Data analysis
Descriptive statistics were employed to summarize both qualitative and quantitative variables. Categorical variables were expressed as percentages (%), while means, standard deviations, and their 95% confidence intervals were computed for quantitative variables. Comparisons of categorical variables were conducted using the chi-square test.The normality of continuous data distribution was assessed through the Shapiro–Wilk test. For non-parametric analyses, the Mann–Whitney U test was utilized for independent samples, and the Friedman test was applied for repeated measures. Post hoc comparisons were performed using the Wilcoxon signed-rank test.Parametric analyses involved Repeated Measures ANOVA. In-depth intragroup and intergroup comparisons were performed through a 2-way ANOVA. This analysis encompassed two groups and three measurement moments, considering repeated measurements over time as a within-subject factor and groups as a between-group factor. The effect size for F-tests was gauged by the partial Eta squared coefficient (ηp2), where values of 0.01, 0.06, and 0.14 indicated small, medium, and large effect sizes respectively. Post-hoc comparisons were adjusted with Bonferroni's correction and adjusted p-values. Effect sizes were determined using Cohen's d, categorized as very small (d < 0.20), small (0.20 ≤ d < 0.50), medium (0.50 ≤ d < 0.80), or large (d ≥ 0.80). The alpha level was set at 