Spss v 22.0 program

The Kolmogorov-Smirnov test (K-S) was used to determine the normal distribution of data. The null hypothesis was rejected for sprints > 19 km/h (%; p = 0.011) and in the high intensity running distance (p = 0.008). For parametric data, descriptive analysis was performed and computed as mean (X) and standard deviation (±SD). In contrast, non-parametric data were described as median (1st quartile – Q1; 3rd quartile – Q3). A paired t-test (parametric data) and the Wilcoxon test (non-parametric data) were conducted to compare two moments (pre- versus post-COVID-19 quarantine). The significance level was set at p < 0.05 for all analyses. Subsequently, the effect size measure for non-parametric analysis was calculated, defined as ES = Z/√N, where ES represents the effect size, Z is derived from the z-score of the Wilcoxon (W) test and N is the total number of observations. This analysis considers ES-values as small effect size (r = 0.10), medium effect size (r = 0.30), or large effect size (r = 0.50). Data were analyzed using the “Statistical Package for the Social Sciences” (SPSS v. 22.0 program, SPSS, Inc., Chicago, IL, United States).

For each herbal blend, three individual samples were analyzed, and all assays were prepared in triplicate. The results were analyzed using one-way analysis of variance (ANOVA), followed by Tukey’s HSD test as p = 0.05, and are expressed as mean values with standard deviation (SD). When less than three results were present in each individual analysis, Student’s t-test p-values were used to determine the significant difference, with p = 0.05. Both of these statistical treatments were carried out using the SPSS v.22.0 program.