The time of dim light melatonin onset (DLMO) is the gold standard for circadian phase assessment in humans, but collection of samples for DLMO is time and resource-intensive. Numerous studies have attempted to estimate circadian phase from actigraphy data, but most of these studies have involved individuals on controlled and stable sleep-wake schedules, with mean errors reported between 0.5 and 1 hour. We found that such algorithms are less successful in estimating DLMO in a population of college students with more irregular schedules: Mean errors in estimating the time of DLMO are approximately 1.5-1.6 hours. We reframed the problem as a classification problem and estimated whether an individual's current phase was before or after DLMO. Using a neural network, we found high classification accuracy of about 90%, which decreased the mean error in DLMO estimation—identifying the time at which the switch in classification occurs—to approximately 1.3 hours. To test whether this classification approach was valid when activity and circadian rhythms are decoupled, we applied the same neural network to data from inpatient forced desynchrony studies in which participants are scheduled to sleep and wake at all circadian phases (rather than their habitual schedules). In participants on forced desynchrony protocols, overall classification accuracy dropped to 55%-65% with a range of 20%-80% for a given day; this accuracy was highly dependent upon the phase angle (ie, time) between DLMO and sleep onset, with the highest accuracy at phase angles associated with nighttime sleep. Circadian patterns in activity, therefore, should be included when developing and testing actigraphy-based approaches to circadian phase estimation. Our novel algorithm may be a promising approach for estimating the onset of melatonin in some conditions and could be generalized to other hormones.

中文翻译：

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从活动记录仪和光度计数据估计昼夜节律调整下人类昼夜节律相位的分类方法

微光褪黑激素发作 (DLMO) 的时间是人类昼夜节律评估的黄金标准，但为 DLMO 收集样本是时间和资源密集型的。许多研究试图从活动记录数据中估计昼夜节律阶段，但这些研究中的大多数都涉及受控和稳定的睡眠-觉醒时间表的个体，平均误差报告在 0.5 到 1 小时之间。我们发现，此类算法在日程安排更不规则的大学生群体中估计 DLMO 的成功率较低：估计 DLMO 时间的平均误差约为 1.5-1.6 小时。我们将问题重新定义为分类问题，并估计个人的当前阶段是在 DLMO 之前还是之后。使用神经网络，我们发现大约 90% 的高分类准确率，这将 DLMO 估计的平均误差（确定分类转换发生的时间）减少到大约 1.3 小时。为了测试这种分类方法在活动和昼夜节律分离时是否有效，我们将相同的神经网络应用于住院强迫不同步研究的数据，其中参与者被安排在所有昼夜节律阶段（而不是他们的习惯性时间表）睡觉和醒来。在强制不同步协议的参与者中，总体分类准确率下降到 55%-65%，范围为 20%-80%；这种精度高度依赖于 DLMO 和睡眠开始之间的相位角（即时间），在与夜间睡眠相关的相位角处精度最高。因此，活动中的昼夜节律模式，在开发和测试基于活动记录的昼夜节律相位估计方法时应包括在内。我们的新算法可能是在某些情况下估计褪黑激素发作的有前途的方法，并且可以推广到其他激素。