Department of Experimental, Diagnostic and Specialty Medicine (DIMES), University of Bologna, Bologna, Italy.
Department of Biomedical and Neuromotor Sciences (DIBINEM), University of Bologna, Bologna, Italy.
J Sleep Res. 2020 Feb;29(1):e12935. doi: 10.1111/jsr.12935. Epub 2019 Oct 31.
An increasing number of sleep applications are currently available and are being widely used for in-home sleep tracking. The present study assessed four smartphone applications (Sleep Cycle-Accelerometer, SCa; Sleep Cycle-Microphone, SCm; Sense, Se; Smart Alarm, SA) designed for sleep-wake detection through sound and movement sensors, by comparing their performance with polysomnography. Twenty-one healthy participants (six males, 15 females) used the four sleep applications running on iPhone (provided by the experimenter) simultaneously with portable polysomnography recording at home, while sleeping alone for two consecutive nights. Whereas all apps showed a significant correlation with polysomnography-time in bed, only SA offered significant correlations for sleep efficacy. Furthermore, SA seemed to be quite effective in reliable detection of total sleep time and also light sleep; however, it underestimated wake and partially overestimated deep sleep. None of the apps resulted capable of detecting and scoring rapid eye movement sleep. To sum up, SC (functioning through both accelerometer and microphone) and Se did not result sufficiently reliable in sleep-wake detection compared with polysomnography. SA, the only application offering the possibility of an epoch-by-epoch analysis, showed higher accuracy than the other apps in comparison with polysomnography, but it still shows some limitations, particularly regarding wake and deep sleep detection. Developing scoring algorithms specific for smartphone sleep detection and adding external sensors to record other physiological parameters may overcome the present limits of sleep tracking through smart phone apps.
目前有越来越多的睡眠应用程序可供使用,并被广泛用于家庭睡眠追踪。本研究通过比较声音和运动传感器设计的四种智能手机应用程序(Sleep Cycle-Accelerometer,SCa;Sleep Cycle-Microphone,SCm;Sense,Se;Smart Alarm,SA)的性能,评估了它们在睡眠-觉醒检测方面的表现与多导睡眠图的相关性。21 名健康参与者(6 名男性,15 名女性)在 iPhone 上同时使用这四种睡眠应用程序(由实验者提供),并在家中进行连续两晚的便携式多导睡眠图记录,独自睡觉。虽然所有应用程序与多导睡眠图的睡眠时间均呈显著相关,但只有 SA 与睡眠效率呈显著相关。此外,SA 似乎在可靠检测总睡眠时间和轻度睡眠方面非常有效;然而,它低估了清醒时间,部分高估了深度睡眠时间。没有一个应用程序能够检测和评分快速眼动睡眠。总之,与多导睡眠图相比,SC(通过加速度计和麦克风两种方式工作)和 Se 在睡眠-觉醒检测方面的可靠性不够高。SA 是唯一提供逐epoch 分析可能性的应用程序,与多导睡眠图相比,其准确性高于其他应用程序,但仍存在一些局限性,特别是在检测清醒和深度睡眠方面。开发专门用于智能手机睡眠检测的评分算法,并添加外部传感器来记录其他生理参数,可能会克服目前通过智能手机应用程序进行睡眠追踪的局限性。