Predicting circadian phase across populations: a comparison of mathematical models and wearable devices
Predicting circadian phase across populations: a comparison of mathematical models and wearable devices
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DOI：
10.1093/sleep/zsab126
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发表时间：
2021-10-01
期刊：
影响因子：
5.6
通讯作者：
Forger, Daniel B.
中科院分区：
文献类型：
--
作者：
Huang, Yitong;Mayer, Caleb;Forger, Daniel B.
从智能工作安排到最佳用药时间，将昼夜节律研究成果转化为现实世界中的精准医学有着巨大的潜力。然而，这种努力的追求需要在实验室之外准确估计昼夜节律阶段的能力。一种方法是使用光和活动测量以及人类生物钟的数学模型来无创地预测昼夜节律阶段。大多数数学模型将光作为输入，并预测光对人类昼夜节律系统的影响。然而，已经有数百万人拥有的消费级可穿戴设备记录的是活动而不是光线，这促使人们对仅使用运动来预测昼夜节律阶段的准确性进行评估。在这里，我们评估了四种不同的人类生物钟模型的能力，从腕戴式可穿戴设备获取的数据中估计昼夜节律阶段。多个数据集跨越不同程度的昼夜节律中断的人群被用于推广。虽然我们测试的模型得出了类似的预测，但对27名昼夜节律高度紊乱的轮班工人的数据分析表明，在数学模型处理后，几乎所有可穿戴设备上记录的活动，比手腕上的设备测量的光照水平更能预测昼夜节律阶段。在那些生活在正常生活条件下的人，即使使用广泛使用的商业设备（苹果手表）的数据，昼夜节律阶段通常也可以预测在1小时内。这些结果表明，可以使用数百万人被动收集的现有数据来预测昼夜节律阶段，其准确性与更具侵入性和昂贵的方法相当。
From smart work scheduling to optimal drug timing, there is enormous potential in translating circadian rhythms research results for precision medicine in the real world. However, the pursuit of such effort requires the ability to accurately estimate circadian phase outside of the laboratory. One approach is to predict circadian phase noninvasively using light and activity measurements and mathematical models of the human circadian clock. Most mathematical models take light as an input and predict the effect of light on the human circadian system. However, consumer-grade wearables that are already owned by millions of individuals record activity instead of light, which prompts an evaluation of the accuracy of predicting circadian phase using motion alone. Here, we evaluate the ability of four different models of the human circadian clock to estimate circadian phase from data acquired by wrist-worn wearable devices. Multiple datasets across populations with varying degrees of circadian disruption were used for generalizability.Though the models we test yield similar predictions, analysis of data from 27 shift workers with high levels of circadian disruption shows that activity, which is recorded in almost every wearable device, is better at predicting circadian phase than measured light levels from wrist-worn devices when processed by mathematical models. In those living under normal living conditions, circadian phase can typically be predicted to within 1 h, even with data from a widely available commercial device (the Apple Watch). These results show that circadian phase can be predicted using existing data passively collected by millions of individuals with comparable accuracy to much more invasive and expensive methods.