Medicine & Science in Sports & Exercise:
Annual Meeting Abstracts: C-28 - Free Communication/Poster: Exercise Evaluation
West Virginia University, Morgantown, WV.
The Wingate test is used to measure components of power. It is known that biometric and training variables contribute to Wingate performance, but it has not been determined how much these contributions vary by sport. Furthermore, the relationship between diet and Wingate performance has not been fully explored. PURPOSE: To determine the relationship of body composition, diet and training to power production in recreational and competitive athletes during the Wingate test. METHODS: Thirtythree resistance-trained men (ages 18–35) participated in a 15-day randomized doubleblind study. Subjects were originally part of a study to test the effects of creatine, but since no differences were found due to treatment, participants were divided into athletic groups: hockey (n = 8), swimmers (n = 9) and recreational athletes (n = 8). Subjects were pre and post tested at 10% and 12.5% of bodyweight. Body composition was assessed by BOD POD®. Diet was assessed via 3-day recall. Subjects were asked to maintain their normal diet and training regimen throughout. Repeated-measures ANOVA with Tukey post-hoc testing was used to identify performance differences between athletic groups. Pearson-product moment correlations were used to identify significant relationships between performance and biometric, training and dietary variables. Step-wise linear regression was used to determine predictors of performance at each resistance. RESULTS: Hockey players produced more mean power than swimmers at 10% (803 ± 19W vs. 735 ± 21W, p = .02) and 12.5% resistance (829 ± 26W vs. 703 ± 28W, p = .002), while recreational athletes had higher mean power than swimmers at the heavier resistance (806 ± 25W vs. 703 ± 28W, p = .02). In the combined group, peak power was higher at 12.5% (1192 ± 30W vs. 1072 ± 24W, p<.001) while mean power was not. Four variables were significantly correlated to peak and mean power at both resistances: age (p<.001), training experience (p<.001), body weight (p<.001) and lean body mass (p<.001). Stepwise regression identified different combinations of these variables as predictors of mean, and peak power at 10% and 12.5% resistances, and accounted for 62–76% of the variance. No macronutrient variable was related to performance. CONCLUSIONS: Age, training experience, body weight, and lean body mass, in various combinations, account for a significant portion of the performance variance in mean and peak power (p<.05) at 10% and 12.5% resistance. Dietary intake did not predict performance. If peak power is the component of interest, 12.5% rather than 10% resistance should be used.