Fitbit Research Library

Last Updated Apr 19, 2024

The following 1221 publications have used Fitbit devices in research. Know of another study not on our list? Send it to us.

Category
Validation
Device(s)
All
Population
All
Data Used
Sleep
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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

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.
Close
x

Comparative assessment of sleep quality estimates using home monitoring technology (2014)

2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society

Abstract

Poor sleep quality is associated with chronic diseases, weight increase and cognitive dysfunction. Home monitoring solutions offer the possibility of offering tailored sleep coaching interventions. There are several new commercially available devices for tracking sleep, and although they have been tested in sleep laboratories, little is known about the errors associated with the use in the home. To address this issue we performed a study in which we compared the sleep monitoring data from two commercially available systems: Fitbit One and Beddit Pro. We studied 23 subjects using both systems over a week each and analyzed the degree of agreement for different aspects of sleep. The results suggest the need for individual-tailoring of the estimation process. Not only do these models address improved accuracy of sleep quality estimates, but they also provide a framework for the representation and harmonization for monitoring data across studies.
Close
x

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.
Close
x

A practical validation study of a commercial accelerometer using good and poor sleepers (2016)

Health Psychology Open

Abstract

We validated a Fitbit sleep tracking device against typical research-use actigraphy across four nights on 38 young adults. Fitbit devices overestimated sleep and were less sensitive to differences compared to the Actiwatch, but nevertheless captured 88 (poor sleepers) to 98?percent (good sleepers) of Actiwatch estimated sleep time changes. Bland–Altman analysis shows that the average difference between device measurements can be sizable. We therefore do not recommend the Fitbit device when accurate point estimates are important. However, when qualitative impacts are of interest (e.g. the effect of an intervention), then the Fitbit device should at least correctly identify the effect's sign.
Close
x

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.
Close
x

Reliability of Sleep Measures from Four Personal Health Monitoring Devices Compared to Research-Based Actigraphy and Polysomnography (2016)

Sensors

Abstract

Polysomnography (PSG) is the “gold standard” for monitoring sleep. Alternatives to PSG are of interest for clinical, research, and personal use. Wrist-worn actigraph devices have been utilized in research settings for measures of sleep for over two decades. Whether sleep measures from commercially available devices are similarly valid is unknown. We sought to determine the validity of five wearable devices: Basis Health Tracker, Misfit Shine, Fitbit Flex, Withings Pulse O2, and a research-based actigraph, Actiwatch Spectrum. We used Wilcoxon Signed Rank tests to assess differences between devices relative to PSG and correlational analysis to assess the strength of the relationship. Data loss was greatest for Fitbit and Misfit. For all devices, we found no difference and strong correlation of total sleep time with PSG. Sleep efficiency differed from PSG for Withings, Misfit, Fitbit, and Basis, while Actiwatch mean values did not differ from that of PSG. Only mean values of sleep efficiency (time asleep/time in bed) from Actiwatch correlated with PSG, yet this correlation was weak. Light sleep time differed from PSG (nREM1 + nREM2) for all devices. Measures of Deep sleep time did not differ from PSG (SWS + REM) for Basis. These results reveal the current strengths and limitations in sleep estimates produced by personal health monitoring devices and point to a need for future development.
Close
x

A Comparison Study Of The Fitbit Activity Monitor And PSG For Assessing Sleep Patterns And Movement In Children (2016)

Journal of Otolaryngology Advances

Abstract

BACKGROUND: Despite its expense, labor and intrusiveness, polysomnography (PSG) is the gold standard for diagnosing obstructive sleep apnea (OSA). Recently, commercially available electronic activity monitors, such as Fitbit, have become widely accepted and can provide an estimate of sleep patterns for screening children with possible OSA. A previous study demonstrated Fitbit to be valid compared to PSG in adults. To date, these devices have not been extensively utilized for research in children with sleep disordered breathing (SDB). OBJECTIVE: To evaluate the validity of the Fitbit activity monitor compared to PSG in children and adolescents with SDB. METHODS: Data was collected from 14 children, ages 3 through 11, who were scheduled for a PSG during the study period. Fitbit was worn concurrently during the night of the PSG. Analyses were performed by comparing total sleep time, number of awakenings, sleep efficiency and wake after sleep onset (WASO) Fitbit parameters with the corresponding parameters measured by PSG using Spearman’s rho. Fitbit movement epochs were also compared to PSG epochs showing movement behavior. RESULTS: Pilot data suggest that Fitbit demonstrates a high sensitivity for sleep, a low specificity for wake and a trend suggesting good association of movement measurements. CONCLUSION: Although Fitbit is not as accurate as PSG for determining wakefulness, it may be a useful screening device to assess gross sleep quality in children. Further studies are indicated to validate these findings.
Close
x

Estimation of sleep stages in a healthy adult population from optical plethysmography and accelerometer signals (2017)

Physiological Measurement

Abstract

Objective: This paper aims to report on the accuracy of estimating sleep stages using a wrist-worn device that measures movement using a 3D accelerometer and an optical pulse photoplethysmograph (PPG). Approach: Overnight recordings were obtained from 60 adult participants wearing these devices on their left and right wrist, simultaneously with a Type III home sleep testing device (Embletta MPR) which included EEG channels for sleep staging. The 60 participants were self-reported normal sleepers (36 M: 24 F, age  =  34  ±  10, BMI  =  28  ±  6). The Embletta recordings were scored for sleep stages using AASM guidelines and were used to develop and validate an automated sleep stage estimation algorithm, which labeled sleep stages as one of Wake, Light (N1 or N2), Deep (N3) and REM (REM). Features were extracted from the accelerometer and PPG sensors, which reflected movement, breathing and heart rate variability. Main results: Based on leave-one-out validation, the overall per-epoch accuracy of the automated algorithm was 69%, with a Cohen's kappa of 0.52  ±  0.14. There was no observable bias to under- or over-estimate wake, light, or deep sleep durations. REM sleep duration was slightly over-estimated by the system. The most common misclassifications were light/REM and light/wake mislabeling. Significance: The results indicate that a reasonable degree of sleep staging accuracy can be achieved using a wrist-worn device, which may be of utility in longitudinal studies of sleep habits.
Close
x

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.
Close
x

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.
Close
x

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.
Close
x

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.
Close
x

Validity of a commercial wearable sleep tracker in adult insomnia disorder patients and good sleepers (2017)

Journal of Psychosomatic Research

Abstract

OBJECTIVES To compare the accuracy of the commercial Fitbit Flex device (FF) with polysomnography (PSG; the gold-standard method) in insomnia disorder patients and good sleepers. METHODS Participants wore an FF and actigraph while undergoing overnight PSG. Primary outcomes were intraclass correlation coefficients (ICCs) of the total sleep time (TST) and sleep efficiency (SE), and the frequency of clinically acceptable agreement between the FF in normal mode (FFN) and PSG. The sensitivity, specificity, and accuracy of detecting sleep epochs were compared among FFN, actigraphy, and PSG. RESULTS The ICCs of the TST between FFN and PSG in the insomnia (ICC=0.886) and good-sleepers (ICC=0.974) groups were excellent, but the ICC of SE was only fair in both groups. The TST and SE were overestimated for FFN by 6.5min and 1.75%, respectively, in good sleepers, and by 32.9min and 7.9% in the insomnia group with respect to PSG. The frequency of acceptable agreement of FFN and PSG was significantly lower (p=0.006) for the insomnia group (39.4%) than for the good-sleepers group (82.4%). The sensitivity and accuracy of FFN in an epoch-by-epoch comparison with PSG was good and comparable to those of actigraphy, but the specificity was poor in both groups. CONCLUSIONS The ICC of TST in the FFN–PSG comparison was excellent in both groups, and the frequency of agreement was high in good sleepers but significantly lower in insomnia patients. These limitations need to be considered when applying commercial sleep trackers for clinical and research purposes in insomnia.
Close
x

Considering interpersonal differences in validating wearable sleep-tracking technologies (2017)

2017 Tenth International Conference on Mobile Computing and Ubiquitous Network

Abstract

This study investigated an important yet usually neglected question in validating consumer sleep tracking devices: does interpersonal difference play a role in the validity of consumer sleep trackers? Most existing validation studies assume that the accuracy of these devices is consistent cross individuals, ignoring the fact that human sleep demonstrates significant interpersonal differences. This study aimed to test this assumption through validating two newest gadgets of home sleep tracking technologies, i.e. Fitbit Charge 2 (wearable wristband) and Neuroon (wearable EEG eye mask), in comparison to a clinical device. Two participants were recruited to track their sleep using a Fitbit, a Neuroon, and a clinical device SLEEP SCOPE simultaneously for approximately 40 nights. Data analysis was conducted for each participant separately. The results showed that the Fitbit measurements completely deviated from the clinical measurements for one participant, but agreed very well for another participant, especially in the dimension of total sleep time. Neuroon measurements agreed well but were uncorrelated to clinical measurements for one participant, and were correlated significantly but agreed poorly for another participant. This study suggested that interpersonal differences should be considered in future validation studies and in designing better consumer sleep-tracking technologies.
Close
x

Are wearable EEG devices more accurate than fitness wristbands for home sleep Tracking? Comparison of consumer sleep trackers with clinical devices (2017)

2017 IEEE 6th Global Conference on Consumer Electronics

Abstract

This study aimed to compare two off-the-shelf sleep tracking devices, Fitbit (wearable fitness wristband) and Neuroon (wearable EEG eye mask), for measuring sleep under free living conditions. Taking the measurements of a clinical sleep monitor as the ground truth, we compared the measurements of Fitbit and Neuroon on total sleep time (TST), wake after sleep onset (WASO), the number of awakenings (NAWK), sleep onset latency (SOL), and sleep efficiency (SE). Although the users of home sleep tracking technologies generally assume that the measurements by EEG-based sleep trackers are more accurate than movement-based wristbands, our study showed that compared to Neuroon EEG eye mask, the measurements by Fitbit wristband agreed better to those of the clinical device.
Close
x

Pilot Validation of Ambulatory Activity Monitors for Sleep Measurement in Huntington's Disease Gene Carriers (2017)

Journal of Huntington's Disease

Abstract

Sleep disturbance occurs early in Huntington's disease (HD). Consumer- and research-grade activity monitors may enable routine assessment of sleep disturbances in HD. We compared Actiwatch Spectrum Pro, Jawbone UP2 and Fitbit One to the gold standard, polysomnography, in four late presymptomatic and three early HD participants. Compared to polysomnography, all ambulatory monitors overestimated total sleep time by textgreater60 minutes and sleep efficiency by ∼15%. Thus, for assessment of specific sleep parameters in HD, none of the activity monitors are sufficiently accurate to replace polysomnography, although they may be sufficient for estimating overall sleep-wake patterns. Larger sample replication is required.
Close
x

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.
Close
x

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.
Close
x

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).
Close
x

Validation of Fitness Tracker for Sleep Measures in Women with Asthma (2018)

Journal of Asthma

Abstract

AbstractObjective: Night-time wakening with asthma symptoms is key to assessment and therapy decisions, with no gold standard objective measure. The study aims were to 1) determine the feasibility, 2) explore equivalence, and 3) test concordance of a consumer-based accelerometer with standard actigraphy for measurement of sleep patterns in women with asthma as an adjunct to self-report.Methods: Panel study design of women with poorly controlled asthma from a university-affiliated primary care clinic system was used. We assessed sensitivity and specificity, equivalence and concordance of sleep time, sleep efficiency, and wake counts between the consumer-based accelerometer Fitbit Charge™ and Actigraph wGT3X+. We linked data between devices for comparison both automatically by 24-hour period and manually by sleep segment.Results: Analysis included 424 938 minutes, 738 nights, and 833 unique sleep segments from 47 women. The fitness tracker demonstrated 97% sensitivity and 40% specificity to identify sleep. ...
Close
x

Comparison of Wearable Trackers’ Ability to Estimate Sleep (2018)

International Journal of Environmental Research and Public Health

Abstract

Tracking physical activity and sleep patterns using wearable trackers has become a current trend. However, little information exists about the comparability of wearable trackers measuring sleep. This study examined the comparability of wearable trackers for estimating sleep measurement with a sleep diary (SD) for three full nights. A convenience sample of 78 adults were recruited in this research with a mean age of 27.6 &plusmn; 11.0 years. Comparisons between wearable trackers and sleep outcomes were analyzed using the mean absolute percentage errors, Pearson correlations, Bland&ndash;Altman Plots, and equivalent testing. Trackers that showed the greatest equivalence with the SD for total sleep time were the Jawbone UP3 and Fitbit Charge Heart Rate (effect size = 0.09 and 0.23, respectively). The greatest equivalence with the SD for time in bed was seen with the SenseWear Armband, Garmin Vivosmart, and Jawbone UP3 (effect size = 0.09, 0.16, and 0.07, respectively). Some of the wearable trackers resulted in closer approximations to self-reported sleep outcomes than a previously sleep research-grade device, these trackers offer a lower-cost alternative to tracking sleep in healthy populations.
Close
x

Validity of Consumer Activity Wristbands and Wearable EEG for Measuring Overall Sleep Parameters and Sleep Structure in Free-Living Conditions (2018)

Journal of Healthcare Informatics Research

Abstract

Consumer sleep tracking technologies offer an unobtrusive and cost-efficient way to monitor sleep in free-living conditions. Technological advances in hardware and software have significantly improved the functionality of the new gadgets that recently appeared in the market. However, whether the latest gadgets can provide valid measurements on overall sleep parameters and sleep structure such as deep and REM sleep has not been examined. In this study, we aimed to investigate the validity of the latest consumer sleep tracking devices including an activity wristband Fitbit Charge 2 and a wearable EEG-based eye mask Neuroon in comparison to a medical sleep monitor. First, we confirmed that Fitbit Charge 2 can automatically detect the onset and offset of sleep with reasonable accuracy. Second, analysis found that both consumer devices produced comparable results in measuring total sleep duration and sleep efficiency compared to the medical device. In addition, Fitbit accurately measured the number of awakenings, while Neuroon with good signal quality had satisfactory performance on total awake time and sleep onset latency. However, measuring sleep structure including light, deep, and REM sleep remains to be challenging for both consumer devices. Third, greater discrepancies were observed between Neuroon and the medical device in nights with more disrupted sleep and when the signal quality was poor, but no trend was observed in Fitbit Charge 2. This study suggests that current consumer sleep tracking technologies may be immature for diagnosing sleep disorders, but they are reasonably satisfactory for general purpose and non-clinical use.
Close
x

How well does a commercially available wearable device measure sleep in young athletes? (2018)

Chronobiology International

Abstract

The validity of a commercially available wearable device for measuring total sleep time was examined in a sample of well-trained young athletes during night-time sleep periods and daytime naps. Participants wore a FitBit HR Charge on their non-dominant wrist and had electrodes attached to their face and scalp to enable polysomnographic recordings of sleep in the laboratory. The FitBit automatically detected 24/30 night-time sleep periods but only 6/20 daytime naps. Compared with polysomnography, the FitBit overestimated total sleep time by an average of 52 ± 152 min for night-time sleep periods, and by 4 ± 8 min for daytime naps. It is important for athletes and practitioners to be aware of the limitations of wearable devices that automatically detect sleep duration.
Close
x

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.
Close
x

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.
Close
x

Preliminary Agreement on Tracking Sleep Between a Wrist-Worn Device Fitbit Alta and Consensus Sleep Diary (2019)

Telemedicine and e-Health

Abstract

Background: Sleep is related to various kinds of health outcomes. Many studies traditionally collect data on sleep using questionnaires or sleep diaries. An increasing popular alternative is a wrist-worn device. The accuracy of these devices is uncertain, and assessment of this accuracy is important. Introduction: The purpose of this study is to compare consensus sleep diary (CSD) and an actigraphy-based wrist-worn device (Fitbit Alta™ [Fitbit, San Francisco, CA]) measurements of total sleep time (TST), sleep onset latency, wake time after sleep onset, number of awakenings, and sleep efficiency. Materials and Methods: Ten healthy young adults (50{\%} female, 100{\%} Asian) in the age range between 20 to 24 years old wore a Fitbit Alta around their nondominated hand during seven consecutive nights. The participants also filled in a CSD every day. Results: On average, the wrist-worn device (Fitbit Alta) recorded a TST per night of 437.15 min, which is 5.46 min shorter on average than the CSD recorded (442.61 min). Bland-Altman plots indicate that there is large variance in the sleep recorded between Fitbit™ (Fitbit, San Francisco, CA) and CSD. For example, Fitbit recorded 2.15 more awakenings per night than CSD, which is equal to 13.09 min on average longer wake time after sleep onset. Conclusion: Fitbit and CSD show significant differences in recording sleep. We find that for most sleep metrics, the level of disagreement is small enough for the devices to be interchangeably used except for recording wakes during the night.
Close
x

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.
Close
x

Performance of a commercial multi-sensor wearable (Fitbit Charge HR) in measuring physical activity and sleep in healthy children (2020)

PLOS ONE

Abstract

Purpose This study sought to assess the performance of the Fitbit Charge HR, a consumer-level multi-sensor activity tracker, to measure physical activity and sleep in children. Methods 59 healthy boys and girls aged 9–11 years old wore a Fitbit Charge HR, and accuracy of physical activity measures were evaluated relative to research-grade measures taken during a combination of 14 standardized laboratory- and field-based assessments of sitting, stationary cycling, treadmill walking or jogging, stair walking, outdoor walking, and agility drills. Accuracy of sleep measures were evaluated relative to polysomnography (PSG) in 26 boys and girls during an at-home unattended PSG overnight recording. The primary analyses included assessment of the agreement (biases) between measures using the Bland-Altman method, and epoch-by-epoch (EBE) analyses on a minute-by-minute basis. Results Fitbit Charge HR underestimated steps ({\~{}}11.8 steps per minute), heart rate ({\~{}}3.58 bpm), and metabolic equivalents ({\~{}}0.55 METs per minute) and overestimated energy expenditure ({\~{}}0.34 kcal per minute) relative to research-grade measures (p{\textless} 0.05). The device showed an overall accuracy of 84.8{\%} for classifying moderate and vigorous physical activity (MVPA) and sedentary and light physical activity (SLPA) (sensitivity MVPA: 85.4{\%}; specificity SLPA: 83.1{\%}). Mean estimates of bias for measuring total sleep time, wake after sleep onset, and heart rate during sleep were 14 min, 9 min, and 1.06 bpm, respectively, with 95.8{\%} sensitivity in classifying sleep and 56.3{\%} specificity in classifying wake epochs.
Close
x

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.
Close
x

Comparing the accuracy of sleep staging data from a new wearable sleep electroencephalography device versus the Fitbit Charge 3 with polysomnography as reference (2021)

Clinical Neurophysiology

Abstract

Although sleep disorders are a common social problem, polysomnography (PSG), the gold standard in sleep diagnostics, can be burdensome and may interfere with natural sleep. We have developed a high-precision wearable electroencephalography (EEG) device for objectively measuring sleep at home. Actigraphy devices with the capability of measuring heart rate variability can also be used to evaluate sleep stages at home (e.g., Fitbit Charge 3). However, the accuracy of these inhome devices has not yet been compared. We hypothesized that the accuracy of our wearable sleep EEG device would be higher than that of the Fitbit Charge 3.
Close
x

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.
Close
x

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.
Close
x

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.
Close
x

Performance of wearable sleep trackers during nocturnal sleep and periods of simulated real-world smartphone use (2024)

Sleep Health

Abstract

GOAL AND AIMS: To test sleep/wake transition detection of consumer sleep trackers and research-grade actigraphy during nocturnal sleep and simulated peri-sleep behavior involving minimal movement. FOCUS TECHNOLOGY: Oura Ring Gen 3, Fitbit Sense, AXTRO Fit 3, Xiaomi Mi Band 7, and ActiGraph GT9X. REFERENCE TECHNOLOGY: Polysomnography. SAMPLE: Sixty-three participants (36 female) aged 20-68. DESIGN: Participants engaged in common peri-sleep behavior (reading news articles, watching videos, and exchanging texts) on a smartphone before and after the sleep period. They were woken up during the night to complete a short questionnaire to simulate responding to an incoming message. CORE ANALYTICS: Detection and timing accuracy for the sleep onset times and wake times. ADDITIONAL ANALYTICS AND EXPLORATORY ANALYSES: Discrepancy analysis both including and excluding the peri-sleep activity periods. Epoch-by-epoch analysis of rate and extent of wake misclassification during peri-sleep activity periods. CORE OUTCOMES: Oura and Fitbit were more accurate at detecting sleep/wake transitions than the actigraph and the lower-priced consumer sleep tracker devices. Detection accuracy was less reliable in participants with lower sleep efficiency. IMPORTANT ADDITIONAL OUTCOMES: With inclusion of peri-sleep periods, specificity and Kappa improved significantly for Oura and Fitbit, but not ActiGraph. All devices misclassified motionless wake as sleep to some extent, but this was less prevalent for Oura and Fitbit. CORE CONCLUSIONS: Performance of Oura and Fitbit is robust on nights with suboptimal bedtime routines or minor sleep disturbances. Reduced performance on nights with low sleep efficiency bolsters concerns that these devices are less accurate for fragmented or disturbed sleep.
Close
x