Department of Psychiatry, University of Arizona College of Medicine, Tucson, AZ, USA.
Happy Health, Inc., Austin, TX, USA.
Sleep. 2023 Jan 11;46(1). doi: 10.1093/sleep/zsac152.
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 mental performance. We tested the performance of a novel device, the Happy Ring, alongside other commercial wearables (Actiwatch 2, Fitbit Charge 4, Whoop 3.0, Oura Ring V2), against in-lab polysomnography (PSG) and at-home electroencephalography (EEG)-derived sleep monitoring device, the Dreem 2 Headband.
Thirty-six healthy adults with no diagnosed sleep disorders and no recent use of medications or substances known to affect sleep patterns were assessed across 77 nights. Subjects participated in a single night of in-lab PSG and two 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 in-lab PSG and to at-home EEG Headband.
Compared to in-lab PSG, the "generalized" and "personalized" algorithms demonstrated good sensitivity (94% and 93%, respectively) and specificity (70% and 83%, respectively). The Happy Personalized model demonstrated a lower bias and more narrow limits of agreement across Bland-Altman measures.
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.
由于技术的进步和对个性化睡眠评估的兴趣增加，以改善健康和精神表现，可穿戴睡眠技术在消费者市场迅速扩展。我们测试了一种新型设备（Happy Ring）的性能，该设备与其他商业可穿戴设备（Actiwatch 2、Fitbit Charge 4、Whoop 3.0、Oura Ring V2）一起，与实验室多导睡眠图（PSG）和家庭脑电图（EEG）衍生的睡眠监测设备（Dreem 2 Headband）进行对比。
36 名无诊断性睡眠障碍且近期未使用已知影响睡眠模式的药物或物质的健康成年人，在 77 个晚上进行了评估。受检者参加了一晚上的实验室 PSG 和两晚的家庭数据收集。Happy Ring 包括皮肤电导率、运动、心率和皮肤温度传感器。Happy Ring 使用了两种机器学习衍生的评分算法：一种适用于所有用户的“通用”算法，以及一种适应个体受检者数据的“个性化”算法。逐时段分析将可穿戴设备与实验室 PSG 和家庭 EEG 头带进行了比较。
与实验室 PSG 相比，“通用”和“个性化”算法的敏感性分别为 94%和 93%，特异性分别为 70%和 83%。Happy Personalized 模型在 Bland-Altman 测量中表现出更低的偏差和更窄的一致性界限。
Happy Ring 在家庭和实验室中的表现良好，尤其是在睡眠/觉醒检测方面。个性化算法在检测准确性方面优于通用方法和其他设备，这表明自适应、动态算法可以提高睡眠检测的准确性。
