Statistical analysis
The data are presented as means and standard deviations for continuous variables and numbers and percentages for categorical variables. Analysis of covariance (ANCOVA) was used to test the differences in outcome variables between the intervention and control groups at 6 months. Separate ANCOVA models were run for each outcome, with the 6-month outcome as the dependent variable, group (intervention, control) as the independent variable, and the baseline value of outcomes as the covariate. ANCOVA model assumptions were checked, and robust ANCOVA was run and compared to ANCOVA where serious departures from the parametric model assumptions were observed. Multiple imputation (MI) was used in the primary analysis; it is considered the best method for addressing the most common and realistic missing data patterns seen in RCTs [Subgroup analyses were proposed Cost analyses were conducted to compare the costs associated with health service use by participants in the intervention and control groups. The service use that patients reported using the HSSUI at baseline and 6 months after the intervention, was multiplied by the unit costs for the service to obtain total service costs. Unit costs were obtained from public sources (e.g., provincial health agency databases). Cost data are often substantially skewed (as in this study), and non-parametric methods based on ranked data are normally used to analyze group differences. The Mann–Whitney U test was used to evaluate differences in median costs between the two groups [