Are Wearable Sleep-Tracking Devices Reliable Alternatives to Polysomnography? A Systematic Review and Meta-Analysis

Polysomnography (PSG) is the reference method for characterizing sleep architecture, but it is resource-intensive and difficult to scale for large cohort assessments. This has in creased interest in wearable devices for naturalistic sleep monitoring. This systematic review and meta-analysis evaluated how wearable sleep-tracking devices compare with laboratory PSG in healthy adults across standard sleep metrics and sleep stage durations.

PubMed, Scopus, Scielo, Web of Science, and the Cochrane Library were searched following PRISMA guidelines. Eligible studies included healthy adults undergoing simultaneous wearable and PSG recordings. Mean differences were synthesized for total sleep time, sleep latency, sleep efficiency, wake after sleep onset, and time spent in light (N1+N2), deep (N3), and REM sleep using fixed or random effects models based on heterogeneity, with significance set at p < .01. Risk of bias and applicability were assessed using QUADAS-2.

Sixteen studies met the inclusion criteria, and ten contributed to the meta-analysis. Wearable devices overestimated total sleep time and sleep efficiency and underestimated wake after sleep onset, with substantial variability between devices. No device demonstrated consistently superior performance. In individual studies, the closest agreement with PSG was observed for the Oura Ring (third generation) for sleep latency and sleep efficiency, and for selected Fitbit models for deep and REM sleep.

Wearable devices provide reasonable estimates of global sleep metrics and may complement PSG for population monitoring and longitudinal self-tracking. However, variable performance, methodological heterogeneity, and risk-of-bias considerations currently limit their use as stand-alone diagnostic tools or for detailed sleep-stage characterization.