Introduction The heart rate variability coefficient of variation (HRV-CV) is an index of day-to-day cardiac autonomic fluctuation that may serve as a scalable digital biomarker for behavioral monitoring and health risk stratification. We investigated how many nights of sleep-derived HRV were needed to reliably estimate seven-day HRV-CV, and examined associations with behavioral and demographic characteristics linked to health. Methods We analyzed ~2 million nocturnal HRV readings from >21,000 wearable device users, stratified by age and sex. Seven-day HRV-CV was calculated, and a simulation determined the minimum nights required for reliable estimates. Associations with alcohol, physical activity, sleep, and variability of these behaviors were evaluated. Additional models examined associations between HRV-CV with age, biological sex, and BMI. Results At least five of seven nights were required to achieve acceptable agreement with full-week HRV-CV values (ICC≥0.80). Higher HRV-CV was associated with greater alcohol consumption, lower physical activity, shorter and less consistent sleep, and greater behavioral variability (Ps<0.001), with stronger associations for alcohol and sleep compared to HRV. HRV-CV increased with age in males after ~40yrs and showed a U-shaped pattern in females, declining through midlife and rising after ~50yrs. HRV-CV increased with BMI in both sexes (Ps<0.01). Conclusions HRV-CV measured during nocturnal sleep can be reliably estimated from at least five nights of data, with higher values associating with less favorable behavioral profiles, older age, and higher BMI. These findings support the use of HRV-CV as a scalable, behavior-sensitive digital biomarker with potential applications in personalized health monitoring and risk stratification.
Grosicki et al. (Thu,) studied this question.