Performance of a Multisensor Smart Ring to Evaluate Sleep: In-Lab and Home-Based Evaluation Relative to Polysomnography and Actigraphy: Importance of Generalized Versus Personalized Scoring

Study ObjectivesWearable sleep technology has rapidly expanded across the consumer market due to advances in technology and increased interest in personalized sleep assessment to improve health and mental performance. In this study, we tested the performance of a novel device, the Happy Ring, alongside other commercial wearables, against in-lab polysomnography (PSG) and an at-home EEG-derived sleep monitoring device, the Dreem 2 Headband. Methods36 healthy adults with no diagnosed sleep disorders and no recent use of medications or substances known to affect sleep pattern were assessed across 77 nights while wearing the Happy Ring, as well as a set of other consumer wearable devices. Subjects participated in a single night of in-lab PSG and 2 nights of at-home data collection. The Happy Ring includes sensors for skin conductance, movement, heart rate, and skin temperature. The Happy Ring utilized two machine-learning derived scoring algorithms: a "generalized" algorithm that applied broadly to all users, and a "personalized" algorithm that adapted to individual subjects data. Epoch-by-epoch analyses compared the wearable devices to both in-lab PSG and to the Dreem 2 EEG Headband ("Dreem 2 Headband") at home. ResultsCompared to in-lab PSG, the "generalized" and "personalized" algorithms demonstrated good sensitivity (94% and 93%, respectively) and specificity (70% and 83%, respectively). Accuracy was 91% for "generalized" and 92% for "personalized" algorithms. The generalized algorithm demonstrated an accuracy of 67%, 85%, and 85% for light, deep, and REM sleep, respectively. The personalized algorithm was 81%, 95%, and 92% accurate for light, deep, and REM sleep, respectively. ConclusionsThe Happy Ring performed well at home and in the lab, especially regarding sleep detection. The personalized algorithm demonstrated improved detection accuracy over the generalized approach and other devices, suggesting that adaptable, dynamic algorithms can enhance sleep detection accuracy.