Efficient assessment of real-world dynamics of circadian rhythms in heart rate and body temperature from wearable data

Aug 23, 2023Journal of the Royal Society, Interface

Quick measurement of daily heart rate and body temperature rhythms using wearable devices

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Abstract

The proposed method for extracting circadian rhythms from wearable measurements has a computational cost approximately 300-fold lower than previous approaches.

Circadian rhythms can be quantified outside laboratory settings to enhance clinical practice.
Wearables are a promising tool for measuring circadian rhythms, but their validation is currently limited.
A new approximation-based least-squares method successfully extracts circadian rhythms from wearable data.
Testing on large-scale datasets demonstrated effective extraction of body temperature and heart rate dynamics.
The method includes a harmonic model for analyzing wearable data and can be integrated into a Kalman filter.

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Laboratory studies have made unprecedented progress in understanding circadian physiology. Quantifying circadian rhythms outside of laboratory settings is necessary to translate these findings into real-world clinical practice. Wearables have been considered promising way to measure these rhythms. However, their limited validation remains an open problem. One major barrier to implementing large-scale validation studies is the lack of reliable and efficient methods for circadian assessment from wearable data. Here, we propose an approximation-based least-squares method to extract underlying circadian rhythms from wearable measurements. Its computational cost is ∼ 300-fold lower than that of previous work, enabling its implementation in smartphones with low computing power. We test it on two large-scale real-world wearable datasets: [Formula: see text] of body temperature data from cancer patients and ∼ 184 000 days of heart rate and activity data collected from the 'Social Rhythms' mobile application. This shows successful extraction of real-world dynamics of circadian rhythms. We also identify a reasonable harmonic model to analyse wearable data. Lastly, we show our method has broad applicability in circadian studies by embedding it into a Kalman filter that infers the state space of the molecular clocks in tissues. Our approach facilitates the translation of scientific advances in circadian fields into actual improvements in health.

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Efficient assessment of real-world dynamics of circadian rhythms in heart rate and body temperature from wearable data

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