Primary outcome analysis
Primary outcome will be first analyzed on an intention-to-treat (ITT) basis, including all patients as originally allocated after randomisation. For missing information due to patient drop-out, we will rely on the linear mixed model and consider the baseline data as part of the outcome matrix. We will also perform a per-protocol (PP) sensitivity analysis including only those patients who completed the treatment originally allocated.Analyses should be considered as hypotheses generating. To explore the effects of the intervention type on the two primary outcome measures, the results will be compared using linear mixed models. More precisely, the moment of the measure (baseline and immediate follow-up), the intervention (nested within the centers), the centers and the interaction of these factors will be considered as fixed factors. A nested random intercepts and slopes for each participant within the centers will be implemented. Age groups, educational level, objective Z-score (Z-score from 0 to -0.50; -0.51 to -1, etc.) and gender will also be included in the model as stratification factors. Following the linear mixed model, linear contrast will be performed between the two levels of the intervention variable on the first follow-up measure (T1) to explicitly compare the potential benefit of the intervention. Bonferroni adjustment will be performed to correct for multiple testing, more precisely 2 tests (the number of primary measures), and p-value < 0.025 will be considered as statistically significant. 95% CI and standardized effect sizes (SES) from the null model will be calculated. The following convention will be used to interpret effect size: small 0.2–0.49, moderate 0.5–0.79, large ≥ 0.8. As a Proof-of-Concept (POC) study, estimation will be focused on ES but will include p-values as help in interpreting the data.