whether financial incentives would elicit greater frequency and/or duration of exercise in AI youth at high risk for developing diabetes. Overweight/obese AI boys and girls, 11-20 years old, were instructed to exercise on 3 days/week for 48 weeks at a tribal wellness center. The program was divided into three, 16-week-long phases to test different financial incentive strategies. Within each phase participants were randomly assigned to one of two groups that received different payments for exercise. Phase 1 was designed to test whether the size of the incentive would affect exercise frequency. In Phase 1, the number of exercise sessions did not differ between the group receiving a modest fixed-value payment per exercise session and the group receiving enhanced incentives to exercise more frequently (26 ± 3 versus 28 ± 2 sessions, respectively, p = 0.568). In Phase 2, the provision of an enhanced financial incentive to increase exercise duration resulted longer sessions, as the incentivized and standard payment groups exercised 38 ± 2 versus 29 ± 1 minutes per session (p = 0.002), respectively. In Phase 3, the effect of reducing the incentives on maintenance of exercise behaviors was inconclusive due to high participant withdrawal. Aerobic fitness increased 10% during Phase 1 but was unchanged thereafter. Insulin sensitivity and body composition were unchanged during the study. In conclusion, enhanced financial incentives increased the duration of exercise sessions, but had minimal effects on exercise participation. These results indicate that financial incentives hold promise in motivating previously sedentary, overweight/obese adolescents to exercise longer, but motivating them to sustain an exercise program remains the major challenge. Trial Registration: ClinicalTrials.gov NCT01848353.

Comparison of Research- and Consumer-grade Energy Expenditure Estimation Methods during 10 Days of Military Training (2018)

Medicine & Science in Sports & Exercise

Abstract

PURPOSE: Wearable physical activity monitoring devices have improved the ability to estimate free-living total energy expenditure (TEE) but their application during arduous military training alongside more well-established techniques has not been widely documented. This study aimed to assess the validity of two wrist-worn activity monitors to estimate TEE by evaluating performance against doubly labelled water (DLW) during British Army Officer Cadet (OC) training. METHODS: Twenty (10 male and 10 female) OCs (mean ± SD: age 22 ± 1 years, height 1.73 ± 0.08 m, body mass 77.0 ± 9.3 kg) wore one research-grade accelerometer (GENEActiv, Cambridge, UK; “RES”) on the dominant wrist and one commercially available (FITBIT SURGE, USA; “COM”) monitor on the non-dominant wrist for 10 days of training. Immediately prior to this 10-day period, participants consumed a bolus of DLW and provided daily urine samples, which were analysed by mass spectrometry to determine TEE. Bivariate correlations and limits of agreement were calculated to compare the 10-day mean TEE from DLW with both activity monitors to evaluate device performance. RESULTS: TEE (mean ± SD) from DLW, RES and COM were 17.2 ± 2.7 MJ.day-1 (4112 ± 652 kcal.day-1), 17.3 ± 2.8 MJ.day-1 (4129 ± 677 kcal.day-1) and 15.1 ± 3.7 MJ.day-1 (3607 ± 888 kcal.day-1), respectively. TEE from DLW was linearly correlated with both RES (r=0.786, p<0.001) and COM (r=0.888, p<0.001). Despite a stronger association with DLW however, COM tended to underestimate TEE (mean bias [95% CI]) by -2.1 [-5.6-1.4] MJ.day-1 (-505 [-1348-339] kcal.day-1; p<0.05). In contrast, mean TEE from RES was similar to DLW (-0.05 [-3.6-3.5] MJ.day-1; -11 [-867-845] kcal.day-1; p>0.05). CONCLUSION: Wearable physical activity monitors provides a cheaper and more practical method for estimating free-living TEE than DLW, and could be useful for military populations. However, this study suggests a consumer monitor may underperform, by underestimating TEE, during physically demanding training in comparison to a research- grade device.

mActive-Smoke: A Prospective Observational Study Using Mobile Health Tools to Assess the Association of Physical Activity With Smoking Urges (2018)

JMIR mHealth and uHealth

Abstract

Background: Evidence that physical activity can curb smoking urges is limited in scope to acute effects and largely reliant on retrospective self-reported measures. Mobile health technologies offer novel mechanisms for capturing real-time data of behaviors in the natural environment. Objective: This study aimed to explore this in a real-world longitudinal setting by leveraging mobile health tools to assess the association between objectively measured physical activity and concurrent smoking urges in a 12-week prospective observational study. Methods: We enrolled 60 active smokers (≥3 cigarettes per day) and recorded baseline demographics, physical activity, and smoking behaviors using a Web-based questionnaire. Step counts were measured continuously using the Fitbit Charge HR. Participants reported instantaneous smoking urges via text message using a Likert scale ranging from 1 to 9. On study completion, participants reported follow-up smoking behaviors in an online exit survey. Results: A total of 53 participants (aged 40 [SD 12] years, 57% [30/53] women, 49% [26/53] nonwhite) recorded at least 6 weeks of data and were thus included in the analysis. We recorded 15,365 urge messages throughout the study, with a mean of 290 (SD 62) messages per participant. Mean urge over the course of the study was positively associated with daily cigarette consumption at follow-up (Pearson r=.33; P=.02). No association existed between daily steps and mean daily urge (beta=−6.95×10−3 per 1000 steps; P=.30). Regression models of acute effects, however, did reveal modest inverse associations between steps within 30-, 60-, and 120-min time windows of a reported urge (beta=−.0191 per 100 steps, P<.001). Moreover, 6 individuals (approximately 10% of the study population) exhibited a stronger and consistent inverse association between steps and urge at both the day level (mean individualized beta=−.153 per 1000 steps) and 30-min level (mean individualized beta=−1.66 per 1000 steps). Conclusions: Although there was no association between objectively measured daily physical activity and concurrently self-reported smoking urges, there was a modest inverse relationship between recent step counts (30-120 min) and urge. Approximately 10% of the individuals appeared to have a stronger and consistent inverse association between physical activity and urge, a provocative finding warranting further study. [JMIR Mhealth Uhealth 2018;6(5):e121]

A Smartphone App to Promote an Active Lifestyle in Lower-Educated Working Young Adults: Development, Usability, Acceptability, and Feasibility Study. (2018)

JMIR mHealth and uHealth

Abstract

BACKGROUND Physical activity (PA) levels are problematic in lower-educated working young adults (18-26 years). To promote PA, smartphone apps have great potential, but there is no evidence for their effectiveness in this population. To increase the likelihood that a newly developed app will be effective, formative research and user testing are required. OBJECTIVE The aim of this study was to describe the development, usability, acceptability, and feasibility of a new theory- and evidence-based smartphone app to promote an active lifestyle in lower-educated working young adults. METHODS The new app was developed by applying 4 steps. First, determinants important to promote an active lifestyle in this population were selected. Second, evidence-based behavior change techniques were selected to convert the determinants into practical applications. Third, a new smartphone app was developed. Fourth, volunteers (n=11, both lower and higher educated) tested the app on usability, and lower-educated working young adults (n=16) tested its acceptability and feasibility via (think aloud) interviews, a questionnaire, and Google Analytics. The app was accordingly adapted for the final version. RESULTS A new Android app, Active Coach, was developed that focused on knowledge, attitude, social support, and self-efficacy (based on outcomes from step 1), and that applied self-regulation techniques (based on outcomes from step 2). The app consists of a 9-week program with personal goals, practical tips, and scientific facts to encourage an active lifestyle. To ensure all-day and automatic self-monitoring of the activity behavior, the Active Coach app works in combination with a wearable activity tracker, the Fitbit Charge. Issues detected by the usability test (eg, text errors, wrong messages) were all fixed. The acceptability and feasibility test showed that participants found the app clear, understandable, and motivating, although some aspects needed to be more personal. CONCLUSIONS By applying a stepwise, user-centered approach that regularly consulted the target group, the new app is adapted to their specific needs and preferences. The Active Coach app was overall positively evaluated by the lower-educated working young adults at the end of the development process.

Water intake reverses dehydration associated impaired executive function in healthy young women (2018)

Physiology & Behavior

Abstract

INTRODUCTION Healthy women do not always consume Recommended Daily Levels of fluid intake ad libitum. We hypothesized that 1) women lose≥1.0% BW during daily activities, 2) that mild body water loss impairs memory and executive function, 3) water intake to recommended daily levels will improve cognitive function. METHODS We tested 12 women (26±5yr, 22.5±2.6kg/m2 BMI). Session 1 was a control (CON) session, during which subjects monitored their food and fluid intake (diary) and activity (Fitbit®). The next two sessions were applied in balanced order: dehydration (DEH) session, where subjects minimized drinking, and a euhydration (EUH) session, where subjects drank Recommended Daily Levels of fluid for their age and sex, or 2500ml/24h. We compared emotion, sensory perception and cognition with computer based visual analog tests and computer based cognitive tasks (Cogstate) at 5PM, i.e. baseline (BL) on the evening prior to the session, and at 7AM, 12PM, and 5PM during the session. RESULTS Urine specific gravity (USG) was similar at BL across conditions (CON 1.013±0.002, DEH 1.015±0.002, EUH 1.014±0.002) and increased with dehydration (CON 1.011±0.003, DEH 1.021±0.002, EUH 1.010±0.002, Ptextless0.05) by 5PM of the session. Uncontrolled fluid intake and physical activity were similar across sessions. The water challenges did not impact Detection, Identification, One-Card Learning, but EUH improved visual and working memory (Groton Maze Learning Test) errors: CON 40.1±11.1, DEH 40.5±10.1, EUH 33.9±10.9, Ptextless0.05. Executive function [Set Shifting (SETS)] also improved under EUH, errors: BL 22.5±12.7 vs. 5PM 17.8±6.2, Ptextless0.05. CONCLUSIONS Mild dehydration caused deficits in visual and working memory and executive function in healthy young women. These deficits were reversed by drinking water to the European Food Safety Authority and Institute of Medicine requirements of 2.5l/day for adult women.

Bouldering psychotherapy reduces depressive symptoms even when general physical activity is controlled for: A randomized controlled trial (2018)

Heliyon

Abstract

Background Bouldering psychotherapy (BPT) combines psychotherapeutic elements with physical activity (PA). It might be effective for reducing symptoms of depression, but so far, no study has assessed individuals' levels of PA to control for whether positive effects on depression can also be found when adjusting for participants' levels of PA. This is important because PA itself has been proven effective in reducing depression and therefore might be an important variable to account for – especially in therapies using sport as one therapeutic mechanism. Methods Using a waitlist control group design, outpatients with depression were assessed at baseline and after eight, 16, and 24 weeks. The intervention group took part in an eight-week bouldering psychotherapy which met once a week for three hours. Self-report measures before and after the intervention included the Symptom Checklist-90-R (SCL-90-R), the Beck Depression Inventory (BDI-II), and the questionnaire on resources and self-management skills (FERUS). PA was assessed during the first 16-week period via FitBit Zip accelerometers. Results Altogether, 47 complete cases (20 men and 27 women) were included in the final analyses. Depression scores dropped by up to 6.74 (CI 2.80–10.67) points on the SCL-90-R depression scale and by up to 8.26 (CI 4.21–12.31) points on the BDI-II during the BPT intervention, the control group remained stable (SCL-90-R Cohen's d = 0.60; BDI-II: Cohen's d = .50). All Participants accrued an average of 6,515 steps per day, which is considered “low-active.” Participants of the BPT intervention were significantly more likely to reduce their depressive symptoms (p = .025) than participants of the control group, even when PA was controlled for in a regression analysis. Limitations Limitations of the study are the relatively small number of patients and the assessment of outcome scores via self-report. Conclusions This study provides evidence that short-term BPT can be effective for reducing symptoms of depression even if controlled for other therapeutically active confounders including antidepressant medication, psychotherapy and general level of PA.

The validity and reliability of consumer-grade activity trackers in older, community-dwelling adults: A systematic review (2018)

Maturitas

Abstract

OBJECTIVE To understand the validity and reliability of consumer-grade activity trackers (consumer wearables) in older, community-dwelling adults. METHODS A systematic review of studies involving adults aged over 65 years who underwent physical activity monitoring with consumer wearables. A total of 7 observational studies qualified, identified from electronic databases: MEDLINE, EMBASE, Cochrane Library and others (2014-2018). Validity was interpreted using correlation coefficients (CC) and percentage error for agreement between reference devices or gold-standard validation methods Reliability was compared using mean differences or ranges (under- or overestimation) of step count and activity time. RESULTS Total sample size was 290 adults, mean age of 70.2 ± 4.8 years and females constituting 46.7 ± 26.1%. The studies evaluated eight different consumer wearables used by community-dwelling adults with a range of co-morbidities. Daily step count for all consumer wearables correlated highly with validation criterion, especially the ActiGraph device: intraclass correlation coefficients (ICC) were 0.94 for Fitbit One, 0.94 for Zip, 0.86 for Charge HR and 0.96 for Misfit Shine. Slower walking pace and impaired ambulation reduced the levels of agreement. Daily step count captured by Fitbit Zip was on average 7117 (±5,880.6), which was overestimated by five of the eight consumer wearables compared with reference devices (range 167.6-2,690.3 steps/day). Measurement of activity duration was accurate compared with reference devices, yet less so than step count. CONCLUSION In older, community-dwelling adults, consumer wearables accurately measure step count and activity duration, as confirmed by reference devices and validation methods Further research is required to understand how co-morbidities, gait and activity levels interact with monitoring in free-living environments.

The Influence Of Non-exercise Physical Activity During Aerobic Exercise On Cardiometabolic Risk Factors (2018)

Medicine & Science in Sports & Exercise

Abstract

PURPOSE: To determine the impact of changes in non-exercise physical activity on changes in cardiometabolic risk factors in participants performing aerobic exercise training. METHODS: Obese adults (N=25) were randomized to an aerobic training group or an aerobic training and increasing non-exercise physical activity group. Both groups performed supervised aerobic training (50%-75% VO2 max) for 24 weeks at a dose of 12 kcals per kg per week. Non-exercise physical activity (total steps, minutes in low, moderate to vigorous [MVPA] physical activity) was quantified during the entire intervention using Fitbit One accelerometers (removed during supervised exercise sessions). Cardiometabolic assessments included lipids, glucose, insulin, 2-hour glucose/insulin from an oral glucose tolerance test, fitness, and body composition measures (% body fat, weight, and waist circumference). Linear regression models were run with change in the cardiometabolic variable as the dependent variable and baseline value, age, race, sex, supervised exercise time, adherence to exercise dose, change in non-exercise physical activity variables (change in total steps, minutes in light intensity and minutes in MVPA) as predictor variables. RESULTS: Change in total steps was a significant predictor for change in weight (r2= 0.17, p=0.04), percent weight loss (r2= 0.18, p=0.03, waist circumference (r2=0.31, p=0.004), triglycerides (r2= 0.30, p=0.01) and relative fitness (r2= 0.19, p=0.03). Change in total steps approached significance as a predictor for absolute fitness (p=0.052) and body fat (p=0.059). Change in minutes in low intensity was a significant predictor of the change in 2-hour glucose (r2= 0.20, p=0.03). Change in MVPA was not associated with change in any cardiometabolic variables (all ps>0.05). Change in non-exercise physical activity did not predict changes in glucose, insulin, 2-hour insulin, low density lipoprotein, high density lipoprotein, total cholesterol, or lean mass levels (all ps>0.05). CONCLUSIONS: Change in non-exercise physical activity outside of aerobic training was associated with changes in several cardiometabolic variables. Increasing total steps or minutes in low intensity may represent a clinical target to maximize the health benefits of aerobic exercise training in obese adults.

Counting Steps in Activities of Daily Living in People With a Chronic Disease Using Nine Commercially Available Fitness Trackers: Cross-Sectional Validity Study. (2018)

JMIR mHealth and uHealth

Abstract

BACKGROUND Measuring physical activity with commercially available activity trackers is gaining popularity. People with a chronic disease can especially benefit from knowledge about their physical activity pattern in everyday life since sufficient physical activity can contribute to wellbeing and quality of life. However, no validity data are available for this population during activities of daily living. OBJECTIVE The aim of this study was to investigate the validity of 9 commercially available activity trackers for measuring step count during activities of daily living in people with a chronic disease receiving physiotherapy. METHODS The selected activity trackers were Accupedo (Corusen LLC), Activ8 (Remedy Distribution Ltd), Digi-Walker CW-700 (Yamax), Fitbit Flex (Fitbit inc), Lumoback (Lumo Bodytech), Moves (ProtoGeo Oy), Fitbit One (Fitbit inc), UP24 (Jawbone), and Walking Style X (Omron Healthcare Europe BV). In total, 130 persons with chronic diseases performed standardized activity protocols based on activities of daily living that were recorded on video camera and analyzed for step count (gold standard). The validity of the trackers' step count was assessed by correlation coefficients, t tests, scatterplots, and Bland-Altman plots. RESULTS The correlations between the number of steps counted by the activity trackers and the gold standard were low (range: -.02 to .33). For all activity trackers except for Fitbit One, a significant systematic difference with the gold standard was found for step count. Plots showed a wide range in scores for all activity trackers; Activ8 showed an average overestimation and the other 8 trackers showed underestimations. CONCLUSIONS This study showed that the validity of 9 commercially available activity trackers is low measuring steps while individuals with chronic diseases receiving physiotherapy engage in activities of daily living.

The Wearable Technological Device as a Means of Physical Activity Monitoring (2018)

Medicine & Science in Sports & Exercise

Abstract

PURPOSE: To assess the use of a wearable technological device for the increase in attainment of physical activity (PA) with the goal of preventing Type II Diabetes Mellitus (T2DM) through weight loss. The Fitbit offers an advantage to other activity trackers when used for study purposes, in that it can provide continuous measurement of PA across the entire study period. METHODS: Men and women over the age of 40, and at risk for prediabetes, were recruited. The study period was four months in length, with an initial four weeks of baseline PA testing, followed by 12 weeks of lifestyle intervention. Individualized PA goals were set. PA monitoring was very successful. Participants averaged 72 days of PA tracking with 46% of participants reaching the maximal number of days (77). The Fitbit Flex measures steps, minutes sedentary, minutes lightly active, minutes fairly active, minutes very active, and total active minutes. RESULTS: Participants (N = 13) were aged 65.03 (SD = 8.3) years. At baseline, participants were performing 220.8 (SD = 249.0) minutes per week of moderate intensity PA. Participants increased moderate intensity PA to 243.3 (SD = 198.8) minutes per week. At baseline, participants were accumulating 7511.6 steps/day (SD = 3271.2) increasing to 8177.6 steps/day (SD = 3078.9) taken during the three month intervention. No statistical significance was found. A Pearson CC (0.598) showed there was a positive trend with minutes of moderate-to-vigorous activity and weight loss. 36% of the variants of weight loss was influenced by minutes of PA. Compliance to wearing the Fitbit was very good with 93.1% of the weeks having data tracked for at least ≥ 6 days/week. Wear time was corroborated by Fitbit data, which showed of the 1415 tracked days, only 9.3% days recorded were of less than 2000 steps. Barriers to technology were low and 100% of participants strongly agreed that continuous monitoring of weight and PA encouraged them to make healthy lifestyle changes. CONCLUSIONS: Of particular importance was that 46% of participants initially self-reported that they were physically active for more than 150 minutes per week, which shows many people underestimate their actual PA. Continuous monitoring of PA through wearable technology can be a useful modality aiding in weight loss.

Self-monitoring and reminder text messages to increase physical activity after colorectal cancer (CRC): A pilot randomized controlled trial. (2018)

JCO

Abstract

615Background: Over 1.3 million people in the US are living with CRC. Physical activity is associated with lower risk of CRC mortality. Interventions are needed to increase physical activity after CRC diagnosis. Methods: We conducted a pilot RCT to determine the feasibility (adherence, attrition) and acceptability of a 12-wk intervention using a Fitbit Flex? and daily text messages to increase physical activity after CRC. Eligible patients had to have colon or rectal cancer of any stage, be disease free or have stable disease, able to speak and read English, and access to Internet and a mobile phone. Individuals with contraindications to moderate-to-vigorous physical activity (MVPA) or exercising ≥30 min ≥5 d/wk were excluded. We explored the impact of the intervention (n = 20) vs. usual care (n = 21) on MVPA via ActiGraph GT3X+ accelerometers pre-/post-intervention. Results: The intervention was feasible and acceptable. On average, participants were 54 y, BMI 28 kg/m2 and enrolled 1.5 y after diagnosis; 59% were women, 73% were White, and 61% were stage III. The intervention arm wore their Fitbits a median of 74 d (89% of study days, IQR: 23-83 d) and responded to 74% (34) of the 46 text messages that asked for a reply (IQR: 28-82%). Older participants were more likely to wear the Fitbit (r: 0.72; p < 0.001). Married participants were more likely to wear the Fitbit and respond to texts compared to unmarried (96% vs. 32% wear time, p: 0.02 and 85% vs. 46% response rate, p: 0.006). Most patients (88%) reported that the intervention motivated them to exercise and that they were satisfied with their experience. On average, the intervention arm increased MVPA by 14 min/d, while the control arm increased by only 1 min/d, but there was no statistically significant difference in change in MVPA between groups (mean difference comparing change in MVPA in the intervention vs. control: 13 min/d; 95% CI: -14, 40; p: 0.33). Conclusions: A 12-wk physical activity intervention with a Fitbit and text messages is feasible and acceptable among CRC patients after treatment. Larger studies are needed to determine whether the intervention increases physical activity. Clinical trial information: NCT02966054.

Sleep Tracking and Exercise in Patients With Type 2 Diabetes Mellitus (Step-D): Pilot Study to Determine Correlations Between Fitbit Data and Patient-Reported Outcomes. (2018)

JMIR mHealth and uHealth

Abstract

BACKGROUND Few studies assessing the correlation between patient-reported outcomes and patient-generated health data from wearable devices exist. OBJECTIVE The aim of this study was to determine the direction and magnitude of associations between patient-generated health data (from the Fitbit Charge HR) and patient-reported outcomes for sleep patterns and physical activity in patients with type 2 diabetes mellitus (T2DM). METHODS This was a pilot study conducted with adults diagnosed with T2DM (n=86). All participants wore a Fitbit Charge HR for 14 consecutive days and completed internet-based surveys at 3 time points: day 1, day 7, and day 14. Patient-generated health data included minutes asleep and number of steps taken. Questionnaires assessed the number of days of exercise and nights of sleep problems per week. Means and SDs were calculated for all data, and Pearson correlations were used to examine associations between patient-reported outcomes and patient-generated health data. All respondents provided informed consent before participating. RESULTS The participants were predominantly middle-aged (mean 54.3, SD 13.3 years), white (80/86, 93%), and female (50/86, 58%). Use of oral T2DM medication correlated with the number of mean steps taken (r=.35, P=.001), whereas being unaware of the glycated hemoglobin level correlated with the number of minutes asleep (r=-.24, P=.04). On the basis of the Fitbit data, participants walked an average of 4955 steps and slept 6.7 hours per day. They self-reported an average of 2.0 days of exercise and 2.3 nights of sleep problems per week. The association between the number of days exercised and steps walked was strong (r=.60, P<.001), whereas the association between the number of troubled sleep nights and minutes asleep was weaker (r=.28, P=.02). CONCLUSIONS Fitbit and patient-reported data were positively associated for physical activity as well as sleep, with the former more strongly correlated than the latter. As extensive patient monitoring can guide clinical decisions regarding T2DM therapy, passive, objective data collection through wearables could potentially enhance patient care, resulting in better patient-reported outcomes.

Accuracy of Activity Trackers in Parkinson Disease: Should We Prescribe Them? (2018)

Physical Therapy

Abstract

Background Wearable, consumer-grade activity trackers have become widely available as a means of monitoring physical activity in the form of step counts. However, step counts may not be accurate in persons with Parkinson disease (PD) due to atypical gait characteristics. Objective This study aimed to investigate the accuracy of 4 consumer-grade activity trackers in individuals with PD while ambulating during continuous and discontinuous walking tasks. Design This study used a cross-sectional design. Methods Thirty-three persons with PD (Hoehn & Yahr stages 1–3) donned 4 models of activity trackers on the less affected side of their bodies. Participants performed 2 continuous walking tasks (2-minute walk tests at comfortable and fast speeds) and 2 discontinuous walking tasks (a simulated household course and an obstacle negotiation course) in an outpatient setting. Bland-Altman plots and intraclass correlation coefficients [ICC(2,1)] were computed as a measure of agreement between actual steps taken (reference standard: video recording) and steps recorded by each tracker. Results The accuracy of the activity trackers varied widely, with ICCs ranging from − 0.03 to 0.98. Overall, the most accurate device across all tasks was the Fitbit Zip, and the least accurate was the Jawbone Up Move during the simulated household course. All activity trackers were more accurate for continuous compared walking tasks compared with discontinuous walking tasks. Waist-mounted devices were more accurate then wrist-mounted devices with continuous tasks. Bland-Altman plots revealed that all activity trackers underestimated step counts. Limitations All walking tasks were measured over relatively short distances. Conclusion In persons with mild-to-moderate PD, waist-worn activity trackers may be prescribed to monitor bouts of continuous walking with reasonable accuracy; however, activity trackers have little utility in monitoring discontinuous walking common in household settings.

Protocol for promoting recovery optimization of walking activity in stroke (PROWALKS): a randomized controlled trial (2018)

BMC Neurology

Abstract

Stroke survivors are more physically inactive than even the most sedentary older adults, and low activity is associated with increased risk of recurrent stroke, medical complications, and mortality. We hypothesize that the combination of a fast walking intervention that improves walking capacity, with a step activity monitoring program that facilitates translation of gains from the clinic to the “real-world”, would generate greater improvements in real world walking activity than with either intervention alone. Using a single-blind randomized controlled experimental design, 225 chronic (textgreater 6 months) stroke survivors complete 12 weeks of fast walking training, a step activity monitoring program or a fast walking training + step activity monitoring program. Main eligibility criteria include: chronic ischemic or hemorrhagic stroke (textgreater 6 months post), no evidence of cerebellar stroke, baseline walking speed between 0.3 m/s and 1.0 m/s, and baseline average steps / day textless 8000. The primary (steps per day), secondary (self-selected and fastest walking speed, walking endurance, oxygen consumption) and exploratory (vascular events, blood lipids, glucose, blood pressure) outcomes are assessed prior to initiating treatment, after the last treatment and at a 6 and 12-month follow-up. Moderation of the changes in outcomes by baseline characteristics are evaluated to determine for whom the interventions are effective. Following completion of this study, we will not only understand the efficacy of the interventions and the individuals for which they are effective, we will have the necessary information to design a study investigating the secondary prevention benefits of improved physical activity post-stroke. This study is, therefore, an important step in the development of both rehabilitative and secondary prevention guidelines for persons with stroke.

Evaluating the Validity of Current Mainstream Wearable Devices in Fitness Tracking Under Various Physical Activities: Comparative Study (2018)

JMIR Mhealth Uhealth

Abstract

Background: Wearable devices have attracted much attention from the market in recent years for their fitness monitoring and other health-related metrics; however, the accuracy of fitness tracking results still plays a major role in health promotion. Objective: The aim of this study was to evaluate the accuracy of a host of latest wearable devices in measuring fitness-related indicators under various seminatural activities. Methods: A total of 44 healthy subjects were recruited, and each subject was asked to simultaneously wear 6 devices (Apple Watch 2, Samsung Gear S3, Jawbone Up3, Fitbit Surge, Huawei Talk Band B3, and Xiaomi Mi Band 2) and 2 smartphone apps (Dongdong and Ledongli) to measure five major health indicators (heart rate, number of steps, distance, energy consumption, and sleep duration) under various activity states (resting, walking, running, cycling, and sleeping), which were then compared with the gold standard (manual measurements of the heart rate, number of steps, distance, and sleep, and energy consumption through oxygen consumption) and calculated to determine their respective mean absolute percentage errors (MAPEs). Results: Wearable devices had a rather high measurement accuracy with respect to heart rate, number of steps, distance, and sleep duration, with a MAPE of approximately 0.10, whereas poor measurement accuracy was observed for energy consumption (calories), indicated by a MAPE of up to 0.44. The measurements varied for the same indicator measured by different fitness trackers. The variation in measurement of the number of steps was the highest (Apple Watch 2: 0.42; Dongdong: 0.01), whereas it was the lowest for heart rate (Samsung Gear S3: 0.34; Xiaomi Mi Band 2: 0.12). Measurements differed insignificantly for the same indicator measured under different states of activity; the MAPE of distance and energy measurements were in the range of 0.08 to 0.17 and 0.41 to 0.48, respectively. Overall, the Samsung Gear S3 performed the best for the measurement of heart rate under the resting state (MAPE of 0.04), whereas Dongdong performed the best for the measurement of the number of steps under the walking state (MAPE of 0.01). Fitbit Surge performed the best for distance measurement under the cycling state (MAPE of 0.04), and Huawei Talk Band B3 performed the best for energy consumption measurement under the walking state (MAPE of 0.17). Conclusions: At present, mainstream devices are able to reliably measure heart rate, number of steps, distance, and sleep duration, which can be used as effective health evaluation indicators, but the measurement accuracy of energy consumption is still inadequate. Fitness trackers of different brands vary with regard to measurement of indicators and are all affected by the activity state, which indicates that manufacturers of fitness trackers need to improve their algorithms for different activity states. [JMIR Mhealth Uhealth 2018;6(4):e94]

Successful adherence and retention to daily monitoring of physical activity: Lessons learned (2018)

PLOS ONE

Abstract

Research utilizing repeated-measures such as daily assessments with self-report and/or objective measures [e.g., physical activity (PA) monitors] are important in understanding health behaviors and informing practice and policy. However, studies that utilize daily assessment often encounter issues with attrition and non-compliance. The current research yielded high levels of retention and adherence with both self-report and objective daily measures. The purpose of this paper is to highlight and discuss strategies utilized in maximizing retention, minimizing missing data, and some lessons learned from the research experience. Fifty community participants took part in a 4-week study utilizing both daily self-report questionnaires and daily use of PA monitors (Fitbit One™). This study focused on typical daily PA and was not an intervention study (e.g., participants were not randomized nor asked to change their PA behavior). Participants completed the study in two waves (wave 1 n = 10, wave 2 n = 40). The research team utilized several retention strategies including automating the data collection process, a prorated incentive structure, having a dedicated and responsive study staff, and utilizing the 2-wave process to optimize data collection during the 2nd wave. The study had 100% retention and generally positive anonymous feedback post-study. Overall, participants completed the vast majority of daily surveys (97%) and wore their Fitbits (for at least part of the day) on almost all days (99.57%) of the study, although there were individual differences. The strategies discussed and lessons learned may be useful to other researchers using daily measurements for whom adherence and retention are important issues. Future research employing these strategies in different populations, with different measurements, and for longer durations is warranted to determine generalizability.

Accuracy of PurePulse photoplethysmography technology of Fitbit Charge 2 for assessment of heart rate during sleep (2019)

Chronobiology International

Abstract

Elevated asleep heart rate (HR) is a risk factor for cardiovascular disease and other-cause morbidity and mortality. We assessed the accuracy of Fitbit Inc. PurePulse{\textregistered} photoplethysmography with reference to three-lead electrocardiography (ECG) in determining HR during sleep. HR of 35 (17 female) healthy adults 25.1 ± 10.6 years of age (mean ± SD) was continuously recorded throughout a single night of sleep. There was no significant difference in asleep HR mean (0.09 beats per minute [bpm], P = 0.426) between Fitbit photoplethysmography and ECG; plus, there was excellent intraclass correlation (0.998) and narrow Bland–Altman agreement range (2.67 bpm). The regression analysis of Bland–Altman plot of mean asleep HR indicates Fitbit tends to slightly overestimate reference values in the lower range of HR (HR {\textless} 50 bpm) by 0.51 bpm and slightly underestimate reference values in the higher range of HR (HR {\textgreater} 80 bpm) by 0.63 bpm. Mixed model analysis of epoch-by-epoch (5-min epochs) asleep HR showed significant “U” shape trend (P {\textless} 0.001) in amount of Fitbit error (absolute amount of difference between ECG and Fitbit values regardless of overestimation or underestimation) in regard to HR, i.e. smaller error in the medium range of HR (60–80 bpm) and slightly larger error for lower ({\textless}60 bpm) and higher ({\textgreater}80 bpm) ranges of HR. However, effect of age, body mass index, gender, and subjective sleep quality measured by Pittsburgh sleep quality index (good/poor sleepers) on error in estimating HR by the Fitbit method was not significant. It is concluded that Fitbit photoplethysmography suitably tracks HR during sleep in healthy young adults.

Accuracy of wristband fitbit models in assessing sleep: Systematic review and meta-analysis (2019)

Journal of Medical Internet Research

Abstract

Background: Wearable sleep monitors are of high interest to consumers and researchers because of their ability to provide estimation of sleep patterns in free-living conditions in a cost-efficient way. Objective: We conducted a systematic review of publications reporting on the performance of wristband Fitbit models in assessing sleep parameters and stages. Methods: In adherence with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement, we comprehensively searched the Cumulative Index to Nursing and Allied Health Literature (CINAHL), Cochrane, Embase, MEDLINE, PubMed, PsycINFO, and Web of Science databases using the keyword Fitbit to identify relevant publications meeting predefined inclusion and exclusion criteria. Results: The search yielded 3085 candidate articles. After eliminating duplicates and in compliance with inclusion and exclusion criteria, 22 articles qualified for systematic review, with 8 providing quantitative data for meta-analysis. In reference to polysomnography (PSG), nonsleep-staging Fitbit models tended to overestimate total sleep time (TST; range from approximately 7 to 67 mins; effect size=-0.51, P{\textless}.001; heterogenicity: I2=8.8{\%}, P=.36) and sleep efficiency (SE; range from approximately 2{\%} to 15{\%}; effect size=-0.74, P{\textless}.001; heterogenicity: I2=24.0{\%}, P=.25), and underestimate wake after sleep onset (WASO; range from approximately 6 to 44 mins; effect size=0.60, P{\textless}.001; heterogenicity: I2=0{\%}, P=.92) and there was no significant difference in sleep onset latency (SOL; P=.37; heterogenicity: I2=0{\%}, P=.92). In reference to PSG, nonsleep-staging Fitbit models correctly identified sleep epochs with accuracy values between 0.81 and 0.91, sensitivity values between 0.87 and 0.99, and specificity values between 0.10 and 0.52. Recent-generation Fitbit models that collectively utilize heart rate variability and body movement to assess sleep stages performed better than early-generation nonsleep-staging ones that utilize only body movement. Sleep-staging Fitbit models, in comparison to PSG, showed no significant difference in measured values of WASO (P=.25; heterogenicity: I2=0{\%}, P=.92), TST (P=.29; heterogenicity: I2=0{\%}, P=.98), and SE (P=.19) but they underestimated SOL (P=.03; heterogenicity: I2=0{\%}, P=.66). Sleep-staging Fitbit models showed higher sensitivity (0.95-0.96) and specificity (0.58-0.69) values in detecting sleep epochs than nonsleep-staging models and those reported in the literature for regular wrist actigraphy. Conclusions: Sleep-staging Fitbit models showed promising performance, especially in differentiating wake from sleep. However, although these models are a convenient and economical means for consumers to obtain gross estimates of sleep parameters and time spent in sleep stages, they are of limited specificity and are not a substitute for PSG.

Validation of Fitbit Charge 2 and Fitbit Alta HR Against Polysomnography for Assessing Sleep in Adults With Obstructive Sleep Apnea (2019)

Journal of Clinical Sleep Medicine

Abstract

Study Objectives: Consumer wearable devices may be a helpful method of assessing sleep, but validation is required for their use in clinical practice. Our aim was to validate two models of Fitbit sleep trackers that rely on both accelerometer and heart rate sensors against polysomnography in participants with obstructive sleep apnea (OSA). Methods: Participants were adults presenting with symptoms of OSA and attending our outpatient sleep clinic. A polysomnography (PSG) was applied to all participants at the same time they were wearing a Fitbit sleep tracker. Using paired t tests and Bland-Altman plots, we compared the sleep measures provided by the wearable devices with those obtained by PSG. Since Fitbit devices' automatic detection of sleep start time can cause bias, we performed a correction using Huber loss function-based linear regression and a leave-one-out strategy. Results: Our sample consisted of 65 patients. Diagnosis of OSA was confirmed on 55 (84.6{\%}). There were statistically significant differences between PSG and Fitbit measures for all sleep outcomes but rapid eye movement sleep. Fitbit devices overestimated total sleep time, and underestimated wake after sleep onset and sleep onset latency. After correction of bias, Fitbit-delivered measures of sleep onset latency did not significantly differ of those provided by PSG. Conclusions: Fitbit wearable devices showed an acceptable sensitivity but poor specificity. Consumer sleep trackers still have insufficient accuracy for clinical settings, especially in clinical populations. Solving technical issues and optimizing clinically-oriented features couldmake themapt for their use in clinical practice in a nondistant future.

Ability of the Fitbit Alta HR to quantify and classify sleep in patients with suspected central disorders of hypersomnolence: A comparison against polysomnography (2019)

Journal of Sleep Research

Abstract

Measuring sleep duration and early onset rapid eye movement sleep (REMS) is critical in the assessment of suspected central disorders of hypersomnolence (CDH). Current multi-sensor activity trackers that integrate accelerometry and heart rate are purported to accurately quantify sleep time and REMS; however, their utility in suspected CDH has not been established. This investigation aimed to determine the ability of a current, multi-sensor tracker, Fitbit Alta HR (FBA-HR), to quantify and classify sleep in patients with suspected CDH relative to polysomnography (PSG). Forty-nine patients (46 female; mean age, 30.3 ± 9.84 years) underwent ad libitum PSG with concurrent use of the FBA-HR. FBA-HR sleep variable quantification was assessed using Bland-Altman analysis. FBA-HR all sleep (AS), light sleep (LS; PSG N1 + N2), deep sleep (DS; PSG N3) and REMS classification was evaluated using epoch-by-epoch comparisons. FBA-HR-detected sleep-onset rapid eye movement periods (SOREMPs) were compared against PSG SOMREMPs. FBA-HR displayed significant overestimation of total sleep time (11.6 min), sleep efficiency (1.98{\%}) and duration of deep sleep (18.2 min). FBA-HR sensitivity and specificity were as follows: AS, 0.96, 0.58; LS, 0.73, 0.72;DS, 0.67, 0.92; REMS, 0.74, 0.93. The device failed to detect any nocturnal SOREMPs. Device performance did not differ appreciably among diagnostic subgroups. These results suggest FBA-HR cannot replace EEG-based measurements of sleep and wake in the diagnostic assessment of suspected CDH, and that improvements in device performance are required prior to adoption in clinical or research settings.

Validation of the Fitbit Charge 2 compared to the ActiGraph GT3X+ in older adults with knee osteoarthritis in free-living conditions (2019)

PLOS ONE

Abstract

Objective To evaluate physical activity (PA) and sedentary time in subjects with knee osteoarthritis (OA) measured by the Fitbit Charge 2 (Fitbit) and a wrist-worn ActiGraph GT3X+ (AGW) compared to the hip-worn ActiGraph (AGH). Design We recruited a cohort of subjects with knee OA from rheumatology clinics. Subjects wore the AGH for four weeks, AGW for two weeks, and Fitbit for two weeks over a four-week study period. We collected accelerometer counts (ActiGraphs) and steps (ActiGraphs, Fitbit) and calculated time spent in sedentary, light, and moderate-to-vigorous activity. We used triaxial PA intensity count cut-points from the literature for ActiGraph and a stride length-based cadence algorithm to categorize Fitbit PA. We compared Fitbit wear times calculated from a step-based algorithm and a novel algorithm that incorporates steps and heart rate (HR). Results We enrolled 15 subjects (67{\%} female, mean age 68 years). Relative to AGH, Fitbit, on average, overestimated steps by 39{\%} and sedentary time by 37{\%} and underestimated MVPA by 5 minutes. Relative to AGH, AGW overestimated steps 116{\%}, underestimated sedentary time by 66{\%}, and captured 281 additional MVPA minutes. The step-based wear time Fitbit algorithm captured 14{\%} less wear time than the HR-based algorithm. Conclusions Fitbit overestimates steps and underestimates MVPA in knee OA subjects. Cut-offs validated for AGW should be developed to support the use of AGW for PA assessment. The HR-based Fitbit algorithm captured more wear time than the step-based algorithm. These data provide critical insight for researchers planning to use commercially-available accelerometers in pragmatic studies.

Identifying behavioral phenotypes of loneliness and social isolation with passive sensing: Statistical analysis, data mining and machine learning of smartphone and fitbit data (2019)

JMIR mHealth and uHealth

Abstract

Background: Feelings of loneliness are associated with poor physical and mental health. Detection of loneliness through passive sensing on personal devices can lead to the development of interventions aimed at decreasing rates of loneliness. Objective: The aim of this study was to explore the potential of using passive sensing to infer levels of loneliness and to identify the corresponding behavioral patterns. Methods: Data were collected from smartphones and Fitbits (Flex 2) of 160 college students over a semester. The participants completed the University of California, Los Angeles (UCLA) loneliness questionnaire at the beginning and end of the semester. For a classification purpose, the scores were categorized into high (questionnaire score{\textgreater}40) and low (≤40) levels of loneliness. Daily features were extracted from both devices to capture activity and mobility, communication and phone usage, and sleep behaviors. The features were then averaged to generate semester-level features. We used 3 analytic methods: (1) statistical analysis to provide an overview of loneliness in college students, (2) data mining using the Apriori algorithm to extract behavior patterns associated with loneliness, and (3) machine learning classification to infer the level of loneliness and the change in levels of loneliness using an ensemble of gradient boosting and logistic regression algorithms with feature selection in a leave-one-student-out cross-validation manner. Results: The average loneliness score from the presurveys and postsurveys was above 43 (presurvey SD 9.4 and postsurvey SD 10.4), and the majority of participants fell into the high loneliness category (scores above 40) with 63.8{\%} (102/160) in the presurvey and 58.8{\%} (94/160) in the postsurvey. Scores greater than 1 standard deviation above the mean were observed in 12.5{\%} (20/160) of the participants in both pre- and postsurvey scores. The majority of scores, however, fell between 1 standard deviation below and above the mean (pre=66.9{\%} [107/160] and post=73.1{\%} [117/160]). Our machine learning pipeline achieved an accuracy of 80.2{\%} in detecting the binary level of loneliness and an 88.4{\%} accuracy in detecting change in the loneliness level. The mining of associations between classifier-selected behavioral features and loneliness indicated that compared with students with low loneliness, students with high levels of loneliness were spending less time outside of campus during evening hours on weekends and spending less time in places for social events in the evening on weekdays (support=17{\%} and confidence=92{\%}). The analysis also indicated that more activity and less sedentary behavior, especially in the evening, was associated with a decrease in levels of loneliness from the beginning of the semester to the end of it (support=31{\%} and confidence=92{\%}). Conclusions: Passive sensing has the potential for detecting loneliness in college students and identifying the associated behavioral patterns. These findings highlight intervention opportunities through mobile technology to reduce the impact of loneliness on individuals' health and well-being.

Is There a Benefit to Patients Using Wearable Devices Such as Fitbit or Health Apps on Mobiles? A Systematic Review (2019)

American Journal of Medicine

Abstract

Wearable devices have become a standard health care intervention with emerging health care technologies. These devices are designed to promote healthy behaviors and decrease risk for chronic disease like cardiovascular disease and diabetes. The purpose of this study was to provide evidence of the benefit of wearable devices in chronic disease outcomes among adults. Systematic search of PubMed, Web of Science, World Health Organization international clinical trials registry platform, BMC ISRCTN registry, and the Institute of Electrical and Electronics Engineers was performed based upon the PRISMA guideline. Included articles were randomized controlled trials or quasi-experimental studies with health outcomes published in English up to October 2018. Studies focusing on adults were selected. Three investigators reviewed the selected publications and made agreement on final selection. Of a total of 550 publications extracted, 6 studies met the final criteria. There was little indication that wearable devices provide a benefit for health outcomes. Of the 6 studies examined, only one study showed a significant reduction for weight loss among participants who used wearable devices. No significant reduction was discovered in cholesterol or blood pressure. Among the 6 studies, only one study examined hemoglobin A1c, and it showed a significant reduction in older patients with type 2 diabetes. The current literature evaluating wearable devices indicates little benefit of the devices on chronic disease health outcomes. Wearable devices play a role as a facilitator in motivating and accelerating physical activity, but current data do not suggest other consistent health benefits.

The Effects of a Mobile Wellness Intervention with Fitbit Use and Goal Setting for Workers (2019)

Telemedicine and e-Health

Abstract

Background and Introduction: There is strong evidence that worksite wellness programs can significantly improve the health profile of participating workers. To date, little is known about research on the effects of mobile wellness interventions in worksite settings. Furthermore, no studies have been conducted to evaluate mobile wellness interventions with activity trackers and tailoring strategies for physically inactive workers in manufacturing companies. This study aimed to examine the effects of a mobile wellness intervention with Fitbit and goal setting using brief counseling and text messaging among workers. Materials and Methods: A total of 79 (n = 79) workers from large manufacturing companies were allocated into an experimental group (n = 41) and a control group (n = 38). All participants were asked to wear an activity tracker (Fitbit Charger HR) during all waking hours for 5 weekdays. Participants in the experimental group received Fitbit, daily motivational text messaging, and biweekly counseling with a specifically designed workbook for 12 weeks, whereas Fitbit was only provided to the control group. Results: At the 12-week measurement, there were significant differences between the experimental group and control group on wellness (p {\textless} 0.001), physical activity behavior (p {\textless} 0.001), daily walking steps (p {\textless} 0.001), and physical activity self-efficacy (p {\textless} 0.001). Discussion and Conclusions: Although Fitbit facilitates an individual's activities by providing information about daily steps, the tracker itself, without additional goal-setting techniques, may be insufficient to encourage behavior change. These findings indicate that the mobile wellness intervention with Fitbit and goal setting using brief counseling and tailored text messaging is more effective for physically inactive workers.

Validity Evaluation of the Fitbit Charge2 and the Garmin vivosmart HR+ in Free-Living Environments in an Older Adult Cohort. (2019)

JMIR mHealth and uHealth

Abstract

BACKGROUND Few studies have investigated the validity of mainstream wrist-based activity trackers in healthy older adults in real life, as opposed to laboratory settings. OBJECTIVE This study explored the performance of two wrist-worn trackers (Fitbit Charge 2 and Garmin vivosmart HR+) in estimating steps, energy expenditure, moderate-to-vigorous physical activity (MVPA) levels, and sleep parameters (total sleep time [TST] and wake after sleep onset [WASO]) against gold-standard technologies in a cohort of healthy older adults in a free-living environment. METHODS Overall, 20 participants ({\textgreater}65 years) took part in the study. The devices were worn by the participants for 24 hours, and the results were compared against validated technology (ActiGraph and New-Lifestyles NL-2000i). Mean error, mean percentage error (MPE), mean absolute percentage error (MAPE), intraclass correlation (ICC), and Bland-Altman plots were computed for all the parameters considered. RESULTS For step counting, all trackers were highly correlated with one another (ICCs{\textgreater}0.89). Although the Fitbit tended to overcount steps (MPE=12.36{\%}), the Garmin and ActiGraph undercounted (MPE 9.36{\%} and 11.53{\%}, respectively). The Garmin had poor ICC values when energy expenditure was compared against the criterion. The Fitbit had moderate-to-good ICCs in comparison to the other activity trackers, and showed the best results (MAPE=12.25{\%}), although it underestimated calories burned. For MVPA levels estimation, the wristband trackers were highly correlated (ICC=0.96); however, they were moderately correlated against the criterion and they overestimated MVPA activity minutes. For the sleep parameters, the ICCs were poor for all cases, except when comparing the Fitbit with the criterion, which showed moderate agreement. The TST was slightly overestimated with the Fitbit, although it provided good results with an average MAPE equal to 10.13{\%}. Conversely, WASO estimation was poorer and was overestimated by the Fitbit but underestimated by the Garmin. Again, the Fitbit was the most accurate, with an average MAPE of 49.7{\%}. CONCLUSIONS The tested well-known devices could be adopted to estimate steps, energy expenditure, and sleep duration with an acceptable level of accuracy in the population of interest, although clinicians should be cautious in considering other parameters for clinical and research purposes.

Accuracy of fitbit wristbands in measuring sleep stage transitions and the effect of user-specific factors (2019)

Journal of Medical Internet Research

Abstract

Background: It has become possible for the new generation of consumer wristbands to classify sleep stages based on multisensory data. Several studies have validated the accuracy of one of the latest models, that is, Fitbit Charge 2, in measuring polysomnographic parameters, including total sleep time, wake time, sleep efficiency (SE), and the ratio of each sleep stage. Nevertheless, its accuracy in measuring sleep stage transitions remains unknown. Objective: This study aimed to examine the accuracy of Fitbit Charge 2 in measuring transition probabilities among wake, light sleep, deep sleep, and rapid eye movement (REM) sleep under free-living conditions. The secondary goal was to investigate the effect of user-specific factors, including demographic information and sleep pattern on measurement accuracy. Methods: A Fitbit Charge 2 and a medical device were used concurrently to measure a whole night's sleep in participants' homes. Sleep stage transition probabilities were derived from sleep hypnograms. Measurement errors were obtained by comparing the data obtained by Fitbit with those obtained by the medical device. Paired 2-tailed t test and Bland-Altman plots were used to examine the agreement of Fitbit to the medical device. Wilcoxon signed-rank test was performed to investigate the effect of user-specific factors. Results: Sleep data were collected from 23 participants. Sleep stage transition probabilities measured by Fitbit Charge 2 significantly deviated from those measured by the medical device, except for the transition probability from deep sleep to wake, from light sleep to REM sleep, and the probability of staying in REM sleep. Bland-Altman plots demonstrated that systematic bias ranged from 0{\%} to 60{\%}. Fitbit had the tendency of overestimating the probability of staying in a sleep stage while underestimating the probability of transiting to another stage. SE{\textgreater}90{\%} (P=.047) was associated with significant increase in measurement error. Pittsburgh sleep quality index (PSQI){\textless}5 and wake after sleep onset (WASO){\textless}30 min could be associated to significantly decreased or increased errors, depending on the outcome sleep metrics. Conclusions: Our analysis shows that Fitbit Charge 2 underestimated sleep stage transition dynamics compared with the medical device. Device accuracy may be significantly affected by perceived sleep quality (PSQI), WASO, and SE.

Reliability of Zephyr Bioharness and Fitbit Charge Measures of Heart Rate and Activity at Rest, During the Modified Canadian Aerobic Fitness Test, and Recovery (2019)

Journal of Strength and Conditioning Research

Abstract

The purpose of this study was to determine the intrasession and intersession reliability of Zephyr Bioharness (ZB) and Fitbit Charge variables in both healthy men and women at rest, during the Modified Canadian Aerobic Fitness Test (mCAFT), and throughout recovery. Stratified convenience and snowball sampling were used to recruit 60 participants (30 women, 48 ± 15 years) and (30 men, 48 ± 15 years) from McMaster University student, staff, and faculty population. At rest, intrasession average heart rate (b{\textperiodcentered}min -1 ). Intraclass correlation coefficients (ICCs) and Standard Error of Measurement [SEM] for Zephyr ranged from (0.94-0.97) [1.17-1.70] to (0.92-0.97) [1.45-2.10] for Fitbit Charge. During the mCAFT, the Zephyr ICCs and (SEM) ranged from 0.31-0.99 (1.28-8.10) to 0.45-0.99 (1.45-8.71) for the Fitbit Charge. Throughout the recovery, the ICCs and (SEM) ranged from 0.44-0.98 (1.26-10.47) to 0.45-0.98 (1.15-11.90) for Zephyr and Fitbit devices, respectively. At rest, intersession ICCs (SEM) for Zephyr and Fitbit ranged from 0.90-0.94 (1.73-2.37) to 0.88-0.94 (1.83-2.67), respectively. At mCAFT, the Zephyr ICCs (SEM) ranged from 0.91-0.97 (3.12-4.64) to 0.85-0.98 (3.28-4.88) for the Fitbit. Throughout the recovery, the ICCs (SEM) ranged from 0.93-0.97 (2.65-4.66) to 0.76-0.91 (3.17-4.67) for Zephyr and Fitbit devices, respectively. To conclude, both the ZB and Fitbit Charge devices demonstrated excellent reliability measures throughout the 3 phases. The findings from our study add to the existing pool of literature regarding the reliability parameters of wearable devices and suggest that stable and consistent measures of heart rate and physical activity can be obtained using ZB and Fitbit Charge devices among healthy male and female participants at rest, during a standardized submaximal fitness test (mCAFT), and throughout recovery.

Concurrent validity of the Fitbit for assessing sedentary behavior and moderate-to-vigorous physical activity (2019)

BMC Medical Research Methodology

Abstract

Background: Recent advances in sensor technologies have promoted the use of consumer-based accelerometers such as Fitbit Flex in epidemiological and clinical research; however, the validity of the Fitbit Flex in measuring sedentary behavior (SED) and physical activity (PA) has not been fully determined against previously validated research-grade accelerometers such as ActiGraph GT3X+. Therefore, the purpose of this study was to examine the concurrent validity of the Fitbit Flex against ActiGraph GT3X+ in a free-living condition. Methods: A total of 65 participants (age: M = 42, SD = 14 years, female: 72{\%}) each wore a Fitbit Flex and GT3X+ for seven consecutive days. After excluding sleep and non-wear time, time spent (min/day) in SED and moderate-to-vigorous PA (MVPA) were estimated using various cut-points for GT3X+ and brand-specific algorithms for Fitbit, respectively. Repeated measures one-way ANOVA and mean absolute percent errors (MAPE) served to examine differences and measurement errors in SED and MVPA estimates between Fitbit Flex and GT3X+, respectively. Pearson and Spearman correlations and Bland-Altman (BA) plots were used to evaluate the association and potential systematic bias between Fitbit Flex and GT3X+. PROC MIXED procedure in SAS was used to examine the equivalence (i.e., the 90{\%} confidence interval with ±10{\%} equivalence zone) between the devices. Results: Fitbit Flex produced similar SED and low MAPE (mean difference [MD] = 37 min/day, P =.21, MAPE = 6.8{\%}), but significantly higher MVPA and relatively large MAPE (MD = 59-77 min/day, P {\textless}.0001, MAPE = 56.6-74.3{\%}) compared with the estimates from GT3X+ using three different cut-points. The correlations between Fitbit Flex and GT3X+ were consistently higher for SED (r = 0.90, $\rho$ = 0.86, P {\textless}.01), but weaker for MVPA (r = 0.65-0.76, $\rho$ = 0.69-0.79, P {\textless}.01). BA plots revealed that there is no apparent bias in estimating SED. Conclusion: In comparison with the GT3X+ accelerometer, the Fitbit Flex provided comparatively accurate estimates of SED, but the Fitbit Flex overestimated MVPA under free-living conditions. Future investigations using the Fitbit Flex should be aware of present findings.

Validity of Cardiorespiratory Fitness Measured with Fitbit Compared to V˙O2max (2019)

Medicine and science in sports and exercise

Abstract

PURPOSE: Cardiorespiratory fitness (CRF), broadly defined as the body's ability to utilize oxygen, is a well-established prognostic marker of health, but it is not routinely measured. This may be due to the difficulty of acquiring high-quality CRF measures. The purpose of this study was to independently determine the validity of the Fitbit Charge 2's measure of CRF (Fitbit CRF). METHODS: Sixty-five healthy adults between the ages of 18 and 45 yr (55{\%} female, 45{\%} male) were recruited to undergo gold standard V˙O2max testing and wear a Fitbit Charge 2 continuously for 1 wk during which they were instructed to complete a qualifying outdoor run to derive the Fitbit CRF (units: mL{\textperiodcentered}kg{\textperiodcentered}min). This measure was compared with V˙O2max measures (units: mL{\textperiodcentered}kg{\textperiodcentered}min) epoched at 15 and 60 s. RESULTS: Bland-Altman analyses revealed that Fitbit CRF had a positive bias of 1.59 mL{\textperiodcentered}kg{\textperiodcentered}min compared with laboratory data epoched at 15 s and 0.30 mL{\textperiodcentered}kg{\textperiodcentered}min compared with data epoched at 60 s (n = 60). F statistics (2.09; 0.08) and P values (0.133; 0.926) from Bradley-Blackwood tests for the concordance of Fitbit CRF with 15- and 60-s laboratory data, respectively, support the null hypothesis of equal means and variances, indicating there is concordance between the two measures. Mean absolute percentage error was less than 10{\%} for each comparison. CONCLUSIONS: The Fitbit Charge 2 provides an acceptable level of validity when measuring CRF in young, healthy, and fit adults who are able to run. Further research is required to determine if it is a potentially useful tool in clinical practice and epidemiological research to quantify, categorize, and longitudinally track risk for adverse outcomes.

Validating Fitbit for Evaluation of Physical Activity in Patients with Knee Osteoarthritis: Do Thresholds Matter? (2019)

ACR Open Rheumatology

Abstract

Objective: We sought to evaluate the performance of Fitbit in estimating ActiGraph-derived moderate-to-vigorous physical activity (MVPA) and sedentary time in the knee osteoarthritis (OA) population. Methods: We used data from two weeks of Fitbit and ActiGraph wear among knee OA subjects. In primary analyses, we used literature-based ActiGraph thresholds of 200 and 1924 counts/min (triaxial vector magnitude) for sedentary and MVPA time as the gold standard to which we compared three sets of Fitbit thresholds informed by literature and data (Youden index). We also considered personalized, stride length-based Fitbit thresholds. In sensitivity analyses, we used uniaxial, vertical axis-based as well as personalized, BMI-based ActiGraph thresholds. We calculated agreement, sensitivity, and specificity of Fitbit in classifying sedentary and MVPA time. Results: In the primary analysis (vector magnitude thresholds), maximum agreement for sedentary and MVPA time was 67.0{\%} from the Youden index-based and 91.1{\%} from the stride length-based Fitbit thresholds. For sedentary time, the 20 strides/min threshold had the highest sensitivity (97.6{\%}), and Youden-derived 1 stride/min had the highest specificity (51.6{\%}). For MVPA, Youden-derived 14 strides/min yielded 72.8{\%} sensitivity, and using stride length yielded 98.6{\%} specificity. MVPA time ranged from 49-323 min/d, depending on threshold used, with literature-based and personalized thresholds leading to more conservative estimates of MVPA than Youden-derived thresholds. Conclusion: Using Fitbit for MVPA and sedentary time assessment may lead to inaccurate estimates of both. Fitbit MVPA estimates were generally more conservative than ActiGraph estimates. Incorporating individuals' characteristics did not meaningfully improve Fitbit performance. Caution should be exercised when measuring activity using Fitbit.

Goal-directed versus outcome-based financial incentives for weight loss among low-income patients with obesity: Rationale and design of the Financial Incentives for Weight Reduction (FIReWoRk) randomised controlled trial (2019)

BMJ Open

Abstract

Introduction Obesity is a major public health challenge and exacerbates economic disparities through employment discrimination and increased personal health expenditures. Financial incentives for weight management may intensify individuals' utilisation of evidence-based behavioural strategies while addressing obesity-related economic disparities in low-income populations. Trials have focused on testing incentives contingent on achieving weight loss outcomes. However, based on social cognitive and self-determination theories, providing incentives for achieving intermediate behavioural goals may be more sustainable than incentivising outcomes if they enhance an individual's skills and self-efficacy for maintaining long-Term weight loss. The objective of this paper is to describe the rationale and design of the Financial Incentives foR Weight Reduction study, a randomised controlled trial to test the comparative effectiveness and cost-effectiveness of two financial incentive strategies for weight loss (goal directed vs outcome based) among low-income adults with obesity, as well as compared with the provision of health behaviour change resources alone. Methods and analysis We are recruiting 795 adults, aged 18-70 years with a body mass index ≥30 kg/m 2, from three primary care clinics serving residents of socioeconomically disadvantaged neighbourhoods in New York City and Los Angeles. All participants receive a 1-year commercial weight loss programme membership, self-monitoring tools (bathroom scale, food journal and Fitbit Alta HR), health education and monthly check-in visits. In addition to these resources, those in the two intervention groups can earn up to 750 over 6 months for: (1) participating in an intensive weight management programme, self-monitoring weight and diet and meeting physical activity guidelines (goal-directed arm); or (2) a ≥1.5{\%} to ≥5{\%} reduction in baseline weight (outcome-based arm). To maximise incentive efficacy, we incorporate concepts from behavioural economics, including immediacy of payments and framing feedback to elicit regret aversion. We will use generalised mixed effect models for repeated measures to examine intervention effects on weight at 6, 9 and 12 months. Ethics and dissemination Human research protection committees at New York University School of Medicine, University of California Los Angeles (UCLA) David Geffen School of Medicine and Olive-View-UCLA Medical Center granted ethics approval. We will disseminate the results of this research via peer-reviewed publications, conference presentations and meetings with stakeholders. Trial registration number NCT03157713.

Agreement between the Apple Series 1, LifeTrak Core C200, and Fitbit Charge HR with Indirect Calorimetry for Assessing Treadmill Energy Expenditure (2019)

International Journal of Environmental Research and Public Health

Abstract

The purpose of this study was to examine agreement in energy expenditure between the Apple Series 1 Watch, LifeTrak Core C200, and Fitbit Charge HR with indirect calorimetry during various treadmill speeds in young adults. Participants were a sample of college-aged students (mean age = 20.1 (1.7) years; 13 females, 17 males). Participants completed six structured 10-minute exercise sessions on a treadmill with speeds ranging from 53.6 m{\textperiodcentered}min−1 to 187.7 m{\textperiodcentered}min−1. Indirect calorimetry was used as the criterion. Participants wore the Apple Watch, LifeTrak, and Fitbit activity monitors on their wrists. Group-level agreement was examined using equivalence testing, relative agreement was examined using Spearman's rho, and individual-level agreement was examined using Mean Absolute Percent Error (MAPE) and Bland-Altman Plots. Activity monitor agreement with indirect calorimetry was supported using the Apple Watch at 160.9 m{\textperiodcentered}min−1 (Mean difference = −2.7 kcals, 90{\%} C.I.: −8.3 kcals, 2.8 kcals; MAPE = 11.9{\%}; rs = 0.64) and 187.7 m{\textperiodcentered}min−1 (Mean difference = 3.7 kcals, 90{\%} C.I.: −2.2 kcals, 9.7 kcals; MAPE = 10.7{\%}; rs = 0.72) and the Fitbit at 187.7 m{\textperiodcentered}min−1 (Mean difference = −0.2 kcals, 90{\%} C.I.: −8.8 kcals, 8.5 kcals; MAPE = 20.1{\%}; rs = 0.44). No evidence for statistical equivalence was seen for the LifeTrak at any speed. Bland-Altman Plot Limits of Agreement were narrower for the Apple Series 1 Watch compared to other monitors, especially at slower treadmill speeds. The results support the utility of the Apple Series 1 Watch and Fitbit Charge HR for assessing energy expenditure during specific treadmill running speeds in young adults.{\textless}/p{\textgreater}

Performance assessment of new-generation Fitbit technology in deriving sleep parameters and stages (2020)

Chronobiology International

Abstract

We compared performance in deriving sleep variables by both Fitbit Charge 2™, which couples body movement (accelerometry) and heart rate variability (HRV) in combination with its proprietary interpretative algorithm (IA), and standard actigraphy (Motionlogger{\textregistered} Micro Watch Actigraph: MMWA), which relies solely on accelerometry in combination with its best performing ‘Sadeh' IA, to electroencephalography (EEG: Zmachine{\textregistered} Insight+ and its proprietary IA) used as reference. We conducted home sleep studies on 35 healthy adults, 33 of whom provided complete datasets of the three simultaneously assessed technologies. Relative to the Zmachine EEG method, Fitbit showed an overall Kappa agreement of 54{\%} in distinguishing wake/sleep epochs and sensitivity of 95{\%} and specificity of 57{\%} in detecting sleep epochs. Fitbit, relative to EEG, underestimated sleep onset latency (SOL) by {\~{}}11 min and overestimated sleep efficiency (SE) by {\~{}}4{\%}. There was no statistically significant difference between Fitbit and EEG methods in measuring wake after sleep onset (WASO) and total sleep time (TST). Fitbit showed substantial agreement with EEG in detecting rapid eye movement and deep sleep, but only moderate agreement in detecting light sleep. The MMWA method showed 51{\%} overall Kappa agreement with the EEG one in detecting wake/sleep epochs, with sensitivity of 94{\%} and specificity of 53{\%} in detecting sleep epochs. MMWA, relative to EEG, underestimated SOL by {\~{}}10 min. There was no significant difference between Fitbit and MMWA methods in amount of bias in estimating SOL, WASO, TST, and SE; however, the minimum detectable change (MDC) per sleep variable with Fitbit was better (smaller) than with MMWA, respectively, by {\~{}}10 min, {\~{}}16 min, {\~{}}22 min, and {\~{}}8{\%}. Overall, performance of Fitbit accelerometry and HRV technology in conjunction with its proprietary IA to detect sleep vs. wake episodes is slightly better than wrist actigraphy that relies solely on accelerometry and best performing Sadeh IA. Moreover, the smaller MDC of Fitbit technology in deriving sleep parameters in comparison to wrist actigraphy makes it a suitable option for assessing changes in sleep quality over time, longitudinally, and/or in response to interventions.

Fitbit wear-time and patterns of activity in cancer survivors throughout a physical activity intervention and follow-up: Exploratory analysis from a randomised controlled trial (2020)

PLOS ONE

Abstract

Objective There has been growing interest in the use of smart wearable technology to promote physical activity (PA) behaviour change. However, little is known concerning PA patterns throughout an intervention or engagement with trackers. The objective of the study was to explore patterns of Fitbit-measured PA and wear-time over 24-weeks and their relationship to changes in Actigraph-derived moderate-to-vigorous PA (MVPA). Methods Twenty-nine intervention participants (88{\%}) from the wearable activity technology and action-planning (WATAAP) trial in colorectal and endometrial cancer survivors accepted a Fitbit friend request from the research team to permit monitoring of Fitbit activity. Daily steps and active minutes were recorded for each participant over the 12-week intervention and throughout the follow-up period to 24-weeks. Accelerometer (GT9X) derived MVPA was assessed at end of intervention (12-weeks) and end of follow-up (24-weeks). Results Fitbit wear-time over the 24-weeks of data was remarkably consistent, with median adherence score of 100{\%} for all weeks. During the intervention, participants recorded a median 8006 steps/day. Daily step count was slightly increased through week-13 to week-24 with a median of 8191 steps/day (p = 0.039). Actigraph and Fitbit derived measures were highly correlated but demonstrated poor agreement overall. Fitbit measured activity was closest to MVPA measured using Freedson cut-points as no bias was observed. Conclusions Step count was maintained throughout the trial displaying promise for the effectiveness of smart-wearable interventions to reduce sedentary behaviour beyond the intervention period. Further worthwhile work should compare more advanced smart-wearable technology with accelerometers in order to improve agreement and explore less resource-intensive methods to assess PA that could be scalable. Copyright:

Estimating physical activity and sedentary behaviour in a free-living environment: A comparative study between Fitbit Charge 2 and Actigraph GT3X (2020)

PLOS ONE

Abstract

Background Activity trackers such as the Fitbit Charge 2 enable users and researchers to monitor physical activity in daily life, which could be beneficial for changing behaviour. However, the accuracy of the Fitbit Charge 2 in a free-living environment is largely unknown. Objective To investigate the agreement between Fitbit Charge 2 and ActiGraph GT3X for the estimation of steps, energy expenditure, time in sedentary behaviour, and light and moderate-to-vigorous physical activity under free-living conditions, and further examine to what extent placing the ActiGraph on the wrist as opposed to the hip would affect the findings. Methods 41 adults (n = 10 males, n = 31 females) were asked to wear a Fitbit Charge 2 device and two ActiGraph GT3X devices (one on the hip and one on the wrist) for seven consecutive days and fill out a log of wear times. Agreement was assessed through Bland-Altman plots combined with multilevel analysis. Results The Fitbit measured 1,492 steps/day more than the hip-worn ActiGraph (limits of agreement [LoA] = -2,250; 5,234), while for sedentary time, it measured 25 min/day less (LoA = -137; 87). Both Bland-Altman plots showed fixed bias. For time in light physical activity, the Fitbit measured 59 min/day more (LoA = -52;169). For time in moderate-to-vigorous physical activity, the Fitbit measured 31 min/day less (LoA = -132; 71) and for activity energy expenditure it measured 408 kcal/day more than the hip-worn ActiGraph (LoA = -385; 1,200). For the two latter outputs, the plots indicated proportional bias. Similar or more pronounced discrepancies, mostly in opposite direction, appeared when comparing to the wrist-worn ActiGraph. Conclusion Moderate to substantial differences between devices were found for most outputs, which could be due to differences in algorithms. Caution should be taken if replacing one device with another and when comparing results.

Participant Perceptions on a Fitbit and Facebook Intervention for Young Adult Cancer Survivors: A Qualitative Study (2020)

Journal of Adolescent and Young Adult Oncology

Abstract

Among cancer survivors, physical activity (PA) is associated with reductions in cancer recurrence, morbidity, and mortality. Most young adult (YA) survivors do not attain adequate PA. Digital modalities, specifically wearable activity monitors with a paired mobile application and private social media group for support offer a promising approach for promoting PA among YAs. We conducted a pilot randomized controlled trial of this intervention. To evaluate its acceptability and perceptions of the intervention components, we conducted qualitative interviews with those in the intervention. The results of our interviews serve to refine future interventions to better serve this population. Methods: Semistructured qualitative interviews with 13 YA cancer survivors ages 20-39 who participated in the intervention assessed perceptions of the digital components of the study and buddy system of nominating a friend to participate in PA with the survivor. Analyses included a qualitative thematic analysis of the interview transcripts and coded interview segments into three predetermined categories: facilitators, limitations, and suggestions. Results: Participants described wide-ranging benefits of the intervention, citing the Fitbit device and buddy system as major motivators to engage in PA and reach goals. Most participants noted feelings of increased physical and emotional wellness. The most-cited limitation of the intervention was the automated text messages, which participants found impersonal. Suggestions for improvement included integrating more elements of competition and group challenges. Conclusion: This digital PA intervention was perceived as feasible and acceptable to YA cancer survivors and appears promising for promoting PA and improving long-term health and quality of life. Clinicaltrial.gov identifier number: NCT03233581; Date of registration: July 28, 2017.

Fitbit-Based Interventions for Healthy Lifestyle Outcomes: Systematic Review and Meta-Analysis (2020)

Journal of Medical Internet Research

Abstract

Background: Unhealthy behaviors, such as physical inactivity, sedentary lifestyle, and unhealthful eating, remain highly prevalent, posing formidable challenges in efforts to improve cardiovascular health. While traditional interventions to promote healthy lifestyles are both costly and effective, wearable trackers, especially Fitbit devices, can provide a low-cost alternative that may effectively help large numbers of individuals become more physically fit and thereby maintain a good health status. Objective: The objectives of this meta-analysis are (1) to assess the effectiveness of interventions that incorporate a Fitbit device for healthy lifestyle outcomes (eg, steps, moderate-to-vigorous physical activity, and weight) and (2) to identify which additional intervention components or study characteristics are the most effective at improving healthy lifestyle outcomes. Methods: A systematic review was conducted, searching the following databases from 2007 to 2019: MEDLINE, EMBASE, CINAHL, and CENTRAL (Cochrane). Studies were included if (1) they were randomized controlled trials, (2) the intervention involved the use of a Fitbit device, and (3) the reported outcomes were related to healthy lifestyles. The main outcome measures were related to physical activity, sedentary behavior, and weight. All the studies were assessed for risk of bias using Cochrane criteria. A random-effects meta-analysis was conducted to estimate the treatment effect of interventions that included a Fitbit device compared with a control group. We also conducted subgroup analysis and fuzzy-set qualitative comparative analysis (fsQCA) to further disentangle the effects of intervention components. Results: Our final sample comprised 41 articles reporting the results of 37 studies. For Fitbit-based interventions, we found a statistically significant increase in daily step count (mean difference [MD] 950.54, 95{\%} CI 475.89-1425.18; P{\textless}.001) and moderate-to-vigorous physical activity (MD 6.16, 95{\%} CI 2.80-9.51; P{\textless}.001), a significant decrease in weight (MD −1.48, 95{\%} CI −2.81 to −0.14; P=.03), and a nonsignificant decrease in objectively assessed and self-reported sedentary behavior (MD −10.62, 95{\%} CI −35.50 to 14.27; P=.40 and standardized MD −0.11, 95{\%} CI −0.48 to 0.26; P=.56, respectively). In general, the included studies were at low risk for bias, except for performance bias. Subgroup analysis and fsQCA demonstrated that, in addition to the effects of the Fitbit devices, setting activity goals was the most important intervention component. Conclusions: The use of Fitbit devices in interventions has the potential to promote healthy lifestyles in terms of physical activity and weight. Fitbit devices may be useful to health professionals for patient monitoring and support.

Outcomes of a text message, Fitbit, and coaching intervention on physical activity maintenance among cancer survivors: a randomized control pilot trial (2020)

Journal of Cancer Survivorship

Abstract

Purpose: We aimed to determine the effect of a health coach, text message, and Fitbit intervention on moderate-to-vigorous physical activity (MVPA) maintenance in cancer survivors following a supervised exercise program compared to provision of a Fitbit alone. Methods: Participants were recruited during the last month of an exercise-based oncology rehabilitation program and randomly assigned to the full intervention (n = 34) or Fitbit-only control groups (n = 32). In total, 59 cancer survivors completed the program (81{\%} female; mean age 61.4 ± 9.0). Group by time differences in accelerometer-measured MVPA was assessed using linear mixed models. Additionally, we examined mean weekly Fitbit-derived MVPA levels. Results: Intervention participants maintained weekly MVPA from pre- (295.7 ± 139.6) to post-intervention (322.0 ± 199.4; p = 0.37), whereas those in the Fitbit-only group had a significant decrease in MVPA (305.5 ± 181.1 pre vs 250.7 ± 166.5 post; p = 0.03, effect size 0.57). The intervention participants maintained recommended levels of MPVA for health benefits during the 8-week intervention, whereas the Fitbit-only control group did not. Conclusions: The findings demonstrate that a remote intervention delivered through health coaching, text messages, and Fitbit can promote maintenance of MVPA after a structured program for cancer survivors. However, use of a Fitbit alone was not sufficient to prevent expected decline in MVPA. Additional research is warranted to examine long-term impacts and efficacy in a more diverse population of cancer survivors. Implications for Cancer Survivors: Use of a wearable tracker alone may not be sufficient for exercise maintenance among cancer survivors after transition to an independent program. Additional planning for relapse prevention is recommended.

Investigating the within-person relationships between activity levels and sleep duration using Fitbit data (2020)

Translational Behavioral Medicine

Abstract

The advancement of wearable technologies provides opportunities to continuously track individuals' daily activity levels and sleep patterns over extended periods of time. These data are useful in examining the reciprocal relationships between physical activity and sleep at the intrapersonal level. The purpose of this study is to test the bidirectional relationships between daily activity levels and sleep duration. The current study analyzed activity and sleep data collected from a Fitbit device as part of a 6 month employer-sponsored weight loss program. A total of 105 overweight/obese adults were included (92{\%} female, 70{\%} obese, and 44{\%} Hispanic). Multilevel models were used to examine (a) whether daily active and sedentary minutes predicted that night's sleep duration and (b) whether sleep duration predicted active and sedentary minutes the following day. Potential extended effects were explored by using a 2 day average of the activity minutes/sleep duration as the predictor. No significant relationships between active minutes and sleep duration were found on a daily basis. However, having less sleep over two nights than one's usual level was associated with an increased likelihood of engaging in some physical activity the following day. There was a significant bidirectional negative association between sedentary minutes and sleep duration for both the daily and 2 day models. Data from wearable trackers, such as Fitbit, can be used to investigate the daily within-person relationship between activity levels and sleep duration. Future studies should investigate other sleep metrics that may be obtained from wearable trackers, as well as potential moderators and mediators of daily activity levels and sleep.

Minimal Detectable Change Thresholds and Responsiveness of Zephyr Bioharness and Fitbit Charge Devices (2020)

Journal of Strength and Conditioning Research

Abstract

Nazari, G and MacDermid, JC. Minimal detectable change thresholds and responsiveness of zephyr bioharness and Fitbit Charge devices. J Strength Cond Res 34(1): 257-263, 2020-The aim of the current study was to define the minimal detectable change (MDC) thresholds for Zephyr Bioharness (ZB) and Fitbit Charge Heart Rate (FC-HR) variables and to establish the internal responsiveness of ZB and FC-HR devices in terms of their HR, respiratory rate variables, and number of steps taken variables, in a healthy sample of men and women at resting, submaximal activity and throughout recovery phases. A total of sixty participants (30 women, 48 ± 15) and (30 men, 48 ± 15) were recruited using stratified convenience and snowball sampling approaches from our university student, staff, and faculty population. At rest, MDC90 values of ±4.89 and ±5.15 were noted for ZB and FC-HR devices, respectively. Throughout the recovery, thresholds of ±8.14 for ZB and 8.74 for FC-HR were reported. During the submaximal phases, scores of ±8.18 and ±9.35 were established for ZB and FC-HR, respectively. From a resting to a submaximal state, the standardized response means (SRMs) for ZB and FC-HR variables exhibited large responsiveness of SRMZB-HR = 4.42; SRMFC-HR = 4.04. In addition, the ZB respiratory rate variable showed the largest effect size of SRMZB-RR = 2.55. Similarly, the large effect size of SRMFC-ST = 1.76 was reported for the FC-HR steps taken variable. From a submaximal to a recovery state, the SRMs for ZB and FC-HR variables demonstrated effect sizes of SRMZB-HR = -3.10; SRMFC-HR = -3.12. Furthermore, the ZB respiratory rate variable displayed similar internal responsiveness measures of SRMZB-RR = -2.45. To conclude, Similar MDC HR thresholds can be expected using the ZB and FC-HR devices and that the two devices demonstrated large internal responsiveness in our healthy male and female cohort when transitioning from resting to submaximal states and vice-versa. Our findings suggest that the ZB and FC-HR devices can be used in monitoring performance improvements or deterioration over time and would allow qualified personnel in assessing the effectiveness of exercise programs in healthy individuals.

Twitter-Based Social Support Added to Fitbit Self-Monitoring for Decreasing Sedentary Behavior: Protocol for a Randomized Controlled Pilot Trial With Female Patients From a Women's Heart Clinic (2020)

JMIR Research Protocols

Abstract

Background: Prolonged sitting is an independent risk behavior for the development of chronic disease. With most interventions focusing on physical activity and exercise, there is a separate need for investigation into innovative and accessible interventions to decrease sedentary behavior throughout the day. Twitter is a social media platform with application for health communications and fostering of social support for health behavior change. Objective: This pilot study aims to test the feasibility, acceptability, and preliminary efficacy of delivering daily behavior change strategies within private Twitter groups to foster peer-to-peer support and decrease sedentary behavior throughout the day in women. The Twitter group was combined with a Fitbit for self-monitoring activity and compared to a Fitbit-only control group. Methods: In a 2-group design, participants were randomized to a Twitter + Fitbit treatment group or a Fitbit-only control group. Participants were recruited via the Stanford Research Repository System, screened for eligibility, and then invited to an orientation session. After providing informed consent, they were randomized. All participants received 13 weeks of tailored weekly step goals and a Fitbit. The treatment group participants, placed in a private Twitter support group, received daily automated behavior change “tweets” informed by theory and regular automated encouragement via text to communicate with the group. Fitbit data were collected daily throughout the treatment and follow-up period. Web-based surveys and accelerometer data were collected at baseline, treatment end (13 weeks), and at 8.5 weeks after the treatment. Results: The initial study design funding was obtained from the Women's Heart Clinic and the Stanford Clayman Institute. Funding to run this pilot study was received from the National Institutes of Health's National Heart, Lung, and Blood Institute under Award Number K01HL136702. All procedures were approved by Stanford University's Institutional Review Board, {\#}32127 in 2018, prior to beginning data collection. Recruitment for this study was conducted in May 2019. Of the 858 people screened, 113 met the eligibility criteria, 68 came to an information session, and 45 consented to participate in this pilot study. One participant dropped out of the intervention, and complete follow-up data were obtained from 39 of the 45 participants (87{\%} of the sample). Data were collected over 6 months from June to December 2019. Feasibility, acceptability, and preliminary efficacy results are being analyzed and will be reported in the winter of 2021. Conclusions: This pilot study is assessing the feasibility, acceptability, and preliminary efficacy of delivering behavior change strategies in a Twitter social support group to decrease sedentary behavior in women. These findings will inform a larger evaluation. With an accessible, tailorable, and flexible platform, Twitter-delivered interventions offer potential for many treatment variations and titrations, thereby testing the effects of different behavior change strategies, peer-group makeups, and health behaviors of interest.

Self-efficacy and physical activity in overweight and obese adults participating in a worksite weight loss intervention: Multistate modeling of wearable device data (2020)

Cancer Epidemiology Biomarkers and Prevention

Abstract

Background: Physical activity is associated with a reduced risk of numerous types of cancer and plays an important role in maintaining a healthy weight. Wearable physical activity trackers may supplement behavioral intervention and enable researchers to study how determinants like self-efficacy predict physical activity patterns over time. Methods: We used multistate models to evaluate how selfefficacy predicted physical activity states among overweight and obese individuals participating in a 26-week weight loss program (N = 96). We specified five states to capture physical activity patterns: (i) active (i.e., meeting recommendations for 2 weeks), (ii) insufficiently active, (iii) nonvalid wear, (iv) favorable transition (i.e., improvement in physical activity over 2 weeks), and (v) unfavorable transition.We calculated HRs of transition probabilities by self-efficacy, body mass index, age, and time. Results: The average prevalence of individuals in the active, insufficiently active, and nonvalid wear states was 13{\%}, 44{\%}, and 16{\%}, respectively. Low self-efficacy negatively predicted entering an active state [HR, 0.51; 95{\%} confidence interval (CI), 0.29- 0.88]. Obesity negatively predicted making a favorable transition out of an insufficiently active state (HR, 0.61; 95{\%} CI, 0.40-0.91). Older participants were less likely to transition to the nonvalid wear state (HR, 0.53; 95{\%} CI, 0.30-0.93). Device nonwear increased in the second half of the intervention (HR, 1.73; 95{\%} CI, 1.07-2.81). Conclusions: Self-efficacy is an important predictor for clinically relevant physical activity change in overweight and obese individuals. Multistate modeling is useful for analyzing longitudinal physical activity data. Impact: Multistate modeling can be used for statistical inference of covariates and allow for explicit modeling of nonvalid wear.

Comparison of Physical Activity Measures Derived From the Fitbit Flex and the ActiGraph GT3X+ in an Employee Population With Chronic Knee Symptoms (2020)

ACR Open Rheumatology

Abstract

Background/Purpose: There is increasing public health interest in the objective measurement of free-living physical activity in persons with arthritis, but research grade wearable monitors can be expensive. We therefore sought to examine the accuracy of the affordable personal monitor (Fitbit Flex) by comparing that output with data from the GT3X+ accelerometer (amount of time spent in light, moderate, and vigorous PA) worn simultaneously for 7 days; high accuracy would support substitution with this less expensive personal activity monitor in future arthritis research. Methods: Subjects (n=35) with chronic knee symptoms were recruited for a pilot intervention study using Fitbits to increase physical activity in employees with chronic knee symptoms at an urban insurance corporation. Subjects simultaneously wore for 7 days a Fitbit Flex (wrist-worn)and ActiGraph GT3x+ (waist-worn) as part of 12-week postintervention follow-upevaluation. Fitbit Flex data was regularly stored on a research storage service (Fitabase)by participants. Correlation tables were constructed to examine the association between the amount of time spent in activity intensity categories(Light, Moderate, Vigorous, and Moderate/Vigorous - the last three categories in bouts of 10 min or more). Due to discrepancy in wear time (Fitbit worn for 24hours/day, ActiGraph unit was not), sedentary minutes were not included for comparison. Comparisons were calculated by matching Fitabase data from calendar days Fitbit was worn with data from valid monitoring days (≥10hours wear time) of the ActiGraph. Results: Participants at baseline were primarily female (69{\%}), Caucasian (57{\%}), with mean age 52 years, BMI 32 kg/m2. Table I provides an overview of the definitions and distinctions of each measured activity intensity level. Table 2 provides the comparison of data from Fitbit Flex and ActiGraph GT3x+, including Spearman correlations, within each of the activity intensity categories. Conclusion: The individual intensity category correlations for the two devices are not strong. However, the bouted Moderate-Vigorous activity categories are moderately well correlated. One potential confounder includes the wrist vs waist location of the monitoring device during data collection. This moderate correlation of moderate/vigorous intensity between devices is reassuring to consumers/researchers using the Fitbit to track bouted higher intensity activities, but may not support substitution of the ActiGraph with the Fitbit in population studies that seek to accurately measure all levels of activity and sedentary time.

Fitbit\textregistered-derived evidence of enhanced mobility of patients with knee osteoarthritis treated with triamcinolone acetonide extended release: Results from a phase 3B, single-ARM, open-label study (2020)

Osteoarthritis and Cartilage

Abstract

Purpose: Repeated treatment with intra-articular corticosteroid is often used to manage recurrent osteoarthritis (OA) symptoms. Triamcinolone acetonide extended-release (TA-ER) is an intra-articular injection approved for the treatment of pain in patients with knee OA. We conducted a Phase 3b, single-arm, open-label study and assessed safety and exploratory efficacy endpoints in patients with knee OA treated with repeat administration of TA-ER (NCT03046446). This analysis assessed the clinical benefit of treatment based on patient mobility defined as numbers of steps per day. Method(s): Enrolled patients were aged {\textgreater}=40 years with symptomatic knee OA for {\textgreater}=6 months, ACR clinical and radiological diagnosis, Kellgren-Lawrence Grade 2-4, Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) pain total sum score {\textgreater}=6, and index-knee pain for {\textgreater}15 days during the past month. Patients received the 1st injection of TA-ER on Day 1 and the 2nd injection at either Week 12, 16, 20, or 24. This analysis collected patient mobility data within the 1st injection period only. Patient movements were analyzed with a measured function device [Fitbit] that was worn beginning at least 7 days prior to Day 1 and continuing to the end of Week 12. Weekly average step values were recorded if 3 out of 7 daily values were obtained with a lower threshold of 1000 steps per day. Exploratory efficacy endpoints included WOMAC A (pain) assessed every 4 weeks. A mixed effects model was generated to assess an outcome of change from baseline in steps per day using data from Weeks 4, 8, and 12. The primary independent variable of interest was change from baseline in WOMAC-A average pain scale. Other covariates examined were age, sex, baseline body mass index (BMI), region, month of the start of trial (categorical), visit (continuous, indicating time in weeks from receiving 1st injection), baseline global physical activity questionnaire (GPAQ) values (average MET-minutes of moderate or vigorous physical activity per day), baseline steps per day, and baseline average pain scale. Result(s): 208 patients were enrolled, most patients were female (55.8{\%}), and 67.8{\%} had radiographic OA severity of Kellgren-Lawrence Grade 3 or 4 at baseline. Mean age was 60.8 years and mean BMI was 31.4 kg/m2. The baseline mean WOMAC pain score was 2.17. Patients treated with TA-ER exhibited an immediate improvement in mobility as shown by increases in weekly mean steps per day (Figure 1) and mean change from baseline in weekly mean steps per day (Figure 2). Treatment with TA-ER was effective with mean changes from baseline in WOMAC-A scores of -1.4, -1.2, and -0.8 at Weeks 4, 8, and 12, respectively. A statistically significant greater change in step count from baseline was observed in association with larger decreases in WOMAC pain scores, male gender, lower BMI, lower baseline GPAQ average MET, and lower baseline steps per day. For every 1-unit decrease (from baseline) in mean WOMAC pain scale, the mean step count per day increased by 339 steps (95{\%} confidence limits, 58 and 620 steps) (Figure 3). [Formula presented] [Formula presented] [Formula presented] Conclusion(s): Patients with knee OA treated with TA-ER exhibited a decrease in pain and increased mobility as measured by their Fitbit. Limitations to this study include incomplete patient adherence to usage of the Fitbit device and that this was an open-label study with no control arm for comparison. Furthermore, it is unclear why the patient population had a relatively high mean number of steps per day at baseline. This study shows the feasibility of using personal fitness monitors to assess the effectiveness of treatments for OA. Sponsor: This study was funded by Flexion Therapeutics (Burlington, MA, USA).Copyright {\textcopyright} 2020

Accuracy and precision of three consumer-grade motion sensors during overground and treadmill walking in people with parkinson disease: Cross-sectional comparative study (2020)

JMIR Rehabilitation and Assistive Technologies

Abstract

Background: Wearable motion sensors are gaining popularity for monitoring free-living physical activity among people with Parkinson disease (PD), but more evidence supporting the accuracy and precision of motion sensors for capturing step counts is required in people with PD. Objective: This study aimed to examine the accuracy and precision of 3 common consumer-grade motion sensors for measuring actual steps taken during prolonged periods of overground and treadmill walking in people with PD. Methods: A total of 31 ambulatory participants with PD underwent 6-min bouts of overground and treadmill walking at a comfortable speed. Participants wore 3 devices (Garmin Vivosmart 3, Fitbit One, and Fitbit Charge 2 HR), and a single researcher manually counted the actual steps taken. Accuracy and precision were based on absolute and relative metrics, including intraclass correlation coefficients (ICCs) and Bland-Altman plots. Results: Participants walked 628 steps over ground based on manual counting, and Garmin Vivosmart, Fitbit One, and Fitbit Charge 2 HR devices had absolute (relative) error values of 6 (6/628, 1.0{\%}), 8 (8/628, 1.3{\%}), and 30 (30/628, 4.8{\%}) steps, respectively. ICC values demonstrated excellent agreement between manually counted steps and steps counted by both Garmin Vivosmart (0.97) and Fitbit One (0.98) but poor agreement for Fitbit Charge 2 HR (0.47). The absolute (relative) precision values for Garmin Vivosmart, Fitbit One, and Fitbit Charge 2 HR were 11.1 (11.1/625, 1.8{\%}), 14.7 (14.7/620, 2.4{\%}), and 74.4 (74.4/598, 12.4{\%}) steps, respectively. ICC confidence intervals demonstrated low variability for Garmin Vivosmart (0.96 to 0.99) and Fitbit One (0.93 to 0.99) but high variability for Fitbit Charge 2 HR (–0.57 to 0.74). The Fitbit One device maintained high accuracy and precision values for treadmill walking, but both Garmin Vivosmart and Fitbit Charge 2 HR (the wrist-worn devices) had worse accuracy and precision for treadmill walking. Conclusions: The waist-worn sensor (Fitbit One) was accurate and precise in measuring steps with overground and treadmill walking. The wrist-worn sensors were accurate and precise only during overground walking. Similar research should inform the application of these devices in clinical research and practice involving patients with PD.

The Physical Activity Wearables in the Police Force (PAW-Force) study: acceptability and impact (2020)

BMC Public Health

Abstract

Background: Policing is a highly stressful and increasingly sedentary occupation. The study aim was to assess the acceptability and impact of a mobile health (mHealth) technology intervention (Fitbit{\textregistered} activity monitor and ‘Bupa Boost' smartphone app) to promote physical activity (PA) and reduce sedentary time in the police force. Methods: Single-group, pre-post, mixed methods pilot study. Police officers and staff (n = 180) were recruited from two police forces in South West England. Participants used the technology for 12 weeks (an ‘individual' then ‘social' phase) followed by 5 months of optional use. Data sources included Fitbit{\textregistered}-recorded objective step count, questionnaire surveys and semi-structured interviews (n = 32). Outcome assessment points were baseline (week 0), mid-intervention (week 6), post-intervention (week 12) and follow-up (month 8). Paired t-tests were used to investigate changes in quantitative outcomes. Qualitative analysis involved framework and thematic analysis. Results: Changes in mean daily step count were non-significant (p {\textgreater} 0.05), but self-reported PA increased in the short term (e.g. + 465.4 MET-minutes/week total PA baseline to week 12, p = 0.011) and longer term (e.g. + 420.5 MET-minutes/week moderate-to-vigorous PA baseline to month 8, p = 0.024). The greatest impact on behaviour was perceived by less active officers and staff. There were no significant changes in sedentary time; the qualitative findings highlighted the importance of context and external influences on behaviour. There were no statistically significant changes (all p-values {\textgreater} 0.05) in any secondary outcomes (physical and mental health-related quality of life, perceived stress and perceived productivity), with the exception of an improvement in mental health-related quality of life (SF-12 mental component score + 1.75 points, p = 0.020) from baseline to month 8. Engagement with and perceived acceptability of the intervention was high overall, but a small number of participants reported negative physical (skin irritation) and psychological (feelings of guilt and anxiety) consequences of technology use. Individual app features (such as goal-setting and self-monitoring) were generally preferred to social components (social comparison, competitions and support). Conclusions: mHealth technology is an acceptable and potentially impactful intervention for increasing PA in the police force. The intervention was less useful for reducing sedentary time and the impact on secondary outcomes is unclear. Trial registration: NCT03169179 (registered 30th May 2017).

Harnessing wearable device data to improve state-level real-time surveillance of influenza-like illness in the USA: a population-based study (2020)

The Lancet Digital Health

Abstract

Background: Acute infections can cause an individual to have an elevated resting heart rate (RHR) and change their routine daily activities due to the physiological response to the inflammatory insult. Consequently, we aimed to evaluate if population trends of seasonal respiratory infections, such as influenza, could be identified through wearable sensors that collect RHR and sleep data. Methods: We obtained de-identified sensor data from 200 000 individuals who used a Fitbit wearable device from March 1, 2016, to March 1, 2018, in the USA. We included users who wore a Fitbit for at least 60 days and used the same wearable throughout the entire period, and focused on the top five states with the most Fitbit users in the dataset: California, Texas, New York, Illinois, and Pennsylvania. Inclusion criteria included having a self-reported birth year between 1930 and 2004, height greater than 1 m, and weight greater than 20 kg. We excluded daily measurements with missing RHR, missing wear time, and wear time less than 1000 min per day. We compared sensor data with weekly estimates of influenza-like illness (ILI) rates at the state level, as reported by the US Centers for Disease Control and Prevention (CDC), by identifying weeks in which Fitbit users displayed elevated RHRs and increased sleep levels. For each state, we modelled ILI case counts with a negative binomial model that included 3-week lagged CDC ILI rate data (null model) and the proportion of weekly Fitbit users with elevated RHR and increased sleep duration above a specified threshold (full model). We also evaluated weekly change in ILI rate by linear regression using change in proportion of elevated Fitbit data. Pearson correlation was used to compare predicted versus CDC reported ILI rates. Findings: We identified 47 249 users in the top five states who wore a Fitbit consistently during the study period, including more than 13{\textperiodcentered}3 million total RHR and sleep measures. We found the Fitbit data significantly improved ILI predictions in all five states, with an average increase in Pearson correlation of 0{\textperiodcentered}12 (SD 0{\textperiodcentered}07) over baseline models, corresponding to an improvement of 6{\textperiodcentered}3–32{\textperiodcentered}9{\%}. Correlations of the final models with the CDC ILI rates ranged from 0{\textperiodcentered}84 to 0{\textperiodcentered}97. Week-to-week changes in the proportion of Fitbit users with abnormal data were associated with week-to-week changes in ILI rates in most cases. Interpretation: Activity and physiological trackers are increasingly used in the USA and globally to monitor individual health. By accessing these data, it could be possible to improve real-time and geographically refined influenza surveillance. This information could be vital to enact timely outbreak response measures to prevent further transmission of influenza cases during outbreaks. Funding: Partly supported by the US National Institutes of Health National Center for Advancing Translational Sciences.

Using Fitbit data to examine factors that affect daily activity levels of college students (2021)

PLOS ONE

Abstract

To date, the effect of both fixed and time-varying individual, social, psychological, environmental, and behavioral characteristics on temporal growth trends in physical activity (PA) among younger individuals remains an under-studied topic. In this paper, we address this gap in previous work by examining how temporal growth trends in PA respond to changing social, environmental, and behavioral characteristics using a large sample of college students (N = 692) who participated in the NetHealth project at the University of Notre Dame and from which fine-grained longitudinal data on physical activity and social interaction were collected unobtrusively via the use of wearables for 637 days (August 16, 2015 to May 13, 2017). These data are augmented by periodic survey data on fixed sociodemographic and psychological variables. We estimate latent growth-curve models for daily activity status, steps, active minutes, and activity calories. We find evidence of both a generalized friendship paradox and a peer effect for PA, with the average PA level of study participants' contacts being on average larger than their own, and with this average level exerting a statistically significant effect on individual PA levels. Notably, there was limited evidence of temporal growth in PA across the 637 days of observation with null temporal effects for three out of the four PA indicators, except for daily steps taken. Finally, we find that social, psychological, and behavioral factors (e.g., large network size, high extroversion levels, and more courses taken) are systematically associated with higher PA levels in this sample. Overall, our findings highlight the importance of social, environmental, and behavioral factors (such as peer networks and daily sociability) in modulating the dynamics of PA levels among college students.

Feasibility and acceptability of a technology-based, rural weight management intervention in older adults with obesity (2021)

BMC Geriatrics

Abstract

Background: Older adults with obesity residing in rural areas have reduced access to weight management programs. We determined the feasibility, acceptability and preliminary outcomes of an integrated technology-based health promotion intervention in rural-living, older adults using remote monitoring and synchronous video-based technology. Methods: A 6-month, non-randomized, non-blinded, single-arm study was conducted from October 2018 to May 2020 at a community-based aging center of adults aged ≥65 years with a body mass index (BMI) ≥30 kg/m2. Weekly dietitian visits focusing on behavior therapy and caloric restriction and twice-weekly physical therapist-led group strength, flexibility and balance training classes were delivered using video-conferencing to participants in their homes. Participants used a Fitbit Alta HR for remote monitoring with data feedback provided by the interventionists. An aerobic activity prescription was provided and monitored. Results: Mean age was 72.9±3.9 years (82{\%} female). Baseline anthropometric measures of weight, BMI, and waist circumference were 97.8±16.3 kg, 36.5±5.2 kg/m2, and 115.5±13.0 cm, respectively. A total of 142 participants were screened (n=27 ineligible), and 53 consented. There were nine dropouts (17{\%}). Overall satisfaction with the trial (4.7+ 0.6, scale: 1 (low) to 5 (high)) and with Fitbit (4.2+ 0.9) were high. Fitbit was worn an average of 81.7±19.3{\%} of intervention days. In completers, mean weight loss was 4.6±3.5 kg or 4.7±3.5{\%} (p{\textless} 0.001). Physical function measures of 30-s sit-to-stand repetitions increased from 13.5±5.7 to 16.7±5.9 (p{\textless} 0.001), 6-min walk improved by 42.0±77.3 m (p=0.005) but no differences were observed in gait speed or grip strength. Subjective measures of late-life function improved (3.4±4.7 points, p{\textless} 0.001). Conclusions: A technology-based obesity intervention is feasible and acceptable to older adults with obesity and may lead to weight loss and improved physical function. Clinical trial registration: Registered on Clinicaltrials.gov {\#}NCT03104205. Registered on April 7, 2017. First participant enrolled on October 1st, 2018.

Annual rhythms in adults' lifestyle and health (ARIA): protocol for a 12-month longitudinal study examining temporal patterns in weight, activity, diet, and wellbeing in Australian adults (2021)

BMC Public Health

Abstract

Background: Almost one in three Australian adults are now obese, and the rate continues to rise. The causes of obesity are multifaceted and include environmental, cultural and lifestyle factors. Emerging evidence suggests there may be temporal patterns in weight gain related, for example, to season and major festivals such as Christmas, potentially due to changes in diet, daily activity patterns or both. The aim of this study is to track the annual rhythm in body weight, 24 h activity patterns, dietary patterns, and wellbeing in a cohort of Australian adults. In addition, through data linkage with a concurrent children's cohort study, we aim to examine whether changes in children's body mass index, activity and diet are related to those of their parents. Methods: A community-based sample of 375 parents aged 18 to 65 years old, residing in or near Adelaide, Australia, and who have access to a Bluetooth-enabled mobile device or a computer and home internet, will be recruited. Across a full year, daily activities (minutes of moderate to vigorous physical activity, light physical activity, sedentary behaviour and sleep) will be measured using wrist-worn accelerometry (Fitbit Charge 3). Body weight will be measured daily using Fitbit wifi scales. Self-reported dietary intake (Dietary Questionnaire for Epidemiological Studies V3.2), and psychological wellbeing (WHOQOL-BREF and DASS-21) will be assessed eight times throughout the 12-month period. Annual patterns in weight will be examined using Lowess curves. Associations between changes in weight and changes in activity and diet compositions will be examined using repeated measures multi-level models. The associations between parent's and children's weight, activity and diet will be investigated using multi-level models. Discussion: Temporal factors, such as day type (weekday or weekend day), cultural celebrations and season, may play a key role in weight gain. The aim is to identify critical opportunities for intervention to assist the prevention of weight gain. Family-based interventions may be an important intervention strategy. Trial registration: Australia New Zealand Clinical Trials Registry, identifier ACTRN12619001430123. Prospectively registered on 16 October 2019.

A digital lifestyle behaviour change intervention for the prevention of type 2 diabetes: a qualitative study exploring intuitive engagement with real-time glucose and physical activity feedback (2021)

BMC Public Health

Abstract

Mobile health technologies have advanced to now allow monitoring of the acute physiological responses to lifestyle behaviours. Our aim was to explore how people engaged with real-time feedback on their physical activity and glucose levels over several weeks. Semi-structured interviews with 26 participants (61.5{\%} female, 56.6 years) at moderate-to-high risk of developing type 2 diabetes were conducted. Interviews were completed after participants took part in an intervention comprising a flash glucose monitor (Freestyle Libre) and a physical activity monitor (Fitbit Charge 2). Purposive sampling ensured representation of ages, genders and group allocations. Inductive thematic analysis revealed how individuals intuitively used, interpreted and acted on feedback from wearable technologies. Six key themes emerged: triggers of engagement with the technologies, links between behaviour and health, lack of confidence, changes to movement behaviours, changes to diet and barriers to lifestyle behaviour change. Our findings demonstrate that accessing behavioural and physiological feedback can increase self-awareness of how lifestyle impacts short-term health. Some participants noticed a link between the feedback presented by the two devices and changed their behaviour but many did not. Training and educational support, as well as efforts to optimize how feedback is presented to users, are needed to sustain engagement and behaviour change. Extensions of this work to involve people with diabetes are also warranted to explore whether behavioural and physiological feedback in parallel can encourage better diabetes self-management. ISRCTN Registry, ISRCTN17545949 , 12/05/2017, prospectively registered.

Measuring Daily Compliance with Physical Activity Tracking in Ambulatory Surgery Patients: Comparative Analysis of Five Compliance Criteria (Preprint) (2021)

JMIR mHealth and uHealth

Abstract

Background: Physical activity trackers such as the Fitbit can allow clinicians to monitor the recovery of their patients following surgery. An important issue when analyzing activity tracker data is to determine patients' daily compliance with wearing their assigned device, using an appropriate criterion to determine a valid day of wear. However, it is currently unclear as to how different criteria can affect the reported compliance of patients recovering from ambulatory surgery. Investigating this issue can help to inform the use of activity data by revealing factors that may impact compliance calculations. Objective: This study aimed to understand how using different criteria can affect the reported compliance with activity tracking in ambulatory surgery patients. It also aimed to investigate factors that explain variation between the outcomes of different compliance criteria. Methods: A total of 62 patients who were scheduled to undergo total knee arthroplasty (TKA, ie, knee replacement) volunteered to wear a commercial Fitbit Zip activity tracker over an 8-week perioperative period. Patients were asked to wear the Fitbit Zip daily, beginning 2 weeks prior to their surgery and ending 6 weeks after surgery. Of the 62 patients who enrolled in the study, 20 provided Fitbit data and underwent successful surgery. The Fitbit data were analyzed using 5 different daily compliance criteria, which consider patients as compliant with daily tracking if they either register {\textgreater}0 steps in a day, register {\textgreater}500 steps in a day, register at least one step in 10 different hours of the day, register {\textgreater}0 steps in 3 distinct time windows, or register {\textgreater}0 steps in 3 out of 4 six-hour time windows. The criteria were compared in terms of compliance outcomes produced for each patient. Data were explored using heatmaps and line graphs. Linear mixed models were used to identify factors that lead to variation between compliance outcomes across the sample. Results: The 5 compliance criteria produce different outcomes when applied to the patients' data, with an average 24{\%} difference in reported compliance between the most lenient and strictest criteria. However, the extent to which each patient's reported compliance was impacted by different criteria was not uniform. Some individuals were relatively unaffected, whereas others varied by up to 72{\%}. Wearing the activity tracker as a clip-on device, rather than on the wrist, was associated with greater differences between compliance outcomes at the individual level (P=.004, r=.616). This effect was statistically significant (P{\textless}.001) in the first 2 weeks after surgery. There was also a small but significant main effect of age on compliance in the first 2 weeks after surgery (P=.040). Gender and BMI were not associated with differences in individual compliance outcomes. Finally, the analysis revealed that surgery has an impact on patients' compliance, with noticeable reductions in activity following surgery. These reductions affect compliance calculations by discarding greater amounts of data under strict criteria. Conclusions: This study suggests that different compliance criteria cannot be used interchangeably to analyze activity data provided by TKA patients. Surgery leads to a temporary reduction in patients' mobility, which affects their reported compliance when strict thresholds are used. Reductions in mobility suggest that the use of lenient compliance criteria, such as {\textgreater}0 steps or windowed approaches, can avoid unnecessary data exclusion over the perioperative period. Encouraging patients to wear the device at their wrist may improve data quality by increasing the likelihood of patients wearing their tracker and ensuring that activity is registered in the 2 weeks after surgery. Trial Registration: ClinicalTrials.gov NCT03518866; https://clinicaltrials.gov/ct2/show/NCT03518866

Phase II trial evaluating efficacy of a Fitbit program for improving the health of endometrial cancer survivors (2021)

Gynecologic Oncology

Abstract

Background: Despite the favorable prognosis of early stage endometrial cancer, mortality from cardiovascular disease is high. We aimed to evaluate the efficacy of a Fitbit program to improve physical activity in endometrial cancer survivors. Methods: Eligible patients were diagnosed with stage IA-IIIA endometrial adenocarcinoma, ≥3 months out from treatment. Participants received a Fitbit Alta and were randomized to receive communication via telephone or electronic methods (email/text). Communication was every two weeks for two months, then once during months four and five. Average daily steps were assessed weekly for nine months. Results: The 46 analyzable patients demonstrated a baseline of 5641 median daily average steps. Average steps increased by 22{\%} at 6 months but decreased to baseline by nine months. Baseline activity level (daily steps and walks per week) was the greatest predictor of activity level. Only the telephone intervention participants demonstrated increased activity level at several timepoints, although not maintained by nine months. BMI was unchanged. There was mild improvement in physical and social well-being in those with low baseline well-being (p = 0.009 and 0.014, respectively), regardless of intervention group. Emotional well-being correlated with step count (p = 0.005). Conclusions: Activity level was low and mildly improved on the Fitbit program with the telephone intervention, but effects did not persist by study completion. The program had the greatest impact on a select group of telephone intervention patients with high baseline walking frequency and low baseline step count. Others may require more intense intervention to promote more robust/persistent lifestyle changes.

Performance of Fitbit Charge 3 against polysomnography in measuring sleep in adolescent boys and girls (2021)

Chronobiology International

Abstract

We evaluated the performance of Fitbit Charge 3™ (FC3), a multi-sensor commercial sleep-tracker, for measuring sleep in adolescents against gold-standard laboratory polysomnography (PSG). Single-night PSG and FC3 sleep outcomes were compared in thirty-nine adolescents (22 girls; 16–19 years), 12 of whom presented with clinical/subclinical DSM-5 insomnia symptoms (7 girls). Discrepancy analysis, Bland–Altman plots, and epoch-by-epoch analyses were used to evaluate FC3 performance. The influence of several factors potentially affecting FC3 performance (e.g., sex, age, body mass index, firmware version, and magnitude of heart rate changes between consecutive PSG epochs) was also tested. In the sample of healthy adolescents, FC3 systematically underestimated PSG total sleep time by about 11 min and sleep efficiency by 2.5%, and overestimated wake after sleep onset by 9 min. Proportional biases were detected for “light” and “deep” sleep duration, resulting in significant underestimation of these parameters for those participants having longer PSG N1+ N2 and N3 durations, respectively. No significant systematic bias was detected for sleep efficiency and sleep onset latency. Epoch-by-epoch analysis showed sleep-stage sensitivity (average proportion of PSG epochs correctly classified by the device for a given sleep stage) of 68% for wake, 78% for “light” sleep, 59% for “deep” sleep, and 69% for rapid eye movement (REM) sleep in healthy sleepers. Similar results were found in the sample of adolescents with insomnia symptoms. Body mass index was positively associated with FC3-PSG discrepancies in wake after sleep onset (R2 = .16, p = .048). The magnitude of the heart rate acceleration/deceleration between consecutive PSG epochs was an important factor affecting FC3 classifications of sleep stages. Our results are in line with a general trend in the literature, suggesting better performance for the recently introduced multi-sensor devices compared to motion-only devices, although further developments are needed to improve accuracy in sleep stage classification and wake detection. Further insight is needed to determine factors potentially affecting device performance, such as accuracy and reliability (consistency of performance over time), in different samples and conditions.

A standardized framework for testing the performance of sleep-tracking technology: step-by-step guidelines and open-source code (2021)

Sleep

Abstract

Sleep-tracking devices, particularly within the consumer sleep technology (CST) space, are increasingly used in both research and clinical settings, providing new opportunities for large-scale data collection in highly ecological conditions. Due to the fast pace of the CST industry combined with the lack of a standardized framework to evaluate the performance of sleep trackers, their accuracy and reliability in measuring sleep remains largely unknown. Here, we provide a step-by-step analytical framework for evaluating the performance of sleep trackers (including standard actigraphy), as compared with gold-standard polysomnography (PSG) or other reference methods. The analytical guidelines are based on recent recommendations for evaluating and using CST from our group and others (de Zambotti and colleagues; Depner and colleagues), and include raw data organization as well as critical analytical procedures, including discrepancy analysis, Bland-Altman plots, and epoch-by-epoch analysis. Analytical steps are accompanied by open-source R functions (depicted at https://sri-human-sleep.github.io/sleep-trackers-performance/AnalyticalPipeline_v1.0.0.html). In addition, an empirical sample dataset is used to describe and discuss the main outcomes of the proposed pipeline. The guidelines and the accompanying functions are aimed at standardizing the testing of CSTs performance, to not only increase the replicability of validation studies, but also to provide ready-to-use tools to researchers and clinicians. All in all, this work can help to increase the efficiency, interpretation, and quality of validation studies, and to improve the informed adoption of CST in research and clinical settings.

Monitoring Beliefs and Physiological Measures Using Wearable Sensors and Smartphone Technology Among Students at Risk of COVID-19: Protocol for a mHealth Study (2021)

JMIR Res Protoc 2021;10(6):e29561 https://www.researchprotocols.org/2021/6/e29561

Abstract

Background: The COVID-19 pandemic has significantly impacted lives and greatly affected the mental health and public safety of an already vulnerable population—college students. Social distancing and isolation measures have presented challenges to students' mental health. mHealth apps and wearable sensors may help monitor students at risk of COVID-19 and support their mental well-being. Objective: This study aimed to monitor students at risk of COVID-19 by using a wearable sensor and a smartphone-based survey. Methods: We conducted a prospective study on undergraduate and graduate students at a public university in the Midwest United States. Students were instructed to download the Fitbit, Social Rhythms, and Roadmap 2.0 apps onto their personal smartphone devices (Android or iOS). Subjects consented to provide up to 10 saliva samples during the study period. Surveys were administered through the Roadmap 2.0 app at five timepoints: at baseline, 1 month later, 2 months later, 3 months later, and at study completion. The surveys gathered information regarding demographics, COVID-19 diagnoses and symptoms, and mental health resilience, with the aim of documenting the impact of COVID-19 on the college student population. Results: This study enrolled 2158 college students between September 2020 and January 2021. Subjects are currently being followed-up for 1 academic year. Data collection and analysis are currently underway. Conclusions: This study examined student health and well-being during the COVID-19 pandemic and assessed the feasibility of using a wearable sensor and a survey in a college student population, which may inform the role of our mHealth tools in assessing student health and well-being. Finally, using data derived from a wearable sensor, biospecimen collection, and self-reported COVID-19 diagnosis, our results may provide key data toward the development of a model for the early prediction and detection of COVID-19. Trial Registration: ClinicalTrials.gov NCT04766788; https://clinicaltrials.gov/ct2/show/NCT04766788

Co-occurrence of pain and dyspnea in Veterans with COPD: Relationship to functional status and a pilot study of neural correlates using structural and functional magnetic resonance imaging (2021)

PLOS ONE

Abstract

Persons with COPD experience co-occurring dyspnea and pain. Little is known about the relationship between symptom co-occurrence with physical activity (PA) and exercise. Novel diagnostic tools are needed for accurate symptom discrimination. In this secondary analysis, we examined relationships between baseline assessments of pain, dyspnea, objectively measured PA, and exercise capacity in persons with COPD who previously enrolled in three PA studies. Pain was assessed with the bodily pain domain of the Veterans RAND-36 (VR-36), and dyspnea with the modified Medical Research Council (mMRC) scale. Average daily step count was assessed with the Omron HJ-720ITC or FitBit Zip pedometer, and exercise capacity with 6-minute walk test (6MWT). We also conducted a pilot neuroimaging study. Neuroimaging data were acquired on a Siemens 3-Tesla Magnetom Prismafit whole-body scanner. Analysis of variance assessed trends in daily step count and 6MWT distance across categories of co-occurring pain and dyspnea. General linear models examined relationships between cortical thickness and resting state functional connectivity (fc) with symptoms and functional status. In 373 Veterans, 98% were male with mean age 70.5± 8.3 years and FEV1% predicted 59 ± 21%. Compared to those with no co-occurrence of pain and dyspnea, those with co-occurrence walked 1,291–1,444 fewer steps per day and had an 80–85 m lower 6MWT distance. Ten males participated in the pilot neuroimaging study. Predominant findings were that lower cortical thickness and greater fc were associated with higher pain and dyspnea, p<0.05. Greater cortical thickness and lower fc were associated with higher daily step count and 6MWT distance, p<0.05. Regional patterns of associations differed for pain and dyspnea, suggesting that cortical thickness and fc may discriminate symptoms. Co-occurring dyspnea and pain in COPD are associated with significant reductions in PA and exercise capacity. It may be feasible for neuroimaging markers to discriminate between pain and dyspnea.

Feasibility, preliminary efficacy, and accessibility of a twitter-based social support group vs Fitbit only to decrease sedentary behavior in women (2021)

Internet Interventions

Abstract

Background Health behavior change interventions delivered by social media allow for real-time, dynamic interaction, peer social support, and experimenter-provided content. Aims We tested the feasibility, acceptability, and preliminary efficacy of a novel Twitter-based walking break intervention with daily behavior change strategies and prompts for social support, combined with a Fitbit, vs. Fitbit alone. Methods In a 2-group pilot, 45 sedentary women from a heart clinic were randomized to Twitter + Fitbit activity tracker (Tweet4Wellness, n = 23) or Fitbit-only (control, n = 22). All received a Fitbit Inspire and 13 weeks of tailored weekly step goals. Tweet4Wellness consisted of a private Twitter support group, with daily automated behavior change “tweets” informed by behavior change theory, and encouragement to communicate within the group. Feasibility outcomes included recruitment and enrollment numbers, implementation challenges, and number and type of help requests from participants throughout the study period. Preliminary efficacy outcomes provided by Fitbit data were sedentary minutes, number of hours with >250 steps, maximum sitting bout, weighted sedentary median bout length, total steps, intensity minutes (>3.0 METS), and ratio of time spent sitting-to-moving. Acceptability outcomes included level of Twitter participation within Tweet4Wellness, and Likert scale plus open-ended survey questions on enjoyment and perceived effectiveness of intervention components. Survey data on acceptability of the features of the intervention were collected at 13 weeks (end-of-treatment [EOT]) and 22 weeks (follow-up). Results The study was feasible, with addressable implementation challenges. Tweet4Wellness participants changed significantly from baseline to EOT relative to control participants on number of active hours p = .018, total steps p = .028, and ratio of sitting-to-moving, p = .014. Only sitting-to-moving was significant at follow-up (p = .047). Among Tweet4Wellness participants, each tweet sent during treatment was associated with a 0.11 increase in active hours per day (p = .04) and a 292-step increase per day (p < .001). Tweet4Wellness participants averaged 54.8 (SD = 35.4) tweets, totaling 1304 tweets, and reported liking the accountability and peer support provided by the intervention. Conclusion A Twitter-delivered intervention for promoting physical activity among inactive women from a heart clinic was feasible, acceptable, and demonstrated preliminary efficacy in increasing daily active hours, daily total steps, and the ratio of sitting-to-moving from pre to post for the intervention compared with the control. Lessons learned from this pilot suggest that the next study should expand the recruitment pool, refine the intervention to increase group engagement, and select active hours, total steps, and ratio of sitting-to-movement as primary sedentary behavior measures.

Using fitbit as an mHealth intervention tool to promote physical activity: Potential challenges and solutions (2021)

JMIR mHealth and uHealth

Abstract

Consumer-based physical activity (PA) trackers, also known as wearables, are increasingly being used in research studies as intervention or measurement tools. One of the most popular and widely used brands of PA trackers is Fitbit. Since the release of the first Fitbit in 2009, hundreds of experimental studies have used Fitbit devices to facilitate PA self-monitoring and behavior change via goal setting and feedback tools. Fitbit's ability to capture large volumes of PA and physiological data in real time creates enormous opportunities for researchers. At the same time, however, it introduces a number of challenges (eg, technological, operational, logistical), most of which are not sufficiently described in study publications. Currently, there are no technical reports, guidelines, nor other types of publications discussing some of these challenges and offering guidance to researchers on how to best incorporate Fitbit devices in their study design and intervention to achieve their research goals. As a result, researchers are often left alone to discover and address some of these issues during the study through “trial and error.” This paper aims to address this gap. Drawing on our cumulative experience of conducting multiple studies with various Fitbit PA trackers over the years, we present and discuss various key challenges associated with the use of Fitbit PA trackers in research studies. Difficulties with the use of Fitbit PA trackers are encountered throughout the entire research process. Challenges and solutions are categorized in 4 main categories: study preparation, intervention delivery, data collection and analysis, and study closeout. Subsequently, we describe a number of empirically tested strategies used in 4 of our interventional studies involving participants from a broad range of demographic characteristics, racial/ethnic backgrounds, and literacy levels. Researchers should be prepared to address challenges and issues in a timely fashion to ensure that the Fitbit effectively assists participants and researchers in achieving research and outcome goals.

Use of Fitbit Devices in Physical Activity Intervention Studies Across the Life Course: Narrative Review (2021)

JMIR mHealth and uHealth

Abstract

Background: Commercial off-the-shelf activity trackers (eg, Fitbit) allow users to self-monitor their daily physical activity (PA), including the number of steps, type of PA, amount of sleep, and other features. Fitbits have been used as both measurement and intervention tools. However, it is not clear how they are being incorporated into PA intervention studies, and their use in specific age groups across the life course is not well understood. Objective: This narrative review aims to characterize how PA intervention studies across the life course use Fitbit devices by synthesizing and summarizing information on device selection, intended use (intervention vs measurement tool), participant wear instructions, rates of adherence to device wear, strategies used to boost adherence, and the complementary use of other PA measures. This review provides intervention scientists with a synthesis of information that may inform future trials involving Fitbit devices. Methods: We conducted a search of the Fitabase Fitbit Research Library, a database of studies published between 2012 and 2018. Of the 682 studies available on the Fitabase research library, 60 interventions met the eligibility criteria and were included in this review. A supplemental search in PubMed resulted in the inclusion of 15 additional articles published between 2019 and 2020. A total of 75 articles were reviewed, which represented interventions conducted in childhood; adolescence; and early, middle, and older adulthood. Results: There was considerable heterogeneity in the use of Fitbit within and between developmental stages. Interventions for adults typically required longer wear periods, whereas studies on children and adolescents tended to have more limited device wear periods. Most studies used developmentally appropriate behavior change techniques and device wear instructions. Regardless of the developmental stage and intended Fitbit use (ie, measurement vs intervention tool), the most common strategies used to enhance wear time included sending participants reminders through texts or emails and asking participants to log their steps or synchronize their Fitbit data daily. The rates of adherence to the wear time criteria were reported using varying metrics. Most studies supplemented the use of Fitbit with additional objective or self-reported measures for PA. Conclusions: Overall, the heterogeneity in Fitbit use across PA intervention studies reflects its relative novelty in the field of research. As the use of monitoring devices continues to expand in PA research, the lack of uniformity in study protocols and metrics of reported measures represents a major issue for comparability purposes. There is a need for increased transparency in the prospective registration of PA intervention studies. Researchers need to provide a clear rationale for the use of several PA measures and specify the source of their main PA outcome and how additional measures will be used in the context of Fitbit-based interventions.

The Association Between Logging Steps Using a Website, App, or Fitbit and Engaging With the 10,000 Steps Physical Activity Program: Observational Study (2021)

Journal of medical Internet research

Abstract

Background: Engagement is positively associated with the effectiveness of digital health interventions. It is unclear whether tracking devices that automatically synchronize data (eg, Fitbit) produce different engagement levels compared with manually entering data. Objective: This study examines how different step logging methods in the freely available 10,000 Steps physical activity program differ according to age and gender and are associated with program engagement. Methods: A subsample of users (n=22,142) of the free 10,000 Steps physical activity program were classified into one of the following user groups based on the step-logging method: Website Only (14,617/22,142, 66.01%), App Only (2100/22,142, 9.48%), Fitbit Only (1705/22,142, 7.7%), Web and App (2057/22,142, 9.29%), and Fitbit Combination (combination of web, app, and Fitbit; 1663/22,142, 7.51%). Generalized linear regression and binary logistic regression were used to examine differences between user groups' engagement and participation parameters. The time to nonusage attrition was assessed using Cox proportional hazards regression. Results: App Only users were significantly younger and Fitbit user groups had higher proportions of women compared with other groups. The following outcomes were significant and relative to the Website Only group. The App Only group had fewer website sessions (odds ratio [OR] −6.9, 95% CI −7.6 to −6.2), whereas the Fitbit Only (OR 10.6, 95% CI 8.8-12.3), Web and App (OR 1.5, 95% CI 0.4-2.6), and Fitbit Combination (OR 8.0; 95% CI 6.2-9.7) groups had more sessions. The App Only (OR −0.7, 95% CI −0.9 to −0.4) and Fitbit Only (OR −0.5, 95% CI −0.7 to −0.2) groups spent fewer minutes on the website per session, whereas the Fitbit Combination group (OR 0.2, 95% CI 0.0-0.5) spent more minutes. All groups, except the Fitbit Combination group, viewed fewer website pages per session. The mean daily step count was lower for the App Only (OR −201.9, 95% CI −387.7 to −116.0) and Fitbit Only (OR −492.9, 95% CI −679.9 to −305.8) groups but higher for the Web and App group (OR 258.0, 95% CI 76.9-439.2). The Fitbit Only (OR 5.0, 95% CI 3.4-6.6), Web and App (OR 7.2, 95% CI 5.9-8.6), and Fitbit Combination (OR 15.6, 95% CI 13.7-17.5) groups logged a greater number of step entries. The App Only group was less likely (OR 0.65, 95% CI 0.46-0.94) and other groups were more likely to participate in Challenges. The mean time to nonusage attrition was 35 (SD 26) days and was lower than average in the Website Only and App Only groups and higher than average in the Web and App and Fitbit Combination groups. Conclusions: Using a Fitbit in combination with the 10,000 Steps app or website enhanced engagement with a real-world physical activity program. Integrating tracking devices that synchronize data automatically into real-world physical activity interventions is one strategy for improving engagement.

Physical Activity Monitoring Using a Fitbit Device in Ischemic Stroke Patients: Prospective Cohort Feasibility Study (2021)

JMIR mHealth and uHealth

Abstract

Background: Continuous tracking of ambulatory activity in real-world settings using step activity monitors has many potential uses. However, feasibility, accuracy, and correlation with performance measures in stroke patients have not been well-established. Objective: The primary study objective was to determine adherence with wearing a consumer-grade step activity monitor, the Fitbit Charge HR, in home-going ischemic stroke patients during the first 90 days after hospital discharge. Secondary objectives were to (1) determine accuracy of step counts of the Fitbit Charge HR compared with a manual tally; (2) calculate correlations between the Fitbit step counts and the mobility performance scores at discharge and 30 days after stroke; (3) determine variability and change in weekly step counts over 90 days; and (4) evaluate patient experience with using the Fitbit Charge HR poststroke. Methods: A total of 15 participants with recent mild ischemic stroke wore a Fitbit Charge HR for 90 days after discharge and completed 3 mobility performance tests from the National Institutes of Health Toolbox at discharge and Day 30: (1) Standing Balance Test, (2) 2-Minute Walk Endurance Test, and (3) 4-Meter Walk Gait Speed Test. Accuracy of step activity monitors was assessed by calculating differences in steps recorded on the step activity monitor and a manual tally during 2-minute walk tests. Results: Participants had a mean age of 54 years and a median modified Rankin scale score of 1. Mean daily adherence with step activity monitor use was 83.6%. Mean daily step count in the first week after discharge was 4376. Daily step counts increased slightly during the first 30 days after discharge (average increase of 52.5 steps/day; 95% CI 32.2-71.8) and remained stable during the 30-90 day period after discharge. Mean step count difference between step activity monitor and manual tally was –4.8 steps (–1.8%). Intraclass correlation coefficients for step counts and 2-minute walk, standing balance, and 4-meter gait speed at discharge were 0.41 (95% CI –0.14 to 0.75), –0.12 (95% CI –0.67 to 0.64), and 0.17 (95% CI –0.46 to 0.66), respectively. Values were similarly poor at 30 days. Conclusions: The use of consumer-grade Fitbit Charge HR in patients with recent mild stroke is feasible with reasonable adherence and accuracy. There was poor correlation between step counts and gait speed, balance, and endurance. Further research is needed to evaluate the association between step counts and other outcomes relevant to patients, including patient-reported outcomes and measures of physical function.

Validity Evaluation of the Fitbit Charge2 and the Garmin vivosmart HR+ in Free-Living Environments in an Older Adult Cohort (2019)

JMIR mHealth and uHealth

Abstract

Background: Few studies have investigated the validity of mainstream wrist-based activity trackers in healthy older adults in real life, as opposed to laboratory settings. Objective: This study explored the performance of two wrist-worn trackers (Fitbit Charge 2 and Garmin vivosmart HR+) in estimating steps, energy expenditure, moderate-to-vigorous physical activity (MVPA) levels, and sleep parameters (total sleep time [TST] and wake after sleep onset [WASO]) against gold-standard technologies in a cohort of healthy older adults in a free-living environment. Methods: Overall, 20 participants (>65 years) took part in the study. The devices were worn by the participants for 24 hours, and the results were compared against validated technology (ActiGraph and New-Lifestyles NL-2000i). Mean error, mean percentage error (MPE), mean absolute percentage error (MAPE), intraclass correlation (ICC), and Bland-Altman plots were computed for all the parameters considered. Results: For step counting, all trackers were highly correlated with one another (ICCs>0.89). Although the Fitbit tended to overcount steps (MPE=12.36%), the Garmin and ActiGraph undercounted (MPE 9.36% and 11.53%, respectively). The Garmin had poor ICC values when energy expenditure was compared against the criterion. The Fitbit had moderate-to-good ICCs in comparison to the other activity trackers, and showed the best results (MAPE=12.25%), although it underestimated calories burned. For MVPA levels estimation, the wristband trackers were highly correlated (ICC=0.96); however, they were moderately correlated against the criterion and they overestimated MVPA activity minutes. For the sleep parameters, the ICCs were poor for all cases, except when comparing the Fitbit with the criterion, which showed moderate agreement. The TST was slightly overestimated with the Fitbit, although it provided good results with an average MAPE equal to 10.13%. Conversely, WASO estimation was poorer and was overestimated by the Fitbit but underestimated by the Garmin. Again, the Fitbit was the most accurate, with an average MAPE of 49.7%. Conclusions: The tested well-known devices could be adopted to estimate steps, energy expenditure, and sleep duration with an acceptable level of accuracy in the population of interest, although clinicians should be cautious in considering other parameters for clinical and research purposes.

Measurement of Heart Rate Using the Polar OH1 and Fitbit Charge 3 Wearable Devices in Healthy Adults During Light, Moderate, Vigorous, and Sprint-Based Exercise: Validation Study (2021)

JMIR mHealth and uHealth

Abstract

Background: Accurate, continuous heart rate measurements are important for health assessment, physical activity, and sporting performance, and the integration of heart rate measurements into wearable devices has extended its accessibility. Although the use of photoplethysmography technology is not new, the available data relating to the validity of measurement are limited, and the range of activities being performed is often restricted to one exercise domain and/or limited intensities. Objective: The primary objective of this study was to assess the validity of the Polar OH1 and Fitbit Charge 3 devices for measuring heart rate during rest, light, moderate, vigorous, and sprint-type exercise. Methods: A total of 20 healthy adults (9 female; height: mean 1.73 [SD 0.1] m; body mass: mean 71.6 [SD 11.0] kg; and age: mean 40 [SD 10] years) volunteered and provided written informed consent to participate in the study consisting of 2 trials. Trial 1 was split into 3 components: 15-minute sedentary activities, 10-minute cycling on a bicycle ergometer, and incremental exercise test to exhaustion on a motorized treadmill (18-42 minutes). Trial 2 was split into 2 components: 4 × 15-second maximal sprints on a cycle ergometer and 4 × 30- to 50-m sprints on a nonmotorized resistance treadmill. Data from the 3 devices were time-aligned, and the validity of Polar OH1 and Fitbit Charge 3 was assessed against Polar H10 (criterion device). Validity was evaluated using the Bland and Altman analysis, Pearson moment correlation coefficient, and mean absolute percentage error. Results: Overall, there was a very good correlation between the Polar OH1 and Polar H10 devices (r=0.95), with a mean bias of −1 beats{\textperiodcentered}min-1 and limits of agreement of −20 to 19 beats{\textperiodcentered}min-1. The Fitbit Charge 3 device underestimated heart rate by 7 beats{\textperiodcentered}min-1 compared with Polar H10, with a limit of agreement of −46 to 33 beats{\textperiodcentered}min-1 and poor correlation (r=0.8). The mean absolute percentage error for both devices was deemed acceptable (<5%). Polar OH1 performed well across each phase of trial 1; however, validity was worse for trial 2 activities. Fitbit Charge 3 performed well only during rest and nonsprint-based treadmill activities. Conclusions: Compared with our criterion device, Polar OH1 was accurate at assessing heart rate, but the accuracy of Fitbit Charge 3 was generally poor. Polar OH1 performed worse during trial 2 compared with the activities in trial 1, and the validity of the Fitbit Charge 3 device was particularly poor during our cycling exercises.

Comparison of free-living physical activity data obtained from a Fitbit Zip, the Apple iPhone Health app and a modified Bouchard Activity Record (2021)

Health promotion journal of Australia : official journal of Australian Association of Health Promotion Professionals

Abstract

Issue addressed: Physical activity tracking devices have potential to improve public health, but their data needs to be reliable. No study has compared movement data between the Fitbit Zip, Apple iPhone Health app and physical activity records in a community setting over 10 days. Methods: University students aged 18+ years wore both a Fitbit Zip and an iPhone at/near their right waist and completed a modified Bouchard Activity Record (BAR) for 10 days in a free-living setting. Comparisons were made between the Fitbit Zip and iPhone for the number of steps and the distance travelled and between the Fitbit Zip and BAR for the minutes of activity in three different intensities. Results: Eighteen students provided sufficient data for inclusion. There were strong correlations between steps per day (r =.87) and distance travelled (r =.88) between the Fitbit Zips and iPhones. However, the Fitbit Zip measured significantly more steps per day (mean 8437 vs 7303; P ≤.001) and greater distances (mean 5.9 vs 4.9; P ≤.001) than the iPhone. Correlations between the Fitbit Zips and the BARs were moderate for minutes of total (r =.51) and light (r =.40) activity and weak for moderate/fairly active (r =.20) and vigorous/very active (r =.25). Conclusions: There were strong correlations between the physical activity data measured by Fitbit Zips and iPhones, but the iPhone Health app significantly underestimated the number of steps per day taken and the distance travelled when compared to the Fitbit Zip. So what?: Understanding the comparability of accelerometer devices provides useful information for future pragmatic physical activity measurement.

Management of fatigue with physical activity and behavioural change support in vasculitis: a feasibility study (2021)

Rheumatology (Oxford, England)

Abstract

OBJECTIVE: Patients with ANCA associated vasculitis (AAV) experience high levels of fatigue, despite disease remission. This study assessed the feasibility and acceptability of a definitive randomised controlled trial of a behavioural-based physical activity intervention to support fatigue self-management in AAV patients. METHODS: AAV patients in disease remission with fatigue (Multidimensional Fatigue Inventory-20 general fatigue domain ≥) were randomly allocated to intervention or standard care in this single-centre open-label randomised controlled feasibility study. The intervention lasted 12 weeks and comprised eight face-to-face physical activity sessions with a facilitator and 12 weekly telephone calls. Participants were encouraged to monitor their physical activity using a tracker device (Fitbit). Standard care involved sign-posting to fatigue websites. The primary outcome was feasibility of a phase III trial assessed against three stop-go traffic light criteria, (recruitment, intervention adherence and study withdrawal). A qualitative study assessed participant views about the intervention. RESULTS: 248 patients were screened and 134 were eligible to participate (54%). Stop-go criteria were amber for recruitment; 43/134 (32%, 95% CI 24-40) eligible participants randomised, amber for adherence; 73% of participants attended all eight physical activity sessions, but only 11/22 (50%, 95% CI 29-71%) completed the intervention as per the intended schedule, and green for study withdrawal; 2/43 participants withdrew before 24 weeks (5%, 95% CI 0-11). Qualitative results suggested the intervention was acceptable. CONCLUSION: This study suggests a behavioural-based physical activity intervention targeting fatigue self-management was acceptable to patients with AAV, although recruitment and protocol adherence will need modification prior to a definitive trial. Clinical Trial Registration Number ISRCTN11929227.

Effect and feasibility of wearable physical activity trackers and pedometers for increasing physical activity and improving health outcomes in cancer survivors: A systematic review and meta-analysis (2021)

Journal of sport and health science

Abstract

Background: The purpose of this systematic review and meta-analysis was to evaluate the effect of wearable devices for improving physical activity and health-related outcomes in cancer survivors. Methods: CINAHL, Cochrane, Ebscohost, MEDLINE, Pubmed, ProQuest Health and Medical Complete, ProQuest Nursing and Allied Health Source, ScienceDirect, and SPORTDiscus databases were searched for randomized controlled trials published before September 1, 2020, that evaluated interventions involving wearable devices in cancer survivors. Standardized mean differences (SMDs) were calculated to assess effects on physical activity and health-related outcomes. Subgroup analyses were conducted to assess whether the effects differed by intervention and cancer characteristics. Risk of bias was assessed using the Cochrane risk of bias tool. Results: Thirty-five trials were included (breast cancer, n = 15, 43%). Intervention durations ranged between 4 weeks and 1 year. Most trials (n = 25, 71%) involved pedometer-based physical activity interventions. Seven (20%) involved Fitbit-based interventions, and three (9%) involved other wearable physical activity trackers (e.g., Polar, Garmin). Compared to usual care, wearable devices had moderate-to-large effects (SMD range = 0.54−0.87, p < 0.001) on moderate-intensity physical activity, moderate-to-vigorous-intensity physical activity, total physical activity, and daily steps. Compared to usual care, those in the intervention had higher quality of life, aerobic fitness, physical function, and reduced fatigue (SMD range = 0.18−0.66, all p < 0.05). Conclusion: Wearable physical activity trackers and pedometers are effective tools that increase physical activity and improve health-related outcomes in individuals with cancer. Identifying how these devices can be implemented for longer-term use with other intervention components remains an area for future research.

Consumer-based activity trackers in evaluation of physical activity in myositis patients (2021)

Rheumatology (Oxford, England)

Abstract

Abstract Objectives Inflammatory myopathies are characterized by muscle weakness that limit activities of daily living. Daily step count is an accepted metric of physical activity. Wearable technologies such as Fitbit® enable tracking of daily step counts. We assessed psychometric properties of Fitbit® and compared the accuracy of Fitbit® step counts to ActiGraph®. Methods This was a pilot, proof of concept, prospective observational study with 4 visits at 0, 1-, 3-, and 6-months in polymyositis (PM), dermatomyositis (DM), necrotizing myopathy (NM) or anti-synthetase syndrome (AS) subjects. Six core set measures [manual muscle testing, physician (MD-GDA), patient (PT-GDA), and extra-muscular disease activity, HAQ-DI and creatine kinase], 3 functional tests [six-minute walk, timed up-and-go, sit-to-stand tests] and SF-36 physical function-10 (PF10) were collected at each visit. Patients wore waist-worn Fitbit® One and ActiGraph® T3X-BT concurrently for 7 days/month for 6-months. Results Twenty-four (10 DM, 8 PM/NM, 6 AS) patients (17 females/7 males; 91% Caucasian) were enrolled. Test-retest reliability of daily steps was strong in 1-month follow-up (ICC 0.89). Daily steps and peak 1-min cadence showed moderate-strong correlations with MD-GDA, PT-GDA, HAQ-DI, SF-36 PF10 and all three functional tests. Fitbit® and ActiGraph® step counts demonstrated good agreement and strong correlation (ICC 0.96). Conclusion Fitbit® daily steps and peak 1-min cadence are reliable and valid measures of physical activity in a cohort of myositis patients. This pilot data suggests that Fitbit® has a potential for use in clinical practice and trials to monitor physical activity in myositis patients, but larger studies are needed for further validation.

Consumer-Based Activity Trackers as a Tool for Physical Activity Monitoring in Epidemiological Studies During the COVID-19 Pandemic: Development and Usability Study (2021)

JMIR public health and surveillance

Abstract

Background: Consumer-based physical activity trackers have increased in popularity. The widespread use of these devices and the long-term nature of the recorded data provides a valuable source of physical activity data for epidemiological research. The challenges include the large heterogeneity between activity tracker models in terms of available data types, the accuracy of recorded data, and how this data can be shared between different providers and third-party systems. Objective: The aim of this study is to develop a system to record data on physical activity from different providers of consumer-based activity trackers and to examine its usability as a tool for physical activity monitoring in epidemiological research. The longitudinal nature of the data and the concurrent pandemic outbreak allowed us to show how the system can be used for surveillance of physical activity levels before, during, and after a COVID-19 lockdown. Methods: We developed a system (mSpider) for automatic recording of data on physical activity from participants wearing activity trackers from Apple, Fitbit, Garmin, Oura, Polar, Samsung, and Withings, as well as trackers storing data in Google Fit and Apple Health. To test the system throughout development, we recruited 35 volunteers to wear a provided activity tracker from early 2019 and onward. In addition, we recruited 113 participants with privately owned activity trackers worn before, during, and after the COVID-19 lockdown in Norway. We examined monthly changes in the number of steps, minutes of moderate-to-vigorous physical activity, and activity energy expenditure between 2019 and 2020 using bar plots and two-sided paired sample t tests and Wilcoxon signed-rank tests. Results: Compared to March 2019, there was a significant reduction in mean step count and mean activity energy expenditure during the March 2020 lockdown period. The reduction in steps and activity energy expenditure was temporary, and the following monthly comparisons showed no significant change between 2019 and 2020. A small significant increase in moderate-to-vigorous physical activity was observed for several monthly comparisons after the lockdown period and when comparing March-December 2019 with March-December 2020. Conclusions: mSpider is a working prototype currently able to record physical activity data from providers of consumer-based activity trackers. The system was successfully used to examine changes in physical activity levels during the COVID-19 period.

Physical activity in hemodialysis patients on nondialysis and dialysis days: Prospective observational study (2021)

Hemodialysis international. International Symposium on Home Hemodialysis

Abstract

Introduction: The physical decline in patients with end-stage kidney disease (ESKD) is associated with morbidity and mortality. Prior studies have attempted to promote physical activity at the time of dialysis; however, physical activity patterns on the nondialysis days are unknown. This study aimed to quantify physical activity on dialysis and nondialysis days in hemodialysis patients using a wearable actigraph. Methods: In this prospective study, subjects receiving hemodialysis were recruited from two outpatient dialysis units in urban San Diego and rural Imperial County, CA, between March 2018 and April 2019. Key inclusion criteria included: (1) receiving thrice weekly hemodialysis for ≥3 months, (2) age ≥ 18 years, and (3) able to walk with or without assistive devices. All participants wore a Fitbit Charge 2 tracker for a minimum of 4 weeks. The primary outcome was the number of steps per day. Each participant completed the Physical Activity Questionnaire, the Patient Health Questionnaire (PHQ)-9, the PROMIS Short form Fatigue Questionnaire at baseline, and the Participant Technology Experience Questionnaire at day 7 after study enrolment. Findings: Of the 52 recruited, 45 participants (urban = 25; rural = 20) completed the study. The mean age was 61 ± 15 years, 42% were women, 64% were Hispanic, and the mean dialysis vintage was 4.4 ± 3.0 years. For those with valid Fitbit data (defined as ≥10 hours of wear per day) for 28 days (n = 45), participants walked an average of 3688 steps per day, and 73% of participants were sedentary (<5000 steps/day). Participants aged >80 years were less active than younger (age < 65 years) participants (1232 vs. 4529 steps, P = 0.01). There were no statistical differences between the groups when stratified by gender (women vs. men [2817 vs. 4324 steps, respectively]), urbanicity (rural vs. urban dialysis unit [3141 vs. 4123 steps, respectively]), and dialysis/nondialysis day (3177 vs. 4133 steps, respectively). Due to the small sample size, we also calculated effect sizes. The effect size was medium for the gender differences (cohen's d = 0.57) and small to medium for urbanicity and dialysis/nondialysis day (d = 0.37 and d = 0.33, respectively). We found no association between physical activity and self-reported depression and fatigue scale. The majority of participants (62%, 28/45) found the Fitbit tracker easy to wear and comfortable. Discussion: ESKD participants receiving hemodialysis are frequently sedentary, and differences appear more pronounced in older patients. These findings may assist in designing patient-centered interventions to increase physical activity among hemodialysis patients.

Dynamics of sleep, sedentary behavior, and moderate-to-vigorous physical activity on school versus nonschool days (2021)

Sleep

Abstract

STUDY OBJECTIVES: Studies examining time-use activity behaviors (sleep, sedentary behavior, and physical activity) on school days compared with nonschool days have examined these behaviors independently, ignoring their interrelated nature, limiting our ability to optimize the health benefits of these behaviors. This study examines the associations of school-day (vs. nonschool day) with time-use activity behaviors. METHODS: Time series data (6,642 days) from Fitbits (Charge-2) were collected (n = 196, 53% female, 5-10 years). We used a variable-centered dynamic structural equation modeling approach to estimate day-to-day associations of time-use activity behaviors on school days for each child. We then used person-centered cluster analyses to group individuals based on these estimates. RESULTS: Within-participant analysis showed that on school days (vs. nonschool days), children (1) slept less (β = -0.17, 95% CI = -0.21, -0.13), (2) were less sedentary (β = -0.05, 95% CI = -0.09, -0.02), and (3) had comparable moderate-to-vigorous physical activity (MVPA; β = -0.05, 95% CI = -0.11, 0.00). Between-participant analysis showed that, on school days, children with higher sleep carryover experienced greater decreases in sleep (β = 0.44, 95% CI = 0.08, 0.71), children with higher body mass index z-score decreased sedentary behavior more (β = -0.41, 95% CI = -0.64, -0.13), and children with lower MVPA increased MVPA more (β = -0.41, 95% CI -0.64, -0.13). Cluster analysis demonstrated four distinct patterns of connections between time-use activity behaviors and school (High Activity, Sleep Resilient, High Sedentary, and Dysregulated Sleep). CONCLUSIONS: Using a combination of person-centered and more traditional variable-centered approaches, we identified patterns of interrelated behaviors that differed on school, and nonschool days. Findings can inform targeted intervention strategies tailored to children's specific behavior patterns.

Physical activity patterns, adherence to using a wearable activity tracker during a 12-week period and correlation between self-reported function and physical activity in working age individuals with hip and/or knee osteoarthritis (2021)

BMC Musculoskeletal Disorders 2021 22:1

Abstract

A majority of individuals with osteoarthritis (OA) are insufficiently physically active. Self-monitoring with wearable activity trackers (WAT) could promote physical activity (PA), and increased knowledge of PA patterns and adherence to using a WAT is needed. The aim of this study was to describe PA patterns and adherence to WAT-use during an intervention among participants of working age with hip and/or knee OA. The study further explores the correlation between self-reported joint function and PA. Individuals of working age with hip and/or knee OA who used a WAT, Fitbit Flex 2, for 12 weeks were included. Participants monitored their PA in the Fitbit-app. An activity goal of 7,000 steps/day was set. Steps and minutes in light (L), moderate and vigorous (MV) PA were collected from the Fitbit. Self-reported joint function (HOOS/KOOS) was completed. Data was analyzed with linear mixed models and Spearman’s rank correlation. Seventy-five participants (45–66 years) walked on average 10 593 (SD 3431) steps/day, spent 248.5 (SD 42.2) minutes in LPA/day, 48.1 (SD 35.5) minutes in MVPA/day, 336.0 (SD 249.9) minutes in MVPA/week and used the Fitbit for an average of 88.4 % (SD 11.6) of the 12-week period. 86.7 % took > 7,000 steps/day and 77.3 % spent > 150 min in MVPA/week. Mean daily steps/week decreased significantly over the 12 weeks (β-coefficient − 117, 95 % CI -166 to -68, p = < 0.001) as well as mean daily minutes in LPA/week (β-coefficient − 2.3, 95 % CI -3.3 to -1.4, p = < 0.001), mean daily minutes in MVPA/week (β-coefficient − 0.58, 95 % CI -1.01 to -0.16, p = 0.008) and mean adherence to Fitbit-use per week (β-coefficient − 1.3, 95 % CI -1.8 to -0.8, p = < 0.001). There were no significant correlations between function (HOOS/KOOS) and PA. The majority of participants reached 7,000 steps/day and the recommended 150 min in MVPA per week. However, PA decreased slightly but gradually over time. Adherence to using the Fitbit was high but also decreased during the intervention. Understanding PA patterns and the use of a Fitbit to promote PA could be beneficial in tailoring interventions for individuals with hip and/or knee OA.

Applying Mobile Technology to Sustain Physical Activity After Completion of Cardiac Rehabilitation: Acceptability Study (2021)

JMIR human factors

Abstract

Background: Many patients do not meet the recommended levels of physical activity after completing a cardiac rehabilitation (CR) program. Wearable activity trackers and mobile phone apps are promising potential self-management tools for maintaining physical activity after CR completion. Objective: This study aims to evaluate the acceptability of a wearable device, mobile app, and push messages to facilitate physical activity following CR completion. Methods: We used semistructured interviews to assess the acceptability of various mobile technologies after participation in a pilot randomized controlled trial. Intervention patients in the randomized controlled trial wore the Fitbit Charge 2, used the Movn mobile app, and received push messages on cardiovascular disease prevention and physical activity for over 2 months. We asked 26 intervention group participants for feedback about their experience with the technology and conducted semistructured individual interviews with 7 representative participants. We used thematic analysis to create the main themes from individual interviews. Results: Our sample included participants with a mean age of 66.7 (SD 8.6) years; 23% (6/26) were female. Overall, there were varying levels of satisfaction with different technology components. There were 7 participants who completed the satisfaction questionnaires and participated in the interviews. The Fitbit and Movn mobile app received high satisfaction scores of 4.86 and 4.5, respectively, whereas push messages had a score of 3.14 out of 5. We identified four main themes through the interviews: technology use increased motivation to be physically active, technology use served as a reminder to be physically active, recommendations for technology to improve user experience, and desire for personal feedback. Conclusions: By applying a wearable activity tracker, mobile phone app, and push messages, our study showed strong potential for the adoption of new technologies by older adults to maintain physical activity after CR completion. Future research should include a larger sample over a longer period using a mixed methods approach to assess the efficacy of technology use for promoting long-term physical activity behavior in older adults.

A Pilot Randomized Controlled Trial of a Fitbit- and Facebook-Based Physical Activity Intervention for Young Adult Cancer Survivors (2021)

Journal of adolescent and young adult oncology

Abstract

Purpose: Most young adult cancer survivors (YACS) do not meet physical activity (PA) guidelines. Although PA can improve health and quality of life (QOL), few randomized controlled trials (RCTs) of PA interventions for YACS exist. We conducted a pilot RCT to test feasibility of a PA intervention among YACS. Methods: We recruited 18-39-year-olds (≥1 and <5 years postcancer therapy) from Seattle Cancer Care Alliance. The 12-week intervention involved a wrist-worn PA-tracking device (Fitbit), a peer-based Facebook support group, step count goal setting, and a self-selected support "buddy." Controls received Fitbit only. Baseline assessments occurred before randomization; follow-up assessments occurred during intervention weeks 10-12. Feasibility criteria are listed below. Exploratory outcomes included PA, sedentary time (ST), QOL measures (e.g., fatigue), and self-determination theory (SDT) construct measures. Results: All feasibility criteria were met: We recruited 50 YACS, intervention participants wore the Fitbit on the majority of intervention days (82.9%), ≥75% of participants completed questionnaires at baseline (100%) and follow-up (93.9%). Exploratory analyses, adjusted for wave, accelerometer wear time, race, and income, showed significant group differences for change in ST (-52.4 vs. 2.5 minutes/day; p = 0.002) but no change in moderate-to-vigorous intensity PA (0.0 vs. -0.2 minutes/day; p = 0.40), comparing intervention participants to controls. The intervention (vs. control) group had a greater increase in fatigue interference (p = 0.03). No other significant differences in SDT or QOL measures were found. Conclusion: This Fitbit and Facebook-based PA intervention was feasible to YACS, with promising effects on reducing ST, and warrants a fully powered RCT. Clinical Trial Registration no.: NCT03233581.

Greater time in bed and less physical activity associate with poorer cognitive functioning performance in Huntington's disease (2021)

Neuropsychology

Abstract

Objective: This study aimed to investigate how sleep and physical activity habits related to cognitive functioning, in naturalistic settings, in early Huntington's disease (HD). Method: Forty-two participants with the expanded HD repeat (20 manifest, 22 premanifest) and 29 healthy controls wore Fitbit One sleep and activity monitors for 7 days and 7 nights. They used a smartphone application to complete daily sleep and activity diaries, sleep and mood inventories, and a brief battery of cognitive tests, which were completed on Day 8 of the study. All data were collected in naturalistic home and community settings. Results: Amongst participants with the expanded HD repeat, greater time spent in bed, measured by Fitbit, was associated with poorer accuracy and response speed on a test of visual memory, whereas lower levels of physical activity, measured by Fitbit, were associated with poorer accuracy on a test involving a working memory component. Neither time in bed nor physical activity is associated with a test of psychomotor speed. Groups were mostly similar across a range of Fitbit and self-report measures of sleep and physical activity, although the Manifest-HD group spent more time in bed than the Premanifest-HD and Healthy Control groups and had better self-reported sleep quality and more self-reported time spent sitting than the Healthy Control group and the Premanifest-HD group, respectively. Conclusions: Sleep timing and physical activity relate to cognitive functioning in HD and may be important targets for management in behavioral intervention studies aimed at improving cognition in HD. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Targeting self-control as a behavior change mechanism to increase physical activity: Study protocol of a randomized controlled trial (2021)

Contemporary clinical trials

Abstract

Despite the highly publicized beneficial effects of physical activity, 51.1% of middle-aged US adults do not achieve the recommended minimum of aerobic physical activity needed to maintain health. A sedentary lifestyle can be attributed in part to a lack of self-control and there is some evidence that self-control strategies can be improved with targeted interventions. The overall aim of this study is to test self-control as a behavior change mechanism for physical activity and to investigate whether a smartphone-based self-control intervention can increase physical activity among sedentary middle-aged adults. This protocol describes the design of a randomized controlled trial with two experimental conditions: The self-control treatment group and the control group. Both groups track their daily physical activity using a Fitbit step counter for eight weeks. Additionally, the self-control intervention group receives a 7-week smartphone-based self-control intervention to learn strategies how to potentiate desirable impulses or weaken undesirable ones. It is expected that the self-control treatment group will show greater increases in physical activity and that changes last longer compared to the control group. All participants will be assessed at pretest (baseline), at the end of each week (weeks 1–7), at posttest (week 8), and at follow-up (week 12). If this self-control intervention proves effective, this digital approach would represent a low-threshold and cost-effective approach to increasing physical activity. Such an intervention could be delivered to a large number of people to improve their health outcomes in the long run. Trial Registration: ClinicalTrials.gov: NCT04522141

Effect of Wearable Technology-Based Physical Activity Interventions on Breast Cancer Survivors' Physiological, Cognitive, and Emotional Outcomes: A Systematic Review (2021)

Journal of Clinical Medicine

Abstract

This systematic review synthesized all randomized controlled trials (RCTs) and controlled trials examining the effects of wearable health technology-based physical activity interventions on physiological, cognitive, and emotional outcomes in breast cancer survivors (BCS). We searched NCBI, Academic Search Premier, EMBASE, Web of Science, PubMed, and Medline from inception to March 2021. We included studies which: (1) were RCTs or controlled trials ≥8 weeks in duration; (2) were peer-reviewed and published in English; (3) sampled BCS in full remission and had not received treatment for at least six months; (4) utilized wearable health technology (e.g., Fitbit, Garmin xGC30); and (5) examined physiological, emotional, and/or cognitive outcomes. Sixty-six studies were identified and 14 were included in the review. Most of the observed effects were statistically significant and those which employed multi-component interventions generally yielded greater effects. Overall, the use of wearable health technology reduced sedentary behavior and increased moderate-to-vigorous intensity physical activity. Further, increased moderate-to-vigorous intensity physical activity was observed to be associated with increased perceived cognition and higher cognitive performance. Multiple studies also observed significant improvements in attitude, worry, and anxiety. Overall, findings suggested wearable health technology-based physical activity interventions to be effective for improving physical activity, attitude, and cognitive functions and for reducing sedentary behavior, anxiety, and worry in BCS.

A Mind-Body Physical Activity Program for Chronic Pain With or Without a Digital Monitoring Device: Proof-of-Concept Feasibility Randomized Controlled Trial (2020)

JMIR formative research

Abstract

Background: Chronic pain is associated with poor physical and emotional functioning. Nonpharmacological interventions can help, but improvements are small and not sustained. Previous clinical trials do not follow recommendations to comprehensively target objectively measured and performance-based physical function in addition to self-reported physical function. Objective: This study aimed to establish feasibility benchmarks and explore improvements in physical (self-reported, performance based, and objectively measured) and emotional function, pain outcomes, and coping through a pilot randomized controlled trial of a mind-body physical activity program (GetActive) with and without a digital monitoring device (GetActive-Fitbit), which were iteratively refined through mixed methods. Methods: Patients with chronic pain were randomized to the GetActive (n=41) or GetActive-Fitbit (n=41) programs, which combine relaxation, cognitive behavioral, and physical restoration skills and were delivered in person. They completed in-person assessments before and after the intervention. Performance-based function was assessed with the 6-min walk test, and step count was measured with an ActiGraph. Results: Feasibility benchmarks (eg, recruitment, acceptability, credibility, therapist adherence, adherence to practice at home, ActiGraph wear, and client satisfaction) were good to excellent and similar in both programs. Within each program, we observed improvement in the 6-min walk test (mean increase=+41 m, SD 41.15; P<.001; effect size of 0.99 SD units for the GetActive group and mean increase=+50 m, SD 58.63; P<.001; effect size of 0.85 SD units for the GetActive-Fitbit group) and self-reported physical function (P=.001; effect size of 0.62 SD units for the GetActive group and P=.02; effect size of 0.38 SD units for the GetActive-Fitbit group). The mean step count increased only among sedentary patients (mean increase=+874 steps for the GetActive group and +867 steps for the GetActive-Fitbit group). Emotional function, pain intensity, pain coping, and mindfulness also improved in both groups. Participants rated themselves as much improved at the end of the program, and those in the GetActive-Fitbit group noted that Fitbit greatly helped with increasing their activity. Conclusions: These preliminary findings support a fully powered efficacy trial of the two programs against an education control group. We present a model for successfully using the Initiative on the Methods, Measurement, and Pain Assessment in Clinical Trials criteria for a comprehensive assessment of physical function and following evidence-based models to maximize feasibility before formal efficacy testing.

A Mental Health-Informed Physical Activity Intervention for First Responders and Their Partners Delivered Using Facebook: Mixed Methods Pilot Study (2021)

JMIR formative research

Abstract

Background: First responders (eg, police, firefighters, and paramedics) are at high risk of experiencing poor mental health. Physical activity interventions can help reduce symptoms and improve mental health in this group. More research is needed to evaluate accessible, low-cost methods of delivering programs. Social media may be a potential platform for delivering group-based physical activity interventions. Objective: This study aims to examine the feasibility and acceptability of delivering a mental health–informed physical activity program for first responders and their self-nominated support partners. This study also aims to assess the feasibility of applying a novel multiple time series design and to explore the impact of the intervention on mental health symptoms, sleep quality, quality of life, and physical activity levels. Methods: We co-designed a 10-week web-based physical activity program delivered via a private Facebook group. We provided education and motivation around different topics weekly (eg, goal setting, overcoming barriers to exercise, and reducing sedentary behavior) and provided participants with a Fitbit. A multiple time series design was applied to assess psychological distress levels, with participants acting as their own control before the intervention. Results: In total, 24 participants (12 first responders and 12 nominated support partners) were recruited, and 21 (88%) completed the postassessment questionnaires. High acceptability was observed in the qualitative interviews. Exploratory analyses revealed significant reductions in psychological distress during the intervention. Preintervention and postintervention analysis showed significant improvements in quality of life (P=.001; Cohen d=0.60); total depression, anxiety, and stress scores (P=.047; Cohen d=0.35); and minutes of walking (P=.04; Cohen d=0.55). Changes in perceived social support from family (P=.07; Cohen d=0.37), friends (P=.10; Cohen d=0.38), and sleep quality (P=.28; Cohen d=0.19) were not significant. Conclusions: The results provide preliminary support for the use of social media and a multiple time series design to deliver mental health–informed physical activity interventions for first responders and their support partners. Therefore, an adequately powered trial is required.

The Feasibility and Acceptability of the Developing Real Incentives and Volition for Exercise (DRIVE) Program: A Pilot Study for Promoting Physical Activity in African American Women (2021)

Health promotion practice

Abstract

Background. The purpose of the current article is to demonstrate how formative process evaluation was used in a pilot study to optimize the design and implementation of two motivationally targeted community-based physical activity (PA) interventions for inactive African American women. Method. Fifteen African American women (M age: 41.6 years) were randomized to a challenge-focused program targeted toward high autonomous motivation or a rewards-focused program targeted toward low autonomous motivation. The challenge-focused program targeted enjoyment and valuation of PA and a team-based positive social climate through competitive intergroup activities and team-based goals, whereas the rewards-focused program targeted PA interest, competency, and partner-based social support through a walking program, individual-based goals with financial incentives, and partner-based action-plans. Results. Feedback from participants revealed high levels of acceptability of essential elements. Average weekly attendance exceeded the a priori goal of ≥75% of members in attendance each week. External systematic observation demonstrated that session content dose was ≥93% in both programs. Facilitator-level fidelity exceeded the a priori goal of averaging ≥3 on a 4-point scale for behavioral skills, communication, autonomy support, and session content. The process evaluation also revealed areas for improvement, including facilitator-level social support and behavioral skills at the group-level. Process data collected through FitBits revealed that participants were engaged in self-monitoring PA during the 6-week programs. Conclusions. The formative process evaluation demonstrated adequate levels of feasibility and acceptability and also provided key insights into adjustments needed before proceeding with implementing the motivationally targeted group-based programs in a larger randomized study.

A Feasibility Study of a Peer-Facilitated Physical Activity Intervention in Methadone Maintenance (2021)

Mental health and physical activity

Abstract

Purpose: While methadone maintenance treatment (MMT) has been effective in improving opioid use outcomes, most patients continue to engage in unhealthy lifestyles that lead to significant mental and physical health consequences. Interventions targeting increases in physical activity (PA) in MMT patients could have a significant impact on reducing the overall morbidity in these individuals. The purpose of this study was to assess acceptability and feasibility of a 12-week peer-facilitated PA intervention for MMT patients called TREC (Transforming Recovery with Exercise and Community). Method: We developed and then pilot-tested TREC in 26 low-active MMT clients (73% female; mean age = 41.2 years). TREC included: 1) an orientation session and intervention materials, 2) weekly PA discussion groups led by trained MMT clients, 3) peer-led walking groups and 4) a Fitbit activity tracker to facilitate self-monitoring of PA. Results: Participants attended 63% of eligible TREC sessions. Sixty-nine percent of the sample wore the Fitbit for at least 6 weeks (of the 12-week intervention). Participants reported that they enjoyed the group walks and that it was helpful to have a peer-facilitated PA group. There were small-to-moderate effect sizes for increases in PA, positive affect, and benefits of PA, and decreases in illicit opioid use and barriers to PA. No changes in depression, anxiety, and negative affect were observed from baseline to the end of the 12-week intervention. Conclusion: Indicators of feasibility and acceptability suggest that a peer-facilitated PA intervention can be incorporated in the context of MMT. Low active, opioid dependent clients showed increases in PA during the 12-week intervention. A future randomized clinical trial is necessary to determine the efficacy of TREC on long-term maintenance of PA and ancillary mental health and substance use outcomes.

Feasibility of a behavioral intervention using mobile health applications to reduce cardiovascular risk factors in cancer survivors: a pilot randomized controlled trial (2021)

Journal of cancer survivorship : research and practice

Abstract

Purpose: Determine the feasibility of a remotely delivered mobile health (mHealth)-supported intervention to improve diet and physical activity in hematologic malignancy survivors. Methods: Pilot randomized controlled trial of a 16-week intervention for improving diet and physical activity: individualized goal-setting (daily steps, sodium, saturated fat, added sugar intake) per feedback from mHealth trackers (Fitbit for activity; Healthwatch360 for diet), supplemented by a Facebook peer support group. Controls accessed the trackers without goal-setting or peer support. Everyone received standardized survivorship counseling with tailored advice from a clinician. Actigraphy and food frequency questionnaires assessed activity and diet at baseline and follow-up. Results: Forty-one participants (51.2% male; median age 45.1 years; 7.0 years from treatment) were randomized (24 intervention; 17 control). Fitbit and Healthwatch360 use were more common among intervention versus control participants (75.0% versus 70.6% and 50.0% versus 17.7% of eligible days, respectively). Most intervention participants (66.7%) engaged with Facebook; overall, 91.7% interacted with the study's mHealth applications. While no comparisons in activity or dietary outcomes between intervention versus control group met statistical significance, the intervention was associated with greater reductions in the targeted dietary factors and improvements in Healthy Eating Index-2015 score, moderate-vigorous physical activity time, and daily steps. Participant retention at 6 months was 90.2%. Conclusions: An intervention for cardiovascular risk reduction based on individualized goal-setting enhanced by mHealth and social media peer support was feasible and acceptable among cancer survivors. Implications for Cancer Survivors: Effective and easily disseminated strategies that improve diet and physical activity in this population are needed. Trial registration: Registered in ClinicalTrials.gov (NCT03574012) on June 29, 2018.

A qualitative study on participants' experiences with a community-based mindful walking intervention and mobile device activity measurement (2021)

Complementary therapies in medicine

Abstract

Background: Mindful walking (MW) interventions employ mindfulness training combined with physical activity. Wearable mobile devices have been increasingly used to measure outcomes of physical activity interventions. The purpose of this study was to understand MW participants' attitudes towards MW and the use of mobile devices in health promotion interventions, including barriers and facilitators of intervention engagement and adherence. Few qualitative studies have documented participant experience with these two types of interventions. Method: The pilot study involved a randomized MW intervention including 38 participants with self-reported inadequate physical activity. Half of them were randomized to receive MW intervention plus a FitBit device and the other received the FitBit device only. We used a qualitative thematic analysis of the narrative data collected through open-ended survey questions at three time points. Participants in the MW intervention were asked to describe their experiences with MW, while all participants were asked to describe their experience with wearing the FitBit to track their step counts. Results: Participants reported a broad range of perceived benefits and challenges related to adopting the MW intervention and using the mobile device. Participants were generally willing to try to adopt the recommended MW practice and to see value of MW in increasing physical activity and improving overall health. Participants reported using a variety of additional device features beyond goal setting and step counts, indicating using the devices may have been effective in providing additional motivation for participants in meeting physical activity goals in both the control and intervention groups. While most of the feedback about MW (in the intervention group) and the device (all participants) was overwhelmingly positive, a minority of participants reported barriers such as lack of patience with meditation and discomfort with wearing the device. Conclusion: Most participants in the MW intervention see the health benefits of this program and most participants using the wearable physical activity tracking device reported the motivational benefits of this device. Issues with the MW intervention (e.g., lack of patience) and the wearable device (e.g., discomfort with wearing) need to be addressed in future interventions.

Measuring Daily Compliance With Physical Activity Tracking in Ambulatory Surgery Patients: Comparative Analysis of Five Compliance Criteria (2021)

JMIR mHealth and uHealth

Abstract

Background: Physical activity trackers such as the Fitbit can allow clinicians to monitor the recovery of their patients following surgery. An important issue when analyzing activity tracker data is to determine patients' daily compliance with wearing their assigned device, using an appropriate criterion to determine a valid day of wear. However, it is currently unclear as to how different criteria can affect the reported compliance of patients recovering from ambulatory surgery. Investigating this issue can help to inform the use of activity data by revealing factors that may impact compliance calculations. Objective: This study aimed to understand how using different criteria can affect the reported compliance with activity tracking in ambulatory surgery patients. It also aimed to investigate factors that explain variation between the outcomes of different compliance criteria. Methods: A total of 62 patients who were scheduled to undergo total knee arthroplasty (TKA, ie, knee replacement) volunteered to wear a commercial Fitbit Zip activity tracker over an 8-week perioperative period. Patients were asked to wear the Fitbit Zip daily, beginning 2 weeks prior to their surgery and ending 6 weeks after surgery. Of the 62 patients who enrolled in the study, 20 provided Fitbit data and underwent successful surgery. The Fitbit data were analyzed using 5 different daily compliance criteria, which consider patients as compliant with daily tracking if they either register >0 steps in a day, register >500 steps in a day, register at least one step in 10 different hours of the day, register >0 steps in 3 distinct time windows, or register >0 steps in 3 out of 4 six-hour time windows. The criteria were compared in terms of compliance outcomes produced for each patient. Data were explored using heatmaps and line graphs. Linear mixed models were used to identify factors that lead to variation between compliance outcomes across the sample. Results: The 5 compliance criteria produce different outcomes when applied to the patients' data, with an average 24% difference in reported compliance between the most lenient and strictest criteria. However, the extent to which each patient's reported compliance was impacted by different criteria was not uniform. Some individuals were relatively unaffected, whereas others varied by up to 72%. Wearing the activity tracker as a clip-on device, rather than on the wrist, was associated with greater differences between compliance outcomes at the individual level (P=.004, r=.616). This effect was statistically significant (P<.001) in the first 2 weeks after surgery. There was also a small but significant main effect of age on compliance in the first 2 weeks after surgery (P=.040). Gender and BMI were not associated with differences in individual compliance outcomes. Finally, the analysis revealed that surgery has an impact on patients' compliance, with noticeable reductions in activity following surgery. These reductions affect compliance calculations by discarding greater amounts of data under strict criteria. Conclusions: This study suggests that different compliance criteria cannot be used interchangeably to analyze activity data provided by TKA patients. Surgery leads to a temporary reduction in patients' mobility, which affects their reported compliance when strict thresholds are used. Reductions in mobility suggest that the use of lenient compliance criteria, such as >0 steps or windowed approaches, can avoid unnecessary data exclusion over the perioperative period. Encouraging patients to wear the device at their wrist may improve data quality by increasing the likelihood of patients wearing their tracker and ensuring that activity is registered in the 2 weeks after surgery.

The Feasibility of a Lifestyle Physical Activity Intervention for Black Women with Asthma (2021)

The journal of allergy and clinical immunology. In practice

Abstract

Background: Black women are disproportionately affected by both physical inactivity and asthma. Lifestyle physical activity (PA) interventions targeted for Black women with asthma are lacking. Objective: To assess the feasibility and acceptability as well as preliminary effects of a lifestyle PA intervention culturally tailored for Black women with asthma. Methods: Black women (age 18-70 years) with uncontrolled asthma (Asthma Control Test <20) were recruited. Outcome assessments at baseline and 24 weeks included measures of: feasibility and acceptability, asthma control, quality of life, health care use, and PA levels. Participants were randomized to the intervention (asthma education, Fitbit, monthly group sessions, text messages, individual step goals, and study manual) or enhanced usual care (EUC) (asthma education plus Fitbit) group. Results: Of the 53 women randomized (EUC = 28; intervention = 25), 92% remained in the intervention (23 of 25) and 76% completing the 24-week outcome assessment. Overall intervention satisfaction (mean score, 6.88 of 7) and individual components were high at 24 weeks. Mean change in asthma control questionnaire between groups was not significant at 24 weeks (intervention = –0.41 vs EUC = 0.03 [P = .08]; effect size = –0.38) but approached clinical significance (0.5). At 24 weeks, more women receiving the intervention had controlled asthma compared with EUC (36.84% vs 9.52%; P = .04). Clinically significant improvements (0.5) in quality of life were found in the intervention group (mean change: intervention = 0.58 vs EUC = 0.10; P = .10) at 24 weeks. Conclusions: A culturally tailored lifestyle PA intervention is feasible and demonstrates improvements in asthma control and quality of life among Black women with asthma. These preliminary findings support the need for PA lifestyle interventions in urban Black women with asthma.

Rates, patterns, and correlates of fitness tracker use among older adults with multiple sclerosis (2021)

Disability and health journal

Abstract

Background: Older adults with multiple sclerosis (MS) engage in alarmingly low levels of physical activity. Fitness trackers may be a promising approach for promoting and monitoring physical activity among older adults with MS. Objective/Hypothesis: This study reports on the rates, patterns of fitness tracker use in adults with MS who are over 60 years of age. We hypothesized that older adults with MS who use fitness trackers “users” would report significantly more physical activity than those who don't “non-users.” Methods: Participants across the United States completed an online survey that included self-reported demographic and clinical characteristics, fitness tracker use questionnaire, and Godin Leisure-Time Exercise Questionnaire (GLTEQ) for measuring total and health-promoting physical activity (GLTEQ-HCS). Results: Of the 440 participants who completed the full survey, 112 (28%) identified as fitness tracker users. The most common activity monitors were Fitbit{\textregistered}, Smartphone app, Apple{\textregistered} watch, and Garmin{\textregistered}. Fitness tracker users mostly reported having relapsing-remitting MS, less disability (i.e., lower Patient Determined Disability Steps), higher income, and higher rates of employment. There was a statistically significant difference in GLTEQ Total (t(438) = −3.8, p = .001) and GLTEQ-HCS (t(438) = −2.8, p = .006) scores between fitness tracker users and non-users. Self-reported step counts were strongly correlated with both GLTEQ Total ($\rho$ = .50) and GLTEQ-HCS ($\rho$ = 0.54) scores in fitness tracker users. Conclusions: Further research is warranted investigating fitness tracker use and interests among older adults with MS and how technology may be applied as a behavioral tool to increase physical activity among this growing portion of the MS population.

The relationship between sleep and physical activity in an in-patient rehabilitation stroke setting: a cross-sectional study (2021)

Topics in stroke rehabilitation

Abstract

Background: Appropriate sleep and physical activity are known to be important for positive neuroplastic changes in the brain and therefore may affect stroke recovery. Objective: To investigate the relationship between sleep and participation in different intensity levels of physical activity; to investigate the convergent validity of a commercially available device (Fitbit Flex) in measuring sleep and physical activity in people with stroke. Methods: A cross-sectional observational study in people with stroke undergoing rehabilitation. Participants wore two accelerometers on their unaffected wrist for seven consecutive days and recorded a sleep log, the Pittsburgh Sleep Quality Index and Fatigue Assessment Scale. Any relationships between sleep and activity were assessed with linear regression. Pearson and intra-class correlation coefficients were used to assess the validity of the two accelerometers (Fitbit Flex against the validated GENEActiv). Results: Twenty-three patients with stroke were recruited. Twenty complete data sets were analyzed. Participants had approximately 9 hours of sleep and 13 hours of sedentary behavior per day, with 99 minutes spent on physical activity (16 min spent on moderate to vigorous activity). Time spent on sleep was significantly related to sedentary, light and moderate physical activity time (r = −.67,.22,.20). The ICC of the Fitbit Flex in measuring light physical activity was.884 p < .001 but was not correlated for other measures. Conclusions: More sleep was related to less sedentary behavior and more time spent on physical activity. Fitbit Flex was only valid for measuring light physical activity.

Using an activity tracker to increase motivation for physical activity in patients with type 2 diabetes in primary care: a randomized pilot trial (2021)

mHealth

Abstract

Background: Adopting healthy lifestyle habits reduces the risk of type 2 diabetes (T2D) and its complications. The use of an activity tracker to monitor physical activity (PA) could favor behavior changes in patients with chronic diseases such as diabetes. The aims of this study were: (I) to evaluate the impact of an activity tracker on PA and cardiometabolic risk variables in patients with T2D; (II) to assess the feasibility of its implantation in a primary care setting. Methods: This 3-month study was a pilot randomized controlled trial of 30 patients with T2D followed at a university-affiliated Family Medicine Group. Patients were randomly assigned to either: (I) control group, including a PA promotion intervention supported by a kinesiologist or (II) intervention group, including a PA promotion intervention supported by a kinesiologist with the addition of an activity tracker (Fitbit). Cardiometabolic risk variables, PA and motivation were assessed at baseline and after three months. Satisfaction and acceptability of wearing the activity tracker were measured in the intervention group. Results: PA assessed by questionnaires increased in both groups, change being greater in the intervention group (P<0.05). Autonomous motivation in both groups was higher than controlled motivation (P<0.001). Eighty-six percent of the participants in the intervention group were satisfied with their activity tracker use and the compliance remained high. High-density lipoprotein cholesterol increased in the intervention group and decreased in the control group (P=0.014). Resting systolic and diastolic blood pressure decreased over time in both groups (P<0.05) whereas glycated hemoglobin tended to decrease in both groups (P=0.080). Significant correlations were observed between average steps per day and changes in waist circumference (pre: -0.721, P=0.044; post: -0.736, P=0.038), body mass index (pre: -0.764, P=0.010; post: -0.771, P=0.009) and fat percentage (pre: -0.654, P=0.040; post: -0.686, P=0.028) in the intervention group. Conclusions: Our pilot study shows that the use of an activity tracker improves cardiometabolic risk variables in patients with T2D and could potentially be a motivation tool to increase PA in primary care setting.

Feasibility, Usability, and Effectiveness of a Machine Learning-Based Physical Activity Chatbot: Quasi-Experimental Study (2021)

JMIR mHealth and uHealth

Abstract

Background: Behavioral eHealth and mobile health interventions have been moderately successful in increasing physical activity, although opportunities for further improvement remain to be discussed. Chatbots equipped with natural language processing can interact and engage with users and help continuously monitor physical activity by using data from wearable sensors and smartphones. However, a limited number of studies have evaluated the effectiveness of chatbot interventions on physical activity. Objective: This study aims to investigate the feasibility, usability, and effectiveness of a machine learning-based physical activity chatbot. Methods: A quasi-experimental design without a control group was conducted with outcomes evaluated at baseline and 6 weeks. Participants wore a Fitbit Flex 1 (Fitbit LLC) and connected to the chatbot via the Messenger app. The chatbot provided daily updates on the physical activity level for self-monitoring, sent out daily motivational messages in relation to goal achievement, and automatically adjusted the daily goals based on physical activity levels in the last 7 days. When requested by the participants, the chatbot also provided sources of information on the benefits of physical activity, sent general motivational messages, and checked participants' activity history (ie, the step counts/min that were achieved on any day). Information about usability and acceptability was self-reported. The main outcomes were daily step counts recorded by the Fitbit and self-reported physical activity. Results: Among 116 participants, 95 (81.9%) were female, 85 (73.3%) were in a relationship, 101 (87.1%) were White, and 82 (70.7%) were full-time workers. Their average age was 49.1 (SD 9.3) years with an average BMI of 32.5 (SD 8.0) kg/m2. Most experienced technical issues were due to an unexpected change in Facebook policy (93/113, 82.3%). Most of the participants scored the usability of the chatbot (101/113, 89.4%) and the Fitbit (99/113, 87.6%) as at least “OK.” About one-third (40/113, 35.4%) would continue to use the chatbot in the future, and 53.1% (60/113) agreed that the chatbot helped them become more active. On average, 6.7 (SD 7.0) messages/week were sent to the chatbot and 5.1 (SD 7.4) min/day were spent using the chatbot. At follow-up, participants recorded more steps (increase of 627, 95% CI 219-1035 steps/day) and total physical activity (increase of 154.2 min/week; 3.58 times higher at follow-up; 95% CI 2.28-5.63). Participants were also more likely to meet the physical activity guidelines (odds ratio 6.37, 95% CI 3.31-12.27) at follow-up. Conclusions: The machine learning-based physical activity chatbot was able to significantly increase participants' physical activity and was moderately accepted by the participants. However, the Facebook policy change undermined the chatbot functionality and indicated the need to use independent platforms for chatbot deployment to ensure successful delivery of this type of intervention.

The Effect of Aerobic Training and Increasing Nonexercise Physical Activity on Cardiometabolic Risk Factors (2021)

Medicine and science in sports and exercise

Abstract

PURPOSE: Epidemiological studies suggest that sedentary behavior is an independent risk factor for cardiovascular mortality independent of meeting physical activity guidelines. However, limited evidence of this relationship is available from prospective interventions. The purpose of the present study is to evaluate the combined effect of aerobic training and increasing nonexercise physical activity on body composition and cardiometabolic risk factors. METHODS: Obese adults (N = 45) were randomized to 6 months of aerobic training (AERO), aerobic training and increasing nonexercise physical activity ($\sim$3000 steps above baseline levels; AERO-PA), or a control (CON) group. The AERO and AERO-PA groups performed supervised aerobic training (3-4 times per week). The AERO-PA group wore Fitbit One accelerometers and received behavioral coaching to increase nonexercise physical activity. RESULTS: There was a larger increase in fitness in the AERO-PA group (0.27 L{\textperiodcentered}min-1; confidence interval (CI), 0.16 to 0.40 L{\textperiodcentered}min-1) compared with the AERO group (0.09 L{\textperiodcentered}min-1; CI, -0.04 to 0.22 L{\textperiodcentered}min-1) and the CON group (0.01; CI, -0.11 to 0.12 L{\textperiodcentered}min-1). Although significant findings were not observed in the entire study sample, when the analysis was restricted to participants compliant to the intervention (n = 33), we observed significant reductions in waist circumference, percent weight loss, body fat, 2-h glucose, and 2-h insulin in comparison to the CON group (P < 0.05), but not the AERO group. Furthermore, linear regression models showed that change in steps was associated with 21% and 26% of the variation in percent weight loss and percent fat loss, respectively. CONCLUSIONS: Increasing nonexercise physical activity with aerobic training may represent a viable strategy to augment the fitness response in comparison to aerobic training alone and has promise for other health indicators.

Validity of Wrist-Wearable Activity Devices for Estimating Physical Activity in Adolescents: Comparative Study (2021)

JMIR mHealth and uHealth

Abstract

Background: The rapid advancements in science and technology of wrist-wearable activity devices offer considerable potential for clinical applications. Self-monitoring of physical activity (PA) with activity devices is helpful to improve the PA levels of adolescents. However, knowing the accuracy of activity devices in adolescents is necessary to identify current levels of PA and assess the effectiveness of intervention programs designed to increase PA. Objective: The study aimed to determine the validity of the 11 commercially available wrist-wearable activity devices for monitoring total steps and total 24-hour total energy expenditure (TEE) in healthy adolescents under simulated free-living conditions. Methods: Nineteen (10 male and 9 female) participants aged 14 to 18 years performed a 24-hour activity cycle in a metabolic chamber. Each participant simultaneously wore 11 commercial wrist-wearable activity devices (Mi Band 2 [XiaoMi], B2 [Huawei], Bong 2s [Meizu], Amazfit [Huamei], Flex [Fitbit], UP3 [Jawbone], Shine 2 [Misfit], GOLiFE Care-X [GoYourLife], Pulse O2 [Withings], Vivofit [Garmin], and Loop [Polar Electro]) and one research-based triaxial accelerometer (GT3X+ [ActiGraph]). Criterion measures were total EE from the metabolic chamber (mcTEE) and total steps from the GT3X+ (AGsteps). Results: Pearson correlation coefficients r for 24-hour TEE ranged from.78 (Shine 2, Amazfit) to.96 (Loop) and for steps ranged from 0.20 (GOLiFE) to 0.57 (Vivofit). Mean absolute percent error (MAPE) for TEE ranged from 5.7% (Mi Band 2) to 26.4% (Amazfit) and for steps ranged from 14.2% (Bong 2s) to 27.6% (Loop). TEE estimates from the Mi Band 2, UP3, Vivofit, and Bong 2s were equivalent to mcTEE. Total steps from the Bong 2s were equivalent to AGsteps. Conclusions: Overall, the Bong 2s had the best accuracy for estimating TEE and total steps under simulated free-living conditions. Further research is needed to examine the validity of these devices in different types of physical activities under real-world conditions.

BAILAMOS With mHealth Technology! Improving Physical Activity and Well-Being in Middle-Aged and Older Latinxs: A Pre-Post Feasibility Study (2021)

Health education & behavior : the official publication of the Society for Public Health Education

Abstract

Older Latinxs engage in lower levels of leisure-time physical activity (PA) compared with non-Latinx Whites. Latin dance is a culturally relevant type of leisure-time PA that may engage older Latinx populations, particularly when coupled with mobile health technologies (mHealth). This single group pre–post feasibility study described the PA and health outcomes of middle-aged and older Latinxs participating in BAILA TECH—an intervention that combines the BAILAMOS Latin dance program with mHealth (Fitbit Charge 2, Fitbit app, and motivational text messages). Participants (n = 20, Mage = 67 ± 7.1, female n = 15, 75%) were enrolled in the 16-week BAILA TECH intervention held twice a week. Participants received a Fitbit Charge 2 to assess PA at baseline, during the intervention, and postintervention. An mHealth platform (iCardia) collected Fitbit data and staff delivered weekly motivational and informational text messages. Participants completed questionnaires about PA, sedentary behavior, cardiorespiratory fitness, social support, quality of life, and cognitive function at baseline and postintervention. Paired t tests evaluated change in pre–post measures. There was a significant increase in device-assessed moderate-to-vigorous PA (d = 0.69), self-reported light-leisure (d = 1.91) and moderate-to-vigorous PA (d = 1.05), moderate-to-vigorous leisure PA (d = 1.55), predicted cardiorespiratory fitness (d = 1.10), and PA social support (d = 0.81 [family]; d = 0.95 [friends]) from baseline to postintervention. Although nonsignificant, there was a small effect on physical health–related quality of life (d = 0.32) and executive function (d = 0.29). These data describe an increase in PA levels and health-related outcomes of middle-aged and older Latinxs from participation in an mHealth-infused Latin dance intervention. An adequately powered trial is necessary to establish efficacy.

Effectiveness of Distance Technology in Promoting Physical Activity in Cardiovascular Disease Rehabilitation: Cluster Randomized Controlled Trial, A Pilot Study (2021)

JMIR rehabilitation and assistive technologies

Abstract

Background: Physical activity is beneficial for cardiovascular rehabilitation. Digitalization suggests using technology in the promotion of physical activity and lifestyle changes. The effectiveness of distance technology interventions has previously been found to be similar to that of conventional treatment, but the added value of the technology has not been frequently studied. Objective: The aim of this pilot study was to investigate whether additional distance technology intervention is more effective in promoting physical activity than non-technology-based treatment in 12 months of cardiac rehabilitation. Methods: The cardiovascular disease rehabilitation intervention consisted of three 5-day inpatient periods in a rehabilitation center and two 6-month self-exercise periods at home in between. Participants were recruited from among cardiac patients who attended the rehabilitation program and were cluster-randomized into unblinded groups: conventional rehabilitation control clusters (n=3) and similar rehabilitation with additional distance technology experimental group clusters (n=3). Experimental groups used Fitbit Charge HR for self-monitoring, and they set goals and reported their activity using Movendos mCoach, through which they received monthly automated and in-person feedback. Physical activity outcomes for all participants were measured using the Fitbit Zip accelerometer and the International Physical Activity Questionnaire. Results: During the first 6 months, the experimental group (n=29) engaged in light physical activity more often than the control group (n=30; mean difference [MD] 324.2 minutes per week, 95% CI 77.4 to 571.0; P=.01). There were no group differences in the duration of moderate to vigorous physical activity (MD 12.6 minutes per week, 95% CI -90.5 to 115.7; P=.82) or steps per day (MD 1084.0, 95% CI -585.0 to 2752.9; P=.20). During the following 6 months, no differences between the groups were observed in light physical activity (MD -87.9 minutes per week, 95% CI -379.2 to 203.3; P=.54), moderate to vigorous physical activity (MD 70.9 minutes per week, 95% CI -75.7 to 217.6; P=.33), or steps per day (MD 867.1, 95% CI -2099.6 to 3833.9; P=.55). Conclusions: The use of additional distance technology increased the duration of light physical activity at the beginning of cardiac rehabilitation (for the first 6 months), but statistically significant differences were not observed between the two groups for moderate or vigorous physical activity or steps per day for both 6-month self-exercise periods.

Mobile Health Intervention Promoting Physical Activity in Adults Post Cardiac Rehabilitation: Pilot Randomized Controlled Trial (2021)

JMIR formative research

Abstract

Background: Cardiac rehabilitation (CR) is an exercise-based program prescribed after cardiac events associated with improved physical, mental, and social functioning; however, many patients return to a sedentary lifestyle leading to deteriorating functional capacity after discharge from CR. Physical activity (PA) is critical to avoid recurrence of cardiac events and mortality and maintain functional capacity. Leveraging mobile health (mHealth) strategies to increase adherence to PA is a promising approach. Based on the social cognitive theory, we sought to determine whether mHealth strategies (Movn mobile app for self-monitoring, supportive push-through messages, and wearable activity tracker) would improve PA and functional capacity over 2 months. Objective: The objectives of this pilot randomized controlled trial were to examine preliminary effects of an mHealth intervention on group differences in PA and functional capacity and group differences in depression and self-efficacy to maintain exercise after CR. Methods: During the final week of outpatient CR, patients were randomized 1:1 to the intervention group or usual care. The intervention group downloaded the Movn mobile app, received supportive push-through messages on motivation and educational messages related to cardiovascular disease (CVD) management 3 times per week, and wore a Charge 2 (Fitbit Inc) activity tracker to track step counts. Participants in the usual care group wore a pedometer and recorded their daily steps in a diary. Data from the 6-minute walk test (6MWT) and self-reported questionnaires were collected at baseline and 2 months. Results: We recruited 60 patients from 2 CR sites at a community hospital in Northern California. The mean age was 68.0 (SD 9.3) years, and 23% (14/60) were female; retention rate was 85% (51/60). Our results from 51 patients who completed follow-up showed the intervention group had a statistically significant higher mean daily step count compared with the control (8860 vs 6633; P=.02). There was no difference between groups for the 6MWT, depression, or self-efficacy to maintain exercise. Conclusions: This intervention addresses a major public health initiative to examine the potential for mobile health strategies to promote PA in patients with CVD. Our technology-based pilot mHealth intervention provides promising results on a pragmatic and contemporary approach to promote PA by increasing daily step counts after completing CR.

A systematic review of the validity, reliability, and feasibility of measurement tools used to assess the physical activity and sedentary behaviour of pre-school aged children (2021)

The international journal of behavioral nutrition and physical activity

Abstract

Physical activity (PA) and sedentary behaviour (SB) of pre-school aged children are associated with important health and developmental outcomes. Accurate measurement of these behaviours in young children is critical for research and practice in this area. The aim of this review was to examine the validity, reliability, and feasibility of measurement tools used to assess PA and SB of pre-school aged children. Searches of electronic databases, and manual searching, were conducted to identify articles that examined the measurement properties (validity, reliability or feasibility) of measurement tools used to examine PA and/or SB of pre-school aged children (3–7 years old). Following screening, data were extracted and risk of bias assessment completed on all included articles. A total of 69 articles, describing 75 individual studies were included. Studies assessed measurement tools for PA (n = 27), SB (n = 5), and both PA and SB (n = 43). Outcome measures of PA and SB differed between studies (e.g. moderate to vigorous activity, step count, posture allocation). Most studies examined the measurement properties of one measurement tool only (n = 65). Measurement tools examined included: calorimetry, direct observation, combined heart rate and accelerometry, heart rate monitors, accelerometers, pedometers, and proxy report (parent, carer or teacher reported) measures (questionnaires or diaries). Studies most frequently assessed the validity (criterion and convergent) (n = 65), face and content validity (n = 2), test-retest reliability (n = 10) and intra-instrument reliability (n = 1) of the measurement tools. Feasibility data was abstracted from 41 studies. Multiple measurement tools used to measure PA and SB in pre-school aged children showed some degree of validity, reliability and feasibility, but often for different purposes. Accelerometers, including the Actigraph (in particular GT3X versions), Actical, ActivPAL and Fitbit (Flex and Zip), and proxy reported measurement tools used in combination may be useful for a range of outcome measures, to measure intensity alongside contextual information.

Optimization of a technology-supported physical activity promotion intervention for breast cancer survivors: Results from Fit2Thrive (2021)

Cancer

Abstract

Background: The benefits of moderate to vigorous physical activity (MVPA) for breast cancer survivors are well established. However, most are insufficiently active. Fit2Thrive used the Multiphase Optimization Strategy methodology to determine the effect of 5 intervention components on MVPA in this population. Methods: Two hundred sixty-nine participants (mean age, 52.5 years; SD, 9.9 years) received a core intervention (the Fit2Thrive self-monitoring app and Fitbit) and were randomly assigned to 5 intervention components set to on/off in a full factorial experiment: support calls, deluxe app, buddy, online gym, and text messages. The intervention was delivered over 12 weeks with a 12-week follow-up. MVPA was measured via accelerometry at the baseline (T1), at 12 weeks (T2), and at 24 weeks (T3). The main effects and interaction effects at each time point were examined for all components. Results: Trial retention was high: 91.8% had valid accelerometer data at T2 or T3. Across all conditions, there were significant increases in MVPA (+53.6 min/wk; P <.001) and in the proportion of survivors meeting MVPA guidelines (+22.3%; P <.001) at T2 that were maintained but attenuated at T3 (MVPA, +24.6 min/wk; P <.001; meeting guidelines, +12.6%; P <.001). No individual components significantly improved MVPA, although increases were greater for the on level versus the off level for support calls, buddy, and text messages at T2 and T3. Conclusions: The Fit2Thrive core intervention (the self-monitoring app and Fitbit) is promising for increasing MVPA in breast cancer survivors, but the components provided no additional increases in MVPA. Future research should evaluate the core intervention in a randomized trial and determine what components optimize MVPA behaviors in breast cancer survivors.

Effect of commercial wearables and digital behaviour change resources on the physical activity of adolescents attending schools in socio-economically disadvantaged areas: the RAW-PA cluster-randomised controlled trial (2021)

The international journal of behavioral nutrition and physical activity

Abstract

Background: There has been increasing interest in using wearable activity trackers to promote physical activity in youth. This study examined the short- and longer-term effects of a wearable activity tracker combined with digital behaviour change resources on the physical activity of adolescents attending schools in socio-economically disadvantaged areas. Methods: The Raising Awareness of Physical Activity (RAW-PA) Study was a 12-week, multicomponent intervention that combined a Fitbit Flex (and accompanying app), and online digital behaviour change resources and weekly challenges delivered via Facebook. RAW-PA was evaluated using a cluster-randomised controlled trial with 275 adolescents (50.2% female; 13.7 ± 0.4 years) from 18 Melbourne secondary schools (intervention n = 9; wait-list control group n = 9). The primary outcome was moderate- to vigorous-intensity physical activity (MVPA), measured using hip-worn ActiGraph accelerometers. The secondary outcome was self-reported physical activity. Data were collected at baseline, 12-weeks (immediately post-intervention), and 6-months post-intervention (follow-up). Multilevel models were used to determine the effects of the intervention on daily MVPA over time, adjusting for covariates. Results: No significant differences were observed between intervention and wait-list control adolescents' device-assessed MVPA immediately post-intervention. At 6-months post-intervention, adolescents in the intervention group engaged in 5 min (95% CI: − 9.1 to − 1.0) less MVPA per day than those in the wait-list control group. Males in the intervention group engaged in 11 min (95% CI: − 17.6 to − 4.5) less MVPA than males in the wait-list control group at 6-months post-intervention. No significant differences were observed for females at either time point. For self-reported physical activity, no significant effects were found at 12-weeks and 6-months post-intervention. Conclusions: Combining a wearable activity tracker with digital behaviour change resources and weekly challenges did not increase inactive adolescents' accelerometer-derived and self-reported physical activity levels immediately post-intervention. This contrasts previous research that has suggested wearable activity tracker may increase youth physical activity levels in the short-term. Lower engagement in MVPA 6-months post-intervention was observed for males but not for females, though it is unclear why this finding was observed. The results suggest wearable activity trackers, in combination with supporting materials, may not be effective for increasing physical activity levels in adolescents. Trial registration: ACTRN12616000899448. Australian and New Zealand Clinical Trials Registry. Registered 7 July 2016.

Stepping into survivorship pilot study: Harnessing mobile health and principles of behavioral economics to increase physical activity in ovarian cancer survivors (2021)

Gynecologic oncology

Abstract

Objective: Physical activity improves physical function, quality of life, and mental health, yet fewer than 80% of ovarian cancer survivors meet activity guidelines. This pilot intervention study aimed to increase physical activity in ovarian cancer survivors by leveraging principles of behavioral economics, gamification, and social support. Methods: This 24-week study (12-week intervention; 12-week follow-up) enrolled women with ovarian cancer after completion of first-line treatment with a self-selected “teammate.” Participants used Fitbits to measure daily steps, select an increased step goal, and enroll in a collaborative game, including points and levels for achieving step goals. Primary outcomes were feasibility (defined a priori as ≥60% approach-to-consent ratio and ≥ 70% adherence to Fitbit), acceptability (≤20% of participants reporting burden or regret for participation) and preliminary efficacy (≥70% reporting increased motivation); exploratory outcomes included change in steps. Results: We recruited 24 participants (mean age = 63 years, range = 37–79 years) with a 94% approach-to-consent ratio. All participants completed the intervention with 94% tracker adherence. At 24-week follow-up, 1/24 (≤5%) of participants reported burden; 0/24 (0%) reported regret for study participation; and 22/24 (>90%) agreed/strongly agreed that “the study motivated me to increase activity levels.” Participants' mean daily steps were 6210.7 (±3328.1) at baseline and increased to 7643 (± 3610.9) steps (p < 0.001) during the 12-week intervention. Conclusions: This pilot study demonstrated feasibility, acceptability, and preliminary efficacy, justifying a larger randomized clinical trial to test efficacy at increasing activity levels. Future studies should examine strategies for maintaining increased activity levels in survivors over time.

Using a consumer-based wearable activity tracker for physical activity goal setting and measuring steps in pregnant women with gestational diabetes mellitus: exploring acceptance and validity (2021)

BMC Pregnancy and Childbirth

Abstract

Background: Activity monitoring devices may be used to facilitate goal-setting, self-monitoring, and feedback towards a step-based physical activity (PA) goal. This study examined the performance of the wrist-worn Fitbit Charge 3™ (FC3) and sought opinions on walking and stepping-in-place from women with gestational diabetes (GDM). Methods: Participants completed six 2-min metronome-assisted over ground bouts that varied by cadence (67, 84, or 100 steps per minute) and mode (walking or stepping-in-place; N = 15), with the sequence randomized. Steps were estimated by FC3 and measured, in duplicate, by direct observation (hand-tally device, criterion). Equivalence testing by the two one-sided tests (TOST) method assessed agreement within ± 15%. Mean absolute percent error (MAPE) of steps were compared to 10%, the accuracy standard of the Consumer Technology Association (CTA)™. A subset (n = 10) completed a timed, 200-m self-paced walk to assess natural walking pace and cadence. All participants completed semi-structured interviews, which were transcribed and analyzed using descriptive and interpretive coding. Results: Mean age was 27.0 years (SD 4.2), prepregnancy BMI 29.4 kg/m2 (8.3), and gestational age 32.8 weeks (SD 2.6). The FC3 was equivalent to hand-tally for bouts of metronome-assisted walking and stepping-in-place at 84 and 100 steps per minute (i.e., P <.05), although walking at 100 steps per minute (P =.01) was no longer equivalent upon adjustment for multiple comparisons (i.e., at P <.007). The FC3 was equivalent to hand-tally during the 200-m walk (i.e., P <.001), in which mean pace was 68.2 m per minute (SD 10.7), or 2.5 miles per hour, and mean cadence 108.5 steps per minute (SD 6.5). For walking at 84 and 100 steps per minute, stepping-in-place at 100 steps per minute, and the 200-m walk, MAPE was within 10%, the accuracy standard of the CTA™. Interviews revealed motivation for PA, that stepping-in-place was an acceptable alternative to walking, and competing responsibilities made it difficult to find time for PA. Conclusions: The FC3 appears to be a valid step counter during the third trimester, particularly when walking or stepping-in-place at or close to women's preferred cadence.

Quantitative activity levels and gestational age at delivery: a prospective cohort study among nulliparous women (2022)

American journal of obstetrics & gynecology MFM

Abstract

OBJECTIVE: Despite the knowledge that bed rest does not reduce the risk of preterm birth (PTB), it continues to be recommended by many providers worldwide. This is because there are no quantitative data assessing the relationship between PTB and physical activity in pregnancy.1–3 We designed a prospective cohort study using a Fitbit activity tracker to quantitatively explore the association between baseline physical activity in pregnancy in steps/day and the risk of PTB (<37 weeks). STUDY DESIGN: This was a prospective cohort study assessing the association between the risk of PTB and physical activity in healthy nulliparous women from 10 to 24 weeks to delivery. The physical activity (San Francisco, California) was measured from the time of entry into the study until the day before admission for delivery using the Fitbit Flex 2. The participants wore the faceless device 24/7 without modifying their activity. The primary exposure was steps/day in low- (<5000 steps/d) and high-level (≥5000 steps/d) activity groups. The primary outcome was the rate of PTB (<37 weeks). An additional unplanned secondary analysis was performed using a 3500 steps/d cutoff. The secondary outcomes included peripartum complications and median steps/day in term vs preterm groups. Adjusted analyses were performed to account for possible confounders. RESULTS: A total of 134 women were enrolled, of which 25 (19%) and 109 (81%) were in the low- and high-level activity groups, respectively. Overall, 11 (8.2%) women delivered preterm. The high-level activity group was older, partnered, employed, and had a higher education level. The PTB did not differ between the groups (adjusted risk ratio, 0.99; 95% confidence interval [CI], 0.99–1.00) (Table). There was no difference in the median steps/d between preterm and term deliveries (7767 interquartile range, [5188–10,387] vs 6986 [5412–8528]); percentile difference, −442; (95% CI, −2233 to 1507) steps. Using a 3500 steps/d cutoff, there was a 75% reduction in the PTB risk (29% vs 7%, respectively; risk ratio, 0.25; 95% CI, 0.05–2.35) (Table). CONCLUSION: This prospective study of nulliparous women showed no difference in the risk of PTB between low- vs high-activity groups using a cutoff of 5000 steps/d. The gestational age at delivery was similar between the groups. No significant difference in the number of steps/d was observed between women who delivered preterm compared with term. The women who were prescribed activity restriction (AR) had a marked reduction in their median number of steps/d after AR was prescribed. However, their median number of steps per day (>5000) reflected that they remained active despite this instruction. An additional analysis using 3500 steps/d as a cutoff for exposure groups showed a significantly increased risk of PTB in the <3500 steps/d group than the ≥3500 steps/d group. It is therefore plausible that activity levels <3500 steps/d are associated with an increased risk of PTB.

A Technology-Based Physical Activity Intervention for Patients With Metastatic Breast Cancer (Fit2ThriveMB): Protocol for a Randomized Controlled Trial (2021)

JMIR research protocols

Abstract

Background: Increased incidence and life expectancy have resulted in a growing population of patients with metastatic breast cancer, and these patients experience high rates of morbidity and premature mortality. Increased physical activity (PA) is consistently associated with improved health and disease outcomes among early-stage survivors. However, there is a paucity of research on PA in patients with metastatic breast cancer, and existing PA interventions have exhibited low feasibility because of their focus on intense PA and/or requirement of on-site visits. Mobile health (mHealth)–based PA interventions may be particularly useful for patients with metastatic breast cancer because they allow for remote monitoring, which facilitates individual tailoring of PA recommendations to patients' abilities and may minimize participant burden. However, no studies have examined mHealth PA interventions in patients with metastatic breast cancer. Objective: We aim to address these critical research gaps by testing a highly tailored technology-based intervention to promote PA of any intensity (ie, light, moderate, or vigorous) by increasing daily steps in patients with metastatic breast cancer. The primary aim of this study is to test the feasibility and acceptability of the Fit2ThriveMB intervention. We will also examine outcome patterns suggesting the efficacy of Fit2ThriveMB on symptom burden, quality of life, and functional performance. Methods: The Fit2ThriveMB trial is a two-arm pilot randomized controlled trial that will compare the effects of a smartphone-delivered, home-based PA intervention and an attention-control education condition on PA and quality of life in low-active female patients with metastatic breast cancer. A subsample (n=25) will also complete functional performance measures. This innovative trial will recruit 50 participants who will be randomized into the study's intervention or control arm. The intervention will last 12 weeks. The Fit2ThriveMB intervention consists of a Fitbit, coaching calls, and the Fit2ThriveMB smartphone app that provides self-monitoring, a tailored goal-setting tool, real-time tailored feedback, app notifications, and a group message board. Assessments will occur at baseline and post intervention. Results: The Fit2ThriveMB study is ongoing. Data collection ended in February 2021. Conclusions: Data from this study will provide the preliminary effect sizes needed to assemble an intervention that is to be evaluated in a fully powered trial. In addition, these data will provide essential evidence to support the feasibility and acceptability of using a technology-based PA promotion intervention, a scalable strategy that could be easily integrated into care, among patients with metastatic breast cancer.

Feasibility and potential efficacy of commercial mHealth/eHealth tools for weight loss in African American breast cancer survivors: pilot randomized controlled trial (2020)

Translational behavioral medicine

Abstract

Weight management after breast cancer (BC) treatment in African American (AA) women is crucial to reduce comorbid conditions and health disparities. We examined feasibility and potential efficacy of commercial eHealth/mHealth tools for weight management in AA BC survivors in New Jersey. Participants (N = 35) were randomized to an intervention (SparkPeople) plus activity tracker, Fitbit Charge (n = 18), or wait-list active control group (Fitbit only, n = 17). Anthropometric, behavioral, and quality of life (QOL) outcomes were collected at baseline, 3, 6, and 12 months. Differences in outcomes were assessed using intent-to-treat analysis. Retention was 97.1%. Both groups lost weight, with no significant differences between groups. At month 6, mean weight change was: intervention: -1.71 kg (SD 2.33; p =. 006), 33.3% lost ≥3% of baseline weight; control: -2.54 kg (SD 4.00, p =. 002), 23.5% lost ≥3% weight. Intervention participants achieved significant improvements in waist circumference (-3.56 cm, SD 4.70, p =. 005), QOL (p =. 030), and use of strategies for healthy eating (p =. 025) and decreasing calories (p <. 001). Number of days logged food per week was associated with decreases in waist circumference at 6 months ($\beta$ -0.79, 95% CI, -1.49, -0.09, p =. 030) and 12 months ($\beta$ -2.16, 95% CI, -4.17, -0.15, p =. 038). Weight loss was maintained at 12 months. This is the first study to demonstrate potential efficacy of commercial eHealth/mHealth tools for weight loss in AA BC survivors, without additional counseling from the research team. If effective, they may be convenient weight loss tools that can be easily and widely disseminated. Clinical Trials registration: ClinicalTrials.gov NCT02699983.

Use of consumer wearable devices to promote physical activity among breast, prostate, and colorectal cancer survivors: a review of health intervention studies (2020)

Journal of cancer survivorship : research and practice

Abstract

Purpose: A growing amount of research has successfully incorporated Fitbit devices and other wearable activity trackers into technology-oriented lifestyle interventions to increase physical activity among cancer survivors. Methods: The present review of this literature is based upon bibliographic searches in PubMed and CINAHL and relevant search terms. Articles published in English from January 1, 2009, through October 16, 2019, were considered. Results: A total of 1726 article citations were identified in PubMed and non-duplicates in CINAHL. After screening the abstracts or full texts of these articles and reviewing the references of previous review articles, we found 13 studies that met the eligibility criteria. Of these, 8 were randomized controlled trials, one was a pre-post-test trial, and 4 were qualitative studies (focus groups, in-depth interviews). The studies focused on breast cancer (n = 8), prostate cancer (n = 2), and colorectal cancer (n = 1), and the remainder focused on more than one cancer site. Conclusions: Additional research is needed to examine the efficacy of consumer wearable devices in promoting physical activity and weight management among cancer survivors. Implications for Cancer Survivors: Cancer survivors show an increase in physical activity when using consumer wearable activity trackers. Increased physical activity plays an important role in alleviating many adverse effects of breast cancer therapy as well as improving morbidity and mortality. Additional research such as clinical trials focused on the development of successful interventions utilizing these devices is warranted.

Wrist-Worn Wearables for Monitoring Heart Rate and Energy Expenditure While Sitting or Performing Light-to-Vigorous Physical Activity: Validation Study (2020)

JMIR mHealth and uHealth

Abstract

Background: Physical activity reduces the incidences of noncommunicable diseases, obesity, and mortality, but an inactive lifestyle is becoming increasingly common. Innovative approaches to monitor and promote physical activity are warranted. While individual monitoring of physical activity aids in the design of effective interventions to enhance physical activity, a basic prerequisite is that the monitoring devices exhibit high validity. Objective: Our goal was to assess the validity of monitoring heart rate (HR) and energy expenditure (EE) while sitting or performing light-to-vigorous physical activity with 4 popular wrist-worn wearables (Apple Watch Series 4, Polar Vantage V, Garmin Fenix 5, and Fitbit Versa). Methods: While wearing the 4 different wearables, 25 individuals performed 5 minutes each of sitting, walking, and running at different velocities (ie, 1.1 m/s, 1.9 m/s, 2.7 m/s, 3.6 m/s, and 4.1 m/s), as well as intermittent sprints. HR and EE were compared to common criterion measures: Polar-H7 chest belt for HR and indirect calorimetry for EE. Results: While monitoring HR at different exercise intensities, the standardized typical errors of the estimates were 0.09-0.62, 0.13-0.88, 0.62-1.24, and 0.47-1.94 for the Apple Watch Series 4, Polar Vantage V, Garmin Fenix 5, and Fitbit Versa, respectively. Depending on exercise intensity, the corresponding coefficients of variation were 0.9%-4.3%, 2.2%-6.7%, 2.9%-9.2%, and 4.1%-19.1%, respectively, for the 4 wearables. While monitoring EE at different exercise intensities, the standardized typical errors of the estimates were 0.34-1.84, 0.32-1.33, 0.46-4.86, and 0.41-1.65 for the Apple Watch Series 4, Polar Vantage V, Garmin Fenix 5, and Fitbit Versa, respectively. Depending on exercise intensity, the corresponding coefficients of variation were 13.5%-27.1%, 16.3%-28.0%, 15.9%-34.5%, and 8.0%-32.3%, respectively. Conclusions: The Apple Watch Series 4 provides the highest validity (ie, smallest error rates) when measuring HR while sitting or performing light-to-vigorous physical activity, followed by the Polar Vantage V, Garmin Fenix 5, and Fitbit Versa, in that order. The Apple Watch Series 4 and Polar Vantage V are suitable for valid HR measurements at the intensities tested, but HR data provided by the Garmin Fenix 5 and Fitbit Versa should be interpreted with caution due to higher error rates at certain intensities. None of the 4 wrist-worn wearables should be employed to monitor EE at the intensities and durations tested.

Functional Connectivity, Physical Activity, and Neurocognitive Performances in Patients with Vascular Cognitive Impairment, No Dementia (2022)

Current Alzheimer research

Abstract

Background: Vascular Cognitive Impairment, No Dementia (VCIND) is a key stage at which early intervention will delay or prevent dementia. The pathophysiology of VCIND posits that a lesion in a single location in the brain has the ability to disrupt brain networks, and the subsequent abnormal Functional Connectivity (FC) of brain networks leads to deficits in corresponding neurobehavioral domains. In this study, we tested the hypothesis that disrupted anterior cingulate cortex and striatal networks mediated the effects of Physical Activity (PA) on neurobehavioral function. Methods: In 27 patients with VCIND, FC within the brain networks and neurobehavioral dysfunction were assessed. The relationship between the cognitive scores, FC, and PA was studied. The Fitbit Charge 2 was used to measure step counts, distance, and calories burned. In patients with VCIND, a cross-sectional Spearman's correlation to analyze the relationship among patient-level measures of PA, cognitive function scores, and FC strength within the brain networks. Results: Average step counts and average distance were associated with Trail Making Test B (TM-B) time to completion (seconds) and Instrumental Activities of Daily Living (IADL) score (P <0.05). The average calories burned were associated with IADL score (P = 0.009). The FC within the brain networks anchored by left caudal Anterior Cingulate Cortex (ACC) seeds (x= -5, y= 0, z= 36) and (x= -5, y= -10, z= 47) were positively correlated with average step counts and average distance, were negatively correlated with TMB time to completion (seconds), and were positively correlated with IADL score (P < 0.05). The FC within the brain networks anchored by left subgenual ACC seed (x= -5, y= 25, z= -10) were negatively correlated with average step counts and average distance were positively correlated with TMB time to completion (seconds), and were negatively correlated with IADL score (P < 0.05). The FC within the striatal networks was positively correlated with average calories burned and IADL score (P < 0.05). Keywords: Actigraphy; brain network; cognition; executive function; functional connectivity; physical activity.

Accuracy and Acceptability of Wrist-Wearable Activity-Tracking Devices: Systematic Review of the Literature (2022)

Journal of medical Internet research

Abstract

Background: Numerous wrist-wearable devices to measure physical activity are currently available, but there is a need to unify the evidence on how they compare in terms of acceptability and accuracy. Objective: The aim of this study is to perform a systematic review of the literature to assess the accuracy and acceptability (willingness to use the device for the task it is designed to support) of wrist-wearable activity trackers. Methods: We searched MEDLINE, Embase, the Cochrane Central Register of Controlled Trials, and SPORTDiscus for studies measuring physical activity in the general population using wrist-wearable activity trackers. We screened articles for inclusion and, for the included studies, reported data on the studies' setting and population, outcome measured, and risk of bias. Results: A total of 65 articles were included in our review. Accuracy was assessed for 14 different outcomes, which can be classified in the following categories: count of specific activities (including step counts), time spent being active, intensity of physical activity (including energy expenditure), heart rate, distance, and speed. Substantial clinical heterogeneity did not allow us to perform a meta-analysis of the results. The outcomes assessed most frequently were step counts, heart rate, and energy expenditure. For step counts, the Fitbit Charge (or the Fitbit Charge HR) had a mean absolute percentage error (MAPE) <25% across 20 studies. For heart rate, the Apple Watch had a MAPE <10% in 2 studies. For energy expenditure, the MAPE was >30% for all the brands, showing poor accuracy across devices. Acceptability was most frequently measured through data availability and wearing time. Data availability was ≥75% for the Fitbit Charge HR, Fitbit Flex 2, and Garmin Vivofit. The wearing time was 89% for both the GENEActiv and Nike FuelBand. Conclusions: The Fitbit Charge and Fitbit Charge HR were consistently shown to have a good accuracy for step counts and the Apple Watch for measuring heart rate. None of the tested devices proved to be accurate in measuring energy expenditure. Efforts should be made to reduce the heterogeneity among studies.

Exploring the Use of Fitbit Consumer Activity Trackers to Support Active Lifestyles in Adults with Type 2 Diabetes: A Mixed-Methods Study (2021)

International journal of environmental research and public health

Abstract

Background: People with type 2 diabetes are less active than those without the condition. Physical activity promotion within diabetes health care is limited. This project explored the use of Fitbit activity trackers (Fitbit, San Francisco, CA, USA) to support active lifestyles in adults with type 2 diabetes through a mixed-methods study. Methods: Two stages were conducted. In stage 1, adults with type 2 diabetes used a Fitbit Charge 4 (Fitbit, San Francisco, CA, USA) for 4 weeks. Fitbit and self-reported physical activity data was examined through quantitative analysis. Qualitative analysis was conducted to explore the experiences of participants. In stage 2, health professionals were interviewed to examine their views on using Fitbit activity trackers within type 2 diabetes care. Results: Adults with type 2 diabetes were recruited for stage 1 and adult health care and fitness professionals were recruited for stage 2. Stage 1 participants' self-reported increases in physical activity (mean weekly minutes of walking increased from 358.75 to 507.50 min, p = 0.046) and a decrease in sedentary behaviour (mean daily hours of sedentary behaviour decreased from 10.65 to 10.05 h, p = 0.575). Fitbit activity data ranges identified individuals who led inactive and sedentary lifestyles below levels recommended and in need of physical activity support to reduce the risk to their health. During interviews, participants stated that the Fitbit activity tracker motivated them to be more active. Stage 2 participants intimated that Fitbit activity trackers could improve the promotion of physical activity within type 2 diabetes care. Interventions involving the Fitbit premium service, community prescription and combined use of Fitbits with physical activity behaviour change models were recommended by stage 2 participants. Conclusion: This study found that there is future scope for using Fitbit activity trackers to support active lifestyles in adults diagnosed with type 2 diabetes.

Is physical activity before the end of the workday a drain or a gain? Daily implications on work focus in regular exercisers (2021)

The Journal of applied psychology

Abstract

Although Organizations Increasingly Offer Wellness Programs That Enable Employees To Work Out Before Or During Work, It Remains Unknown What Implications Physical Activity Before Or During The Workday Might Have For Work Outcomes. Whereas A Workout Might Be Rewarding, Especially For Those Who Enjoy Exercise, Working Out Might Also Be Draining, Especially For Those Who Are Less Intrinsically Motivated To Exercise. Integrating The Work–Home Resources Model With Self-Determination Theory, We Develop And Test Theory Which Identifies How Physical Activity Before The End Of The Workday Might Exert Countervailing Effects By Impeding Work Focus Through Drained Personal Resources (I.E., Ego Depletion), While Also Improving Work Focus Via Enhanced Personal Resources (I.E., Self-Efficacy). We Further Theorized That Motivation For Exercise—Whether It Is Intrinsically Or Extrinsically Motivated—Serves As A Cross-Level Moderator Of These Relations. In A 5-Day Experience Sampling Study Tracking 74 Regularly Exercising Employees With Fitbit Activity Monitors, Results Indicated That Physical Activity Was Not Significantly Related To Ego Depletion. However, We Found That Light Physical Activity Was Positively Related To Self-Efficacy And Self-Efficacy Positively Related To Work Focus (As Rated By Coworkers). Further, Vigorous Physical Activity Only Resulted In Better Work Focus Among Employees With An Intrinsic (Vs. Extrinsic) Motivation For Exercise. Finally, Moderate Physical Activity Resulted In Better Work Focus Via Self-Efficacy Among Extrinsically Motivated Exercises, Whereas This Relation Was Negative For Intrinsically Motivated Exercisers. Combined, Our Results Highlight That Physical Activity Can Improve Work Focus When There Is A Match Between Physical Activity Intensity And Exercise Motivation

Feasibility and Acceptability of a Physical Activity Tracker and Text Messages to Promote Physical Activity During Chemotherapy for Colorectal Cancer: Pilot Randomized Controlled Trial (Smart Pace II) (2022)

JMIR cancer

Abstract

Background: We conducted a pilot 2-arm randomized controlled trial to assess the feasibility of a digital health intervention to increase moderate-to-vigorous physical activity in patients with colorectal cancer (CRC) during chemotherapy. Objective: This study aimed to determine whether a digital health physical activity intervention is feasible and acceptable during chemotherapy for CRC. Methods: Potentially eligible patients with CRC expected to receive at least 12 weeks of chemotherapy were identified in person at the University of California, San Francisco, and on the web through advertising. Eligible patients were randomized 1:1 to a 12-week intervention (Fitbit Flex, automated SMS text messages) versus usual care. At 0 and 12 weeks, patients wore an Actigraph GT3X+ accelerometer for 7 days and completed surveys, body size measurements, and an optional 6-minute walk test. Participants could not be masked to their intervention arm, but people assessing the body size and 6-minute walk test outcomes were masked. The primary outcomes were adherence (eg, Fitbit wear and text response rate) and self-assessed acceptability of the intervention. The intervention would be considered feasible if we observed at least 80% complete follow-up and 70% adherence and satisfaction, a priori. Results: From 2018 to 2020, we screened 240 patients; 53.3% (128/240) of patients were ineligible and 26.7% (64/240) declined to participate. A total of 44 patients (44/240, 18%) were randomized to the intervention (n=22) or control (n=22) groups. Of these, 57% (25/44) were women; 68% (30/44) identified as White and 25% (11/44) identified as Asian American or Pacific Islander; and 77% (34/44) had a 4-year college degree. The median age at enrollment was 54 years (IQR 45-62 years). Follow-up at 12 weeks was 91% (40/44) complete. In the intervention arm, patients wore Fitbit devices on a median of 67 out of 84 (80%) study days and responded to a median of 17 out of 27 (63%) questions sent via SMS text message. Among 19 out of 22 (86%) intervention patients who completed the feedback survey, 89% (17/19) were satisfied with the Fitbit device; 63% (12/19) were satisfied with the SMS text messages; 68% (13/19) said the SMS text messages motivated them to exercise; 74% (14/19) said the frequency of SMS text messages (1-3 days) was ideal; and 79% (15/19) said that receiving SMS text messages in the morning and evening was ideal. Conclusions: This pilot study demonstrated that many people receiving chemotherapy for CRC are interested in participating in digital health physical activity interventions. Fitbit adherence was high; however, participants indicated a desire for more tailored SMS text message content. Studies with more socioeconomically diverse patients with CRC are required.

Monitoring work-related physical activity and estimating lower-limb loading: a proof-of-concept study (2021)

BMC musculoskeletal disorders

Abstract

Background: Physical activity (PA) is important to general health and knee osteoarthritis (OA). Excessive workplace PA is an established risk factor for knee OA however, appropriate methods of measurement are unclear. There is a need to examine and assess the utility of new methods of measuring workplace PA and estimating knee load prior to application to large-scale, knee OA cohorts. Our aims, therefore, were to monitor workplace PA and estimate lower-limb loading across different occupations in health participants. Methods: Twenty-four healthy adults, currently working full-time in a single occupation (≥ 35 h/week) and free of musculoskeletal disease, comorbidity and had no history of lower-limb injury/surgery (past 12-months) were recruited across New South Wales (Australia). A convenience sample was recruited with occupations assigned to levels of workload; sedentary, light manual and heavy manual. Metrics of workplace PA including tasks performed (i.e., sitting), step-count and lower-limb loading were monitored over 10 working days using a daily survey, smartwatch, and a smartphone. Results: Participants of light manual occupations had the greatest between-person variations in mean lower-limb load (from 2 to 59 kg*m/s3). Lower-limb load for most participants of the light manual group was similar to a single participant in heavy manual work (30 kg*m/s3) and was at least three times greater than the sedentary group (2 kg*m/s3). The trends of workplace PA over working hours were largely consistent, per individual, but rare events of extreme loads were observed across all participants (up to 760 kg*m/s3). Conclusions: There are large interpersonal variations in metrics of workplace PA, particularly among light and heavy manual occupations. Our estimates of lower-limb loading were largely consistent with pre-conceived levels of physical demand. We present a new approach to monitoring PA and estimating lower-limb loading, which could be applied to future occupational studies of knee OA.

Risk Factors for COVID-19 in College Students Identified by Physical, Mental, and Social Health Reported During the Fall 2020 Semester: Observational Study Using the Roadmap App and Fitbit Wearable Sensors (2022)

JMIR mental health

Abstract

BACKGROUND: The coronavirus disease 2019 (COVID-19) pandemic triggered a seismic shift in education, to online learning. With nearly 20 million students enrolled in colleges across the U.S., the long-simmering mental health crisis in college students was likely further exacerbated by the pandemic. OBJECTIVE: This study leveraged mobile health (mHealth) technology and sought to: i) characterize self-reported outcomes of physical, mental, and social health by COVID-19 status; ii) assess physical activity through consumer-grade wearable sensors (Fitbit{\textregistered}); and iii) identify risk factors associated with COVID-19 positivity in a population of college students prior to release of the vaccine. METHODS: Detailed methods were previously published in JMIR Res Protocols (Cislo et al). After completing a baseline assessment (i.e., Time 0 [T0]) of demographics, mental, and social health constructs through the Roadmap 2.0 app, participants were instructed to use the app freely, to wear the Fitbit{\textregistered}, and complete subsequent assessments at T1, T2 and T3, followed by a COVID-19 assessment of history and timing of COVID-19 testing and diagnosis (T4: $\sim$14 days after T3). Continuous measures were described using means (M) and standard deviations (SD), while categorical measures were summarized using frequencies and proportions. Formal comparisons were made based on COVID-19 status. The multivariate model was determined by entering all statistically significant variables (P<.05) in univariable associations at once and then removing one variable at a time by backward selection until the optimal model was obtained. RESULTS: During the fall 2020 semester, 1,997 participants consented, enrolled, and met criteria for data analyses. There was a high prevalence of anxiety, as assessed by the State Trait Anxiety Index (STAI), with moderate and severe levels in N=465 (24%) and N=970 (49%) students, respectively. Approximately, one-third of students reported having a mental health disorder (N=656, 33%). The average daily steps recorded in this student population was approximately 6500 (M=6474, SD=3371). Neither reported mental health nor step count were significant based on COVID-19 status (P=.52). Our analyses revealed significant associations of COVID-positivity with use of marijuana and alcohol (P =.020 and .046, respectively) and lower belief in public health measures (P=.003). In addition, graduate students were less likely and those with ≥20 roommates were more likely to report a COVID-19 diagnosis (P=.009). CONCLUSIONS: Mental health problems were common in this student population. Several factors, including substance use, were associated with risk of COVID-19. These data highlight important areas for further attention, such as prioritizing innovative strategies that address health and well-being, considering the potential long-term effects of COVID-19 on college students. CLINICALTRIAL: ClinicalTrials.gov NCT04766788. INTERNATIONAL REGISTERED REPORT: RR2-10.2196/29561.

Moving from intention to behaviour: a randomised controlled trial protocol for an app-based physical activity intervention (i2be) (2022)

BMJ open

Abstract

Introduction Efficacy tests of physical activity interventions indicate that many have limited or short-term efficacy, principally because they do not sufficiently build on theory-based processes that determine behaviour. The current study aims to address this limitation. Methods and analysis The efficacy of the 8-week intervention will be tested using a three-condition randomised controlled trial delivered through an app, in women with a prior hypertensive pregnancy disorder. The intervention is based on the integrated behaviour change model, which outlines the motivational, volitional and automatic processes that lead to physical activity. The mechanisms by which the behaviour change techniques lead to physical activity will be tested. Following stratification on baseline factors, participants will be randomly allocated in-app to one of three conditions (1:1:1). The information condition will receive information, replicating usual care. Additionally to what the information condition receives, the motivation condition will receive content targeting motivational processes. Additionally to what the motivation condition receives, the action condition will receive content targeting volitional and automatic processes. The primary outcome is weekly minutes of moderate-to-vigorous physical activity, as measured by an activity tracker (Fitbit Inspire 2). Secondary outcomes include weekly average of Fitbit-measured daily resting heart rate, and self-reported body mass index, waist-hip ratio, cardiorespiratory fitness and subjective well-being. Tertiary outcomes include self-reported variables representing motivational, volitional, and automatic processes. Outcome measures will be assessed at baseline, immediately post-intervention, and at 3 and 12 months post-intervention. Physical activity will also be investigated at intervention midpoint. Efficacy will be determined by available case analysis. A process evaluation will be performed based on programme fidelity and acceptability measures. Ethics and dissemination The Medical Ethics Committee of the Erasmus MC has approved this study (MEC-2020-0981). Results will be published in peer reviewed scientific journals and presented at scientific conferences. Trial registration number Netherlands trial register, NL9329.

Acceptability of a Mobile Health Behavior Change Intervention for Cancer Survivors With Obesity or Overweight: Nested Mixed Methods Study Within a Randomized Controlled Trial (2021)

JMIR mHealth and uHealth

Abstract

Background: A significant proportion of cancer survivors have overweight or obesity. Although this has negative implications for health, weight management is not a standard component of oncology aftercare. Mobile health (mHealth) technology, in combination with behavior change techniques (BCTs), has the potential to support positive lifestyle changes. Few studies have been carried out with cancer survivors; therefore, the acceptability of these tools and techniques requires further investigation. Objective: The aim of this study is to examine the acceptability of a behavior change intervention using mHealth for cancer survivors with a BMI of 25 or more and to gather constructive feedback from participants. Methods: The intervention consisted of educational sessions and an 8-week physical activity goal setting intervention delivered using mobile technology (ie, Fitbit activity monitor plus SMS contact). In the context of a two-arm randomized controlled trial, semistructured interviews were conducted to assess the retrospective acceptability of the intervention from the perspective of the recipients. The theoretical framework for the acceptability of health care interventions was used to inform a topic guide. The interviews were transcribed and analyzed using thematic analysis. A quantitative survey was also conducted to determine the acceptability of the intervention. A total of 13 participants were interviewed, and 36 participants completed the quantitative survey. Results: The results strongly support the acceptability of the intervention. The majority of the survey respondents held a positive attitude toward the intervention (35/36, 97%). In qualitative reports, many of the intervention components were enjoyed and the mHealth components (ie, Fitbit and goal setting through text message contact) were rated especially positively. Responses were mixed as to whether the burden of participating in the intervention was high (6/36, 17%) or low (5/36, 14%). Participants perceived the intervention as having high efficacy in improving health and well-being (34/36, 94%). Most respondents said that they understood how the intervention works (35/36, 97%), and qualitative data show that participants' understanding of the aim of the intervention was broader than weight management and focused more on moving on psychologically from cancer. Conclusions: On the basis of the coherence of responses with theorized aspects of intervention acceptability, we are confident that this intervention using mHealth and BCTs is acceptable to cancer survivors with obesity or overweight. Participants made several recommendations concerning the additional provision of social support. Future studies are needed to assess the feasibility of delivery in clinical practice and the acceptability of the intervention to those delivering the intervention.

A 'novel' multi-component approach to promote physical activity among older cancer survivors: a pilot randomized controlled trial (2021)

Acta oncologica (Stockholm, Sweden)

Abstract

Background: Physical activity (PA) provides many benefits for recovery from cancer treatments. Many older (65+ years) cancer survivors which comprise the majority of the cancer survivor population, do not meet recommended PA guidelines. This study explored the feasibility and acceptability of using audiobooks as part of a 12-week multi-component intervention to increase steps/day, light and moderate-to-vigorous intensity PA among older survivors. Methods: Twenty older cancer survivors (95% female, mean age = 71.55 years, 90% White, 85% overweight/obese, 75% breast cancer survivors, mean 1.96 years since treatment completion) were randomized into one of the two study groups (Audiobook Group, n = 12, Comparison Group, n = 8). Both study groups were provided a tailored step goal program over the 12-week intervention; weekly step increases were based on a percent increase from baseline. Participant self-monitored their steps using a Fitbit Charge 2. In addition, the Audiobook group were encouraged to listen to audiobooks (downloaded onto a smartphone device via an app available at no cost from the local library) during PA to add enjoyment and increase PA. Regression analyses on steps/day, light and moderate-to-vigorous PA/week and sedentary time/week as assessed by the Actigraph were conducted, after adjusting for Actigraph wear time. Data from the post-intervention questionnaire were summarized. Results: Overall, majority of participants (89%) stated they were very satisfied with their participation and 100% reported that they were able to maintain their activity upon study completion. Retention rates were high. At post-intervention, there were significant differences favoring the Audiobook group for steps/day and moderate-to-vigorous PA/week. No significant group differences were found for minutes of light intensity PA/week and sedentary time/week. Conclusion: Piloting the implementation of a sustainable, innovative intervention among older survivors to increase their PA has significance for this group of survivors.

¡Mi Vida Saludable! A randomized, controlled, 2 × 2 factorial trial of a diet and physical activity intervention among Latina breast cancer survivors: Study design and methods (2021)

Contemporary clinical trials

Abstract

Background: Most Latina breast cancer survivors do not meet diet and physical activity (PA) guidelines for cancer survivors and effective lifestyle interventions to adopt and maintain these recommendations are limited, especially among underserved populations. Here we describe the design, methods and enrollment of a 2 × 2 factorial-designed trial testing the separate effects of the ¡Mi Vida Saludable! (My Healthy Life!) intervention program on changes in diet and PA behaviors among Latina breast cancer survivors. Methods: Latinas with a history of stage 0-III breast cancer, no evidence of recurrent/metastatic disease, and > 90 days post-treatment were primarily identified via cancer registries and physician referral. Participants were randomized to four arms: 1) 4 weeks of in-person group sessions plus 11 months of eHealth communication, 2) in-person group sessions alone, 3) eHealth alone, or 4) control. All participants received a Fitbit to self-monitor PA. Assessments at baseline, 6 and 12 months include diet, PA, anthropometrics, predictors and mediators of behavior change, psychosocial and quality of life outcomes, and blood draw. Results: Of 884 women screened between January 2016 and September 2018, 27% were eligible. Primary reasons for ineligibility included not being willing/able to participate due to work/life responsibilities, health reasons, or transportation. Of 241 eligible women, 167 completed baseline assessment and enrolled. Conclusions: We successfully enrolled a diverse group of breast cancer survivors representing more than 15 Latin American nationalities to a diet and physical activity trial. If effective, the ¡Mi Vida Saludable! program can be implemented by community groups and medical centers. Trial registration: ClinicalTrials.gov, NCT02780271, registered May 2016.

Connecting breast cancer survivors for exercise: protocol for a two-arm randomized controlled trial (2021)

BMC sports science, medicine & rehabilitation

Abstract

Background: Peer-based exercise interventions that cultivate new opportunities for support with a fellow cancer survivor may result in increased exercise volume. It is not clear whether adding qualified exercise professional (QEP) support to peer-based interventions improves health outcomes. Therefore, the purpose of this study is to determine whether breast cancer survivor (BCS) dyads who receive 10 weekly sessions of virtually delivered QEP support have improved outcomes compared to BCS dyads who do not receive QEP support. Methods: Participants Adult BCS with medical clearance for exercise, who have an internet-connected device, and currently engage in < 150 min of moderate-intensity exercise per week. Intervention BCS will be matched using evidence-based criteria. The intervention group will receive dyadic exercise information sessions and a program tailored by a QEP for 10 weeks (intervention period) and have access to the QEP for an additional 4 weeks (tapering period). The control will not receive any QEP support. Outcomes The primary outcome is post-intervention self-reported exercise volume. Secondary outcomes include device-assessed exercise volume (i.e., Fitbit), social support, and health-related quality of life. Randomization 108 participants, matched in dyads, will be randomized 1:1 to the MatchQEP or Match groups using a web-based scheme. Statistical analysis Outcomes will be measured at baseline, post-intervention, post-tapering, and at 12 weeks post-intervention follow-up. Discussion: The findings from this RCT will determine if matched BCS dyads who receive 10 weeks of virtually delivered QEP support have higher levels of self-report and device-measured exercise, social support, and health related quality of life compared to matched dyads without QEP-delivered exercise guidance. To our knowledge this will be the first study to assess the combined effect of peer- and QEP support on exercise volume. Project findings will inform and optimize intervention methods aimed to increase exercise among BCS through accessible exercise supports. Trial Registration: The study is registered on ClinicalTrials.gov (study identifier: NCT04771975, protocol Version Number: 2, date: July 22, 2021).

Feasibility and efficacy of enteral tube feeding on weight stability, lean body mass, and patient-reported outcomes in pancreatic cancer cachexia (2021)

Journal of cachexia, sarcopenia and muscle

Abstract

Background: Advanced pancreatic ductal adenocarcinoma (PDAC) is characterized by progressive weight loss and nutritional deterioration. This wasting has been linked to poor survival outcomes, alterations in host defenses, decreased functional ability, and diminished health-related quality of life (HRQOL) in pancreatic cancer patients. There are currently no standardized approaches to the management of pancreatic cancer cachexia. This study explores the feasibility and efficacy of enteral tube feeding of a peptide-based formula to improve weight stability and patient-reported outcomes (PROs) in advanced PDAC patients with cachexia. Methods: This was a single-institution, single-arm prospective trial conducted between April 2015 and March 2019. Eligible patients were adults (>18 years) diagnosed with advanced or locally advanced PDAC and cachexia, defined as greater than 5% unexplained weight loss within 6 months from screening. The study intervention included three 28 day cycles of a semi-elemental peptide-based formula, administered through a jejunal or gastrojejunal feeding tube. The primary outcome was weight stability at 3 months (Cycle 3), defined as weight change less than 0.1 kg/baseline BMI unit from baseline. Secondary outcomes included changes in lean body mass, appendicular lean mass, bone mineral density, fat mass, and percent body fat, as measured with a DEXA scan, HRQOL (EORTC QLQC30) and NIH PROMIS PROs assessed at each cycle. Daily activity (steps, distance, active minutes, heart rate, and sleep) were remotely monitored using a wearable activity monitor (Fitbit) over the 3 month study period. Results: Thirty-six patients were screened for eligibility, 31 patients consented onto study and underwent jejunal tube placement, and 16 patients completed treatment: mean age 67 years (SD 9.3), 43.8% male. Among evaluable patients (n = 16), weight stability was achieved in 10 patients (62.5%), thus completing the trial early. Increases in lean body mass (1273.1, SD: 4078, P = 0.01) and appendicular lean mass (0.45, SD: 0.6, P = 0.02) were observed. Statistically significant improvements at Cycle 3 from baseline were also observed for QLQC30 role function [mean difference (MD): 20.1, P = 0.03], appetite (MD: 27.4, P = 0.02), and global health scores (MD: 13.3, P = 0.05) as well as for NIH PROMIS t-scores for depression (MD: −10.4, P = 0.006) and pain interference (MD: −7.5, P = 0.05). Objectively monitored (Fitbit) activity levels increased, although statistical significance was not reached. Conclusions: Our findings suggest that enteral nutrition support may improve weight stability, lean body mass, appendicular lean mass and PROs in PDAC patients with cachexia who completed treatment, representing a subsample of the study population. The feasibility and role of enteral feeding in routine care remain unclear, and larger and randomized controlled trials are warranted.

Feasibility of Fitness Tracker Usage to Assess Activity Level and Toxicities in Patients With Colorectal Cancer (2021)

JCO clinical cancer informatics

Abstract

PURPOSE: Performance status (PS) is a subjective assessment of patients' overall health. Quantification of physical activity using a wearable tracker (Fitbit Charge [FC]) may provide an objective measure of patient's overall PS and treatment tolerance. MATERIALS AND METHODS: Patients with colorectal cancer were prospectively enrolled into two cohorts (medical and surgical) and asked to wear FC for 4 days at baseline (start of new chemotherapy [± 4 weeks] or prior to curative resection) and follow-up (4 weeks [± 2 weeks] after initial assessment in medical and postoperative discharge in surgical cohort). Primary end point was feasibility, defined as 75% of patients wearing FC for at least 12 hours/d, 3 of 4 assigned days. Mean steps per day (SPD) were correlated with toxicities of interest (postoperative complication or ≥ grade 3 toxicity). A cutoff of 5,000 SPD was selected to compare outcomes. RESULTS: Eighty patients were accrued over 3 years with 55% males and a median age of 59.5 years. Feasibility end point was met with 68 patients (85%) wearing FC more than predefined duration and majority (91%) finding its use acceptable. The mean SPD count for patients with PS 0 was 6,313, and for those with PS 1, it was 2,925 (122 and 54 active minutes, respectively) (P = .0003). Occurrence of toxicity of interest was lower among patients with SPD > 5,000 (7 of 33, 21%) compared with those with SPD < 5,000 (14 of 43, 32%), although not significant (P = .31). CONCLUSION: Assessment of physical activity with FC is feasible in patients with colorectal cancer and well-accepted. SPD may serve as an adjunct to PS assessment and a possible tool to help predict toxicities, regardless of type of therapy. Future studies incorporating FC can standardize patient assessment and help identify vulnerable population.

Wrist-worn devices for the measurement of heart rate and energy expenditure: A validation study for the Apple Watch 6, Polar Vantage V and Fitbit Sense (2022)

European journal of sport science

Abstract

The purpose of this study was to investigate the accuracy of 3 recently released wrist-worn devices (Apple Watch 6, Polar Vantage V and Fitbit Sense) for heart rate and energy expenditure during various activities. The study population consisted of 60 young healthy individuals (30 men and 30 women; age: 24.9 ± 3.0 years, BMI: 23.1 ± 2.7 kg/m2). Heart rate and energy expenditure were measured using the Polar H10 and Metamax 3B, respectively (reference measures) as well as with the 3 wrist-worn devices during 5 different activities (sitting, walking, running, resistance exercises and cycling). The Apple Watch 6 displayed the highest level of accuracy for heart rate measurement with a coefficient of variation (CV) (%) of less than 5% for all 5 activities, whereas the Polar Vantage V and the Fitbit Sense presented various degrees of accuracy (from high to poor accuracy) dependent on the activity (CVs between 2.44-8.80% and 4.14-10.76%, respectively). As for energy expenditure, all 3 devices displayed poor accuracy for all 5 physical activities (CVs between 14.68-24.85% for Apple Watch 6, 16.54-25.78% for Polar Vantage V and 13.44-29.66% for Fitbit Sense). Results of the present study indicate that the Apple Watch 6 was the most accurate for measuring heart rate across all 5 activities, whereas variable levels of accuracy for heart rate measurement for the Polar Vantage V and the Fitbit Sense were observed depending on the activity. As for energy expenditure, all 3 devices showed poor accuracy during all activities. Highlights The Apple Watch 6 was the most accurate for measuring heart rate, whereas the Polar Vantage V and Fitbit Sense showed variable results dependent on the activity The Apple Watch 6, Polar Vantage V and Fitbit Sense showed poor accuracy for energy expenditure during 5 different physical activities Healthcare care professionals, athletes/coaches and the general population may want to proceed with caution on the clinical utility of energy expenditure of these devices during the implementation of an exercise training or nutritional programme.

Concurrent and prospective associations between fitbit wearable-derived RDoC arousal and regulatory constructs and adolescent internalizing symptoms (2022)

Journal of child psychology and psychiatry, and allied disciplines

Abstract

Background: Adolescence is characterized by alterations in biobehavioral functioning, during which individuals are at heightened risk for onset of psychopathology, particularly internalizing disorders. Researchers have proposed using digital technologies to index daily biobehavioral functioning, yet there is a dearth of research examining how wearable metrics are associated with mental health. Methods: We preregistered analyses using the Adolescent Brain Cognitive Development Study dataset using wearable data collection in 5,686 adolescents (123,862 person-days or 2,972,688 person-hours) to determine whether wearable indices of resting heart rate (RHR), step count, and sleep duration and variability in these measures were cross-sectionally associated with internalizing symptomatology. All models were also run controlling for age, sex, body mass index, socioeconomic status, and race. We then performed prospective analyses on a subset of this sample (n = 143) across 25 months that had Fitbit data available at baseline and follow-up in order to explore directionality of effects. Results: Cross-sectional analyses revealed a small, yet significant, effect size (R2 =.053) that higher RHR, lower step count and step count variability, and greater variability in sleep duration were associated with greater internalizing symptoms. Cross-lagged panel model analysis revealed that there were no prospective associations between wearable variables and internalizing symptoms (partial R2 =.026), but greater internalizing symptoms and higher RHR predicted lower step count 25 months later (partial R2 =.010), while higher RHR also predicted lower step count variability 25 months later (partial R2 =.008). Conclusions: Findings indicate that wearable indices concurrently associate with internalizing symptoms during early adolescence, while a larger sample size is likely required to accurately assess prospective or directional effects between wearable indices and mental health. Future research should capitalize on the temporal resolution provided by wearable devices to determine the intensive longitudinal relations between biobehavioral risk factors and acute changes in mental health.

Heart Rate Measurement Accuracy of Fitbit Charge 4 and Samsung Galaxy Watch Active2: Device Evaluation Study (2022)

JMIR formative research

Abstract

Background: Fitness trackers and smart watches are frequently used to collect data in longitudinal medical studies. They allow continuous recording in real-life settings, potentially revealing previously uncaptured variabilities of biophysiological parameters and diseases. Adequate device accuracy is a prerequisite for meaningful research. Objective: This study aims to assess the heart rate recording accuracy in two previously unvalidated devices: Fitbit Charge 4 and Samsung Galaxy Watch Active2. Methods: Participants performed a study protocol comprising 5 resting and sedentary, 2 low-intensity, and 3 high-intensity exercise phases, lasting an average of 19 minutes 27 seconds. Participants wore two wearables simultaneously during all activities: Fitbit Charge 4 and Samsung Galaxy Watch Active2. Reference heart rate data were recorded using a medically certified Holter electrocardiogram. The data of the reference and evaluated devices were synchronized and compared at 1-second intervals. The mean, mean absolute error, mean absolute percentage error, Lin concordance correlation coefficient, Pearson correlation coefficient, and Bland-Altman plots were analyzed. Results: A total of 23 healthy adults (mean age 24.2, SD 4.6 years) participated in our study. Overall, and across all activities, the Fitbit Charge 4 slightly underestimated the heart rate, whereas the Samsung Galaxy Watch Active2 overestimated it (-1.66 beats per minute [bpm]/3.84 bpm). The Fitbit Charge 4 achieved a lower mean absolute error during resting and sedentary activities (seated rest: 7.8 vs 9.4; typing: 8.1 vs 11.6; laying down [left]: 7.2 vs 9.4; laying down [back]: 6.0 vs 8.6; and walking slowly: 6.8 vs 7.7 bpm), whereas the Samsung Galaxy Watch Active2 performed better during and after low- and high-intensity activities (standing up: 12.3 vs 9.0; walking fast: 6.1 vs 5.8; stairs: 8.8 vs 6.9; squats: 15.7 vs 6.1; resting: 9.6 vs 5.6 bpm). Conclusions: Device accuracy varied with activity. Overall, both devices achieved a mean absolute percentage error of just <10%. Thus, they were considered to produce valid results based on the limits established by previous work in the field. Neither device reached sufficient accuracy during seated rest or keyboard typing. Thus, both devices may be eligible for use in respective studies; however, researchers should consider their individual study requirements.

Consistency With and Disengagement From Self-monitoring of Weight, Dietary Intake, and Physical Activity in a Technology-Based Weight Loss Program: Exploratory Study (2022)

JMIR formative research

Abstract

Background: Digital self-monitoring tools offer promise to improve adherence to self-monitoring of weight and weight-related behaviors; however, less is known regarding the patterns of participant consistency and disengagement with these tools. Objective: This study characterizes the consistency of use and time to disengagement with digital self-monitoring tools during a 6-month weight loss intervention and investigates whether the provision of phone-based intervention improved self-monitoring adherence. Methods: Participants were 54 adults with overweight or obesity (mean age 49.6 years, SD 12.4 years; mean BMI 32.6 kg/m2, SD 3.2 kg/m2) enrolled in a pilot trial assessing the impact of self-monitoring technology (Fitbit Zip, Aria scale, and smartphone app), with and without additional interventionist contact, on weight loss. All participants received weight loss education and were asked to self-monitor weight, dietary intake, and physical activity daily throughout the 6-month program. Consistency was defined as the number of weeks that participants adhered to self-monitoring recommendations (7 out of 7 days). Disengagement was defined as the first of 2 consecutive weeks that the 7-day self-monitoring adherence goal was not met. Wilcoxon signed-rank tests were used to examine differences in consistency and disengagement by behavioral targets. t tests (2-tailed) and Cox proportional hazards models were used to examine whether providing additional interventionist contact would lead to significant improvements in consistency and time to disengagement from self-monitoring tools, respectively. Linear regressions were used to examine associations between consistency, time to disengagement, and weight loss. Results: Participants consistently self-monitored physical activity for more weeks (mean 17.4 weeks, SD 8.5 weeks) than weight (mean 11.1 weeks, SD 8.5 weeks) or dietary intake (mean 10.8 weeks, SD 8.7 weeks; P<.05). Similarly, participants had a significantly longer time to disengagement from self-monitoring of physical activity (median 19.5 weeks) than weight (4 weeks) or dietary intake (10 weeks; P<.001). Participants randomized to receive additional interventionist contact had significantly greater consistency and longer time to disengagement for self-monitoring of dietary intake compared with participants who did not (P=.006); however, there were no statistically significant differences between groups for self-monitoring of weight or physical activity (P=.24 and P=.25, respectively). Greater consistency and longer time to disengagement were associated with greater weight loss for self-monitoring of weight and dietary intake (P<.001 and P=.004, respectively) but not for physical activity (P=.57). Conclusions: Results demonstrated that self-monitoring adherence differed by behavioral target, with greater consistency and longer time to disengagement associated with lower-burden tools (ie, self-monitoring of physical activity). Consistent with supportive accountability theory, additional interventionist contact improved consistency and lengthened time to disengagement from self-monitoring of dietary intake. Given the observed associations between consistency, disengagement, and weight loss outcomes, it is important to identify additional methods of increasing consistency and engagement with digital self-monitoring tools.

HRV Monitoring Using Commercial Wearable Devices as a Health Indicator for Older Persons during the Pandemic (2022)

Sensors (Basel, Switzerland)

Abstract

Remote monitoring platforms based on advanced health sensors have the potential to become important tools during the COVID-19 pandemic, supporting the reduction in risks for affected populations such as the elderly. Current commercially available wearable devices still have limitations to deal with heart rate variability (HRV), an important health indicator of human aging. This study analyzes the role of a remote monitoring system designed to support health services to older people during the complete course of the COVID-19 pandemic in Brazil, since its beginning in Brazil in March 2020 until November 2021, based on HRV. Using different levels of analysis and data, we validated HRV parameters by comparing them with reference sensors and tools in HRV measurements. We compared the results obtained for the cardiac modulation data in time domain using samples of 10 elderly people's HRV data from Fitbit Inspire HR with the results provided by Kubios for the same population using a cardiac belt, with the data divided into train and test, where 75% of the data were used for training the models, with the remaining 25% as a test set for evaluating the final performance of the models. The results show that there is very little difference between the results obtained by the remote monitoring system compared with Kubios, indicating that the data obtained from these devices might provide accurate results in evaluating HRV in comparison with gold standard devices. We conclude that the application of the methods and techniques used and reported in this study are useful for the creation and validation of HRV indicators in time series obtained by means of wearable devices based on photoplethysmography sensors; therefore, they can be incorporated into remote monitoring processes as seen during the pandemic.

Commercial Devices Provide Estimates of Energy Balance with Varying Degrees of Validity in Free-Living Adults (2022)

The Journal of nutrition

Abstract

Background: The challenges of accurate estimation of energy intake (EI) are well-documented, with self-reported values 12%-20% below expected values. New approaches rely on gold-standard assessments of the other components of energy balance, energy expenditure (EE) and energy storage (ES), to estimate EI. Objectives: The purpose of this study was to evaluate the validity, repeatability, and measurement error of consumer devices when estimating energy balance in a free-living population. Methods: Twenty-four healthy adults (14 women, 10 men; mean ± SD age: 30.7 ± 8.2 y) completed two 14-d assessment periods, including assessments of EE and ES using gold-standard [doubly labeled water (DLW) and DXA] and commercial devices [Fitbit Alta HR activity monitor (Alta) and Fitbit Aria wireless body composition scale (Aria)], and of EI by dietician-administered recalls. Accuracy and validity were assessed using Spearman correlation, interclass correlation, mean absolute percentage error, and equivalency testing. We also applied linear measurement error modeling including error in gold-standard devices and within-subject repeated-measures design to calibrate consumer devices and quantify error. Results: There was moderate to strong agreement for EE between the Fitbit Alta and DLW at each time point (rs = 0.82 and 0.66 for Times 1 and 2, respectively). There was weak agreement for ES between the Fitbit Aria and DXA (rs = 0.15 and 0.49 for Times 1 and 2, respectively). Correlations between methods to assess EI ranged from weak to strong, with agreement between the DXA/DLW-calculated EI and dietary recalls being the highest (rs = 0.63 for Time 1 and 0.73 for Time 2). Only EE from the Fitbit Alta at Time 1 was equivalent to the DLW value using equivalency testing. Conclusions: Commercial devices provide estimates of energy balance in free-living adults with varying degrees of validity compared to gold-standard techniques. EE estimates were the most robust overall, whereas ES estimates were generally poor.

A multiple technology-based physical activity intervention for Latina adolescents in the USA: randomized controlled trial study protocol for Chicas Fuertes (2022)

Trials

Abstract

Background: Latina adolescents in the USA report some of the lowest rates of physical activity of any demographic subgroup; this is paralleled by a markedly higher lifetime risk of obesity, type 2 diabetes, and other conditions related to inactivity. Despite this, to date, no fully powered clinical trials have tested physical activity interventions specifically for this population. High use of mobile technologies (including text messages, smartphone apps, and social media) suggests this could be an appropriate intervention channel, while also having potential for broad reach. This paper describes the protocol for Chicas Fuertes, a fully powered randomized trial of a mobile technology-based physical activity intervention for Latina adolescents. Methods: We plan to recruit 200 Latina teens (age 13-18) in San Diego, CA, currently engaging in ≤ 150 min/week of moderate-to-vigorous physical activity (MVPA) to be assigned 1:1 to the intervention or control groups. Those randomly assigned to the intervention group receive a one-on-one goal setting session followed by 6 months of mobile technology-based intervention, including a personalized website, Fitbit activity tracker and app, individually tailored text messages based on Fitbit data, and daily intervention content on Instagram. Those randomized to the control group receive only a Fitbit activity tracker. The main outcome is change in weekly minutes of MVPA from baseline to 6 months, measured both objectively (ActiGraph accelerometers and Fitbit Inspire HR) and subjectively (7-Day Physical Activity Recall Interview). Additional outcomes are maintenance of activity change at 12 months and changes in psychosocial constructs, including social support and self-efficacy, engagement with mobile technology channels, and costs of intervention delivery. We are also examining the potential mediators and moderators of the intervention. The efficacy of the intervention is analyzed using a mixed effects regression model, adjusting for any potential confounders not balanced by randomization. All analyses of accelerometer measured MVPA are also adjusted for wear time.

A randomized controlled trial of the effect of supervised exercise on functional outcomes in older African American and non-Hispanic White breast cancer survivors: Are there racial differences in the effects of exercise on functional outcomes? (2022)

Cancer

Abstract

BACKGROUND: The objective of this study was to test the hypothesis that exercise would be more effective than a support group plus Fitbit (SG+Fitbit) program in improving functional outcomes in older breast cancer survivors (BCSs) and that race would moderate the exercise effect on outcomes. METHODS: Older African American (AA) and non-Hispanic White (NHW) BCSs were purposively recruited and enrolled into the 52-week randomized controlled trial. The interventions included 20 weeks of supervised moderate-intensity aero-bic and resistance training followed by 32 weeks of unsupervised exercise called IMPROVE (n = 108) and a 20-week SG+Fitbit program followed by 32 weeks of unsupervised activity (n = 105). Study outcomes were assessed at 20 and 52 weeks. The primary outcome was the change in Short Physical Performance Battery (SPPB) scores 20 weeks from the baseline between arms. Secondary outcomes included change in the 6-Minute Walk Test (6MWT) in meters 20 weeks from the baseline between arms. General linear regression and multivariable logistic regression analyses were used. RESULTS: The mean age was 71.9 years (SD, 5.9 years), and 44% were AA. SPPB scores did not differ between arms (adjusted difference in mean change, 0.13; 95% CI,-0.28 to 0.55; P = .53). However, the exercise arm (vs the SG+Fitbit arm) improved on the 6MWT (21.6 m; 95% CI, 2.5-40.6 m; P = .03). Race moderated the exercise effect on the 6MWT (adjusted interaction effect, 43.3 m; 95% CI, 6.3-80.2 m; P = .02); this implied that the change in the adjusted mean for the 6MWT at 20 weeks from the baseline was 43.3 m higher in AA exercise participants versus NHW exercise participants. CONCLUSIONS: Combined aerobic and resistance exercise appears to improve physical performance in older BCSs, and the exercise effect might be moderated by race, with AAs appearing to derive larger benefits in comparison with NHWs. Larger studies are warranted to confirm the study findings. Cancer 2022;0:1-19.

Accuracy and Precision of Energy Expenditure, Heart Rate, and Steps Measured by Combined-Sensing Fitbits Against Reference Measures: Systematic Review and Meta-analysis (2022)

JMIR mHealth and uHealth

Abstract

Background: Although it is widely recognized that physical activity is an important determinant of health, assessing this complex behavior is a considerable challenge. Objective: The purpose of this systematic review and meta-analysis is to examine, quantify, and report the current state of evidence for the validity of energy expenditure, heart rate, and steps measured by recent combined-sensing Fitbits. Methods: We conducted a systematic review and Bland-Altman meta-analysis of validation studies of combined-sensing Fitbits against reference measures of energy expenditure, heart rate, and steps. Results: A total of 52 studies were included in the systematic review. Among the 52 studies, 41 (79%) were included in the meta-analysis, representing 203 individual comparisons between Fitbit devices and a criterion measure (ie, n=117, 57.6% for heart rate; n=49, 24.1% for energy expenditure; and n=37, 18.2% for steps). Overall, most authors of the included studies concluded that recent Fitbit models underestimate heart rate, energy expenditure, and steps compared with criterion measures. These independent conclusions aligned with the results of the pooled meta-analyses showing an average underestimation of -2.99 beats per minute (k comparison=74), -2.77 kcal per minute (k comparison=29), and -3.11 steps per minute (k comparison=19), respectively, of the Fitbit compared with the criterion measure (results obtained after removing the high risk of bias studies; population limit of agreements for heart rate, energy expenditure, and steps: -23.99 to 18.01, -12.75 to 7.41, and -13.07 to 6.86, respectively). Conclusions: Fitbit devices are likely to underestimate heart rate, energy expenditure, and steps. The estimation of these measurements varied by the quality of the study, age of the participants, type of activities, and the model of Fitbit. The qualitative conclusions of most studies aligned with the results of the meta-analysis. Although the expected level of accuracy might vary from one context to another, this underestimation can be acceptable, on average, for steps and heart rate. However, the measurement of energy expenditure may be inaccurate for some research purposes.

A Digital Health Program Targeting Physical Activity Among Adolescents With Overweight or Obesity: Open Trial (2022)

JMIR pediatrics and parenting

Abstract

Background: Prior studies suggest that mobile health physical activity programs that provide only weekly or daily text-based health coaching evidence limit the efficacy in improving physical activity in adolescents with overweight or obesity. It is possible that incentives, combined with health coaching and daily feedback on goal success, may increase program efficacy; however, such programs have not yet been tested with adolescents with overweight and obesity. Objective: This study aims to examine the feasibility and acceptability of a 12-week, incentive-based, mobile health physical activity program with text-based health coaching, goal setting, and self-monitoring for adolescents with overweight or obesity. Program adherence and changes in tracked physical activity (ie, steps and active minutes while wearing a Fitbit [Google LLC]), body mass, and body fat are assessed. Methods: A total of 28 adolescents aged 13 to 18 years with a BMI ≥90th percentile participated in the program. Of the 28 participants, 2 (7%) were lost to follow-up; thus, data from 26 (93%) participants were used in analyses. Results: Participant-reported acceptability was high, with all mean ratings of text-based coaching, Fitbit use, and the overall program being >5 on a 7-point scale. In addition, 85% (23/26) of participants reported that they would like to continue to wear the Fitbit. Program adherence was also high, as participants wore the Fitbit on 91.1% (SD 12.6%) of days on average and met their weekly goals for an average of 7 (SD 3.5) of 11 possible weeks. There were no demographic (ie, sex, age, and baseline body mass) differences in the percentage of days participants wore their Fitbit. Across the 12-week study, there were significant improvements in tracked daily active minutes (P=.006) and steps (P<.001) and significant pre- to posttest improvements in body fat percentage (P=.04). Conclusions: The pilot program improved adolescent physical activity and physical health. A larger factorial design trial with adaptive daily goals may clarify the role of each program component in driving physical activity.

Virtual AppLication-supported Environment To INcrease Exercise (VALENTINE) during cardiac rehabilitation study: Rationale and design (2022)

American heart journal

Abstract

Background: In-person, exercise-based cardiac rehabilitation improves physical activity and reduces morbidity and mortality for patients with cardiovascular disease. However, activity levels may not be optimized and decline over time after patients graduate from cardiac rehabilitation. Scalable interventions through mobile health (mHealth) technologies have the potential to augment activity levels and extend the benefits of cardiac rehabilitation. Methods: The VALENTINE Study is a prospective, randomized-controlled, remotely-administered trial designed to evaluate an mHealth intervention to supplement cardiac rehabilitation for low- and moderate-risk patients (ClinicalTrials.gov NCT04587882). Participants are randomized to the control or intervention arms of the study. Both groups receive a compatible smartwatch (Fitbit Versa 2 or Apple Watch 4) and usual care. Participants in the intervention arm of the study additionally receive a just-in-time adaptive intervention (JITAI) delivered as contextually tailored notifications promoting low-level physical activity and exercise throughout the day. In addition, they have access to activity tracking and goal setting through the mobile study application and receive weekly activity summaries via email. The primary outcome is change in 6-minute walk distance at 6-months and, secondarily, change in average daily step count. Exploratory analyses will examine the impact of notifications on immediate short-term smartwatch-measured step counts and exercise minutes. Conclusions: The VALENTINE study leverages innovative techniques in behavioral and cardiovascular disease research and will make a significant contribution to our understanding of how to support patients using mHealth technologies to promote and sustain physical activity.

Efficacy and acceptability of using wearable activity trackers in older adults living in retirement communities: a mixed method study (2022)

BMC geriatrics

Abstract

Background: Wearable activity trackers hold the potential for enhancing health and fitness, but the use of wearable activity trackers has remained largely unexplored in older adults. The purpose of the current study was to examine the effectiveness and acceptability of wearable activity trackers for promoting physical activity (PA) in older adults living in retirement communities. Methods: Forty older adult participants (mean age = 85.4 years) used a wearable activity tracker (Fitbit InspireHR) for 12 weeks. Participants were provided with personalized activity goals and weekly feedback of PA during the 12 weeks. The main outcomes were daily step counts collected at baseline and the end of the intervention, and participants' experiences of using the wearable activity tracker assessed after the 12-week intervention through an 8-item questionnaire and individual interviews. Results: Participants used the activity tracker on 97.5% of measured days and had an average increase of 900 steps/day (p < 0.001). The Acceptance questionnaire revealed that the wearable activity tracker was acceptable, useful, and easy to use. Participants found that wearable activity trackers helped improve self-awareness and motivation of PA but reported a few concerns regarding the comfort of wearing the activity trackers and the ease of reading visual feedback. Conclusions: Wearable activity trackers lead to a small but significant increase of PA and are perceived as acceptable and useful in older adults. Given the rapidly growing older population, wearable activity trackers are promising tools that could be used in large-scale interventions to improve PA and health in older adults. Trial registration: Registered on Clinicaltrials.gov # NCT05233813 (Registered on 10/02/2022).

Sex-based differences in remote monitoring of biometric, psychometric and biomarker indices in stable ischemic heart disease (2022)

Biology of sex differences

Abstract

Background: Sex-based differences are crucial to consider in the formulation of a personalized treatment plan. We evaluated sex-based differences in adherence and remotely monitored biometric, psychometric, and biomarker data among patients with stable ischemic heart disease (IHD). Methods: The Prediction, Risk, and Evaluation of Major Adverse Cardiac Events (PRE-MACE) study evaluated patients with stable IHD over a 12-week period. We collected biometric and sleep data using remote patient monitoring via FitBit and psychometric data from Patient-Reported Outcomes Measurement Information System (PROMIS), Kansas City Cardiomyopathy (KCC) and Seattle Angina Questionnaire-7 (SAQ-7) questionnaires. Serum biomarker levels were collected at the baseline visit. We explored sex-based differences in demographics, adherence to study protocols, biometric data, sleep, psychometric data, and biomarker levels. Results: There were 198 patients enrolled, with mean age 65.5 ± 11 years (± Standard deviation, SD), and 60% were females. Females were less adherent to weekly collection of PROMIS, KCC and SAQ-7 physical limitations questionnaires (all p < 0.05), compared to males. There was no difference in biometric physical activity. There was a statistically significant (p < 0.05) difference in sleep duration between sexes, with females sleeping 6 min longer. However, females reported higher PROMIS sleep disturbance scores (p < 0.001) and poorer psychometric scores overall (p < 0.05). A higher proportion of males had clinically significant elevations of median N-terminal pro-brain natriuretic peptide (p = 0.005) and high-sensitivity cardiac troponin levels (p < 0.001) compared to females. Conclusions: Among females and males with stable IHD, there are sex-based differences in remote monitoring behavior and data. Females are less adherent to psychometric data collection and report poorer psychometric and sleep quality scores than males. Elevated levels of biomarkers for MACE are more common in males. These findings may improve sex-specific understanding of IHD using remote patient monitoring.

“The Ripple Effect”: The influence of social support on participation in a small workplace health promotion program (2022)

Health Promotion Journal of Australia

Abstract

Issue addressed: Workplaces are key settings for health promotion. There is limited evidence pertaining to workplace health promotion [WHP] in Australian small and medium enterprises [SMEs], particularly in regional areas. This qualitative study explored employee perceptions of a pilot workplace health promotion program, LifeMAP, conducted in a small health service enterprise in regional Victoria 2014-2017, including facilitators of participation and perceived benefits of participation. Method: Ten LifeMAP participants were recruited using convenience sampling. Individual semi-structured interviews (n = 7) and one focus group (n = 3) were conducted between August and September 2017. Data were analysed inductively and thematically to elicit emergent themes. Results: Social support emerged as the overarching theme influencing participation in LifeMAP, and a perceived benefit of participation. Using FitBits{\textregistered} and setting exercise challenges enabled social support to be fostered through role modelling, staff collegiality and community. There may be gendered differences in the experiences of role modelling in WHP. Conclusions: This SME, and others like it, often feature high levels of employee social connectedness, with a strong workplace and community networks. Social support is a critical influence in social connectedness which needs to be considered and incorporated into the design, implementation and evaluation of WHP programs as a means of overcoming WHP recruitment and participation challenges in regional SMEs. So what?: The identification of social support as a key factor for WHP participation and employee satisfaction is a valuable finding providing insight into how similar programs may be better designed and implemented to enhance WHP program recruitment and retention.

Wrist-mounted accelerometers provide objective evidence of disease and recovery in patients with frozen shoulder (2021)

JSES international

Abstract

Background: Commercially available wrist-mounted exercise monitors may offer objective data on disease and recovery. This study is the first to evaluate the potential of such devices in the assessment of frozen shoulder and the effects of treatment. Methods: Twenty-one patients with isolated, unilateral frozen shoulder wore a wrist-mounted accelerometer (Fitbit Fire II, Fitbit Inc. 2007, California, USA) on each wrist for two separate seven-day periods, one week before and six months after treatment. The monitors produced an activity count for each 24-hour period, accounting for all movements of the upper limb. Three values were calculated for each time period: (1) the mean activity count for each limb, (2) the total activity count for both limbs, and (3) an activity count ratio calculated by dividing the activity of the frozen limb by the unaffected limb. Constant score, American Shoulder and Elbow Surgeons, visual analog scale–pain, and range of movement were recorded before and after treatment. Results: Mean activity counts were significantly lower in the frozen shoulder limb than those in the unaffected limb over the initial seven-day period (6066 vs. 7516; P = .04). The activity count ratio significantly improved after treatment (0.83 vs. 096; p 0.01), whereas the mean total activity count remained similar before and after treatment (14915 vs. 12371; P = .18), demonstrating that activity transferred from the unaffected limb back to the previously frozen limb. Range of movement (P < .01), Constant (P < .01), American Shoulder and Elbow Surgeons (P < .01), and visual analog scale–pain (P < .01) scores all significantly improved after treatment, but there was no correlation with the data from the activity monitor. Discussion: Wrist-mounted accelerometers are sufficiently sensitive to detect a difference in limb activity in patients affected by frozen shoulder. The movement deficit between the affected and unaffected limbs improved by 14% after treatment. These data could be used in conjunction with subjective scores to offer a clearer insight into patient disease burden and recovery.

Impact of a Pilot Structured Mobile Technology Based Lifestyle Intervention for Patients with Nonalcoholic Fatty Liver Disease (2022)

Digestive diseases and sciences

Abstract

Background and Aims: Lifestyle modification is the main treatment for nonalcoholic fatty liver disease (NAFLD), but remains challenging to implement. The aim of this pilot was to assess the acceptability and feasibility of a mobile-technology based lifestyle program for NAFLD patients. Methods: We enrolled adult patients with NAFLD in a 6-month mobile-technology based program where participants received a FitBit with weekly tailored step count goals and nutritional assessments. Anthropometrics, hepatic and metabolic parameters, Fibroscan, physical function and activity, and health-related quality of life measures were obtained at enrollment and month 6. Semi-structured exit interviews were conducted to assess patient's experience with the program. Results: 40 (63%) eligible patients were enrolled. Median age was 52.5 with 53% males, 93% whites, 43% with diabetes and median BMI 33.9. On baseline Fibroscan, 59% had F0-2 fibrosis and 70% had moderate-severe steatosis. 33 patients completed the study. Median percentage of days with valid FitBit data collection was 91. 4 patients increased and maintained, 19 maintained, and 8 increased but subsequently returned to baseline weekly step count. 59% of patients reported Fitbit was easy to use and 66% felt step count feedback motivated them to increase their activity. Roughly 50% of patients had reduction in weight, triglycerides and Fibroscan liver stiffness, and 75% had improvement in controlled attenuation parameter and physical function. Conclusions: A 6-month mobile-technology based pilot lifestyle intervention was feasible and acceptable to NAFLD patients. The program promoted physical activity and was associated with improvement in clinical parameters in some patients.

A distance-based, randomized controlled trial for reducing sedentary behavior among prostate cancer survivors: a study protocol (2022)

BMC public health

Abstract

Background: Prostate cancer survivors (PCS) experience long-term side effects beyond treatment such as fatigue, depression and anxiety. Quality and engaging supportive care programs are needed to reduce these chronic and debilitating effects. Independent of physical activity (PA), high volumes of sedentary behavior (SB) are associated with chronic disease-related risk factors and poorer cancer-specific quality of life (QoL). Simultaneously increasing PA and decreasing SB may be an effective health promotion strategy. Given that PCS may face several barriers to engaging in supervised programs, there is a need to develop and assess the efficacy of interventions that employ distance-based approaches for behavior change. The primary aim of this study is to determine the effects of a 12-week intervention (Fitbit + behavioral counselling) vs. Fitbit-only control group in reducing SB among PCS. Secondary outcomes include light-intensity PA, QoL, motivational outcomes, and patient satisfaction. Methods: This two-armed, randomized controlled trial will recruit inactive PCS (stage I-IV) across Canada who self-report engaging in >8 hours/day of SB. Participants will be randomized to the intervention (n=60; Fitbit and behavioral support) or active control group (n=60; Fitbit-only). The intervention consists of the use of a Fitbit and a series of six behavioral support sessions (two group, four individual) to aid PCS in gradually replacing SB with light-intensity PA by increasing their daily step counts to 3,000 steps above their baseline values. The Fitbit-only control condition will receive a Fitbit and public health PA resources. The primary outcome is change in SB measured objectively using activPAL inclinometers. All secondary outcomes will be measured via self-report, except for PA which will be measuring using Fitbits. Data will be collected at baseline, post-intervention, and at 6-month post-intervention. Discussion: Reducing SB and increasing light-intensity PA plays an important, yet often undervalued role in the health and well-being of PCS. This study will create a unique distance-based platform that can be used by clinical and community-based organizations as a low-cost, supportive care tool to improve health outcomes for PCS.

Responsiveness of an activity tracker as a measurement tool in a knee osteoarthritis clinical trial (ACTIVe-OA study) (2022)

Annals of physical and rehabilitation medicine

Abstract

Background: In osteoarthritis (OA) clinical trials, reliable and responsive outcome measures to document physical and functional improvements are limited. Objective: This study aimed to assess whether the use of an activity tracker in an OA clinical trial is a responsive measurement tool. Secondary objectives assessed feasibility and validity. Methods: We recruited 65 participants in a prospective cohort study nested in a placebo-controlled clinical trial of platelet-rich plasma injection in knee OA. Participants wore an activity tracker (Fitbit Flex 2), and a smartphone was preloaded with a mobile application (OApp) designed to monitor load rates as a surrogate of knee loading. Participants used the systems for 7 days at baseline and for 7 days before the 2-month follow-up assessment. Effect size (ES) and standardised response mean (SRM) were calculated for change in step count and knee loading rate and regularly used knee OA outcome measures. Correlation coefficients (r) were calculated to examine the strength of the association between outcome measures. Results:. Step count showed a trivial ES and SRM and mean knee loading rate measurements a moderate ES and SRM. We found a weak but significant correlation between change in mean steps per day and global improvement overall (r= 0.28) and Western Ontario and McMaster Universities Osteoarthritis Index function (r = -0.28). Compliance was high with the activity trackers. Conclusions: Despite good feasibility, this study did not show significant responsiveness or validity of the activity trackers as compared with currently recommended outcome measures in OA clinical trials. The main challenge is the lack of a gold standard outcome measure to validate against, and because of the complex interplay between pain and measured function, a lack of correlation does not necessarily represent a failed validation in this context. Australian New Zealand Clinical Trials Registry: ACTRN12617000853347. This trial is a substudy of the “Platelet-rich plasma as a symptom-and disease-modifying treatment for knee osteoarthritis – the RESTORE trial”.

Associations of COVID-19 lockdown restrictions with longer-term activity levels of working adults with type 2 diabetes (2022)

JMIR diabetes

Abstract

Background: Lockdown restrictions reduce COVID-19 community transmission; however, they may pose challenges for non-communicable disease management. A 112-day hard lockdown in Victoria, Australia (commencing March 23, 2020), coincided with an intervention trial of reducing and breaking up sitting time in desk workers with type 2 diabetes who were using a provided consumer grade activity tracker (Fitbit). Objective: To compare continuously recorded activity levels preceding and during COVID-19 lockdown restrictions among working adults with type 2 diabetes participating in a sitting less and moving more intervention. Methods: Eleven participants (8/11 male; mean [SD] age 52.8 [5.0] years) in Melbourne, Australia had Fitbit activity tracked before (mean [SD]: 122.7 [47.9] days) and during (99.7 [62.5] days) city-wide COVID-19 lockdown restrictions. Regression models compared device (Fitbit Inspire HR)-derived activity (steps; METs [metabolic equivalents], mean time in sedentary, lightly, fairly, and very active minutes, and usual bout durations) during restrictions, to pre-restriction. Changes in activity were statistically significant when estimates (Δ%) did not intercept zero. Results: Overall, there was a decrease in mean: steps (-1,584 steps/day; Δ% : 9%; 95%CI: -11, -7); METs (-83 METs/day; Δ% : -5%; 95%CI: -6, -5); and, lightly active (Δ% : -4%; 95%CI: -8, -1), fairly active (Δ% : -8%; 95%CI: -21, -15), and very active (Δ% : -8%; 95%CI: -11, -5) intensity minutes per day, and increases in mean sedentary minutes per day (+51 mins/day; Δ% : +3%; 95%CI: 1, 6). Only very active (+5.1 mins) and sedentary (+4.3 mins) bout durations changed significantly. Conclusions: In a convenience sample of adults with type 2 diabetes, COVID-19 lockdown restrictions were associated with decreases in overall activity levels and increases in very active and sedentary bout durations. A Fitbit monitor provided meaningful continuous long-term data in this context.

Continuous Monitoring of Vital Signs With Wearable Sensors During Daily Life Activities: Validation Study (2022)

JMIR formative research

Abstract

Background: Continuous telemonitoring of vital signs in a clinical or home setting may lead to improved knowledge of patients' baseline vital signs and earlier detection of patient deterioration, and it may also facilitate the migration of care toward home. Little is known about the performance of available wearable sensors, especially during daily life activities, although accurate technology is critical for clinical decision-making. Objective: The aim of this study is to assess the data availability, accuracy, and concurrent validity of vital sign data measured with wearable sensors in volunteers during various daily life activities in a simulated free-living environment. Methods: Volunteers were equipped with 4 wearable sensors (Everion placed on the left and right arms, VitalPatch, and Fitbit Charge 3) and 2 reference devices (Oxycon Mobile and iButton) to obtain continuous measurements of heart rate (HR), respiratory rate (RR), oxygen saturation (SpO2), and temperature. Participants performed standardized activities, including resting, walking, metronome breathing, chores, stationary cycling, and recovery afterward. Data availability was measured as the percentage of missing data. Accuracy was evaluated by the median absolute percentage error (MAPE) and concurrent validity using the Bland-Altman plot with mean difference and 95% limits of agreement (LoA). Results: A total of 20 volunteers (median age 64 years, range 20-74 years) were included. Data availability was high for all vital signs measured by VitalPatch and for HR and temperature measured by Everion. Data availability for HR was the lowest for Fitbit (4807/13,680, 35.14% missing data points). For SpO2 measured by Everion, median percentages of missing data of up to 100% were noted. The overall accuracy of HR was high for all wearable sensors, except during walking. For RR, an overall MAPE of 8.6% was noted for VitalPatch and that of 18.9% for Everion, with a higher MAPE noted during physical activity (up to 27.1%) for both sensors. The accuracy of temperature was high for VitalPatch (MAPE up to 1.7%), and it decreased for Everion (MAPE from 6.3% to 9%). Bland-Altman analyses showed small mean differences of VitalPatch for HR (0.1 beats/min [bpm]), RR (−0.1 breaths/min), and temperature (0.5 °C). Everion and Fitbit underestimated HR up to 5.3 (LoA of −39.0 to 28.3) bpm and 11.4 (LoA of −53.8 to 30.9) bpm, respectively. Everion had a small mean difference with large LoA (−10.8 to 10.4 breaths/min) for RR, underestimated SpO2 (>1%), and overestimated temperature up to 2.9 °C. Conclusions: Data availability, accuracy, and concurrent validity of the studied wearable sensors varied and differed according to activity. In this study, the accuracy of all sensors decreased with physical activity. Of the tested sensors, VitalPatch was found to be the most accurate and valid for vital signs monitoring.

Reference Values for Wristband Accelerometry Data in Children Aged 6-11 Years of Age (2022)

Frontiers in pediatrics

Abstract

Objectives: Wristband activity trackers (accelerometers) could serve as a convenient monitoring tool to continuously quantify physical activity throughout the day. We aim to provide reference values for the use of these devices in healthy children. Methods: Children were recruited at a local school and provided with activity trackers (Fitbit Charge 2). Pupils were instructed to wear devices during all normal daytime activities over a period of 11-15 days. Demographic data, total number of daily steps and heart rate were recorded. In addition, all children/parents were asked to complete a questionnaire providing information about daily physical routine (mode of transport to school, sporting activities as well as sport club memberships). Results: Three hundred two children (54.6% boys; median age 8.7 years) participated in this prospective study. Median wearing time of the device was 12.1 h/day. Overall, the median daily total step count was 12,095. Median step counts/day were significantly higher in boys compared to girls (13,015 vs. 11,305 steps/day; p < 0.0001). In addition, step counts were significantly higher during the week, compared to weekend days. The effect of age on daily step count was found to be non-linear: the total daily step count increased from 6 to 8.5 years of age, while older children (aged >8.5 years) had lower step counts compared to the younger children. Significant predictors of the daily step count were male gender (+1,324.9 steps, p = 0.0008), mode of transportation to school (walking, bicycle, scooter: +865.5 steps p = 0.049), active membership in a sports club (+1,324.9 steps/day, p = 0.0008), and number of structured units of physical exercise performed (+336.5/per 45 min, p < 0.0001). Severe obesity was associated with a significant reduction in total daily step count (-3037.7 steps/day, p = 0.015). Conclusion: Our prospective cohort study of healthy school children provides reference values for wristband accelerometers in normal individuals. In addition, it clarifies the effect of age, body weight and lifestyle on normal daily step counts in school children. This data should be helpful to judge the degree of physical limitation of patients compared to healthy peers.

Home-based cardiac rehabilitation using information and communication technology for heart failure patients with frailty (2022)

ESC heart failure

Abstract

Aims: Cardiac rehabilitation (CR) is an evidence-based, secondary preventive strategy that improves mortality and morbidity rates in patients with heart failure (HF). However, the implementation and continuation of CR remains unsatisfactory, particularly for outpatients with physical frailty. This study investigated the efficacy and safety of a comprehensive home-based cardiac rehabilitation (HBCR) programme that combines patient education, exercise guidance, and nutritional guidance using information and communication technology (ICT). Methods and results: This study was a single-centre, open-label, randomized, controlled trial. Between April 2020 and November 2020, 30 outpatients with chronic HF (New York Heart Association II-III) and physical frailty were enrolled. The control group (n = 15) continued with standard care, while the HBCR group (n = 15) also received comprehensive, individualized CR, including ICT-based exercise and nutrition guidance using ICT via a Fitbit® device for 3 months. The CR team communicated with each patient in HBCR group once a week via the application messaging tool and planned the training frequency and intensity of training individually for the next week according to each patient's symptoms and recorded pulse data during exercise. Dietitians conducted a nutritional assessment and then provided individual nutritional advice using the picture-posting function of the application. The primary outcome was the change in the 6 min walking distance (6MWD). The participants' mean age was 63.7 ± 10.1 years, 53% were male, and 87% had non-ischaemic heart disease. The observed change in the 6MWD was significantly greater in the HBCR group (52.1 ± 43.9 m vs. -4.3 ± 38.8 m; P < 0.001) at a 73% of adherence rate. There was no significant change in adverse events in either group. Conclusions: Our comprehensive HBCR programme using ICT for HF patients with physical frailty improved exercise tolerance and improved lower extremity muscle strength in our sample, suggesting management with individualized ICT-based programmes as a safe and effective approach. Considering the increasing number of HF patients with frailty worldwide, our approach provides an efficient method to keep patients engaged in physical activity in their daily life.

A Remote Health Coaching, Text-Based Walking Program in Ethnic Minority Primary Care Patients With Overweight and Obesity: Feasibility and Acceptability Pilot Study (2022)

JMIR formative research

Abstract

Background: Over half of US adults have at least one chronic disease, including obesity. Although physical activity is an important component of chronic disease self-management, few reach the recommended physical activity goals. Individuals who identify as racial and ethnic minorities are disproportionally affected by chronic diseases and physical inactivity. Interventions using consumer-based wearable devices have shown promise for increasing physical activity among patients with chronic diseases; however, populations with the most to gain, such as minorities, have been poorly represented to date. Objective: This study aims to assess the feasibility, acceptability, and preliminary outcomes of an 8-week text-based coaching and Fitbit program aimed at increasing the number of steps in a predominantly overweight ethnic minority population. Methods: Overweight patients (BMI >25 kg/m2) were recruited from an internal medicine clinic located in an inner-city academic medical center. Fitbit devices were provided. Using 2-way SMS text messaging, health coaches (HCs) guided patients to establish weekly step goals that were specific, measurable, attainable, realistic, and time-bound. SMS text messaging and Fitbit activities were managed using a custom-designed app. Program feasibility was assessed via the recruitment rate, retention rate (the proportion of eligible participants completing the 8-week program), and patient engagement (based on the number of weekly text message goals set with the HC across the 8-week period). Acceptability was assessed using a qualitative, summative evaluation. Exploratory statistical analysis included evaluating the average weekly steps in week 1 compared with week 8 using a paired t test (2-tailed) and modeling daily steps over time using a linear mixed model. Results: Of the 33 patients initially screened; 30 (91%) patients were enrolled in the study. At baseline, the average BMI was 39.3 (SD 9.3) kg/m2, with 70% (23/33) of participants presenting as obese. A total of 30% (9/30) of participants self-rated their health as either fair or poor, and 73% (22/30) of participants set up ≥6 weekly goals across the 8-week program. In total, 93% (28/30) of participants completed a qualitative summative evaluation, and 10 themes emerged from the evaluation: patient motivation, convenient SMS text messaging experience, social support, supportive accountability, technology support, self-determined goals, achievable goals, feedback from Fitbit, challenges, and habit formation. There was no significant group change in the average weekly steps for week 1 compared with week 8 (mean difference 7.26, SD 6209.3; P=.99). However, 17% (5/30) of participants showed a significant increase in their daily steps. Conclusions: Overall, the results demonstrate the feasibility and acceptability of a remotely delivered walking study that included an HC; SMS text messaging; a wearable device (Fitbit); and specific, measurable, attainable, realistic, and time-bound goals within an ethnic minority patient population. Results support further development and testing in larger samples to explore efficacy.

Factors related to daily step counts of stroke patients during hospitalization in a convalescent rehabilitation ward (2022)

Journal of stroke and cerebrovascular diseases : the official journal of National Stroke Association

Abstract

Objectives: Clarifying the factors related to decreased physical activity in post-stroke patients is essential for effective disease management. This study aimed to examine the factors influencing the amount of daily steps taken by post-stroke patients in a convalescent rehabilitation ward during activities other than rehabilitation (non-rehabilitation steps). Materials and methods: Eighty-nine post-stroke patients (60.8±14.4 years; 55 men) were enrolled. The inclusion criteria were walking independently within the ward and having a walking speed of ≥24 m/min. Data on patient clinical characteristics including age, sex, body mass index, stroke type, hemiparetic side, and time from stroke onset were collected. Stroke impairment and motor and cognitive functional disabilities were assessed using the Stroke Impairment Assessment Set and the Functional Independence Measure, respectively. The non-rehabilitation steps were calculated by subtracting the steps during the rehabilitation activities from the total steps using Fitbit Flex2. Results: The average number of non-rehabilitation steps was 4,523±2,339 steps/day. The hierarchical multiple regression analysis revealed that sex, motor disability, and the interaction term of stroke impairment with cognitive disability were significantly related to non-rehabilitation steps. Simple slope analysis demonstrated that the stroke impairment slope was steeper at lower levels than at higher levels of cognitive disability for non-rehabilitation steps. Conclusions: In addition to independent effects of sex and motor disability, this study found that stroke impairment and cognitive disability were interactively related to non-rehabilitation steps in post-stroke patients in a convalescent rehabilitation ward. These findings may provide useful information for managing physical activity in post-stroke patients after hospital discharge.

The Effectiveness of a Computer-Tailored Web-Based Physical Activity Intervention Using Fitbit Activity Trackers in Older Adults (Active for Life): Randomized Controlled Trial (2022)

Journal of medical Internet research

Abstract

Background Physical activity is an integral part of healthy aging; yet, most adults aged ≥65 years are not sufficiently active. Preliminary evidence suggests that web-based interventions with computer-tailored advice and Fitbit activity trackers may be well suited for older adults. Objective The aim of this study was to examine the effectiveness of Active for Life, a 12-week web-based physical activity intervention with 6 web-based modules of computer-tailored advice to increase physical activity in older Australians. Methods Participants were recruited both through the web and offline and were randomly assigned to 1 of 3 trial arms: tailoring+Fitbit, tailoring only, or a wait-list control. The computer-tailored advice was based on either participants’ Fitbit data (tailoring+Fitbit participants) or self-reported physical activity (tailoring-only participants). The main outcome was change in wrist-worn accelerometer (ActiGraph GT9X)–measured moderate to vigorous physical activity (MVPA) from baseline to after the intervention (week 12). The secondary outcomes were change in self-reported physical activity measured by means of the Active Australia Survey at the midintervention point (6 weeks), after the intervention (week 12), and at follow-up (week 24). Participants had a face-to-face meeting at baseline for a demonstration of the intervention and at baseline and week 12 to return the accelerometers. Generalized linear mixed model analyses were conducted with a γ distribution and log link to compare MVPA and self-reported physical activity changes over time within each trial arm and between each of the trial arms. Results A total of 243 participants were randomly assigned to tailoring+Fitbit (n=78, 32.1%), tailoring only (n=96, 39.5%), and wait-list control (n=69, 28.4%). Attrition was 28.8% (70/243) at 6 weeks, 31.7% (77/243) at 12 weeks, and 35.4% (86/243) at 24 weeks. No significant overall time by group interaction was observed for MVPA (P=.05). There were no significant within-group changes for MVPA over time in the tailoring+Fitbit group (+3%, 95% CI –24% to 40%) or the tailoring-only group (–4%, 95% CI –24% to 30%); however, a significant decline was seen in the control group (–35%, 95% CI –52% to –11%). The tailoring+Fitbit group participants increased their MVPA 59% (95% CI 6%-138%) more than those in the control group. A significant time by group interaction was observed for self-reported physical activity (P=.02). All groups increased their self-reported physical activity from baseline to week 6, week 12, and week 24, and this increase was greater in the tailoring+Fitbit group than in the control group at 6 weeks (+61%, 95% CI 11%-133%). Conclusions A computer-tailored physical activity intervention with Fitbit integration resulted in improved MVPA outcomes in comparison with a control group in older adults.

The effectiveness of the Structured Health Intervention For Truckers (SHIFT): a cluster randomised controlled trial (RCT) (2022)

BMC Medicine

Abstract

Background Long distance heavy goods vehicle (HGV) drivers exhibit higher than nationally representative rates of obesity, and obesity-related co-morbidities, and are underserved in terms of health promotion initiatives. The purpose of this study was to evaluate the effectiveness of the multicomponent ‘Structured Health Intervention For Truckers’ (SHIFT), compared to usual care, at 6- and 16–18-month follow-up. Methods We conducted a two-arm cluster RCT in transport sites throughout the Midlands, UK. Outcome measures were assessed at baseline, at 6- and 16–18-month follow-up. Clusters were randomised (1:1) following baseline measurements to either the SHIFT arm or usual practice control arm. The 6-month SHIFT programme included a group-based interactive 6-h education and behaviour change session, health coach support and equipment provision (Fitbit® and resistance bands/balls to facilitate a ‘cab workout’). The primary outcome was device-assessed physical activity (mean steps/day) at 6 months. Secondary outcomes included the following: device-assessed sitting, physical activity intensity and sleep; cardiometabolic health, diet, mental wellbeing and work-related psychosocial variables. Data were analysed using mixed-effect linear regression models using a complete-case population. Results Three hundred eighty-two HGV drivers (mean ± SD age: 48.4 ± 9.4 years, BMI: 30.4 ± 5.1 kg/m2, 99% male) were recruited across 25 clusters (sites) and randomised into either the SHIFT (12 clusters, n = 183) or control (13 clusters, n = 199) arms. At 6 months, 209 (55%) participants provided primary outcome data. Significant differences in mean daily steps were found between groups, in favour of the SHIFT arm (adjusted mean difference: 1008 steps/day, 95% CI: 145–1871, p = 0.022). Favourable differences were also seen in the SHIFT group, relative to the control group, in time spent sitting (− 24 mins/day, 95% CI: − 43 to − 6), and moderate-to-vigorous physical activity (6 mins/day, 95% CI: 0.3–11). Differences were not maintained at 16–18 months. No differences were observed between groups in the other secondary outcomes at either follow-up. Conclusions The SHIFT programme led to a potentially clinically meaningful difference in daily steps, between trial arms, at 6 months. Whilst the longer-term impact is unclear, the programme offers potential to be incorporated into driver training courses to promote activity in this at-risk, underserved and hard-to-reach essential occupational group.

Investigation of physical activity, sleep, and mental health recovery in treatment resistant depression (TRD) patients receiving repetitive transcranial magnetic stimulation (rTMS) treatment (2022)

Journal of affective disorders reports

Abstract

Background: Repetitive transcranial magnetic stimulation (rTMS) is effective in treating depression; however, the effect on physical activity, sleep and recovery is unclear. This study investigated rTMS effect on physical activity and sleep through providing patients with a Fitbit and software apps; and reports the impact of rTMS on depression, anxiety and mental health recovery. Methods: Study design was a pre and post data collection without a control, with twenty-four participants with treatment-resistant depression (TRD). Measures used were Fitbit activity and sleep data, and patient-rated Recovering Quality of Life (ReQoL-20), Patient Health Questionnaire (PHQ-9) and Generalised Anxiety Disorder (GAD-7). Results: Response and remission rates were, respectively: 34.8% and 39% for PHQ-9; 34.8% and 47.8% for GAD-7. ReQoL-20 response and reliable improvement were 29.4% and 53%. PHQ-9, GAD-7 and ReQol-20 scores significantly improved, with large effect sizes. Analysis of Fitbit activity and sleep data yielded non-significant results. The Fitbit data machine learning model classified two levels of depression to 82% accuracy. Limitations: rTMS treatment was open-label and adjunct to existing antidepressant medication. No control group. Female patients were overrepresented. Conclusions: Improvements on the ReQoL-20 and aspects of sleep and activity indicate the positive impact of rTMS on the individual's real world functioning and quality of life. A wearable activity tracker can provide feedback to patients and clinicians on sleep, physical activity and depression levels. Further research could be undertaken through a sufficiently powered RCT comparing rTMS versus rTMS with use of a Fitbit, its software applications, and sleep and physical activity advice.

Human-Centered Design of a Digital Health Tool to Promote Effective Self-care in Patients With Heart Failure: Mixed Methods Study (2022)

JMIR Formative Research

Abstract

Background Effective self-care is an important factor in the successful management of patients with heart failure (HF). Despite the importance of self-care, most patients with HF are not adequately taught the wide range of skills required to become proficient in self-care. Digital health technology (DHT) may provide a novel solution to support patients at home in effective self-care, with the view to enhancing the quality of life and ultimately improving patient outcomes. However, many of the solutions developed to date have failed to consider users’ perspectives at the point of design, resulting in poor effectiveness. Leveraging a human-centered design (HCD) approach to the development of DHTs may lead to the successful promotion of self-care behaviors in patients with HF. Objective This study aimed to outline the HCD, development, and evaluation process of a DHT designed to promote effective self-care in patients with HF. Methods A design thinking approach within the HCD framework was undertaken, as described in the International Organization for Standardization 9241-210:2019 regulations, using a 5-step process: empathize, ideate, design, develop, and test. Patients with HF were involved throughout the design and evaluation of the system. The designed system was grounded in behavior change theory using the Theoretical Domains Framework and included behavior change techniques. Mixed methods were used to evaluate the DHT during the testing phase. Results Steps 1 to 3 of the process resulted in a set of evidence- and user-informed design requirements that were carried forward into the iterative development of a version 1 system. A cross-platform (iOS and Android) mobile app integrated with Fitbit activity trackers and smart scales was developed. A 2-week user testing phase highlighted the ease of use of the system, with patients demonstrating excellent adherence. Qualitative analysis of semistructured interviews identified the early potential for the system to positively influence self-care. Specifically, users perceived that the system increased their confidence and motivation to engage in key self-care behaviors, provided them with skills and knowledge that made them more aware of the importance of self-care behaviors, and might facilitate timely help seeking. Conclusions The use of an HCD methodology in this research has resulted in the development of a DHT that may engage patients with HF and potentially affect their self-care behaviors. This comprehensive work lays the groundwork for further development and evaluation of this solution before its implementation in health care systems. A detailed description of the HCD process used in this research will help guide the development and evaluation of future DHTs across a range of disease use cases.

A Facebook-Delivered Weight Loss Intervention Using Open Enrollment: Randomized Pilot Feasibility Trial (2022)

JMIR Formative Research

Abstract

Background Behavioral weight loss programs typically enroll 12-40 people into groups that then suffer from declining engagement over time. Web-based patient communities, on the other hand, typically offer no limits on capacity and membership is fluid. This model may be useful for boosting engagement in behavioral weight loss interventions, which could lead to better outcomes. Objective In this study, we aimed to examine the feasibility and acceptability of continuously enrolling participants into a Facebook-delivered weight loss intervention for the first 8 of 16 weeks relative to the same intervention where no new participants were enrolled after randomization. Methods We conducted a randomized pilot trial to compare a Facebook weight loss group that used open enrollment with a group that used closed enrollment on feasibility and acceptability in adults with BMI 27-45 kg/m2. The feasibility outcomes included retention, engagement, and diet tracking adherence. We described the percentage loss of ≥5% weight in both groups as an exploratory outcome. We also explored the relationship between total volume of activity in the group and weight loss. The participants provided feedback via web-based surveys and focus groups. Results Randomized participants (68/80, 85% women) were on average, aged 40.2 (SD 11.2) years with a mean BMI of 34.4 (SD 4.98) kg/m2. We enrolled an additional 54 participants (50/54, 93% female) in the open enrollment condition between weeks 1 and 8, resulting in a total group size of 94. Retention was 88% and 98% under the open and closed conditions, respectively. Randomized participants across conditions did not differ in engagement (P=.72), or diet tracking adherence (P=.42). Participant feedback in both conditions revealed that sense of community was what they liked most about the program and not enough individualized feedback was what they liked the least. Weight loss of ≥5% was achieved by 30% (12/40) of the participants randomized to the open enrollment condition and 18% (7/40) of the participants in the closed enrollment condition. Exploratory analyses revealed that the open condition (median 385, IQR 228-536.5) had a greater volume of engagement than the closed condition (median 215, IQR 145.5-292; P=.007). Furthermore, an increase of 100 in the total volume of engagement in the Facebook group each week was associated with an additional 0.1% weekly weight loss among the randomized participants (P=.02), which was independent of time, individual participant engagement, and sociodemographic characteristics. Conclusions Open enrollment was as feasible and acceptable as closed enrollment. A greater volume of engagement in the Facebook group was associated with weight loss, suggesting that larger groups that produce more engagement overall may be beneficial. Future research should examine the efficacy of the open enrollment approach for weight loss in a fully powered randomized trial.

Predictors of engagement with remote sensing technologies for symptom measurement in Major Depressive Disorder (2022)

Journal of Affective Disorders

Abstract

Background Remote sensing for the measurement and management of long-term conditions such as Major Depressive Disorder (MDD) is becoming more prevalent. User-engagement is essential to yield any benefits. We tested three hypotheses examining associations between clinical characteristics, perceptions of remote sensing, and objective user engagement metrics. Methods The Remote Assessment of Disease and Relapse – Major Depressive Disorder (RADAR-MDD) study is a multicentre longitudinal observational cohort study in people with recurrent MDD. Participants wore a FitBit and completed app-based assessments every two weeks for a median of 18 months. Multivariable random effects regression models pooling data across timepoints were used to examine associations between variables. Results A total of 547 participants (87.8% of the total sample) were included in the current analysis. Higher levels of anxiety were associated with lower levels of perceived technology ease of use; increased functional disability was associated with small differences in perceptions of technology usefulness and usability. Participants who reported higher system ease of use, usefulness, and acceptability subsequently completed more app-based questionnaires and tended to wear their FitBit activity tracker for longer. All effect sizes were small and unlikely to be of practical significance. Limitations Symptoms of depression, anxiety, functional disability, and perceptions of system usability are measured at the same time. These therefore represent cross-sectional associations rather than predictions of future perceptions. Conclusions These findings suggest that perceived usability and actual use of remote measurement technologies in people with MDD are robust across differences in severity of depression, anxiety, and functional impairment.

Predictive Modeling of Mental Illness Onset Using Wearable Devices and Medical Examination Data: Machine Learning Approach (2022)

Frontiers in Digital Health

Abstract

The prevention and treatment of mental illness is a serious social issue. Prediction and intervention, however, have been difficult because of lack of objective biomarkers for mental illness. The objective of this study was to use biometric data acquired from wearable devices as well as medical examination data to build a predictive model that can contribute to the prevention of the onset of mental illness. This was an observational study of 4,612 subjects from the health database of society-managed health insurance in Japan provided by JMDC Inc. The inputs to the predictive model were 3-months of continuous wearable data and medical examinations within and near that period; the output was the presence or absence of mental illness over the following month, as defined by insurance claims data. The features relating to the wearable data were sleep, activity, and resting heart rate, measured by a consumer-grade wearable device (specifically, Fitbit). The predictive model was built using the XGBoost algorithm and presented an area-under-the-receiver-operating-characteristic curve of 0.712 (SD = 0.02, a repeated stratified group 10-fold cross validation). The top-ranking feature importance measure was wearable data, and its importance was higher than the blood-test values from medical examinations. Detailed verification of the model showed that predictions were made based on disrupted sleep rhythms, mild physical activity duration, alcohol use, and medical examination data on disrupted eating habits as risk factors. In summary, the predictive model showed useful accuracy for grouping the risk of mental illness onset, suggesting the potential of predictive detection, and preventive intervention using wearable devices. Sleep abnormalities in particular were detected as wearable data 3 months prior to mental illness onset, and the possibility of early intervention targeting the stabilization of sleep as an effective measure for mental illness onset was shown.

Sleep habits of high school student-athletes and non-athletes during a semester (2022)

Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine

Abstract

Study objectives: Lack of sleep has been shown to be harmful to athletic and academic performance as well as health and well-being. The primary purpose of this study was to analyze the sleep and physical activity differences between U.S. high school student-athletes and non-athletes during a semester of school and competition. Methods: Participants included 34 student-athletes (18 males and 16 females), age 15.8 ± 0.8 years and 38 non-athletes (10 males and 28 females), age 16.3 ± 0.7 years. Objective sleep and physical activity outcomes were collected using Fitbit wrist-worn activity trackers for 8-14 consecutive days and nights, measuring total sleep time (TST), sleep efficiency (SE), bedtimes, wake times, and steps counted. Results: Student-athletes and non-athletes did not differ in TST (440.4 ± 46.4 vs. 438.1 ± 41.7 min, p = 0.82) and SE (93.6 ± 2.3 vs. 92.9 ± 2.3%, p = 0.20). Fitbit data revealed that 79% of student-athletes and 87% of non-athletes failed to get greater than the minimally recommended 8 hours of total sleep time per night. Student-athletes had significantly more steps per day (10,163 ± 2,035 vs. 8,418 ± 2,489, p < 0.01). Student-athletes had earlier bedtimes and wake times. Earlier bedtimes were significantly correlated with increased TST (p < 0.01). Earlier wake times were significantly correlated to increased steps per day (p < 0.01). Conclusions: Participation in high school sports may not have a detrimental effect on a student's sleep habits. High school students are not meeting the recommended 8-10 hours of sleep per night. Going to bed and waking up early were linked to healthier outcomes. Consistent and earlier sleep/wake schedules may optimize students sleep and health.

Low- and High-Intensity Physical Activity Among People with HIV: Multilevel Modeling Analysis Using Sensor- and Survey-Based Predictors (2022)

JMIR mHealth and uHealth

Abstract

Background: High-intensity physical activity improves the health of people with HIV. Even when people have good intentions to engage in physical activity, they often find it difficult to maintain physical activity behavior in the long term. Two Minds Theory is a neurocognitive model that explains gaps between people's intentions and behaviors based on the operations of 2 independent mental systems. This model predicts that everyday experiences will affect physical activity and that factors outside people's awareness, such as sleep and stress, can have particularly strong effects on physical activity behaviors. Objective: We designed this study to test the effects of daily experiences on physical activity among people with HIV, including measures of people's conscious experiences using daily electronic surveys and measures of nonconscious influences using sensor devices. Methods: In this study, 55 people with HIV wore a Fitbit Alta for 30 days to monitor their physical activity, sleep, and heart rate variability (HRV) as a physiological indicator of stress. Participants also used their smartphones to complete daily electronic surveys for the same 30 days about fatigue, self-efficacy, mood, stress, coping, motivation, and barriers to self-care. Time-lagged, within-person, multilevel models were used to identify the best prospective predictors of physical activity, considering the daily survey responses of people with HIV and sensor data as predictors of their physical activity the following day. We also tested baseline surveys as predictors of physical activity for comparison with daily variables. Results: Different people had different average levels of physical activity; however, physical activity also varied substantially from day to day, and daily measures were more predictive than baseline surveys. This suggests a chance to intervene based on day-to-day variations in physical activity. High-intensity physical activity was more likely when people with HIV reported less subjective fatigue on the prior day (r=-0.48) but was unrelated to actual sleep based on objective sensor data. High-intensity physical activity was also predicted by higher HRV (r=0.56), indicating less stress, lower HIV-related stigma (r=-0.21), fewer barriers to self-care (r=-0.34), and less approach coping (r=-0.34). Similar variables predicted lower-level physical activity measured based on the number of steps per day of people with HIV. Conclusions: Some predictors of physical activity, such as HRV, were only apparent based on sensor data, whereas others, such as fatigue, could be measured via self-report. Findings about coping were unexpected; however, other findings were in line with the literature. This study extends our prior knowledge on physical activity by demonstrating a prospective effect of everyday experiences on physical activity behavior, which is in line with the predictions of Two Minds Theory. Clinicians can support the physical activity of people with HIV by helping their patients reduce their daily stress, fatigue, and barriers to self-care.

Quality Evaluation of Free-living Validation Studies for the Assessment of 24-Hour Physical Behavior in Adults via Wearables: Systematic Review (2022)

JMIR mHealth and uHealth

Abstract

Background: Wearable technology is a leading fitness trend in the growing commercial industry and an established method for collecting 24-hour physical behavior data in research studies. High-quality free-living validation studies are required to enable both researchers and consumers to make guided decisions on which study to rely on and which device to use. However, reviews focusing on the quality of free-living validation studies in adults are lacking. Objective: This study aimed to raise researchers' and consumers' attention to the quality of published validation protocols while aiming to identify and compare specific consistencies or inconsistencies between protocols. We aimed to provide a comprehensive and historical overview of which wearable devices have been validated for which purpose and whether they show promise for use in further studies. Methods: Peer-reviewed validation studies from electronic databases, as well as backward and forward citation searches (1970 to July 2021), with the following, required indicators were included: protocol must include real-life conditions, outcome must belong to one dimension of the 24-hour physical behavior construct (intensity, posture or activity type, and biological state), the protocol must include a criterion measure, and study results must be published in English-language journals. The risk of bias was evaluated using the Quality Assessment of Diagnostic Accuracy Studies-2 tool with 9 questions separated into 4 domains (patient selection or study design, index measure, criterion measure, and flow and time). Results: Of the 13,285 unique search results, 222 (1.67%) articles were included. Most studies (153/237, 64.6%) validated an intensity measure outcome such as energy expenditure. However, only 19.8% (47/237) validated biological state and 15.6% (37/237) validated posture or activity-type outcomes. Across all studies, 163 different wearables were identified. Of these, 58.9% (96/163) were validated only once. ActiGraph GT3X/GT3X+ (36/163, 22.1%), Fitbit Flex (20/163, 12.3%), and ActivPAL (12/163, 7.4%) were used most often in the included studies. The percentage of participants meeting the quality criteria ranged from 38.8% (92/237) to 92.4% (219/237). On the basis of our classification tree to evaluate the overall study quality, 4.6% (11/237) of studies were classified as low risk. Furthermore, 16% (38/237) of studies were classified as having some concerns, and 72.9% (173/237) of studies were classified as high risk. Conclusions: Overall, free-living validation studies of wearables are characterized by low methodological quality, large variability in design, and focus on intensity. Future research should strongly aim at biological state and posture or activity outcomes and strive for standardized protocols embedded in a validation framework. Standardized protocols for free-living validation embedded in a framework are urgently needed to inform and guide stakeholders (eg, manufacturers, scientists, and consumers) in selecting wearables for self-tracking purposes, applying wearables in health studies, and fostering innovation to achieve improved validity.

Integrating a physical activity coaching intervention into diabetes care: a mixed-methods evaluation of a pilot pragmatic trial (2022)

Translational behavioral medicine

Abstract

Physical activity (PA) counseling is under-utilized in primary care for patients with type 2 diabetes mellitus (T2D), despite improving important health outcomes, including physical function. We adapted evidence-based PA counseling programs to primary care patients, staff, and leader's needs, resulting in "Be ACTIVE" comprised of shared PA tracker data (FitBit), six theory-informed PA coaching calls, and three in-person clinician visits. In a pilot randomized pragmatic trial, we evaluated the feasibility, acceptability, and effectiveness of Be ACTIVE. Sedentary patients with T2D were randomized to Be ACTIVE versus an enhanced control condition. Mixed methods assessments of feasibility and acceptability included costs. Objective pilot effectiveness outcomes included PA (primary outcome, accelerometer steps/week), the Short Physical Performance Battery (SPPB) physical function measure, and behavioral PA predictors. Fifty patients were randomized to Be ACTIVE or control condition. Acceptability was >90% for patients and clinic staff. Coaching and PA tracking costs of ~$90/patient met Medicare reimbursement criteria. Pre-post PA increased by ~11% (Be ACTIVE) and ~6% in controls (group difference: 1574 ± 4391 steps/week, p = .72). As compared to controls, Be ACTIVE participants significantly improved SPPB (0.9 ± 0.3 vs. -0.1 ± 0.3, p = .01, changes >0.5 points prevent falls clinically), and PA predictors of self-efficacy (p = .02) and social-environmental support (p < .01). In this pilot trial, Be ACTIVE was feasible and highly acceptable to stakeholders and yielded significant improvements in objective physical function consistent with lower fall risk, whereas PA changes were less than anticipated. Be ACTIVE may need additional adaptation or a longer duration to improve PA outcomes.

Evaluation of the Immediate Effects of Web-Based Intervention Modules for Goals, Planning, and Coping Planning on Physical Activity: Secondary Analysis of a Randomized Controlled Trial on Weight Loss Maintenance (2022)

Journal of medical Internet research

Abstract

Background: The use of digital interventions can be accurately monitored via log files. However, monitoring engagement with intervention goals or enactment of the actual behaviors targeted by the intervention is more difficult and is usually evaluated based on pre-post measurements in a controlled trial. Objective: The objective of this paper is to evaluate if engaging with 2 digital intervention modules focusing on (1) physical activity goals and action plans and (2) coping with barriers has immediate effects on the actual physical activity behavior. Methods: The NoHoW Toolkit (TK), a digital intervention developed to support long-term weight loss maintenance, was evaluated in a 2 x 2 factorial randomized controlled trial. The TK contained various modules based on behavioral self-regulation and motivation theories, as well as contextual emotion regulation approaches, and involved continuous tracking of weight and physical activity through connected commercial devices (Fitbit Aria and Charge 2). Of the 4 trial arms, 2 had access to 2 modules directly targeting physical activity: a module for goal setting and action planning (Goal) and a module for identifying barriers and coping planning (Barriers). Module visits and completion were determined based on TK log files and time spent in the module web page. Seven physical activity metrics (steps; activity; energy expenditure; fairly active, very active and total active minutes; and distance) were compared before and after visiting and completing the modules to examine whether the modules had immediate or sustained effects on physical activity. Immediate effect was determined based on 7-day windows before and after the visit, and sustained effects were evaluated for 1 to 8 weeks after module completion. Results: Out of the 811 participants, 498 (61.4%) visited the Goal module and 406 (50.1%) visited the Barriers module. The Barriers module had an immediate effect on very active and total active minutes (very active minutes: before median 24.2, IQR 10.4-43.0 vs after median 24.9, IQR 10.0-46.3; P=.047; total active minutes: before median 45.1, IQR 22.9-74.9 vs after median 46.9, IQR 22.4-78.4; P=.03). The differences were larger when only completed Barriers modules were considered. The Barriers module completion was also associated with sustained effects in fairly active and total active minutes for most of the 8 weeks following module completion and for 3 weeks in very active minutes. Conclusions: The Barriers module had small, significant, immediate, and sustained effects on active minutes measured by a wrist-worn activity tracker. Future interventions should pay attention to assessing barriers and planning coping mechanisms to overcome them.

A Longitudinal Study of Fitbit Usage Behavior Among College Students (2022)

Cyberpsychology, behavior and social networking

Abstract

Fitbit wearable devices provide users with objective data on their physical activity and sleep habits. However, little is known about how users develop their usage patterns and the key mechanisms underlying the development of such patterns. In this article, we report results from a longitudinal analysis of Fitbit usage behavior among a sample of college students. Survey and Fitbit data were collected from 692 undergraduates at the University of Notre Dame across two waves. We use a structural equation modeling strategy to examine the relationships among three dimensions of Fitbit usage behavior corresponding to three elements of the habit loop model: trust in the accuracy of Fitbit physical activity and sleep data (cue), intensity of Fitbit device use (routine), and adjustment of physical activity and sleep behaviors based on Fitbit data (reward). More than 75 percent of participants trusted the accuracy of Fitbit data and nearly half of the participants reported they adjusted their physical activities based on the data reported by their devices. Participants who trusted the Fitbit physical activity data also tended to trust the sleep data, and those who intensively used Fitbit devices tended to adjust both their physical activities and then sleep habits. Psychological states and traits such as depression, extroversion, agreeableness, and neuroticism help predict multiple dimensions of Fitbit usage behaviors. However, we find little evidence that trust, Fitbit usage, or perceived adjustment of activity or sleep were associated with actual changes in levels of sleep and activity. We discuss the implications of these findings for understanding when and how this new monitoring technology results in changes in people's behavior.

Fitbit Use and Activity Levels From Intervention to 2 Years After: Secondary Analysis of a Randomized Controlled Trial (2022)

JMIR mHealth and uHealth

Abstract

Background: There has been a rapid increase in the use of commercially available activity trackers, such as Fitbit, in physical activity intervention research. However, little is known about the long-term sustained use of trackers and behavior change after short-term interventions. Objective: This study aims to use minute-level data collected from a Fitbit tracker for up to 2 years after the end of a randomized controlled trial to examine patterns of Fitbit use and activity over time. Methods: Participants in this secondary data analysis were 75 female breast cancer survivors who had been enrolled in a 12-week physical activity randomized controlled trial. Participants randomized to the exercise intervention (full intervention arm) received a Fitbit One, which was worn daily throughout the 12-week intervention, and then were followed for 2 years after the intervention. Participants randomized to the waitlist arm, after completing the randomized controlled trial, received a Fitbit One and a minimal version of the exercise intervention (light intervention arm), and then were followed for 2 years after the intervention. Average and daily adherence and MVPA were compared between the 2 groups in the interventional and postinterventional periods using both linear and generalized additive mixed effects models. Results: Adherence to wearing the Fitbit during the 12-week intervention period was significantly higher in the full intervention arm than in the light intervention arm (85% vs 60%; P<.001). Average adherence was significantly lower for both study arms during the follow-up period than in the intervention period; however, there were statistically different patterns of adherence during the follow-up period, with the light intervention arm having steeper declines than the full intervention arm over time (P<.001). Similar to the adherence results, mean minutes of Fitbit-measured MVPA was higher for the full intervention arm than for the light intervention arm during the 12-week intervention period (mean MVPA 27.89 minutes/day, SD 16.38 minutes/day vs 18.35 minutes/day, SD 12.64 minutes/day; P<.001). During the follow-up period, average MVPA was significantly lower than the 12-week intervention period for both the full intervention arm (21.74 minutes/day, SD 24.65 minutes/day; P=.002) and the light intervention arm (15.03 minutes/day, SD 13.27 minutes/day; P=.004). Although the mean MVPA in each arm was similar across the follow-up period (P=.33), the pattern of daily MVPA was significantly different between the 2 groups (P<.001). Conclusions: While adherence to wearing activity trackers and maintaining physical activities declined after completion of a 12-week exercise intervention, a more active interventional strategy resulted in greater wear time and activity levels during the intervention and more stable patterns of adherence and activity in the long term. An improved understanding of long-term maintenance patterns may inform improved exercise interventions that result in sustained increases in physical activity.

Comparison of free-living physical activity measurements between ActiGraph GT3X-BT and Fitbit Charge 3 in young people with haemophilia (2022)

Haemophilia : the official journal of the World Federation of Hemophilia

Abstract

Introduction: Measurement of physical activity (PA) using commercial activity trackers such as Fitbit devices has become increasingly popular, also for people with haemophilia (PWH). The accuracy of the Fitbit model Charge 3 has not yet been examined. Aims: To compare the Fitbit Charge 3 against the research-grade accelerometer ActiGraph GT3X-BT in measuring average daily steps and minutes spent in different PA intensities. Methods: Twenty-four young PWH wore a wrist-worn Fitbit Charge 3 and hip-worn ActiGraph GT3X-BT simultaneously for seven consecutive days in free-living conditions. Correlation of and differences between the devices for daily averages of PA parameters were assessed using Pearson's correlation coefficient and paired t-test, respectively. Agreement between devices was assessed using Bland-Altman plots. Results: Twenty participants (mean age 21.8) were included in the analyses. We found moderate to high correlations between Fitbit and ActiGraph measured daily averages for all PA variables, but statistically significant differences between devices for all variables except daily minutes of moderate PA. Fitbit overestimated average daily steps, minutes of light, vigorous and moderate-to-vigorous PA. Bland-Altman plots showed a measurement bias between devices for all parameters with increasing overestimation by the Fitbit for higher volumes of PA. Conclusion: The Fitbit Charge 3 overestimated steps and minutes of light, moderate and moderate-to-vigorous PA as compared to the ActiGraph GT3X-BT, and this bias increased with PA volume. The Fitbit should therefore be used with caution in research, and we advise users of the device to be cognizant of this overestimation.

Effects of Sleep-Extend on glucose metabolism in women with a history of gestational diabetes: a pilot randomized trial (2022)

Pilot and feasibility studies

Abstract

Women with a history of gestational diabetes (GDM) are at 7-fold increase in the risk of developing diabetes. Insufficient sleep has also been shown to increase diabetes risk. This study aimed to explore the feasibility of a sleep extension in women with a history of GDM and short sleep, and effects on glucose metabolism. Women age 18–45 years with a history of GDM and actigraphy confirmed short sleep duration (<7 h/night) on weekdays were randomized at a ratio of 1 control (heathy living information) to 2 cases (6 weeks of “Sleep-Extend” intervention: use of a Fitbit, weekly digital content, and weekly coaching to increase sleep duration). An oral glucose tolerance test (OGTT), 7-day actigraphy recording, and questionnaires were obtained at baseline and 6 weeks. Mean differences between baseline and end-of-intervention parameters were compared using independent samples t-tests. Mean (SD) sleep duration increased within the Sleep-Extend group (n=9, +26.9 (42.5) min) but decreased within the controls (n=5, − 9.1 (20.4) min), a mean difference (MD) of 35.9 min (95% confidence interval (CI) − 8.6, 80.5). Fasting glucose increased, but less in Sleep-Extend vs. control groups (1.6 (9.4) vs 10.4 (8.2) mg/dL, MD − 8.8 mg/dL (95% CI − 19.8, 2.1), while 2-h glucose levels after an OGTT did not differ. Compared to controls, Sleep-Extend had decreased fatigue score (MD − 10.6, 95%CI − 20.7, − 0.6), and increased self-report physical activity (MD 5036 MET- minutes/week, 95%CI 343, 9729. Fitbit compliance and satisfaction in Sleep-Extend group was high. Sleep extension is feasible in women with a history of GDM, with benefits in fatigue and physical activity, and possibly glucose metabolism. These data support a larger study exploring benefits of sleep extension on glucose metabolism in these high-risk women. ClinicalTrials.gov , NCT03638102 (8/20/2018)

A Randomized Clinical Trial Demonstrating Feasibility and Preliminary Efficacy of a Videoconference-Delivered Physical Activity Lifestyle Intervention Among Adolescents With a Congenital Heart Defect (2022)

Annals of behavioral medicine : a publication of the Society of Behavioral Medicine

Abstract

Background: Individuals with congenital heart defects are at increased risk for developing further cardiovascular complications, which can be mitigated by increasing physical activity. Given that positive health behaviors begin declining during older adolescence, it is vital to promote lifestyle changes in this population. Purpose: The current study aims to (a) determine the feasibility/acceptability of the Congenital Heart Disease Physical Activity Lifestyle (CHD-PAL) intervention among adolescents (ages 15-18) with moderate and complex congenital heart defects, and (b) estimate the preliminary efficacy of CHD-PAL for increasing time spent in moderate-to-vigorous physical activity (MVPA) and cardiorespiratory fitness and decreasing sedentary behavior. Methods: Eligible participants were randomized into either CHD-PAL (eight 30-min videoconferencing sessions over 20 weeks with an interventionist + Fitbit + exercise prescription) or a comparator (Fitbit + exercise prescription). Results: Sixty adolescents were randomized (76% recruitment rate; 94% of participants were retained from baseline to follow-up). Most adolescents (73%) and their parents/guardians (76%) reported that the trial was enjoyable. While there was no effect of arm on change in MVPA, sedentary behavior, or cardiorespiratory fitness for the entire sample, among those who engaged in <21 min of MVPA on average at baseline, adolescents in the CHD-PAL intervention had an increase of 16 min/day of MVPA more than comparators (d = 0.90). Conclusions: The CHD-PAL intervention warrants examination in a larger trial to establish efficacy among those adolescents with a congenital heart defect who engage in <21 min of MVPA/day and should include follow-up assessments to examine effect durability.

Performance of a Multisensor Smart Ring to Evaluate Sleep: In-Lab and Home-Based Evaluation of Generalized and Personalized Algorithms (2022)

Sleep

Abstract

Study objectives: Wearable sleep technology has rapidly expanded across the consumer market due to advances in technology and increased interest in personalized sleep assessment to improve health and mental performance. We tested the performance of a novel device, the Happy Ring, alongside other commercial wearables (Actiwatch 2, Fitbit Charge 4, Whoop 3.0, Oura Ring V2), against in-lab polysomnography (PSG) and an at-home EEG-derived sleep monitoring device, the Dreem 2 Headband. Methods: 36 healthy adults with no diagnosed sleep disorders and no recent use of medications or substances known to affect sleep pattern were assessed across 77 nights. Subjects participated in a single night of in-lab PSG and 2 nights of at-home data collection. The Happy Ring includes sensors for skin conductance, movement, heart rate, and skin temperature. The Happy Ring utilized two machine-learning derived scoring algorithms: a "generalized" algorithm that applied broadly to all users, and a "personalized" algorithm that adapted to individual subjects' data. Epoch-by-epoch analyses compared the wearable devices to in-lab PSG and to at-home EEG Headband. Results: Compared to in-lab PSG, the "generalized" and "personalized" algorithms demonstrated good sensitivity (94% and 93%, respectively) and specificity (70% and 83%, respectively). The Happy Personalized model demonstrated a lower bias and more narrow limits of agreement across Bland-Altman measures. Conclusion: The Happy Ring performed well at home and in the lab, especially regarding sleep/wake detection. The personalized algorithm demonstrated improved detection accuracy over the generalized approach and other devices, suggesting that adaptable, dynamic algorithms can enhance sleep detection accuracy.

The Need for Objective Physical Activity Measurements in Routine Bariatric Care (2022)

Obesity surgery

Abstract

Purpose This study aims to (1) quantify physical behavior through self-reports and sensor-based measures, (2) examine the correlation between self-reported and sensor-based physical activity (PA) and (3) assess whether bariatric patients adhere to PA guidelines. Methods A Fitbit accelerometer was used to collect minute-to-minute step count and heart rate data for 14 consecutive days. Total physical activity levels (PAL), moderate-to-vigorous intensity physical activity (MVPA) and sedentary behavior (SB) were used to quantify physical behavior. Self-reported PA was assessed with the International Physical Activity Questionnaire (IPAQ). To analyze the association between sensor-based and self-reported PA, Spearman’s correlation was used. A minimum of 150 MVPA minutes per week was considered as compliance with the PA guidelines. Results Fitbit data of 37 pre- and 18 post-surgery patients was analyzed. Participants averaged 7403 ± 3243 steps/day and spent most of their time sedentary (832 min, IQR: 749 – 879), especially in prolonged periods of ≥ 30 min (525, IQR: 419 – 641). Median MVPA time was 5.6 min/day (IQR: 1.7 – 10.6). Correlations between self-reported and sensor-based MVPA and SB were respectively 0.072 and 0.455. Only 17.1% was objectively adherent to MVPA guidelines ≥ 150 min/week, while 94.3% met the guidelines in case of self-reports. Conclusion PA quantification confirmed that bariatric patients are highly sedentary and rarely engage in MVPA, despite a relatively high daily step count. Moreover, bariatric patients are not able to assess MVPA and moderately their SB by self-reports. Our results indicate the need for sensor-based PA monitoring in routine bariatric care.

Sign of the times: Community engagement to refine a cardiovascular mHealth intervention through a virtual focus group series during the COVID-19 Pandemic (2022)

Digital health

Abstract

Background: African-Americans are underrepresented in mobile health intervention research studies which can perpetuate health inequities and the digital divide. A community-based, user-centered approach to designing mobile health interventions may increase their sociocultural relevance and effectiveness, especially with increased smartphone use during the coronavirus disease 2019 pandemic. We aimed to refine an existing mobile health intervention via a virtual focus group series. Methods: African-American community members (n = 15) from churches in Minneapolis-St. Paul and Rochester, Minnesota were enrolled in a virtual (via videoconferencing), three-session focus group series over five months to refine a cardiovascular health-focused mobile health application (FAITH! [Fostering African-American Improvement in Total Health!] App). Participants accessed the app via their smartphones and received a Fitbit synced to the app. Participants engaged with multimedia cardiovascular health-focused education modules, a sharing board for social networking, and diet/physical activity self-monitoring. Participant feedback on app features prompted iterative revisions to the FAITH! App. Primary outcomes were app usability (assessed via Health Information Technology Usability Evaluation Scale range: 0-5) and user satisfaction. Results: Participants (mean age [SD]: 56.9 [12.3] years, 86.7% female) attended a mean 2.8 focus groups (80% attended all sessions). The revised FAITH! App exceeded the goal Health Information Technology Usability Evaluation Scale score threshold of ≥4 (mean: 4.39, range: 3.20-4.95). Participants positively rated updated app content, visual appeal, and use of social incentives to maintain engagement. Increasing user control and refinement of the moderated sharing board were identified as areas for future improvement. Conclusions: Community-partnered, virtual focus groups can optimize usability and increase participant satisfaction of mobile health lifestyle interventions that aim to promote cardiovascular health in African-Americans.

Daily steps are associated with walking ability in hospitalized patients with sub-acute stroke (2022)

Scientific reports

Abstract

Increased physical activity is required in patients with stroke that are hospitalized in the rehabilitation unit. This study investigated the association between the daily number of steps and walking independence in order to determine the cutoff value of daily number of steps that can predict walking independence in hospitalized patients with sub-acute stroke. This cross-sectional observational study included 85 stroke patients admitted to the rehabilitation unit. The average daily number of steps was measured using Fitbit One for 4 days starting at 30 days after stroke onset. 6-min walk test, and Fugl-Meyer assessment of the lower extremities were measured The category of walking independence was classified using the Functional Ambulation Category (FAC). The subjects were divided into two groups according to the FAC score: a walking independence group (FAC ≥ 4) and a walking non-independence group (FAC ≤ 3). Logistic regression analysis was conducted to investigate the association of daily number of steps with walking independence and a receiver operating characteristic curve was used to identify the cutoff value of daily number of steps for predicting walking independence. The daily number of steps (per 1000 steps) was independently associated with walking independence (odds ratio (OR); 2.53, 95% confidence interval (CI); 1.40-5.73, p = 0.009). The cutoff value of daily number of steps for predicting independent walking was 4286 steps (area under the curve = 0.914, sensitivity of 0.731, and specificity of 0.949). The daily number of steps was associated with independent walking in hospitalized patients with sub-acute stroke. The daily number of steps may be a useful target in rehabilitation for patients with sub-acute stroke.

Protocol for a multi-center randomized controlled trial to evaluate the benefits of exercise incentives and corticosteroid injections in osteoarthritis of the knee (MOVE-OK) (2022)

Trials

Abstract

Background: Knee osteoarthritis (KOA) is a high-priority problem among the aging population. While exercise has been shown to be beneficial in management of the disease, scalable and low-cost interventions to improve exercise in this population are lacking. Recent controversy over the value of corticosteroid injections for palliation has also arisen. Therefore, we designed a randomized, double-blind, placebo-controlled clinical trial with a 2-period crossover design to study (1) behavioral incentives to promote exercise and (2) corticosteroid injections to reduce pain and improve function in patients with KOA when compared to lidocaine only. Methods: The study design is a pragmatic factorial and crossover randomized clinical trial. Patients with KOA who are deemed eligible by their provider to receive knee injections and are able to walk without assistive devices will be recruited from clinical practices at four sites within the Veterans Affairs (VA) Health System in the USA. In total, 220 participants will be randomized to receive social incentives with gamification (i.e., incorporation of game elements) to promote exercise and compared to controls that receive a Fitbit but no incentive. Each patient will also be assigned to receive a blinded corticosteroid injection and a lidocaine-only injection in random order. The primary outcomes are the change in average daily step counts from baseline and the change in Knee Osteoarthritis Outcome Score (KOOS) from baseline. The study team will continuously collect step count, heart rate, and sleep data using activity monitors and patient-reported outcomes using the Way to Health (WTH) platform at two four-week intervals over eight months of follow-up. Mixed effects regression incorporating all available data points will be used for analysis. Discussion: The "Marching on for Veterans with Osteoarthritis of the Knee" (MOVE-OK) trial will take a pragmatic approach to evaluate (1) whether incentives based on behaviorally enhanced gamification can improve physical activity in this patient population and (2) whether corticosteroids injections reduce pain and disability in patients with KOA. Results of this trial will help to direct clinical practice and inform management guidelines.

Using wearables to promote physical activity in old age : Feasibility, benefits, and user friendliness (2022)

Zeitschrift fur Gerontologie und Geriatrie

Abstract

Background: Wearables provide new opportunities to promote physical activity also among older adults but data on effectiveness and user friendliness are rare. Objective: The effects of a comprehensive self-regulative intervention on moderate to vigorous physical activity (MVPA) and number of steps were examined using commercially available activity trackers. Acceptance regarding the devices was analysed in various domains. Methods: In this study 80 older adults (mean = 67.03 years, standard deviation = 3.97 years; 59% women) wore a Fitbit Charge HR for 21 days including a baseline, a postintervention and a follow-up week. The intervention comprised feedback, goal setting and planning and 50% of the participants were additionally randomized to a role model component. Social cognitive predictors based on the health action process approach (HAPA) and user experience were assessed via questionnaires. Results: The MVPA increased by an average of 19 min per week and steps by 1317 per day. An additional benefit of the role model component could be observed for MVPA. In the follow-up, the intervention effect was still significant for the number of steps, while MVPA dropped back to baseline. Multilevel models including HAPA variables explained small but significant amounts of variance in MVPA (8% within-person, 26% between-person) and steps (11% within-person, 12% between-person). User experience was rated as very high. Conclusion: Providing an intervention based on established behavior change techniques and self-monitoring via wearables seems to be effective for increasing physical activity among older adults. The HAPA variables seem to play a limited role to explain activity levels. Acceptance of wearables can be expected to be high.

The Effect of Tailored, Daily, Smartphone Feedback to Lifestyle Self-Monitoring on Weight Loss at 12 Months: the SMARTER Randomized Clinical Trial (2022)

Journal of medical Internet research

Abstract

Background: Self-monitoring (SM) is the centerpiece of behavioral weight loss treatment, but the efficacy of smartphone-delivered SM feedback (FB) has not been tested in large, long-term, randomized trials. Objective: The aim of this study was to establish the efficacy of providing remote FB to diet, physical activity (PA), and weight SM on improving weight loss outcomes when comparing the SM plus FB (SM+FB) condition to the SM-only condition in a 12-month randomized controlled trial. The study was a single-site, population-based trial that took place in southwestern Pennsylvania, USA, conducted between 2018 and 2021. Participants were smartphone users age ≥18 years, able to engage in moderate PA, with a mean BMI between 27 and 43 kg/m2. Methods: All participants received a 90-minute, one-to-one, in-person behavioral weight loss counseling session addressing behavioral strategies, establishing participants' dietary and PA goals, and instructing on use of the PA tracker (Fitbit Charge 2), smart scale, and diet SM app. Only SM+FB participants had access to an investigator-developed smartphone app that read SM data, in which an algorithm selected tailored messages sent to the smartphone up to 3 times daily. The SM-only participants did not receive any tailored FB based on SM data. The primary outcome was percent weight change from baseline to 12 months. Secondary outcomes included engagement with digital tools (eg, monthly percentage of FB messages opened and monthly percentage of days adherent to the calorie goal). Results: Participants (N=502) were on average 45.0 (SD 14.4) years old with a mean BMI of 33.7 (SD 4.0) kg/m2. The sample was 79.5% female (n=399/502) and 82.5% White (n=414/502). At 12 months, retention was 78.5% (n=394/502) and similar by group (SM+FB: 202/251, 80.5%; SM: 192/251, 76.5%; P=.28). There was significant percent weight loss from baseline in both groups (SM+FB: -2.12%, 95% CI -3.04% to -1.21%, P<.001; SM: -2.39%, 95% CI -3.32% to -1.47%; P<.001), but no difference between the groups (-0.27%; 95% CI -1.57% to 1.03%; t =-0.41; P=.68). Similarly, 26.3% (66/251) of the SM+FB group and 29.1% (73/251) of the SM group achieved ≥5% weight loss (chi-square value=0.49; P=.49). A 1% increase in FB messages opened was associated with a 0.10 greater percent weight loss at 12 months (b=-0.10; 95% CI -0.13 to -0.07; t =-5.90; P<.001). A 1% increase in FB messages opened was associated with 0.12 greater percentage of days adherent to the calorie goal per month (b=0.12; 95% CI 0.07-0.17; F=22.19; P<.001). Conclusions: There were no significant between-group differences in weight loss; however, the findings suggested that the use of commercially available digital SM tools with or without FB resulted in a clinically significant weight loss in over 25% of participants. Future studies need to test additional strategies that will promote greater engagement with digital tools.

The structured health intervention for truckers (SHIFT) cluster randomised controlled trial: a mixed methods process evaluation (2022)

The international journal of behavioral nutrition and physical activity

Abstract

BACKGROUND: Long distance heavy goods vehicle (HGV) drivers exhibit higher than nationally representative rates of obesity, and obesity-related co-morbidities, and are underserved in terms of health promotion initiatives. The purpose of this study was to evaluate the effectiveness of the multicomponent 'Structured Health Intervention For Truckers' (SHIFT), compared to usual care, at 6- and 16-18-month follow-up. METHODS: We conducted a two-arm cluster RCT in transport sites throughout the Midlands, UK. Outcome measures were assessed at baseline, at 6- and 16-18-month follow-up. Clusters were randomised (1:1) following baseline measurements to either the SHIFT arm or usual practice control arm. The 6-month SHIFT programme included a group-based interactive 6-h education and behaviour change session, health coach support and equipment provision (Fitbit{\textregistered} and resistance bands/balls to facilitate a 'cab workout'). The primary outcome was device-assessed physical activity (mean steps/day) at 6 months. Secondary outcomes included the following: device-assessed sitting, physical activity intensity and sleep; cardiometabolic health, diet, mental wellbeing and work-related psychosocial variables. Data were analysed using mixed-effect linear regression models using a complete-case population. RESULTS: Three hundred eighty-two HGV drivers (mean ± SD age: 48.4 ± 9.4 years, BMI: 30.4 ± 5.1 kg/m(2), 99% male) were recruited across 25 clusters (sites) and randomised into either the SHIFT (12 clusters, n = 183) or control (13 clusters, n = 199) arms. At 6 months, 209 (55%) participants provided primary outcome data. Significant differences in mean daily steps were found between groups, in favour of the SHIFT arm (adjusted mean difference: 1008 steps/day, 95% CI: 145-1871, p = 0.022). Favourable differences were also seen in the SHIFT group, relative to the control group, in time spent sitting (- 24 mins/day, 95% CI: - 43 to - 6), and moderate-to-vigorous physical activity (6 mins/day, 95% CI: 0.3-11). Differences were not maintained at 16-18 months. No differences were observed between groups in the other secondary outcomes at either follow-up. CONCLUSIONS: The SHIFT programme led to a potentially clinically meaningful difference in daily steps, between trial arms, at 6 months. Whilst the longer-term impact is unclear, the programme offers potential to be incorporated into driver training courses to promote activity in this at-risk, underserved and hard-to-reach essential occupational group. TRIAL REGISTRATION: ISRCTN10483894 (date registered: 01/03/2017).

Utilizing daily mood diaries and wearable sensor data to predict depression and suicidal ideation among medical interns (2022)

Journal of affective disorders

Abstract

Background: Intensive longitudinal methods (ILMs) for collecting self-report (e.g., daily diaries, ecological momentary assessment) and passive data from smartphones and wearable sensors provide promising avenues for improved prediction of depression and suicidal ideation (SI). However, few studies have utilized ILMs to predict outcomes for at-risk, non-clinical populations in real-world settings. Methods: Medical interns (N = 2881; 57 % female; 58 % White) were recruited from over 300 US residency programs. Interns completed a pre-internship assessment of depression, were given Fitbit wearable devices, and provided daily mood ratings (scale: 1-10) via mobile application during the study period. Three-step hierarchical logistic regressions were used to predict depression and SI at the end of the first quarter utilizing pre-internship predictors in step 1, Fitbit sleep/step features in step 2, and daily diary mood features in step 3. Results: Passively collected Fitbit features related to sleep and steps had negligible predictive validity for depression, and no incremental predictive validity for SI. However, mean-level and variability in mood scores derived from daily diaries were significant independent predictors of depression and SI, and significantly improved model accuracy. Limitations: Work schedules for interns may result in sleep and activity patterns that differ from typical associations with depression or SI. The SI measure did not capture intent or severity. Conclusions: Mobile self-reporting of daily mood improved the prediction of depression and SI during a meaningful at-risk period under naturalistic conditions. Additional research is needed to guide the development of adaptive interventions among vulnerable populations.

A Smartwatch Step-Counting App for Older Adults: Development and Evaluation Study (2022)

JMIR aging

Abstract

Background: Older adults who engage in physical activity can reduce their risk of mobility impairment and disability. Short amounts of walking can improve quality of life, physical function, and cardiovascular health. Various programs have been implemented to encourage older adults to engage in physical activity, but sustaining their motivation continues to be a challenge. Ubiquitous devices, such as mobile phones and smartwatches, coupled with machine-learning algorithms, can potentially encourage older adults to be more physically active. Current algorithms that are deployed in consumer devices (eg, Fitbit) are proprietary, often are not tailored to the movements of older adults, and have been shown to be inaccurate in clinical settings. Step-counting algorithms have been developed for smartwatches, but only using data from younger adults and, often, were only validated in controlled laboratory settings. Objective: We sought to develop and validate a smartwatch step-counting app for older adults and evaluate the algorithm in free-living settings over a long period of time. Methods: We developed and evaluated a step-counting app for older adults on an open-source wrist-worn device (Amulet). The app includes algorithms to infer the level of physical activity and to count steps. We validated the step-counting algorithm in the lab (counting steps from a video recording, n=20) and in free-living conditions-one 2-day field study (n=6) and two 12-week field studies (using the Fitbit as ground truth, n=16). During app system development, we evaluated 4 walking patterns: normal, fast, up and down a staircase, and intermittent speed. For the field studies, we evaluated 5 different cut-off values for the algorithm, using correlation and error rate as the evaluation metrics. Results: The step-counting algorithm performed well. In the lab study, for normal walking (R2=0.5), there was a stronger correlation between the Amulet steps and the video-validated steps; for all activities, the Amulet's count was on average 3.2 (2.1%) steps lower (SD 25.9) than the video-validated count. For the 2-day field study, the best parameter settings led to an association between Amulet and Fitbit (R2=0.989) and 3.1% (SD 25.1) steps lower than Fitbit, respectively. For the 12-week field study, the best parameter setting led to an R2 value of 0.669. Conclusions: Our findings demonstrate the importance of an iterative process in algorithm development before field-based deployment. This work highlights various challenges and insights involved in developing and validating monitoring systems in real-world settings. Nonetheless, our step-counting app for older adults had good performance relative to the ground truth (a commercial Fitbit step counter). Our app could potentially be used to help improve physical activity among older adults.

Feasibility of Conducting Long-term Health and Behaviors Follow-up in Adolescents: Longitudinal Observational Study (2022)

JMIR formative research

Abstract

Background: Machine learning uses algorithms that improve automatically through experience. This statistical learning approach is a natural extension of traditional statistical methods and can offer potential advantages for certain problems. The feasibility of using machine learning techniques in health care is predicated on access to a sufficient volume of data in a problem space. Objective: This study aimed to assess the feasibility of data collection from an adolescent population before and after a posterior spine fusion operation. Methods: Both physical and psychosocial data were collected. Adolescents scheduled for a posterior spine fusion operation were approached when they were scheduled for the surgery. The study collected repeated measures of patient data, including at least 2 weeks prior to the operation and 6 months after the patients were discharged from the hospital. Patients were provided with a Fitbit Charge 4 (consumer-grade health tracker) and instructed to wear it as often as possible. A third-party web-based portal was used to collect and store the Fitbit data, and patients were trained on how to download and sync their personal device data on step counts, sleep time, and heart rate onto the web-based portal. Demographic and physiologic data recorded in the electronic medical record were retrieved from the hospital data warehouse. We evaluated changes in the patients' psychological profile over time using several validated questionnaires (ie, Pain Catastrophizing Scale, Patient Health Questionnaire, Generalized Anxiety Disorder Scale, and Pediatric Quality of Life Inventory). Questionnaires were administered to patients using Qualtrics software. Patients received the questionnaire prior to and during the hospitalization and again at 3 and 6 months postsurgery. We administered paper-based questionnaires for the self-report of daily pain scores and the use of analgesic medications. Results: There were several challenges to data collection from the study population. Only 38% (32/84) of the patients we approached met eligibility criteria, and 50% (16/32) of the enrolled patients dropped out during the follow-up period-on average 17.6 weeks into the study. Of those who completed the study, 69% (9/13) reliably wore the Fitbit and downloaded data into the web-based portal. These patients also had a high response rate to the psychosocial surveys. However, none of the patients who finished the study completed the paper-based pain diary. There were no difficulties accessing the demographic and clinical data stored in the hospital data warehouse. Conclusions: This study identifies several challenges to long-term medical follow-up in adolescents, including willingness to participate in these types of studies and compliance with the various data collection approaches. Several of these challenges-insufficient incentives and personal contact between researchers and patients-should be addressed in future studies.

Pre S cription Digita L Th E rap E utic for P atients with I nsomnia ( SLEEP-I): a protocol for a pragmatic randomised controlled trial (2022)

BMJ open

Abstract

Introduction Cognitive behavioural therapy for insomnia (CBT-I) is effective at treating chronic insomnia, yet in-person CBT-I can often be challenging to access. Prior studies have used technology to bridge barriers but have been unable to extensively assess the impact of the digital therapeutic on real-world patient experience and multidimensional outcomes. Among patients with insomnia, our aim is to determine the impact of a prescription digital therapeutic (PDT) (PEAR-003b, FDA-authorised as Somryst; herein called PDT) that provides mobile-delivered CBT-I on patient-reported outcomes (PROs) and healthcare utilisation. Methods and analysis We are conducting a pragmatically designed, prospective, multicentre randomised controlled trial that leverages Hugo, a unique patient-centred health data-aggregating platform for data collection and patient follow-up from Hugo Health. A total of 100 participants with insomnia from two health centres will be enrolled onto the Hugo Health platform, provided with a linked Fitbit (Inspire 2) to track activity and then randomised 1:1 to receive (or not) the PDT for mobile-delivered CBT-I (Somryst). The primary outcome is a change in the insomnia severity index score from baseline to 9-week postrandomisation. Secondary outcomes include healthcare utilisation, health utility scores and clinical outcomes; change in sleep outcomes as measured with sleep diaries and a change in individual PROs including depressive symptoms, daytime sleepiness, health status, stress and anxiety. For those allocated to the PDT, we will also assess engagement with the PDT. Ethics and dissemination The Institutional Review Boards at Yale University and the Mayo Clinic have approved the trial protocol. This trial will provide important data to patients, clinicians and policymakers about the impact of the PDT device delivering CBT-I on PROs, clinical outcomes and healthcare utilisation. Findings will be disseminated to participants, presented at professional meetings and published in peer-reviewed journals. Trial registration number NCT04909229.

Utility of Fitbit devices among children and adolescents with chronic health conditions: a scoping review (2022)

mHealth

Abstract

Background: While Fitbit® devices were initially intended for leisurely, consumer use, there has been recent interest among scientific and medical communities in the prospective use of Fitbit devices for clinical and research purposes. Those who have chronic health conditions are often required to spend considerable amounts of money and time undergoing physiological tests and activity monitoring to support, stabilize, and manage their health. This disease burden is only amplified in pediatric populations. Devices that are used to collect these data can be invasive, uncomfortable, and disconcerting. Using the Fitbit tracker to acquire such biometric data could ease this burden. Our scoping review seeks to summarize the research that has been conducted on the utilization of Fitbit devices in studies of children and adolescents with chronic health conditions and the feasibility, accuracy, and potential benefits of doing so. Methods: Searches were conducted on PubMed for articles relating pediatric health to Fitbit device usage (using a Boolean search strategy). The eligibility criteria included trials being clinical and/or randomized controlled and articles being in English. Once articles were obtained, they underwent screening and exclusion processes and were charted for their titles, authors, objectives, results, and respective chronic illnesses. In the subsequent full-text review, further charting was conducted, collecting study designs, Fitbit parameters, feasibility, accuracy, and related health and clinical outcomes. Results: Fitbit trackers were unanimously demonstrated to be feasible devices in this population for physical activity monitoring and were determined to be potentially beneficial in measuring and improving overall wellbeing and physical health in children with chronic illness. Nevertheless, sufficient evidence was not found in support of Fitbit accuracy. Additional biases were identified against the population of children with chronic health conditions that may further enable inaccurate data. Conclusions: While Fitbit devices may be beneficial for those interested in improving physical health, discretion is advised for those seeking to collect accurate and/or medically necessitated data. Given the existing literature evaluated, medical-grade technologies are preferred in instances of the latter, as Fitbit devices have not been found to provide reliably accurate data.

A catalog of validity indices for step counting wearable technologies during treadmill walking: the CADENCE-adults study (2022)

The international journal of behavioral nutrition and physical activity

Abstract

Background: Standardized validation indices (i.e., accuracy, bias, and precision) provide a comprehensive comparison of step counting wearable technologies. Purpose: To expand a previously published child/youth catalog of validity indices to include adults (21-40, 41-60 and 61-85 years of age) assessed across a range of treadmill speeds (slow [0.8-3.2 km/h], normal [4.0-6.4 km/h], fast [7.2-8.0 km/h]) and device wear locations (ankle, thigh, waist, and wrist). Methods: Two hundred fifty-eight adults (52.5 ± 18.7 years, 49.6% female) participated in this laboratory-based study and performed a series of 5-min treadmill bouts while wearing multiple devices; 21 devices in total were evaluated over the course of this multi-year cross-sectional study (2015-2019). The criterion measure was directly observed steps. Computed validity indices included accuracy (mean absolute percentage error, MAPE), bias (mean percentage error, MPE), and precision (correlation coefficient, r; standard deviation, SD; coefficient of variation, CoV). Results: Over the range of normal speeds, 15 devices (Actical, waist-worn ActiGraph GT9X, activPAL, Apple Watch Series 1, Fitbit Ionic, Fitbit One, Fitbit Zip, Garmin vivoactive 3, Garmin vivofit 3, waist-worn GENEActiv, NL-1000, PiezoRx, Samsung Gear Fit2, Samsung Gear Fit2 Pro, and StepWatch) performed at < 5% MAPE. The wrist-worn ActiGraph GT9X displayed the worst accuracy across normal speeds (MAPE = 52%). On average, accuracy was compromised across slow walking speeds for all wearable technologies (MAPE = 40%) while all performed best across normal speeds (MAPE = 7%). When analyzing the data by wear locations, the ankle and thigh demonstrated the best accuracy (both MAPE = 1%), followed by the waist (3%) and the wrist (15%) across normal speeds. There were significant effects of speed, wear location, and age group on accuracy and bias (both p < 0.001) and precision (p ≤ 0.045). Conclusions: Standardized validation indices cataloged by speed, wear location, and age group across the adult lifespan facilitate selecting, evaluating, or comparing performance of step counting wearable technologies. Speed, wear location, and age displayed a significant effect on accuracy, bias, and precision. Overall, reduced performance was associated with very slow walking speeds (0.8 to 3.2 km/h). Ankle- and thigh-located devices logged the highest accuracy, while those located at the wrist reported the worst accuracy.

Feasibility, acceptability, and effectiveness of school-based dance movement psychotherapy for children with emotional and behavioral difficulties (2022)

Frontiers in psychology

Abstract

Background: Schools have been increasingly employing dance movement psychotherapists to support children cope with daily worries and stress, express and understand their emotions, develop self-awareness and self-esteem. However, evidence on the impact of dance movement psychotherapy as a tool for prevention of mental health difficulties in childhood remains limited. Methods: Sixteen children (aged 7-9) with mild emotional and behavioral difficulties from two primary schools were randomly assigned to a Dance Movement Psychotherapy (DMP) intervention or to a waiting list, within a larger pilot cross-over randomized controlled study which aimed to (a) test whether all elements of study design can work together and run smoothly in a full-scale RCT; and (b) investigate the effectiveness of arts therapies in improving children's health related quality of life (HRQOL; EQ-5D-Y), wellbeing and life functioning (Child Outcome Rating Scale; CORS), emotional and behavioral difficulties (Strengths and Difficulties Questionnaire; SDQ), and duration of sleep (Fitbits). The therapeutic process was also evaluated through interviews with children, participant observations, the Children's Session Rating Scale (CSRS), and ratings of adherence to the therapeutic protocol. Results: The findings indicated that DMP led to improvements in children's life functioning, wellbeing, duration of sleep, emotional and behavioral difficulties, but not in quality of life. The improvements were maintained at the follow-up stages, up to 6 months post-intervention. Interviews with children also suggested positive outcomes, such as self-expression; emotional regulation; mastery and acceptance of emotions; improved self-confidence and self-esteem; reduced stress; and development of positive relationships. However, children would have preferred smaller groups and longer sessions. Conclusion: This study indicated that all outcome measures would be suitable for inclusion in a larger randomized controlled trial, though the EQ-5D-Y is not recommended as a stand-alone measure due to its lack of sensitivity and specificity for young participants. The adherence to the therapeutic protocol ratings differed between children and adults, highlighting the need to include children's voice in future research. Strategies are also proposed of how to conduct randomization of participants in ways that do not hinder the therapeutic process.

A method for intelligent allocation of diagnostic testing by leveraging data from commercial wearable devices: a case study on COVID-19 (2022)

NPJ digital medicine

Abstract

Mass surveillance testing can help control outbreaks of infectious diseases such as COVID-19. However, diagnostic test shortages are prevalent globally and continue to occur in the US with the onset of new COVID-19 variants and emerging diseases like monkeypox, demonstrating an unprecedented need for improving our current methods for mass surveillance testing. By targeting surveillance testing toward individuals who are most likely to be infected and, thus, increasing the testing positivity rate (i.e., percent positive in the surveillance group), fewer tests are needed to capture the same number of positive cases. Here, we developed an Intelligent Testing Allocation (ITA) method by leveraging data from the CovIdentify study (6765 participants) and the MyPHD study (8580 participants), including smartwatch data from 1265 individuals of whom 126 tested positive for COVID-19. Our rigorous model and parameter search uncovered the optimal time periods and aggregate metrics for monitoring continuous digital biomarkers to increase the positivity rate of COVID-19 diagnostic testing. We found that resting heart rate (RHR) features distinguished between COVID-19-positive and -negative cases earlier in the course of the infection than steps features, as early as 10 and 5 days prior to the diagnostic test, respectively. We also found that including steps features increased the area under the receiver operating characteristic curve (AUC-ROC) by 7–11% when compared with RHR features alone, while including RHR features improved the AUC of the ITA model's precision-recall curve (AUC-PR) by 38–50% when compared with steps features alone. The best AUC-ROC (0.73 ± 0.14 and 0.77 on the cross-validated training set and independent test set, respectively) and AUC-PR (0.55 ± 0.21 and 0.24) were achieved by using data from a single device type (Fitbit) with high-resolution (minute-level) data. Finally, we show that ITA generates up to a 6.5-fold increase in the positivity rate in the cross-validated training set and up to a 4.5-fold increase in the positivity rate in the independent test set, including both symptomatic and asymptomatic (up to 27%) individuals. Our findings suggest that, if deployed on a large scale and without needing self-reported symptoms, the ITA method could improve the allocation of diagnostic testing resources and reduce the burden of test shortages.

A randomized controlled trial for reduction of risk factors for cardiovascular disease in young adults: Methods of the Aspire study (2022)

Contemporary clinical trials

Abstract

Background: Being overweight or obese is a large public health problem, especially in the African American/Black and Hispanic communities. Having a body mass index (BMI) > 25 can lead to chronic health conditions, such as heart disease, stroke, and type 2 diabetes. The transition into adulthood may be an excellent time to intervene due to young adults gaining independence; however, little research has focused on community college students and weight change. The purpose of this randomized controlled trial is to test the efficacy of an intervention tool that is personalized for weight loss through healthy eating and increased physical activity in Black and Hispanic community college students. Methods: Community college students who identify as Black or Hispanic (N = 256) will be recruited and randomized 1:1 to a 12-month, health coach and smartphone application (app) driven intervention or an attention-control condition. The intervention is implemented by health coaches, trained in a health coach specialty program at a university. The intervention includes a Smartphone app for tracking dietary intake, a Fitbit Inspire 2 for tracking physical activity, and daily personalized text messages from a health coach. The main outcome is percent weight loss at 6- and 12-month follow-up assessments. Secondary outcomes include self-efficacy for eating and physical activity, adherence to self-monitoring, Healthy Eating Index scores, and physical activity levels. Discussion: If the intervention is demonstrated to be efficacious, broader dissemination across college/university campuses to assist young adults to develop sustainable healthy habits may be possible. ClinicalTrials.gov Identifier: NCT04412954 R01 Trial Number: 1R01NR018699–02.

Lifestyle and health changes in wheelchair users with a chronic disability after 12 weeks of using the WHEELS mHealth application (2022)

Disability and Rehabilitation. Assistive Technology

Abstract

PURPOSE: The aim of this study was to determine changes in physical activity, nutrition, sleep behaviour and body composition in wheelchair users with a chronic disability after 12 weeks of using the WHEELS mHealth application (app). METHODS: A 12-week pre-post intervention study was performed, starting with a 1-week control period. Physical activity and sleep behaviour were continuously measured with a Fitbit charge 3. Self-reported nutritional intake, body mass and waist circumference were collected. Pre-post outcomes were compared with a paired-sample t-test or Wilcoxon signed-rank test. Fitbit data were analysed with a mixed model or a panel linear model. Effect sizes were determined and significance was accepted at p {\textless} .05. RESULTS: Thirty participants completed the study. No significant changes in physical activity (+1.5 √steps) and sleep quality (-9.7 sleep minutes; -1.2\% sleep efficiency) were found. Significant reduction in energy (-1022 kJ, d = 0.71), protein (-8.3 g, d = 0.61) and fat (-13.1 g, d = 0.87) intake, body mass (-2.2 kg, d = 0.61) and waist circumference (-3.3 cm, d = 0.80) were found. CONCLUSION: Positive changes were found in nutritional behaviour and body composition, but not in physical activity and sleep quality. The WHEELS app seems to partly support healthy lifestyle behaviour.Implications for RehabilitationHealthy lifestyle promotion is crucial, especially for wheelchair users as they tend to show poorer lifestyle behaviour despite an increased risk of obesity and comorbidity.The WHEELS lifestyle app seems to be a valuable tool to support healthy nutrition choices and weight loss and to improve body satisfaction, mental health and vitality.

Detection of Atrial Fibrillation in a Large Population Using Wearable Devices: The Fitbit Heart Study (2022)

Circulation

Abstract

BACKGROUND: Morbidity from undiagnosed atrial fibrillation (AF) may be preventable with early detection. Many consumer wearables contain optical photoplethysmography (PPG) sensors to measure pulse rate. PPG-based software algorithms that detect irregular heart rhythms may identify undiagnosed AF in large populations using wearables, but minimizing false-positive detections is essential. METHODS: We performed a prospective remote clinical trial to examine a novel PPG-based algorithm for detecting undiagnosed AF from a range of wrist-worn devices. Adults aged ≥22 years in the United States without AF, using compatible wearable Fitbit devices and Android or iOS smartphones, were included. PPG data were analyzed using a novel algorithm that examines overlapping 5-minute pulse windows (tachograms). Eligible participants with an irregular heart rhythm detection (IHRD), defined as 11 consecutive irregular tachograms, were invited to schedule a telehealth visit and were mailed a 1-week ambulatory ECG patch monitor. The primary outcome was the positive predictive value of the first IHRD during ECG patch monitoring for concurrent AF. RESULTS: A total of 455 699 participants enrolled (median age 47 years, 71\% female, 73\% White) between May 6 and October 1, 2020. IHRDs occurred for 4728 (1\%) participants, and 2070 (4\%) participants aged ≥65 years during a median of 122 (interquartile range, 110-134) days were at risk for an IHRD. Among 1057 participants with an IHRD notification and subsequent analyzable ECG patch monitor, AF was present in 340 (32.2\%). Of the 225 participants with another IHRD during ECG patch monitoring, 221 had concurrent AF on the ECG and 4 did not, resulting in an IHRD positive predictive value of 98.2\% (95\% CI, 95.5\%-99.5\%). For participants aged ≥65 years, the IHRD positive predictive value was 97.0\% (95\% CI, 91.4\%-99.4\%). CONCLUSIONS: A novel PPG software algorithm for wearable Fitbit devices exhibited a high positive predictive value for concurrent AF and identified participants likely to have AF on subsequent ECG patch monitoring. Wearable devices may facilitate identifying individuals with undiagnosed AF. REGISTRATION: URL: https://www. CLINICALTRIALS: gov; Unique identifier: NCT04380415.

Physical Activity Dynamics During a Digital Messaging Intervention Changed After the Pandemic Declaration (2022)

Annals of Behavioral Medicine: A Publication of the Society of Behavioral Medicine

Abstract

BACKGROUND: The COVID-19 pandemic adversely impacted physical activity, but little is known about how contextual changes following the pandemic declaration impacted either the dynamics of people's physical activity or their responses to micro-interventions for promoting physical activity. PURPOSE: This paper explored the effect of the COVID-19 pandemic on the dynamics of physical activity responses to digital message interventions. METHODS: Insufficiently-active young adults (18-29 years; N = 22) were recruited from November 2019 to January 2020 and wore a Fitbit smartwatch for 6 months. They received 0-6 messages/day via smartphone app notifications, timed and selected at random from three content libraries (Move More, Sit Less, and Inspirational Quotes). System identification techniques from control systems engineering were used to identify person-specific dynamical models of physical activity in response to messages before and after the pandemic declaration on March 13, 2020. RESULTS: Daily step counts decreased significantly following the pandemic declaration on weekdays (Cohen's d = -1.40) but not on weekends (d = -0.26). The mean overall speed of the response describing physical activity (dominant pole magnitude) did not change significantly on either weekdays (d = -0.18) or weekends (d = -0.21). In contrast, there was limited rank-order consistency in specific features of intervention responses from before to after the pandemic declaration. CONCLUSIONS: Generalizing models of behavioral dynamics across dramatically different environmental contexts (and participants) may lead to flawed decision rules for just-in-time physical activity interventions. Periodic model-based adaptations to person-specific decision rules (i.e., continuous tuning interventions) for digital messages are recommended when contexts change.

Effectiveness of wearable technology to optimize youth soccer players' off-training behaviour and training responses: a cluster-randomized trial (2022)

Science & Medicine in Football

Abstract

PURPOSE: A cluster-randomized trial examined the effect of activity wristbands on young players' off-training PA and SB profiles; assessed the added value of wearable wristbands with PA warnings; and investigated whether manipulating off-training PA can affect the players' training responses. METHODS: Thirty-two adolescent soccer players (16.1 ± 0.9 years old) were monitored during weekdays for two weeks (interspersed with one week). Players were randomly assigned to a reminder to move (REM) and a non-reminder to move group (nREM). The REM wore an activity wristband (Fitbit Charge 2) with PA warnings in the last week of research, while the nREM wore identical monitors without PA feedback. Throughout the study, off-training PA was assessed using tri-axial accelerometers, and training responses were analysed using wearable inertial monitoring units. Gardner-Altman estimation plots and a Fisher's Exact Test of Independence estimated each group's off-training PA changes between the monitored weeks. Complementary, an analysis of covariance identified the effect of the activity wristband configuration (REM vs nREM) on players' off-training PA profiles and soccer training responses. RESULTS: Interestingly, results showed that different wearable wristbands did not influence the players' off-training PA profiles (p {\textgreater} 0.05). Concomitantly, no differences were observed in training responses (p {\textgreater} 0.05). CONCLUSION: Findings emphasize the importance of health and youth sports organizations in developing newer approaches for promoting healthier lifestyles, beyond training practices, with potentially favourable implications for sports performance.

mHealth Support to Stimulate Physical Activity in Individuals With Intellectual Disability: Protocol for a Mixed Methods Pilot Study (2022)

JMIR research protocols

Abstract

BACKGROUND: Several studies have shown that individuals with intellectual disabilities (IDs) have low levels of physical activity (PA), and intervention studies on PA suggest inconsistent evidence. The use of technology as a means of motivation for PA has yet to be extensively explored and needs to be further investigated. OBJECTIVE: We aim to assess the feasibility and acceptability of procedures for an intervention arm in a future trial on mobile health (mHealth) to support PA for individuals with IDs. In addition, we aim to examine how the use of technology can influence motivation for PA among participants, their caregivers, and staff members. METHODS: A mixed methods pilot study of an intervention arm will be carried out in a planned randomized controlled trial (RCT). Ten participants with ID and their caregivers or a staff member will be included. Information will always be provided by a caregiver or a staff member, or participants with ID if possible. Assessments will be carried out at baseline, follow-up after 4 weeks, and 12 weeks, and include questionnaires on PA, social support, self-efficacy, and challenging behavior. PA will be measured with 2 different activity trackers (Fitbit and Axivity) for 1 week at all assessments. Feasibility will be assessed as recruitment and adherence rate, missing data, usability of the motivational mHealth tool, and estimates of effectiveness. Acceptability of study procedures, activity measures, and motivation for participation in PA will be additionally assessed with qualitative methods at the end of the intervention. RESULTS: Enrollment commenced in May 2021. Data collection was completed in March 2022. CONCLUSIONS: This pilot study will evaluate the feasibility and acceptability of study procedures of the intervention arm of a planned RCT to address feasibility issues, improve study procedures, and estimate effectiveness of the study measures. How the use of technology can influence motivation for PA will also be examined, which can help guide and improve future PA interventions involving the use of technology. TRIAL REGISTRATION: ClinicalTrials.gov NCT04929106; https://clinicaltrials.gov/ct2/show/NCT04929106. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/37849.

Step-count outcomes of 13 different activity trackers: Results from laboratory and free-living experiments (2022)

Gait \& Posture

Abstract

BACKGROUND: Many activity trackers have been developed, but steps can still be inconsistent from one monitor to another. RESEARCH QUESTION: What are the differences and associations between the steps of 13 selected consumer-based and research-grade wearable devices during 1 standardized day in a metabolic chamber and 15-day free-living trials? METHODS: In total, 19 healthy adults between 21 and 50 years-old participated in this study. Participants were equipped with 12 accelerometer-based active trackers and one pedometer (Yamasa) in order to monitor the number of steps per day. The devices were worn on the waist (ActiGraph, Omron, Actimarker, Lifedorder, Withings, and Yamasa) or non-dominant wrist (Fitbit, Garmin, Misfit, EPSON, and Jawbone), or placed in a pocket (Omron CaloriScan, and TANITA). Participants performed structured activities over a 24 h period in a chamber (Standardized day), and steps were monitored in the same participants in free-living trials for 15 successive days using the same monitors (free-living days). RESULTS: When the 13 monitors were ranked by their steps, waist-worn ActiGraph was located at the center (7th) of the monitors both in the Standardized (12,252 ± 598 steps/day, mean ± SD) and free-living days (9295 ± 4027 steps/day). The correlation between the accelerometer-based devices was very high (r = 0.87-0.99). However, the steps of Yamasa was significantly lower in both trials than ActiGraph. The wrist-worn accelerometers had significantly higher steps than other devices both trials (P {\textless} 0.05). The differences between ActiGraph and Actimarker or Lifecorder was less than 100 steps/day in the Standardized day, and the differences between ActiGraph and Active Style Pro was less than 100 steps/day in the free-living days. Regression equation was also performed for inter-device compatibility. SIGNIFICANCE: Step obtained from the wrist-worn, waist-worn, and pocket-type activity trackers were significantly different from each other but still highly correlated in free-living conditions.

Activity monitoring and patient-reported outcome measures in Myalgic Encephalomyelitis/Chronic Fatigue Syndrome patients (2022)

PloS One

Abstract

INTRODUCTION: Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS) is a disease with no validated specific and sensitive biomarker, and no standard approved treatment. In this observational study with no intervention, participants used a Fitbit activity tracker. The aims were to explore natural symptom variation, feasibility of continuous activity monitoring, and to compare activity data with patient reported outcome measures (PROMs). MATERIALS AND METHODS: In this pilot study, 27 patients with mild to severe ME/CFS, of mean age 42.3 years, used the Fitbit Charge 3 continuously for six months. Patients wore a SenseWear activity bracelet for 7 days at baseline, at 3 and 6 months. At baseline and follow-up they completed the Short Form 36 Health Survey (SF-36) and the DePaul Symptom Questionnaire-Short Form (DSQ-SF). RESULTS: The mean number of steps per day decreased with increasing ME/CFS severity; mild 5566, moderate 4991 and severe 1998. The day-by-day variation was mean 47\% (range 25\%-79\%). Mean steps per day increased from the first to the second three-month period, 4341 vs 4781 steps, p = 0.022. The maximum differences in outcome measures between 4-week periods (highest vs lowest), were more evident in a group of eight patients with milder disease (baseline SF-36 PF {\textgreater} 50 or DSQ-SF {\textless} 55) as compared to 19 patients with higher symptom burden (SF-36 PF {\textless} 50 and DSQ-SF {\textgreater} 55), for SF-36 PF raw scores: 16.9 vs 3.4 points, and for steps per day: 958 versus 479 steps. The correlations between steps per day and self-reported SF-36 Physical function, SF-36 Social function, and DSQ-SF were significant. Fitbit recorded significantly higher number of steps than SenseWear. Resting heart rates were stable during six months. CONCLUSION: Continuous activity registration with Fitbit Charge 3 trackers is feasible and useful in studies with ME/CFS patients to monitor steps and resting heart rate, in addition to self-reported outcome measures. CLINICAL TRIAL REGISTRATION: Clinicaltrials.gov: NCT04195815.

Physical activity measurement in older adults: Wearables versus self-report (2022)

Frontiers in Digital Health

Abstract

Physical activity (PA) is associated with preserved age-related body and brain health. However, PA quantification can vary. Commercial-grade wearable monitors are objective, low burden tools to capture PA but are less well validated in older adults. Self-report PA questionnaires are widely accepted and more frequently used but carry inherent limitations. We aimed to compare these commonly used PA measures against one another and examine their convergent validity with a host of relevant outcomes. We also examined the factors that drive differences in PA self-reporting styles in older adults. 179 older adults completed 30-day Fitbit Flex2™ monitoring and reported PA levels via two widely used PA questionnaires: PASE and CHAMPS-METs (metabolic expenditure calories burned). Participants also completed measures of cardiometabolic (hypertension diagnosis, resting heart rate, A1C levels), cognitive (memory, processing speed, executive functioning), and brain MRI (medial temporal lobe volume) outcomes. The discrepancy between objective Fitbit monitoring and self-reported PA was evaluated using a sample-based z difference score. There were only modest relationships across all PA metrics. Fitbit step count demonstrated a stronger association with the PASE, whereas Fitbit calories burned was more strongly associated with CHAMPS-MET. Fitbit outcomes had more consistent convergence with relevant outcomes of interest (e.g., cardiometabolic and brain health indices) when compared to subjective measures; however, considerable heterogeneity within these associations was observed. A higher degree of overreporting was associated with worse memory and executive performances, as well as hypertension diagnoses. We build on prior findings that wearable, digital health indicators of PA demonstrate greater construct validity than self-report in older adults. We further show important clinical features (e.g., poorer cognitive status) of older adults that could contribute to a higher level of overreporting on self-report measures. Characterization of what PA measures truly operationalize will help elucidate relationships between most relevant facets of PA and outcomes of interest.

Mediators of Effects on Physical Activity and Sedentary Time in an Activity Tracker and Behavior Change Intervention for Adolescents: Secondary Analysis of a Cluster Randomized Controlled Trial (2022)

JMIR mHealth and uHealth

Abstract

BACKGROUND: Adolescence is a critical age where steep declines in physical activity and increases in sedentary time occur. Promoting physical activity should therefore be a priority for short- and long-term health benefits. Wearable activity trackers in combination with supportive resources have the potential to influence adolescents' physical activity levels and sedentary behavior. Examining the pathways through which such interventions work can inform which mediators to target in future studies. OBJECTIVE: The aim of this paper is to examine the impact of the Raising Awareness of Physical Activity (RAW-PA) intervention on potential mediators of behavior change after intervention, and whether these mediated the intervention effects on physical activity and sedentary time at 6-month follow-up. METHODS: RAW-PA was a 12-week intervention, grounded in social cognitive theory and behavioral choice theory, aimed at increasing physical activity among inactive adolescents through combining a wearable activity tracker with digital resources delivered via a private Facebook group (n=159 complete cases). The targeted potential mediators were identified from previous studies conducted in adolescents and included self-efficacy, peer support, family support, teacher support, self-regulation strategies, barriers, and enjoyment. Outcomes included sedentary time as well as light- and moderate-to-vigorous-intensity physical activity. A series of mixed linear models were used to estimate intervention effects on physical activity and sedentary behavior at follow-up and on potential mediators after intervention and to test whether there were indirect effects of the intervention on physical activity and sedentary behavior via mediators. RESULTS: Adolescents in the intervention group (n=75) engaged in higher sedentary time and lower light intensity at 6-month follow-up compared to the wait-list controls (n=84). There were no intervention effects for moderate-to-vigorous-intensity physical activity. The intervention group perceived more barriers to physical activity than the wait-list control group at 6-month follow-up (mean adjusted difference=1.77; 95\% CI 0.19-3.34; P=.03). However, indirect effects for each outcome were not statistically significant, indicating that perceived barriers to physical activity did not mediate intervention effects for physical activity or sedentary time. CONCLUSIONS: RAW-PA did not beneficially impact hypothesized mediators in these inactive adolescents, despite strategies being designed to target them. This suggests that the lack of overall intervention effects on physical activity and sedentary time observed in the RAW-PA study could be due to the limited impact of the intervention on the targeted mediators. Future studies should consider different strategies to target theoretically informed potential mediators and identify intervention strategies that effectively target key mediators to improve physical activity among inactive adolescents. Finally, intervention effects according to level of wearable tracker use or level of engagement with the intervention should be explored. This may provide important insights for designing successful wearable activity tracker interventions. TRIAL REGISTRATION: Australian New Zealand Clinical Trials Registry ACTRN12616000899448; https://anzctr.org.au/Trial/Registration/TrialReview.aspx?id=370716\&isReview=true. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.1186/s12889-016-3945-5.

A Smartphone Physical Activity App for Patients in Alcohol Treatment: Single-Arm Feasibility Trial (2022)

JMIR Formative Research

Abstract

Background: Alcohol use disorder (AUD) is a significant public health concern worldwide. Alcohol consumption is a leading cause of death in the United States and has a significant negative impact on individuals and society. Relapse following treatment is common, and adjunct intervention approaches to improve alcohol outcomes during early recovery continue to be critical. Interventions focused on increasing physical activity (PA) may improve AUD treatment outcomes. Given the ubiquity of smartphones and activity trackers, integrating this technology into a mobile app may be a feasible, acceptable, and scalable approach for increasing PA in individuals with AUD. Objective: This study aims to test the Fit\&Sober app developed for patients with AUD. The goals of the app were to facilitate self-monitoring of PA engagement and daily mood and alcohol cravings, increase awareness of immediate benefits of PA on mood and cravings, encourage setting and adjusting PA goals, provide resources and increase knowledge for increasing PA, and serve as a resource for alcohol relapse prevention strategies. Methods: To preliminarily test the Fit\&Sober app, we conducted an open pilot trial of patients with AUD in early recovery (N=22; 13/22, 59\% women; mean age 43.6, SD 11.6 years). At the time of hospital admission, participants drank 72\% of the days in the last 3 months, averaging 9 drinks per drinking day. The extent to which the Fit\&Sober app was feasible and acceptable among patients with AUD during early recovery was examined. Changes in alcohol consumption, PA, anxiety, depression, alcohol craving, and quality of life were also examined after 12 weeks of app use. Results: Participants reported high levels of satisfaction with the Fit\&Sober app. App metadata suggested that participants were still using the app approximately 2.5 days per week by the end of the intervention. Pre-post analyses revealed small-to-moderate effects on increase in PA, from a mean of 5784 (SD 2511) steps per day at baseline to 7236 (SD 3130) steps per day at 12 weeks (Cohen d=0.35). Moderate-to-large effects were observed for increases in percentage of abstinent days (Cohen d=2.17) and quality of life (Cohen d=0.58) as well as decreases in anxiety (Cohen d=−0.71) and depression symptoms (Cohen d=−0.58). Conclusions: The Fit\&Sober app is an acceptable and feasible approach for increasing PA in patients with AUD during early recovery. A future randomized controlled trial is necessary to determine the efficacy of the Fit\&Sober app for long-term maintenance of PA, ancillary mental health, and alcohol outcomes. If the efficacy of the Fit\&Sober app could be established, patients with AUD would have a valuable adjunct to traditional alcohol treatment that can be delivered in any setting and at any time, thereby improving the overall health and well-being of this population. Trial Registration: ClinicalTrials.gov NCT02958280; https://www.clinicaltrials.gov/ct2/show/NCT02958280

Validation of Fitbit Charge 4 for assessing sleep in Chinese patients with chronic insomnia: A comparison against polysomnography and actigraphy (2022)

PloS One

Abstract

Our research aims to assess the performance of a new generation of consumer activity trackers (Fitbit Charge 4TM: FBC) to measure sleep variables and sleep stage classifications in patients with chronic insomnia, compared to polysomnography (PSG) and a widely used actigraph (Actiwatch Spectrum Pro: AWS). We recruited 37 participants, all diagnosed with chronic insomnia disorder, for one night of sleep monitoring in a sleep laboratory using PSG, AWS, and FBC. Epoch-by-epoch analysis along with Bland-Altman plots was used to evaluate FBC and AWS against PSG for sleep-wake detection and sleep variables: total sleep time (TST), sleep efficiency (SE), waking after sleep onset (WASO), and sleep onset latency (SOL). FBC sleep stage classification of light sleep (LS), deep sleep (DS), and rapid eye movement (REM) was also compared to that of PSG. When compared with PSG, FBC notably underestimated DS (-41.4, p {\textless} 0.0001) and SE (-4.9\%, p = 0.0016), while remarkably overestimating LS (37.7, p = 0.0012). However, the TST, WASO, and SOL assessed by FBC presented no significant difference from that assessed by PSG. Compared with PSG, AWS and FBC showed great accuracy (86.9\% vs. 86.5\%) and sensitivity (detecting sleep; 92.6\% vs. 89.9\%), but comparatively poor specificity (detecting wake; 35.7\% vs. 62.2\%). Both devices showed better accuracy in assessing sleep than wakefulness, with the same sensitivity but statistically different specificity. FBC supplied equivalent parameters estimation as AWS in detecting sleep variables except for SE. This research shows that FBC cannot replace PSG thoroughly in the quantification of sleep variables and classification of sleep stages in Chinese patients with chronic insomnia; however, the user-friendly and low-cost wearables do show some comparable functions. Whether FBC can serve as a substitute for actigraphy and PSG in patients with chronic insomnia needs further investigation.

Intra-individual impact of the COVID-19 pandemic on mental health and sleep in young adults (2022)

PloS One

Abstract

The COVID-19 pandemic has posed unique academic, social, financial, and health-related challenges for young adults. While numerous studies have documented average increases in reported mental health issues in the general population, few have measured the magnitude of changes in mental health symptoms and sleep difficulties within individuals. Here, we measure the impact of the COVID-19 pandemic on mental health and sleep of university students pre- and mid-pandemic. Prior to the pandemic (Fall 2019), individuals (n = 23) were recruited to participate in an eight-day, comprehensive sleep study using Fitbit® actigraphy. Participants also completed detailed mental health and sleep surveys, including depression (BDI-II), anxiety (STAI), and sleep disturbance (PROMIS) surveys. One year later, these individuals repeated the study during the pandemic (Fall 2020); participants completed the original surveys and sleep study, in addition to a targeted survey on mental and sleep health due to the pandemic. Self-reported levels of anxiety, depression, and sleep disturbance, and sleep parameters, measured by actigraphy, were compared within the same individuals pre- and mid-pandemic. Self-report survey data revealed that three-quarters of participants experienced an increase in stress and anxiety due to the pandemic. In addition, intra-individual depression and anxiety symptoms increased to clinically significant levels within individuals from pre- to mid-pandemic. Over two-thirds of participants reported sleeping less, and more than half reported that their sleep health had worsened during the pandemic. Changes in sleep disturbance were positively associated with changes in depression and anxiety, reinforcing the robust relationship between poor sleep quality and mental health. Furthermore, individuals who reported greater sleep disturbance during the pandemic experienced lower relative proportions of both REM and deep sleep. The impact of the COVID-19 pandemic on university students is multi-faceted-mental health, sleep quality, and the amount of restorative sleep are negatively affected by the pandemic environment. These compounded effects exacerbate the health consequences of the pandemic and highlight a need for increased attention to the prevention and treatment of mental health disorders, particularly in vulnerable populations of young adults.

Fitbits for monitoring depressive symptoms in older aged persons: Qualitative outcomes of a feasibility study (2022)

JMIR formative research

Abstract

BACKGROUND: In 2022, an estimated 1.105 billion people used smart wearables and 31 million used Fitbit devices, worldwide. While there is growing evidence for the use of smart wearables to benefit physical health, more research is required on the feasibility of using these devices for mental health and wellbeing. In studies focusing on emotion recognition, emotions are often inferred and dependent on external cues, which may not be representative of true emotional states. OBJECTIVE: The aim of this study was to evaluate the feasibility and acceptability of utilizing consumer-grade activity trackers for applications in remote mental health monitoring of older aged people. METHODS: Older adults were recruited using criterion sampling. Participants were provided an activity tracker (Fitbit Alta HR) and completed weekly online questionnaires, including the Geriatric Depression Scale, for 4 weeks. Before and after the study period semi-structured qualitative interviews were conducted to provide insight on the acceptance and feasibility of performing the protocol over a 4-week period. Interview transcripts were analyzed using a hybrid inductive-deductive thematic analysis. RESULTS: Twelve participants enrolled in the study, and 9 returned for interviews after the study period. Participants had positive attitudes towards being remotely monitored with 78\% (7/9) participants experiencing no inconvenience throughout the study period. Sixty-seven percent (6/9) of were interested in trialing our prototype when it is implemented. Participants stated they would feel more comfortable that mental wellbeing was being monitored by carers remotely. CONCLUSIONS: Fitbit-like devices were an unobtrusive and convenient tool to collect physiological user data. Future research should integrate physiological user inputs to differentiate and predict depressive tendencies in users. CLINICALTRIAL:

Digital phenotyping by wearable-driven artificial intelligence in older adults and people with Parkinson's disease: Protocol of the mixed method, cyclic ActiveAgeing study (2022)

PloS One

Abstract

BACKGROUND: Active ageing is described as the process of optimizing health, empowerment, and security to enhance the quality of life in the rapidly growing population of older adults. Meanwhile, multimorbidity and neurological disorders, such as Parkinson's disease (PD), lead to global public health and resource limitations. We introduce a novel user-centered paradigm of ageing based on wearable-driven artificial intelligence (AI) that may harness the autonomy and independence that accompany functional limitation or disability, and possibly elevate life expectancy in older adults and people with PD. METHODS: ActiveAgeing is a 4-year, multicentre, mixed method, cyclic study that combines digital phenotyping via commercial devices (Empatica E4, Fitbit Sense, and Oura Ring) with traditional evaluation (clinical assessment scales, in-depth interviews, and clinical consultations) and includes four types of participants: (1) people with PD and (2) their informal caregiver; (3) healthy older adults from the Helgetun living environment in Norway, and (4) people on the Helgetun waiting list. For the first study, each group will be represented by N = 15 participants to test the data acquisition and to determine the sample size for the second study. To suggest lifestyle changes, modules for human expert-based advice, machine-generated advice, and self-generated advice from accessible data visualization will be designed. Quantitative analysis of physiological data will rely on digital signal processing (DSP) and AI techniques. The clinical assessment scales are the Unified Parkinson's Disease Rating Scale (UPDRS), Montreal Cognitive Assessment (MoCA), Geriatric Depression Scale (GDS), Geriatric Anxiety Inventory (GAI), Apathy Evaluation Scale (AES), and the REM Sleep Behaviour Disorder Screening Questionnaire (RBDSQ). A qualitative inquiry will be carried out with individual and focus group interviews and analysed using a hermeneutic approach including narrative and thematic analysis techniques. DISCUSSION: We hypothesise that digital phenotyping is feasible to explore the ageing process from clinical and lifestyle perspectives including older adults and people with PD. Data is used for clinical decision-making by symptom tracking, predicting symptom evolution, and discovering new outcome measures for clinical trials.

Virtual cardiac fitness training in pediatric heart transplant patients: A pilot study (2022)

Pediatric Transplantation

Abstract

BACKGROUND: Cardiac fitness training in pediatric heart transplant recipients can improve functional capacity. Widespread implementation has been limited mostly due to logistical constraints, specifically related to travel. The aim of this study was to implement and assess a virtual cardiac fitness program for pediatric heart transplant patients. METHODS: Participants were between the age of 10 and 20 years old. All subjects completed an initial 6MWT, strength/flexibility assessment, and QOL assessment with the PROMIS measurement. Participants then underwent a 16-week intervention with exercise sessions twice weekly for 30 min with a trained exercise physiologist over a virtual platform. At the end of the intervention period, participants repeated a 6MWT, strength/flexibility assessment, and PROMIS measurement. Throughout the study, patients wore a FitBit accelerometer to monitor daily activity levels. RESULTS: Thirteen individuals were enrolled. Mean age was 15.4 years (SD =3.4) with a mean post-transplant period of 9.7 years (SD = 4.3). Session attendance was 83\%. Post-intervention measurements showed improvements in 6MWT (median, +21 m, p = .02), push-up repetitions (median, +5 rep, p = .0005), wall-sit duration (median, +10 s, p = .001), plank duration (median, +9 s, p = .03), sit-up repetitions (median, +7 rep, p = .002), and sit and reach distance (median, +5 cm, p = .04). PROMIS measurement showed significant improvements in self-reported fatigue (Δz-score, -7.7, p = .008) and sleep impairment (Δz-score, -5.9, p = .002). Average daily step count increased 1464 steps per day per patient (p = .008). CONCLUSION: We have demonstrated the successful implementation of a virtual cardiac fitness with excellent adherence and improvement in physical fitness and QOL metrics.

A qualitative study to explore the acceptability and usefulness of personalized biofeedback to motivate physical activity in cancer survivors (2022)

Digital Health

Abstract

Objective: Many cancer survivors do not meet recommended levels of exercise, despite the benefits physical activity offers. This study aimed to understand experiences of insufficiently active overweight/obese breast or colorectal cancer survivors, in efforts to (1) examine regular physical activity barriers, and (2) determine perceptions and acceptability of a remotely delivered physical activity intervention utilizing wearable sensors and personalized feedback messages. Methods: In-person and virtual small group interviews were conducted engaging overweight/obese cancer survivors (n = 16, 94\% female, 94\% breast cancer survivors) in discussions resulting in 314 pages of transcribed data analyzed by multiple coders. Results: All participants expressed needing to increase physical activity, identifying lack of motivation centering on survivorship experiences and symptom management as the most salient barrier. They indicated familiarity with activity trackers (i.e., Fitbit) and expressed interest in biosensors (i.e., continuous glucose monitors [CGMs]) as CGMs show biological metrics in real-time. Participants reported (1) personalized feedback messages can improve motivation and accountability; (2) CGM acceptability is high given survivors' medical history; and (3) glucose data is a relevant health indicator and they appreciated integrated messages (between Fitbit and CGM) in demonstrating how behaviors immediately affect one's body. Conclusions: This study supports the use of wearable biosensors and m-health interventions to promote physical activity in cancer survivors. Glucose-based biofeedback provides relevant and motivating information for cancer survivors regarding their daily activity levels by demonstrating the immediate effects of physical activity. Integrating biofeedback into physical activity interventions could be an effective behavioral change strategy to promote a healthy lifestyle in cancer survivors.

A Remotely Delivered Progressive Walking Intervention for Adults With Persistent Symptoms of a Mild Traumatic Brain Injury: Feasibility and Exploration of Its Impact (2022)

Frontiers in Rehabilitation Sciences

Abstract

Introduction: Persistent post-concussion symptoms following a mild traumatic brain injury (mTBI) can impact function and participation of adults. Physical activity is recommended to reduce symptoms and foster return to normal activities. Adults with a mTBI may have personal factors or experience accessibility issues restricting physical activity. Walking is a physical activity accessible to most that could be delivered remotely. Objectives: Determine the feasibility, safety, and acceptability of a remotely delivered progressive walking intervention designed for adults with persistent mTBI symptoms and explore its effects on health-related outcomes. Methodology: This feasibility study using a single-group pre-post mixed methods convergent parallel design was conducted remotely. Adults aged 18-65 years with a mTBI reporting persistent symptoms for ≥3 months were recruited. The 8-week remote progressive walking intervention aimed to increase the weekly number of steps walked by 40\% based on a 1-week baseline measured by a Fitbit Inspire 2 activity monitor. Feasibility measures were about the intervention, its remote delivery, safety, and acceptability. Health-related outcomes were post-concussion symptoms, kinesiophobia, mood, sleep, fatigue, and quality of life. Semi-structured exit interviews were recorded and transcribed verbatim. Quantitative and qualitative data were analyzed separately, and results merged, compared, and contrasted. Descriptive statistics and paired samples t-tests were used. The qualitative analyses followed an iterative content analysis approach using reflexivity and triangulation of sources. Results: Twenty adults (16 women) aged 42.5 ± 11.51 years with persisting symptoms for 9.25 ± 6.43 months participated, adhered to 94.38\% of sessions, completed the intervention, and found it to be feasible, safe and acceptable. Participants increased weekly total number of steps walked (change = 14,886 ± 18,283; t = 3.55, p = 0.002). Severity of post-concussion symptoms (change = -6.42 ± 10.69; t = -2.62, p = 0.018), kinesiophobia (change = -5 ± 6.86; t = 3.18, p = 0.005), anxiety (change = -1.53 ± 3.01; t = -2.21, p = 0.04), and fatigue (change = -10.21 ± 10.20; t = -4.37, p {\textless} 0.001) were reduced, whilst quality of life improved (change = 10.58 ± 13.35; t = 3.46, p = 0.003). Participants' perceptions corroborate most quantitative results; they felt improved self-efficacy about physical activity and provided five key recommendations. Discussion: This study demonstrates the feasibility, safety, and acceptability of the remote 8-week progressive walking intervention, a promising approach to reduce persisting symptoms, improve physical activity level health-related outcomes and quality of life of adults with persistent post-concussion symptoms following a mTBI.

An app-based just-in-time-adaptive self-management intervention for care partners: The CareQOL feasibility pilot study (2022)

Rehabilitation Psychology

Abstract

PURPOSE/OBJECTIVE: The primary objective of this study was to establish the feasibility and acceptability of an intensive data collection protocol that involves the delivery of a personalized just-in-time adaptive intervention (JITAI) in three distinct groups of care partners (care partners of persons with spinal cord injury [SCI], Huntington's disease [HD], or hematopoietic cell transplantation [HCT]). RESEARCH METHOD/DESIGN: Seventy care partners were enrolled in this study (n = 19 SCI; n = 21 HD, n = 30 HCT). This three-month (90 day) randomized control trial involved wearing a Fitbit to track sleep and steps, providing daily reports of health-related quality of life (HRQOL), and completing end of month HRQOL surveys. Care partners in the JITAI group also received personalized pushes (i.e., text-based phone notifications that include brief tips or suggestions for improving self-care). At the end of three-months, care partners in both groups completed a feasibility and acceptability questionnaire. RESULTS: Most (98.6\%) care partners completed the study, average compliance was 88\% for daily HRQOL surveys, 96\% for daily steps, and 85\% for daily sleep (from wearing the Fitbit), and all monthly surveys were completed with the exception of one missed 3-month assessment. The acceptability of the protocol was high; ratings exceeded 80\% agreement for the different elements of the study. Improvements were seen for the majority of the HRQOL measures. There was no evidence of measurement reactivity. CONCLUSIONS/IMPLICATIONS: Findings provide strong support for the acceptability and feasibility of an intensive data collection protocol that involved the administration of a JITAI. Although this trial was not powered to establish efficacy, findings indicated improvements across a variety of different HRQOL measures ({\textasciitilde}1/3 of which were statistically significant). (PsycInfo Database Record (c) 2022 APA, all rights reserved).

A Positive Emotion-Focused Intervention to Increase Physical Activity After Bariatric Surgery: Protocol for a Pilot Randomized Controlled Trial (2022)

JMIR research protocols

Abstract

BACKGROUND: Physical activity levels after bariatric surgery are usually low, despite the significant protective health benefits of physical activity in this population. Positive psychological well-being is associated with improved adherence to health behaviors, but bariatric surgery patients often have negative associations with physical activity that prevent sustained engagement. OBJECTIVE: The Gaining Optimism After weight Loss Surgery (GOALS) pilot randomized controlled trial is aimed at testing a novel intervention to increase physical activity after bariatric surgery, which incorporates positive psychological skill-building with motivational interviewing and goal-setting. METHODS: The GOALS trial is a 2-arm, 24-week pilot randomized controlled trial that aims to enroll 58 adults who report less than 200 minutes per week of moderate to vigorous physical activity and a desire to become more active 6-12 months after bariatric surgery. GOALS is testing the feasibility, acceptability, and preliminary efficacy of a positive psychology-motivational interviewing telephone intervention targeting to increase physical activity and associated positive affect. Intervention components include positive psychology, goal-setting, self-monitoring via provided Fitbits, and motivational interviewing to overcome barriers and increase motivation. The intervention is compared to a physical activity education control that includes mailings with psychoeducation around physical activity and provision of a Fitbit. The primary outcomes of the pilot trial are feasibility and acceptability, measured as session completion rates and participant ratings of ease and helpfulness of each session. The main secondary outcome is change in accelerometer-measured moderate to vigorous physical activity post intervention and at 24-week follow-up. Additional outcomes include changes in attitudes related to physical activity, psychological well-being, and physical health measures. RESULTS: This multiphase project was funded in 2020 and institutional review board approval was obtained for the proposed trial in 2021. Recruitment for the randomized controlled trial began in July 2022. Upon completion of the pilot trial, we will examine the feasibility, acceptability, and preliminary efficacy of the intervention. CONCLUSIONS: Although bariatric surgery is the most effective treatment available for severe obesity, weight regain occurs, often in the context of low psychological well-being. Many individuals would benefit from learning strategies to increase positive psychological well-being after bariatric surgery, which could help them maintain lifestyle changes. Positive psychology is a novel approach to improve adherence by increasing positive associations with health behaviors including physical activity. The GOALS pilot trial will determine whether this type of intervention is feasible and acceptable to this population and will provide a foundation for a future full-scale randomized controlled efficacy trial. TRIAL REGISTRATION: ClinicalTrials.gov NCT04868032; https://clinicaltrials.gov/ct2/show/NCT04868032. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/39856.

How is the usability of commercial activity monitors perceived by older adults and by researchers? A cross-sectional evaluation of community-living individuals (2022)

BMJ open

Abstract

OBJECTIVES: Using commercial activity monitors may advance research with older adults. However, usability for the older population is not sufficiently established. This study aims at evaluating the usability of three wrist-worn monitors for older adults. In addition, we report on usability (including data management) for research. DESIGN: Data were collected cross-sectionally. Between-person of three activity monitor type (Apple Watch 3, Fitbit Charge 4, Polar A370) were made. SETTING: The activity monitors were worn in normal daily life in an urban community in Germany. The period of wear was 2 weeks. PARTICIPANTS: Using convenience sampling, we recruited N=27 healthy older adults (≥60 years old) who were not already habitual users of activity monitors. OUTCOMES: To evaluate usability from the participant perspective, we used the System Usability Scale (SUS) as well as a study-specific qualitative checklist. Assessment further comprised age, highest academic degree, computer proficiency and affinity for technology interaction. Usability from the researchers' perspective was assessed using quantitative data management markers and a study-specific qualitative check-list. RESULTS: There was no significant difference between monitors in the SUS. Female gender was associated with higher SUS usability ratings. Qualitative participant-usability reports revealed distinctive shortcomings, for example, in terms of battery life and display readability. Usability for researchers came with problems in data management, such as completeness of the data download. CONCLUSION: The usability of the monitors compared in this work differed qualitatively. Yet, the overall usability ratings by participants were comparable. Conversely, from the researchers' perspective, there were crucial differences in data management and usability that should be considered when making monitor choices for future studies.

Validity and Acceptability of Wearable Devices for Monitoring Step-Count and Activity Minutes Among People With Multiple Sclerosis (2021)

Frontiers in Rehabilitation Sciences

Abstract

Multiple wearable devices that purport to measure physical activity are widely available to consumers. While they may support increases in physical activity among people with multiple sclerosis (MS) by providing feedback on their performance, there is little information about the validity and acceptability of these devices. Providing devices that are perceived as inaccurate and difficult to use may have negative consequences for people with MS, rather than supporting participation in physical activity. The aim of this study was, therefore, to assess the validity and acceptability of commercially available devices for monitoring step-count and activity time among people with MS. Nineteen ambulatory adults with MS [mean (SD) age 52.1 (11.9) years] participated in the study. Step-count was assessed using five commercially available devices (Fitbit Alta, Fitbit Zip, Garmin Vivofit 4, Yamax Digi Walker SW200, and Letscom monitor) and an activPAL3μ while completing nine everyday activities. Step-count was also manually counted. Time in light activity, moderate-to-vigorous activity, and total activity were measured during activities using an Actigraph GT3X accelerometer. Of the 19 participants who completed the validity study, fifteen of these people also wore the five commercially available devices for three consecutive days each, and participated in a semi-structured interview regarding their perception of the acceptability of the monitors. Mean percentage error for step-count ranged from 12.1\% for the Yamax SW200 to -112.3\% for the Letscom. Mean step-count as manually determined differed to mean step-count measured by the Fitbit Alta (p = 0.002), Garmin vivofit 4 (p {\textless} 0.001), Letscom (p {\textless} 0.001) and the research standard device, the activPAL3μ (p {\textless} 0.001). However, 95\% limits of agreement were smallest for the activPAL3μ and largest for the Fitbit Alta. Median percentage error for activity minutes was 52.9\% for the Letscom and 100\% for the Garmin Vivofit 4 and Fitbit Alta compared to minutes in total activity. Three inductive themes were generated from participant accounts: Interaction with device; The way the device looks and feels; Functionality. In conclusion, commercially available devices demonstrated poor criterion validity when measuring step-count and activity time in people with MS. This negatively affected the acceptability of devices, with perceived inaccuracies causing distrust and frustration. Additional considerations when designing devices for people with MS include an appropriately sized and lit display and ease of attaching and charging devices.

A digital health intervention to improve nutrition and physical activity in breast cancer survivors: Rationale and design of the Cook and Move for Your Life pilot and feasibility randomized controlled trial (2022)

Contemporary Clinical Trials

Abstract

Background: The design of a randomized pilot trial evaluating the feasibility of two doses of a digital health intervention promoting changes in nutrition and physical activity in breast cancer (BC) survivors is described. Methods: Eligible women were adults with history of early-stage breast cancer and > 60 days post-treatment, consumed <5 servings/day of fruits/vegetables and/or engaged in <150 min/week of aerobic moderate-to-vigorous physical activity, and had internet access. Participants were randomized to 6 months of either a "low" (1 session) or "high" (12 sessions) dose digital health intervention. Zoom-delivered sessions focused on improving diet and physical activity through didactic and experiential classes delivered by a registered dietitian, chef, exercise physiologist, and culinary educator. All study participants received weekly motivational texts, a Fitbit, and study website access. Diet, accelerometry, anthropometric, psychosocial, and biospecimen data were collected remotely at baseline and six months. Primary outcome was feasibility measured via accrual rate, adherence, retention, and acceptability. Results: Recruitment began in December 2019, was suspended in March 2020 due to the COVID-19 pandemic, resumed September 2020, and concluded in January 2021. Women were identified from the local BC registry and flyers posted in the oncology clinic. Of 929 women recruited, 321 completed the screening assessment, and of these, 138 were eligible. A total of 74 women were enrolled and randomized to the study. Conclusion: BC survivors were successfully enrolled in a digital health nutrition and physical activity intervention. If feasible, this intervention will be tested in larger and more diverse populations of cancer survivors.

Correlation Between Right Ventricular Echocardiography Measurements and Functional Capacity in Patients With Pulmonary Arterial Hypertension (2022)

Texas Heart Institute Journal

Abstract

BACKGROUND: Accelerometry is an emerging option for real-time evaluation of functional capacity in patients with pulmonary arterial hypertension (PAH). This prospective pilot study assesses the relationship between functional capacity by accelerometry and right ventricular measurements on echocardiography for this high-risk cohort. METHODS: Patients with PAH were prospectively enrolled and underwent 6-Minute Walk Test and cardiopulmonary exercise testing. They were given a Fitbit, which collected steps and sedentary time per day. Echocardiographic data included right ventricular global longitudinal, free wall, and septal strain; tricuspid regurgitant peak velocity; tricuspid annular plane systolic excursion; tricuspid annular plane systolic velocity; right ventricular myocardial performance index; and pulmonary artery acceleration time. Pairwise correlations were performed. RESULTS: The final analysis included 22 patients aged 13 to 59 years. Tricuspid regurgitant peak velocity had a negative correlation with 6-Minute Walk Test (r = -0.58, P = .02), peak oxygen consumption on exercise testing (r = -0.56, P = .03), and average daily steps on accelerometry (r = -0.59, P = .03), but a positive correlation with median sedentary time on accelerometry (r = 0.64, P = .02). Pulmonary artery acceleration time positively correlated with peak oxygen consumption on exercise testing (r = 0.64, P = .002). There was no correlation between right ventricular strain measurements and functional capacity testing. CONCLUSION: In this pilot study, tricuspid regurgitant jet and pulmonary artery acceleration time were the echocardiographic variables that correlated most with accelerometry data. With further echocardiographic validation, accelerometry can be a useful, noninvasive, and cost-effective tool to monitor disease progression in patients with PAH.

Feasibility and preliminary efficacy of a physiotherapy-led remotely delivered physical activity intervention in cancer survivors using wearable technology. The IMPETUS trial (2022)

Physiotherapy Theory and Practice

Abstract

BACKGROUND: Physical activity levels are low in cancer survivors. Remotely delivered programs which harness wearable technology may potentially be beneficial. OBJECTIVE: To evaluate the feasibility and preliminary efficacy of a remotely delivered, physical activity intervention which harnessed wearable technology. METHODS: This single arm pre-post longitudinal study included cancer survivors who had completed treatment in the preceding 3 years. Participants were supplied with a Fitbit One® or Flex® for 12 weeks. Physical activity goals were discussed during support phone calls. Outcome measures, assessed at baseline (T1), 12 weeks (T2), and 24 weeks (T3), included feasibility (recruitment, adherence, safety, acceptability) and efficacy [physical activity (Godin leisure time Index, ActiGraph GT3X+), quality of life (functional assessment of cancer therapy - general, short form 36 physical functioning component), functional capacity (six-minute walk test)]. RESULTS: Forty-five participants completed T1 assessments (10 males, 35 females). Thirty-nine (86.6\%) of those underwent assessment at T2 and 31 (68.8\%) at T3. The intervention was perceived positively with no adverse effects. There were increases in functional capacity (six-minute walk test, p = .002) between T1-T3, an increase in quality of life [short form 36 physical functioning measure (p = .0035), functional assessment of cancer total score (p = .02)] and self-report physical activity levels (p = .000123) between T1-T2, although effect sizes were generally low (d = 0.180 to d = 0.418). Objectively measured physical activity did not change. CONCLUSION: A physical activity intervention including wearable technology was safe, feasible, and well received by cancer survivors. An intervention based on this proof of concept should be followed up in further studies.

Comparing three wearable accelerometers to measure early activity after cardiac surgery (2022)

JTCVS open

Abstract

OBJECTIVE: Wearable activity monitors can provide detailed data on activity after cardiac surgery and discriminate a patient's risk for hospital-based outcomes. However, comparative data for different monitoring approaches, as well as predictive ability over clinical characteristics, are lacking. In addition, data on specific thresholds of activity are needed. The objective of this study was to compare 3 wearable activity monitors and 1 observational mobility scale in discriminating risk for 3 hospital-based outcomes, and to establish clinically relevant step thresholds. METHODS: Cardiac surgery patients were enrolled between June 2016 and August 2017 in a cohort study. Postoperative activity was measured by 3 accelerometry monitors (StepWatch Ambulation Monitor, Fitbit Charge HR, and ActiGraph GT9X) and 1 nurse-based observation scale. Monitors represent a spectrum of characteristics, including wear location (ankle/wrist), output (activity counts/steps), consumer accessibility, and cost. Primary outcomes were duration of hospitalization {\textgreater}7 days, discharge to a nonhome location, and 30-day readmission. RESULTS: Data were available from 193 patients (median age 67 years [interquartile range, 58-72]). All postoperative day 2 activity metrics (ie, from StepWatch, Fitbit, ActiGraph, and the observation scale) were independently associated with prolonged hospitalization and discharge to a nonhome location. Only steps as measured by StepWatch was independently associated with 30-day readmission. Overall, StepWatch provided the greatest discrimination (C-statistics 0.71-0.76 for all outcomes). Step thresholds between 250 and 500 steps/day identified between 74\% and 96\% of patients with any primary outcome. CONCLUSIONS: Data from wearable accelerometers provide additive value in early postoperative risk-stratification for hospital-based outcomes. These results both support and provide guidance for activity-monitoring programs after cardiac surgery.

Steps Ahead: Optimising physical activity in adults with cystic fibrosis: A pilot randomised trial using wearable technology, goal setting and text message feedback (2022)

Journal of Cystic Fibrosis: Official Journal of the European Cystic Fibrosis Society

Abstract

BACKGROUND: Regular participation in physical activity (PA) is encouraged for people with Cystic Fibrosis (CF). This study aimed to assess the effectiveness of an intervention using wearable technology, goal setting and text message feedback on PA and health outcomes in people with CF. METHODS: This was a pilot randomised trial conducted at University Hospital Limerick. Participants were randomly assigned to the intervention (INT) or active comparator (AC). The 12-week intervention consisted of wearable technology (Fitbit Charge 2) which was remotely monitored, and participants set step count goals. Participants were sent a one-way text message once a week over 12 weeks to positively reinforce and encourage PA participation. The AC group received the wearable technology alone. Follow up was assessed at 24 weeks. Outcomes assessed were PA, aerobic capacity, lung function, sleep, quality of life and wellbeing. RESULTS: Step count increased significantly for the INT group over 12 weeks when compared to the AC group (p=0.019). The INT group had a 28\% week-to-week percentage change (Weeks 1-12), while the AC group reduced by 1\%, p=0.023. Within group changes demonstrated that VO2 peak (ml/kg/min) significantly increased for the INT group at 12 weeks (24.4 ±7.65 to 26.13 ±7.79, p=0.003) but not at 24 weeks (24.45 ±7.05, p=0.776). There were no significant differences observed for VO2 peak (ml/kg/min) for the AC group. There was no significant effect on lung function, sleep, well-being, or quality of life for either group. CONCLUSIONS: A personalised PA intervention using wearable technology, goal setting and text message feedback increased PA and aerobic capacity in people with CF. Integration of this intervention into usual care may encourage regular PA participation for people with CF.

mHealth-Supported Gender- and Culturally Sensitive Weight Loss Intervention for Hispanic Men With Overweight and Obesity: Single-Arm Pilot Study (2022)

JMIR formative research

Abstract

BACKGROUND: Hispanic men have disproportionate rates of overweight and obesity compared with other racial and ethnic subpopulations. However, few weight loss interventions have been developed specifically for this high-risk group. Furthermore, the use of mobile health (mHealth) technologies to support lifestyle behavior changes in weight loss interventions for Hispanic men is largely untested. OBJECTIVE: This single-arm pilot study examined the feasibility and acceptability of integrating mHealth technology into a 12-week gender- and culturally sensitive weight loss intervention (GCSWLI) for Hispanic men with overweight and obesity. METHODS: A total of 18 Hispanic men (mean age 38, SD 10.9 years; mean BMI 34.3, SD 5.5 kg/m²; 10/18, 56\% Spanish monolingual) received a GCSWLI, including weekly in-person individual sessions, a daily calorie goal, and prescription of ≥225 minutes of moderate-intensity physical activity per week. mHealth technology support included tailored SMS text messaging, behavior self-monitoring support using Fitbit Charge 2, and weight tracking using a Fitbit Aria Wi-Fi Smart Scale. Changes in weight from baseline to 12 weeks were estimated using a paired 2-tailed t test. Descriptive analyses characterized the use of Fitbit and smart scales. Semistructured interviews were conducted immediately after intervention to assess the participants' weight loss experiences and perspectives on mHealth technologies. RESULTS: Of 18 participants, 16 (89\%) completed the 12-week assessments; the overall attrition rate was 11.1\%. The mean weight loss at week 12 was -4.7 kg (95\% CI 7.1 to -2.4 kg; P{\textless}.001). Participants wore the Fitbit 71.58\% (962/1344) of the intervention days and logged their body weight using the smart scale (410/1344, 30.51\% of the intervention days). Participants identified barriers to the use of the technology, such as lack of technological literacy and unreliable internet access for the smart scale. CONCLUSIONS: Although clinically significant weight loss was achieved by integrating mHealth technology into the GCSWLI, adherence to the prescribed use of technology was modest. Addressing barriers to the use of such technologies identified in our work may help to refine an mHealth intervention approach for Hispanic men. TRIAL REGISTRATION: ClinicalTrials.gov NCT02783521; https://clinicaltrials.gov/ct2/show/NCT02783521.

Mining associations between glycemic variability in awake-time and in-sleep among non-diabetic adults (2022)

Frontiers in Medical Technology

Abstract

It is often assumed that healthy people have the genuine ability to maintain tight blood glucose regulation. However, a few recent studies revealed that glucose dysregulation such as hyperglycemia may occur even in people who are considered normoglycemic by standard measures and were more prevalent than initially thought, suggesting that more investigations are needed to fully understand the within-day glucose dynamics of healthy people. In this paper, we conducted an analysis on a multi-modal dataset to examine the relationships between glycemic variability when people were awake and that when they were sleeping. The interstitial glucose levels were measured with a wearable continuous glucose monitoring (CGM) technology FreeStyle Libre 2 at every 15 min interval. In contrast to the traditional single-time-point measurements, the CGM data allow the investigation into the temporal patterns of glucose dynamics at high granularity. Sleep onset and offset timestamps were recorded daily with a Fitbit Charge 3 wristband. Our analysis leveraged the sleep data to split the glucose readings into segments of awake-time and in-sleep, instead of using fixed cut-off time points as has been done in existing literature. We combined repeated measure correlation analysis and quantitative association rules mining, together with an original post-filtering method, to identify significant and most relevant associations. Our results showed that low overall glucose in awake time was strongly correlated to low glucose in subsequent sleep, which in turn correlated to overall low glucose in the next day. Moreover, both analysis techniques identified significant associations between the minimal glucose reading in sleep and the low blood glucose index the next day. In addition, the association rules discovered in this study achieved high confidence (0.75-0.88) and lift (4.1-11.5), which implies that the proposed post-filtering method was effective in selecting quality rules.

A randomized, double-blind, placebo-controlled trial investigating the effects of an Ocimum tenuiflorum (Holy Basil) extract (HolixerTM) on stress, mood, and sleep in adults experiencing stress (2022)

Frontiers in Nutrition

Abstract

Background: In Ayurveda, Ocimum tenuiflorum (Holy Basil) is referred to as "the elixir of life" and is believed to promote longevity and general wellbeing. Although limited, there are clinical trials to suggest Ocimum tenuiflorum has anti-stress effects. Purpose: Examine the effects of a standardized Ocimum tenuiflorum extract (HolixerTM) on subjective and objective measures of stress and sleep quality in adults experiencing stress. Study design: Two-arm, parallel-group, 8-week, randomized, double-blind, placebo-controlled trial. Australian and New Zealand Clinical Trials Registry trial registration number ACTRN12621000609853. Methods: One hundred volunteers aged 18-65 years received either 125 mg of Ocimum tenuiflorum twice daily or a placebo. Outcome measures included the Perceived Stress Scale (PSS) (primary outcome measure), Profile of Mood States, Athens Insomnia Scale (AIS), Restorative Sleep Questionnaire, and the Patient-Reported Outcomes Measurement Information System-29. Sleep quality was also assessed using a wrist-worn sleep tracker (Fitbit), and stress changes were examined by measuring between-group differences in hair cortisol and stress responses after exposure to an experiment stress procedure known as the Maastricht Acute Stress Test (MAST). Results: Compared to the placebo, Ocimum tenuiflorum supplementation was associated with greater improvements in PSS (p = 0.003) and AIS (p = 0.025) scores; and at week 8, concentrations in hair cortisol were also lower (p = 0.025). Moreover, Ocimum tenuiflorum supplementation was associated with a buffered stress responses after exposure to the MAST as demonstrated by significantly lower concentrations in salivary cortisol (p = 0.001), salivary amylase (p = 0.001), systolic (p = 0.010) and diastolic (p = 0.025) blood pressure, and subjective stress ratings (p < 0.001). Ocimum tenuiflorum supplementation was well-tolerated with no reports of major adverse effects. Conclusion: The results from this trial suggest that 8 weeks of supplementation with an Ocimum tenuiflorum extract (HolixerTM) may reduce objective and subjective measures of stress, and improve subjective measures of sleep quality. However, further research using gold-standard objective sleep measures will be required to substantiate the sleep-related findings.

Feasibility, acceptability, and preliminary efficacy of a single-arm, remotely-delivered health coaching intervention to increase physical activity and reduce sedentary behavior during pregnancy (2022)

BMC pregnancy and childbirth

Abstract

BACKGROUND: Interventions targeting physical activity and sedentary behavior concurrently in pregnancy may be an ideal strategy to reduce the risk of pregnancy complications. We assessed the feasibility, acceptability, and preliminary efficacy of a single-arm, remotely-delivered health coaching intervention to promote physical activity and reduce sedentary behavior in pregnancy. METHODS: Women (n = 34) between 8 and 12 weeks gestation were recruited to take part in the INcreasing Steps in PREgnancy (INSPiRE) study. Participants were given an activity tracker (Fitbit Inspire) and met virtually with their health coach throughout the second and third trimesters of pregnancy. Feasibility was based on enrollment, retention, and adherence rates. Acceptance was assessed using a process evaluation survey. Intervention efficacy was based on activPAL data obtained at baseline and the end of the second trimester. RESULTS: Feasibility objectives were met, with greater than 70\% enrollment, 97\% retention, and 99\% adherence. All participants reported high levels of satisfaction with the program. ActivPAL data indicated statistically significant increases in daily steps (+ 1715.8 steps/day, Cohen's d = 0.97), stepping time (+ 1.9\%, d = 0.75), standing time (+ 2.3\%, d = 0.29), and decreases in total sedentary time (- 4.2\%, d = 0.43) and sedentary bouts of 30 minutes (- 4.1\%, d = 0.36) from baseline to the end of the second trimester, all p {\textless} 0.05. Decreases were also observed in sedentary bouts of 60 minutes (- 3.9\%, d = 0.40), but this was not statistically significant. CONCLUSIONS: The INSPiRE study demonstrated feasibility, high acceptability, and preliminary efficacy for improving movement behaviors in women during pregnancy, supporting future testing in a randomized controlled trial.

Feasibility of Fitbit Use in Adolescent Survivors of Pediatric Cancer: Who Consistently Uses It and for How Long? (2022)

Journal of Adolescent and Young Adult Oncology

Abstract

Purpose: Wearable activity trackers with real-time feedback and goal-setting features are being incorporated into programs to increase physical activity among childhood cancer survivors. This analysis describes the adoption and use of a Fitbit® Flex™ among adolescent-aged survivors of childhood cancer without incentives, reminders, or interventions to encourage use. Procedures: Cancer survivors aged 13-18 and ≥2 years from therapy received a Fitbit Flex with instructions to wear it daily. Researchers downloaded participants' daily total steps and active minutes for 1 year. Participants were classified as consistent (≥5 days/week for {\textgreater}4 weeks during the first 12 weeks) or inconsistent users, and comparisons between user types were made. Longitudinal use of the Fitbit and participants' 1-year acceptability evaluations are described. Results: Overall, 67.1\% (47/70) of survivors enrolled, and Fitbit data were available for 36 participants. Initially, 30.6\% (11/36) were consistent users. Consistent users had lower body mass index z-scores at enrollment (0.4 ± 0.7 vs. 1.2 ± 0.9; p = 0.01), but were otherwise comparable with inconsistent users. Over time survivors' use declined; at 12 months, only one participant was using his or her Fitbit. Survivors who completed a survey (n = 22) reported their Fitbit helped them self-monitor their exercise (72.7\%, 16/22) and lead a more active lifestyle (63.6\%, 14/22), but found it challenging to charge and not lose, forget about, or break the device. Conclusions: In the real-world setting, only a small subset of adolescent-aged survivors will initially consistently use a Fitbit and their interest diminishes over time. To maximize engagement, interventions incorporating wearable activity trackers likely need to include additional behavior change strategies.

The Development of a Novel mHealth Tool for Obstructive Sleep Apnea: Tracking Continuous Positive Airway Pressure Adherence as a Percentage of Time in Bed (2022)

Journal of Medical Internet Research

Abstract

Background: Continuous positive airway pressure (CPAP) is the mainstay obstructive sleep apnea (OSA) treatment; however, poor adherence to CPAP is common. Current guidelines specify 4 hours of CPAP use per night as a target to define adequate treatment adherence. However, effective OSA treatment requires CPAP use during the entire time spent in bed to optimally treat respiratory events and prevent adverse health effects associated with the time spent sleeping without wearing a CPAP device. Nightly sleep patterns vary considerably, making it necessary to measure CPAP adherence relative to the time spent in bed. Weight loss is an important goal for patients with OSA. Tools are required to address these clinical challenges in patients with OSA. Objective: This study aimed to develop a mobile health tool that combined weight loss features with novel CPAP adherence tracking (ie, percentage of CPAP wear time relative to objectively assessed time spent in bed) for patients with OSA. Methods: We used an iterative, user-centered process to design a new CPAP adherence tracking module that integrated with an existing weight loss app. A total of 37 patients with OSA aged 20 to 65 years were recruited. In phase 1, patients with OSA who were receiving CPAP treatment (n=7) tested the weight loss app to track nutrition, activity, and weight for 10 days. Participants completed a usability and acceptability survey. In phase 2, patients with OSA who were receiving CPAP treatment (n=21) completed a web-based survey about their interpretations and preferences for wireframes of the CPAP tracking module. In phase 3, patients with recently diagnosed OSA who were CPAP naive (n=9) were prescribed a CPAP device (ResMed AirSense10 AutoSet) and tested the integrated app for 3 to 4 weeks. Participants completed a usability survey and provided feedback. Results: During phase 1, participants found the app to be mostly easy to use, except for some difficulty searching for specific foods. All participants found the connected devices (Fitbit activity tracker and Fitbit Aria scale) easy to use and helpful. During phase 2, participants correctly interpreted CPAP adherence success, expressed as percentage of wear time relative to time spent in bed, and preferred seeing a clearly stated percentage goal ("Goal: 100%"). In phase 3, participants found the integrated app easy to use and requested push notification reminders to wear CPAP before bedtime and to sync Fitbit in the morning. Conclusions: We developed a mobile health tool that integrated a new CPAP adherence tracking module into an existing weight loss app. Novel features included addressing OSA-obesity comorbidity, CPAP adherence tracking via percentage of CPAP wear time relative to objectively assessed time spent in bed, and push notifications to foster adherence. Future research on the effectiveness of this tool in improving OSA treatment adherence is warranted.

A remotely delivered, peer-led intervention to improve physical activity and quality of life in younger breast cancer survivors (2022)

Journal of Behavioral Medicine

Abstract

Younger breast cancer survivors (YBCS) consistently report poorer quality of life (QOL) than older survivors. Increasing physical activity (PA) may improve QOL, but this has been understudied in YBCS. This single arm pilot study evaluated the feasibility and acceptability of a 3-month, peer-delivered, remote intervention to increase PA and improve QOL in YBCS. Data were collected from October 2019 - July 2020. Participants (n = 34, 43.1 ± 5.5 years old, 46 ± 34.4 months post-diagnosis, BMI = 30.2 ± 7.4 kg/m2) completed six video sessions with a trained peer mentor; self-monitored PA with a Fitbit activity tracker; and interacted with a private Fitbit Community for social support. At baseline, 3-and 6-months, participants completed QOL questionnaires and PA was measured through accelerometer (moderate-to-vigorous PA [MVPA]) and self-report (strength and flexibility). A parallel mixed-methods approach (qualitative interviews and quantitative satisfaction survey at 3-months) explored intervention feasibility and acceptability. One-way repeated-measures ANOVAs examined impacts on PA and QOL at 3-and 6-months. The intervention was feasible as evidenced by efficient recruitment, high retention, and adherence to intervention components. Remote delivery, working with a peer mentor, and using Fitbit tools were highly acceptable. From baseline to 3-months, participants increased time spent in objectively measured MVPA, strength, and flexibility exercises, and reported meaningful improvements to body image, fatigue, anxiety, and emotional support. A fully remote, peer-to-peer intervention is an acceptable and promising strategy to increase PA and improve QOL in YBCS. Refinements to the intervention and its delivery should be further assessed in future studies, toward the goal of disseminating an evidence-based, scalable intervention to the growing number of YBCS.Trial registration Prospectively registered as NCT04064892.

PreScription DigitaL ThErapEutic for Patients with Insomnia (SLEEP-I): a protocol for a pragmatic randomised controlled trial (2022)

BMJ open

Abstract

INTRODUCTION: Cognitive behavioural therapy for insomnia (CBT-I) is effective at treating chronic insomnia, yet in-person CBT-I can often be challenging to access. Prior studies have used technology to bridge barriers but have been unable to extensively assess the impact of the digital therapeutic on real-world patient experience and multidimensional outcomes. Among patients with insomnia, our aim is to determine the impact of a prescription digital therapeutic (PDT) (PEAR-003b, FDA-authorised as Somryst; herein called PDT) that provides mobile-delivered CBT-I on patient-reported outcomes (PROs) and healthcare utilisation. METHODS AND ANALYSIS: We are conducting a pragmatically designed, prospective, multicentre randomised controlled trial that leverages Hugo, a unique patient-centred health data-aggregating platform for data collection and patient follow-up from Hugo Health. A total of 100 participants with insomnia from two health centres will be enrolled onto the Hugo Health platform, provided with a linked Fitbit (Inspire 2) to track activity and then randomised 1:1 to receive (or not) the PDT for mobile-delivered CBT-I (Somryst). The primary outcome is a change in the insomnia severity index score from baseline to 9-week postrandomisation. Secondary outcomes include healthcare utilisation, health utility scores and clinical outcomes; change in sleep outcomes as measured with sleep diaries and a change in individual PROs including depressive symptoms, daytime sleepiness, health status, stress and anxiety. For those allocated to the PDT, we will also assess engagement with the PDT. ETHICS AND DISSEMINATION: The Institutional Review Boards at Yale University and the Mayo Clinic have approved the trial protocol. This trial will provide important data to patients, clinicians and policymakers about the impact of the PDT device delivering CBT-I on PROs, clinical outcomes and healthcare utilisation. Findings will be disseminated to participants, presented at professional meetings and published in peer-reviewed journals. TRIAL REGISTRATION NUMBER: NCT04909229.

Adverse Effects of Sit and Stand Workstations on the Health Outcomes of Assembly Line Workers: A Cross-sectional Study (2022)

Journal of Preventive Medicine and Hygiene

Abstract

INTRODUCTION: Sitting and standing workstations can affect individual's health outcomes differently. This study aimed to assess the effects of sit and stand workstations on energy expenditure and blood parameters, including glucose and triglyceride, musculoskeletal symptoms/pain and discomfort, fatigue, and productivity among workers of assembly line of a belt factory. METHODS: This cross-sectional study was conducted on 47 male assembly line workers (24 workers in sitting workstation and 23 workers in standing workstation) with at least one year of working experience. Data were gathered via demographic/occupational characteristics, Fitbit system, medical records, the Persian version of the Nordic Musculoskeletal Questionnaire (P-NMQ), the Persian version of the Numeric Rating Scale (P-NRS), the Persian version of the Swedish Occupational Fatigue (P-SOFI), and Persian version of the Health and Work Questionnaire (P-HWQ). RESULTS: The results showed that there were no statistically significant between the demographic/occupational details of the participants in sitting and standing groups, except work experience. The findings of the present study revealed that the energy expenditure, and blood glucose/triglyceride there are not statistically differences between sitting and standing groups. In addition, the prevalence of musculoskeletal symptoms in the neck, lower back, knees, and ankles/feet in standing group was significantly higher than the sitting group. The means of severity of discomfort/pain in all body regions were significantly higher in standing group compared to other group. Generally, occupational fatigue was higher among the standing group compared to sitting group. About productivity, the 'concentration/focus' and 'impatience/irritability' subscales in sitting group were higher than the standing group. Contrariwise, other subscales of the productivity, including 'productivity', 'supervisor relations', 'non-work satisfaction', 'work satisfaction' in the standing group were higher than the sitting group. CONCLUSIONS: To reduce the adverse effects of sitting and standing workstations on individual's health outcomes, planning to use sit-stand workstations is recommended.

Identifying Predictors of Adherence to the Physical Activity Goal: A Secondary Analysis of the SMARTER Weight Loss Trial (2022)

Medicine and Science in Sports and Exercise

Abstract

INTRODUCTION/PURPOSE: Research is needed to inform tailoring supportive strategies for promoting physical activity (PA) in the context of behavioral treatment of obesity. We aimed to identify baseline participant characteristics and short-term intervention response predictors associated with adherence to the study-defined PA goal in a mobile health (mHealth) weight loss trial. METHODS: A secondary analysis was conducted of a 12-month weight loss trial (SMARTER) that randomized 502 adults with overweight or obesity to either self-monitoring of diet, PA, and weight with tailored feedback messages (n = 251) or self-monitoring alone (n = 251). The primary outcome was average adherence to the PA goal of ≥150 min/week of moderate- and vigorous-intensity aerobic activities (MVPA) from Fitbit Charge 2™ trackers over 52 weeks. Twenty-five explanatory variables were considered. Machine learning methods and linear regression were used to identify predictors of adherence to the PA goal. RESULTS: The sample (N = 502) was mostly female (80\%), White (83\%) with the average age of 45 ± 14.4 years and body mass index of 33.7 ± 4.0 kg/m2. Machine learning methods identified PA goal adherence for the first week as the most important of long-term PA goal adherence. In the parsimonious linear regression model, higher PA goal adherence for the first week, greater PA FB messages opened, older age, being male, higher education, being single and not having obstructive sleep apnea were associated with higher long-term PA goal adherence. CONCLUSIONS: To our knowledge, this is the first study using machine learning approaches to identify predictors of long-term PA goal adherence in a mHealth weight loss trial. Future studies focusing on facilitators or barriers to PA among young and middle-aged adults and women with low PA goal adherence are warranted.

Comparing a Fitbit Wearable to an Electrocardiogram Gold Standard as a Measure of Heart Rate Under Psychological Stress: A Validation Study (2022)

JMIR formative research

Abstract

BACKGROUND: Wearable devices collect physiological and behavioral data that have the potential to identify individuals at risk of declining mental health and well-being. Past research has mainly focused on assessing the accuracy and the agreement of heart rate (HR) measurement of wearables under different physical exercise conditions. However, the capacity of wearables to sense physiological changes, assessed by increasing HR, caused by a stressful event has not been thoroughly studied. OBJECTIVE: This study followed 3 objectives: (1) to test the ability of a wearable device (Fitbit Versa 2) to sense an increase in HR upon induction of psychological stress in the laboratory; (2) to assess the accuracy of the wearable device to capture short-term HR variations caused by psychological stress compared to a gold-standard electrocardiogram (ECG) measure (Biopac); and (3) to quantify the degree of agreement between the wearable device and the gold-standard ECG measure across different experimental conditions. METHODS: Participants underwent the Trier Social Stress Test protocol, which consists of an oral phase, an arithmetic stress phase, an anticipation phase, and 2 relaxation phases (at the beginning and the end). During the stress protocol, the participants wore a Fitbit Versa 2 and were also connected to a Biopac. A mixed-effect modeling approach was used (1) to assess the effect of experimental conditions on HR, (2) to estimate several metrics of accuracy, and (3) to assess the agreement: the Bland-Altman limits of agreement (LoA), the concordance correlation coefficient, the coverage probability, the total deviation index, and the coefficient of an individual agreement. Mean absolute error and mean absolute percent error were calculated as accuracy indices. RESULTS: A total of 34 university students were recruited for this study (64\% of participants were female with a mean age of 26.8 years, SD 8.3). Overall, the results showed significant HR variations across experimental phases. Post hoc tests revealed significant pairwise differences for all phases. Accuracy analyses revealed acceptable accuracy according to the analyzed metrics of accuracy for the Fitbit Versa 2 to capture the short-term variations in psychological stress levels. However, poor indices of agreement between the Fitbit Versa 2 and the Biopac were found. CONCLUSIONS: Overall, the results support the use of the Fitbit Versa 2 to capture short-term stress variations. The Fitbit device showed acceptable levels of accuracy but poor agreement with an ECG gold standard. Greater inaccuracy and smaller agreement were found for stressful experimental conditions that induced a higher HR. Fitbit devices can be used in research to measure HR variations caused by stress, although they cannot replace an ECG instrument when precision is of utmost importance.

Objective and subjective measures of daily physical activity in persons with Multiple Sclerosis beginning a rehabilitation regime: A cross-sectional study (2022)

Multiple Sclerosis and Related Disorders

Abstract

INTRODUCTION: Fatigue, and balance and gait disorders can impact on physical activity (PA) levels in persons with Multiple Sclerosis (pwMS). To date, several studies have examined PA in pwMS during daily life, but little is known about PA levels in pwMS during a rehabilitation period. The present study investigated PA levels (daily steps, light physical activity (LPA), and moderate-vigorous physical activity (MVPA)) in inpatient and outpatients with MS during their rehabilitation period and described the relationship between objectively measured PA and levels of disability, quality of life, fatigue, and self-efficacy. METHODS: In this exploratory cross-sectional study, we examined 40 pwMS during their inpatient or outpatient rehabilitation regime. Participants included in the study (N = 34) wore a Fitbit Versa tracker for one week recording daily steps, minutes of LPA, and minutes of MVPA (primary outcomes). They underwent a clinical assessment of physical activity levels (Godin Leisure-Time Exercise Questionnaire(GLTEQ)), fatigue (Fatigue Severity Scale(FSS)), walking ability (10 Meter Walk Test(10MWT), 2-Min Walk Test(2MWT), 12-item Multiple Sclerosis Walking scale(MSWS-12)), quality of life (12-Item Short Form Survey (SF-12)), and self-efficacy (Self-Efficacy in Multiple Sclerosis scale (SEMS)) (secondary outcomes). Multiple linear regressions (MLR) models were used to test whether the demographic difference between the two groups influenced the estimation of objective variables measured by Fitbit. Finally, correlations between objectively measured physical activity and subjective clinical scales were estimated with Spearman correlations. RESULTS: Our sample consisted of 21 females and 13 males with a mean (interquartile range) age of 52 (20) years and an Expanded Disability Status scale(EDSS) score of 6.0 (1.50) points; baseline characteristics of inpatients (N = 18) and outpatients (N = 16) differed statistically only in EDSS levels (p-value = 0.008) and use of assistive devices (p=0.007). The whole sample performed (mean±standard deviation) 3969±2190 steps per day, with no significant difference between inpatients (3318±1515) and outpatients (4660±2606). No statistical difference was found between the groups in LPA (p-value=0.064). A significant difference in MVPA (p-value {\textless} 0.001) was found between inpatients and outpatients, 1.52±3.98 and 14.69±11.56 min per day, respectively. Significant correlations were found between FSS and MVPA both in the whole sample (r(32)= -0.62, p {\textless} 0.001) and in the outpatients group (r(14) = -0.66, p = 0.005), and between 10MWT and daily steps (whole sample:(r(32) = 0.48, p = 0.005), outpatients:(r(14)= -0.51, p = 0.05)). CONCLUSIONS: PwMS in our study overall engaged in reduced and less intense levels of daily PA with respect to the guidelines with inpatients performing almost no vigorous activities. Considering the importance of PA in improving physical and mental well-being, clinicians and researchers should develope strategies to increase daily PA of PwMS during their rehabilitation programs and daily life.

Usability and acceptability of wearable technology in the early detection of dementia (2022)

Alzheimer's \& Dementia: The Journal of the Alzheimer's Association

Abstract

BACKGROUND: The Early Detection of Neurodegeneration (EDoN) is a global initiative that aims to explore the potential of wearable technologies and smartphone applications to detect preclinical dementia, with aspirations to validate a digital toolkit for clinical practice. To enhance the development of an inclusive digital toolkit, we conducted a study to assess the usability and acceptability of different digital devices in people with cognitive impairments and their carers. METHOD: Recruitment was conducted across various UK networks such as Join Dementia Research. Participants received the EDoN toolkit, which includes a smartwatch (Fitbit Charge 4), EEG headband (Dreem 3) and two smartphone applications (Longevity and Mezurio). Guides were provided to support the setup process. Initial interviews were conducted approximately three days after the participant received the devices, to explore initial perspectives regarding the toolkit and experiences of the setup process. Follow-up interviews were conducted two weeks later to explore the acceptability and usability of the toolkit. NVivo was used to thematically analyse the interview transcripts. Emerging themes were discussed and refined by the research group. RESULT: Sixteen semi-structured interviews were conducted with nine participants, at two-time points. Four participants had mild cognitive impairment, two had frontotemporal dementia, one had Alzheimer's and two were carers. We identified three key themes, which centred around usability, acceptability and inequity. Participants expressed the wearable devices were comfortable but individuals with physical disabilities or cognitive impairments struggled to use some devices. Participants valued the feedback the devices provided such as information on sleep and heart rate, although some information was not fully understood. Participants also shared their concerns around detecting preclinical dementia and the increased anxiety around the consequences of this such as being put in a home". Various inequities of the toolkit were uncovered such as digital exclusion relating to a lack of access to strong WiFi connection

Use of coaching and technology to improve blood pressure control in Black women with hypertension: Pilot randomized controlled trial study (2023)

Journal of Clinical Hypertension (Greenwich, Conn.)

Abstract

Hypertension is the main cause of cardiovascular disease, especially in women. Black women (58\%) are affected by higher rates of hypertension than other racial/ethnic groups contributing to increased cardio-metabolic disorders. To decrease blood pressure (BP) in this population, a pilot randomized controlled trial was conducted to examine the effects of Interactive Technology Enhanced Coaching (ITEC) versus Interactive Technology (IT) alone in achieving BP control, adherence to antihypertensive medication, and adherence to lifestyle modifications among Black women diagnosed with and receiving medication for their hypertension. Participants completed a 6-week Chronic Disease Self-Management Program (CDSMP), and 83 participants were randomly assigned to ITEC versus IT. Participants were trained to use three wireless tools and five apps that were synchronized to smartphones to monitor BP, weight, physical activity (steps), diet (caloric and sodium intake), and medication adherence. Fitbit Plus, a cloud-based collaborative care platform was used to collect, track, and store data. Using a mixed-effects repeated measures model, the main effect of group means indicated no significant difference between the treatment and referent groups on study variables. The main effect of time indicated significant differences between repeated measures for systolic BP (p {\textless} .0001), weight (p {\textless} .0001), and steps (p = .018). An interaction effect revealed differences over time and was significant for study measures except diastolic BP. An important goal of this preliminary analysis is to help Black women prioritize self-care management in their everyday environment. Future research is warranted in a geographically broader population of hypertensive Black women.

Detailed Versus Simplified Dietary Self-monitoring in a Digital Weight Loss Intervention Among Racial and Ethnic Minority Adults: Fully Remote, Randomized Pilot Study (2022)

JMIR formative research

Abstract

BACKGROUND: Detailed self-monitoring (or tracking) of dietary intake is a popular and effective weight loss approach that can be delivered via digital tools, although engagement declines over time. Simplifying the experience of self-monitoring diet may counteract this decline in engagement. Testing these strategies among racial and ethnic minority groups is important as these groups are often disproportionately affected by obesity yet underrepresented in behavioral obesity treatment. OBJECTIVE: In this 2-arm pilot study, we aimed to evaluate the feasibility and acceptability of a digital weight loss intervention with either detailed or simplified dietary self-monitoring. METHODS: We recruited racial and ethnic minority adults aged ≥21 years with a BMI of 25 kg/m2 to 45 kg/m2 and living in the United States. The Pacific time zone was selected for a fully remote study. Participants received a 3-month stand-alone digital weight loss intervention and were randomized 1:1 to either the detailed arm that was instructed to self-monitor all foods and drinks consumed each day using the Fitbit mobile app or to the simplified arm that was instructed to self-monitor only red zone foods (foods that are highly caloric and of limited nutritional value) each day via a web-based checklist. All participants were instructed to self-monitor both steps and body weight daily. Each week, participants were emailed behavioral lessons, action plans, and personalized feedback. In total, 12 a priori benchmarks were set to establish feasibility, including outcomes related to reach, retention, and self-monitoring engagement (assessed objectively via digital tools). Acceptability was assessed using a questionnaire. Weight change was assessed using scales shipped to the participants' homes and reported descriptively. RESULTS: The eligibility screen was completed by 248 individuals, of whom 38 (15.3\%) were randomized, 18 to detailed and 20 to simplified. At baseline, participants had a mean age of 47.4 (SD 14.0) years and BMI of 31.2 (SD 4.8) kg/m2. More than half (22/38, 58\%) were identified as Hispanic of any race. The study retention rate was 92\% (35/38) at 3 months. The detailed arm met 9 of 12 feasibility benchmarks, while the simplified arm met all 12. Self-monitoring engagement was moderate to high (self-monitoring diet: median of 49\% of days for detailed, 97\% for simplified; self-monitoring steps: 99\% for detailed, 100\% for simplified; self-monitoring weight: 67\% for detailed, 80\% for simplified). Participants in both arms reported high satisfaction, with 89\% indicating that they would recommend the intervention. Weight change was -3.4 (95\% CI -4.6 to -2.2) kg for detailed and -3.3 (95\% CI -4.4 to -2.2) kg for simplified. CONCLUSIONS: A digital weight loss intervention that incorporated either detailed or simplified dietary self-monitoring was feasible, with high retention and engagement, and acceptable to racial and ethnic minority adults. TRIAL REGISTRATION: ASPREDICTED \#66674; https://aspredicted.org/ka478.pdf.

Sleep and seizure risk in epilepsy: bed and wake times are more important than sleep duration (2022)

Brain: A Journal of Neurology

Abstract

Sleep duration, sleep deprivation and the sleep-wake cycle are thought to play an important role in the generation of epileptic activity and may also influence seizure risk. Hence, people diagnosed with epilepsy are commonly asked to maintain consistent sleep routines. However, emerging evidence paints a more nuanced picture of the relationship between seizures and sleep, with bidirectional effects between changes in sleep and seizure risk in addition to modulation by sleep stages and transitions between stages. We conducted a longitudinal study investigating sleep parameters and self-reported seizure occurrence in an ambulatory at-home setting using mobile and wearable monitoring. Sixty subjects wore a Fitbit smartwatch for at least 28 days while reporting their seizure activity in a mobile app. Multiple sleep features were investigated, including duration, oversleep and undersleep, and sleep onset and offset times. Sleep features in participants with epilepsy were compared to a large (n = 37921) representative population of Fitbit users, each with 28 days of data. For participants with at least 10 seizure days (n = 34), sleep features were analysed for significant changes prior to seizure days. A total of 4956 reported seizures (M = 83, SD = 130) and 30485 recorded sleep nights (M = 508, SD = 445) were included in the study. There was a trend for participants with epilepsy to sleep longer than the general population, although this difference was not significant. Just 5 of 34 participants showed a significant difference in sleep duration the night before seizure days compared to seizure-free days. However, 14 of 34 subjects showed significant differences between their sleep onset (bed) and/or offset (wake) times prior to seizure occurrence. In contrast to previous studies, the current study found undersleeping was associated with a marginal 2\% decrease in seizure risk in the following 48 h (p {\textless} 0.01). Nocturnal seizures were associated with both significantly longer sleep durations and increased risk of a seizure occurring in the following 48 h. Overall, the presented results demonstrated that day-to-day changes in sleep duration had a minimal effect on reported seizures, while patient-specific changes in bed- and wake-times were more important for identifying seizure risk the following day. Nocturnal seizures were the only factor that significantly increased the risk of seizures in the following 48 h on a group level. Wearables can be utilised to identify these sleep-seizure relationships and guide clinical recommendations or improve seizure forecasting algorithms.

Living labs for a mobile app-based health program: effectiveness of a 24-week walking intervention for cardiovascular disease risk reduction among female Korean-Chinese migrant workers: a randomized controlled trial (2022)

Archives of Public Health = Archives Belges De Sante Publique

Abstract

BACKGROUND: The risk of Cardiovascular disease (CVDs) among adult populations is influenced by environmental factors, and immigrant populations tend to be more vulnerable. This study examined the effectiveness of a 24-week walking program based on social-cognitive determinants through mobile app for CVD risk reduction among female Korean-Chinese middle-aged workers. METHODS: This study used a parallel randomized controlled trial. Participants were recruited by distributing posters and flyers and randomly assigned to either the standard treatment (ST, n = 22) or enhanced treatment group (ET, n = 28). Participants were provided with a mobile app linked to Fitbit Alta for 24 weeks and instructed to walk at least 30 minutes five times a week and moderate-intensity physical activity. The ET group had additional interventions that enhanced social-cognitive determinants such as self-efficacy, social support. All participants were guided to voluntary physical activity during the 12-week maintenance period. Data were analyzed by the Mann Whitney U-test and a generalized estimating equation. RESULTS: There were significant between-group differences regarding the number of steps (B = 1.295, P {\textless} .001) and moderate physical activity time (OR = 6.396, P = .030) at week 12. ET group had significant changes in high-density lipoprotein cholesterol (B = 10.522, P = .007), low-density lipoprotein cholesterol (B = -16.178, P = .024), total cholesterol (B = -20.325, P = .039), fasting blood sugar (B = - 8.138, P = -.046). In addition, there was a significant reduction of 10-year CVD risk for the ET group over 12 weeks compared to the ST group (B = -0.521, P{\textless}. 001). CONCLUSIONS: Long-term studies are needed to reduce the risk of cardiovascular disease in large-scale migrant workers and to confirm the direct and insdirect effects of social-cognitive determinants on health outcomes. TRIAL REGISTRATION: The trial was retrospectively registered in WHO ICTRP (KCT0006467) August 19th, 2021. ( https://trialsearch.who.int/Trial2.aspx?TrialID=KCT0006467.

Prescribing Time in Nature for Human Health and Well-Being: Study Protocol for Tailored Park Prescriptions (2022)

Frontiers in Digital Health

Abstract

BACKGROUND: eHealth technologies offer an efficient method to integrate park prescriptions into clinical practice by primary health care (PHC) providers to help patients improve their health via tailored, nature-based health behavior interventions. This paper describes the protocol of the GoalRx Prescription Intervention (GPI) which was designed to leverage community resources to provide tailored park prescriptions for PHC patients. METHODS: The GPI study was designed as a 3-arm, multi-site observational study. We enrolled low-income, rural adults either at-risk of or living with hypertension or diabetes (n = 75) from Federally Qualified Health Centers (FQHC) in two counties in North Carolina, USA into the 3-month intervention. Eligible participants self-selected to receive (1) a tailored park prescription intervention; (2) a tailored home/indoor PA prescription intervention; or (3) a healthy eating prescription (with no PA prescription beyond standard PA counseling advice that is already routinely provided in PHC) as the comparison group. The GPI app paired patient health data from the electronic health record with stated patient preferences and triggered app-integrated SMS motivation and compliance messaging directly to the patient. Patients were assessed at baseline and at a 3-month follow-up upon the completion of the intervention. The primary outcome (mean difference in weekly physical activity from baseline (T0) to post-intervention (T1) as measured by the Fitbit Flex 2) was assessed at 3 months. Secondary outcomes included assessment of the relationship between the intervention and biological markers of health, including body mass index (BMI), systolic and diastolic blood pressure, HbA1c or available glucose test (if applicable), and a depression screen score using the Patient Health Questionnaire 9. Secondary outcomes also included the total number of SMS messages sent, number of SMS messages responded to, number of SMS messages ignored, and opt-out rate. DISCUSSION: The goal was to create a protocol utilizing eHealth technologies that addressed the specific needs of rural low-income communities and fit into the natural rhythms and processes of the selected FQHC clinics in North Carolina. This protocol offered a higher standard of health care by connecting patients to their PHC teams and increasing patient motivation to make longer-lasting health behavior changes.

The Feasibility of Implementing Remote Measurement Technologies in Psychological Treatment for Depression: Mixed Methods Study on Engagement (2023)

JMIR mental health

Abstract

BACKGROUND: Remote measurement technologies (RMTs) such as smartphones and wearables can help improve treatment for depression by providing objective, continuous, and ecologically valid insights into mood and behavior. Engagement with RMTs is varied and highly context dependent; however, few studies have investigated their feasibility in the context of treatment. OBJECTIVE: A mixed methods design was used to evaluate engagement with active and passive data collection via RMT in people with depression undergoing psychotherapy. We evaluated the effects of treatment on 2 different types of engagement: study attrition (engagement with study protocol) and patterns of missing data (engagement with digital devices), which we termed data availability. Qualitative interviews were conducted to help interpret the differences in engagement. METHODS: A total of 66 people undergoing psychological therapy for depression were followed up for 7 months. Active data were gathered from weekly questionnaires and speech and cognitive tasks, and passive data were gathered from smartphone sensors and a Fitbit (Fitbit Inc) wearable device. RESULTS: The overall retention rate was 60\%. Higher-intensity treatment (χ21=4.6; P=.03) and higher baseline anxiety (t56.28=-2.80, 2-tailed; P=.007) were associated with attrition, but depression severity was not (t50.4=-0.18; P=.86). A trend toward significance was found for the association between longer treatments and increased attrition (U=339.5; P=.05). Data availability was higher for active data than for passive data initially but declined at a sharper rate (90\%-30\% drop in 7 months). As for passive data, wearable data availability fell from a maximum of 80\% to 45\% at 7 months but showed higher overall data availability than smartphone-based data, which remained stable at the range of 20\%-40\% throughout. Missing data were more prevalent among GPS location data, followed by among Bluetooth data, then among accelerometry data. As for active data, speech and cognitive tasks had lower completion rates than clinical questionnaires. The participants in treatment provided less Fitbit data but more active data than those on the waiting list. CONCLUSIONS: Different data streams showed varied patterns of missing data, despite being gathered from the same device. Longer and more complex treatments and clinical characteristics such as higher baseline anxiety may reduce long-term engagement with RMTs, and different devices may show opposite patterns of missingness during treatment. This has implications for the scalability and uptake of RMTs in health care settings, the generalizability and accuracy of the data collected by these methods, feature construction, and the appropriateness of RMT use in the long term.

Systematic Review of Fitbit Charge 2 Validation Studies for Exercise Tracking (2022)

Translational Journal of the American College of Sports Medicine

Abstract

CONTEXT: There are research-grade devices that have been validated to measure either heart rate (HR) by electrocardiography (ECG) with a Polar chest strap, or step count with ACTiGraph accelerometer. However, wearable activity trackers that measure HR and steps concurrently have been tested against research-grade accelerometers and HR monitors with conflicting results. This review examines validation studies of the Fitbit Charge 2 (FBC2) for accuracy in measuring HR and step count and evaluates the device's reliability for use by researchers and clinicians. DESIGN: This registered review was conducted using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The robvis (risk-of-bias visualization) tool was used to assess the strength of each considered article. ELIGIBILITY CRITERIA: Eligible articles published between 2018 and 2019 were identified using PubMed, CINHAL, Embase, Cochran, and World of Science databases and hand-searches. All articles were HR and/or step count validation studies for the FBC2 in adult ambulatory populations. STUDY SELECTION: Eight articles were examined in accordance with the eligibility criteria alignment and agreement among the authors and research librarian. MAIN OUTCOME MEASURES: Concordance correlation coefficients (CCC) were used to measure agreement between the tracker and criterion devices. Mean absolute percent error (MAPE) was used to average the individual absolute percent errors. RESULTS: Studies that measured CCC found agreement between the FBC2 and criterion devices ranged between 26\% and 92\% for HR monitoring, decreasing in accuracy as exercise intensity increased. Inversely, CCC increased from 38\% to 99\% for step count when exercise intensity increased. HR error between MAPE was 9.21\% to 68\% and showed more error as exercise intensity increased. Step measurement error MAPE was 12\% for healthy persons aged 24-72 years but was reported at 46\% in an older population with heart failure. CONCLUSIONS: Relative agreement with criterion and low-to-moderate MAPE were consistent in most studies reviewed and support validation of the FBC2 to accurately measure HR at low or moderate exercise intensities. However, more investigation controlling testing and measurement congruency is needed to validate step capabilities. The literature supports the validity of the FBC2 to accurately monitor HR, but for step count is inconclusive so the device may not be suitable for recommended use in all populations.

An online mental health informed physical activity intervention for emergency service workers and their families: A stepped-wedge trial (2023)

Digital Health

Abstract

OBJECTIVE: Emergency service workers are at risk of experiencing poor mental health due to repeated exposure to potentially traumatic events. Promoting healthy lifestyle factors may help improve health outcomes and quality of life among this population. This study aimed to evaluate the efficacy of a 10-week physical activity (PA) and diet programme delivered via Facebook for sedentary emergency service workers and their support partners on levels of psychological distress. METHODS: We delivered a 10-week intervention via a private Facebook group facilitated by exercise physiologists, a dietitian and peer-facilitators. Weekly education modules and telehealth calls were delivered, and participants were provided with a PA tracking device (Fitbit accelerometer). A stepped-wedge design was applied to compare levels of psychological distress (K6) during baseline, to intervention by comparing slopes of change. Secondary pre-post outcomes included mental health symptoms, PA, quality of life, social support to exercise, sleep quality and suicidal ideation. RESULTS: In total, N=90 participants (n=47 emergency service workers and n=43 support partners) were recruited in 4 cohorts (aged 42.3±11.5 years, 51\% male). Levels of psychological distress did not change significantly during the baseline (control) slope and reduced significantly during the first 6 weeks of intervention (intervention slope 1). The slopes were significantly different, b=-0.351, p = 0.003 (i.e. the trajectories of change) and improvements plateaued until follow up. Retention was high (92\%) and improvements in mental health symptoms, minutes of PA, sedentary time and quality of life were significant. CONCLUSIONS: Our intervention delivered via social media is feasible and associated with reduced levels of psychological distress among emergency service workers and support partners. TRIAL REGISTRATION: Australian New Zealand Clinical Trials Registry (ACTRN): 12619000877189.

Piloting HealthScore: Feasibility and acceptability of a clinically integrated health coaching program for people living with cancer (2023)

Cancer Medicine

Abstract

BACKGROUND: Cancer supportive care interventions often have limited generalizability, goal misalignment, and high costs. We developed and piloted a health coaching intervention, UNC HealthScore, in patients undergoing cancer treatment (ClinicalTrials.gov identifier NCT04923997). We present feasibility, acceptability, and preliminary outcome data. METHODS: HealthScore is a six-month, theory-based, multicomponent intervention delivered through participant-driven coaching sessions. For the pilot study, participants were provided a Fitbit, responded to weekly symptom and physical function digital surveys, and met with a health coach weekly to develop and monitor goals. Coaching notes were discussed in weekly interdisciplinary team meetings and provided back to the treating oncology team. Symptom alerts were monitored and triaged through a study resource nurse to relevant supportive care services. Feasibility was determined based on intervention enrollment and completion. Acceptability was based on satisfaction with coaching and Fitbit-wearing and was informed by semistructured exit interviews. Outcomes evaluated for signs of improvement included several PROMIS (Patient-Reported Outcomes Measurement Information System) measures, including the primary intervention target, physical function. RESULTS: From May 2020 to March 2022, 50 participants completed the single-arm pilot. Feasibility was high: 66\% enrolled and 71\% completed the full intervention. Participants reported an average of 4.8 and 4.7 (out of 5) on the acceptability of coaching calls and using the Fitbit, respectively. Physical function scores rose 3.1 points (SE = 1.1) from baseline to 3 months, and 4.3 (SE = 1.0) from baseline to 6 months, above established minimal clinically important difference (MCID). Improvements above MCID were also evident in anxiety and depression, and smaller improvements were demonstrated for emotional support, social isolation, cognitive function, symptom burden, and self-efficacy. DISCUSSION: HealthScore shows feasibility, acceptability, and promising preliminary outcomes. Randomized studies are underway to determine the efficacy of preserving physical function in patients with advanced cancer.

Selection of and Response to Physical Activity-Based Social Comparisons in a Digital Environment: Series of Daily Assessment Studies (2023)

JMIR human factors

Abstract

BACKGROUND: Innovative approaches are needed to understand barriers to and facilitators of physical activity among insufficiently active adults. Although social comparison processes (ie, self-evaluations relative to others) are often used to motivate physical activity in digital environments, user preferences and responses to comparison information are poorly understood. OBJECTIVE: We used an iterative approach to better understand users' selection of comparison targets, how they interacted with their selected targets, and how they responded to these targets. METHODS: Across 3 studies, different samples of insufficiently active college students used the Fitbit system (Fitbit LLC) to track their steps per day as well as a separate, adaptive web platform each day for 7 to 9 days (N=112). The adaptive platform was designed with different layouts for each study; each allowed participants to select their preferred comparison target from various sets of options, view the desired amount of information about their selected target, and rate their physical activity motivation before and after viewing information about their selected target. Targets were presented as achieving physical activity at various levels below and above their own, which were accessed via the Fitbit system each day. We examined the types of comparison target selections, time spent viewing and number of elements viewed for each type of target, and day-level associations between comparison selections and physical activity outcomes (motivation and behavior). RESULTS: Study 1 (n=5) demonstrated that the new web platform could be used as intended and that participants' interactions with the platform (ie, the type of target selected, the time spent viewing the selected target's profile, and the number of profile elements viewed) varied across the days. Studies 2 (n=53) and 3 (n=54) replicated these findings; in both studies, age was positively associated with time spent viewing the selected target's profile and the number of profile elements viewed. Across all studies, upward targets (who had more steps per day than the participant) were selected more often than downward targets (who had fewer steps per day than the participant), although only a subset of either type of target selection was associated with benefits for physical activity motivation or behavior. CONCLUSIONS: Capturing physical activity-based social comparison preferences is feasible in an adaptive digital environment, and day-to-day differences in preferences for social comparison targets are associated with day-to-day changes in physical activity motivation and behavior. Findings show that participants only sometimes focus on the comparison opportunities that support their physical activity motivation or behavior, which helps explain previous, equivocal findings regarding the benefits of physical activity-based comparisons. Additional investigation of day-level determinants of comparison selections and responses is needed to fully understand how best to harness comparison processes in digital tools to promote physical activity.

The development of a family-based wearable intervention using behaviour change and co-design approaches: move and connect (2023)

Public Health

Abstract

BACKGROUND: Previous research has explored the effectiveness of wearable activity trackers (wearables) for increasing child physical activity (PA) levels, but there have been mixed results. The use of theoretical frameworks and co-design techniques are recognised ways of increasing an intervention's acceptability and effectiveness. AIMS: This study aims to use co-design workshops and an evidence-based theoretical framework (the Behaviour Change Wheel) to develop a family-based PA intervention using wearables. METHODS: Three stages of intervention development outlined by the Behaviour Change Wheel were used. Co-design workshops with seven families (11 parents and 12 children) and seven PA experts were conducted where stakeholders discussed how to overcome previously identified barriers to families being active and using wearables. This resulted in the intervention's components being developed, with each component's mechanisms of action (e.g. intervention functions and behaviour change techniques) being retrospectively identified. RESULTS: The 'Move \& Connect' intervention was developed, which targets family PA and wearable use. The intervention takes a flexible approach and includes eight components, including wearable devices (Fitbit Alta HR), support resources, an introductory workshop, collective challenges, goal setting and reviewing, engagement prompts, social support and health-related resources (e.g. educational videos). The intervention incorporates six intervention functions targeting PA and wearable use: education, training, modelling, persuasion, incentivisation and environmental restructuring and 24 behaviour change techniques, including goal setting, social comparison, feedback on behaviour and graded task. CONCLUSIONS: This is the first known study to use an evidence-based framework and co-design to develop a family-based wearable intervention. The identification of the intervention's mechanisms of action will prove useful when implementing and evaluating the 'Move \& Connect' intervention and allow researchers to replicate its components.

Non-equivalent, but still valid: Establishing the construct validity of a consumer fitness tracker in persons with multiple sclerosis (2023)

PLOS digital health

Abstract

Tools for monitoring daily physical activity (PA) are desired by persons with multiple sclerosis (MS). However, current research-grade options are not suitable for longitudinal, independent use due to their cost and user experience. Our objective was to assess the validity of step counts and PA intensity metrics derived from the Fitbit Inspire HR, a consumer-grade PA tracker, in 45 persons with MS (Median age: 46, IQR: 40-51) undergoing inpatient rehabilitation. The population had moderate mobility impairment (Median EDSS 4.0, Range 2.0-6.5). We assessed the validity of Fitbit-derived PA metrics (Step count, total time in PA, time in moderate to vigorous PA (MVPA)) during scripted tasks and free-living activity at three levels of data aggregation (minute, daily, and average PA). Criterion validity was assessed though agreement with manual counts and multiple methods for deriving PA metrics via the Actigraph GT3X. Convergent and known-groups validity were assessed via relationships with reference standards and related clinical measures. Fitbit-derived step count and time in PA, but not time in MVPA, exhibited excellent agreement with reference measures during scripted tasks. During free-living activity, step count and time in PA correlated moderately to strongly with reference measures, but agreement varied across metrics, data aggregation levels, and disease severity strata. Time in MVPA weakly agreed with reference measures. However, Fitbit-derived metrics were often as different from reference measures as reference measures were from each other. Fitbit-derived metrics consistently exhibited similar or stronger evidence of construct validity than reference standards. Fitbit-derived PA metrics are not equivalent to existing reference standards. However, they exhibit evidence of construct validity. Consumer-grade fitness trackers such as the Fitbit Inspire HR may therefore be suitable as a PA tracking tool for persons with mild or moderate MS.

Using Wearable Technology to Quantify Physical Activity Recovery: Secondary Report From the AFTER (App-Facilitated Tele-Rehabilitation) Program for COVID-19 Survivors Randomized Study (2023)

JMIR rehabilitation and assistive technologies

Abstract

Background: Knowledge on physical activity recovery after COVID-19 survival is limited. The AFTER (App-Facilitated Tele-Rehabilitation) program for COVID-19 survivors randomized participants, following hospital discharge, to either education and unstructured physical activity or a telerehabilitation program. Step count data were collected as a secondary outcome, and we found no significant differences in total step count trajectories between groups at 6 weeks. Further step count data were not analyzed. Objective: The purpose of this analysis was to examine step count trajectories and correlates among all participants (combined into a single group) across the 12-week study period. Methods: Linear mixed models with random effects were used to model daily steps over the number of study days. Models with 0, 1, and 2 inflection points were considered, and the final model was selected based on the highest log-likelihood value. Results: Participants included 44 adults (41 with available Fitbit [Fitbit LLC] data). Initially, step counts increased by an average of 930 (95% CI 547-1312; P<.001) steps per week, culminating in an average daily step count of 7658 (95% CI 6257-9059; P<.001) at the end of week 3. During the remaining 9 weeks of the study, weekly step counts increased by an average of 67 (95% CI -30 to 163; P<.001) steps per week, resulting in a final estimate of 8258 (95% CI 6933-9584; P<.001) steps. Conclusions: Participants showed a marked improvement in daily step counts during the first 3 weeks of the study, followed by more gradual improvement in the remaining 9 weeks. Physical activity data and step count recovery trajectories may be considered surrogates for physiological recovery, although further research is needed to examine this relationship.

Harnessing digital health to optimise the delivery of guideline-based cardiac rehabilitation during COVID-19: an observational study (2023)

Open Heart

Abstract

Background: The COVID-19 pandemic accelerated the uptake of digital health interventions for the delivery of cardiac rehabilitation (CR). However, there is a need to evaluate these interventions. Methods: We examined the impact of an evidence-based, digital CR programme on medical, lifestyle and psychosocial outcomes. Delivered by an interdisciplinary team of healthcare professionals, the core components of this 12-week programme included lifestyle modification, medical risk factor management, psychosocial and behavioural change support. To support self-management, patients were provided with a Fitbit, a home blood pressure (BP) monitor and an interactive workbook. Patients received access to a bespoke web-based platform and were invited to attend weekly, online group-based supervised exercise sessions and educational workshops. Outcomes were assessed at baseline, end of programme and at 6-month follow-up. Results: Over a 3-month period, 105 patients (88% with coronary heart disease) were referred with 74% (n=77) attending initial assessment. Of these, 97% (n=75) enrolled in the programme, with 85% (n=64) completing the programme, 86% (n=55) of completers attended 6-month follow-up. Comparing baseline to end of programme, we observed significant improvements in the proportion of patients meeting guideline-recommended targets for physical activity (+68%, p<0.001), BP (+44%, p<0.001) and low-density lipoprotein cholesterol (+27%, p<0.001). There were significant reductions in mean weight (-2.6 kg, p<0.001). Adherence to the Mediterranean diet score improved from 5.2 to 7.3 (p<0.001). Anxiety and depression levels (Hospital Anxiety and Depression score) both reduced by more than 50% (p<0.001). The majority of these improvements were sustained at 6-month follow-up. Conclusion: Outcomes from this study suggest that interdisciplinary digital CR programmes can be successfully implemented and help patients achieve guideline recommended lifestyle, medical and therapeutic targets.

Learning in a Virtual Environment to Improve Type 2 Diabetes Outcomes: A Randomized Controlled Trial (2023)

JMIR formative research

Abstract

Background: Given the importance of self-management in type 2 diabetes mellitus (T2DM) (T2D), a major aspect of health is providing diabetes self-management education and support (DSME/S). There are known barriers including access, availability, and lack of follow through on referral to education programs. Virtual education and support have increased in use, particularly over the last few years. Objective: The purpose of the Diabetes LIVE (Learning in a Virtual Environment) study was to compare the effects of the LIVE intervention (educational 3D world) on diet and physical activity behaviors, behavioral and metabolic outcomes in adults with T2DM over 12 months to an online DSME/S control website. Methods: The LIVE study was a 52-week multi-site randomized controlled trial with longitudinal repeated measures. Participants were randomized to Diabetes LIVE (n =101) or a web control site (n=110). Both contained the same educational materials, but the VE was synchronous and interactive and the other was a flat website. Data were collected at baseline, 3, 6, and 12 months using surveys, clinical, laboratory, and Fitbit measures. Descriptive statistics for baseline characteristics and demographics were used. The effects of the intervention on outcomes were initially examined by comparing the means and standard deviations across the four time points of the outcomes between study arms, followed by multilevel modeling on trajectories of the outcomes over the 12 months. Results: 211 participants consented to participate in this trial. The mean age of participants was 58.85 (+10.1) years, and a majority were White (60.2%), non-Hispanic (93.8%), married (56.3%), and female (59.2%). Mean A1c at baseline was 7.64% (+1.79) and the mean BMI was 33.51 (+7.25). We examined weight loss status vs. randomized group, where data with no weight change were eliminated, and we found a statistically significant difference with the LIVE group experiencing more weight loss than the WebControl group (P=.04). There were no statistically significant differences between groups in changes in physical activity and dietary outcomes, but each group showed an increase in physical activity. Both groups experienced a decrease in mean A1c, systolic and diastolic blood pressure, cholesterol, and triglycerides over the course of 12 months of study participation, including those participants whose baseline A1c was 8.6 or higher. Conclusions: This study confirmed that there were minor positive changes on metabolic controlglycemic targets in both groups over the 12-month study period, however, the majority of the participants began with optimal metabolic controlA1c levels. We did find clinically relevant metabolic changes in those who began with an A1c level >8.6% in both groups. This study provided a variety of resources to our participants in both study groups, and we conclude that a toolkit with a variety of services would be helpful to improving self-care in the future for persons with T2DM.

Real-time Assessment of the Bidirectional Relationship Between Affective States and Glucose: Protocol for a 14-Day Observational Study (2023)

JMIR research protocols

Abstract

Background: Glucose variability increases cardiometabolic disease risk. While many factors can influence glucose levels, postprandial glucose response is the primary driver of glucose variability. Furthermore, affect may directly and indirectly impact glucose variability through its effect on eating behavior. Continuous glucose monitors (CGMs) facilitate the real-time evaluation of blood glucose, and ecological momentary assessment (EMA) can be used to assess affect in real time. Together, data collected from these sources provide the opportunity to further understand the role of affect in glucose levels. Objective: This paper presents the protocol for a study that aims to (1) evaluate the feasibility and acceptability of using CGMs along with EMA in nondiabetic populations and (2) examine the bidirectional relationship between affect and glucose in nondiabetic adults with overweight or obesity using a CGM and EMA. Methods: Eligibility criteria for the study include participants (1) aged 18 to 65 years old, (2) with a BMI of ≥25 kg/m2, (3) who are able to read and write in English, and (4) who own a smartphone. Individuals will be excluded if they (1) have type 1 or 2 diabetes or have any other condition that requires glucose monitoring, (2) are pregnant, (3) use any medications that have the potential to alter blood glucose levels or interfere with the glucose sensing process, or (4) have a diagnosed gastrointestinal condition or eating disorder. In a 14-day observational study, participants will wear a FreeStyle Libre Pro CGM sensor (Abbott) and will receive mobile phone-based EMA prompts 6 times per day (randomly within six 2-hour windows between 8 AM and 8 PM) to assess positive and negative affect. Participants will also wear a Fitbit Inspire 2 (Fitbit) to continuously monitor physical activity and sleep, which will be included as covariates in the analysis. Multilevel linear regression models will be used to evaluate the acute relationship between glucose level and affect. Results: Recruitment started in October 2022 and is expected to be completed in March 2023. We will aim to recruit 100 participants. As of December 12, 2022, a total of 39 participants have been enrolled. Conclusions: The results of this study will further elucidate the role of affect in glucose variability. By identifying affective states that may lead to glucose excursions, our findings could inform just-in-time behavioral interventions by indicating opportunities for intervention delivery.

Feasibility and scalability of a fitness tracker study: Results from a longitudinal analysis of persons with multiple sclerosis (2023)

Frontiers in Digital Health

Abstract

Background: Consumer-grade fitness trackers offer exciting opportunities to study persons with chronic diseases in greater detail and in their daily-life environment. However, attempts to bring fitness tracker measurement campaigns from tightly controlled clinical environments to home settings are often challenged by deteriorating study compliance or by organizational and resource limitations. Objectives: By revisiting the study design and patient-reported experiences of a partly remote study with fitness trackers (BarKA-MS study), we aimed to qualitatively explore the relationship between overall study compliance and scalability. On that account, we aimed to derive lessons learned on strengths, weaknesses, and technical challenges for the conduct of future studies. Methods: The two-phased BarKA-MS study employed Fitbit Inspire HR and electronic surveys to monitor physical activity in 45 people with multiple sclerosis in a rehabilitation setting and in their natural surroundings at home for up to 8 weeks. We examined and quantified the recruitment and compliance in terms of questionnaire completion and device wear time. Furthermore, we qualitatively evaluated experiences with devices according to participants' survey-collected reports. Finally, we reviewed the BarKA-MS study conduct characteristics for its scalability according to the Intervention Scalability Assessment Tool checklist. Results: Weekly electronic surveys completion reached 96%. On average, the Fitbit data revealed 99% and 97% valid wear days at the rehabilitation clinic and in the home setting, respectively. Positive experiences with the device were predominant: only 17% of the feedbacks had a negative connotation, mostly pertaining to perceived measurement inaccuracies. Twenty-five major topics and study characteristics relating to compliance were identified. They broadly fell into the three categories: "effectiveness of support measures", "recruitment and compliance barriers", and "technical challenges". The scalability assessment revealed that the highly individualized support measures, which contributed greatly to the high study compliance, may face substantial scalability challenges due to the strong human involvement and limited potential for standardization. Conclusion: The personal interactions and highly individualized participant support positively influenced study compliance and retention. But the major human involvement in these support actions will pose scalability challenges due to resource limitations. Study conductors should anticipate this potential compliance-scalability trade-off already in the design phase.

mHealth intervention delivered in general practice to increase physical activity and reduce sedentary behaviour of patients with prediabetes and type 2 diabetes (ENERGISED): rationale and study protocol for a pragmatic randomised controlled trial (2023)

BMC public health

Abstract

Background: The growing number of patients with type 2 diabetes and prediabetes is a major public health concern. Physical activity is a cornerstone of diabetes management and may prevent its onset in prediabetes patients. Despite this, many patients with (pre)diabetes remain physically inactive. Primary care physicians are well-situated to deliver interventions to increase their patients' physical activity levels. However, effective and sustainable physical activity interventions for (pre)diabetes patients that can be translated into routine primary care are lacking. Methods: We describe the rationale and protocol for a 12-month pragmatic, multicentre, randomised, controlled trial assessing the effectiveness of an mHealth intervention delivered in general practice to increase physical activity and reduce sedentary behaviour of patients with prediabetes and type 2 diabetes (ENERGISED). Twenty-one general practices will recruit 340 patients with (pre)diabetes during routine health check-ups. Patients allocated to the active control arm will receive a Fitbit activity tracker to self-monitor their daily steps and try to achieve the recommended step goal. Patients allocated to the intervention arm will additionally receive the mHealth intervention, including the delivery of several text messages per week, with some of them delivered just in time, based on data continuously collected by the Fitbit tracker. The trial consists of two phases, each lasting six months: the lead-in phase, when the mHealth intervention will be supported with human phone counselling, and the maintenance phase, when the intervention will be fully automated. The primary outcome, average ambulatory activity (steps/day) measured by a wrist-worn accelerometer, will be assessed at the end of the maintenance phase at 12 months. Discussion: The trial has several strengths, such as the choice of active control to isolate the net effect of the intervention beyond simple self-monitoring with an activity tracker, broad eligibility criteria allowing for the inclusion of patients without a smartphone, procedures to minimise selection bias, and involvement of a relatively large number of general practices. These design choices contribute to the trial's pragmatic character and ensure that the intervention, if effective, can be translated into routine primary care practice, allowing important public health benefits.

Daytime Sleep-Tracking Performance of Four Commercial Wearable Devices During Unrestricted Home Sleep (2023)

Nature and Science of Sleep

Abstract

Purpose: Previous studies have found that many commercial wearable devices can accurately track sleep-wake patterns in laboratory or home settings. However, nearly all previous studies tested devices under conditions with fixed time in bed (TIB) and during nighttime sleep episodes only. Despite its relevance to shift workers and others with irregular sleep schedules, it is largely unknown how devices track daytime sleep. Therefore, we tested the sleep-tracking performance of four commercial wearable devices during unrestricted home daytime sleep. Participants and Methods: Participants were 16 healthy young adults (6 men, 10 women; 26.6 ± 4.6 years, mean ± SD) with habitual daytime sleep schedules. Participants slept at home for 1 week under unrestricted conditions (ie, self-selecting TIB) using a set of four commercial wearable devices and completed reference sleep logs. Wearables included the Fatigue Science ReadiBand, Fitbit Inspire HR, Oura Ring, and Polar Vantage V Titan. Daytime sleep episode TIB biases and frequencies of missed and false-positive daytime sleep episodes were examined. Results: TIB bias was low in general for all devices on most daytime sleep episodes, but some exhibited large biases (eg, > 1 h). Total missed daytime sleep episodes were as follows: Fatigue Science: 3.6%; Fitbit: 4.8%; Oura: 6.0%; Polar: 37.3%. Missed episodes occurred most often when TIB was short (eg, naps < 4 h). Conclusion: When daytime sleep episodes were recorded, the devices generally exhibited similar performance for tracking TIB (ie, most episodes had low bias). However, the devices failed to detect some daytime episodes, which occurred most often when TIB was short, but varied across devices (especially Polar, which missed over one-third of episodes). Findings suggest that accurate daytime sleep tracking is largely achievable with commercial wearable devices. However, performance differences for missed recordings suggest that some devices vary in reliability (especially for naps), but improvements could likely be made with changes to algorithm sensitivities.

Performance of seven consumer sleep-tracking devices compared with polysomnography (2021)

Sleep

Abstract

STUDY OBJECTIVES: Consumer sleep-tracking devices are widely used and becoming more technologically advanced, creating strong interest from researchers and clinicians for their possible use as alternatives to standard actigraphy. We, therefore, tested the performance of many of the latest consumer sleep-tracking devices, alongside actigraphy, versus the gold-standard sleep assessment technique, polysomnography (PSG). METHODS: In total, 34 healthy young adults (22 women; 28.1 ± 3.9 years, mean ± SD) were tested on three consecutive nights (including a disrupted sleep condition) in a sleep laboratory with PSG, along with actigraphy (Philips Respironics Actiwatch 2) and a subset of consumer sleep-tracking devices. Altogether, four wearable (Fatigue Science Readiband, Fitbit Alta HR, Garmin Fenix 5S, Garmin Vivosmart 3) and three nonwearable (EarlySense Live, ResMed S+, SleepScore Max) devices were tested. Sleep/wake summary and epoch-by-epoch agreement measures were compared with PSG. RESULTS: Most devices (Fatigue Science Readiband, Fitbit Alta HR, EarlySense Live, ResMed S+, SleepScore Max) performed as well as or better than actigraphy on sleep/wake performance measures, while the Garmin devices performed worse. Overall, epoch-by-epoch sensitivity was high (all ≥0.93), specificity was low-to-medium (0.18-0.54), sleep stage comparisons were mixed, and devices tended to perform worse on nights with poorer/disrupted sleep. CONCLUSIONS: Consumer sleep-tracking devices exhibited high performance in detecting sleep, and most performed equivalent to (or better than) actigraphy in detecting wake. Device sleep stage assessments were inconsistent. Findings indicate that many newer sleep-tracking devices demonstrate promising performance for tracking sleep and wake. Devices should be tested in different populations and settings to further examine their wider validity and utility.

Performance of Four Commercial Wearable Sleep-Tracking Devices Tested Under Unrestricted Conditions at Home in Healthy Young Adults (2022)

Nature and Science of Sleep

Abstract

PURPOSE: Commercial wearable sleep-tracking devices are growing in popularity and in recent studies have performed well against gold standard sleep measurement techniques. However, most studies were conducted in controlled laboratory conditions. We therefore aimed to test the performance of devices under naturalistic unrestricted home sleep conditions. PARTICIPANTS AND METHODS: Healthy young adults (n = 21; 12 women, 9 men; 29.0 ± 5.0 years, mean ± SD) slept at home under unrestricted conditions for 1 week using a set of commercial wearable sleep-tracking devices and completed daily sleep diaries. Devices included the Fatigue Science Readiband, Fitbit Inspire HR, Oura ring, and Polar Vantage V Titan. Participants also wore a research-grade actigraphy watch (Philips Respironics Actiwatch 2) for comparison. To assess performance, all devices were compared with a high performing mobile sleep electroencephalography headband device (Dreem 2). Analyses included epoch-by-epoch and sleep summary agreement comparisons. RESULTS: Devices accurately tracked sleep-wake summary metrics (ie, time in bed, total sleep time, sleep efficiency, sleep latency, wake after sleep onset) on most nights but performed best on nights with higher sleep efficiency. Epoch-by-epoch sensitivity (for sleep) and specificity (for wake), respectively, were as follows: Actiwatch (0.95, 0.35), Fatigue Science (0.94, 0.40), Fitbit (0.93, 0.45), Oura (0.94, 0.41), and Polar (0.96, 0.35). Sleep stage-tracking performance was mixed, with high variability. CONCLUSION: As in previous studies, all devices were better at detecting sleep than wake, and most devices compared favorably to actigraphy in wake detection. Devices performed best on nights with more consolidated sleep patterns. Unrestricted sleep TIB differences were accurately tracked on most nights. High variability in sleep stage-tracking performance suggests that these devices, in their current form, are still best utilized for tracking sleep-wake outcomes and not sleep stages. Most commercial wearables exhibited promising performance for tracking sleep-wake in real-world conditions, further supporting their consideration as an alternative to actigraphy.

Fitbit Data to Assess Functional Capacity in Patients Before Elective Surgery: Pilot Prospective Observational Study (2023)

Journal of Medical Internet Research

Abstract

Background: Preoperative assessment is crucial to prevent the risk of complications of surgical operations and is usually focused on functional capacity. The increasing availability of wearable devices (smartwatches, trackers, rings, etc) can provide less intrusive assessment methods, reduce costs, and improve accuracy. Objective: The aim of this study was to present and evaluate the possibility of using commercial smartwatch data, such as those retrieved from the Fitbit Inspire 2 device, to assess functional capacity before elective surgery and correlate such data with the current gold standard measure, the 6-Minute Walk Test (6MWT) distance. Methods: During the hospital visit, patients were evaluated in terms of functional capacity using the 6MWT. Patients were asked to wear the Fitbit Inspire 2 for 7 days (with flexibility of -2 to +2 days) after the hospital visit, before their surgical operation. Resting heart rate and daily steps data were retrieved directly from the smartwatch. Feature engineering techniques allowed the extraction of heart rate over steps (HROS) and a modified version of Non-Exercise Testing Cardiorespiratory Fitness. All measures were correlated with 6MWT. Results: In total, 31 patients were enrolled in the study (n=22, 71% men; n=9, 29% women; mean age 76.06, SD 4.75 years). Data were collected between June 2021 and May 2022. The parameter that correlated best with the 6MWT was the Non-Exercise Testing Cardiorespiratory Fitness index (r=0.68; P<.001). The average resting heart rate over the whole acquisition period for each participant had r=-0.39 (P=.03), even if some patients did not wear the device at night. The correlation of the 6MWT distance with the HROS evaluated at 1% quantile was significant, with Pearson coefficient of -0.39 (P=.04). Fitbit step count had a fair correlation of 0.59 with 6MWT (P<.001). Conclusions: Our study is a promising starting point for the adoption of wearable technology in the evaluation of functional capacity of patients, which was strongly correlated with the gold standard. The study also identified limitations in the availability of metrics, variability of devices, accuracy and quality of data, and accessibility as crucial areas of focus for future studies.

Influence of sleep duration and sex on age-related differences in heart rate variability: Findings from program 4 of the HAIE study (2023)

Sleep Medicine

Abstract

Heart rate variability (HRV) is an important marker of cardiac autonomic regulation and health. We examined the influence of sleep duration and sex on HRV in younger and middle-aged adults. Cross-sectional data (888 participants, 44% women) were analyzed from Program 4 of the Healthy Aging in Industrial Environment study (HAIE). Sleep duration was measured across 14 days using Fitbit Charge monitors. Short-term EKG recordings were used to evaluate HRV in the time (RMSSD) and frequency domains (low frequency (LF) and high frequency (HF) power). Regression analysis showed age was associated with lower HRV across all HRV variables (all P < 0.001). Sex was a significant predictor for LF (β = 0.52) and HF (β = 0.54; both P < 0.001) in normalized units. Similarly, sleep duration was only associated with HF in normalized units (β = 0.06, P = 0.04). To explore this finding further, participants within each sex were separated into groups based on age (<40 and ≥ 40y) and adequate sleep duration (<7 and ≥7 h). Middle-aged women with sleep durations <7 h, but not ≥7 h, had lower HRV than younger women after adjusting for medications, respiratory frequency, and cardiorespiratory fitness (peak VO2). Middle-aged women with sleep durations <7 h also had lower RMSSD (33 ± 2 vs. 41±4 ms, P = 0.04), HF power (5.6 ± 0.1 vs. 6.0 ± 0.1 log ms2, P = 0.04), and HF in normalized units (39 ± 1 vs. 48 ± 2, P = 0.01) than middle-aged women with sleep durations ≥7 h. In contrast, middle-aged men irrespective of sleep duration had lower HRV than younger men. These results suggest that adequate sleep duration may positively influence HRV in middle-aged women but not men.

The Added Value of Remote Technology in Cardiac Rehabilitation on Physical Function, Anthropometrics, and Quality of Life: Cluster Randomized Controlled Trial (2023)

Journal of Medical Internet Research

Abstract

Background: Cardiovascular diseases (CVDs) cause most deaths globally and can reduce quality of life (QoL) of rehabilitees with cardiac disease. The risk factors of CVDs are physical inactivity and increased BMI. With physical activity, it is possible to prevent CVDs, improve QoL, and help maintain a healthy body mass. Current literature shows the possibilities of digitalization and advanced technology in supporting independent self-rehabilitation. However, the interpretation of the results is complicated owing to the studies' high heterogeneity. In addition, the added value of this technology has not been studied well, especially in cardiac rehabilitation. Objective: We aimed to examine the effectiveness of added remote technology in cardiac rehabilitation on physical function, anthropometrics, and QoL in rehabilitees with CVD compared with conventional rehabilitation. Methods: Rehabilitees were cluster randomized into 3 remote technology intervention groups (n=29) and 3 reference groups (n=30). The reference group received conventional cardiac rehabilitation, and the remote technology intervention group received conventional cardiac rehabilitation with added remote technology, namely, the Movendos mCoach app and Fitbit charge accelerometer. The 12 months of rehabilitation consisted of three 5-day in-rehabilitation periods in the rehabilitation center. Between these periods were two 6-month self-rehabilitation periods. Outcome measurements included the 6-minute walk test, body mass, BMI, waist circumference, and World Health Organization QoL-BREF questionnaire at baseline and at 6 and 12 months. Between-group differences were assessed using 2-tailed t tests and Mann-Whitney U test. Within-group differences were analyzed using a paired samples t test or Wilcoxon signed-rank test. Results: Overall, 59 rehabilitees aged 41 to 66 years (mean age 60, SD 6 years; n=48, 81% men) were included in the study. Decrement in waist circumference (6 months: 1.6 cm; P=.04; 12 months: 3 cm; P<.001) and increment in self-assessed QoL were greater (environmental factors: 0.5; P=.02) in the remote technology intervention group than the reference group. Both groups achieved statistically significant improvements in the 6-minute walk test in both time frames (P=.01-.03). Additionally, the remote technology intervention group achieved statistically significant changes in the environmental domain at 0-6 months (P=.03) and waist circumference at both time frames (P=.01), and reference group achieve statistically significant changes in waist circumference at 0-6 months (P=.02). Conclusions: Remote cardiac rehabilitation added value to conventional cardiac rehabilitation in terms of waist circumference and QoL. The results were clinically small, but the findings suggest that adding remote technology to cardiac rehabilitation may increase beneficial health outcomes. There was some level of systematic error during rehabilitation intervention, and the sample size was relatively small. Therefore, care must be taken when generalizing the study results beyond the target population. To confirm assumptions of the added value of remote technology in rehabilitation interventions, more studies involving different rehabilitees with cardiac disease are required.

Validation of an open-source smartphone step counting algorithm in clinical and non-clinical settings (2023)

medRxiv: The Preprint Server for Health Sciences

Abstract

Background: Step counts are increasingly used in public health and clinical research to assess wellbeing, lifestyle, and health status. However, estimating step counts using commercial activity trackers has several limitations, including a lack of reproducibility, generalizability, and scalability. Smartphones are a potentially promising alternative, but their step-counting algorithms require robust validation that accounts for temporal sensor body location, individual gait characteristics, and heterogeneous health states. Objective: Our goal was to evaluate an open-source step-counting method for smartphones under various measurement conditions against step counts estimated from data collected simultaneously from different body locations ("internal" validation), manually ascertained ground truth ("manual" validation), and step counts from a commercial activity tracker (Fitbit Charge 2) in patients with advanced cancer ("wearable" validation). Methods: We used eight independent datasets collected in controlled, semi-controlled, and free-living environments with different devices (primarily Android smartphones and wearable accelerometers) carried at typical body locations. Five datasets (N=103) were used for internal validation, two datasets (N=107) for manual validation, and one dataset (N=45) used for wearable validation. In each scenario, step counts were estimated using a previously published step-counting method for smartphones that uses raw sub-second level accelerometer data. We calculated mean bias and limits of agreement (LoA) between step count estimates and validation criteria using Bland-Altman analysis. Results: In the internal validation datasets, participants performed 751.7±581.2 (mean±SD) steps, and the mean bias was -7.2 steps (LoA -47.6, 33.3) or -0.5%. In the manual validation datasets, the ground truth step count was 367.4±359.4 steps while the mean bias was -0.4 steps (LoA -75.2, 74.3) or 0.1 %. In the wearable validation dataset, Fitbit devices indicated mean step counts of 1931.2±2338.4, while the calculated bias was equal to -67.1 steps (LoA -603.8, 469.7) or a difference of 0.3 %. Conclusions: This study demonstrates that our open-source step counting method for smartphone data provides reliable step counts across sensor locations, measurement scenarios, and populations, including healthy adults and patients with cancer.

Wearable Sensor and Mobile App-Based mHealth Approach for Investigating Substance Use and Related Factors in Daily Life: Protocol for an Ecological Momentary Assessment Study (2023)

JMIR research protocols

Abstract

Background: Digital health technologies using mobile apps and wearable devices are a promising approach to the investigation of substance use in the real world and for the analysis of predictive factors or harms from substance use. Moreover, consecutive repeated data collection enables the development of predictive algorithms for substance use by machine learning methods. Objective: We developed a new self-monitoring mobile app to record daily substance use, triggers, and cravings. Additionally, a wearable activity tracker (Fitbit) was used to collect objective biological and behavioral data before, during, and after substance use. This study aims to describe a model using machine learning methods to determine substance use. Methods: This study is an ongoing observational study using a Fitbit and a self-monitoring app. Participants of this study were people with health risks due to alcohol or methamphetamine use. They were required to record their daily substance use and related factors on the self-monitoring app and to always wear a Fitbit for 8 weeks, which collected the following data: (1) heart rate per minute, (2) sleep duration per day, (3) sleep stages per day, (4) the number of steps per day, and (5) the amount of physical activity per day. Fitbit data will first be visualized for data analysis to confirm typical Fitbit data patterns for individual users. Next, machine learning and statistical analysis methods will be performed to create a detection model for substance use based on the combined Fitbit and self-monitoring data. The model will be tested based on 5-fold cross-validation, and further preprocessing and machine learning methods will be conducted based on the preliminary results. The usability and feasibility of this approach will also be evaluated. Results: Enrollment for the trial began in September 2020, and the data collection finished in April 2021. In total, 13 people with methamphetamine use disorder and 36 with alcohol problems participated in this study. The severity of methamphetamine or alcohol use disorder assessed by the Drug Abuse Screening Test-10 or the Alcohol Use Disorders Identification Test-10 was moderate to severe. The anticipated results of this study include understanding the physiological and behavioral data before, during, and after alcohol or methamphetamine use and identifying individual patterns of behavior. Conclusions: Real-time data on daily life among people with substance use problems were collected in this study. This new approach to data collection might be helpful because of its high confidentiality and convenience. The findings of this study will provide data to support the development of interventions to reduce alcohol and methamphetamine use and associated negative consequences.

Opportune warning of COVID-19 in a Mexican health care worker cohort: Discrete beta distribution entropy of smartwatch physiological records (2023)

Biomedical Signal Processing and Control

Abstract

We present a statistical study of heart rate, step cadence, and sleep stage registers of health care workers in the Hospital General de México "Dr. Eduardo Liceaga" (HGM), monitored continuously and non-invasively during the COVID-19 contingency from May to October 2020, using the Fitbit Charge 3® Smartwatch device. The HGM-COVID cohort consisted of 115 participants assigned to areas of COVID-19 exposure. We introduce a novel biomarker for an opportune signal for the likelihood of SARS-CoV-2 infection based on the Shannon Entropy of the Discrete Generalized Beta Distribution fit of rank ordered smartwatch registers. Our statistical test indicated infection for 94% of patients confirmed by positive polymer chain reaction (PCR+) test, 47% before the test, and 47% in coincidence. These results required innovative data preprocessing for the definition of a new biomarker index. The statistical method parameters are data-driven, confidence estimates were calibrated based on sensitivity tests using appropriately derived surrogate data as a benchmark. Our surrogate tests can also provide a benchmark for comparing results from other anomaly detection methods (ADMs). Biomarker comparison of the negative Immunoglobulin G Antibody (IgG-) subgroup with the PCR+ subgroup showed a statistically significant difference (p < 0.01, effect size = 1.44). The distribution of the uninfected population had a lower median and less dispersion than the PCR+ population. A retrospective study of our results confirmed that the biomarker index provides an early warning of the likelihood of COVID-19, even several days before the onset of symptoms or the PCR+ test request. The method can be calibrated for the analysis of different SARS-CoV-2 strains, the effect of vaccination, and previous infections. Furthermore, our biomarker screening could be implemented to provide general health profiles for other population sectors based on physiological signals from smartwatch wearable devices.

Motivational Message Framing Effects on Physical Activity Dynamics in a Digital Messaging Intervention: Secondary Analysis (2023)

JMIR formative research

Abstract

Background: Digital smartphone messaging can be used to promote physical activity to large populations with limited cost. It is not clear which psychological constructs should be targeted by digital messages to promote physical activity. This gap presents a challenge for developing optimal content for digital messaging interventions. Objective: The aim of this study is to compare affectively framed and social cognitively framed messages on subsequent changes in physical activity using dynamical modeling techniques. Methods: We conducted a secondary analysis of data collected from a digital messaging intervention in insufficiently active young adults (18-29 years) recruited between April 2019 and July 2020 who wore a Fitbit smartwatch for 6 months. Participants received 0 to 6 messages at random per day across the intervention period. Messages were drawn from 3 content libraries: affectively framed, social cognitively framed, or inspirational quotes. Person-specific dynamical models were identified, and model features of impulse response and cumulative step response were extracted for comparison. Two-way repeated-measures ANOVAs evaluated the main effects and interaction of message type and day type on model features. This early-phase work with novel dynamic features may have been underpowered to detect differences between message types so results were interpreted descriptively. Results: Messages (n=20,689) were paired with valid physical activity monitoring data from 45 participants for analysis. Received messages were distributed as 40% affective (8299/20,689 messages), 39% social-cognitive (8187/20,689 messages), and 20% inspirational quotes (4219/20,689 messages). There were no statistically significant main effects for message type when evaluating the steady state of step responses. Participants demonstrated heterogeneity in intervention response: some had their strongest responses to affectively framed messages, some had their strongest responses to social cognitively framed messages, and some had their strongest responses to the inspirational quote messages. Conclusions: No single type of digital message content universally promotes physical activity. Future work should evaluate the effects of multiple message types so that content can be continuously tuned based on person-specific responses to each message type.

Considering depression as a secondary outcome in the optimization of physical activity interventions for breast cancer survivors in the PACES trial: a factorial randomized controlled trial (2023)

The International Journal of Behavioral Nutrition and Physical Activity

Abstract

Background: Depressive symptoms result in considerable burden for breast cancer survivors. Increased physical activity may reduce these burdens but existing evidence from physical activity interventions in equivocal. Furthermore, physical activity intervention strategies may differentially impact depressive symptoms, which should be considered in designing and optimizing behavioral interventions for breast cancer survivors. Methods: The Physical Activity for Cancer Survivors (PACES) trial enrolled 336 participants breast cancer survivors, who were 3 months to 10 years post-treatment, and insufficiently active (< 150 min of moderate-to-vigorous physical activity per week). Participants were randomly assigned to a combination of 4 intervention strategies in a full-factorial design: 1) supervised exercise sessions, 2) facility access, 3) Active Living Every Day, and 4) Fitbit self-monitoring. Depressive symptoms were assessed at baseline, mid-intervention (3 months), and post-intervention (6 months) using the Quick Inventory for Depressive Symptoms. Change in depressive symptoms were analyzed using a linear mixed-effects model. Results: Results from the linear mixed-effects model indicated that depressive symptoms decreased significantly across the entire study sample over the 6-month intervention (F = 4.09, p = 0.044). A significant ALED x time interaction indicated participants who received the ALED intervention experienced greater reductions in depressive symptoms (F = 5.29, p = 0.022). No other intervention strategy significantly impacted depressive symptoms. Conclusions: The ALED intervention consists of strategies (i.e., goal setting, social support) that may have a beneficial impact on depressive symptoms above and beyond the effect of increased physical activity. Our findings highlight the need to consider secondary outcomes when designing and optimizing physical activity interventions.

An overview of the together everyone achieves more physical activity (TEAM-PA) trial to increase physical activity among African American women (2023)

Contemporary Clinical Trials

Abstract

Background: The Together Everyone Achieves More Physical Activity (TEAM-PA) trial is a randomized controlled trial testing the efficacy of a group-based intervention for increasing physical activity (PA) among insufficiently active African American women. Design: The TEAM-PA trial uses a group cohort design, is implemented at community sites, and will involve 360 African American women. The trial compares a 10-week group-based intervention vs. a standard group-delivered PA comparison program. Measures include minutes of total PA/day using 7-day accelerometer estimates (primary outcome), and body mass index, blood pressure, waist circumference, walking speed, sedentary behavior, light physical activity, and the percentage achieving ≥150 min of moderate to vigorous PA/week (secondary outcomes) at baseline, post-intervention, and 6-months post-intervention. Intervention: The intervention integrates elements from Social Cognitive Theory, Self-Determination Theory, Group Dynamics Theory, and a focus on collectivism to evaluate different components of social affiliation (relatedness, reciprocal support, group cohesion, and collective efficacy). The intervention integrates shared goal-setting via Fitbits, group-based problem-solving, peer-to-peer positive communication, friendly competition, and cultural topics related to collectivism. Compared to the standard group-delivered PA program, participants in the intervention are expected to show greater improvements from baseline to post- and 6-month follow-up on minutes of total PA/day and secondary outcomes. Social affiliation variables (vs. individual-level factors) will be evaluated as mediators of the treatment effect. Implications: The results of the TEAM-PA trial will determine the efficacy of the intervention and identify which aspects of social affiliation are most strongly related to increased PA among African American women. Trial registration: This study was registered on Clinicaltrials.gov (# NCT05519696) in August 2022 prior to initial participant enrollment.

Efficacy of the Aim2Be Intervention in Changing Lifestyle Behaviors Among Adolescents With Overweight and Obesity: Randomized Controlled Trial (2023)

Journal of Medical Internet Research

Abstract

Background: Aim2Be is a gamified lifestyle app designed to promote lifestyle behavior changes among Canadian adolescents and their families. Objective: The primary aim was to test the efficacy of the Aim2Be app with support from a live coach to reduce weight outcomes (BMI Z score [zBMI]) and improve lifestyle behaviors among adolescents with overweight and obesity and their parents versus a waitlist control group over 3 months. The secondary aim was to compare health trajectories among waitlist control participants over 6 months (before and after receiving access to the app), assess whether support from a live coach enhanced intervention impact, and evaluate whether the app use influenced changes among intervention participants. Methods: A 2-arm parallel randomized controlled trial was conducted from November 2018 to June 2020. Adolescents aged 10 to 17 years with overweight or obesity and their parents were randomized into an intervention group (Aim2Be with a live coach for 6 months) or a waitlist control group (Aim2Be with no live coach; accessed after 3 months). Adolescents' assessments at baseline and at 3 and 6 months included measured height and weight, 24-hour dietary recalls, and daily step counts measured with a Fitbit. Data on self-reported physical activity, screen time, fruit and vegetable intake, and sugary beverage intake of adolescents and parents were also collected. Results: A total of 214 parent-child participants were randomized. In our primary analyses, there were no significant differences in zBMI or any of the health behaviors between the intervention and control groups at 3 months. In our secondary analyses, among waitlist control participants, zBMI (P=.02), discretionary calories (P=.03), and physical activity outside of school (P=.001) declined, whereas daily screen time increased (P<.001) after receiving access to the app compared with before receiving app access. Adolescents randomized to Aim2Be with live coaching reported more time being active outside of school compared with adolescents who used Aim2Be with no coaching over 3 months (P=.001). App use did not modify any changes in outcomes among adolescents in the intervention group. Conclusions: The Aim2Be intervention did not improve zBMI and lifestyle behaviors in adolescents with overweight and obesity compared with the waitlist control group over 3 months. Future studies should explore the potential mediators of changes in zBMI and lifestyle behaviors as well as predictors of engagement. Trial registration: ClinicalTrials.gov NCT03651284; https://clinicaltrials.gov/ct2/show/study/NCT03651284.

Health-related impact of illness associated with excessive daytime sleepiness in patients with obstructive sleep apnea (2023)

Postgraduate Medicine

Abstract

OBJECTIVES: This real-world study aimed to characterize the impact of illness of excessive daytime sleepiness (EDS) in patients with obstructive sleep apnea (OSA) who are adherent to continuous positive airway pressure (CPAP). METHODS: This cross-sectional study surveyed participants in Evidation Health's Achievement app (November 2020-January 2021), a mobile consumer platform that encourages users to develop healthy habits and provides incentives to participate in research. Participants were US-resident adults who self-reported a physician diagnosis of OSA and adherence to CPAP (≥4 hours/night, ≥5 nights/week) for≥6 months. The survey included the Functional Outcomes of Sleep Questionnaire-Short Version (FOSQ-10), Epworth Sleepiness Scale (ESS), and questions regarding comorbidities, CPAP use, caffeine consumption, and physical activity. EDS was defined as ESS score {\textgreater}10. There were more female than male participants; therefore, data were reported separately for females/males. RESULTS: In total, 476 participants (female, n = 283 [59\%]; mean [SD] age, 49.7 [10.8] years; obese, 74.4\%) completed the survey; 209 had EDS (mean [SD] ESS, 13.8 [2.5]) and 267 did not (mean [SD] ESS, 6.3 [2.5]). Self-reported duration of CPAP use was consistent between the EDS/no EDS cohorts, with most participants using CPAP for 7 to 9 hours/night, 7 nights/week. Participants with EDS commonly reported anxiety ([EDS/no EDS] males: 31.5\%/20.0\%; females: 53.7\%/39.5\%), depression (males: 35.6\%/24.2\%; females: 55.9\%/44.9\%), and insomnia (males: 19.2\%/6.7\%; females: 25.7\%/12.9\%) and showed impairment on the FOSQ-10 ([EDS/no EDS] males: 80.8\%/35.0\%; females: 91.9\%/53.1\%). Participants with EDS reported that sleepiness 'very often' prevented physical activity and influenced dietary choices. CONCLUSION: EDS influences choices related to physical activity, caffeine consumption, and diet in patients who are adherent with CPAP. More research is needed to understand the association between EDS and choices of CPAP-adherent patients. Future research should explore the health-related consequences of residual EDS associated with OSA and whether they can be mitigated by improving EDS.

Gut microbiota composition and functionality are associated with REM sleep duration and continuous glucose levels (2023)

The Journal of Clinical Endocrinology and Metabolism

Abstract

CONTEXT: Sleep disruption is associated with poorer glucose metabolic control and altered gut microbiota in animal models. OBJECTIVE: We aimed to evaluate the possible links among REM sleep duration, continuous glucose levels and gut microbiota composition. DESIGN: This is an observational, prospective, real-life, cross-sectional case-control study. SETTING: Tertiary Hospital recruiting healthy volunteers. PATIENTS OR OTHER PARTICIPANTS: One-hundred and eighteen (60 with obesity), middle-aged (39.1-54.8) subjects. INTERVENTION(S): Glucose variability and REM sleep duration were assessed by 10-day continuous glucose monitoring (CGM) (Dexcom G6®) and wrist-actigraphy (Fitbit Charge 3®), respectively. MAIN OUTCOME MEASURE(S): . Glucose variability was evaluated through standard deviation (SD), coefficient of variation (CV) and interquartile range (IQR). The percentage of time in range (\% TIR), 126-139 mg/dL (TIR2) and 140-199 mg/dL (TIR3) were calculated. Shotgun metagenomics sequencing was applied to study gut microbiota taxonomy and functionality. RESULTS: Increased glycemic variability (SD, CV and IQR) was observed among subjects with obesity in parallel to increased \% TIR2 and \% TIR3. REM sleep duration was independently associated with the \% TIR3 (β=-0.339; p {\textless} 0.001) and glucose variability (SD, β=-0.350; p {\textless} 0.001). Microbial taxa from Christensenellaceae family (Firmicutes phylum) were positively associated with REM sleep and negatively with glucose continuous monitoring levels, while bacteria from Enterobacteriacea family and bacterial functions involved in iron metabolism showed opposite associations. CONCLUSIONS: Decreased REM sleep duration was independently associated with a worse glucose profile. The associations of species from Christensenellaceae and Enterobacteriaceae families with REM sleep duration and continuous glucose values suggest an integrated picture of metabolic health.

Effect of an Automated Advice Algorithm (CloudConnect) on Adolescent-Parent Diabetes-Specific Communication and Glycemic Management: A Randomized Trial (2023)

Diabetes Therapy: Research, Treatment and Education of Diabetes and Related Disorders

Abstract

INTRODUCTION: Because adolescence is a time of difficult management of Type 1 diabetes (T1D) in part from adolescent-parent shared responsibility of T1D management, our objective was to assess the effects of a decision support system (DSS) CloudConnect on T1D-related communication between adolescents and their parents and on glycemic management. METHODS: We followed 86 participants including 43 adolescents with T1D (not on automated insulin delivery systems, AID) and their parents/care-giver for a 12-week intervention of UsualCare + CGM or CloudConnect, which included a Weekly Report of automated T1D advice, including insulin dose adjustments, based on data from continuous glucose monitors (CGM), Fitbit and insulin use. Primary outcome was T1D-specific communication and secondary outcomes were hemoglobin A1c, time-in-target range (TIR) 70-180 mg/dl, and additional psychosocial scales. RESULTS: Adolescents and parents reported a similar amount of T1D-related communication in both the UsualCare + CGM or CloudConnect groups and had similar levels of final HbA1c. Overall blood glucose time in range 70-180 mg/dl and time below 70 mg/dl were not different between groups. Parents but not children in the CloudConnect group reported less T1D-related conflict; however, compared to the UsualCare + CGM group, adolescents and parents in the CloudConnect reported a more negative tone of T1D-related communication. Adolescent-parent pairs in the CloudConnect group reported more frequent changes in insulin dose. There were no differences in T1D quality of life between groups. CONCLUSIONS: While feasible, the CloudConnect DSS system did not increase T1D communication or provide improvements in glycemic management. Further efforts are needed to improve T1D management in adolescents with T1D not on AID systems.

Mindfulness Combined with Exercise Online (MOVE) Compared with a Self-Management Guide for Adults with Chronic Pain: A Feasibility Randomised Controlled Trial (2023)

The Clinical Journal of Pain

Abstract

OBJECTIVE: Limited studies exist combining mindfulness-based stress reduction (MBSR) and exercise in a pain management programme (PMP), with none thus far delivering a combined intervention as an online PMP. This study aimed to explore the acceptability and feasibility of a combined MBSR and exercise online PMP for adults with chronic pain and to examine the feasibility of conducting a Randomised Controlled Trial (RCT) comparing MBSR and exercise delivered online with an online self-management guide. METHODS: A feasibility RCT was conducted with participants randomised into the MOVE Group (8-week MBSR and exercise live online) or the self-management (SM) group (8-week online self-management guide). Primary outcomes included recruitment, attrition, intervention adherence, and satisfaction. Participants wore a Fitbit watch during the study and completed patient reported outcome measures at baseline, post-intervention and 12-week follow-up. RESULTS: Ninety-six participants were randomised and 80 (83.3\%) completed the interventions. Higher mean satisfaction (Client Satisfaction Questionnaire-8) was reported in the MOVE group 26.2(±5.5) than the SM group 19.4(±5.6). The Patient Global Impression of Change scale showed favourable changes in both groups; 65.1\% of the MOVE group, 42.3\% of the SM Group reporting improvement. 73 participants (76.3\%) adhered to wearing the Fitbit for 8-weeks. Comparable improvements post-intervention and at a 12-week follow-up were noted within both groups for Brief Pain Inventory, Pain Self Efficacy Questionnaire, Pain Disability Index, Pain Catastrophising Scale, Fear Avoidance Belief Questionnaire and Short Form-36 Health Survey. DISCUSSION: The findings suggest both interventions explored are acceptable and feasible. A fully powered RCT examining the effectiveness of MBSR combined with exercise, delivered live online is warranted.

Machine Learning Approaches to Classify Self-Reported Rheumatoid Arthritis Health Scores Using Activity Tracker Data (2023)

JMIR formative research

Abstract

BACKGROUND: The increasing use of activity trackers in mobile health studies to passively collect physical data has shown promise to lessen participation burden to provide actively contributed patient reported outcome (PRO) information. Our goal was to develop machine learning models to classify patient-reported outcome (PRO) scores using Fitbit data from a cohort of patients with rheumatoid arthritis (RA). OBJECTIVE: The increasing use of activity trackers in mobile health studies to passively collect physical data has shown promise to lessen participation burden to provide actively contributed patient reported outcome (PRO) information. Our goal was to develop machine learning models to classify patient-reported outcome (PRO) scores using Fitbit data from a cohort of patients with rheumatoid arthritis (RA). METHODS: Two different models were built to classify PRO scores; a random forest (RF) Classifier model that treated each week of observations independently when making weekly predictions of PRO scores, and a hidden Markov model (HMM) that additionally took correlations between successive weeks into account. Analyses compared model evaluation metrics for: 1) a binary task of distinguishing a normal PRO score from a severe PRO score, and 2) a multiclass task of classifying a PRO score state for a given week. RESULTS: For both the binary and multiclass tasks, the HMM significantly (p {\textless} 0.05) outperformed the RF for most PRO scores, and the highest AUC, Pearson's Correlation coefficient, and Cohen's Kappa coefficient were 0.751, 0.458, and 0.450 respectively. CONCLUSIONS: While further validation of our results and evaluation in a real-world setting yet remains, this study demonstrates the ability of physical activity tracker data to classify health status over time in patients with RA and enables the possibility of scheduling preventive clinical interventions as needed. If patient outcomes can be monitored in real time, there is potential to improve clinical care for patients with other chronic conditions.

Performance evaluation of Fitbit Charge 3 and actigraphy vs. polysomnography: Sensitivity, specificity, and reliability across participants and nights (2023)

Sleep Health

Abstract

GOAL AND AIMS: Compare the accuracy and reliability of sleep/wake classification between the Fitbit Charge 3 and the Micro Motionlogger actigraph when applying either the Cole-Kripke or Sadeh scoring algorithms. Accuracy was established relative to simultaneous Polysomnography recording. Focus technology: Fitbit Charge 3 and actigraphy. Reference technology: Polysomnography. SAMPLE: Twenty-one university students (10 females). DESIGN: Simultaneous Fitbit Charge 3, actigraphy, and polysomnography were recorded over 3 nights at the participants' homes. CORE ANALYTICS: Total sleep time, wake after sleep onset, sensitivity, specificity, positive predictive value, and negative predictive value. ADDITIONAL ANALYTICS AND EXPLORATORY ANALYSES: Variability of specificity and negative predictive value across subjects and across nights. CORE OUTCOMES: Fitbit Charge 3 and actigraphy using the Cole-Kripke or Sadeh algorithms exhibited similar sensitivity in classifying sleep segments relative to polysomnography (sensitivity of 0.95, 0.96, and 0.95, respectively). Fitbit Charge 3 was significantly more accurate in classifying wake segments (specificity of 0.69, 0.33, and 0.29, respectively). Fitbit Charge 3 also exhibited significantly higher positive predictive value than actigraphy (0.99 vs. 0.97 and 0.97, respectively) and a negative predictive value that was significantly higher only relative to the Sadeh algorithm (0.41 vs. 0.25, respectively). IMPORTANT ADDITIONAL OUTCOMES: Fitbit Charge 3 exhibited significantly lower standard deviation in specificity values across subjects and negative predictive value across nights. CORE CONCLUSION: This study demonstrates that Fitbit Charge 3 is more accurate and reliable in identifying wake segments than the examined FDA-approved Micro Motionlogger actigraphy device. The results also highlight the need to create devices that record and save raw multi-sensor data, which are necessary for developing open-source sleep or wake classification algorithms.

The feasibility of remotely monitoring physical, cognitive, and psychosocial function in individuals with stroke or chronic obstructive pulmonary disease (2023)

Digital Health

Abstract

OBJECTIVE: Clinical implementation of remote monitoring of human function requires an understanding of its feasibility. We evaluated adherence and the resources required to monitor physical, cognitive, and psychosocial function in individuals with either chronic obstructive pulmonary disease or stroke during a three-month period. METHODS: Seventy-three individuals agreed to wear a Fitbit to monitor physical function and to complete monthly online assessments of cognitive and psychosocial function. During a three-month period, we measured adherence to monitoring (1) physical function using average daily wear time, and (2) cognition and psychosocial function using the percentage of assessments completed. We measured the resources needed to promote adherence as (1) the number of participants requiring at least one reminder to synchronize their Fitbit, and (2) the number of reminders needed for each completed cognitive and psychosocial assessment. RESULTS: After accounting for withdrawals, the average daily wear time was 77.5 ± 19.9\% of the day and did not differ significantly between months 1, 2, and 3 (p = 0.30). To achieve this level of adherence, 64.9\% of participants required at least one reminder to synchronize their device. Participants completed 61.0\% of the cognitive and psychosocial assessments; the portion of assessments completed each month didnot significantly differ (p = 0.44). Participants required 1.13 ± 0.57 reminders for each completed assessment. Results did not differ by disease diagnosis. CONCLUSIONS: Remote monitoring of human function in individuals with either chronic obstructive pulmonary disease or stroke is feasible as demonstrated by high adherence. However, the number of reminders required indicates that careful consideration must be given to the resources available to obtain high adherence.

Working-from-home persistently influences sleep and physical activity 2 years after the Covid-19 pandemic onset: a longitudinal sleep tracker and electronic diary-based study (2023)

Frontiers in Psychology

Abstract

Objective Working from home (WFH) has become common place since the Covid-19 pandemic. Early studies observed population-level shifts in sleep patterns (later and longer sleep) and physical activity (reduced PA), during home confinement. Other studies found these changes to depend on the proportion of days that individuals WFH (vs. work from office; WFO). Here, we examined the effects of WFH on sleep and activity patterns in the transition to normality during the later stages of the Covid-19 pandemic (Aug 2021–Jan 2022). Methods Two-hundred and twenty-five working adults enrolled in a public health study were followed for 22 weeks. Sleep and activity data were collected with a consumer fitness tracker (Fitbit Versa 2). Over three 2-week periods (Phase 1/week 1–2: August 16–29, 2021; Phase 2/week 11–12: October 25–November 7, 2021; Phase 3/week 21–22: January 3–16, 2022), participants provided daily Fitbit sleep and activity records. Additionally, they completed daily phone-based ecological momentary assessment (EMA), providing ratings of sleep quality, wellbeing (mood, stress, motivation), and information on daily work arrangements (WFH, WFO, no work). Work arrangement data were used to examine the effects of WFH vs. WFO on sleep, activity, and wellbeing. Results The proportion of WFH vs. WFO days fluctuated over the three measurement periods, mirroring evolving Covid restrictions. Across all three measurement periods WFH days were robustly associated with later bedtimes (+14.7 min), later wake times (+42.3 min), and longer Total Sleep Time (+20.2 min), compared to WFO days. Sleep efficiency was not affected. WFH was further associated with lower daily step count than WFO (−2,471 steps/day). WFH was associated with higher wellbeing ratings compared to WFO for those participants who had no children. However, for participants with children, these differences were not present. Conclusion Pandemic-initiated changes in sleep and physical activity were sustained during the later stage of the pandemic. These changes could have longer term effects, and conscious effort is encouraged to harness the benefits (i.e., longer sleep), and mitigate the pitfalls (i.e., less physical activity). These findings are relevant for public health as hybrid WHF is likely to persist in a post-pandemic world.

Natural patterns of social support for physical activity participation in newly matched breast cancer survivor dyads (2023)

BMC women's health

Abstract

BACKGROUND: Physical activity (PA) can be a beneficial strategy to mitigate physical, emotional, and social-related challenges in women living beyond breast cancer treatment (WBC). However, PA levels among WBC remain low. Optimizing social support provided in a peer-matched setting may increase PA behavior. Unfortunately, factors that lead to an ideal peer-match among WBC are not well understood. The purpose of this study was to contextualize the natural social support environment and PA behavior in newly formed peer WBC dyads participating in an ecological momentary assessment study. METHODS: WBC were matched with a partner and provided with a Fitbit activity tracker. Social support was measured using 21-daily surveys, and a 3-week follow-up survey. Descriptive statistics were calculated. Open-ended survey questions were analyzed using content analysis. Data were analyzed based on (i) social support types (informational, tangible, esteem, and emotional support); and (ii) WBC' reports of being in a good, neutral, or poor match at the end of the study. RESULTS: Women (n = 46; Mage = 42.4 ± 7.6 years; 89.2\% stage I-III breast cancer) connected with their partner (58.1\%) and participated in moderate-vigorous PA (MVPA)(77.1\%) on most days over the 21-day study period. Women were identified as being in good (63\%), neutral (20\%), or poor (17\%) dyad matches. The most frequently documented social support received by WBC was esteem support. Participants in a good match were more likely to report receiving all types of social support compared to neutral or poor matches. CONCLUSION \& CLINICAL IMPLICATIONS: Findings describe the social support characteristics important to WBC for facilitating their PA participation in a partner-based setting. This study provides valuable insight that can inform the development of partner-based PA interventions for WBC.

Development of a Mobile Health Snacktivity App to Promote Physical Activity in Inactive Adults (SnackApp): Intervention Mapping and User Testing Study (2023)

JMIR formative research

Abstract

BACKGROUND: Despite the unequivocal evidence demonstrating the benefits of being physically active, many people do not meet the recommended guidelines of at least 150 minutes of moderate- to vigorous-intensity physical activity per week. This can be changed with the development and implementation of innovative interventions. The use of mobile health (mHealth) technologies has been suggested as a mechanism to offer people innovative health behavior change interventions. OBJECTIVE: This study aims to outline the systematic, theory-driven processes and user testing applied to the development of a smartphone-based physical activity app (SnackApp) to promote participation in a novel physical activity intervention called Snacktivity. The acceptability of the app was explored and reported. METHODS: Intervention mapping involves a 6-step process, the first 4 of which were presented in this study. These steps were used to develop the SnackApp for use within the Snacktivity intervention. The first step involved a needs assessment, which included composing an expert planning group, patient and public involvement group, and gathering the views of the public on Snacktivity and the public perception of the use of wearable technology to support Snacktivity. This first step aimed to determine the overall purpose of the Snacktivity intervention. Steps 2 to 4 involved determining the intervention objectives, the behavior change theory and techniques on which the intervention is based, and the development of the intervention resources (ie, SnackApp). After the completion of steps 1 to 3 of the intervention mapping process, the SnackApp was developed and linked to a commercial physical activity tracker (Fitbit Versa Lite) for the automated capture of physical activity. SnackApp includes provisions for goal setting, activity planning, and social support. Stage 4 involved users (inactive adults, N=15) testing the SnackApp for 28 days. App engagement (mobile app use analytics) was analyzed to determine app use and to inform the further development of SnackApp. RESULTS: Over the study period (step 4), participants engaged with SnackApp an average of 77 (SD 80) times. On average, participants used the SnackApp for 12.6 (SD 47) minutes per week, with most of the time spent on the SnackApp dashboard and engaging, on average, 14 (SD 12.1) times, lasting 7 to 8 minutes per week. Overall, male participants used the SnackApp more than female participants did. The app rating score was 3.5 (SD 0.6) out of 5, suggesting that SnackApp was rated as fair to good. CONCLUSIONS: This study outlines and reports data regarding the development of an innovative mHealth app using a systematic, theory-driven framework. This approach can guide the development of future mHealth programs. User testing of the SnackApp suggested that physically inactive adults will engage with the SnackApp, indicating its applicability of use in the Snacktivity physical activity intervention.

Accuracy of a Standalone Atrial Fibrillation Detection Algorithm Added to a Popular Wristband and Smartwatch: Prospective Diagnostic Accuracy Study (2023)

Journal of Medical Internet Research

Abstract

BACKGROUND: Silent paroxysmal atrial fibrillation (AF) may be difficult to diagnose, and AF burden is hard to establish. In contrast to conventional diagnostic devices, photoplethysmography (PPG)-driven smartwatches or wristbands allow for long-term continuous heart rhythm assessment. However, most smartwatches lack an integrated PPG-AF algorithm. Adding a standalone PPG-AF algorithm to these wrist devices might open new possibilities for AF screening and burden assessment. OBJECTIVE: The aim of this study was to assess the accuracy of a well-known standalone PPG-AF detection algorithm added to a popular wristband and smartwatch, with regard to discriminating AF and sinus rhythm, in a group of patients with AF before and after cardioversion (CV). METHODS: Consecutive consenting patients with AF admitted for CV in a large academic hospital in Amsterdam, the Netherlands, were asked to wear a Biostrap wristband or Fitbit Ionic smartwatch with Fibricheck algorithm add-on surrounding the procedure. A set of 1-min PPG measurements and 12-lead reference electrocardiograms was obtained before and after CV. Rhythm assessment by the PPG device-software combination was compared with the 12-lead electrocardiogram. RESULTS: A total of 78 patients were included in the Biostrap-Fibricheck cohort (156 measurement sets) and 73 patients in the Fitbit-Fibricheck cohort (143 measurement sets). Of the measurement sets, 19/156 (12\%) and 7/143 (5\%), respectively, were not classifiable by the PPG algorithm due to bad quality. The diagnostic performance in terms of sensitivity, specificity, positive predictive value, negative predictive value, and accuracy was 98\%, 96\%, 96\%, 99\%, 97\%, and 97\%, 100\%, 100\%, 97\%, and 99\%, respectively, at an AF prevalence of {\textasciitilde}50\%. CONCLUSIONS: This study demonstrates that the addition of a well-known standalone PPG-AF detection algorithm to a popular PPG smartwatch and wristband without integrated algorithm yields a high accuracy for the detection of AF, with an acceptable unclassifiable rate, in a semicontrolled environment.

Assessment of 24-hour physical behaviour in adults via wearables: a systematic review of validation studies under laboratory conditions (2023)

The International Journal of Behavioral Nutrition and Physical Activity

Abstract

Background: Wearable technology is used by consumers and researchers worldwide for continuous activity monitoring in daily life. Results of high-quality laboratory-based validation studies enable us to make a guided decision on which study to rely on and which device to use. However, reviews in adults that focus on the quality of existing laboratory studies are missing. Methods: We conducted a systematic review of wearable validation studies with adults. Eligibility criteria were: (i) study under laboratory conditions with humans (age ≥ 18 years); (ii) validated device outcome must belong to one dimension of the 24-hour physical behavior construct (i.e., intensity, posture/activity type, and biological state); (iii) study protocol must include a criterion measure; (iv) study had to be published in a peer-reviewed English language journal. Studies were identified via a systematic search in five electronic databases as well as back- and forward citation searches. The risk of bias was assessed based on the QUADAS-2 tool with eight signaling questions. Results: Out of 13,285 unique search results, 545 published articles between 1994 and 2022 were included. Most studies (73.8% (N = 420)) validated an intensity measure outcome such as energy expenditure; only 14% (N = 80) and 12.2% (N = 70) of studies validated biological state or posture/activity type outcomes, respectively. Most protocols validated wearables in healthy adults between 18 and 65 years. Most wearables were only validated once. Further, we identified six wearables (i.e., ActiGraph GT3X+, ActiGraph GT9X, Apple Watch 2, Axivity AX3, Fitbit Charge 2, Fitbit, and GENEActiv) that had been used to validate outcomes from all three dimensions, but none of them were consistently ranked with moderate to high validity. Risk of bias assessment resulted in 4.4% (N = 24) of all studies being classified as "low risk", while 16.5% (N = 90) were classified as "some concerns" and 79.1% (N = 431) as "high risk". Conclusion: Laboratory validation studies of wearables assessing physical behaviour in adults are characterized by low methodological quality, large variability in design, and a focus on intensity. Future research should more strongly aim at all components of the 24-hour physical behaviour construct, and strive for standardized protocols embedded in a validation framework.

Nonlinear, Multicomponent Physical Exercise with Heart Rate Variability-Guided Prescription in Women with Breast Cancer during Treatment: Feasibility and Preliminary Results (ATOPE Study) (2023)

Physical Therapy

Abstract

Objective: The purpose of this study was to examine the feasibility, safety, adherence, and preliminary efficacy of the ATOPE program during radiotherapy (RT) or chemotherapy (CT) for women with breast cancer. Methods: This single-blind, pretest-posttest feasibility study included 38 women with breast cancer at the beginning of their treatment. The ATOPE program consisted of 12 to 18 sessions of a multimodal physical exercise program, prescribed based on daily heart rate variability (HRV) and clinimetric assessments using the ATOPE+ mHealth system. Overall health was assessed with quality of life, autonomous balance, and body composition, whereas health-related fitness (HRF) was measured through functional capacity, physical activity levels, and upper and lower limb strength. Results: The rates of recruitment, retention, and adherence were 52.35%, 73.68%, and 84.37%, respectively, and the satisfaction rating was 9.2 out of a possible 10 points. The perceived health status change score was 3.83 points, scored on a - 5 to 5 point scale. No adverse effects were found. Compliance results showed that the ATOPE+ mHealth system was used on 73.38% of the days, and the Fitbit bracelet (Google, Mountain View, CA, USA) was used on 84.91% of the days. Women stayed physically active 55% of days. Regarding preliminary results, for overall health, the percentage of body fat in the RT group decreased by 1.93%, whereas it increased by 5.03% in the CT group. Lower limb strength increased in the RT group, specifically knee extensor isometric strength (6.07%), isokinetic knee flexors 180°/s (1.53%) and isokinetic knee extensors 300°/s (4.53%), in contrast with the reductions found in the CT group (11.07%, 18.67%, and 14.89%, respectively). Conclusions: The ATOPE program, through nonlinear prescription based on daily monitoring with the ATOPE+ mHealth system, is feasible and safe for application during breast cancer treatment. The results suggest that overall health can be maintained or even improved regarding most variables. Impact: This study focused on the feasibility, safety, and completion of a physical therapist-led program at early diagnosis for adults with breast cancer. The multimodal, supervised, tailored, nonlinear physical exercise program is feasible and safe, showed a good completion rate, and was able to prevent the quality-of-life deficits that are often triggered by systemic breast cancer treatment. This study highlights the importance of daily morning assessments using the ATOPE+ mHealth system in patients with breast cancer to prescribe nonlinear physical exercise.

Heart rate responses in critical care trainees during airway intubation: a comparison between the simulated and clinical environments (2023)

BMC Emergency Medicine

Abstract

Objective This study aimed to compare the heart rate response to stress during airway intubations in clinical practice and a simulated environment. Methods Twenty-five critical care registrars participated in the study over a 3-month period. Heart rate data during intubations was recorded by a FitBit® Charge 2 worn by each participant during their clinical practice, and during a single simulated airway management scenario. The heart rate range was calculated by subtracting the baseline working heart rate (BWHR) from the maximum functional heart rate (MFHR). For each airway intubation performed participants recorded an airway diary entry. Data from intubations performed in the clinical environment was compared to data from a simulated environment. Heart rate changes were observed in two ways: percentage rise (median) across the 20-min intubation period and; percentage rise at point of intubation (median). Results Eighteen critical care registrars completed the study, mean age 31.8 years (SD = 2.015, 95% CI = 30.85–32.71). Throughout the 20-min peri-intubation recording period there was no significant difference in the median change in heart rates between the clinical (14.72%) and simulation (15.96%) environment (p = 0.149). At the point of intubation there was no significant difference in the median change in heart rate between the clinical (16.03%) and the simulation (25.65%) environment groups (p = 0.054). Conclusion In this small population of critical care trainees, a simulation scenario induced a comparable heart rate response to the clinical environment during intubation. This provides evidence that simulation scenarios are able to induce a comparable physiological stress response to the clinical environment and thus facilitates effective teaching of a high-risk procedure in a safe manner.

Validation of the relationship between rapid eye movement sleep and sleep-related erections in healthy adults by a feasible instrument Fitbit Charge2 (2023)

Andrology

Abstract

Background: Sleep, particularly rapid eye movement sleep, has been found to be associated with sleep-related erections. While RigiScan is currently a more accurate method for monitoring nocturnal erectile events, the Fitbit, a smart wearable device, shows great potential for sleep monitoring. Objectives: To analyze the relationship between sleep-related erections and sleep by recruiting sexually active, healthy men for simultaneous monitoring of sleep and nocturnal penile tumescence and rigidity. Patients and methods: Using Fitbit Charge2 and RigiScan, we simultaneously monitored nocturnal sleep and erections in 43 healthy male volunteers, and analyzed the relationship between sleep periods and erectile events with the Statistical Package for Social Sciences. Results: Among all erectile events, 89.8% were related to rapid eye movement, and 79.2% of all rapid eye movement periods were associated with erectile events. Moreover, a statistical correlation was shown between the duration of rapid eye movement and the time of total erectile events (first night: 𝜌 = 0.316, p = 0.039; second night: 𝜌 = 0.370, p = 0.015). Discussion and conclusion: Our study shows a potential link between sleep-related erections and rapid eye movement sleep, which has implications for the current examination of sleep-related erections and further research into the mechanisms of erectile function. Meanwhile, the wearable device Fitbit has shown a potential promise for sleep monitoring in patients with erectile dysfunction. The results provide an alternative approach for further research on the relationship between erectile function and sleep with large sample sizes in the future.

Social epidemiology of Fitbit daily steps in early adolescence (2023)

Pediatric Research

Abstract

Background: Sociodemographic disparities in adolescent physical activity have been documented but mostly rely on self-reported data. Our objective was to examine differences in device-based step metrics, including daily step count (steps d-1), by sociodemographic factors among a diverse sample of 10-to-14-year-old adolescents in the US. Methods: We analyzed prospective cohort data from Year 2 (2018-2020) of the Adolescent Brain Cognitive Development (ABCD) Study (N = 6460). Mixed-effects models were conducted to estimate associations of sociodemographic factors (sex, sexual orientation, race/ethnicity, household income, parental education, and parental marital status) with repeated measures of steps d-1 over the course of 21 days. Results: Participants (49.6% female, 39.0% racial/ethnic minority) accumulated an average of 9095.8 steps d-1. In mixed-effects models, 1543.6 more steps d-1 were recorded for male versus female sex, Black versus White race (328.8 more steps d-1), heterosexual versus sexual minority sexual orientation (676.4 more steps d-1), >$200,000 versus <$25,000 household income (1003.3 more steps d-1), and having married/partnered parents versus unmarried/unpartnered parents (326.3 more steps d-1). We found effect modification by household income for Black adolescents and by sex for Asian adolescents. Conclusions: Given sociodemographic differences in adolescent steps d-1, physical activity guidelines should focus on key populations and adopt strategies optimized for adolescents from diverse backgrounds. Impact: Sociodemographic disparities in physical activity have been documented but mostly rely on self-reported data, which can be limited by reporting and prevarication bias. In this demographically diverse sample of 10-14-year-old early adolescents in the U.S., we found notable and nuanced sociodemographic disparities in Fitbit steps per day. More daily steps were recorded for male versus female sex, Black versus White race, heterosexual versus sexual minority, >$100,000 versus <$25,000 household income, and having married/partnered versus unmarried/unpartnered parents. We found effect modification by household income for Black adolescents and by sex for Asian adolescents.

Pain self-management plus activity tracking and nurse-led support in adults with chronic low back pain: feasibility and acceptability of the problem-solving pain to enhance living well (PROPEL) intervention (2023)

BMC nursing

Abstract

Background: Chronic low back pain can lead to individual suffering, high medical expenditures, and impaired social well-being. Although the role of physical activity in pain management is well established, the underlying mechanisms of biological and clinical outcomes are unknown. This study aimed to assess the feasibility and acceptability of a pain self-management intervention, Problem-Solving Pain to Enhance Living Well, which employs wearable activity tracking technology and nurse consultations for people with chronic low back pain. Methods: This one-arm longitudinal study recruited 40 adults aged 18-60 years with chronic low back pain. Over 12 weeks, participants watched 10 short video modules, wore activity trackers, and participated in nurse consultations every 2 weeks. At baseline and the 12-week follow-up, they completed study questionnaires, quantitative sensory testing, and blood sample collection. Results: Forty participants were recruited, and their mean age was 29.8. Thirty-two participants completed the survey questionnaire, quantitative sensory testing, Fitbit activity tracker, and bi-weekly nurse consultation, and 25 completed the evaluation of biological markers. The overall satisfaction with the Problem-Solving Pain to Enhance Living Well video modules, nurse consultations, and Fitbit in pain management was rated as excellent. No adverse events were reported. Between the baseline and 12-week follow-up, there was a significant decrease in pain intensity and interference and an increase in the warm detection threshold at the pain site. Conclusions: Despite concerns about the participant burden due to multidimensional assessment and intensive education, the feasibility of the Problem-Solving Pain to Enhance Living Well intervention was favorable. Technology-based self-management interventions can offer personalized strategies by integrating pain phenotypes, genetic markers, and physical activity types affecting pain conditions. Trial registration: This pilot study was registered with ClinicalTrials.gov [NCT03637998, August 20, 2018]. The first participant was enrolled on September 21, 2018.

A Series of Remote Melatonin Supplement Interventions for Poor Sleep: Protocol for a Feasibility Pilot Study for a Series of Personalized (N-of-1) Trials (2023)

JMIR Research Protocols

Abstract

Background: Poor sleep, defined as short-duration or poor-quality sleep, is a frequently reported condition with many deleterious effects including poorer cognitive functioning, increased accidents, and poorer health. Melatonin has been shown to be an efficacious treatment to manage symptoms of poor sleep. However, the treatment effects of melatonin on sleep can vary greatly between participants. Personalized, or N-of-1, trial designs represent a method for identifying the best treatment for individual participants. Although using N-of-1 trials of melatonin to treat poor sleep is possible, the feasibility, acceptability, and effectiveness of N-of-1 trials using melatonin are unknown. Using the National Institutes of Health Stage Model for Behavioral Intervention Development, a stage IB (intervention refinement, modification, and adaptation and pilot testing) design appeared to be needed to address these feasibility questions. Objective: This trial series evaluates the feasibility, acceptability, and effectiveness of a series of personalized interventions for remote delivery of melatonin dose (3 and 0.5 mg) versus placebo supplements for self-reported poor sleep among 60 participants. The goal of this study is to provide valuable information about implementing remote N-of-1 randomized controlled trials to improve poor sleep. Methods: Participants will complete a 2-week baseline followed by six 2-week alternating intervention periods of 3 mg of melatonin, 0.5 mg of melatonin, and placebo. Participants will be randomly assigned to 2 intervention orders. The feasibility and acceptability of the personalized trial approach will be determined with participants’ ratings of usability and satisfaction with the remote, personalized intervention delivery system. The effectiveness of the intervention will be measured using participants’ self-reported sleep quality and duration and Fitbit tracker–measured sleep duration and efficiency. Additional measures will include ecological momentary assessment measures of fatigue, stress, pain, mood, concentration, and confidence as well as measures of participant adherence to the intervention, use of the Fitbit tracker, and survey data collection. Results: As of the submission of this protocol, recruitment for this National Institutes of Health stage IB personalized trial series is approximately 78.3\% complete (47/60). We expect recruitment and data collection to be finalized by June 2023. Conclusions: Evaluating the feasibility, acceptability, and effectiveness of a series of personalized interventions of melatonin will address the longer term aim of this program of research—is integrating N-of-1 trials useful patient care? The personalized trial series results will be published in a peer-reviewed journal and will follow the CONSORT (Consolidated Standards of Reporting Trials) extension for N-of-1 trials (CENT 2015) reporting guidelines. This trial series was approved by the Northwell Health institutional review board. Trial Registration: ClinicalTrials.gov NCT05349188; https://www.clinicaltrials.gov/study/NCT05349188

Ecological Momentary Assessments and Passive Sensing in the Prediction of Short-Term Suicidal Ideation in Young Adults (2023)

JAMA network open

Abstract

IMPORTANCE: Advancements in technology, including mobile-based ecological momentary assessments (EMAs) and passive sensing, have immense potential to identify short-term suicide risk. However, the extent to which EMA and passive data, particularly in combination, have utility in detecting short-term risk in everyday life remains poorly understood. OBJECTIVE: To examine whether and what combinations of self-reported EMA and sensor-based assessments identify next-day suicidal ideation. DESIGN, SETTING, AND PARTICIPANTS: In this intensive longitudinal prognostic study, participants completed EMAs 4 times daily and wore a sensor wristband (Fitbit Charge 3) for 8 weeks. Multilevel machine learning methods, including penalized generalized estimating equations and classification and regression trees (CARTs) with repeated 5-fold cross-validation, were used to optimize prediction of next-day suicidal ideation based on time-varying features from EMAs (affective, cognitive, behavioral risk factors) and sensor data (sleep, activity, heart rate). Young adult patients who visited an emergency department with recent suicidal ideation and/or suicide attempt were recruited. Identified via electronic health record screening, eligible individuals were contacted remotely to complete enrollment procedures. Participants (aged 18 to 25 years) completed 14 708 EMA observations (64.4\% adherence) and wore a sensor wristband approximately half the time (55.6\% adherence). Data were collected between June 2020 and July 2021. Statistical analysis was performed from January to March 2023. MAIN OUTCOMES AND MEASURES: The outcome was presence of next-day suicidal ideation. RESULTS: Among 102 enrolled participants, 83 (81.4\%) were female; 6 (5.9\%) were Asian, 5 (4.9\%) were Black or African American, 9 (8.8\%) were more than 1 race, and 76 (74.5\%) were White; mean (SD) age was 20.9 (2.1) years. The best-performing model incorporated features from EMAs and showed good predictive accuracy (mean [SE] cross-validated area under the receiver operating characteristic curve [AUC], 0.84 [0.02]), whereas the model that incorporated features from sensor data alone showed poor prediction (mean [SE] cross-validated AUC, 0.56 [0.02]). Sensor-based features did not improve prediction when combined with EMAs. Suicidal ideation-related features were the strongest predictors of next-day ideation. When suicidal ideation features were excluded, an alternative EMA model had acceptable predictive accuracy (mean [SE] cross-validated AUC, 0.76 [0.02]). Both EMA models included features at different timescales reflecting within-day, end-of-day, and time-varying cumulative effects. CONCLUSIONS AND RELEVANCE: In this prognostic study, self-reported risk factors showed utility in identifying near-term suicidal thoughts. Best-performing models required self-reported information, derived from EMAs, whereas sensor-based data had negligible predictive accuracy. These results may have implications for developing decision algorithms identifying near-term suicidal thoughts to guide risk monitoring and intervention delivery in everyday life.

Integrating Genome-wide information and Wearable Device Data to Explore the Link of Anxiety and Antidepressants with Heart Rate Variability (2023)

medRxiv: The Preprint Server for Health Sciences

Abstract

BACKGROUND: Anxiety disorders are associated with decreased heart rate variability (HRV), but the underlying mechanisms remain elusive. METHODS: We selected individuals with whole-genome sequencing, Fitbit, and electronic health record data (N=920; 61,333 data points) from the All of Us Research Program. Anxiety PRS were derived with PRS-CS after meta-analyzing anxiety genome-wide association studies from three major cohorts-UK Biobank, FinnGen, and the Million Veterans Program (N Total =364,550). The standard deviation of average RR intervals (SDANN) was calculated using five-minute average RR intervals over full 24-hour heart rate measurements. Antidepressant exposure was defined as an active antidepressant prescription at the time of the HRV measurement in the EHR. The associations of daily SDANN measurements with the anxiety PRS, antidepressant classes, and antidepressant substances were tested. Participants with lifetime diagnoses of cardiovascular disorders, diabetes mellitus, and major depression were excluded in sensitivity analyses. One-sample Mendelian randomization (MR) was employed to assess potential causal effect of anxiety on SDANN. RESULTS: Anxiety PRS was independently associated with reduced SDANN (beta=-0.08; p=0.003). Of the eight antidepressant medications and four classes tested, venlafaxine (beta=-0.12, p=0.002) and bupropion (beta=-0.071, p=0.01), tricyclic antidepressants (beta=-0.177, p=0.0008), selective serotonin reuptake inhibitors (beta=-0.069; p=0.0008) and serotonin and norepinephrine reuptake inhibitors (beta=-0.16; p=2×10 -6 ) were associated with decreased SDANN. One-sample MR indicated an inverse effect of anxiety on SDANN (beta=-2.22, p=0.03). CONCLUSIONS: Anxiety and antidepressants are independently associated with decreased HRV, and anxiety appears to exert a causal effect on HRV. Our observational findings provide novel insights into the impact of anxiety on HRV.

The Effects of Auricular Acupressure on Menopausal Symptoms, Stress, and Sleep in Postmenopausal Middle-Aged Women: A Randomized Single-Blind Sham-Controlled Trial (2023)

Journal of Midwifery \& Women's Health

Abstract

INTRODUCTION: Middle-aged women in the postmenopausal period experience menopause symptoms, stress, and poor sleep quality due to hormonal changes. Nonetheless, most of them recognize such symptoms as an aging process without receiving proper treatment, and there are few nonpharmacologic interventions available. METHODS: This was a randomized single-masked, sham-controlled trial. For the intervention group, a vaccaria seed was applied to the auricular points of Shenmen, anterior lobe, adrenal glands, central rim, and endocrine that are related to the menopause symptoms, stress, and sleep while applying a seed to the auricular points not related to the forementioned symptoms to the control group. The Menopause Rating Scale (MRS), Perceived Stress Scale (PSS), heart rate variability, and electroencephalogram (EEG) were measured before the intervention, 4 weeks after the intervention, and 8 weeks after the intervention. Actigraphy was measured with a Fitbit, and the Pittsburgh Sleep Quality Index (PSQI) was measured before and after the intervention. The study was registered with the World Health Organization International Clinical Trials Registry Platform (KCT0007364). RESULTS: The MRS showed significant differences over time in the intervention group (F, 22.057; P {\textless} .001). There was a significant difference over time in the PSS (F, 22.576; P {\textless} .001), stress index measured by heart rate variability (F, 14.027; P = .001), and antistress quotient of the right brain measured by EEG (F, 4.865; P = .033). Sleep quality, measured by the PSQI (t = -4.050, P {\textless} .001), and sleep efficiency measured by actigraphy (t = 5.996, P {\textless} .001) were found to be significantly different over time in the intervention group. DISCUSSION: This study demonstrated that auricular acupressure is effective in improving menopause symptoms, stress, and sleep in postmenopausal middle-aged women. Therefore, auricular acupressure may be a useful nonpharmacologic intervention for alleviating these symptoms in this population.

Validity of activity wristbands for estimating daily physical activity in primary schoolchildren under free-living conditions: School-Fit study (2023)

Frontiers in Public Health

Abstract

INTRODUCTION: The use of activity wristbands to monitor and promote schoolchildren's physical activity (PA) is increasingly widespread. However, their validity has not been sufficiently studied, especially among primary schoolchildren. Consequently, the main purpose was to examine the validity of the daily steps and moderate-to-vigorous PA (MVPA) scores estimated by the activity wristbands Fitbit Ace 2, Garmin Vivofit Jr 2, and the Xiaomi Mi Band 5 in primary schoolchildren under free-living conditions. MATERIALS AND METHODS: An initial sample of 67 schoolchildren (final sample = 62; 50\% females), aged 9-12 years old (mean = 10.4 ± 1.0 years), participated in the present study. Each participant wore three activity wristbands (Fitbit Ace 2, Garmin Vivofit Jr 2, and Xiaomi Mi Band 5) on his/her non-dominant wrist and a research-grade accelerometer (ActiGraph wGT3X-BT) on his/her hip as the reference standard (number of steps and time in MVPA) during the waking time of one day. RESULTS: Results showed that the validity of the daily step scores estimated by the Garmin Vivofit Jr 2 and Xiaomi Mi Band 5 were good and acceptable (e.g., MAPE = 9.6/11.3\%, and lower 95\% IC of ICC = 0.87/0.73), respectively, as well as correctly classified schoolchildren as meeting or not meeting the daily 10,000/12,000-step-based recommendations, obtaining excellent/good and good/acceptable results (e.g., Garmin Vivofit Jr 2, k = 0.75/0.62; Xiaomi Mi Band 5, k = 0.73/0.53), respectively. However, the Fitbit Ace 2 did not show an acceptable validity (e.g., daily steps: MAPE = 21.1\%, and lower 95\% IC of ICC = 0.00; step-based recommendations: k = 0.48/0.36). None of the three activity wristbands showed an adequate validity for estimating daily MVPA (e.g., MAPE = 36.6-90.3\%, and lower 95\% IC of ICC = 0.00-0.41) and the validity for the MVPA-based recommendation tended to be considerably lower (e.g., k = -0.03-0.54). CONCLUSIONS: The activity wristband Garmin Vivofit Jr 2 obtained the best validity for monitoring primary schoolchildren's daily steps, offering a feasible alternative to the research-grade accelerometers. Furthermore, this activity wristband could be used during PA promotion programs to provide accurate feedback to primary schoolchildren to ensure their accomplishment with the PA recommendations.

'I saw it as a marriage-You can't have one without the other': A qualitative study of patient and physiotherapist experiences with a therapeutic combined strength and aerobic physical activity exercise programme for hip osteoarthritis (2023)

Musculoskeletal Care

Abstract

INTRODUCTION: To explore physiotherapist and patient experiences with, and acceptability of, a 12-week physiotherapist-guided combined strength and aerobic physical activity exercise programme for hip osteoarthritis (OA). METHODS: A qualitative study using semi-structured interviews with 13 people with hip OA and four physiotherapists. Patients underwent a 12-week home exercise programme customised by weekly visits with one of the four physiotherapists. The programme aimed for patients to participate in 150 min of moderate-intensity aerobic physical activity, and 20-30 min of strengthening exercise 2-3 times per week in concordance with American College of Sports Medicine (ACSM) dosage guidelines. Following the programme, patients and physiotherapists participated in individual semi-structured interviews to explore the acceptability of the exercise programme and barriers and facilitators to participation. Data were audio-recorded, transcribed and analysed using a thematic approach. RESULTS: Five over-arching themes (supporting subthemes) were identified from both patient and physiotherapist interviews: (i) positive outcomes (functional improvements, pain relief, empowerment through experience, commitment to continue); (ii) combined benefits of aerobic and strength exercises (complimentary effects, strengthening exercises key); (iii) valuing support from the physiotherapist (personalised care, skill performance feedback, coach effect, accountability); (iv) motivation and opportunity for exercise (positive symptom loop, integration into daily routine, prior exercise experience, Fitbit motivation); and (v) time-consuming commitment (physiotherapy visit frequency, travel inconvenience, time for exercise). CONCLUSIONS: Experiences of participants in this study indicate that, although time-consuming, a combined aerobic physical activity and strengthening programme prescribed at ACSM dosage guidelines is acceptable and confers positive outcomes in individuals with hip OA.

Clinical and Lifestyle Determinants of Continuous Glucose Monitoring Metrics in Insulin-Treated Patients with Type 2 Diabetes Mellitus (2023)

Diabetes \& Metabolism Journal

Abstract

BACKGROUND: There was limited evidence to evaluate the association between lifestyle habits and continuous glucose monitoring (CGM) metrics. Thus, we aimed to depict the behavioral and metabolic determinants of CGM metrics in insulin-treated patients with type 2 diabetes mellitus (T2DM). METHODS: This is a prospective observational study. We analyzed data from 122 insulin-treated patients with T2DM. Participants wore Dexcom G6 and Fitbit, and diet information was identified for 10 days. Multivariate-adjusted logistic regression analysis was performed for the simultaneous achievement of CGM-based targets, defined by the percentage of time in terms of hyper, hypoglycemia and glycemic variability (GV). Intake of macronutrients and fiber, step counts, sleep, postprandial C-peptide-to-glucose ratio (PCGR), information about glucose lowering medications and metabolic factors were added to the analyses. Additionally, we evaluated the impact of the distribution of energy and macronutrient during a day, and snack consumption on CGM metrics. RESULTS: Logistic regression analysis revealed that female, participants with high PCGR, low glycosylated hemoglobin (HbA1c) and daytime step count had a higher probability of achieving all targets based on CGM (odds ratios [95\% confidence intervals] which were 0.24 [0.09 to 0.65], 1.34 [1.03 to 1.25], 0.95 [0.9 to 0.99], and 1.15 [1.03 to 1.29], respectively). And participants who ate snacks showed a shorter period of hyperglycemia and less GV compared to those without. Conclusion: We confirmed that residual insulin secretion, daytime step count, HbA1c, and women were the most relevant determinants of adequate glycemic control in insulin-treated patients with T2DM. In addition, individuals with snack consumption were exposed to lower times of hyperglycemia and GV.

Association between the use of weight management strategies and weight change among Australian adults over 12 months: an observational study (2023)

BMC public health

Abstract

BACKGROUND: Obesity is a growing, global public health issue. This study aimed to describe the weight management strategies used by a sample of Australian adults; examine the socio-demographic characteristics of using each strategy; and examine whether use of each strategy was associated with 12-month weight change. METHODS: This observational study involved a community-based sample of 375 healthy adults (mean age: 40.1 ± 5.8 years, 56.8\% female). Participants wore a Fitbit activity monitor, weighed themselves daily, and completed eight online surveys on socio-demographic characteristics. Participants also recalled their use of weight management strategies over the past month, at 8 timepoints during the 12-month study period. RESULTS: Most participants (81\%) reported using at least one weight management strategy, with exercise/physical activity being the most common strategy at each timepoint (40-54\%). Those who accepted their current bodyweight were less likely to use at least one weight management strategy (Odds ratio = 0.38, 95\% CI = 0.22-0.64, p {\textless} 0.01) and those who reported being physically active for weight maintenance had a greater reduction in bodyweight, than those who did not (between group difference: -1.2 kg, p {\textless} 0.01). The use of supplements and fasting were associated with poorer mental health and quality of life outcomes (p {\textless} 0.01). CONCLUSIONS: The use of weight management strategies appears to be common. Being physically active was associated with greater weight loss. Individuals who accepted their current body weight were less likely to use weight management strategies. Fasting and the use of supplements were associated with poorer mental health. Promoting physical activity as a weight management strategy appears important, particularly considering its multiple health benefits.

Evaluation of the effectiveness of sleep hygiene education and FITBIT devices on quality of sleep and psychological worry: a pilot quasi-experimental study among first-year college students (2023)

Frontiers in Public Health

Abstract

BACKGROUND: College students report disturbed sleep patterns that can negatively impact their wellbeing and academic performance. OBJECTIVES: This study examined the effect of a 4-week sleep hygiene program that included sleep education and actigraph sleep trackers (FITBITs) on improving sleep quality and reducing psychological worry without control group. DESIGN SETTINGS AND PARTICIPANTS: A pilot quasi-experimental design, participants were randomly selected medical and health sciences from a university students in the United-Arab-Emirates. METHODS: Students were asked to wear FITBITs and log their daily sleep data and completed the Pittsburgh Sleep Quality Index (PSQI) and Penn State Worry Questionnaire (PSWQ). Extensive sleep hygiene education was delivered via lectures, a WhatsApp group, and the Blackboard platform. In total, 50 students completed pre-and post-assessments and returned FITBIT data. RESULTS: There was a significant difference in the prevalence of good sleep postintervention compared with pre-intervention (46\% vs. 28\%; p = 0.0126). The mean PSQI score was significantly lower post-intervention compared with pre-intervention (6.17 ± 3.16 vs. 7.12.87; p = 0.04, Cohen's d 0.33). After the intervention, subjective sleep quality, sleep latency, and daytime dysfunction were significantly improved compared with pre-intervention (p {\textless} 0.05). In addition, FITBIT data showed total sleep time and the number of restless episodes per night were significantly improved postintervention compared with pre-intervention (p = 0.013). The mean PSWQ score significantly decreased from pre-intervention to p = 0.049, Cohen' d = 0.25. The correlation between PSQI and PSWQ scores was significant post-intervention (β = 0.40, p = 0.02). CONCLUSION: Our results may inform university educational policy and curricular reform to incorporate sleep hygiene awareness programs to empower students and improve their sleep habits.

Examining changes in vascular function, arterial stiffness and systemic inflammation during hospitalization and recovery from an acute exacerbation of chronic obstructive pulmonary disease (2023)

Scientific Reports

Abstract

An acute exacerbation of COPD (AECOPD) is associated with increased risk of cardiovascular (CV) events. The elevated risk during an AECOPD may be related to changes in vascular function, arterial stiffness, and systemic inflammation; the time course of these measures and their corresponding recovery are poorly understood. Further, physical activity is reduced during an AECOPD, and physical activity may influence the cardiovascular responses to an AECOPD. The purpose of the study was to examine the acute impact of an AECOPD requiring hospitalization on vascular function, arterial stiffness, and systemic inflammation and examine whether physical activity modulates these variables during recovery. Patients hospitalized for an AECOPD were prospectively recruited and compared to control patients with stable COPD. Vascular function, arterial stiffness, and systemic inflammation (CRP, IL-6) were measured at hospital admission, hospital discharge and within 14 days of discharge. Physical activity was electronically tracked daily while in hospital and for 7 days following discharge using a Fitbit. One hundred and twenty-one patients with an AECOPD requiring hospitalization and 33 control patients with stable COPD were enrolled in the study. Vascular function was significantly lower, and systemic inflammation higher at hospital admission in patients with an AECOPD compared to stable COPD. Significant improvements in vascular function and inflammation were observed within 14 days of hospital discharge; however, vascular function remained lower than stable COPD. Physical activity was low at admission and increased following discharge; however, physical activity was unrelated to measures of vascular function or inflammation at any time point. An AECOPD requiring hospitalization is associated with impaired vascular function that persists during recovery. These findings provide a mechanistic link to help explain the enduring increase in CV risk and mortality following a severe AECOPD event.Clinical trial registration: ClinicalTrials.gov \#NCT01949727; Registered: 09/20/2013.

The impact of mindfulness training on posttraumatic stress disorder symptoms, subjective sleep quality, and objective sleep outcomes in police officers (2023)

Psychological Trauma: Theory, Research, Practice and Policy

Abstract

OBJECTIVE: Sleep disturbances cooccur with posttraumatic stress disorder (PTSD) and are often correlated with PTSD severity. Previous research has shown that sleep problems mediate the relationship between PTSD and negative physical and mental health outcomes but has relied on self-reported sleep quality. We tested the effects of mindfulness training-previously shown to improve sleep quality and reduce PTSD symptoms-on subjective and objective sleep metrics and relationships with reduced PTSD symptoms. METHOD: Following baseline data collection in 114 law enforcement officers, we randomly assigned participants to either an 8-week mindfulness training group or a waitlist control group. We repeated assessments immediately posttraining and at 3-month follow-up. Self-reported PTSD symptoms and subjective sleep quality were measured at each visit with the PTSD checklist and Pittsburgh Sleep Quality Index (PSQI), respectively. Participants also wore a Fitbit Charge 2 continuously over the course of a 4- to 6-day work week following each visit, from which we extracted two distinct objective sleep metrics: total minutes of sleep and sleep efficiency. RESULTS: At baseline, PTSD symptoms were correlated with PSQI scores but not objective Fitbit metrics. Relative to waitlist, mindfulness training led to improved subjective sleep quality and reduced PTSD symptoms. Reduced PTSD symptoms mediated the improvement in subjective sleep quality following mindfulness training. Neither objective sleep metric demonstrated improvements following mindfulness training, nor did these metrics mediate reduced PTSD symptoms. CONCLUSIONS: This study provides evidence linking improved subjective sleep quality, but not objective sleep markers, to reductions in PTSD symptoms following mindfulness training. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Promoting participation in physical activity through Snacktivity: A qualitative mixed methods study (2023)

PloS One

Abstract

BACKGROUND: Public health guidance acknowledges the benefits of physical activity of any duration. We have proposed a whole-day approach to promoting physical activity called Snacktivity™, which encourages frequent 2-5 minute 'activity snacks' of moderate-to-vigorous intensity. METHODS: Using repeated semi-structured interviews and a think aloud protocol, this study aimed to understand participants' experiences of integrating Snacktivity™ into daily life, to provide insights to refine the delivery of Snacktivity™ interventions. Physically inactive adults recruited via primary care and a community health service engaged with an intervention to encourage Snacktivity™ over three weeks, which included using a Fitbit and linked mobile phone app (SnackApp). Participants took part in semi-structured interviews on two occasions during the intervention, with a sub-group participating in a think aloud study. Three study data sets were generated and independently explored using inductive thematic analysis, with findings combined into a single set of themes. RESULTS: Eleven adults participated in the interview study who were interviewed twice (total interviews completed n = 21, 1 participant declined the second interview), of whom six completed the think aloud study (total voice recordings n = 103). Three main themes emerged from the combined data; lived experience of participating in Snacktivity™, motivation for Snacktivity™ and experiences with the Snacktivity™ technology. Participants undertook a variety of activity snacks, utilising their environment, which they believed improved their psychological wellbeing. Participants were enthusiastic about Snacktivity™, with some stating that activity snacks were more accessible than traditional exercise, but perceived they were often prevented from doing so in the presence of others. Participants were mostly enthusiastic about using the Snacktivity™ technology. CONCLUSION: Participants were able to incorporate Snacktivity™ into their lives, particularly at home, and found this approach acceptable. Participants felt they experienced health benefits from Snacktivity™ although barriers to participation were reported. This study offers insights for translating guidance into practice and supporting people to become more physically active.

Using mobile health to encourage physical activity in individuals with intellectual disability: a pilot mixed methods feasibility study (2023)

Frontiers in Rehabilitation Sciences

Abstract

BACKGROUND: Many individuals with intellectual disability (ID) have a sedentary lifestyle. Few interventions aimed at increasing their level of physical activity (PA) have shown lasting effects. AIM: To assess the feasibility and acceptability of a pilot intervention study using innovative mobile health (mHealth) support systems to encourage PA in individuals with ID. METHODS: Nine individuals with ID and a low level of PA, aged 16-36 years, were included in the present convergent triangulation mixed method design. Two mHealth support systems (apps) were developed and tested. PA was measured with a Fitbit smartwatch, accelerometer, the International Physical Activity Questionnaire-Short Form (IPAQ-S), and Goal attainment scaling. Data were collected through online pre-, mid- (4 weeks), and post-intervention (12 weeks) questionnaires and activity trackers. Semi-structured qualitative interviews with participants and/or a family or staff member were held after the 12-week follow-up. Data were analyzed using conventional nonparametric statistics and thematic analyses. RESULTS: The response rate and retention to the trial were 16\% and 100\%, respectively. Data quality was high, except for missing data from Fitbit activity trackers of approximately 30\% from the 4- and 12-week follow-up stages. The feasibility challenges with activity trackers include rashes, size, non-acceptance, and loss of motivation. Participants and family members/staff reported interest in the study theme and were pleased with the data collection method. All but one participant achieved their PA goals. Most participants reported being satisfied with the apps as they were enjoyable or provided a reminder for performing physical and other activities. Social support for PA among family members also increased. However, app support from staff and family members was needed, and apps were not used regularly. Two of nine participants (22\%) had increased their PA measured as steps per day with Fitbit at the 12-week follow-up. CONCLUSIONS: The acceptability and feasibility of using tailored mobile applications in natural settings to increase PA among adults with ID are promising. This study aligns with previous studies in showing the challenges to increasing PA, which requires the inclusion of family members, staff, and stakeholders. The intervention requires modifications before a randomized controlled trial can be conducted.

Wellness in Nursing Education to Promote Resilience and Reduce Burnout: Protocol for a Holistic Multidimensional Wellness Intervention and Longitudinal Research Study Design in Nursing Education (2023)

JMIR research protocols

Abstract

BACKGROUND: The United States faces a nursing shortage driven by a burnout epidemic among nurses and nursing students. Nursing students are an integral population to fuel the nursing workforce at high risk of burnout and increased rates of perceived stress. OBJECTIVE: The aim of this paper is to describe WellNurse, a holistic, interdisciplinary, multidimensional longitudinal research study that examines evidence-based interventions intended to reduce burnout and increase resilience among graduate and undergraduate nursing students. METHODS: Graduate and undergraduate nursing students matriculated at a large public university in the northeastern United States are eligible to enroll in this ongoing, longitudinal cohort study beginning in March 2021. Participants complete a battery of health measurements twice each semester during the fourth week and the week before final examinations. The measures include the Perceived Stress Scale, the Satisfaction with Life Scale, the Oldenburg Burnout Inventory, the Brief Resilience Scale, and the Pittsburgh Sleep Quality Index. Participants are eligible to enroll in a variety of interventions, including mindfulness-based stress reduction, mindful eating, fitness training, and massage therapy. Those who enroll in specific, targeted interventions complete additional measures designed to target the aim of the intervention. All participants receive a free Fitbit device. Additional environmental changes are being implemented to further promote a culture that supports academic well-being, including recruiting a diverse student population through evidence-based holistic admissions, inclusive teaching design, targeted resilience and stress reduction workshops, and cultural shifts within classrooms and curricula. The study design protocol is registered at Open Science Framework (DOI 10.17605/OSF.IO/NCBPE). RESULTS: The project was funded on January 1, 2022. Data collection started in March 2022. A total of 267 participants have been recruited. Results will be published after each semester starting in December 2023. WellNurse evaluation follows the Rapid Cycle Quality Improvement framework to continuously monitor ongoing project processes, activity outcomes, and progress toward reducing burnout and increasing resilience. Rapid Cycle Quality Improvement promotes the ability to alter WellNurse interventions, examine multiple interventions, and test their effectiveness among the nursing education population to identify the most effective interventions. CONCLUSIONS: Academic nursing organizations must address student burnout risk and increase resilience to produce a future workforce that provides high-quality patient care to a diverse population. Findings from WellNurse will support evidence-based implementations for public baccalaureate and master's nursing programs in the United States. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/49020.

Minute level smartphone derived exposure to greenness and consumer wearable derived physical activity in a cohort of US women (2023)

Environmental Research

Abstract

BACKGROUND: Inconsistent results have been found in the literature on associations of greenness, or vegetation quantity, and physical activity. However, few studies have assessed associations between mobility-based greenness and physical activity from mobile health data from smartphone and wearable devices with fine spatial and temporal resolution. METHODS: We assessed mobility-based greenness exposure and wearable accelerometer data from participants in the US-based prospective Nurses' Health Study 3 cohort Mobile Health (mHealth) Substudy (2018-2020). We recruited 500 female participants with instructions to wear devices over four 7-day sampling periods equally spaced throughout the year. After restriction criteria there were 337 participants (mean age 36 years) with n = 639,364 unique observations. Normalized Difference Vegetation Index (NDVI) data were derived from 30 m x 30 m Landsat-8 imagery and spatially joined to GPS points recorded every 10 min. Fitbit proprietary algorithms provided physical activity summarized as mean number of steps per minute, which we averaged during the 10-min period following a GPS-based greenness exposure assessment. We utilized Generalized Additive Mixed Models to examine associations (every 10 min) between greenness and physical activity adjusting for neighborhood and individual socioeconomic status, Census region, season, neighborhood walkability, daily mean temperature and precipitation. We assessed effect modification through stratification and interaction models and conducted sensitivity analyses. RESULTS: Mean 10-min step count averaged 7.0 steps (SD 14.9) and greenness (NDVI) averaged 0.3 (SD 0.2). Contrary to our hypotheses, higher greenness exposure was associated non-linearly with lower mean steps per minute after adjusting for confounders. We observed statistically significant effect modification by Census region and season. DISCUSSION: We utilized objective physical activity data at fine temporal and spatial scales to present novel estimates of the association between mobility-based greenness and step count. We found higher levels of greenness were inversely associated with steps per minute.

Measurement properties of device-based physical activity instruments in ambulatory adults with physical disabilities and/or chronic diseases: a scoping review (2023)

BMC sports science, medicine \& rehabilitation

Abstract

BACKGROUND: People with physical disabilities and/or chronic diseases tend to have an inactive lifestyle. Monitoring physical activity levels is important to provide insight on how much and what types of activities people with physical disabilities and/or chronic diseases engage in. This information can be used as input for interventions to promote a physically active lifestyle. Therefore, valid and reliable physical activity measurement instruments are needed. This scoping review aims 1) to provide a critical mapping of the existing literature and 2) directions for future research on measurement properties of device-based instruments assessing physical activity behavior in ambulant adults with physical disabilities and/or chronic diseases. METHODS: Four databases (MEDLINE, CINAHL, Web of Science, Embase) were systematically searched from 2015 to April 16th 2023 for articles investigating measurement properties of device-based instruments assessing physical activity in ambulatory adults with physical disabilities and/or chronic diseases. For the majority, screening and selection of eligible studies were done in duplicate. Extracted data were publication data, study data, study population, device, studied measurement properties and study outcome. Data were synthesized per device. RESULTS: One hundred three of 21566 Studies were included. 55 Consumer-grade and 23 research-grade devices were studied on measurement properties, using 14 different physical activity outcomes, in 23 different physical disabilities and/or chronic diseases. ActiGraph (n = 28) and Fitbit (n = 39) devices were most frequently studied. Steps (n = 68) was the most common used physical activity outcome. 97 studies determined validity, 11 studies reliability and 6 studies responsiveness. CONCLUSION: This scoping review shows a large variability in research on measurement properties of device-based instruments in ambulatory adults with physical disabilities and/or chronic diseases. The variability highlights a need for standardization of and consensus on research in this field. The review provides directions for future research.

GamerFit-ASD beta test: adapting an evidence-based exergaming and telehealth coaching intervention for autistic youth (2023)

Frontiers in Pediatrics

Abstract

BACKGROUND: Health disparities faced by autistic youth are exacerbated by inadequate physical activity (PA) and sleep, whereas healthy PA and sleep may improve mood and function. Adaptive Game Squad (AGS) is an evidence-based telehealth coaching and exergaming intervention to improve PA and sleep for adolescents with diverse neurodevelopmental and psychiatric conditions. This study aimed to adapt AGS for autistic youth ages 10-15 years; beta-test the modified intervention for feasibility, accessibility, and engagement; and further refine the intervention for a larger planned demonstration pilot. METHODS: Interdisciplinary experts adapted AGS to create GamerFit-ASD, a 12-week intervention that included a progressive exergame schedule, Fitbit step-tracking, weekly health coaching, and health tip/exercise videos. For beta testing, the intervention was shortened to a 4-week trial with 5 parent/child dyads. Children completed exit surveys and parents and children were interviewed about intervention feasibility, accessibility, and engagement. Exit survey data were summarized with descriptive statistics. Qualitative data were analyzed using a modified grounded-theory approach. RESULTS: All participants (n = 5; ages 10-14 years) attended all 4 planned coaching sessions and completed an average of 9 of 12 planned exergame challenges for a weekly average of 50 min. All participants reported enjoying coaching sessions, 4 of 5 reported enjoying exergames, and 3 of 5 reported enjoying on-demand exercise videos. In interviews, children generally reported finding participation feasible, exergaming challenges active and fun, and coaches friendly and helpful. Parents reported high feasibility of supporting their children's involvement and valued child goal-setting and intervention flexibility; however, some found telehealth sessions overly scripted. Several adaptations to coaching scripts, coach training, and parent materials were made for the larger demonstration pilot, including changes to reduce scriptedness of coaching sessions, to provide parents with more information specific to autism, and to make video content more appropriate to children's needs/preferences. DISCUSSION: A telehealth coaching and exergaming intervention appears feasible, accessible, and engaging for autistic youth aged 10-15. Future studies with larger, more diverse samples, longer study durations and/or follow-up periods, and more rigorous study designs are needed to advance understanding of the appropriateness and effectiveness of this type of intervention for this population.

Advancing Understanding of Just-in-Time States for Supporting Physical Activity (Project JustWalk JITAI): Protocol for a System ID Study of Just-in-Time Adaptive Interventions (2023)

JMIR research protocols

Abstract

BACKGROUND: Just-in-time adaptive interventions (JITAIs) are designed to provide support when individuals are receptive and can respond beneficially to the prompt. The notion of a just-in-time (JIT) state is critical for JITAIs. To date, JIT states have been formulated either in a largely data-driven way or based on theory alone. There is a need for an approach that enables rigorous theory testing and optimization of the JIT state concept. OBJECTIVE: The purpose of this system ID experiment was to investigate JIT states empirically and enable the empirical optimization of a JITAI intended to increase physical activity (steps/d). METHODS: We recruited physically inactive English-speaking adults aged ≥25 years who owned smartphones. Participants wore a Fitbit Versa 3 and used the study app for 270 days. The JustWalk JITAI project uses system ID methods to study JIT states. Specifically, provision of support systematically varied across different theoretically plausible operationalizations of JIT states to enable a more rigorous and systematic study of the concept. We experimentally varied 2 intervention components: notifications delivered up to 4 times per day designed to increase a person's steps within the next 3 hours and suggested daily step goals. Notifications to walk were experimentally provided across varied operationalizations of JIT states accounting for need (ie, whether daily step goals were previously met or not), opportunity (ie, whether the next 3 h were a time window during which a person had previously walked), and receptivity (ie, a person previously walked after receiving notifications). Suggested daily step goals varied systematically within a range related to a person's baseline level of steps per day (eg, 4000) until they met clinically meaningful targets (eg, averaging 8000 steps/d as the lower threshold across a cycle). A series of system ID estimation approaches will be used to analyze the data and obtain control-oriented dynamical models to study JIT states. The estimated models from all approaches will be contrasted, with the ultimate goal of guiding rigorous, replicable, empirical formulation and study of JIT states to inform a future JITAI. RESULTS: As is common in system ID, we conducted a series of simulation studies to formulate the experiment. The results of our simulation studies illustrated the plausibility of this approach for generating informative and unique data for studying JIT states. The study began enrolling participants in June 2022, with a final enrollment of 48 participants. Data collection concluded in April 2023. Upon completion of the analyses, the results of this study are expected to be submitted for publication in the fourth quarter of 2023. CONCLUSIONS: This study will be the first empirical investigation of JIT states that uses system ID methods to inform the optimization of a scalable JITAI for physical activity. TRIAL REGISTRATION: ClinicalTrials.gov NCT05273437; https://clinicaltrials.gov/ct2/show/NCT05273437. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/52161.

Moderate and increased physical activity is not detrimental to live birth rates among women with unexplained infertility and obesity (2023)

F\&S Reports

Abstract

OBJECTIVE: To determine if moderate physical activity is associated with live birth rates in women with unexplained infertility and obesity. DESIGN: Secondary analysis of the Improving Reproductive Fitness through Pretreatment with Lifestyle Modification in Obese Women with Unexplained Infertility trial. SETTING: US fertility centers, 2015-2019. PATIENTS: A total of 379 women participated in Improving Reproductive Fitness through Pretreatment with Lifestyle Modification in Obese Women with Unexplained Infertility trial, a lifestyle modification program with increased physical activity (phase I, 16 weeks) and up to three cycles of clomiphene citrate treatment and intrauterine insemination (phase II). INTERVENTIONS: Participants were instructed to add 500 steps/day weekly until a maximum of 10,000 steps/day was reached and maintained. Participants were stratified as active (top third, N = 125) and less active (lower third, N = 125) on the basis of the average number of steps per day recorded using a FitBit activity tracker. MAIN OUTCOME MEASURES: Live birth rate. RESULTS: Active participants were more physically active at the time of enrollment than less active participants (average baseline steps per day, 8,708 [7,079-10,000] vs. 4,695 [3,844-5,811]; P ≤ 0.001) and were more likely to reach 10,000 steps/day than less active participants (average steps per day, 10,526 [9,481-11,810] vs. 6,442 [4,644-7,747]; P ≤ 0.001), although both groups increased their average steps per day by a similar amount (1,818 vs.1,747; P = 0.57). There was no difference in live birth rates (24/125 [19.2\%] vs. 25/125 [20\%]; P = 0.87) between active and less active participants nor were there differences in clinical pregnancy rates (P = 0.45) or miscarriage rates (P = 0.49) between the two groups. CONCLUSIONS: Active participants were more likely to achieve the physical activity goal, although this was not associated with benefit or harm with respect to live birth. CLINICAL TRIAL REGISTRATION NUMBER: ClinicalTrials.gov (NCT02432209), first posted: May 4, 2015.

Addressing the Chronic Pain-Early Cognitive Decline Comorbidity Among Older Adults: Protocol for the Active Brains Remote Efficacy Trial (2023)

JMIR research protocols

Abstract

BACKGROUND: Chronic pain and early cognitive decline, which are costly to treat and highly prevalent among older adults, commonly co-occur, exacerbate one another over time, and can accelerate the development and progression of Alzheimer disease and related dementias. We developed the first mind-body activity program (Active Brains [AB]) tailored to the needs of older adults with chronic pain and early cognitive decline. Results from our previous study strongly supported the feasibility of conducting AB remotely and provided evidence for improvements in outcomes. OBJECTIVE: We are conducting a single-blinded, National Institutes of Health stage-2, randomized clinical trial to establish the efficacy of AB versus a time-matched and dose-matched education control (Health Enhancement Program [HEP]) in improving self-reported and objective outcomes of physical, cognitive, and emotional functions in 260 participants. The methodology described in this paper was informed by the lessons learned from the first year of the trial. METHODS: Participants are identified and recruited through multidisciplinary clinician-referred individuals (eg, pain psychologists and geriatricians), the Rally Research platform, social media, and community partnerships. Interested participants complete eligibility screening and electronic informed consent. Baseline assessments include self-report, performance-based measures (eg, 6-min walk test) and objective measures (eg, Repeatable Battery for the Assessment of Neuropsychological Status). Participants are mailed a wrist-worn ActiGraph device (ActiGraph LLC) to passively monitor objective function (eg, steps) during the week between the baseline assessment and the beginning of the programs, which they continue to wear throughout the programs. After baseline assessments, participants are randomized to either AB or HEP and complete 8 weekly, remote, group sessions with a Massachusetts General Hospital psychologist. The AB group receives a Fitbit (Fitbit Inc) to help reinforce increased activity. Assessments are repeated after the intervention and at the 6-month follow-up. Coprimary outcomes include multimodal physical function (self-report, performance based, and objective). Secondary outcomes are cognitive function (self-report and objective), emotional function, and pain. RESULTS: We began recruitment in July 2022 and recruited 37 participants across 4 cohorts. Of them, all (n=37, 100\%) have completed the baseline assessment, 26 (70\%) have completed the posttest assessment, and 9 (24\%) are actively enrolled in the intervention (total dropout: n=2, 5\%). In the three cohorts (26/37, 70\%) that have completed the AB or HEP, 26 (100\%) participants completed all 8 group sessions (including minimal makeups), and watch adherence (1937/2072, 93.48\%, average across ActiGraph and Fitbit devices) has been excellent. The fourth cohort is ongoing (9/37, 24\%), and we plan to complete enrollment by March 2026. CONCLUSIONS: We aim to establish the efficacy of the AB program over a time-matched and dose-matched control in a live video-based trial and test the mechanisms through theoretically driven mediators and moderators. Findings will inform the development of a future multisite effectiveness-implementation trial. TRIAL REGISTRATION: ClinicalTrials.gov NCT05373745; https://classic.clinicaltrials.gov/ct2/show/NCT05373745. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/47319.

Accuracy in detecting atrial fibrillation in single-lead ECGs: an online survey comparing the influence of clinical expertise and smart devices (2023)

Swiss Medical Weekly

Abstract

BACKGROUND: Manual interpretation of single-lead ECGs (SL-ECGs) is often required to confirm a diagnosis of atrial fibrillation. However accuracy in detecting atrial fibrillation via SL-ECGs may vary according to clinical expertise and choice of smart device. AIMS: To compare the accuracy of cardiologists, internal medicine residents and medical students in detecting atrial fibrillation via SL-ECGs from five different smart devices (Apple Watch, Fitbit Sense, KardiaMobile, Samsung Galaxy Watch, Withings ScanWatch). Participants were also asked to assess the quality and readability of SL-ECGs. METHODS: In this prospective study (BaselWearableStudy, NCT04809922), electronic invitations to participate in an online survey were sent to physicians at major Swiss hospitals and to medical students at Swiss universities. Participants were asked to classify up to 50 SL-ECGs (from ten patients and five devices) into three categories: sinus rhythm, atrial fibrillation or inconclusive. This classification was compared to the diagnosis via a near-simultaneous 12-lead ECG recording interpreted by two independent cardiologists. In addition, participants were asked their preference of each manufacturer's SL-ECG. RESULTS: Overall, 450 participants interpreted 10,865 SL-ECGs. Sensitivity and specificity for the detection of atrial fibrillation via SL-ECG were 72\% and 92\% for cardiologists, 68\% and 86\% for internal medicine residents, 54\% and 65\% for medical students in year 4-6 and 44\% and 58\% for medical students in year 1-3; p {\textless}0.001. Participants who stated prior experience in interpreting SL-ECGs demonstrated a sensitivity and specificity of 63\% and 81\% compared to a sensitivity and specificity of 54\% and 67\% for participants with no prior experience in interpreting SL-ECGs (p {\textless}0.001). Of all participants, 107 interpreted all 50 SL-ECGs. Diagnostic accuracy for the first five interpreted SL-ECGs was 60\% (IQR 40-80\%) and diagnostic accuracy for the last five interpreted SL-ECGs was 80\% (IQR 60-90\%); p {\textless}0.001. No significant difference in the accuracy of atrial fibrillation detection was seen between the five smart devices; p = 0.33. SL-ECGs from the Apple Watch were considered as having the best quality and readability by 203 (45\%) and 226 (50\%) participants, respectively. CONCLUSION: SL-ECGs can be challenging to interpret. Accuracy in correctly identifying atrial fibrillation depends on clinical expertise, while the choice of smart device seems to have no impact.

Minimally invasive versus open pancreatoduodenectomy for pancreatic and peri-ampullary neoplasm (DIPLOMA-2): study protocol for an international multicenter patient-blinded randomized controlled trial (2023)

Trials

Abstract

Background: Minimally invasive pancreatoduodenectomy (MIPD) aims to reduce the negative impact of surgery as compared to open pancreatoduodenectomy (OPD) and is increasingly becoming part of clinical practice for selected patients worldwide. However, the safety of MIPD remains a topic of debate and the potential shorter time to functional recovery needs to be confirmed. To guide safe implementation of MIPD, large-scale international randomized trials comparing MIPD and OPD in experienced high-volume centers are needed. We hypothesize that MIPD is non-inferior in terms of overall complications, but superior regarding time to functional recovery, as compared to OPD. Methods/design: The DIPLOMA-2 trial is an international randomized controlled, patient-blinded, non-inferiority trial performed in 14 high-volume pancreatic centers in Europe with a minimum annual volume of 30 MIPD and 30 OPD. A total of 288 patients with an indication for elective pancreatoduodenectomy for pre-malignant and malignant disease, eligible for both open and minimally invasive approach, are randomly allocated for MIPD or OPD in a 2:1 ratio. Centers perform either laparoscopic or robot-assisted MIPD based on their surgical expertise. The primary outcome is the Comprehensive Complication Index (CCI®), measuring all complications graded according to the Clavien-Dindo classification up to 90 days after surgery. The sample size is calculated with the following assumptions: 2.5% one-sided significance level (α), 80% power (1-β), expected difference of the mean CCI® score of 0 points between MIPD and OPD, and a non-inferiority margin of 7.5 points. The main secondary outcome is time to functional recovery, which will be analyzed for superiority. Other secondary outcomes include post-operative 90-day Fitbit™ measured activity, operative outcomes (e.g., blood loss, operative time, conversion to open surgery, surgeon-reported outcomes), oncological findings in case of malignancy (e.g., R0-resection rate, time to adjuvant treatment, survival), postoperative outcomes (e.g., clinically relevant complications), healthcare resource utilization (length of stay, readmissions, intensive care stay), quality of life, and costs. Postoperative follow-up is up to 36 months. Discussion: The DIPLOMA-2 trial aims to establish the safety of MIPD as the new standard of care for this selected patient population undergoing pancreatoduodenectomy in high-volume centers, ultimately aiming for superior patient recovery. Trial registration: ISRCTN27483786. Registered on August 2, 2023.

Protocol for randomized personalized trial for stress management compared to standard of care (2023)

Frontiers in Psychology

Abstract

Stress is a significant public health burden in the United States, with most Americans reporting unhealthy levels of stress. Stress management techniques include various evidence-based treatments shown to be effective but with heterogeneous treatment responses, indicating a lack of uniform benefits for all individuals. Designed to assess a participant's response to a specific intervention, personalized (N-of-1) trials provide guidance for which treatment (s) work (s) best for the individual. Prior studies examining the effects of mindfulness meditation, yoga, and walking for stress reduction found all three interventions to be associated with significant reductions in self-reported measures of stress. Delivering these treatments using a personalized trial approach has the potential to assist clinicians in identifying the best stress management techniques for individuals with persistently high stress while fostering treatment decisions that consider their personal condition/barriers. This trial will evaluate a personalized approach compared to standard of care for three interventions (guided mindfulness meditation; guided yoga; and guided brisk walking) to manage perceived stress. Participants will respond to daily surveys and wear a Fitbit device for 18 weeks. After a 2-week baseline period, participants in the personalized trial groups will receive 12 weeks of interventions in randomized order, while participants in the standard-of-care group will have access to all interventions for self-directed stress management. After intervention, all participants will undergo 2 weeks of observation, followed by two additional weeks of the stress management intervention of their choosing while continuing outcome measurement. At study completion, all participants will be sent a satisfaction survey. The primary analysis will compare perceived stress levels between the personalized and standard of care arms. The results of this trial will provide further support for the use of personalized designs for managing stress. Clinical Trial Registration: clinicaltrials.gov, NCT05408832. Protocol version: 9/14/2022, 21-0968-MRB. Keywords: N-of-1; mindfulness; personalized trials; physical activity; stress management techniques; yoga.

Assessment of glycemic variability and lifestyle behaviors in healthy nondiabetic individuals according to the categories of body mass index (2023)

PloS One

Abstract

Background: There are limited data about the association between body mass index (BMI), glycemic variability (GV), and life-related factors in healthy nondiabetic adults. Methods: This cross-sectional study was carried out within our ethics committee-approved study called "Exploring the impact of nutrition advice on blood sugar and psychological status using continuous glucose monitoring (CGM) and wearable devices". Prediabetes was defined by the HbA1c level of 5.7-6.4% and /or fasting glucose level of 100-125 mg/dL. Glucose levels and daily steps were measured for 40 participants using Free Style Libre and Fitbit Inspire 2 under normal conditions for 14 days. Dietary intakes and eating behaviors were assessed using a brief-type self-administered dietary history questionnaire and a modified questionnaire from the Obesity Guidelines. Results: All indices of GV were higher in the prediabetes group than in the healthy group, but a significant difference was observed only in mean amplitude of glycemic excursions (MAGE). In the multivariate analysis, only the presence of prediabetes showed a significant association with the risk of higher than median MAGE (Odds, 6.786; 95% CI, 1.596-28.858; P = 0.010). Additionally, the underweight (BMI < 18.5) group had significantly higher value in standard deviation (23.7 ± 3.5 vs 19.8 ± 3.7 mg/dL, P = 0.038) and coefficient variability (22.6 ± 4.6 vs 18.4 ± 3.2%, P = 0.015), compared to the normal group. This GV can be partially attributed to irregularity of eating habits. On the contrary, the overweight (BMI ≥ 25) group had the longest time above the 140 or 180 mg/dL range, which may be due to eating style and taking fewer steps (6394 ± 2337 vs 9749 ± 2408 steps, P = 0.013). Conclusions: Concurrent CGM with diet and activity monitoring could reduce postprandial hyperglycemia through assessment of diet and daily activity, especially in non- normal weight individuals.

Association Between Health Information‒Seeking Behavior on YouTube and Physical Activity Among U.S. Adults: Results From Health Information Trends Survey 2020 (2022)

AJPM focus

Abstract

Introduction: Although physical activity has many health benefits, 45.8% of U.S. adults did not meet the WHO recommendation in 2018. Delivering health-related content, particularly physical activity, through YouTube may help to overcome some barriers, such as lack of access to resources. This study aimed to examine the association between watching health-related information on YouTube and increased levels of physical activity among U.S. adults. Methods: Using the U.S. national cross-sectional survey-Health Information National Trends Survey 2020 (n=3,865), we conducted a multivariable logistic regression on obtaining 150 minutes of at least moderate-intensity physical activity per week (WHO guidelines) by watching health-related information on YouTube, controlling for demographics (age, sex, race/ethnicity), socioeconomics (income, education level, insurance coverage, employment), current use of cigarettes and e-cigarettes, use of electronic wearable devices (e.g., Fitbit), self-reported health status, BMI, and the presence of chronic conditions (e.g., diabetes, heart disease, cancer) and depression or anxiety disorders. Results: Overall, 40.8% (weighted) of respondents reported using YouTube to watch health-related videos, and 39.2% reported meeting the WHO-recommended physical activity level. After controlling for covariates, adults who reported watching health-related videos on YouTube in the past 12 months (versus not watching) were 1.33 times more likely to do 150 minutes or more of moderate physical activity a week (AOR=1.33; 95% CI=1.01, 1.76). Conclusions: This study suggests that adults who view health-related YouTube videos may be more likely to meet the WHO-recommended level of physical activity. This finding could inform future behavioral interventions using online video platforms to promote physical activity. Keywords: HINTS; Physical activity; health promotion; social media.

Testing the Effects of App-Based Motivational Messages on Physical Activity and Resting Heart Rate Through Smartphone App Compliance in Patients With Vulnerable Coronary Artery Plaques: Protocol for a Microrandomized Trial (2023)

JMIR Research Protocols

Abstract

Background: Achieving the weekly physical activity recommendations of at least 150-300 minutes of moderate-intensity or 75-150 minutes of vigorous-intensity aerobic exercise is important for reducing cardiometabolic risk, but evidence shows that most people struggle to meet these goals, particularly in the mid to long term. Objective: The Messages Improving Resting Heart Health (MIRTH) study aims to determine if (1) sending daily motivational messages through a research app is effective in improving motivation and in promoting adherence to physical activity recommendations in men and women with coronary heart disease randomized to a 12-month intensive lifestyle intervention, and (2) the time of the day when the message is delivered impacts compliance with exercise training. Methods: We will conduct a single-center, microrandomized trial. Participants will be randomized daily to either receive or not receive motivational messages over two 90-day periods at the beginning (phase 1: months 4-6) and at the end (phase 2: months 10-12) of the Lifestyle Vulnerable Plaque Study. Wrist-worn devices (Fitbit Inspire 2) and Bluetooth pairing with smartphones will be used to passively collect data for proximal (ie, physical activity duration, steps walked, and heart rate within 180 minutes of receiving messages) and distal (ie, change values for resting heart rate and total steps walked within and across both phases 1 and 2 of the trial) outcomes. Participants will be recruited from a large academic cardiology office practice (Central Sydney Cardiology) and the Royal Prince Alfred Hospital Departments of Cardiology and Radiology. All clinical investigations will be undertaken at the Charles Perkins Centre Royal Prince Alfred clinic. Individuals aged 18-80 years (n=58) with stable coronary heart disease who have low attenuation plaques based on a coronary computed tomography angiography within the past 3 months and have been randomized to an intensive lifestyle intervention program will be included in MIRTH. Results: The Lifestyle Vulnerable Plaque Study was funded in 2020 and started enrolling participants in February 2022. Recruitment for MIRTH commenced in November 2022. As of September 2023, 2 participants were enrolled in the MIRTH study and provided baseline data. Conclusions: This MIRTH microrandomized trial will represent the single most detailed and integrated analysis of the effects of a comprehensive lifestyle intervention delivered through a customized mobile health app on smart devices on time-based motivational messaging for patients with coronary heart disease. This study will also help inform future studies optimizing for just-in-time adaptive interventions. Trial registration: Australian New Zealand Clinical Trials Registry ACTRN12622000731796; https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=382861. International registered report identifier (irrid): DERR1-10.2196/46082. Keywords: adherence; coronary artery disease; coronary heart disease; digital health; health behavior; heart rate; messages; mhealth; microrandomized trial; mobile app; physical activity; smartphone; telehealth; user motivation.

Latent Profiles of Sleep Patterns in Early Adolescence: Associations With Behavioral Health Risk (2023)

The Journal of Adolescent Health: Official Publication of the Society for Adolescent Medicine

Abstract

Purpose: The present study characterized sleep profiles in a national longitudinal sample of early adolescents and examined whether profiles predicted later behavioral problems. Methods: Three waves of data (2016-2021) were obtained from the Adolescent Behavior and Cognitive Development study, including 3,326 participants with both weekday and weekend sleep data measured by Fitbit wearables (age range 10.58-13.67 years; 49.3% female). Latent profile analysis was utilized to identify sleep profiles using multiple sleep indicators (duration, latency, efficiency, wake minutes, wake counts, and midpoint). We then explored whether demographic predictors predicted profile membership and tested the latent sleep profiles' predictive utility of internalizing and externalizing symptoms. Results: Four profiles were identified: average sleep (40.39%), high duration & high wakefulness (28.58%), high efficiency, low duration &low wakefulness (16.86%), and low duration & low efficiency (14.17%). Participants with older age, males, higher body mass index, and advanced pubertal status were more likely to be classified in the low duration & low efficiency profile than the average group. Participants with lower income, minority identification, older age, and higher body mass index were more likely to be classified in the high efficiency, low duration &low wakefulness than the average group. Participants with lower parental education and males were more likely to be in the high sleep duration & high wakefulness than the average group. The low duration & low efficiency group had the highest attention problems, social problems, and rule-breaking behaviors. Discussion: Our findings highlight unique sleep patterns in early adolescence and their prospective links with internalizing and externalizing problems. Keywords: Externalizing problems; Internalizing problems; Latent profile; Risk; Sleep.

Physical and Mental Health in Adolescence: Novel Insights from a transdiagnostic examination of FitBit data in the ABCD Study (2023)

Research Square

Abstract

Adolescence is among the most vulnerable period for the emergence of serious mental illnesses. Addressing this vulnerability has generated interest in identifying markers of risk for symptoms and opportunities for early intervention. Physical fitness has been linked to psychopathology and may be a useful risk marker and target for early intervention. New wearable technology has made assessing fitness behavior more practical while avoiding recall and self-report bias. Still, questions remain regarding the clinical utility of physical fitness metrics for mental health, both transdiagnostically and along specific symptom dimensions. The current study includes 5007 adolescents (ages 10 to 13) who participated in the Adolescent Brain Cognitive Development (ABCD) study and additional sub-study that collected fitness data from wearable technology and clinical symptom measures. Physical fitness metrics included resting heart rate (RHR- an index of cardiovascular health), time spent sedentary (associated with increased inflammation and cardiovascular disease), and time spent in moderate physical activity (associated with increased neurogenesis, neuroplasticity, and healthy neurodevelopment). Self-report clinical symptoms included measures of internalizing symptoms, externalizing symptoms, and psychosis-like experiences - PLE). Increased RHR- lower cardiovascular fitness- related only to greater internalizing symptoms (t = 3.63). More sedentary behavior related to elevated PLE severity (t = 5.49). More moderate activity related to lower PLE (t=-2.69) and internalizing (t=-6.29) symptom severity. Wearable technology fitness metrics linked physical health to specific mental health dimensions, which emphasizes the utility of detailed digital health data as a marker for risk and the need for precision in targeting physical health behaviors to benefit symptoms of psychopathology.

A Digital Coach (E-Supporter 1.0) to Support Physical Activity and a Healthy Diet in People With Type 2 Diabetes: Acceptability and Limited Efficacy Testing (2023)

JMIR formative research

Abstract

Background: A healthy lifestyle, including regular physical activity and a healthy diet, is increasingly part of type 2 diabetes (T2D) management. As many people with T2D have difficulty living and maintaining a healthy lifestyle, there is a need for effective interventions. eHealth interventions that incorporate behavior change theories and tailoring are considered effective tools for supporting a healthy lifestyle. The E-Supporter 1.0 digital coach contains eHealth content for app-based eHealth interventions and offers tailored coaching regarding physical activity and a healthy diet for people with T2D. Objective: This study aimed to assess the acceptability of E-Supporter 1.0 and explore its limited efficacy on physical activity, dietary behavior, the phase of behavior change, and self-efficacy levels. Methods: Over a span of 9 weeks, 20 individuals with T2D received daily motivational messages and weekly feedback derived from behavioral change theories and determinants through E-Supporter 1.0. The acceptability of the intervention was assessed using telephone-conducted, semistructured interviews. The interview transcripts were coded using inductive thematic analysis. The limited efficacy of E-Supporter 1.0 was explored using the Fitbit Charge 2 to monitor step count to assess physical activity and questionnaires to assess dietary behavior (using the Dutch Healthy Diet index), phase of behavior change (using the single-question Self-Assessment Scale Stages of Change), and self-efficacy levels (using the Exercise Self-Efficacy Scale). Results: In total, 5 main themes emerged from the interviews: perceptions regarding remote coaching, perceptions regarding the content, intervention intensity and duration, perceived effectiveness, and overall appreciation. The participants were predominantly positive about E-Supporter 1.0. Overall, they experienced E-Supporter 1.0 as a useful and easy-to-use intervention to support a better lifestyle. Participants expressed a preference for combining E-Supporter with face-to-face guidance from a health care professional. Many participants found the intensity and duration of the intervention to be acceptable, despite the coaching period appearing relatively short to facilitate long-term behavior maintenance. As expected, the degree of tailoring concerning the individual and external factors that influence a healthy lifestyle was perceived as limited. The limited efficacy testing showed a significant improvement in the daily step count (z=-2.040; P=.04) and self-efficacy levels (z=-1.997; P=.046) between baseline and postintervention. Diet was improved through better adherence to Dutch dietary guidelines. No significant improvement was found in the phase of behavior change (P=.17), as most participants were already in the maintenance phase at baseline. Conclusions: On the basis of this explorative feasibility study, we expect E-Supporter 1.0 to be an acceptable and potentially useful intervention to promote physical activity and a healthy diet in people with T2D. Additional work needs to be done to further tailor the E-Supporter content and evaluate its effects more extensively on lifestyle behaviors. Keywords: acceptability; eHealth; formative evaluation; lifestyle change; limited efficacy; mobile phone; nutrition; physical activity; type 2 diabetes.

Efficacy of a Commercial Physical Activity Monitor in Longitudinal Tracking of Patients With Pulmonary Hypertension: A Pilot Study (2023)

Texas Heart Institute Journal

Abstract

Background: Patients with pulmonary arterial hypertension have quality-of-life limitations, decreased exercise capacity, and poor prognosis if the condition is left untreated. Standard exercise testing is routinely performed to evaluate patients with pulmonary arterial hypertension but may be limited in its ability to monitor activity levels in daily living. Objective: To evaluate the validity of the commercial Fitbit Charge HR as a tool to assess real-time exercise capacity as compared with standard exercise testing. Methods: Ambulatory pediatric and adult patients were enrolled and given a Fitbit with instructions to continuously wear the device during waking hours. Patients underwent a 6-minute walk test, cardiopulmonary exercise test, and a 36-Item Short Form Health Survey on the day of enrollment and follow-up. Twenty-seven ambulatory patients with pulmonary arterial hypertension were enrolled, and 21 had sufficient data for analyses (median age, 25 years [range, 13-59 years]; 14 female participants). Results: Daily steps measured by the Fitbit had a positive correlation with 6-minute walk distance (r = 0.72, P = .03) and an inverse trend with World Health Organization functional class. On the 36-Item Short Form Health Survey, 77% of patients reported improvement in vitality (P = .055). At follow-up, there was a strong correlation between number of steps recorded by Fitbit and role limitations because of physical problems (r = 0.88, P = .02) and weaker correlations with other quality-of-life markers. Conclusion: The findings of this pilot study suggest activity monitors may have potential as a simple and novel method of assessing longitudinal exercise capacity and activity levels in patients with pulmonary hypertension. Further study in larger cohorts of patients is warranted to determine which accelerometer measures correlate best with outcomes.

Accuracy of 11 Wearable, Nearable, and Airable Consumer Sleep Trackers: Prospective Multicenter Validation Study (2023)

JMIR mHealth and uHealth

Abstract

Background: Consumer sleep trackers (CSTs) have gained significant popularity because they enable individuals to conveniently monitor and analyze their sleep. However, limited studies have comprehensively validated the performance of widely used CSTs. Our study therefore investigated popular CSTs based on various biosignals and algorithms by assessing the agreement with polysomnography. Objective: This study aimed to validate the accuracy of various types of CSTs through a comparison with in-lab polysomnography. Additionally, by including widely used CSTs and conducting a multicenter study with a large sample size, this study seeks to provide comprehensive insights into the performance and applicability of these CSTs for sleep monitoring in a hospital environment. Methods: The study analyzed 11 commercially available CSTs, including 5 wearables (Google Pixel Watch, Galaxy Watch 5, Fitbit Sense 2, Apple Watch 8, and Oura Ring 3), 3 nearables (Withings Sleep Tracking Mat, Google Nest Hub 2, and Amazon Halo Rise), and 3 airables (SleepRoutine, SleepScore, and Pillow). The 11 CSTs were divided into 2 groups, ensuring maximum inclusion while avoiding interference between the CSTs within each group. Each group (comprising 8 CSTs) was also compared via polysomnography. Results: The study enrolled 75 participants from a tertiary hospital and a primary sleep-specialized clinic in Korea. Across the 2 centers, we collected a total of 3890 hours of sleep sessions based on 11 CSTs, along with 543 hours of polysomnography recordings. Each CST sleep recording covered an average of 353 hours. We analyzed a total of 349,114 epochs from the 11 CSTs compared with polysomnography, where epoch-by-epoch agreement in sleep stage classification showed substantial performance variation. More specifically, the highest macro F1 score was 0.69, while the lowest macro F1 score was 0.26. Various sleep trackers exhibited diverse performances across sleep stages, with SleepRoutine excelling in the wake and rapid eye movement stages, and wearables like Google Pixel Watch and Fitbit Sense 2 showing superiority in the deep stage. There was a distinct trend in sleep measure estimation according to the type of device. Wearables showed high proportional bias in sleep efficiency, while nearables exhibited high proportional bias in sleep latency. Subgroup analyses of sleep trackers revealed variations in macro F1 scores based on factors, such as BMI, sleep efficiency, and apnea-hypopnea index, while the differences between male and female subgroups were minimal. Conclusions: Our study showed that among the 11 CSTs examined, specific CSTs showed substantial agreement with polysomnography, indicating their potential application in sleep monitoring, while other CSTs were partially consistent with polysomnography. This study offers insights into the strengths of CSTs within the 3 different classes for individuals interested in wellness who wish to understand and proactively manage their own sleep. Keywords: Fitbit Sense 2, Amazon Halo Rise, SleepRoutine; airables; artificial intelligence; comparative study; consumer sleep trackers; deep learning; multicenter study; nearables; polysomnography; sleep monitoring; sleep stage; wearables.

Harnessing technology and gamification to increase adult physical activity: a cluster randomized controlled trial of the Columbia Moves pilot (2023)

The International Journal of Behavioral Nutrition and Physical Activity

Abstract

Background: The use of health technologies and gamification to promote physical activity has increasingly been examined, representing an opportunistic method for harnessing social support inherent within existing social ties. However, these prior studies have yielded mixed findings and lacked long-term follow-up periods. Thus, a pilot cluster randomized controlled trial was conducted to gauge the feasibility and preliminary efficacy of a digital gamification-based physical activity promotion approach among teams of insufficiently active adults with existing social ties. Methods: Teams (N = 24; 116 total participants) were randomized to either a 12-week intervention (Fitbit, step goals, app, feedback; TECH) or the same program plus gamification (TECH + Gamification). Mixed effects models were used to compare group differences in treatment adherence, and changes in social support, steps, and moderate-to-vigorous physical activity at 12 weeks and 52 weeks from baseline, adjusted for sociodemographic characteristics and team size. Results: TECH had a lower mean number of days of Fitbit self-monitoring versus TECH + Gamification during the intervention (adjusted difference: -.30; 95% CI, -.54 to -.07; P = .01). Post-intervention, TECH had 47% lower odds of self-monitoring 7 days per week versus TECH + Gamification (.53; 95% CI, .31 to .89; P = .02). No differences were observed between TECH + Gamification and TECH in increases in social support (0.04; 95% CI, -.21 to .29; P = .76), ActiGraph-measured daily steps (-425; 95% CI, -1065 to 215; P = .19), or moderate-to-vigorous physical activity minutes (-3.36; 95% CI, -8.62 to 1.91; P = .21) from baseline to 12 weeks or in the regression of these improvements by 1 year (Ps > .05). Although not significant in the adjusted models (Ps > .05), clinically meaningful differences in Fitbit-measured daily steps (TECH, 7041 ± 2520; TECH + Gamification, 7988 ± 2707) and active minutes (TECH, 29.90 ± 29.76; TECH + Gamification, 36.38 ± 29.83) were found during the intervention. Conclusions: A gamified physical activity intervention targeting teams of adults with existing social ties was feasible and facilitated favorable, clinically meaningful additive physical activity effects while in place but did not drive enhanced, long-term physical activity participation. Future investigations should explore optimal team dynamics and more direct ways of leveraging social support (training teams; gamifying social support).

Ambulation for Latency during Expectant Management of PPROM: A Randomized Controlled Trial (AMBLE) (2023)

American journal of obstetrics \& gynecology MFM

Abstract

Background: Individuals hospitalized with preterm prelabor rupture of the membranes (PPROM) are often advised to limit their activity or adhere to bedrest. Some evidence suggests that greater activity is associated with longer latency and improved outcomes, but no high-quality evidence from a randomized-controlled trial exists. Objective: To evaluate whether encouragement to ambulate at least 2,000 steps daily impacts latency among individuals with PPROM compared with usual care. Study design: Multisite unblinded, two-arm randomized trial of individuals at 23 0/7 to 35 0/7 weeks gestation undergoing inpatient expectant management of PPROM with planned delivery at least 7 days away. Each participant wore a Fitbit InspireTM that tracked steps. The intervention arm was encouraged (verbal and Fitbit-based reminders) to reach a goal of 2,000 steps per day. The usual care arm was allowed ad lib activity with no step goal or reminders. The primary outcome was latency (days) from randomization to delivery. Secondary analyses included composite neonatal and maternal clinical outcomes and maternal mental health survey results. Statistical analyses were intent-to-treat under a Bayesian framework using neutral priors (a priori assumed 50:50 likelihood of longer latency in either arm). 100 participants were required to have 80% power to demonstrate a 4-day difference in latency with 75% certainty (Bayesian probability). Results: Among 163 eligible individuals, 100 (61%) were randomized, and after loss to follow-up, 95 were analyzed. Gestational age at randomization was 29 3/7 weeks (interquartile range [IQR] 26 2/7, 31 5/7) in the intervention arm and 27 4/7 weeks (IQR 25 4/7, 29 6/7) in the usual care arm. Median step counts were 1,690/day in the intervention arm (IQR 1,031-2,641) and 1,338/day in the usual care arm (IQR 784-1,913). Median days of latency were 9 days in the intervention arm (IQR 4-17) and 6 days in the usual care arm (IQR 2-14). The primary analysis indicated a 65% posterior probability that the intervention increased latency versus usual care (posterior relative risk [RR] 1.09; 95% credible interval [CrI], 0.70-1.71). The RR for composite neonatal adverse outcome was 0.55 (95% CrI 0.32-0.82) with 99% posterior probability of intervention benefit and for composite maternal adverse outcome was 0.94 (95% CrI 0.72-1.20), with 70% posterior probability of intervention benefit. There was a 94% posterior probability of the intervention arm having a greater decrease in maternal stress score from baseline to before delivery compared with the usual care arm (mean arm difference 3.24 points [95% CrI -7.23-0.79]). Adjustment for gestational age at randomization had minimal impact on secondary outcome results. Conclusions: Individuals with PPROM randomized to encouragement to ambulate had a longer latency to delivery and improved neonatal and mental health outcomes with similar maternal clinical outcomes compared to usual care. Keywords: PROM; Walking; bedrest; exercise; limited activity; modified activity; pregnancy; prematurity; preterm delivery; preterm labor; preterm prelabor rupture of membranes; preterm premature rupture of membranes.

Open-Source, Step-Counting Algorithm for Smartphone Data Collected in Clinical and Nonclinical Settings: Algorithm Development and Validation Study (2023)

JMIR cancer

Abstract

Background: Step counts are increasingly used in public health and clinical research to assess well-being, lifestyle, and health status. However, estimating step counts using commercial activity trackers has several limitations, including a lack of reproducibility, generalizability, and scalability. Smartphones are a potentially promising alternative, but their step-counting algorithms require robust validation that accounts for temporal sensor body location, individual gait characteristics, and heterogeneous health states. Objective: Our goal was to evaluate an open-source, step-counting method for smartphones under various measurement conditions against step counts estimated from data collected simultaneously from different body locations ("cross-body" validation), manually ascertained ground truth ("visually assessed" validation), and step counts from a commercial activity tracker (Fitbit Charge 2) in patients with advanced cancer ("commercial wearable" validation). Methods: We used 8 independent data sets collected in controlled, semicontrolled, and free-living environments with different devices (primarily Android smartphones and wearable accelerometers) carried at typical body locations. A total of 5 data sets (n=103) were used for cross-body validation, 2 data sets (n=107) for visually assessed validation, and 1 data set (n=45) was used for commercial wearable validation. In each scenario, step counts were estimated using a previously published step-counting method for smartphones that uses raw subsecond-level accelerometer data. We calculated the mean bias and limits of agreement (LoA) between step count estimates and validation criteria using Bland-Altman analysis. Results: In the cross-body validation data sets, participants performed 751.7 (SD 581.2) steps, and the mean bias was -7.2 (LoA -47.6, 33.3) steps, or -0.5%. In the visually assessed validation data sets, the ground truth step count was 367.4 (SD 359.4) steps, while the mean bias was -0.4 (LoA -75.2, 74.3) steps, or 0.1%. In the commercial wearable validation data set, Fitbit devices indicated mean step counts of 1931.2 (SD 2338.4), while the calculated bias was equal to -67.1 (LoA -603.8, 469.7) steps, or a difference of 3.4%. Conclusions: This study demonstrates that our open-source, step-counting method for smartphone data provides reliable step counts across sensor locations, measurement scenarios, and populations, including healthy adults and patients with cancer. Keywords: accelerometer; cancer; open-source; smartphone; step count; validation; wearable.

The Role of Daily Steps in the Treatment of Major Depressive Disorder: Secondary Analysis of a Randomized Controlled Trial of a 6-Month Internet-Based, Mindfulness-Based Cognitive Behavioral Therapy Intervention for Youth (2023)

Interactive Journal of Medical Research

Abstract

BACKGROUND: Current evidence supports physical activity (PA) as an adjunctive treatment for major depressive disorder (MDD). Few studies, however, have examined the relationship between objectively measured PA and MDD treatment outcomes using prospective data. OBJECTIVE: This study is a secondary analysis of data from a 24-week internet-based, mindfulness-based cognitive behavioral therapy program for MDD. The purpose of this analysis was twofold: (1) to examine average daily step counts in relation to MDD symptom improvement, and whether pain moderated this relationship; and (2) to examine whether changes in step activity (ie, step trajectories) during treatment were associated with baseline symptoms and symptom improvement. METHODS: Patients from the Centre for Addiction and Mental Health were part of a randomized controlled trial evaluating the effects of internet-based, mindfulness-based cognitive behavioral therapy for young adults (aged 18-30 years old) with MDD. Data from 20 participants who had completed the intervention were analyzed. PA, in the form of objectively measured steps, was measured using the Fitbit-HR Charge 2 (Fitbit Inc), and self-reported depression severity was measured with the Beck Depression Inventory-II (BDI-II). Linear regression analysis was used to test PA's relationship with depression improvement and the moderating effect of pain severity and pain interference. Growth curve and multivariable regression models were used to test longitudinal associations. RESULTS: Participants walked an average of 8269 steps per day, and each additional +1000-step difference between participants was significantly associated with a 2.66-point greater improvement (reduction) in BDI-II, controlling for anxiety, pain interference, and adherence to Fitbit monitoring (P=.02). Pain severity appeared to moderate (reduce) the positive effect of average daily steps on BDI-II improvement (P=.03). Higher baseline depression and anxiety symptoms predicted less positive step trajectories throughout treatment (Ps≤.001), and more positive step trajectories early in the trial predicted greater MDD improvement at the end of the trial (Ps{\textless}.04). However, step trajectories across the full duration of the trial did not significantly predict MDD improvement (Ps=.40). CONCLUSIONS: This study used objective measurements to demonstrate positive associations between PA and depression improvement in the context of cognitive behavioral treatment. Pain appeared to moderate this relationship, and baseline symptoms of anxiety and depression predicted PA trajectories. The findings inform future interventions for major depression. Future research with larger samples should consider additional moderators of PA-related treatment success and the extent to which outcomes are related to PA change in multimodal interventions. TRIAL REGISTRATION: Clinical Trials.gov NCT03406052; https://www.clinicaltrials.gov/ct2/show/NCT03406052. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.2196/11591.

Comparison of Polysomnography, Single-Channel Electroencephalogram, Fitbit, and Sleep Logs in Patients With Psychiatric Disorders: Cross-Sectional Study (2023)

Journal of Medical Internet Research

Abstract

BACKGROUND: Sleep disturbances are core symptoms of psychiatric disorders. Although various sleep measures have been developed to assess sleep patterns and quality of sleep, the concordance of these measures in patients with psychiatric disorders remains relatively elusive. OBJECTIVE: This study aims to examine the degree of agreement among 3 sleep recording methods and the consistency between subjective and objective sleep measures, with a specific focus on recently developed devices in a population of individuals with psychiatric disorders. METHODS: We analyzed 62 participants for this cross-sectional study, all having data for polysomnography (PSG), Zmachine, Fitbit, and sleep logs. Participants completed questionnaires on their symptoms and estimated sleep duration the morning after the overnight sleep assessment. The interclass correlation coefficients (ICCs) were calculated to evaluate the consistency between sleep parameters obtained from each instrument. Additionally, Bland-Altman plots were used to visually show differences and limits of agreement for sleep parameters measured by PSG, Zmachine, Fitbit, and sleep logs. RESULTS: The findings indicated a moderate agreement between PSG and Zmachine data for total sleep time (ICC=0.46; P{\textless}.001), wake after sleep onset (ICC=0.39; P=.002), and sleep efficiency (ICC=0.40; P=.006). In contrast, Fitbit demonstrated notable disagreement with PSG (total sleep time: ICC=0.08; wake after sleep onset: ICC=0.18; sleep efficiency: ICC=0.10) and exhibited particularly large discrepancies from the sleep logs (total sleep time: ICC=-0.01; wake after sleep onset: ICC=0.05; sleep efficiency: ICC=-0.02). Furthermore, subjective and objective concordance among PSG, Zmachine, and sleep logs appeared to be influenced by the severity of the depressive symptoms and obstructive sleep apnea, while these associations were not observed between the Fitbit and other sleep instruments. CONCLUSIONS: Our study results suggest that Fitbit accuracy is reduced in the presence of comorbid clinical symptoms. Although user-friendly, Fitbit has limitations that should be considered when assessing sleep in patients with psychiatric disorders.

Effect of digital monitoring and counselling on self-management ability in patients with rheumatoid arthritis: a randomised controlled trial (2023)

Rheumatology (Oxford, England)

Abstract

OBJECTIVES: To assess a remote physiotherapist (PT) counselling intervention using self-monitoring tools for improving self-management ability, physical activity participation, and health outcomes in people with rheumatoid arthritis (RA). METHODS: Eligible participants were randomly assigned to receive group education, a Fitbit®, a self-monitoring app, and PT counselling phone calls (Immediate Group). The Delayed Group received a monthly e-newsletter until week 26, and then the intervention. The primary outcome was Patient Activation Measure (PAM-13). Participants were assessed at baseline, 27 weeks (the primary end point) and 53 weeks. Secondary outcomes included disease activity, pain, fatigue, depression, sitting/walking habits, daily physical activity time, and daily awake sedentary time. Generalized Linear Mixed-effect Models (GLMMs) were used to assess the effect of the intervention on the change of each outcome measure from the initiation to 27 weeks after the intervention. RESULTS: Analysis included 131 participants (91.6\% women; 80.2\% completed during the COVID-19 pandemic). The mean change of PAM-13 at 27 weeks was 4.6 (SD = 14.7) in the Immediate Group vs -1.6 (SD = 12.5) in the Delayed Group. The mean change in Delayed Group at 53 weeks (after the 26-week intervention) was 3.6 (SD = 14.6). Overall, the intervention improved PAM-13 at 27 weeks post-intervention from the GLMM analysis (adjusted coefficient: 5.3; 95\% CI: 2.0, 8.7; p = {\textless}0.001). Favourable intervention effects were also found in disease activity, fatigue, depression, and self-reported walking habit. CONCLUSION: Remote counselling paired with self-monitoring tools improved self-management ability in people with RA. Findings of secondary outcomes indicate that the intervention had a positive effect on symptom management.

Association of self-directed walking with toxicity moderation during chemotherapy for the treatment of early breast cancer (2023)

Supportive Care in Cancer: Official Journal of the Multinational Association of Supportive Care in Cancer

Abstract

BACKGROUND: In the field of exercise oncology, there is a need to quantify the potential benefits of moderate, self-directed physical activity during active treatment. In a pooled analysis of three identical single-arm intervention studies, we investigate the association of activity tracker steps with patient-reported toxicities during chemotherapy. METHODS: Women with early breast cancer who were enrolled in the intervention studies reported their symptom severity every 2-3 weeks throughout chemotherapy, and daily steps were documented through a Fitbit activity tracker. Relative risks (RR) and 95\% confidence intervals (CI) were calculated using Poisson regression models with robust variance. For outcomes significant in unadjusted models, adjusted RRs were calculated controlling for race, age, and education level. Tracker step cut point (high step, low step) was determined by the means. Cumulative incidence functions of moderate, severe, and very severe (MSVS) symptoms were estimated using the Kaplan-Meier method and compared using a Cox proportional hazard model. RESULTS: In a sample of 283 women, mean age was 56 years and 76\% were White. Mean tracker-documented steps/week were 29,625, with 55\% walking below the mean (low step) and 45\% above (high step). In multivariable analysis, high step patients had lower risk for fatigue [RR 0.83 (0.70, 0.99)] (p = 0.04), anxiety [RR 0.59 (0.42, 0.84)] (p = 0.003), nausea [RR 0.66 (0.46, 0.96)] (p = 0.03), depression [RR 0.59 (0.37, 0.03)] (p = 0.02), and ≥ 6 MSVS symptoms [RR 0.73 (0.54, 1.00)] (p = 0.05) and had 36\% lower risk for dose reductions [RR 0.64 (95\% CI 0.43, 0.97)] (p = 0.03). CONCLUSION: Self-directed walking at a rate of at least 30,000 steps/week may moderate the severity of treatment side effects during chemotherapy for early breast cancer. TRIAL NUMBERS: NCT02167932, NCT02328313, NCT03761706.

Selecting a sleep tracker from EEG-based, iteratively improved, low-cost multisensor, and actigraphy-only devices (2023)

Sleep Health

Abstract

AIMS: Evaluate the performance of 6 wearable sleep trackers across 4 classes (EEG-based headband, research-grade actigraphy, iteratively improved consumer tracker, low-cost consumer tracker). FOCUS TECHNOLOGY: Dreem 3 headband, Actigraph GT9X, Oura Ring Gen3, Fitbit Sense, Xiaomi Mi Band 7, Axtro Fit3. REFERENCE TECHNOLOGY: In-lab polysomnography with 3-reader, consensus sleep scoring. SAMPLE: Sixty participants (26 males) across 3 age groups (18-30, 31-50, and 51-70years). DESIGN: Overnight in a sleep laboratory from habitual sleep time to wake time. CORE ANALYTICS: Discrepancy and epoch-by-epoch analyses for sleep/wake (2-stage) and sleep-stage (4-stage; wake/light/deep/rapid eye movement) classification (devices vs. polysomnography). CORE OUTCOMES: EEG-based Dreem performed the best (2-stage kappa=0.76, 4-stage kappa=0.76-0.86) with the lowest total sleep time, sleep efficiency, sleep onset latency, and wake after sleep onset discrepancies vs. polysomnography. This was followed by the iteratively improved consumer trackers: Oura (2-stage kappa=0.64, 4-stage kappa=0.55-0.70) and Fitbit (2-stage kappa=0.58, 4-stage kappa=0.45-0.60) which had comparable total sleep time and sleep efficiency discrepancies that outperformed accelerometry-only Actigraph (2-stage kappa=0.47). The low-cost consumer trackers had poorest overall performance (2-stage kappa{\textless}0.31, 4-stage kappa{\textless}0.33). IMPORTANT ADDITIONAL OUTCOMES: Proportional biases were driven by nights with poorer sleep (longer sleep onset latencies and/or wake after sleep onset). CORE CONCLUSION: EEG-based Dreem is recommended when evaluating poor quality sleep or when highest accuracy sleep-staging is required. Iteratively improved non-EEG sleep trackers (Oura, Fitbit) balance classification accuracy with well-tolerated, and economic deployment at-scale, and are recommended for studies involving mostly healthy sleepers. The low-cost trackers, can log time in bed but are not recommended for research use.

Using Consumer Wearable Devices to Profile Postoperative Complications After Pediatric Appendectomy (2023)

The Journal of Surgical Research

Abstract

INTRODUCTION: Markers of postoperative recovery in pediatric patients are difficult for parents to evaluate after hospital discharge, who use subjective proxies to assess recovery and the onset of complications. Consumer-grade wearable devices (e.g., Fitbit) generate objective recovery data in near real time and thus may provide an opportunity to remotely monitor postoperative patients and identify complications beyond the initial hospitalization. The aim of this study was to use daily step counts from a Fitbit to compare recovery in patients with complications to those without complications after undergoing appendectomy for complicated appendicitis. METHODS: Children ages 3-17 years old undergoing laparoscopic appendectomy for complicated appendicitis were recruited. Patients wore a Fitbit device for 21 d after operation. After collection, patient data were included in the analysis if minimum wear-time criteria were achieved. Postoperative complications were identified through chart review, and step count trajectories for patients recovering with and without complications were compared. Additionally, to account for the patients experiencing a complication on different postoperative days, median daily step count for pre- and post-complication were analyzed. RESULTS: Eighty-six patients with complicated appendicitis were enrolled in the study, and fourteen children developed a postoperative complication. Three patients were excluded because they did not meet the minimum wear time requirements. Complications were divided into abscesses (n = 7, 64\%), surgical site infections (n = 2, 18\%), and other, which included small bowel obstruction and Clostridioides difficile infection (n = 2, 18\%). Patients presented with a complication on mean postoperative day 8, while deviation from the normative recovery trajectory was evident 4 d prior. When compared to children with normative recovery, the patients with surgical complications experienced a slower increase in step count postoperatively, but the recovery trajectory was specific to each complication type. When corrected for day of presentation with complication, step count remained low prior to the discovery of the complication and increased after treatment resembling the normative recovery trajectory. CONCLUSIONS: This study profiled variations from the normative recovery trajectory in patients with complication after appendectomy for complicated appendicitis, with distinct trajectory patterns by complication type. Our findings have potentially profound clinical implications for monitoring pediatric patients postoperatively, particularly in the outpatient setting, thus providing objective data for potentially earlier identification of complications after hospital discharge.

A single-armed proof-of-concept study of Lymfit: A personalized, virtual exercise intervention to improve health outcomes in lymphoma survivors in the pandemic (2024)

PloS One

Abstract

Background and objective: Treatments of lymphoma can lead to reduced physical functioning, cancer-related fatigue, depression, anxiety, and insomnia. These side effects can negatively impact the cancer survivor's quality of life. Mounting evidence indicates that physical activities are highly therapeutic in mitigating the short- and long-term side effects of cancer treatments. Yet, lymphoma survivors' participation in physical activities remains suboptimal, which has been further exacerbated by the deleterious effects of isolation during the COVID-19 pandemic. The Lymfit intervention aims to offer motivational support, expert guidance, and a personalized exercise prescription to optimize physical activities among lymphoma survivors. This proof-of-concept study explores implementation feasibility (retention, technical and safety), and the preliminary effects of Lymfit on various health outcomes. Method: This was a single-armed trial with a pre-and post-test design. Twenty lymphoma survivors were recruited to participate in the 12-week Lymfit intervention. Wearable activity trackers (Fitbit) were given to participants as a motivational tool and for data collection purposes. Participants received a personalized exercise prescription designed by a kinesiologist. Physiologic metrics were collected by the Fitbit monitors and were stored in the Lymfit database. Self-reported questionnaires measuring health outcomes were collected at baseline and post-intervention. Results: The retention rate of this trial was 70%. Minimal technical issues and no adverse effects were reported. Lymfit led to significant improvements in sleep disturbances and the ability to participate in social activities and decreased fear of cancer recurrence. It also increased daily steps and decreased sedentary time in participants who did not meet the recommended physical activity guidelines. Significance: With access to resources and fitness centers being limited during the pandemic, the Lymfit intervention filled an immediate need to provide physical activity guidance to lymphoma survivors. Findings provide preliminary support that implementing the Lymfit intervention is feasible and demonstrated promising results.

Step cadence as a novel objective postoperative recovery metric in children who undergo laparoscopic appendectomy (2024)

Surgery

Abstract

Background: Daily step counts from consumer wearable devices have been used to objectively assess postsurgical recovery in children. However, step cadence, defined as steps taken per minute, may be a more specific measure of physiologic status. The purpose of this study is to define objective normative physical activity recovery trajectories after laparoscopic appendectomy using this novel metric. We hypothesized that patients would have a progressive increase in peak cadence until reaching a plateau representing baseline status, and this would occur earlier for simple compared with complicated appendicitis. Methods: Children aged 3 to 18 years were enrolled after laparoscopic appendectomy for simple or complicated appendicitis between March 2019 and December 2022 at a tertiary children's hospital. Participants wore a Fitbit for 21 postoperative days. The peak 1-minute cadence and peak 30-minute cadence were determined each postoperative day. Piecewise linear regression was conducted to generate normative peak step cadence recovery trajectories for simple and complicated appendicitis. Results: A total of 147 children met criteria (53.7% complicated appendicitis). Patients with simple appendicitis reached plateau postoperative day 10 at a mean peak 1-minute cadence of 111 steps/minute and a mean peak 30-minute cadence of 77 steps/minute. The complicated appendicitis recovery trajectory reached a plateau postoperative day 13 at a mean peak 1-minute cadence of 106 steps/minute and postoperative day 15 at a mean peak 30-minute cadence of 75 steps/minute. Conclusion: Using step cadence, we defined procedure-specific normative peak cadence recovery trajectories after laparoscopic appendectomy. This can empower clinicians to set data-driven expectations for recovery after surgery and establish the groundwork for consumer wearable devices as a post-discharge remote monitoring tool.

Promoting positive physical activity behaviours in children undergoing acute cancer treatment: feasibility of the CanMOVE intervention (2023)

Brazilian Journal of Physical Therapy

Abstract

Background: Supporting children and adolescents with cancer to be physically active can improve medium- and long-term health outcomes. Objective: To assess the feasibility of CanMOVE, a 10-week complex, theoretically-informed, behaviour change intervention to promote physical activity for children and adolescents undergoing acute cancer treatment. Methods: A feasibility study using a single-group, repeated measures, mixed methods design. Participants completed CanMOVE, which included provision of a Fitbit (child/adolescent and carer) and structured support from a physical therapist. Feasibility domains of demand, acceptability, implementation, practicality, limited efficacy, and integration were evaluated. Data sources included service level data, objective assessment of physical activity, physical function, and health-related quality of life; and qualitative data collected via semi-structured interviews with participants and focus groups with staff. Results: Twenty children/adolescents (median age 13yrs, interquartile-range 9-14) with a mix of cancer diagnoses, 20 parents, and 16 clinicians participated. There was high demand with 95% enrolment rate. CanMOVE was acceptable for participants. All feasibility thresholds set for implementation were met. Under practicality, there were no serious adverse events related to the intervention. Limited efficacy data indicated CanMOVE showed positive estimates of effect in influencing child/adolescent physical activity behaviour, physical function, and health-related quality of life. Positive impacts were also seen in parent and staff attitudes towards physical activity promotion. To improve integration into the clinical setting, it was suggested the duration and scope of CanMOVE could be expanded. Conclusion: CanMOVE was feasible to implement in a paediatric cancer setting. CanMOVE is appropriate to be tested in a large-scale trial.

Examining a Remote Group-Based Type 2 Diabetes Self-Management Education Program in the COVID-19 Era Using the ORBIT Model: Small 6-Week Feasibility Study (2024)

JMIR formative research

Abstract

Background: To date, most group-based diabetes self-management education (DSME) programs for type 2 diabetes (T2D) have been delivered in person. The rapid transition to remote care at the outset of the COVID-19 pandemic presented opportunities to test, evaluate, and iterate a new remote DSME program. Objective: We aim to refine the delivery and evaluation of a multicomponent remote DSME program for adults living with T2D by examining several feasibility outcomes. Methods: We recruited a convenience sample of patients from a London, Canada, outpatient diabetes clinic (serving high-risk, low-income adults) to participate in a 6-week, single cohort feasibility study from November 2020 to March 2021. This small ORBIT phase 1b feasibility study represents the first in a planned series guided by the ORBIT model for developing behavioral interventions for chronic diseases (phase 1: design; phase 2: preliminary testing; phase 3: efficacy; and phase 4: effectiveness). The feasibility of delivering and evaluating a remote DSME program, including (1) live video education classes, (2) individualized physical activity (PA) prescription and counseling, and (3) intermittently scanned continuous glucose and wearable PA monitoring, was assessed. Feasibility outcomes included recruitment and retention rates, program adherence, and acceptability (ie, technology issues and exit survey feedback). PA was assessed with Fitbit Inspire 2 (Fitbit Inc) and estimated glycated hemoglobin (HbA1c) using the FreeStyle Libre (Abbot). Given the small study sample, group- and individual-level data are reported descriptively. Results: A total of 10 adults living with T2D were recruited (female 60%; age 49.9, SD 14.3 years; estimated HbA1c 6.2%, SD 0.5%). Recruitment and retention rates were 29% and 80%, respectively. Participants attended 83% (25/30) and 93% (37/40) of education classes and PA counseling phone calls, respectively. There were 3.2 (SD 2.6) technology issues reported per person, most of which were related to study data transfer. Exit survey responses suggest most participants (8/9, 89%) were "satisfied" with the program. Recognizing the small sample size and the fact that no inferential statistics were conducted, the mean (SD) for the weekly daily step count and estimated HbA1c are provided for illustrative purposes. Participants accumulated 7103 (SD 2900) and 7515 (SD 3169) steps per day at baseline and week 6, respectively. The estimated HbA1c was 6.2% (SD 0.5%) and 6.2% (SD 0.6%) at baseline and week 6, respectively. Conclusions: This ORBIT phase 1b study served to refine the delivery (eg, automatic study data upload process recommended to reduce participant burden) and evaluation (eg, purposeful sampling of participants with baseline HbA1c >8% recommended to address selection bias) of a remote DSME program. Preliminary proof-of-concept testing (ORBIT phase 2) incorporating some of these learnings is now warranted.

Improving multimodal physical function in adults with heterogeneous chronic pain; Protocol for a multisite feasibility RCT (2024)

Contemporary Clinical Trials

Abstract

Background: Chronic pain is associated with substantial impairment in physical function, which has been identified as a top concern among persons with pain. GetActive-Fitbit, a mind-body activity program, is feasible, acceptable, and associated with improvement in physical function among primarily White, sedentary individuals with pain. In preparation for a multisite efficacy trial, we must examine feasibility across multiple sites with diverse patient populations. Here we describe the protocol of a multisite, feasibility RCT comparing GetActive-Fitbit with a time- and attention-matched educational comparison (Healthy Living for Pain). We aim to 1) test multisite fidelity of clinician training; 2) evaluate multisite feasibility benchmarks, including recruitment of chronic pain patients taking <5000 steps/day and racial and ethnic minorities; and 3) optimize fidelity and study protocol in preparation for a future multisite efficacy trial. Methods: Clinician training fidelity was assessed via roleplays and mock group sessions. Feasibility (i.e., recruitment, acceptability, credibility, adherence, satisfaction), multimodal physical function (e.g., self-report, 6-Minute Walk Test, step-count), and other psychosocial outcomes are assessed at baseline, posttest, and 6 months. Protocol optimization will be assessed using exit interviews and cross-site meetings. Results: The trial is ongoing. Clinician training is complete. 87 participants have been recruited. 54 completed baseline assessments and randomization, 44 are mid-intervention, and 9 have completed the intervention and posttest. Conclusions: This study addresses the critical need for feasible, acceptable mind-body-activity interventions for chronic pain that follow evidence-based guidelines and improve all aspects of physical function across diverse populations. Results will inform a future fully-powered multisite efficacy trial.

Family-Focused Facilitated Fitness: Feasibility and Acceptability of a Couple-Based Physical Activity Intervention for Hematopoietic Cell Transplant Recipients and their Caregiving Partners (2024)

Transplantation and Cellular Therapy

Abstract

Background: Reductions in physical activity (PA) are common among patients following hematopoietic cell transplantation, and a risk factor for poor physical functioning. PA among spouses/cohabiting partners, who frequently serve as the patient's primary caregiver, may also be reduced due to caregiving demands and limited bandwidth for self-care. In addition, the patient-caregiver relationship can be compromised, and communication patterns disrupted. All PA interventions in the hematopoietic cell transplantation setting have focused entirely on patients, ignoring an opportunity to synergistically engage and benefit the caregiver as well. Objective: To establish the feasibility and acceptability of a couple-based intervention entitled Family-Focused Facilitated Fitness (FFFF), designed to improve PA as assessed by daily step counts among both patients undergoing hematopoietic cell transplantation and their caregivers. Guided by interdependence and communal coping perspectives, FFFF is an 8-week, remotely-delivered intervention that provides training in communication skills and joint problem-solving to help patients and caregivers support one another in PA. Participants are also given a Fitbit to track their steps and weekly individualized step prescription based on the 75th percentile ranked value of their last 7 recorded daily step counts. A priori benchmarks for feasibility and acceptability were as follows: 50% of eligible couples would agree to participate, 70% of couples would attend all 8 sessions, 80% of participants would provide valid Fitbit wear data 4/7 days/ week, and among sessions reviewed for treatment fidelity, 85% of treatment protocol elements would be covered. Study design: Single-arm pilot intervention. Results: Couples were recruited prior to transplant. Among 26 couples approached and deemed eligible, 17 enrolled (65% agreement) and completed baseline assessment. Three couples later withdrew after transplantation but prior to receiving the intervention, resulting in 14 couples commencing the intervention, on average 21 days post-transplant. Four couples subsequently discontinued due to medical complications (n=3) or caregiver schedule (n=1). Among the 10 couples who completed the intervention, the percentage of participants meeting our benchmark of valid Fitbit wear at least 4 days per week was 85% in week 1, 90% in weeks 2-7, and 80% in week 8. Treatment fidelity was 95% on average across 24 sessions reviewed (3 cases). Treatment satisfaction scores were uniformly high across multiple dimensions, with all means above 4 on the 1-5 scale. Daily step counts among those attending all 8 intervention sessions increased from 2249 ± 302 steps/day in week 1 to 4975 ± 1377 steps/day in week 8 among patients, and from 8676 ± 3760 steps/day in week 1 to 9838 ± 3723 steps/day in week 8 among caregivers. Qualitative feedback indicated perceived mental and physical health benefits of the program. Participants also offered suggestions for adaptations to accommodate medical setbacks and constraints. Conclusion: All a priori feasibility benchmarks were met or exceeded. Results offer promise for utility of the program to engage and leverage patient-caregiver dyads to increase PA following transplant. An investigation using a randomized controlled design will be necessary to adequately examine change over time relative to control and its possible impact on clinical and patient-reported outcomes. Keywords: caregiver; couples; hematopoietic cell transplantation; physical activity.

Menstruation-related symptoms are associated with physical activity and midpoint of sleep: a pilot study (2023)

Frontiers in Global Women's Health

Abstract

Introduction: Menstruation-related symptoms (MRSs) significantly impact women's health and contribute to economic burdens worldwide. Current interventions, primarily pharmacological ones, have limitations and side effects that underscore the need for alternative management strategies. This study explores the association between MRSs and lifestyle factors, specifically physical activity and sleep timing across menstrual cycle phases, to inform non-pharmacological intervention development. Methods: Fourteen female students from Ritsumeikan University, Japan, with regular menstrual cycles (25-38 days), not on hormonal treatment or engaged in shift work, participated in this observational study. Using a Fitbit Inspire 2, total daily energy expenditure (TDEE) and sleep timing were monitored over a complete cycle. Menstrual cycle phases were defined based on ovulation day, predicted using home luteinizing hormone tests. Participants completed daily electronic questionnaires rating MRSs using a modified menstrual distress questionnaire. Data were analyzed using a generalized linear mixed model with a gamma distribution and logarithmic link function, examining the relationship of TDEE and the midpoint of sleep time (MS time) with MRS severity. Results and discussion: The following observations were noted: first, MRS severity, except for behavioral change symptoms, significantly increased during the menstrual and luteal phases compared to the follicular phase. Second, delayed MS time was associated with reduced pain, concentration symptoms, water retention, and negative affect during the menstrual phase and reduced negative affect during the luteal phase. Finally, an increase in TDEE was associated with reduced concentration symptoms, autonomic reaction symptoms, and negative affect during the menstrual and luteal phases and reduced water retention only during the luteal phase. This study provides insights into the relationship between MRSs and TDEE/MS time, suggesting potential non-therapeutic approaches for symptom management, though further research is needed to substantiate these findings for practical applications. Keywords: generalized linear mixed model; menstrual cycle; menstrual cycle phase; menstruation-related symptoms; physical activity; sleep; wearable device.

Data-efficient machine learning methods in the ME-TIME study: Rationale and design of a longitudinal study to detect atrial fibrillation and heart failure from wearables (2023)

Cardiovascular Digital Health Journal

Abstract

Background: Smartwatches enable continuous and noninvasive time series monitoring of cardiovascular biomarkers like heart rate (from photoplethysmograms), step counter, skin temperature, et cetera; as such, they have promise in assisting in early detection and prevention of cardiovascular disease. Although these biomarkers may not be directly useful to physicians, a machine learning (ML) model could find clinically relevant patterns. Unfortunately, ML models typically need supervised (ie, annotated) data, and labeling of large amounts of continuous data is very labor intensive. Therefore, ML methods that are data efficient, ie, needing a low number of labels, are required to detect potential clinical value in patterns found in wearable data. Objective: The primary study objective of the ME-TIME (Machine Learning Enabled Time Series Analysis in Medicine) study is to design an ML model that can detect atrial fibrillation (AF) and heart failure (HF) from wearable data in a data-efficient manner. To achieve this, self-supervised and weakly supervised learning techniques are used. Methods: Two hundred subjects (100 reference, 50 AF, and 50 HF) are being invited to participate in wearing a Fitbit fitness tracker for 3 months. Interested volunteers are sent a questionnaire to determine their health, in particular cardiovascular health. Volunteers without any (history of) serious illness are assigned to the reference group. Participants with AF and HF are recruited in the Haga teaching hospital in The Hague, The Netherlands. Results: Enrollment commenced on May 1, 2022, and as of the time of this report, 62 subjects have been included in the study. Preliminary analysis of the data reveals significant inter-subject variability. Notably, we identified heart rate recovery curves and time-delayed correlations between heart rate and step count as potential strong indicators for heart disease. Conclusion: Using self-supervised and multiple-instance learning techniques, we hypothesize that patterns specific to AF and HF can be found in continuous data obtained from smartwatches. Keywords: Artificial intelligence; Atrial fibrillation; Heart failure; Machine learning; Multiple-instance learning; Self-supervised learning; Smartwatch; Wearables; mHealth.

Engagement With a Remote Symptom-Tracking Platform Among Participants With Major Depressive Disorder: Randomized Controlled Trial (2024)

JMIR mHealth and uHealth

Abstract

Background: Multiparametric remote measurement technologies (RMTs), which comprise smartphones and wearable devices, have the potential to revolutionize understanding of the etiology and trajectory of major depressive disorder (MDD). Engagement with RMTs in MDD research is of the utmost importance for the validity of predictive analytical methods and long-term use and can be conceptualized as both objective engagement (data availability) and subjective engagement (system usability and experiential factors). Positioning the design of user interfaces within the theoretical framework of the Behavior Change Wheel can help maximize effectiveness. In-app components containing information from credible sources, visual feedback, and access to support provide an opportunity to promote engagement with RMTs while minimizing team resources. Randomized controlled trials are the gold standard in quantifying the effects of in-app components on engagement with RMTs in patients with MDD. Objective: This study aims to evaluate whether a multiparametric RMT system with theoretically informed notifications, visual progress tracking, and access to research team contact details could promote engagement with remote symptom tracking over and above the system as usual. We hypothesized that participants using the adapted app (intervention group) would have higher engagement in symptom monitoring, as measured by objective and subjective engagement. Methods: A 2-arm, parallel-group randomized controlled trial (participant-blinded) with 1:1 randomization was conducted with 100 participants with MDD over 12 weeks. Participants in both arms used the RADAR-base system, comprising a smartphone app for weekly symptom assessments and a wearable Fitbit device for continuous passive tracking. Participants in the intervention arm (n=50, 50%) also had access to additional in-app components. The primary outcome was objective engagement, measured as the percentage of weekly questionnaires completed during follow-up. The secondary outcomes measured subjective engagement (system engagement, system usability, and emotional self-awareness). Results: The levels of completion of the Patient Health Questionnaire-8 (PHQ-8) were similar between the control (67/97, 69%) and intervention (66/97, 68%) arms (P value for the difference between the arms=.83, 95% CI -9.32 to 11.65). The intervention group participants reported slightly higher user engagement (1.93, 95% CI -1.91 to 5.78), emotional self-awareness (1.13, 95% CI -2.93 to 5.19), and system usability (2.29, 95% CI -5.93 to 10.52) scores than the control group participants at follow-up; however, all CIs were wide and included 0. Process evaluation suggested that participants saw the in-app components as helpful in increasing task completion. Conclusions: The adapted system did not increase objective or subjective engagement in remote symptom tracking in our research cohort. This study provides an important foundation for understanding engagement with RMTs for research and the methodologies by which this work can be replicated in both community and clinical settings. Trial registration: ClinicalTrials.gov NCT04972474; https://clinicaltrials.gov/ct2/show/NCT04972474. International registered report identifier (irrid): RR2-10.2196/32653. Keywords: app; community; depression; engagement; mobile phone; remote measurement; self-awareness; smartphones; symptom tracking; technology; wearable devices.

Tailor Made: A Pilot Virtual Weight Loss Intervention Individually Tailored for African American Men (2023)

Health Promotion Practice

Abstract

This study tests the acceptability and feasibility of the first virtual weight loss study individually tailored for middle-aged African American men. Tailor Made is a 3-month randomized controlled pilot of a weight loss intervention that included 58 overweight or obese African American men (mean age of 50.4; SD = 7.9). Control group participants received a Fitbit activity tracker and Bluetooth-enabled scale and copies of the self-led Tailor Made curriculum. Intervention group participants received the same Fitbit, Bluetooth-enabled scale, and curriculum and also participated in weekly, 45-minute virtual small group, professional-led education sessions using the Tailor Made curriculum and received three SMS text messages weekly: (a) a message individually tailored on African American Manhood that links men's values, goals, and motivation to health-promoting behavior; (b) a goal-tracking message to monitor physical activity, healthier eating, and lifestyle changes; and (c) a reminder 24 hours before their session. Participation rates in weekly small group sessions, randomization, and attendance at the assessments suggest that Tailor Made was feasible and acceptable. Only among intervention group participants, we found a small and significant decrease in BMI between baseline and final. In addition, active minutes of physical activity decreased for the control group while active minutes for the intervention group remained steady throughout the intervention. In sum, we demonstrated that a virtual, individually tailored weight loss intervention is feasible and acceptable to African American men. Participants valued the convenience of a virtual intervention, but there were a number of ways we may be able to enhance the potential benefits of this approach. Keywords: African American men; men’s health equity; nutrition; obesity; physical activity; weight loss.

Exploring Variations in Sleep Perception: Comparative Study of Chatbot Sleep Logs and Fitbit Sleep Data (2023)

JMIR mHealth and uHealth

Abstract

Background: Patient-generated health data are important in the management of several diseases. Although there are limitations, information can be obtained using a wearable device and time-related information such as exercise time or sleep time can also be obtained. Fitbits can be used to acquire sleep onset, sleep offset, total sleep time (TST), and wakefulness after sleep onset (WASO) data, although there are limitations regarding the depth of sleep and satisfaction; therefore, the patient's subjective response is still important information that cannot be replaced by wearable devices. Objective: To effectively use patient-generated health data related to time such as sleep, it is first necessary to understand the characteristics of the time response recorded by the user. Therefore, the aim of this study was to analyze the characteristics of individuals' time perception in comparison with wearable data. Methods: Sleep data were acquired for 2 weeks using a Fitbit. Participants' sleep records were collected daily through chatbot conversations while wearing the Fitbit, and the two sets of data were statistically compared. Results: In total, 736 people aged 30-59 years were recruited for this study, and the sleep data of 543 people who wore a Fitbit and responded to the chatbot for more than 7 days on the same day were analyzed. Research participants tended to respond to sleep-related times on the hour or in 30-minute increments, and each participant responded within the range of 60-90 minutes from the value measured by the Fitbit. On average for all participants, the chat responses and the Fitbit data were similar within a difference of approximately 15 minutes. Regarding sleep onset, the participant response was 8 minutes and 39 seconds (SD 58 minutes) later than that of the Fitbit data, whereas with respect to sleep offset, the response was 5 minutes and 38 seconds (SD 57 minutes) earlier. The participants' actual sleep time (AST) indicated in the chat was similar to that obtained by subtracting the WASO from the TST measured by the Fitbit. The AST was 13 minutes and 39 seconds (SD 87 minutes) longer than the time WASO was subtracted from the Fitbit TST. On days when the participants reported good sleep, they responded 19 (SD 90) minutes longer on the AST than the Fitbit data. However, for each sleep event, the probability that the participant's AST was within ±30 and ±60 minutes of the Fitbit TST-WASO was 50.7% and 74.3%, respectively. Conclusions: The chatbot sleep response and Fitbit measured time were similar on average and the study participants had a slight tendency to perceive a relatively long sleep time if the quality of sleep was self-reported as good. However, on a participant-by-participant basis, it was difficult to predict participants' sleep duration responses with Fitbit data. Individual variations in sleep time perception significantly affect patient responses related to sleep, revealing the limitations of objective measures obtained through wearable devices. Keywords: Fitbit; PGHD; chat; patient-generated health data; self-report; sleep; sleep diary; sleep log; sleep time; wearables.

From intention to behavior: Within- and between-person moderators of the relationship between intention and physical activity (2024)

Psychology of Sport and Exercise

Abstract

Intention is a proximal predictor of behavior in many theories of behavior change, but intentions to be physically active do not always translate to actual physical activity. Little research has examined intensive longitudinal changes in physical activity and corresponding within-person moderators needed to elucidate the mechanisms, hurdles, and facilitators of individuals' everyday physical activity behaviors. The present study set out to evaluate the possible moderators of the intention-physical activity relationship across within-person and between-person levels, including cross-level interactions. Data comprise the first intensive measurement burst (14 days) of the longitudinal prospective Healthy Aging in Industrial Environment (HAIE) study, with N = 1135 participants (N = 10,030 person-days), aged 18-65. Physical activity was operationalized as step counts measured objectively using Fitbit Charge 3/4 fitness monitor. Intention, barriers to physical activity, and social support for physical activity were measured daily via smartphone surveys. Stable characteristics, i.e., physical activity habit and exercise identity, were measured using an online questionnaire. A multilevel moderation regression model with Bayesian estimator was fitted. At the within-person level, the relation between intention and steps was weaker on days when barriers were more severe than usual for a given person (Estimate = -0.267; CI95 = [-0.340, -0.196]) and social support was below average for a given person (Est = 0.143; CI95 = [0.023, 0.262]). Additionally, the daily intention-behavior relationship was stronger for people with lower average severity of barriers (Est = -0.153; CI95 = [-0.268, -0.052]), higher exercise identity (Est = 0.300; CI95 = [0.047, 0.546]), men (Est = -1.294, CI95 = [-1.854, -0.707]), and older individuals (Est = 0.042, CI95 = [0.017, 0.064]). At the between-person level, only physical activity habit strengthened the intention-behavior link (Est = 0.794; CI95 = [0.090, 1.486]). Our results underscore the need to separate the between-person differences from the within-person fluctuations to better understand the individual dynamics in physical activity behaviors. Personalized interventions aimed at helping individuals translate intentions to actual physical activity could be tailored and become more intensive when there is a higher risk of intention-behavior gap on a given day for a specific individual (i.e., a day with more severe barriers and less social support), by increasing the dosage or deploying more precisely targeted intervention strategies and components. In addition, interventionists should take gender and age into account when tailoring everyday strategies to help individuals act on their intentions. Keywords: Barriers; Intention; Intention-behavior gap; Physical activity; Social support.

Patient Engagement and Provider Effectiveness of a Novel Sleep Telehealth Platform and Remote Monitoring Assessment in the US Military: Pilot Study Providing Evidence-Based Sleep Treatment Recommendations (2023)

JMIR formative research

Abstract

Background: Sleep problems are common and costly in the US military. Yet, within the military health system, there is a gross shortage of trained specialist providers to address sleep problems. As a result, demand for sleep medicine care far exceeds the available supply. Telehealth including telemedicine, mobile health, and wearables represents promising approaches to increase access to high-quality and cost-effective care. Objective: The purpose of this study was to evaluate patient engagement and provider perceived effectiveness of a novel sleep telehealth platform and remote monitoring assessment in the US military. The platform includes a desktop web portal, native mobile app, and integrated wearable sensors (ie, a commercial off-the-shelf sleep tracker [Fitbit]). The goal of the remote monitoring assessment was to provide evidence-based sleep treatment recommendations to patients and providers. Methods: Patients with sleep problems were recruited from the Internal Medicine clinic at Walter Reed National Military Medical Center. Patients completed intensive remote monitoring assessments over 10 days (including a baseline intake questionnaire, daily sleep diaries, and 2 daily symptom surveys), and wore a Fitbit sleep tracker. Following the remote monitoring period, patients received assessment results and personalized sleep education in the mobile app. In parallel, providers received a provisional patient assessment report in an editable electronic document format. Patient engagement was assessed via behavioral adherence metrics that were determined a priori. Patients also completed a brief survey regarding ease of completion. Provider effectiveness was assessed via an anonymous survey. Results: In total, 35 patients with sleep problems participated in the study. There were no dropouts. Results indicated a high level of engagement with the sleep telehealth platform, with all participants having completed the baseline remote assessment, reviewed their personalized sleep assessment report, and completed the satisfaction survey. Patients completed 95.1% of sleep diaries and 95.3% of symptom surveys over 10 days. Patients reported high levels of satisfaction with most aspects of the remote monitoring assessment. In total, 24 primary care providers also participated and completed the anonymous survey. The results indicate high levels of perceived effectiveness and identified important potential benefits from adopting a sleep telehealth approach throughout the US military health care system. Conclusions: Military patients with sleep problems and military primary care providers demonstrated high levels of engagement and satisfaction with a novel sleep telehealth platform and remote monitoring assessment. Sleep telehealth approaches represent a potential pathway to increase access to evidence-based sleep medicine care in the US military. Further evaluation is warranted. Keywords: application; behavioral; care; effective care; effectiveness; insomnia; monitoring; obstructive sleep apnea; patient engagement; remote monitoring; sleep; sleep disorders; telehealth; wearables.

Using Wearable Devices to Profile Demographic-Specific Recovery After Pediatric Appendectomy (2023)

The Journal of Surgical Research

Abstract

Introduction: Counseling patients and parents about the postoperative recovery expectations for physical activity after pediatric appendectomy varies significantly and is not specific to patients' demographic characteristics. Consumer wearable devices (CWD) can be used to objectively assess patients' normative postoperative recovery of physical activity. This study aimed to develop demographic-specific normative physical activity recovery trajectories using CWD in pediatric patients undergoing appendectomy. Methods: Children ages 3-18 y old undergoing laparoscopic appendectomy for acute appendicitis were recruited. Patients wore a Fitbit device for 21 d postoperatively and daily step counts were measured. Patients with postoperative complications were excluded. Segmented regression models were fitted and time-to-plateau was estimated for patients with simple and complicated appendicitis separately for each age group, sex, race/ethnicity, and body mass index category. Results: Among 147 eligible patients; 76 (51.7%) were female, 86 (58.5%) were in the younger group, and 79 (53.7%) had complicated appendicitis. Patients 3-11 y old demonstrated a faster trajectory to a physical activity plateau compared to those 12-18 in both simple (postoperative day [POD] 9 versus POD 17) and complicated appendicitis (POD 17 versus POD 21). Males and females had a similar postoperative recovery trajectory in simple and complicated appendicitis. There was no clear pattern differentiating trajectories based on race/ethnicity. Overweight/obese patients demonstrated a slower recovery trajectory in simple appendicitis. Conclusions: This study demonstrates that factors other than the disease itself, such as age, may affect recovery, suggesting the need for more tailored discharge instructions. CWDs can improve our understanding of recovery and allow for better data-driven counseling perioperatively. Keywords: Appendectomy; Appendicitis; BMI; Demographics; Fitbit; Recovery; Wearable devices.

Association between sleep quality and nocturnal erection monitor by RigiScan in erectile dysfunction patients: a prospective study using fitbit charge 2 (2023)

Basic and Clinical Andrology

Abstract

Background: Few studies were conducted to explore the association between sleep quality and nocturnal erection. Here, we intended to explore the association between sleep quality and nocturnal erection monitor when conducting nocturnal erection monitor. All erectile dysfunction (ED) patients underwent sleep monitors using Fitbit Charge 2™ (Fitbit Inc.) and nocturnal penile tumescence and rigidity (NPTR) monitors using RigiScan® (GOTOP medical, Inc., USA) for two nights. Subsequently, the patients were divided into two groups: Group A included patients who experienced effective erections only on the second night, while Group B included patients who had effective erections on both nights. To explore the associations between NPTR parameters and sleep parameters, a comparative analysis was performed between Group A and Group B for both nights. Results: Finally, our study included 103 participants, with 47 patients in Group A and 56 patients in Group B. Notably, the Group A patients showed significant improvements in NPTR parameters on the second night compared to the first night. Conversely, the NPTR parameters on Group B of the second night did not demonstrate a superior outcome when compared to the second night of Group A. Interestingly, it was found that only the disparities in sleep parameters accounted for the variation in NPTR parameters between the two groups on the first night. After correlation and ROC analysis, we identified the rapid eye movement (REM) sleep time and wake after sleep onset (WASO) time monitoring by the Fitbit Charge 2 as the primary parameters for predicting abnormal NPTR results in the first night. Conclusions: Therefore, our study strongly suggests a close association between sleep parameters and NPTR parameters. It emphasizes the importance of incorporating sleep monitoring alongside nocturnal erection monitoring to enhance the reliability of the NPTR results. Keywords: Erectile dysfunction; Fitbit Charge 2™; Nocturnal erection monitor; RigiScan; Sleep quality.

Physical activity and menopausal symptoms: evaluating the contribution of obesity, fitness, and ambient air pollution status (2024)

Menopause (New York, N.Y.)

Abstract

OBJECTIVE: The menopausal transition is accompanied by transient symptoms that have been linked to subclinical cardiovascular disease (CVD); CVD has also been linked to air pollution. Physical activity (PA) reduces CVD, improves body composition, and can reduce menopausal symptoms. The purpose of this study was to assess the links between PA and menopausal symptoms and whether obesity, fitness, and air pollution status play a role in this relationship. METHODS: Women (40-60 y; N = 243; mean [SD] age, 47.8 [5.6] y) from areas with high versus low air pollution enrolled in the Healthy Aging in Industrial Environment Program 4 prospective cohort study completed psychological, cardiorespiratory fitness, body composition, and menopausal status screening followed by a 14-day prospective assessment of menopausal symptoms (Menopause Rating Scale) using a mobile application. Daily PA was assessed objectively across 14 days via Fitbit Charge 3 monitor. General linear mixed models were conducted and controlled for age, menopausal status, day in the study, wear time, and neuroticism. RESULTS: Peri/postmenopausal women (β = 0.43, P {\textless} 0.001) and those residing in a high-air-pollution environment (β = 0.45, P {\textless} 0.05) reported more somatovegetative symptoms. Hot flashes alone were associated with peri/postmenopausal status (β = 0.45, P {\textless} 0.001), and for women residing in a high-air-pollution environment, lower reporting of hot flashes was observed on days when a woman was more physically active than usual (β = -0.15, P {\textless} 0.001). No associations were found for cardiorespiratory fitness and visceral fat with any of the symptoms. CONCLUSIONS: PA may enhance resilience to hot flashes, especially when residing in high-air-pollution environments where we also observed higher reporting of somatovegetative menopausal symptoms.

Comparison of daily step count between the Fitbit Inspire 3 and the activPAL 3 in adults with transtibial amputation (2024)

Frontiers in Rehabilitation Sciences

Abstract

INTRODUCTION: Physical activity has significant positive effects on health. Accelerometers can be used to track daily physical activity. The Fitbit Inspire 3 is a commercially available health and fitness tracker, but its validity for tracking steps among individuals with transtibial amputation has not been examined. Therefore, the purpose of this study was to evaluate the concurrent validity of the Fitbit Inspire 3 for assessing free-living daily steps in adults with transtibial amputation. METHODS: Participants (n = 79) completed a general health survey and were provided with a Fitbit Inspire 3 and activPAL 3 accelerometer to wear concurrently for seven days in their home environment. Relationships between the activPAL and Fitbit Inspire 3 were examined using Pearson's Correlation. Paired samples t-tests, mean difference, mean absolute difference, and equivalence testing were used to compared daily step counts between Fitbit Inspire 3 and activPAL 3. RESULTS: Average step counts were 5,768 ± 3,750 (mean ± SD) and 4,674 ± 3,081 by the Fitbit Inspire 3 and activPAL, respectively. A high correlation (r = 0.93) but significant mean difference was found between the activPAL 3 and Fitbit Inspire 3 (p {\textless} 0.001). The mean absolute difference between the devices was 1,347 ± 1,184 steps. On average, the Fitbit Inspire 3 counted 1,094 ± 1,423 more daily steps than the activPAL 3. Equivalency could not be claimed between the devices. DISCUSSION: The Fitbit Inspire 3 counted more steps compared to the activPAL. Because of the significant mean differences and the large mean absolute difference between the devices, the activPAL 3 and Fitbit Inspire 3 are not interchangeable for estimating physical activity in individuals with transtibial amputation. However, due to the high correlation, the devices will produce similar classification rankings based on step counts.

Effects of a mobile health intervention on health-related outcomes in Japanese office workers: a pilot study (2024)

International Journal of Occupational Medicine and Environmental Health

Abstract

OBJECTIVES: The purpose of the current study was to explore the effects of a mobile health (mHealth) intervention based on the Persuasive System Design (PSD) model on health-related outcomes among office workers. MATERIAL AND METHODS: The authors conducted a trial that consisted of a 4-week baseline and an 8-week intervention period by reference to 23 office workers in a private research company. The mHealth application was developed to improve these workers' daily step count, decrease their sedentary time, and increase their sleep duration in accordance with the PSD model. The app features included at least 1 principal factor from each of the 4 main categories of the PSD model (primary task support, dialogue support, system credibility support, and social support). The objective health-related variables were measured using a smartwatch (Fitbit Luxe) that was synchronized with the application using the Fitbit Web Application Programming Interface. Subjects used the app, which included self-monitoring, personalized messages, education, and a competition system for users, during the intervention period. RESULTS: Sedentary time exhibited a significant decrease (a median reduction of 14 min/day, p {\textless} 0.05) during the intervention period. No significant differences in daily step count and sleep duration were observed between the baseline and intervention periods. CONCLUSIONS: This study suggests that the mHealth intervention based on the PSD model was useful for reducing sedentary time among office workers. Given that many previous studies on this topic have not been based on any theories, future studies should investigate the impact of structured selection behavior change theories on health-related outcomes among office workers. Int J Occup Med Environ Health. 2024;37(2).

Reducing the burden of inconclusive smart device single-lead ECG tracings via a novel artificial intelligence algorithm (2024)

Cardiovascular Digital Health Journal

Abstract

BACKGROUND: Multiple smart devices capable of automatically detecting atrial fibrillation (AF) based on single-lead electrocardiograms (SL-ECG) are presently available. The rate of inconclusive tracings by manufacturers' algorithms is currently too high to be clinically useful. METHOD: This is a prospective, observational study enrolling patients presenting to a cardiology service at a tertiary referral center. We assessed the clinical value of applying a smart device artificial intelligence (AI)-based algorithm for detecting AF from 4 commercially available smart devices (AliveCor KardiaMobile, Apple Watch 6, Fitbit Sense, and Samsung Galaxy Watch3). Patients underwent a nearly simultaneous 12-lead ECG and 4 smart device SL-ECGs. The novel AI algorithm (PulseAI, Belfast, United Kingdom) was compared with each manufacturer's algorithm. RESULTS: We enrolled 206 patients (31\% female, median age 64 years). AF was present in 60 patients (29\%). Sensitivity and specificity for the detection of AF by the novel AI algorithm vs manufacturer algorithm were 88\% vs 81\% (P = .34) and 97\% vs 77\% (P {\textless} .001) for the AliveCor KardiaMobile, 86\% vs 81\% (P = .45) and 95\% vs 83\% (P {\textless} .001) for the Apple Watch 6, 91\% vs 67\% (P {\textless} .01) and 94\% vs 82\% (P {\textless} .001) for the Fitbit Sense, and 86\% vs 82\% (P = .63) and 94\% vs 80\% (P {\textless} .001) for the Samsung Galaxy Watch3, respectively. In addition, the proportion of SL-ECGs with an inconclusive diagnosis (1.2\%) was significantly lower for all smart devices using the AI-based algorithm compared to manufacturer's algorithms (14\%-17\%), P {\textless} .001. CONCLUSION: A novel AI algorithm reduced the rate of inconclusive SL-ECG diagnosis massively while maintaining sensitivity and improving the specificity compared to the manufacturers' algorithms.

Assessment of Fitbit Charge 4 for sleep stage and heart rate monitoring against polysomnography and during home monitoring in Huntington's disease (2024)

Journal of clinical sleep medicine: JCSM: official publication of the American Academy of Sleep Medicine

Abstract

Study objectives: Wearable devices, monitoring sleep stages and heart rate (HR), bring the potential for longitudinal sleep monitoring in patients with neurodegenerative diseases. Sleep quality reduces with disease progression in Huntington's disease (HD). However, the involuntary movements characteristic of HD may affect the accuracy of wrist-worn devices. This study compares sleep stage and heart rate data from the Fitbit Charge 4 (FB) against polysomnography (PSG) in participants with HD. Methods: Ten participants with manifest HD wore a FB during overnight hospital-based PSG, and for nine of these participants continued to wear the FB for seven nights at home. Sleep stages (30s epochs) and minute-by-minute HR were extracted and compared against PSG data. Results: FB-estimated total sleep and wake times, and sleep stage times were in good agreement with PSG, with intra-class correlations 0.79-0.96. However, poor agreement was observed for Wake After Sleep Onset, and the number of awakenings. FB detected wake with 68.6±15.5% sensitivity and 93.7±2.5% specificity, rapid eye movement (REM) sleep with high sensitivity and specificity (78.7±31.9%, 95.6±2.3%), and deep sleep with lower sensitivity but high specificity (56.4±28.8%, 95.0±4.8%). FB HR was strongly correlated with PSG, and the mean absolute error between FB and PSG HR data was 1.16 ± 0.42 bpm. At home, longer sleep and shorter wake times were observed compared to hospital data, while percentage sleep stage times were consistent with hospital data. Conclusions: Results suggest the potential for long-term monitoring of sleep patterns using wrist-worn wearable devices as part of symptom management in HD. Keywords: PPG; PSG; home monitoring; neurodegeneration; wearable sensor.

Imagery rescripting and cognitive restructuring for inpatients with moderate and severe depression - a controlled pilot study (2024)

BMC psychiatry

Abstract

Background: This controlled pilot study investigates the effect of the combined use of cognitive restructuring (CR) and imagery rescripting (IR) compared to treatment as usual among inpatients with moderate and severe depression. Alongside expert ratings and self-report tools, fitness wristbands were used as an assessment tool. Methods: In addition to the standard inpatient care (SIC) program, 33 inpatients with moderate and severe depression were randomly assigned to an intervention group (two sessions of IR and CR) or an active treatment-as-usual (TAU) control group (two sessions of problem-solving and build-up of positive activity). Depression severity was assessed by the Hamilton Depression Rating Scale-21 (HDRS-21), the Beck Depression Inventory-II (BDI-II), and as a diagnostic adjunct daily step count via the Fitbit Charge 3™. We applied for analyses of HDRS-21 and BDI-II, 2 × 2 repeated-measures analysis of variance (ANOVA), and an asymptotic Wilcoxon test for step count. Results: The main effect of time on both treatments was η2 = .402. Based on the data from the HDRS-21, patients in the intervention group achieved significantly greater improvements over time than the TAU group (η2 = .34). The BDI-II data did not demonstrate a significant interaction effect by group (η2 = .067). The daily hourly step count for participants of the intervention group was significantly higher (r = .67) than the step count for the control group. Conclusions: The findings support the utilization of imagery-based interventions for treating depression. They also provide insights into using fitness trackers as psychopathological assessment tools for depressed patients. Trial registration: The trial is registered at the German Clinical Trials Register (Deutsches Register Klinischer Studien) under the registration number: DRKS00030809. Keywords: Cognitive restructuring; Depression; Fitness wristband; Imagery rescripting.

Feasibility Study on Menstrual Cycles With Fitbit Device (FEMFIT): Prospective Observational Cohort Study (2024)

JMIR mHealth and uHealth

Abstract

Background: Despite its importance to women's reproductive health and its impact on women's daily lives, the menstrual cycle, its regulation, and its impact on health remain poorly understood. As conventional clinical trials rely on infrequent in-person assessments, digital studies with wearable devices enable the collection of longitudinal subjective and objective measures. Objective: The study aims to explore the technical feasibility of collecting combined wearable and digital questionnaire data and its potential for gaining biological insights into the menstrual cycle. Methods: This prospective observational cohort study was conducted online over 12 weeks. A total of 42 cisgender women were recruited by their local gynecologist in Berlin, Germany, and given a Fitbit Inspire 2 device and access to a study app with digital questionnaires. Statistical analysis included descriptive statistics on user behavior and retention, as well as a comparative analysis of symptoms from the digital questionnaires with metrics from the sensor devices at different phases of the menstrual cycle. Results: The average time spent in the study was 63.3 (SD 33.0) days with 9 of the 42 individuals dropping out within 2 weeks of the start of the study. We collected partial data from 114 ovulatory cycles, encompassing 33 participants, and obtained complete data from a total of 50 cycles. Participants reported a total of 2468 symptoms in the daily questionnaires administered during the luteal phase and menses. Despite difficulties with data completeness, the combined questionnaire and sensor data collection was technically feasible and provided interesting biological insights. We observed an increased heart rate in the mid and end luteal phase compared with menses and participants with severe premenstrual syndrome walked substantially fewer steps (average daily steps 10,283, SD 6277) during the luteal phase and menses compared with participants with no or low premenstrual syndrome (mean 11,694, SD 6458). Conclusions: We demonstrate the feasibility of using an app-based approach to collect combined wearable device and questionnaire data on menstrual cycles. Dropouts in the early weeks of the study indicated that engagement efforts would need to be improved for larger studies. Despite the challenges of collecting wearable data on consecutive days, the data collected provided valuable biological insights, suggesting that the use of questionnaires in conjunction with wearable data may provide a more complete understanding of the menstrual cycle and its impact on daily life. The biological findings should motivate further research into understanding the relationship between the menstrual cycle and objective physiological measurements from sensor devices. Keywords: PMS; digital health; menstrual cycle; mobile app; premenstrual syndrome; sensor data; wearable device; women’s health.

Optimization of mHealth behavioral interventions for patients with chronic lymphocytic leukemia: the HEALTH4CLL study (2024)

Journal of Cancer Survivorship: Research and Practice

Abstract

Purpose: This pilot study of a diet and physical activity intervention (HEALTH4CLL) was conducted to reduce fatigue and improve physical function (PF) in patients with chronic lymphocytic leukemia (CLL). Methods: The HEALTH4CLL study used a randomized factorial design based on the multiphase optimization strategy (MOST). Patients received diet, exercise, and body weight management instructional materials plus a Fitbit and were randomized to undergo one of 16 combinations of 4 evidence-based mHealth intervention strategies over 16 weeks. Patients' fatigue, PF, health-related quality of life, behavior changes, and program satisfaction and retention were assessed. Paired t-tests were used to examine changes in outcomes from baseline to follow-up among patients. Factorial analysis of variance examined effective intervention components and their combinations regarding improvement in fatigue and PF scores. Results: Among 31 patients, we observed significant improvements in fatigue (+ 11.8; t = 4.08, p = 0.001) and PF (+ 2.6; t = 2.75, p = 0.01) scores. The combination of resistance and aerobic exercise with daily self-monitoring was associated with improved fatigue scores (β = 3.857, SE = 1.617, p = 0.027). Analysis of the individual components of the MOST design demonstrated greater improvement in the PF score with resistance plus aerobic exercise than with aerobic exercise alone (β = 2.257, SE = 1.071, p = 0.048). Conclusions: Combined aerobic and resistance exercise and daily self-monitoring improved PF and reduced fatigue in patients with CLL. Implications for cancer survivors: This pilot study supported the feasibility of a low-touch mHealth intervention for survivors of CLL and provided preliminary evidence that exercising, particularly resistance exercise, can improve their symptoms and quality of life. Keywords: Cancer; Exercise; Non-Hodgkin lymphoma; Nutrition; Physical activity; Skeletal muscle function.

Criterion-Related Validity of Consumer-Wearable Activity Trackers for Estimating Steps in Primary Schoolchildren under Controlled Conditions: Fit-Person Study (2024)

Journal of Sports Science \& Medicine

Abstract

The purposes were to examine the criterion-related validity of the steps estimated by consumer-wearable activity trackers (wrist-worn activity trackers: Fitbit Ace 2, Garmin Vivofit Jr, and Xiomi Mi Band 5; smartphone applications: Pedometer, Pedometer Pacer Health, and Google Fit/Apple Health) and their comparability in primary schoolchildren under controlled conditions. An initial sample of 66 primary schoolchildren (final sample = 56; 46.4% females), aged 9-12 years old (mean = 10.4 ± 1.0 years), wore three wrist-worn activity trackers (Fitbit Ace 2, Garmin Vivofit Jr 2, and Xiaomi Mi Band 5) on their non-dominant wrist and had three applications in two smartphones (Pedometer, Pedometer Pacer Health, and Google Fit/Apple Health for Android/iOS installed in Samsung Galaxy S20+/iPhone 11 Pro Max) in simulated front trouser pockets. Primary schoolchildren's steps estimated by the consumer-wearable activity trackers and the video-based counting independently by two researchers (gold standard) were recorded while they performed a 200-meter course in slow, normal and brisk pace walking, and running conditions. Results showed that the criterion-related validity of the step scores estimated by the three Samsung applications and the Garmin Vivofit Jr 2 were good-excellent in the four walking/running conditions (e.g., MAPE = 0.6-2.3%; lower 95% CI of the ICC = 0.81-0.99), as well as being comparable. However, the Apple applications, Fitbit Ace 2, and Xiaomi Mi Band 5 showed poor criterion-related validity and comparability on some walking/running conditions (e.g., lower 95% CI of the ICC < 0.70). Although, as in real life primary schoolchildren also place their smartphones in other parts (e.g., schoolbags, hands or even somewhere away from the body), the criterion-related validity of the Garmin Vivofit Jr 2 potentially would be considerably higher than that of the Samsung applications. The findings of the present study highlight the potential of the Garmin Vivofit Jr 2 for monitoring primary schoolchildren's steps under controlled conditions.

Assessing the current utilization status of wearable devices in clinical research (2024)

Clinical Trials (London, England)

Abstract

Background/aims: Information regarding the use of wearable devices in clinical research, including disease areas, intervention techniques, trends in device types, and sample size targets, remains elusive. Therefore, we conducted a comprehensive review of clinical research trends related to wristband wearable devices in research planning and examined their applications in clinical investigations. Methods: As this study identified trends in the adoption of wearable devices during the planning phase of clinical research, including specific disease areas and targeted number of intervention cases, we searched ClinicalTrials.gov-a prominent platform for registering and disseminating clinical research. Since wrist-worn devices represent a large share of the market, we focused on wrist-worn devices and selected the most representative models among them. The main analysis focused on major wearable devices to facilitate data analysis and interpretation, but other wearables were also surveyed for reference. We searched ClinicalTrials.gov with the keywords "ActiGraph,""Apple Watch,""Empatica,""Fitbit,""Garmin," and "wearable devices" to obtain studies published up to 21 August 2022. This initial search yielded 3214 studies. After excluding duplicate National Clinical Trial studies (the overlap was permissible among different device types except for wearable devices), our analysis focused on 2930 studies, including simple, time-series, and type-specific assessments of various variables. Results: Overall, an increasing number of clinical studies have incorporated wearable devices since 2012. While ActiGraph and Fitbit initially dominated this landscape, the use of other devices has steadily increased, constituting approximately 10% of the total after 2015. Observational studies outnumbered intervention studies, with behavioral and device-based interventions being particularly prevalent. Regarding disease types, cancer and cardiovascular diseases accounted for approximately 20% of the total. Notably, 114 studies adopted multiple devices simultaneously within the context of their clinical investigations. Conclusions: Our findings revealed that the utilization of wearable devices for data collection and behavioral interventions in various disease areas has been increasing over time since 2012. The increase in the number of studies over the past 3 years has been particularly significant, suggesting that this trend will continue to accelerate in the future. Devices and their evaluation methods that have undergone thorough validation, confirmed their accuracy, and adhered to established legal regulations will likely assume a pivotal role in evaluations, allowing for remote clinical trials. Moreover, behavioral intervention therapy utilizing apps is becoming more extensive, and we expect to see more examples that will lead to their approval as programmed medical devices in the future. Keywords: ActiGraph; Apple Watch; Empatica; Fitbit; Garmin; clinical trial; disease area; intervention; observational study; wearable device.

Benefits of physical activity self-monitoring in patients with haemophilia: a prospective study with one-year follow-up (2024)

Haemophilia: The Official Journal of the World Federation of Hemophilia

Abstract

Introduction: Activity wristbands have been shown to be effective in relation to self-monitoring activity levels and increasing exercise adherence. However, previous reports have been based on short-term follow-ups in people with haemophilia (PWH). Aim: (1) To evaluate compliance with physical activity (PA) recommendations in PWH during a 1-year follow-up period using activity wristbands to record daily steps and intensity; (2) To determine the effect of PA self-monitoring on clinical outcomes. Methods: A prospective observational study was conducted in 27 adults with severe haemophilia undergoing prophylactic treatment. The Fitbit Charge HR was used to track daily PA for an entire year. The participants were encouraged to try to reach a goal of 10,000 steps/day and to track their progress. The pre- and post-evaluation included quality of life (A36 Hemophilia-QoL Questionnaire), joint health (Haemophilia Joint Health Score), functionality (Timed Up and Go test), and muscle strength. Results: A total of 323.63 (95%CI: 194-364) valid days (i.e., > 2000 steps) were recorded. The annual average number of steps per day taken by participants was 10,379. Sixteen (59%) PWH reached 10,000 steps/day at baseline and 17 (63%) at 1 year follow-up, with no significant differences (x2 = .33; p = .56). A statistically significant improvement was observed in daily moderate activity time (p = .012) and in the 'physical health' quality of life subscale (mean difference: 2.15 points; 95%CI: .64-3.65; p = .007). Conclusion: Our results suggest that patients with severe haemophilia who self-managed their PA can improve their long-term quality of life in the domain of physical health and also the daily time spent in moderate-intensity PA. Keywords: blood coagulation disorders; exercise; fitness tracker; haemophilic arthropathy; walking.

Actívatexto: Feasibility and Acceptability of a Mobile Intervention That Promotes Smoking Cessation and Physical Activity among Latinos (2024)

Cancer Research Communications

Abstract

The purpose of this study was to assess the feasibility and acceptability of a mobile intervention that promotes smoking cessation and physical activity among Latinos living in the United States. Twenty Latino adults who smoked and did not meet recommended levels of physical activity were recruited using community-based recruitment strategies. Participants received Actívatexto, a theory-based, culturally accommodated, 12-week text messaging intervention (available in English and Spanish) that promotes smoking cessation and physical activity. Actívatexto integrates four components: (i) a text messaging program, (ii) a wearable device, (iii) an online dashboard where the research team manages participants' incoming and outgoing data from both the text messaging program and wearable device, and (iv) nicotine replacement therapies (NRT). We assessed self-reported 7-day point prevalence smoking abstinence at month 3, weekly minutes of moderate to vigorous physical activity (MVPA), pharmacotherapy adherence, and satisfaction. At baseline, participants' mean age was 47.3 years old (SD 16.0), 55\% of participants were female, and 60\% indicated that Spanish was their language of preference. Seventy-five percent of participants smoked 1-10 cigarettes daily and 35\% smoked their first cigarette within 5 minutes after waking up. At month 3, 14 participants (70\%) self-reported 7-day point prevalence smoking abstinence (90\% follow-up rate). Fitbit-assessed mean minutes of MVPA per week increased from 113 (SD 127; range: 0-399) at week 1 to 177 (SD 163; range: 0-513) at month 3 (P = 0.15). Sixteen participants (88.9\%, 16/18) reported NRT use in the past 90 days. Most participants reported being extremely satisfied or satisfied with the intervention (88.9\%, 16/18). SIGNIFICANCE: Actívatexto resulted in a noteworthy cessation rate at month 3 (70\%), increased mean weekly minutes of MVPA, produced high use of NRT, and was well received by participants. Additional testing in a randomized clinical trial is warranted to assess the efficacy of the intervention.

Physical activity decreases in patients on the liver transplant waiting list and influences postoperative outcome-a prospective cohort study (2024)

Hepatobiliary Surgery and Nutrition

Abstract

BACKGROUND: Physical deconditioning affects patients suffering from end-stage liver disease (ESLD). Liver transplantation (LT) is the only curative option for ESLD. Growing evidence suggests that pre-habilitation is beneficial in reducing post-surgical morbidity and mortality. We investigated physical activity (PA) in patients awaiting LT in a country with long waiting times. METHODS: Prospective, single center, longitudinal study in Bern, Switzerland between June 2019 and February 2020 (halted due to SARS-CoV-2 pandemic), with follow-up data up to six months post-transplant. Patients were instructed to use a wrist tracker (FitBit) to monitor PA, which was assessed using mixed-effects generalized linear models. The study was approved by the local ethics committee (BASEC ID 2019-00606). RESULTS: Thirty-five patients were included [71\% male, median 59 years, body mass index (BMI) 28 kg/m2, lab Model End-Stage Liver Disease (MELD) 11], 17 (49\%) pre-frail and 5 (14\%) frail according to the Liver Frailty Index (LFI). Twenty-eight patients underwent transplantation with 0 ninety-day mortality and 15 (53.6\%) composite adverse clinical outcome. Median daily steps were 4,661 [interquartile range (IQR), 1,685-8,609] and weekly moderate PA (MPA) was 41 min (IQR, 0-127 min). Longitudinal analysis showed that female patients and patients on nutritional support had an increase in MPA between weeks 20 and 40. A significant decrease was seen in MPA after week 40, whilst no significant association was seen with age, Child-Pugh Score, LFI or quality of life at time of inclusion. MPA was significantly associated with the occurrence of the composite clinical endpoint after week 30 of waiting time (odds ratio 0.882, P=0.026). World Health Organization (WHO)-recommended MPA was significantly associated with less adverse composite clinical outcomes (P{\textless}0.001). CONCLUSIONS: In patients listed for LT, MPA decreased over time, showing a significant association with adverse outcome, specifically after week 30 on the waiting list. Our data support the implementation of routine pre-habilitation in patients awaiting LT.

Participatory development of an mHealth intervention delivered in general practice to increase physical activity and reduce sedentary behaviour of patients with prediabetes and type 2 diabetes (ENERGISED) (2024)

BMC public health

Abstract

BACKGROUND: The escalating global prevalence of type 2 diabetes and prediabetes presents a major public health challenge. Physical activity plays a critical role in managing (pre)diabetes; however, adherence to physical activity recommendations remains low. The ENERGISED trial was designed to address these challenges by integrating mHealth tools into the routine practice of general practitioners, aiming for a significant, scalable impact in (pre)diabetes patient care through increased physical activity and reduced sedentary behaviour. METHODS: The mHealth intervention for the ENERGISED trial was developed according to the mHealth development and evaluation framework, which includes the active participation of (pre)diabetes patients. This iterative process encompasses four sequential phases: (a) conceptualisation to identify key aspects of the intervention; (b) formative research including two focus groups with (pre)diabetes patients (n = 14) to tailor the intervention to the needs and preferences of the target population; (c) pre-testing using think-aloud patient interviews (n = 7) to optimise the intervention components; and (d) piloting (n = 10) to refine the intervention to its final form. RESULTS: The final intervention comprises six types of text messages, each embodying different behaviour change techniques. Some of the messages, such as those providing interim reviews of the patients' weekly step goal or feedback on their weekly performance, are delivered at fixed times of the week. Others are triggered just in time by specific physical behaviour events as detected by the Fitbit activity tracker: for example, prompts to increase walking pace are triggered after 5 min of continuous walking; and prompts to interrupt sitting following 30 min of uninterrupted sitting. For patients without a smartphone or reliable internet connection, the intervention is adapted to ensure inclusivity. Patients receive on average three to six messages per week for 12 months. During the first six months, the text messaging is supplemented with monthly phone counselling to enable personalisation of the intervention, assistance with technical issues, and enhancement of adherence. CONCLUSIONS: The participatory development of the ENERGISED mHealth intervention, incorporating just-in-time prompts, has the potential to significantly enhance the capacity of general practitioners for personalised behavioural counselling on physical activity in (pre)diabetes patients, with implications for broader applications in primary care.

Accuracy of Fitbit Charge 4, Garmin Vivosmart 4, and WHOOP Versus Polysomnography: Systematic Review (2024)

JMIR mHealth and uHealth

Abstract

BACKGROUND: Despite being the gold-standard method for objectively assessing sleep, polysomnography (PSG) faces several limitations as it is expensive, time-consuming, and labor-intensive; requires various equipment and technical expertise; and is impractical for long-term or in-home use. Consumer wrist-worn wearables are able to monitor sleep parameters and thus could be used as an alternative for PSG. Consequently, wearables gained immense popularity over the past few years, but their accuracy has been a major concern. OBJECTIVE: A systematic review of the literature was conducted to appraise the performance of 3 recent-generation wearable devices (Fitbit Charge 4, Garmin Vivosmart 4, and WHOOP) in determining sleep parameters and sleep stages. METHODS: Per the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) statement, a comprehensive search was conducted using the PubMed, Web of Science, Google Scholar, Scopus, and Embase databases. Eligible publications were those that (1) involved the validity of sleep data of any marketed model of the candidate wearables and (2) used PSG or an ambulatory electroencephalogram monitor as a reference sleep monitoring device. Exclusion criteria were as follows: (1) incorporated a sleep diary or survey method as a reference, (2) review paper, (3) children as participants, and (4) duplicate publication of the same data and findings. RESULTS: The search yielded 504 candidate articles. After eliminating duplicates and applying the eligibility criteria, 8 articles were included. WHOOP showed the least disagreement relative to PSG and Sleep Profiler for total sleep time (-1.4 min), light sleep (-9.6 min), and deep sleep (-9.3 min) but showed the largest disagreement for rapid eye movement (REM) sleep (21.0 min). Fitbit Charge 4 and Garmin Vivosmart 4 both showed moderate accuracy in assessing sleep stages and total sleep time compared to PSG. Fitbit Charge 4 showed the least disagreement for REM sleep (4.0 min) relative to PSG. Additionally, Fitbit Charge 4 showed higher sensitivities to deep sleep (75\%) and REM sleep (86.5\%) compared to Garmin Vivosmart 4 and WHOOP. CONCLUSIONS: The findings of this systematic literature review indicate that the devices with higher relative agreement and sensitivities to multistate sleep (ie, Fitbit Charge 4 and WHOOP) seem appropriate for deriving suitable estimates of sleep parameters. However, analyses regarding the multistate categorization of sleep indicate that all devices can benefit from further improvement in the assessment of specific sleep stages. Although providers are continuously developing new versions and variants of wearables, the scientific research on these wearables remains considerably limited. This scarcity in literature not only reduces our ability to draw definitive conclusions but also highlights the need for more targeted research in this domain. Additionally, future research endeavors should strive for standardized protocols including larger sample sizes to enhance the comparability and power of the results across studies.

Home-based circuit training and community walking for intermittent claudication (2024)

Annals of Vascular Surgery

Abstract

OBJECTIVE: Supervised exercise training is recommended for people with peripheral artery disease (PAD), yet it remains underutilised. Home-based exercise programmes are a potential alternative. The aim of this study was to assess the feasibility of conducting a full scale trial of a 12-week home-based exercise programme (HBEP) for people living with symptomatic PAD. METHODS: In a randomised feasibility trial, patients with intermittent claudication (IC) were allocated to either a HBEP or a non-exercise control. The HBEP group were given a Fitbit to use during a 12-week exercise programme comprising of personalised step goals and a resistance-based circuit to be undertaken at home twice weekly. The primary outcome was feasibility, assessed via eligibility, recruitment, attrition, tolerability, and adherence. Acceptability was assessed via semi-structured interviews. Secondary analysis was undertaken to determine the feasibility of collecting clinical outcome data. RESULTS: 188 people were screened, 133 were eligible (70.7\%), 30 were recruited (22.6\%) and one withdrew (3.33\%). Mean adherence to the daily step goal was 53.5\% (range=29.8-90.5\%), and 58.6\% of prescribed circuits were completed of which 56.4\% were at the desired intensity. Six adverse events were recorded, three of which were related to study involvement. No significant differences were observed in exploratory outcomes. Small clinically important differences were seen in walking speed and pain-free treadmill walking distance which should be confirmed or refuted in a larger trial. CONCLUSION: The HBEP was feasible and well tolerated, with successful recruitment and minimal attrition. The intervention was acceptable, with walking seen as more enjoyable than circuit exercise. The WALKSTRONG programme may be suitable for those who will not, or cannot, take part in supervised exercise outside of the home.

Changes in rest-activity rhythms in adolescents as they age: associations with brain changes and behavior in the ABCD study (2024)

medRxiv: The Preprint Server for Health Sciences

Abstract

BACKGROUND: Adolescents with disrupted rest-activity rhythms (RAR) including shorter sleep duration, later sleep timing and low physical activity levels have higher risk for mental and behavioral problems. However, it remains unclear whether the same associations can be observed for within-subject changes in RAR. METHODS: Our longitudinal investigation on RAR used Fitbit data from the Adolescent Brain Cognitive Development (ABCD) Study at the 2-year (FL2: aged 10-13 years) and 4-year follow-up (FL4: aged 13-16 years). 963 youths had good-quality Fitbit data at both time points. In this study we examined changes in RAR from FL2 to FL4, their environmental and demographic contributors as well as brain and behavioral correlates. RESULTS: From FL2 to FL4, adolescents showed decreases in sleep duration and physical activity as well as delayed sleep timing (Cohen's d .44-.75). The contributions of environmental and demographic factors to RAR changes were greatest to sleep timing (explained 10\% variance) and least to sleep duration (explained 1\% variance). Delays in sleep timing had stronger correlations with behavioral problems including greater impulsivity and poor academic performance than reductions in sleep duration or physical activity. Additionally, the various brain measures differed in their sensitivity to RAR changes. Reductions in sleep duration were associated with decreased brain functional connectivity between subcortical regions and sensorimotor and cingulo-opercular networks and with enhanced functional connectivity between sensorimotor, visual and auditory networks. Delays in sleep timing were mainly associated with grey matter changes in subcortical regions. CONCLUSIONS: The current findings corroborate the role of sleep and physical activity in adolescent's brain neurodevelopment and behavior problems. RAR might serve as biomarkers for monitoring behavioral problems in adolescents and to serve as potential therapeutic targets for mental disorders.

A pilot randomised controlled trial exploring the feasibility and efficacy of a human-AI sleep coaching model for improving sleep among university students (2024)

Digital Health

Abstract

OBJECTIVE: Sleep quality is a crucial concern, particularly among youth. The integration of health coaching with question-answering (QA) systems presents the potential to foster behavioural changes and enhance health outcomes. This study proposes a novel human-AI sleep coaching model, combining health coaching by peers and a QA system, and assesses its feasibility and efficacy in improving university students' sleep quality. METHODS: In a four-week unblinded pilot randomised controlled trial, 59 university students (mean age: 21.9; 64\% males) were randomly assigned to the intervention (health coaching and QA system; n = 30) or the control conditions (QA system; n = 29). Outcomes included efficacy of the intervention on sleep quality (Pittsburgh Sleep Quality Index; PSQI), objective and self-reported sleep measures (obtained from Fitbit and sleep diaries) and feasibility of the study procedures and the intervention. RESULTS: Analysis revealed no significant differences in sleep quality (PSQI) between intervention and control groups (adjusted mean difference = -0.51, 95\% CI: [-1.55-0.77], p = 0.40). The intervention group demonstrated significant improvements in Fitbit measures of total sleep time (adjusted mean difference = 32.5, 95\% CI: [5.9-59.1], p = 0.02) and time in bed (adjusted mean difference = 32.3, 95\% CI: [2.7-61.9], p = 0.03) compared to the control group, although other sleep measures were insignificant. Adherence was high, with the majority of the intervention group attending all health coaching sessions. Most participants completed baseline and post-intervention self-report measures, all diary entries, and consistently wore Fitbits during sleep. CONCLUSIONS: The proposed model showed improvements in specific sleep measures for university students and the feasibility of the study procedures and intervention. Future research may extend the intervention period to see substantive sleep quality improvements.

Associations between sleep health, negative reinforcement learning, and alcohol use among South Florida college students with elevated internalizing symptoms (2024)

Alcohol (Fayetteville, N.Y.)

Abstract

Negative reinforcement is proposed to mediate associations between sleep and alcohol use, especially among people with depression and/or anxiety symptoms. Worse sleep (e.g., shorter duration, less efficiency, more irregular timing) exacerbates negative emotions, which alcohol may temporarily relieve. Not yet examined, we propose sleep indirectly impacts early stages of alcohol use via differences in negative reinforcement learning (NRL), since sleep impacts emotion, reward response, and learning. The current study aimed to replicate associations between sleep and alcohol use, test associations with NRL, and examine indirect associations between sleep health and alcohol use via NRL among 60 underage college students (ages 18-20 years, 77\% female) varying in depression and anxiety symptoms. Participants wore Fitbit smartwatches and completed daily diaries measuring sleep and substance use for ∼14 days before completing two computer tasks assessing social (SNRL) and monetary (MNRL) negative reinforcement learning. Robust generalized linear models tested direct associations within the proposed model. SNRL performance was positively associated with alcohol use, but no other associations were observed. Statistical mediation models failed to indicate indirect effects of sleep on alcohol use via SNRL or MNRL performance. Post-hoc exploratory models examining depression and anxiety symptoms as moderators of direct associations indicated several interactions. Positive associations between sleep timing variability and alcohol use were weakened at higher anxiety symptom severity and stronger at higher depression symptom severity. The positive association between SNRL performance and alcohol use was also stronger at higher depression symptom severity. Among students with elevated depression symptoms, variable sleep timing and stronger SNRL performance were independently associated with more alcohol use, but indirect effects were not supported. Future research should replicate findings, confirm causality of interactions, and examine sleep timing and behavioral responses to negative social stimuli as targets for improving alcohol-related outcomes among underage college students with elevated depressive symptoms.

Effectiveness of a Lifestyle Improvement Support App in Combination with a Wearable Device in Japanese People with Type 2 Diabetes Mellitus: STEP-DM Study (2024)

Diabetes Therapy: Research, Treatment and Education of Diabetes and Related Disorders

Abstract

INTRODUCTION: Although the use of application (app)s and wearable devices supporting diabetes treatment has spread rapidly in recent years, evidence of their impact, especially in combination of them, is limited. TOMOCO™ is a lifestyle improvement support app that features interactive virtual conversations according to the programmed algorithm guiding users toward their goals of lifestyle improvement. We hypothesized that TOMOCO™ in combination with Fitbit, which accurately tracks users' activity level, would encourage people with type 2 diabetes mellitus (T2DM) to change their lifestyles and improve their glycated hemoglobin (HbA1c) levels without changes in conventional therapy. Thus, we performed the present study to explore the effectiveness of this combination in Japanese participants with T2DM who had not achieved their glycemic targets. METHODS: In this single-arm exploratory study, participants with T2DM used the TOMOCO™ and Fitbit in addition to the conventional diet/exercise therapy and anti-diabetic drug for 12 weeks. They were provided with feedback/advice by health care providers based on the TOMOCO™ and Fitbit records. The primary endpoint was the change in HbA1c from baseline to the end of the observation period. Data were expressed as mean ± standard deviation. RESULTS: Fifty-nine (96.7\%) of the 61 participants (male, 42 [71.2\%]; age, 60.1 ± 8.7 years; HbA1c level, 7.48 ± 0.37\% at screening) completed the study. At the end of the observation period, the HbA1c was significantly reduced (- 0.41 ± 0.41\%, p {\textless} 0.001). This trend was consistent across the preselected patient characteristics, including sex, age, and body mass index. However, it was more pronounced in the participants with earlier stages of behavioral changes defined by the transtheoretical model at baseline. CONCLUSIONS: The unique features of TOMOCO™ in combination with Fitbit, together with conventional therapy, may promote a healthy lifestyle and thus contribute to improving HbA1c in people with T2DM. CLINICAL TRIAL REGISTRATION: jRCT1070220007.

Implementation of a randomized mobile-technology lifestyle program in individuals with nonalcoholic fatty liver disease (2024)

Scientific Reports

Abstract

Identifying effective, feasible, low-cost interventions that promote sustainable lifestyle changes in nonalcoholic fatty liver disease (NAFLD) is a key unmet need. The aim of this study was to assess predictors of lifestyle practice patterns of NAFLD patients and evaluate the implementation of a mobile technology-based intervention. We prospectively enrolled adults with NAFLD (diagnosed by imaging or biopsy). Individuals with additional liver diseases or decompensated cirrhosis were excluded. Patient were randomized to usual care or a FitBit based program for 6-months. We obtained anthropometrics, labs, vibration controlled transient elastography (VCTE), health-related quality of life (HRQOL), physical activity, diet and motivation to change data. 70 patients were enrolled, 33\% with cirrhosis. Median age was 52.1 years, 47\% males, 83\% white, body mass index 32.3, liver stiffness 7.6 kPa, controlled attenuation parameter 319 db/m, and 50\% had diabetes. Baseline HRQOL was 5.4/7 and independently negatively correlated with level of concern about their disease and positively with physical function. Younger age was independently associated with unhealthy diets whereas diabetes was independently associated with unhealthy diets and higher VCTE kPa. 6-month follow-up data available on 31 patients showed trends in improvement in weight. In a cohort of NAFLD patients, we identified independent correlates of lifestyle behaviors and HRQOL. Implementation of interventions that improve physical function may improve HRQOL in NAFLD. Younger patients and those with diabetes appeared to have the greatest need for dietary interventions. Structured mobile technology lifestyle interventions using Fitbit and personalized coaching showed promise but require further validation with a focus on sustainability of intervention and improvement in outcomes.

Comparison of raw accelerometry data from ActiGraph, Apple Watch, Garmin, and Fitbit using a mechanical shaker table (2024)

PloS One

Abstract

The purpose of this study was to evaluate the reliability and validity of the raw accelerometry output from research-grade and consumer wearable devices compared to accelerations produced by a mechanical shaker table. Raw accelerometry data from a total of 40 devices (i.e., n = 10 ActiGraph wGT3X-BT, n = 10 Apple Watch Series 7, n = 10 Garmin Vivoactive 4S, and n = 10 Fitbit Sense) were compared to reference accelerations produced by an orbital shaker table at speeds ranging from 0.6 Hz (4.4 milligravity-mg) to 3.2 Hz (124.7mg). Two-way random effects absolute intraclass correlation coefficients (ICC) tested inter-device reliability. Pearson product moment, Lin's concordance correlation coefficient (CCC), absolute error, mean bias, and equivalence testing were calculated to assess the validity between the raw estimates from the devices and the reference metric. Estimates from Apple, ActiGraph, Garmin, and Fitbit were reliable, with ICCs = 0.99, 0.97, 0.88, and 0.88, respectively. Estimates from ActiGraph, Apple, and Fitbit devices exhibited excellent concordance with the reference CCCs = 0.88, 0.83, and 0.85, respectively, while estimates from Garmin exhibited moderate concordance CCC = 0.59 based on the mean aggregation method. ActiGraph, Apple, and Fitbit produced similar absolute errors = 16.9mg, 21.6mg, and 22.0mg, respectively, while Garmin produced higher absolute error = 32.5mg compared to the reference. ActiGraph produced the lowest mean bias 0.0mg (95\%CI = -40.0, 41.0). Equivalence testing revealed raw accelerometry data from all devices were not statistically significantly within the equivalence bounds of the shaker speed. Findings from this study provide evidence that raw accelerometry data from Apple, Garmin, and Fitbit devices can be used to reliably estimate movement; however, no estimates were statistically significantly equivalent to the reference. Future studies could explore device-agnostic and harmonization methods for estimating physical activity using the raw accelerometry signals from the consumer wearables studied herein.

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