Characterizing Diurnal Heart Rate Variability Patterns with Continuous Wearable Monitoring and Polynomial Modeling

Heart rate variability (HRV), a well-established marker of cardiovascular health and autonomic nervous system function, exhibits dynamic fluctuations throughout the day. This study aimed to characterize these diurnal HRV patterns with high precision and detail, utilizing 30 days of continuous heart rate monitoring (Polar H10) in a cohort of 35 healthy adults. The cumulative sum of population standard deviations (SDNN) was employed as the HRV metric, capturing both short-term and long-term variations.A cubic polynomial model was used to characterize the relationship between HRV and time of day, revealing a statistically significant and non-linear pattern. HRV peaked during sleep and early morning hours, gradually declining throughout the day and reaching its lowest point in the evening. This pattern was consistent across participants, yet significant inter-individual variability was observed in the specific shape and magnitude of the diurnal curves. The model demonstrated excellent fit to the data (R-squared = 0.98) and good predictive validity on a separate dataset (Mean of Absolute Error = 58.5 ms).These findings underscore the complex interplay of circadian rhythms, sleep-wake cycles, and behavioral factors in shaping diurnal HRV patterns. The cubic polynomial model effectively captured the non-linearity and individual variability in these patterns, highlighting the importance of using statistical models for characterizing this nuanced physiological measure. The observed inter-individual variability emphasizes the need for personalized health monitoring and intervention strategies that consider an individual’s unique HRV profile for optimizing cardiovascular health and overall well-being.