Quantitative analysis
The characteristics of the study population were described using descriptive statistics. The analysis was performed with an intent-to-treat approach, i.e. on all patients randomized to the initial group regardless of their participation in activities. Missing data regarding the outcome were dealt with by implementing a multiple imputation approach with a predictive mean matching method. Five datasets were generated and pooled in the analysis. Data were described with frequency or percentage for qualitative variables, and with mean and standard deviation for quantitative variables. Data from the DERS questionnaires were described by item and the overall score and sub-scores were calculated.The comparison of groups G1 and G2 between T1 and T0 was carried out by calculating the difference in means in the overall DERS score within each group. The differences were presented with their 95% confidence interval in brackets. The statistical significance of these differences was tested with Student's t-test. Alongside the comparison of the global DERS score, we also compared with the same procedure the evolutions of each of the DERS sub-scores between groups. In order to investigate the effect of assiduity on the results, we also performed a unplanned subgroup analysis, using the same procedure as described above. This analysis was based on the comparison of the subset of patients who participated in 80% or more of the planned sessions at T1 with the control group used in the main analysis. In case of multiplicity of test, the inflation of error risk was accounted using the Benjamini Hochberg correction [Considering the most pessimistic effect observed in the literature [