Monitoring external workloads and countermovement jump performance may be useful for coaches. PURPOSE: The purpose of this study was to determine the effects of external load on player performance as measured by a CMJ and specific blood biomarkers throughout the preseason. METHODS:10 female division 1 basketball athletes had PlayerLoadTM (PL) monitored for all mandatory basketball training during six weeks of the preseason and CMJs were performed weekly. Blood biomarkers were collected before preseason and at the end of preseason. Data were analyzed via the Catapult Sport software (Openfield, Catapult, Innovations, Melbourne, VIC, Australia) to quantify all participant movement. Data from CMJs were analyzed via Sparta Science technology (SpartaTrac; SPARTA Performance Science, v1.2.4). Cumulative effect of physical activity (CTPL) was estimated as a sum of total PL up to each jump testing session divided by the number of days. Linear mixed-effects models were used to model data related to the efficacy of PL and CTPL. Athletes (id) and their positions were examined as potential random effects. RESULTS: The best fit model suggested a high-order polynomial pattern between PL and the number of days since the first jump testing session with a random effect for the intercept (marginal R2 = 0.290; conditional R2 = 0.471). The fixed effect for the slope of the first order term was found to be positive. There was a significant negative effect of CTPL on JH (p = 0.0037). The boot strapped model showed a marginal R2 of 0.0183 (95% CI [0.000952, 0.0744]) and a conditional R2 of 0.884 (95% CI [0.762, 0.956]). For RSImod, a significant negative association between RSImod and CTPL (p = 0.0039, 95% CI [-0.0002214, -4.597081e-05]). CONCLUSION: Workloads increase during preseason. CMJ height and RSImod may have limited utility in displaying the effects cumulative workloads. Position played did not impact workload or the impact of that workload on the player. PRACTICAL APPLICATION: Cumulative effect of physical activity may be tracked using CTPL derived from PL. Practitioners may be encouraged to monitor alternative countermovement variables to better understand performance response to the cumulative effect of physical activity.
| Identifer | oai:union.ndltd.org:ETSU/oai:dc.etsu.edu:etd-5833 |
| Date | 01 December 2023 |
| Creators | Van Dyke, Michelle |
| Publisher | Digital Commons @ East Tennessee State University |
| Source Sets | East Tennessee State University |
| Language | English |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Electronic Theses and Dissertations |
| Rights | Copyright by the authors. |
Page generated in 0.0021 seconds