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

Abstract

Study Objectives: Wearable 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 performance. In this study, we tested the performance of a novel device, alongside other commercial wearables, against in-lab and at-home polysomnography (PSG). Methods: 36 healthy adults were assessed across 77 nights while wearing the Happy Ring, as well as the Actiwatch, Fitbit, Whoop, and Oura Ring devices. Subjects participated in a single night of in-lab PSG and 2 nights of at-home PSG. The Happy Ring includes sensors for skin conductance, movement, heart rate, and skin temperature. Epoch-by-epoch analyses compared the wearable de-vices to both in-lab and at-home PSG. The Happy Ring utilized two machine-learning derived scor-ing algorithms: a generalized algorithm that applied broadly to all users, and a personalized algorithm that adapted to the data of individual subjects. Results: Compared to in-lab PSG, the generalized and personalized algorithms demonstrated good sensitivity (94% and 93%, respectively) and specificity (70% and 83%, respectively). The other wearable devices also demonstrated good sensitivity (89%-94%) but lower specificity (19%-54%), relative to the Happy Ring. Accuracy was 91% for generalized and 92% for personalized algorithms, compared to other devices that ranged from 84%-88%. 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, re-spectively. Conclusions: The Happy Ring performed well at home and in the lab, especially regarding sleep-wake 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.