Chronotherapy aims to treat patients according to their endogenous biological rhythms and requires, therefore, knowing their circadian phase. Circadian phase is partially determined by genetics and, under natural conditions, is normally entrained by environmental signals ( zeitgebers ), predominantly by light. Physiological data such as melatonin concentration and core body temperature (CBT) have been used to estimate circadian phase. However, due to their expensive and intrusive obtention, other physiological variables that also present circadian rhythmicity, such as heart rate variability, skin temperature, activity, and body position, have recently been proposed in several studies to estimate circadian phase. This study aims to predict circadian phase using minimally intrusive ambulatory physiological data modeled with machine learning techniques. Two approaches were considered; first, time-series were used to train artificial neural networks (ANNs) that predict CBT and melatonin dynamics and, second, a novel approach that uses scalar variables to build regression models that predict the time of the minimum CBT and the dim light melatonin onset (DLMO). ANNs require less than 48 hours of minimally intrusive data collection to predict circadian phase with an accuracy of less than one hour. On the other hand, regression models that use only three variables (body mass index, activity, and heart rate) are simpler and show higher accuracy with less than one minute of error, although they require longer times of data collection. This is a promising approach that should be validated in further studies considering a broader population and a wider range of conditions, including circadian misalignment.

中文翻译：

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来自非侵入性和动态生理数据的昼夜节律相位预测

计时疗法旨在根据患者的内源性生物节律进行治疗，因此需要了解他们的昼夜节律。昼夜节律部分由遗传决定，在自然条件下，通常由环境信号携带。 时代周刊 )，主要靠光。褪黑激素浓度和核心体温 (CBT) 等生理数据已被用于估计昼夜节律阶段。然而，由于其昂贵且侵入性的获取，最近在几项研究中提出了其他也呈现昼夜节律性的生理变量，例如心率变异性、皮肤温度、活动和身体位置，以估计昼夜节律阶段。本研究旨在使用机器学习技术建模的最小侵入性动态生理数据来预测昼夜节律阶段。考虑了两种方法；首先，时间序列用于训练预测 CBT 和褪黑激素动力学的人工神经网络 (ANN)，其次，一种使用标量变量构建回归模型的新方法，该模型预测最小 CBT 和昏暗的褪黑激素开始 (DLMO) 的时间。人工神经网络需要少于 48 小时的最少侵入性数据收集来预测昼夜节律阶段，准确度不到一小时。另一方面，仅使用三个变量（体重指数、活动和心率）的回归模型更简单，并且显示出更高的准确度，误差不到一分钟，尽管它们需要更长的数据收集时间。这是一种很有前景的方法，考虑到更广泛的人群和更广泛的条件，包括昼夜节律失调，应该在进一步的研究中进行验证。人工神经网络需要少于 48 小时的最少侵入性数据收集来预测昼夜节律阶段，准确度不到一小时。另一方面，仅使用三个变量（体重指数、活动和心率）的回归模型更简单，并且显示出更高的准确度，误差不到一分钟，尽管它们需要更长的数据收集时间。这是一种很有前景的方法，考虑到更广泛的人群和更广泛的条件，包括昼夜节律失调，应该在进一步的研究中进行验证。人工神经网络需要少于 48 小时的最少侵入性数据收集来预测昼夜节律阶段，准确度不到一小时。另一方面，仅使用三个变量（体重指数、活动和心率）的回归模型更简单，并且显示出更高的准确度，误差不到一分钟，尽管它们需要更长的数据收集时间。这是一种很有前景的方法，考虑到更广泛的人群和更广泛的条件，包括昼夜节律失调，应该在进一步的研究中进行验证。