2.4. Statistical Analysis
RA
The data were first tested for normality using standardized skewness and kurtosis values. Normally distributed data were presented as the mean and standard deviation, while non-normally distributed data were presented as the interquartile range. One-way analysis of variance (ANOVA) and the Kruskal–Wallis H test were used to examine differences during the pre-experimental stage among groups. The matched samples t-test and Wilcoxon rank-sum test were used to examine the differences of the physical fitness tests in groups before and after intervention. Generalized Linear models (GLMs) were used to assess the differential impacts of the intervention conditions on all outcome indicators for normally distributed data. Generalized Linear mixed models (GLMMs) were used to assess the differential effects of the intervention conditions on all outcome indicators for non-normally distributed data. The intervention condition (CG, BM, RA, BG, and MA) models were adjusted for potential confounders explaining main outcome variance (baseline test results, age, gender, height, weight, and BMI). Bonferroni adjusted pairwise comparisons were employed to analyze differences among conditions, and 