Alexa Jenny Chandler1, Mallory Dixon1, Bridget A. McFadden2, Harry P. Cintineo3, Blaine S. Lints1, Gianna F. Mastrofini1, Shawn M. Arent, FACSM1. 1University of South Carolina, Columbia, SC. 2Queens College, Flushing, NY. 3Lindenwood University, St. Charles, MO.

BACKGROUND: Volleyball is primarily an anaerobic sport, as players need to perform bursts of high intensity exercise followed by short rest repeatedly throughout a match. Managing athlete workloads to maintain high levels of performance is an important part of team success. The purpose this study was to assess workload, performance, training distress, and sleep, as well as the relationships between these variables. METHODS: Female collegiate volleyball players (n=19) were monitored throughout the 12-week fall 2021 competitive season (T1-T12). Workloads were quantified by a rolling 7-day sum of session rating of perceived exertion (sRPE). Athletes participated in weekly testing to assess physical readiness, training distress, and sleep quality. Physical readiness was assessed by weekly countermovement jump (CMJ). Training distress was determined using the Multicomponent Training Distress Scale (MTDS) composite score and sleep quality was assessed using the Groningen Sleep Quality Score (GSQS). Linear mixed-effects models were used to determine change over time in workload, CMJ, MTDS scores, and GSQS scores with post-hoc tests comparing each timepoint back to T1. Relationships between changes in each metric were assessed via repeated measures correlations. An alpha level of 0.05 was used to determine statistical significance.RESULTS: There were time main effects for sRPE (P<0.001), CMJ (P<0.001), MTDS scores (P<0.001), and GSQS scores (P=0.032). sRPE was significantly lower at T6 (P<0.0001) while there were no differences from baseline in CMJ at any timepoint. MTDS scores were significantly elevated at T5 (P=0.049), T8 (P=0.025), T11 (P=0.001), and T12 (P=0.022) and GSQS scores were higher than baseline at T2-T4, T6-T8, and T10-T12 (P<0.05). There was a significant weak correlation between sRPE and CMJ (r=0.2; P=0.33) but no significant relationships between any other variable. CONCLUSIONS: This study showed fluctuations in subjective training distress and sleep while objective workload and performance measures remained stable throughout the season. Future analyses should investigate the relationship between objective and subjective measures on an individual level, rather than as a team, as some individuals may respond differently to both workload and non-training related stressors.

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