Mayer Caleb, Kim Dae Wook, Zhang Meina, Lee Minki P, Forger Daniel B, Burgess Helen J, Moon Chooza
Department of Mathematics, University of Michigan, Ann Arbor, Michigan, USA.
Department of Genetics, Stanford University, Stanford, California, USA.
J Sleep Res. 2024 Dec 8:e14425. doi: 10.1111/jsr.14425.
The accurate estimation of circadian phase in the real-world has a variety of applications, including chronotherapeutic drug delivery, reduction of fatigue, and optimal jet lag or shift work scheduling. Recent work has developed and adapted algorithms to predict time-consuming and costly laboratory circadian phase measurements using mathematical models with actigraphy or other wearable data. Here, we validate and extend these results in a home-based cohort of later-life adults, ranging in age from 58 to 86 years. Analysis of this population serves as a valuable extension to our understanding of phase prediction, since key features of circadian timekeeping (including circadian amplitude, response to light stimuli, and susceptibility to circadian misalignment) may become altered in older populations and when observed in real-life settings. We assessed the ability of four models to predict ground truth dim light melatonin onset, and found that all the models could generate predictions with mean absolute errors of approximately 1.4 h or below using actigraph activity data. Simulations of the model with activity performed as well or better than the light-based modelling predictions, validating previous findings in this novel cohort. Interestingly, the models performed comparably to actigraph-derived sleep metrics, with the higher-order and nonphotic activity-based models in particular demonstrating superior performance. This work provides evidence that circadian rhythms can be reasonably estimated in later-life adults living in home settings through mathematical modelling of data from wearable devices.
在现实世界中准确估计昼夜节律相位有多种应用，包括时辰治疗给药、减轻疲劳以及优化时差或轮班工作安排。最近的研究工作已经开发并调整了算法，以使用带有活动记录仪或其他可穿戴数据的数学模型来预测耗时且昂贵的实验室昼夜节律相位测量结果。在此，我们在一个年龄范围为58至86岁的居家老年成年人队列中验证并扩展了这些结果。对这一人群的分析是对我们昼夜节律相位预测理解的一个有价值的扩展，因为昼夜节律计时的关键特征（包括昼夜节律振幅、对光刺激的反应以及对昼夜节律失调的易感性）在老年人群中以及在现实生活环境中观察时可能会发生改变。我们评估了四种模型预测暗光褪黑素开始分泌时间的能力，发现所有模型使用活动记录仪活动数据生成的预测结果平均绝对误差约为1.4小时或更低。基于活动的模型模拟表现与基于光的建模预测相当或更好，从而在这个新队列中验证了先前的研究结果。有趣的是，这些模型的表现与活动记录仪得出的睡眠指标相当，尤其是高阶和基于非光活动的模型表现出卓越性能。这项工作提供了证据，表明通过对可穿戴设备数据进行数学建模，可以合理估计居家老年成年人的昼夜节律。
