(52) ASSOCIATION BETWEEN SMART RING MEASURED SLEEP VARIABLES AND IN-GAME BASKETBALL PERFORMANCE

Background: Sleep is critical to athlete health and has been shown to influence basketball performance, including player efficiency. Modern wearable devices claim improved precision and convenience in tracking sleep over traditional methods of data collection like subjective sleep journals. However, research regarding the accuracy of newer methods and their relationships to sports performance is limited.

Purpose: The purpose was to evaluate the relationship between sleep data recorded by a smart ring sleep tracker and in-game basketball performance.

Methods: National Collegiate Athletic Association (NCAA) Division I men’s basketball athletes (n=4; age: 19.8±1.3 years; height: 189.2±3.0 cm; mass: 82.5±5.0 kg;) wore smart ring sleep trackers the night before games. Sleep variables collected included hours (hr) of total sleep (TS), rapid eye movement sleep duration (RES), deep sleep duration (DS), and heart rate variability (HRV). In-game performance was evaluated using field goal percentage (FG%) and a composite stat, efficiency rating (EFF), which was calculated from NCAA offensive and defensive statistics. Data were collected for 13 non-conference games from those who played at least 15 minutes. R Studio was used for statistical analysis (p< 0.05). Relationships between variables were evaluated via Pearson correlation coefficients computed for the group and for individual athletes. Correlation coefficients were defined as very weak: < 0.20; weak: 0.20–0.39; moderate: 0.40–0.59; strong: 0.60–0.79; and very strong: >0.80.  

Results: Group and individual mean values for sleep and sports performance metrics are included in Table 1. The grouped analysis showed TS, RES, DS, and HRV demonstrated very weak relationships with EFF and FG%. When analyzed for individuals, there were no consistent relationships observed among variables. As shown in Table 1, Athlete 2 exhibited strong to moderate correlations for TS-EFF (r=0.67), RS-EFF (r=0.41), TS-FG% (r=0.51), and RS-FG% (r=0.67).

Conclusion: Results suggest a lack of association between smart ring-measured sleep variables and in-game basketball performance. Homogeneity of the small sample size of athletes, consistency of sufficient sleep durations, and lack of sleep variations may have affected the inconsistent correlations. Athlete 2's data suggest that TS and RS may have a positive association with some measures of in-game performance. The extent to which these variables affect performance appears to be individualized, highlighting the need for ongoing athlete monitoring to assess individual responses. PRACTICAL APPLICATIONS: Findings do not support a direct link between sleep the night prior to games, and next-day measures of in-game performance. The results underscore the improved capacity to monitor athlete sleep using wearable devices and suggest that an individualized approach should be integrated into a comprehensive athlete readiness program.

Acknowledgements: None