Accuracy of Fitbit Wristbands in Measuring Sleep Stage Transitions and the Effect of User-Specific Factors
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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>90% (P=.047) was associated with significant increase in measurement error. Pittsburgh sleep quality index (PSQI)<5 and wake after sleep onset (WASO)<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.
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Sleep-stage transition dynamics measured by Fitbit devices are inaccurate compared to PSG.
“Also sleep-stage transition dynamics measured by Fitbit devices were found to be inaccurate compared to PSG [46].”
From Digital Biomarkers for Depression Screening With Wearable Devices: Cross-sectional Study With Machine Learning Modeling (2021) · cites this paper as [46]
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Liang, Z. (2022) · Electronics · doi:10.3390/electronics11203384 · Cited by 9 · see citing works
Self-citationThe Interactive Role of Sleep and Circadian Rhythms in Episodic Memory in Older Adults
Carlson, E. J., Wilckens, K. A., & Wheeler, M. E. (2023) · The Journals of Gerontology Series A · doi:10.1093/gerona/glad112 · Cited by 10 · see citing works
Liang, Z., & Martell, M. A. C. (2019) · IEEE Intl Conf on Dependable, Autonomic and Secure Computing, Intl Conf on Pervasive Intelligence and Computing, Intl Conf on Cloud and Big Data Computing, Intl Conf on Cyber Science and Technology Congress (DASC/PiCom/CBDCom/CyberSciTech) · doi:10.1109/dasc/picom/cbdcom/cyberscitech.2019.00143 · Cited by 10 · see citing works
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Doplor Sleep: Monitoring Hospital Soundscapes for Better Sleep Hygiene
Özcan, E., Liu, Y., Vroon, J., Kamphuis, D. J., & Spagnol, S. (2022) · International Conference on Medical and Health Informatics (ICMHI) · doi:10.1145/3545729.3545745 · Cited by 8 · see citing works
Zhu, Y., Stephenson, C., Moghimi, E., Jagayat, J., Nikjoo, N., Kumar, A., Shirazi, A., Patel, C., Omrani, M., & Alavi, N. (2023) · PLOS ONE · doi:10.1371/journal.pone.0285757 · Cited by 7 · see citing works
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Sleep habits of high school student-athletes and nonathletes during a semester
Ungaro, C. T., & Chavez, P. J. D. D. (2022) · Journal of Clinical Sleep Medicine · doi:10.5664/jcsm.10076 · Cited by 10 · see citing works
Recognizing Eating Activities in Free-Living Environment Using Consumer Wearable Devices
Bertrand, L., Cleyet-Marrel, N., & Liang, Z. (2021) · International Symposium on Sensor Science (I3S) · doi:10.3390/i3s2021dresden-10141 · Cited by 7 · see citing works
Self-citationPilot Sleep Behavior across Time during Ultra-Long-Range Flights
Devine, J. K., Choynowski, J. J., Garcia, C. R., Simoes, A. S., Guelere, M. R., de Godoy, B., Silva, D. S., Pacheco, P., & Hursh, S. R. (2021) · Clocks & Sleep · doi:10.3390/clockssleep3040036 · Cited by 6 · see citing works
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Sleep stage prediction using multimodal body network and circadian rhythm
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Enhancing Active Learning in Web Development Classes Using Pairwise Pre-and-Post Lecture Quizzes
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Self-citationEnvironmental open-source data sets and sleep-wake rhythms of populations: an overview
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Multiscale Attention Entropy (MSAE) of Overnight Pulse Oximetry for Assessing Sleep Apnea
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Self-citationSrivali, N., & Cheungpasitporn, W. (2026) · Sleep Medicine · doi:10.1016/j.sleep.2026.108941 · Cited by 2 · see citing works
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Mining Contrast Rules in a Sleep Apnea Dataset
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Self-citationDeveloping and Validating Ensemble Classifiers for At-Home Sleep Apnea Screening
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A Pervasive Sensing Approach to Automatic Assessment of Trunk Coordination Using Mobile Devices
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Self-citationNight Float Rotations: Continued Questions With Few Answers
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ОГЛЯД СУЧАСНИХ ТЕХНОЛОГІЙ ДЛЯ ДІАГНОСТИКИ ЯКОСТІ СНУ
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Wearable and app-based resilience modelling in employees
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- Authors
- Liang, Z., & Chapa-Martell, M. A.
- Affiliations
- The University of Tokyo (JP)
- Corresponding author
- Liang, Zilu
- Venue
- JMIR mHealth and uHealth
- Publisher
- JMIR Publications Inc.
- Published
- 2019-06-06
- Volume
- 7
- Issue
- 6
- Pages
- e13384
- ISSN
- 2291-5222 (ISSN-L)
- PMC
- PMC6592508
- OpenAlex
- W2940755444
- Cited by
- 76
- References
- 64
- Citations by year
- 2026: 9 · 2025: 2 · 2024: 10 · 2023: 12 · 2022: 13 · 2021: 13 · 2020: 12 · 2019: 4 · 2018: 1
- FWCI
- 12.38
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- JMIR mhealth and uhealth (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Japan Society for the Promotion of Science (16H07469, 19K20141)
- Topics
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Sleep and related disorders
- Physical Sciences › Engineering › Biomedical Engineering › Non-Invasive Vital Sign Monitoring
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Keywords
- sleep stage transitions, wearable devices, sleep stage classification, measurement accuracy, polysomnography, sleep efficiency, wake after sleep onset, Pittsburgh Sleep Quality Index
- MeSH terms
- Wearable Electronic Devices, Adult, Female, Humans, Japan, Male, Surveys and Questionnaires, Sleep Stages, Weights and Measures, Reproducibility of Results, Polysomnography
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