Accuracy of Fitbit Charge 4, Garmin Vivosmart 4, and WHOOP Versus Polysomnography: Systematic Review.
Level 1 - systematic review of randomized trials
Systematic review of diagnostic/validation studies against reference standards (PSG/EEG)
PubMed 38557808 · doi:10.2196/52192
What was done
A systematic review following PRISMA guidelines evaluated the validity of three consumer wearable devices (Fitbit Charge 4, Garmin Vivosmart 4, and WHOOP) for measuring sleep parameters and sleep stages. Five databases (PubMed, Web of Science, Google Scholar, Scopus, and Embase) were searched for studies comparing these devices against polysomnography (PSG) or ambulatory electroencephalography (EEG) in adults, excluding review articles, pediatric cohorts, and studies using subjective sleep diaries as reference standards.
What was found
From 504 candidate articles, 8 studies met inclusion criteria. WHOOP showed the lowest discrepancy relative to reference standards for total sleep time (-1.4 min), light sleep (-9.6 min), and deep sleep (-9.3 min), but had the largest discrepancy for rapid eye movement (REM) sleep (21.0 min). Fitbit Charge 4 demonstrated the lowest discrepancy for REM sleep (4.0 min) and achieved higher sensitivity for deep sleep (75%) and REM sleep (86.5%) than Garmin Vivosmart 4 and WHOOP. Both Fitbit Charge 4 and Garmin Vivosmart 4 demonstrated moderate overall accuracy against PSG for sleep staging and total sleep time.
Why it matters
Wrist-worn consumer trackers offer a practical alternative to PSG for long-term or at-home sleep monitoring, but users and researchers must account for device-specific biases in multistate sleep stage classification.
Limits
The review included only 8 studies, and the abstract does not report total human participant numbers or demographic breakdowns. Inherent heterogeneity across validation protocols and small sample sizes in the primary literature limit definitive comparisons between devices.
Cited by
- supports Consumer wearable sleep staging algorithms have approximately 60% to 80% accuracy relative to polysomnography.
- supports Deep sleep detection on consumer wearable devices is only about 60% to 70% accurate at best in most people.