INTRODUCTION: There is a high prevalence of poor sleep quantity and quality in athletes, and similarly, there is often a high prevalence of low energy availability (EA) in endurance athletes. Poor sleep quality during high training loads has been identified as an early sign of overreaching, which may result in improper recovery and limit training adaptations. Similarly, adequate energy availability (EA) is necessary to sustain physiological function and prevent training maladaptation in athletes. Previous data support worsened sleep quality during periods of heavy training in swimmers and low EA has been demonstrated in athletes during periods of heavy training. Endurance athletes during heavy training may experience both poor sleep quality and low EA, which may affect a range of training metrics. As well, emerging evidence supports a role of EA in sleep quantity, however, it is still unclear how EA affects sleep quality among athletes. Therefore, our objective was to determine the interrelationships between EA and sleep quality, where we assessed EA via 3-day diet log and daily exercise energy expenditure, and matched data collection for sleep quality (sleep duration (hrs), sleep debt (hrs), and hours and % of: slow wave sleep (SWShrs and SWS%), rapid-eye movement (REMhrs and REM%)), via a wearable device. METHODS: In 26 elite male (n=10) and female (n=16) collegiate swimmers, data were collected over 2-weeks of heavy training. Frequency analysis was used to determine the number of athletes meeting or exceeding current athlete sleep duration recommendations. 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. RESULTS: In all swimmers, 42% (11/26) had a sleep duration * 7hrs. 38% (6/16) female swimmers and 50% (5/10) male swimmers achieved a sleep duration * 7hrs. There was a trend toward a correlation between EA and sleep duration in all swimmers (R=0.33; P=0.06). EA positively correlated with REMhrs (R=0.64; p=0.001). EA was not related to sleep debt. Regression analyses revealed that when controlling for sex, EA was a predictor of SWShrs (R2 = 0.448; F=9.35, p
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