Monitoring with multiple wearable biosensors revealed that physical activity and circadian rhythms explain 40%-65% of heart rate variance and up to 15% of additional glucose variability.
Observational (n=25)
Can a Bayesian dynamical model integrating multiple wearable biosensors uncover personalized glucose responses and circadian rhythms in healthy individuals?
Integrating multiple wearable biosensors with Bayesian dynamical modeling can uncover personalized circadian rhythms and improve the prediction of glucose dynamics.
Wearable biosensors and smartphone applications can measure physiological variables over multiple days in free-living conditions. We measure food and drink ingestion, glucose dynamics, physical activity, heart rate (HR), and heart rate variability (HRV) in 25 healthy participants over 14 days. We develop a Bayesian inference framework to learn personal parameters that quantify circadian rhythms and physiological responses to external stressors. Modeling the effects of ingestion events on glucose levels reveals that slower glucose decay kinetics elicit larger postprandial glucose spikes, and we uncover a circadian baseline rhythm for glucose with high amplitudes in some individuals. Physical activity and circadian rhythms explain as much as 40%-65% of the HR variance, whereas the variance explained for HRV is more heterogeneous across individuals. A more complex model incorporating activity, HR, and HRV explains up to 15% of additional glucose variability, highlighting the relevance of integrating multiple biosensors to better predict glucose dynamics.
Phillips et al. (2023) conducted an observational in Healthy (n=25). Multiple wearable biosensors was evaluated on Glucose dynamics, heart rate variance, and heart rate variability. Monitoring with multiple wearable biosensors revealed that physical activity and circadian rhythms explain 40%-65% of heart rate variance and up to 15% of additional glucose variability.