Author(s): LUNDSTROM, E., DE SOUZA, M.J., WILLIAMS, N. , Institution: PENNSYLVANIA STATE UNIVERSITY, Country: UNITED STATES, Abstract-ID: 1402

INTRODUCTION:
High-quality sleep of a sufficient duration is necessary for optimal health and several factors contributing to athletic performance, such as promoting physical recovery from training. Sleep quality can include metrics of sleep duration and other more specific sleep measures. Previous data suggests a high prevalence of poor sleep duration and other sleep metrics in athletes. Poor sleep quality during high training loads has been identified as an early sign of overreaching, which may yield improper recovery and limit training adaptations, and subsequently impact sport performance. Reduced sleep duration has been shown to be deleterious to performance but less is known regarding other sleep metrics and their relationship to training and performance. Therefore, our objective was to determine the associations between sleep quality, training metrics, and performance, we assessed sleep quality (sleep duration (hrs), sleep debt (hrs), percentage and hours of: slow wave sleep (SWShrs and SWS%), and rapid-eye movement (REMhrs and REM%)), training measures (strain (AU), average heart rate (HR) (ExHRavg) and maximum exercising HR (ExHRmax)), and performance via 200yd Time Trial Swim (TTperf).
METHODS:
Twenty six elite male (n=10) and female (n=16) collegiate swimmers were studied during heavy training. Collection of sleep data were matched to days of training metric data collection, and also to the day preceding TTperf. Training measures include; strain (AU), average exercising heart rate (ExHRavg) and maximum exercising heart rate (ExHRmax) collected via a wearable device. Pearson correlations were utilized to determine relationships between variables unless sex effects existed in which case linear regression analyses were utilized to control for sex-differences in variables.
RESULTS:
In all swimmers, there were associations between sleep and training metrics, where sleep duration and sleep debt were related to strain (R=-0.85; p=0.01, R=0.35; p=0.045, respectively). Similarly, sleep duration and sleep debt were related to ExHRavg (R=-0.65; p=0.001, R=0.51; p=0.01, respectively) and ExHRmax (R=-0.48; p=0.01, R=0.57; p=0.003, respectively). SWShrs was inversely related to ExHRavg (R=-0.41; p=0.04). Regarding the effects of sleep on performance, when controlling for sex, sleep duration the night preceding the race predicted TTperf (R2 = 0.881; p<0.001), where swimmers with greater sleep durations exhibited faster TTperf race times. Similarly, when controlling for sex, SWS% the night preceding the race predicted TTperf (R2 = 0.883; p<0.001), whereby swimmers with a greater SWS% exhibited faster TTperf race times.
CONCLUSION:
Sleep quality measures were related to swim training in all swimmers. Similarly, in all swimmers, swimming performance was predicted by sleep quantity and quality when assessing sleep the night prior to the race. Athletes should get adequate sleep to support recovery and optimize training and performance.