Estimating circadian rhythms and their association with sleep efficiency using long-term biological signals

Over several decades, various methods and devices have been used to analyze circadian rhythms and sleep quality. However, conventional approaches struggle to capture long-term biological signal fluctuations. This study explores circadian rhythms and sleep quality in real-world settings using Fitbit and multiscale fuzzy entropy analysis. Findings indicate that heart rate variability (HRV) over a long timescale (approximately 3000 s) is associated with sleep efficiency. Although the underlying neural mechanisms remain unclear, understanding this pattern is crucial. Future research integrating long-term multimodal measurements with devices like Fitbit and portable electroencephalography devices could offer deeper insights into the sleep-HRV relationship.