Key result
Machine learning models using ambulatory physiological data predicted circadian phase with less than one hour of error (ANNs) or less than one minute of error (regression models).
Why the study?
Traditional physiological markers for estimating circadian phase for chronotherapy, such as melatonin concentration and core body temperature, are expensive and intrusive to obtain.
Can machine learning techniques using non-intrusive ambulatory physiological data accurately predict circadian phase?
Can machine learning techniques using non-intrusive ambulatory physiological data accurately predict circadian phase?
Machine learning models using non-intrusive ambulatory physiological data can accurately predict circadian phase, offering a potential tool for personalized chronotherapy.
This is the authors' abstract. We don't add key points for this paper.
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.
May enable ambulatory circadian monitoring; leaves open validation and clinical utility in larger cohorts.
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Suarez et al. (2020) studied Circadian phase prediction. Machine learning models using ambulatory physiological data was evaluated on Accuracy of circadian phase prediction (time of minimum CBT and DLMO). Machine learning models using ambulatory physiological data predicted circadian phase with less than one hour of error (ANNs) or less than one minute of error (regression models).
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