The Relationship Between Sleep Quality Heart Rate Variability (HRV) and Functional Recovery in Players at the Samarra Basketball Club

Abstract

This study examined the relationship between sleep quality, heart rate variability (HRV) and the level of functional recovery among players at the Samarra basketball club. The researcher adopted a descriptive, correlational approach, in line with the nature of the research problem and the objectives the study aimed to achieve. The study population consisted of the players in the first team of the Samarra Basketball Club for the 2025–2026 season, totalling 14 players. Given the small size of the population, the research procedures covered all the players, representing 100 per cent of the initial population. To assess the sleep quality of the sample members, the researcher used the Pittsburgh Sleep Quality Index (PSQI), whilst heart rate variability (HRV) was measured using the RMSSD and SDNN indices. The Functional Recovery Scale (TQR) was also used to assess the players’ level of recovery following training sessions. The measurement tools used included a heart rate monitor, dedicated HRV measurement apps, as well as data-recording questionnaires and SPSS software for data processing and statistical analysis. The results showed that the club’s players exhibited relatively good levels with regard to the variables of sleep quality, heart rate variability and functional recovery. A positive and statistically significant correlation was also observed between sleep quality and HRV (0.71), as well as between sleep quality and functional recovery (0.76), whilst the correlation between HRV and functional recovery reached a correlation coefficient of 0.81. Furthermore, the variables of sleep quality and HRV together accounted for 68 per cent of the variance observed in the players’ functional recovery. These results highlight the importance of using sleep indicators and heart rate variability to monitor the functional status, level of recovery and physical condition of basketball players, as well as the potential to use them to monitor fatigue and stress associated with training loads.