(21) TRAINING LOAD AND SUBJECTIVE WELLNESS SCORES DIFFER THROUGHOUT A COLLEGIATE VOLLEYBALL SEASON

Advances in GPS and accelerometry technology have changed the way sports professionals monitor athletic performance. Many studies in field-based sports have found training loads to be highest during the pre-season. However, evidence is lacking on how these metrics change and the relationship to perceptions of wellness across the season in the sport of volleyball.

Purpose: The aim of this study was to compare player load (PL), total jumps, and wellness across different phases of the volleyball season.

Methods: Data were collected from eighteen collegiate athletes from a Division 1 university during the 2021-2022 season. Of the 18 players, 6 were classified as defensive specialists, 4 middle blockers, 6 outside hitters, and 2 setters. Athletes wore tri-axial accelerometers (ClearSky T6, Catapult Sports, Melbourne Australia) during all team-related activities to track movement and intensities in all three planes of motion. PL was calculated for each activity by taking the sum of instantaneous acceleration in all three planes of motion divided by 100. Wellness was measured via a daily subjective questionnaire (Kinduct, Halifax, Canada) that participants completed each morning. Questions included status about mood state, sleep quality, sleep duration, energy level, muscle readiness, diet yesterday, and academic pressure. Every question except sleep duration was evaluated on a scale of 1-5, with 1 being worst and 5 the best. The number of hours of sleep obtained the previous night was recorded. Separate linear mixed model regressions using least square means were used to examine each accelerometer and wellness variable of interest. The model was then examined to determine if differences existed across pre-, in-, and post-season. Data were analyzed using R statistical software (R Core Team, Vienna Austria) and the level of significance was set at P ≤ 0.05.

Results: The results can be found in Table 1. Player load and total jumps were higher during the pre-season compared to in- and post-season (p< .001). No differences were observed between in- and post-season (p=0.098). Muscle readiness scores were lower (worse) during the pre-season compared to in- and post-season (p< 0.01). No differences were observed between in-season and post-season (p=0.35). Academic pressure scores were highest (less academic stress) during the pre-season and lowest during the post-season (p< 0.01).

Conclusions: The combination of higher training loads and worse wellness scores in the pre-season may be the result of greater practice volume and intensity as teams prepare for the upcoming season. As the season continued, training volume leveled off, and athletes became more adapted which led to better wellness scores. PRACTICAL APPLICATIONS: Knowing the seasonal trends in accelerometer and wellness variables will better inform strength coaches on the exact demands of competition so they can best prepare their athletes to compete at the highest level.

Acknowledgements: None