Wu Fan, Langer Patrick, Shim Jinjoo, Fleisch Elgar, Barata Filipe
IEEE J Biomed Health Inform. 2025 Feb;29(2):900-908. doi: 10.1109/JBHI.2024.3471254. Epub 2025 Feb 10.
Circadian rhythms govern biological patterns that follow a 24-hour cycle. Dysfunctions in circadian rhythms can contribute to various health problems, such as sleep disorders. Current circadian rhythm assessment methods, often invasive or subjective, limit circadian rhythm monitoring to laboratories. Hence, this study aims to investigate scalable consumer-centric wearables for circadian rhythm monitoring outside traditional laboratories. In a two-week longitudinal study conducted in real-world settings, 36 participants wore an Actigraph, a smartwatch, and a core body temperature sensor to collect activity, temperature, and heart rate data. We evaluated circadian rhythms calculated from commercial wearables by comparing them with circadian rhythm reference measures, i.e., Actigraph activities and chronotype questionnaire scores. The circadian rhythm metric acrophases, determined from commercial wearables using activity, heart rate, and temperature data, significantly correlated with the acrophase derived from Actigraph activities (r = 0.96, r = 0.87, r = 0.79; all p 0.001) and chronotype questionnaire (r = -0.66, r = -0.73, r = -0.61; all p 0.001). The acrophases obtained concurrently from consumer sensors significantly predicted the chronotype ( = 0.64; p 0.001). Our study validates commercial sensors for circadian rhythm assessment, highlighting their potential to support maintaining healthy rhythms and provide scalable and timely health monitoring in real-life scenarios.
昼夜节律控制着遵循24小时周期的生物模式。昼夜节律功能失调会导致各种健康问题，如睡眠障碍。当前的昼夜节律评估方法通常具有侵入性或主观性，将昼夜节律监测限制在实验室环境中。因此，本研究旨在探索以消费者为中心的可扩展可穿戴设备，用于在传统实验室之外进行昼夜节律监测。在一项为期两周的现实环境纵向研究中，36名参与者佩戴了活动记录仪、智能手表和核心体温传感器，以收集活动、体温和心率数据。我们通过将商业可穿戴设备计算得出的昼夜节律与昼夜节律参考指标（即活动记录仪活动和昼夜类型问卷得分）进行比较，来评估这些昼夜节律。利用活动、心率和温度数据从商业可穿戴设备确定的昼夜节律指标峰相位，与从活动记录仪活动得出的峰相位（r = 0.96，r = 0.87，r = 0.79；所有p < 0.001）以及昼夜类型问卷（r = -0.66，r = -0.73，r = -0.61；所有p < 0.001）显著相关。同时从消费者传感器获得的峰相位显著预测了昼夜类型（β = 0.64；p < 0.001）。我们的研究验证了用于昼夜节律评估的商业传感器，突出了它们在支持维持健康节律以及在现实生活场景中提供可扩展且及时的健康监测方面的潜力。
