A performance validation of six commercial wrist-worn wearable sleep-tracking devices for sleep stage scoring compared to polysomnography.

An-Marie Schyvens, Brent Peters, Nina Catharina Van Oost, Jean-Marie Aerts, Federica Masci, An Neven, Hélène Dirix, Geert Wets, Veerle Ross, Johan Verbraecken
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Abstract

Study objectives: The aim of this study is to assess the performance of six different consumer wearable sleep-tracking devices, namely the Fitbit Charge 5, Fitbit Sense, Withings Scanwatch, Garmin Vivosmart 4, Whoop 4.0, and the Apple Watch Series 8, for detecting sleep parameters compared to the gold standard, polysomnography (PSG).

Methods: Sixty-two adults (52 males and 10 females, mean age ± SD = 46.0 ± 12.6 years) spent a single night in the sleep laboratory with PSG while simultaneously using two to four wearable devices.

Results: The results indicate that most wearables displayed significant differences with PSG for total sleep time, sleep efficiency, wake after sleep onset, and light sleep (LS). Nevertheless, all wearables demonstrated a higher percentage of correctly identified epochs for deep sleep and rapid eye movement sleep compared to wake (W) and LS. All devices detected >90% of sleep epochs (ie, sensitivity), but showed lower specificity (29.39%-52.15%). The Cohen's kappa coefficients of the wearable devices ranged from 0.21 to 0.53, indicating fair to moderate agreement with PSG.

Conclusions: Our results indicate that all devices can benefit from further improvement for multistate categorization. However, the devices with higher Cohen's kappa coefficients, such as the Fitbit Sense (κ = 0.42), Fitbit Charge 5 (κ = 0.41), and Apple Watch Series 8 (κ = 0.53), could be effectively used to track prolonged and significant changes in sleep architecture.

六种商用腕戴式可穿戴睡眠跟踪设备的睡眠阶段评分与多导睡眠仪的性能验证。
研究目的:本研究的目的是评估六种不同的消费者可穿戴睡眠跟踪设备的性能,即Fitbit Charge 5、Fitbit Sense、Withings Scanwatch、Garmin Vivosmart 4、Whoop 4.0和Apple Watch Series 8,用于检测睡眠参数,并与黄金标准多道睡眠图(PSG)进行比较。方法:62名成年人(男52名,女10名,平均年龄±SD = 46.0±12.6岁)在睡眠实验室进行PSG,同时使用2 - 4个可穿戴设备。结果表明,大多数可穿戴设备与PSG在总睡眠时间、睡眠效率、睡眠后觉醒和浅睡眠(LS)方面存在显著差异。然而,与清醒(W)和LS相比,所有可穿戴设备都显示出更高的深度睡眠和快速眼动睡眠的正确识别时间百分比。所有设备都能检测到90%的睡眠时间(即灵敏度),但特异性较低(29.39%-52.15%)。可穿戴设备的Cohen’s kappa系数在0.21到0.53之间,表明与PSG的一致性大致中等。结论:我们的研究结果表明,所有设备都可以从进一步改进的多状态分类中获益。然而,具有较高科恩kappa系数的设备,如Fitbit Sense (κ = 0.42), Fitbit Charge 5 (κ = 0.41)和Apple Watch Series 8 (κ = 0.53),可以有效地用于跟踪睡眠结构的长时间和显著变化。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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