Association Between Sleep Characteristics and Musculoskeletal Injuries in Elite Paralympic Powerlifters

Abstract

  This study aims to investigate the relationship between sleep and injuries among Paralympic weightlifters while taking advantage of physiological indicators related to recovery. The study sample consisted of 23 male athletes, and their data was monitored daily for 55 days using the wearable WHOOP 5.0 device, aiming to record sleep duration and quality, as well as to track changes in heart rate variability (HRV) and recovery levels. To analyze the relationship between sleep variables and injuries, the researcher used Spearman's correlation coefficient, and a Random Forest model was built to estimate injury risk based on each athlete's daily data. The study results showed a link between some sleep and recovery indicators and the physiological markers related to the players' condition, which highlights the importance of continuously monitoring these variables. The Random Forest model also showed a good ability to predict injury risk based on a set of daily variables, with recovery indicators, HRV, and sleep appearing among the important factors in the prediction process. These results suggest that daily monitoring of sleep and physiological indicators can provide useful information about recovery status and physical readiness, and that integrating this data into predictive models may help in early identification of high injury risk and support decisions related to training, recovery, and injury prevention for Paralympic weightlifters.