Statistical Analyses
REGRESSION
All analyses were done using 2-tailed Variables on the DWELL questionnaire were rated on a scale from 0 to 4 and were considered ordinal. All analyses on individual items of the DWELL questionnaire used a nonparametric approach, as did the engagement variables representing levels of participation in the Facebook group, which were on the same scale from 0 to 4. The overall score of the DWELL index, as well as the overall score of the engagement index, was from 0% to 100%, enabling us to use a parametric approach for these analyses. We also used the parametric approach for the wellness variables (I COPPE and WHO-5). I COPPE variables were on a 10-point scale, and the distributions were close to normal, so we considered these variables as continuous and not ordinal. For the WHO-5 variables, we used both parametric and nonparametric tests. Variables were measured on a 6-point scale, and most distributions were close to normal at both time points. We analyzed the WHO-5 variables as continuous and ordinal scales and obtained similar results.The parametric analyses included paired-sample The nonparametric analyses included paired Wilcoxon rank sum tests to compare between time 1 (before the intervention) and time 2 (after the intervention) in each of the study groups and Wilcoxon-Mann-Whitney tests (unpaired) to compare between the study groups at each of the time points. Chi-square tests were used for comparison of categorical characteristics between the study groups and between dropouts and completers. Spearman tests were used to assess correlations between levels of individual items of engagement and levels of individual items of the DWELL index at the second administration, as well as to assess correlations between individual items of the DWELL index and between WHO-5 and I COPPE variables at baseline in each of the study groups.To assess the effectiveness of the intervention, a one-way analysis of covariance was conducted to compare the effect of study group on the overall DWELL score at the end of the study while controlling for the effect of overall DWELL score at the beginning of the study. Multivariable regression analyses using the enter and forward methods were used to statistically predict the dependent variable—the overall DWELL index at the second questionnaire administration using different scenarios from explanatory variables. As the DWELL index was a new variable that we built in the context of this research, we entered all potential confounders into the regression to control for all possible confounding effects.