Accepted for/Published in: Journal of Medical Internet Research
Date Submitted: Sep 16, 2019
Date Accepted: Oct 17, 2019
(closed for review but you can still tweet)
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
Accuracy of wristband Fitbit models in assessing sleep: A systematic review and meta-analysis
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 condition in a cost-efficient way.
Objective:
We conducted a systematic review of publications reporting 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 PubMed, CINAHL, Cochran, Medline, PsycInfo, and Web of Science databases using keyword ‘Fitbit’ to identify relevant publications meeting predefined inclusion/exclusion criteria.
Results:
The search yielded 1649 candidate articles, with 11 others identified through citations. After eliminating duplicates and in compliance with inclusion/exclusion criteria, 20 qualified for systematic review and with 8 providing quantitative data for meta-analysis. In reference to polysomnography (PSG), non-sleep-staging Fitbit wristband models tended to overestimate Total Sleep Time and Sleep Efficiency and underestimate Wake After Sleep Onset, with accuracy between 0.81 and 0.91, sensitivity between 0.87 and 0.99, and specificity between 0.10 and 0.52 in correctly identifying sleep epochs. In comparison to PSG, Fitbit models that collectively utilized heart rate variability and body movement to assess sleep-stages performed better, and with higher sensitivity (0.95-0.96) and specificity (0.58-0.69), than early-generation non-sleep-staging Fitbit models that utilized only body movement. Moreover, relative to standard PSG, performance of sleep-staging Fitbit models was better than that reported for actigraphy.
Conclusions:
Sleep-staging Fitbit models show promising performance, especially in differentiating wake from sleep. However, although 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 not a substitute for PSG.
Citation
