Institute of Population Health Sciences, National Health Research Institutes, Miaoli, Taiwan.
Department of Psychiatry, National Taiwan University Hospital, Taipei, Taiwan.
JMIR Mhealth Uhealth. 2019 May 16;7(5):e13421. doi: 10.2196/13421.
Modern smartphone use is pervasive and could be an accessible method of evaluating the circadian rhythm and social jet lag via a mobile app.
This study aimed to validate the app-recorded sleep time with daily self-reports by examining the consistency of total sleep time (TST), as well as the timing of sleep onset and wake time, and to validate the app-recorded circadian rhythm with the corresponding 30-day self-reported midpoint of sleep and the consistency of social jetlag.
The mobile app, Rhythm, recorded parameters and these parameters were hypothesized to be used to infer a relative long-term pattern of the circadian rhythm. In total, 28 volunteers downloaded the app, and 30 days of automatically recorded data along with self-reported sleep measures were collected.
No significant difference was noted between app-recorded and self-reported midpoint of sleep time and between app-recorded and self-reported social jetlag. The overall correlation coefficient of app-recorded and self-reported midpoint of sleep time was .87.
The circadian rhythm for 1 month, daily TST, and timing of sleep onset could be automatically calculated by the app and algorithm.
现代智能手机的使用非常普遍,通过移动应用程序,智能手机可能成为评估昼夜节律和社交时差的一种便捷方法。
本研究旨在通过检查总睡眠时间(TST)、睡眠开始时间和醒来时间的一致性,验证应用程序记录的睡眠时间与日常自我报告的睡眠时间的一致性,并验证应用程序记录的昼夜节律与相应的 30 天自我报告的睡眠中点以及社会时差的一致性。
移动应用程序“Rhythm”记录参数,这些参数被假设用于推断昼夜节律的相对长期模式。共有 28 名志愿者下载了该应用程序,并收集了 30 天的自动记录数据以及自我报告的睡眠测量数据。
应用程序记录的睡眠中点时间和自我报告的睡眠中点时间以及应用程序记录的社会时差和自我报告的社会时差之间没有显著差异。应用程序记录的和自我报告的睡眠中点时间的总体相关系数为 0.87。
应用程序和算法可以自动计算 1 个月的昼夜节律、每日总睡眠时间和睡眠开始时间。