Statistical analysis
Analyses were conducted using IBM SPSS v28. Descriptive statistics were calculated for baseline demographics and clinical characteristics using unweighted data. Differences in demographic and clinical characteristics by BMI range were tested using ANOVAs and chi-squared analyses. Multiple imputation was used to address missing data (11.7% at post-treatment, and 16.3% at 3-month follow-up); the model included baseline symptom severity, number of modules completed, and BMI [To examine the effect of BMI categories on clinical outcomes, marginal models were employed. The model was fit using generalised estimating equations (GEE) with a gamma distribution and a log-link function. An unstructured working correlation matrix with robust standard errors were specified [To examine clinically significant change, we reported the proportion of individuals who achieved a minimal treatment-related symptom improvement (≥30% reduction) [The longitudinal GEE models estimating symptom changes were also modelled to include available potential moderators of the effect of BMI range on symptom change. These models included the adjustment for a covariate with a time x covariate interaction (i.e. a three-way interaction between time, BMI range and moderator). The moderator variables explored included demographics (age, sex), treatment-related variables (satisfaction, adherence), as well as clinical and weight characteristics (baseline BMI, perception of weight as a problem, and number of chronic conditions). These analyses were conducted as sensitivity analyses (consistent with STROBE guidelines [