Abstract
Alongside their full-time academic commitments, high school athletes are involved in all-year-round sports activities. It has become increasingly common for learners to participate in two or more sport codes in one season, as they play cricket and basketball in the summer season of the 1st quarter (January–April) of the school year (in-season) while concurrently partaking in an off-season (2nd quarter competitive season) winter sport such as hockey and rugby. Moreover, during the in-season winter sport (namely rugby), learners concurrently partake in another summer sport (such as soccer and/or athletics) for the 3rd quarter of the calendar year. The learners who are multisport athletes are confronted by overlaps in sport seasons that cause physical and mental challenges owing to the accumulated training workload. For the multisport learner, academic and cultural workloads add to the general stress of the learner, and which can create pressure to perform athletically. This in turn results in difficulties in sporting performance. Learners are compelled to deal with stresses concerning family life, social skills, psychological health, and physiological issues, yet coaches demand and expect these learners to perform at optimal levels during training and competition. Shouldering numerous responsibilities both within and outside of the school framework leaves these learners vulnerable to being overwhelmed and, this affects both their physical and mental health. Ultimately, this leads to discouraged performances that result from burnout and overuse injuries. The primary aim of the study was to evaluate the effects of training workload on 15 male multiple-discipline learners, aged 15–16 years, over overlapping sport seasons. The secondary aim was to assess physical performance of these learners at the start of each playing season. This was done while tracking the load for the duration of a particular season. The objective was to quantify and monitor the physical and physiological performance parameters at the start of the playing season and to track the training workloads for the duration of the season. To do so, a test battery aimed at profiling the performance parameters of the sample was employed. The descriptive data taken from the test battery included mass, vertical jump height, medicine ball throw distance, running agility, 5 m and 20 m sprint times, and anaerobic and aerobic capacities. Finally, the effects of the workloads were assessed by observing the changes caused by workload on the performance parameters during the overlap. The mean value and standard deviations were v computed for each of the performance parameters. All data distributions were assessed using Shapiro-Wilk tests. Paired t-tests were used to determine the differences in performance metrics before and after the 10-week monitoring period and one-way analysis of variance (ANOVA) with post hoc pairwise comparisons were calculated to determine the differences between the before and after test values of the same sample. A significance level of p < 0.05 was enforced. The interactions between two independent variables (workload and performance parameters) on the dependant variable (performance) were observed using appropriate post hoc tests. Effect sizes were calculated using Cohen’s d, and Hopkins’ (2002) qualitative descriptors. Differences were noted for agility (8.9 ± 0.9 s, d = 1.58, p = 0.002), vertical jump power (4117 ± 582 W, d = 0.08, p = 0.001) yo-yo intermittent recovery (Level 1) (14.5 ± 1.0, d = 0.11, p = 0.001) and the estimated VO2max (ml/kg/min) (42.4 ± 2.2, d = 0.11, p=0.001) parameters. The mean for the training loads over the 10 weeks was 2283.28 ± 390.21 arbitrary units (AU). A statistically significant difference was seen between workloads across the 10-week (p = 0.002) monitoring period. Specifically, weeks 1 (2048.1 AU) and 2 (2973.0 AU, p = 0.048), weeks 1 and 3 (3691.1 AU, p = 0.0001), weeks 2 and 3 (p = 0.0001), week 3 (3691.1 AU), week 9 (3842.7 AU) and week 10 (3567.5 AU) showed a significant difference. The acute: chronic workload ratios calculated across the 10-week monitoring window presented significant differences across the weeks. The mean of the acute: chronic workload ratio (ACWR) between weeks 4 and 10 was 1.03 ± 0.11. Differences were observed between weeks 4 and 5 (p = 0.0001), weeks 8 and 10 (p = 0.003), weeks 6 and 9 (p = 0.0001), weeks 7 and 9 (p = 0.0001) and weeks 9 and 10 (p = 0.014). Finally, the self-reported wellness scores (%) over 10 weeks showed significant differences. The mean of the wellness across the weeks was 78.2 ± 30%. Significant differences are observed between weeks 1 and 9 (p = 0.0001), weeks 1 and 10 (p = 0.0001), weeks 2 and 9 (p = 0.000) and weeks 2 and 10 (p = 0.000). Very good percentage scores were recorded for mean wellness in week 2 (81.6% ± 0.7%), week 3 (85.1% ± 5.0%), week 4 (85.0% ± 5.4%), week 5 (83.9% ± 10.9%), week 7 (83.1% ± 5.7%) and week 8 (86.1% ± 1.4%). Given the useful information on the positive and negative consequences associated with training workload, performance is affected by either very high training loads or very low training loads, both of which cause injury or even burnout. Conversely, a substantial training workload ought to bring about physiological adaptations from improved performance...
M.Phil. (Sport Science)