Abstract
Background: Sleep is essential for overall health and well-being, but assessing sleep architecture is often costly and time-con suming, relying primarily on polysomnography (PSG). While wrist-worn wearables offer alternatives, they face limitations regarding user compliance, such as battery charging and physical discomfort. Nearable devices address these burdens, but they regularly lack rigorous validation, especially in real-world settings. Objective: This study evaluates the accuracy and reliability of the Withings Sleep Analyzer (WSA), a contactless sleep monitoring device, compared to PSG in a home setting using a large and diverse cohort of healthy individuals. Methods: A total of 117 healthy volunteers (69 women; mean 39.9, SD 11.4 years), prospectively recruited from the general population, underwent home-based PSG and simultaneous WSA recording. The study was conducted under free-living conditions, without constraints on substance intake, prebedtime activity, or forced sleep schedules. The main outcomes were the device’s performance in sleep-wake distinction and sleep stage identification using accuracy, kappa, sensitivity, specific ity, and the mean absolute error of sleep measures on the entire population and demographic, clinical, and environmental subgroups. Results: WSA demonstrates high sensitivity (93%, 95% CI 92%-94%) for sleep detection and moderate sensitivity (73%, 95% CI 69%-77%) for wakefulness, achieving an overall accuracy of 87% (95% CI 86%-87%) for sleep-wake distinction. The device showed consistent performance across various demographic subgroups, including different age, BMI, mattress, and sleep arrangements (with or without bed partner) categories. Challenges were noted in accurately classifying specific sleep stages, particularly in distinguishing between light and deep sleep, with a mean accuracy of 63% (95% CI 62%-65%) and a Cohen κ of 0.49 (95% CI 0.47-0.51). The WSA tended to overestimate total sleep time (20 min, 95% CI 10 min to 31 min) and light sleep (1 h 21 min, 95% CI 1 h 8 min to 1 h 36 min) while underestimating rapid eye movement (−15 min, 95% CI −23 min to −8 min) and deep sleep (−46 min, 95% CI −59 min to −34 min) durations. Disagreements between expert reviewers were mirrored in part by the WSA’s misclassifications. Participants reported altered perceived sleep quality during the night with the PSG, suggesting discomfort during sleep. Conclusions: Being contactless and placed under the mattress, the WSA offers a promising approach to long-term sleep monitoring in natural home environments. It shows competitive performance in sleep-wake and sleep stage identification compared to other consumer devices. Progress in wearable and nearable devices is necessary to enhance their accuracy to better support the monitoring of populations with impaired sleep, although limited by an imperfect gold standard. This work also emphasizes the importance of using large, diverse, and challenging datasets, as well as the need for a standardized methodology for accurate sleep stage classification.