Data Analysis
The data were analyzed using SPSS Statistics Software version 23. Preliminary analyses examined the normality of the variables using the Kolmogorov–Smirnov Test. We also inspected inconsistent data as of the responses to the attentional control items, and no data was excluded as a result. Some of the variables were found to not follow a normal distribution whereas others did, hence non-parametric and parametric statistical methods were adopted as appropriate. A complete case analysis and intention-to-treat analysis were performed to evaluate possible differences in main outcome variables. Multiple imputation using the Fully Conditional Specification (FCS) imputation method was performed and five datasets with a maximum of 10 iterations per imputation were generated.Correlations at baseline were calculated using Spearman correlation tests to assess levels of dependence between the selected variables.Comparisons between the intervention and control arms in the primary outcome variables were performed using Student´s independent t test (parametric test) for continuous variables, the Mann–Whitney U test (non parametric test) for continuous and ordinal variables depending on the assumption of normality. Binary variables were calculated using the Cohen´s effect size formulas for the independent T-test (see Formula G power Software version 3.1 was used to estimate the statistical power post hoc, with a power level of 80% set as a reasonable balance between alpha and beta risk.Finally, a moderation analysis was performed using the PROCESS Hayes macro as implemented in SPSS. Specifically, it tested the moderating effects of sex on the relationship between the study group (intervention vs. wait-list) and statistically significant main outcomes. This analysis was conducted on the total sample (161), following an intention-to-treatment approach.