ArticleJMIR mHealth and uHealth2019
Accuracy of Fitbit Wristbands in Measuring Sleep Stage Transitions and the Effect of User-Specific Factors.
Article in JMIR mHealth and uHealth, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers, 1 of them a synthesis that pooled it.
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Who cites it
38 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Clinical Applications of Mobile Health Wearable-Based Sleep Monitoring: Systematic Review.JMIR mHealth and uHealth · 2020Pooled it
- Magnetic Garments Promote Parasympathetic Dominance and Improve Sleep Quality in Male Long-Distance Runners Following a 30 km Run.Sensors (Basel, Switzerland) · 2024Trial
- Links of fitbit-derived sleep architecture and fragmentation with ADHD symptoms and externalizing behaviors in adolescents.European child & adolescent psychiatry · 2026Article
- Sleep and Activity Patterns in Depression From Wearable Data: Unsupervised Clustering Study.Journal of medical Internet research · 2026Article
- Article
- Examining the Agreement Between Subjective and Objective Measures of Sleep: A Comparison of Munich Chronotype Questionnaire and Fitbit-Derived Sleep Metrics.Journal of sleep research · 2025Article
- Accuracy Bias and Factors Influencing Polysomnography and Consumer Sleep-Monitoring Device Measuring of Total Sleep Time: A Mixed-Methods Study.Nature and science of sleep · 2025Article
- Developing probabilistic ensemble machine learning models for home-based sleep apnea screening using overnight SpO2 data at varying data granularity.Sleep & breathing = Schlaf & Atmung · 2024Article
- The Role of Relevance in Shaping Perceptions of Sleep Hygiene Games Among University Students: Mixed Methods Study.JMIR serious games · 2024Article
- From wearable sensor data to digital biomarker development: ten lessons learned and a framework proposal.NPJ digital medicine · 2024Review
- Association between sleep health and rates of self-reported medical errors in intern physicians: an ancillary analysis of the Intern Health Study.Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine · 2024Article
- Sleep stage prediction using multimodal body network and circadian rhythm.PeerJ. Computer science · 2024Article
- Exploring Variations in Sleep Perception: Comparative Study of Chatbot Sleep Logs and Fitbit Sleep Data.JMIR mHealth and uHealth · 2023Article
- The Interactive Role of Sleep and Circadian Rhythms in Episodic Memory in Older Adults.The journals of gerontology. Series A, Biological sciences and medical sciences · 2023Review
- Sleep and seizure risk in epilepsy: bed and wake times are more important than sleep duration.Brain : a journal of neurology · 2023Article
- Knowledge Discovery in Ubiquitous and Personal Sleep Tracking: Scoping Review.JMIR mHealth and uHealth · 2023Article
- Article
- Validity of Two Consumer Multisport Activity Tracker and One Accelerometer against Polysomnography for Measuring Sleep Parameters and Vital Data in a Laboratory Setting in Sleep Patients.Sensors (Basel, Switzerland) · 2022Article
- Sleep habits of high school student-athletes and nonathletes during a semester.Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine · 2022Article
- End-to-End Sleep Staging Using Nocturnal Sounds from Microphone Chips for Mobile Devices.Nature and science of sleep · 2022Article
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundIt 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.
objectiveThis 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.
methodsA 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.
resultsSleep 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.
conclusionsOur 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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