15, 17 Additionally, these wearables are integrated with smartphone applications, offering convenient collection, display and trend analysis of sleep as well as remote monitoring of participants. For example, the inclusion of photoplethysmography (PPG) to measure heart rate (HR) and heart rate variability (HRV) has improved sleep-wake detection and has been used to facilitate sleep staging. Recent models of consumer sleep trackers use multiple sensors to collect physiological data in order to overcome limitations of motion-based sleep detection. For a variety of reasons, early products demonstrated mixed performance when measuring sleep compared to research-grade devices. Manufacturers soon realized that with minor adaptations, their devices could mimic the function of far more expensive research actigraphs. Most consumer wearables used to track sleep started as fitness trackers used to monitor daytime activity levels using accelerometry. 8 At the other end of the age spectrum, children and adolescents tend to move more during their sleep, resulting in underestimation of sleep duration. 6, 7 For individuals such as older adults who are more likely to have such periods while they are in bed, actigraphy tends to overestimate sleep. 1, 5 Actigraphy shows good sensitivity in detecting sleep, but it has lower specificity in detecting periods of wakefulness, misclassifying periods when one is awake but motionless as sleep. 1–4 It has been validated against PSG in both healthy and clinical populations, across a range of age groups. It is thus less well suited for the characterization of sleep patterns studied over multiple nights outside a laboratory.Īctigraphy is a well-accepted means of gathering population sleep data for such purposes having been refined since its introduction in the 1970s. Polysomnography (PSG), the reference standard for measuring sleep in clinical settings requires the engagement of trained staff, is time-consuming to conduct and score, making it expensive. Keywords: validation, adolescents, wearable, polysomnography, actigraphy However, the device appears adequate for characterizing the effect of sleep duration manipulation on adolescent sleep macro-architecture. EBE analyses demonstrated excellent sleep-wake accuracies, specificities, and sensitivities – between 0.88 and 0.89 across all TIBs.Ĭonclusion: The Oura ring yielded comparable sleep measurement to research grade actigraphy at the latter’s default settings. Relative to PSG, Oura significantly underestimated REM sleep (12.8 to 19.5 minutes) and light sleep (51.1 to 81.2 minutes) but overestimated N3 by 31.5 to 46.8 minutes ( Ps < 0.01).
It was comparable to Actiwatch 2 at default sensitivity in the 6.5, and 8h TIB conditions. Oura significantly overestimated WASO by an average of 30.7 to 46.3 minutes. Results: Compared with PSG, Oura consistently underestimated TST by an average of 32.8 to 47.3 minutes ( Ps < 0.001) across the different TIB conditions Actiwatch 2 at its default setting underestimated TST by 25.8 to 33.9 minutes. Discrepancies in estimated sleep measures as well as sleep-wake, and sleep stage agreements were evaluated using Bland–Altman plots and epoch-by-epoch (EBE) analyses. Actiwatch data at two sensitivity settings were analyzed. Measurements were made over multiple nights and across three levels of sleep opportunity (5 nights with either 6.5 or 8h, and 3 nights with 9h). Methods: Fifty-three adolescents (28 females aged 15– 19 years) underwent overnight PSG monitoring while wearing both an Oura ring and Actiwatch 2 (Philips Respironics, USA). We compared the performance of the multisensor Oura ring (Oura Health Oy, Oulu, Finland) to polysomnography (PSG) and a research actigraph in healthy adolescents. *These authors contributed equally to this workĬentre for Sleep and Cognition, Yong Loo Lin School of Medicine, MD1 Level 13 Rm 05B, National University of Singapore, 117549, Singaporeīackground: Wearable devices have tremendous potential for large-scale longitudinal measurement of sleep, but their accuracy needs to be validated. Nicholas IYN Chee, * Shohreh Ghorbani, * Hosein Aghayan Golkashani, Ruth LF Leong, Ju Lynn Ong, Michael WL CheeĬentre for Sleep and Cognition, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
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