Statistical Analysis
Continuous variables were expressed as mean and SD values (ie, age and time since injury) or first-third quartiles (ie, psychometric and outcome measures), as appropriate. Categorical variables (ie, education and etiology) were expressed as frequencies and percentages. The normal distribution of the sample was investigated through the Shapiro-Wilk test, whereas the Levene test was performed to assess the equality of variances among times for each variable. According to the reduced sample dimension and nonnormality of all variables, we chose a nonparametric analysis. Thus, where appropriate, outcome measures and psychometric variables were analyzed using the 1- or 2-tailed Wilcoxon signed-rank test to detect changes between baseline (T0) and posttreatment (T1) periods within the same group (intragroup analysis); in contrast, the comparison of continuous variables and outcome measures between the 2 groups (intergroup analysis), at both T0 and T1, was performed considering the mean of the Mann-Whitney U test. Linear correlation between variables was calculated with the nonparametric Spearman rank correlation coefficient. In addition, we calculated the effect sizes (ESs) for each psychometric and outcome measure through the Cohen d test between 2 groups to describe whether the treatment effects achieved had a sufficient magnitude. Statistical significance was set at a bilateral α level of .05. Each analysis was performed on the open source software R 4.1.3 (R Foundation for Statistical Computing) for Windows (Microsoft Corporation) [In this study, we did not perform an intention-to-treat analysis because heterogeneity might have been introduced if noncompliant subjects, dropouts, and compliant subjects were mixed in the final analysis, with potential concerns also around the real/potential efficacy of the treatments [