Abstract
This study investigated the complex interplay between sleep quality, wearable sensor-derived heart rate variability (HRV), and training load in semi-professional endurance runners over a 12-week training cycle. Twenty-five male and female runners (mean age 28.5 ± 4.1 years) were monitored using commercially available wearable devices for daily sleep metrics (total sleep time, sleep efficiency, REM/deep sleep) and morning resting HRV (RMSSD). Weekly training load was quantified using session RPE (sRPE) and GPS-derived external load (total distance, duration). Sleep quality was also assessed weekly via the Pittsburgh Sleep Quality Index (PSQI). Our findings revealed significant inverse correlations between high weekly training loads and subsequent decreases in sleep efficiency (r = -0.48, p < 0.01) and morning RMSSD (r = -0.55, p < 0.001). Conversely, improved subjective sleep quality (lower PSQI scores) was positively associated with higher average RMSSD values (r = 0.62, p < 0.001). Regression analysis indicated that a combination of sleep efficiency and RMSSD explained 45% of the variance in perceived recovery status (p < 0.001). These results underscore the critical role of adequate sleep and autonomic nervous system balance, as reflected by HRV, in managing training stress and optimizing recovery in endurance athletes. Wearable technology offers a practical, non-invasive method for continuous monitoring, providing actionable insights for coaches and athletes to individualize training prescription and mitigate overtraining risk.
