3. Statistical Analysis
muscle soreness
Statistical analyses were performed using SPSS software (IBM SPSS Statistics for Windows, Version 25.0 IBM Corp., Armonk, NY, USA) and graphs were made using Graphpad Prism 9. All continuous variables and the residuals of the variables used in the linear-effects model were visually inspected and tested for normality with the Kolmogorov–Smirnov test. Logarithmic transformations were applied when data were not normally distributed and subsequently the normality was retested. Logarithmic transformations of CK and LDH were used in the statistical analyses in all cases. Data were displayed as mean ± SD or median (interquartile range [IQR]) for parametric and non-parametric continuous variables, respectively. Group differences were analyzed using a One-Way ANOVA or Kruskal–Wallis for parametric or non-parametric continuous variables, respectively. Repeated observations over days, such as CK, LDH and skeletal muscle mass, were analyzed using linear mixed-effects models with compound symmetry as the repeated covariance type in which group and time (e.g., 24 h, 48 h and 72 h upon walking) were treated as independent variables. The pre-exercise values were used as a covariate in the linear mixed model analyses. When significant main effects or interactions were detected, Bonferroni post hoc comparisons were made in case of parametric variables and Mann–Whitney U tests in case of non-parametric variables. Repeated measures analyses for the non-parametric muscle soreness values were performed using a Friedman analysis. The level of significance was set at 