A method for characterizing daily physiology from widely used wearables
A method for characterizing daily physiology from widely used wearables
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DOI：
10.1016/j.crmeth.2021.100058
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发表时间：
2021-08-23
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影响因子：
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通讯作者：
Forger, Daniel B.
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文献类型：
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作者：
Bowman, Clark;Huang, Yitong;Forger, Daniel B.
数百万可穿戴设备用户记录他们的心率（HR）和活动。我们引入了一种统计方法，从这些数据中提取并跟踪六个关键的生理参数，包括潜在的昼夜节律在HR （CRHR）中，活动的直接影响，以及饮食，姿势和压力通过皮质醇等激素的影响。我们用来自轮岗实习医生的13万多天的真实数据测试了我们的方法，结果表明，CRHR动态与睡眠-觉醒或身体活动模式不同，并且在个体之间差异很大。我们的方法还估计了每个个体的CRHR对活动的个性化相位响应曲线，代表了对人类昼夜节律计时如何因现实世界的刺激而不断变化的被动和个性化的确定。我们在“Social rhythm”iPhone和Android应用程序中实现了我们的方法，该应用程序匿名收集可穿戴设备用户的数据，并根据我们的方法提供分析。
Millions of wearable-device users record their heart rate (HR) and activity. We introduce a statistical method to extract and track six key physiological parameters from these data, including an underlying circadian rhythm in HR (CRHR), the direct effects of activity, and the effects of meals, posture, and stress through hormones like cortisol. We test our method on over 130,000 days of real-world data from medical interns on rotating shifts, showing that CRHR dynamics are distinct from those of sleep-wake or physical activity patterns and vary greatly among individuals. Our method also estimates a personalized phase-response curve of CRHR to activity for each individual, representing a passive and personalized determination of how human circadian timekeeping continually changes due to real-world stimuli. We implement our method in the ``Social Rhythms'' iPhone and Android app, which anonymously collects data from wearable-device users and provides analysis based on our method.