Characterizing the Human Heart Rate Circadian Pacemaker through Widely Available Wearable Devices

Real world passive measures of circadian timekeeping have the potential to improve the efficacy of medical treatments and improve human performance on a broad scale. Here, we present algorithms for extracting and quantifying the uncertainty of estimates of the underlying circadian rhythm in heart rate (CRHR) from several widely used wearables after removing the masking effects of sleep and activity on heart rate. Analysis of over 130,000 days of data from medical interns on rotating shifts shows how CRHR dynamics are distinct from those of sleep-wake timing. We determine a personalized phase response curve (PRC) of CRHR to activity for each individual, representing the first passive determination of personalized circadian parameters, and validate our methods against previous clinical data. We also validate a simplified algorithm that can be implemented on a smartphone and use this algorithm to test a hypothesis about the role of circadian timekeeping in timing sleep using data from individuals in 5 continents. These results collectively establish CRHR as a practical method to study circadian rhythms in the real world and to understand how circadian disruption affects human health and functioning. Significance Statement We show how the circadian rhythm in heart rate can be extracted from real world data collected by the most popular commercial wearables. Our methods work with many devices and are validated against clinical measurements. Studying data from a large cohort of medical interns working on shifts, we find important dynamics of this circadian rhythm that are independent of the acute effects of activity or sleep-wake timing. These techniques can also determine personalized parameters of circadian timekeeping. Our work allows for large-scale study of human circadian rhythms in the real world.