Intervention effectiveness
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To evaluate the success of randomization, we will first compare between interventions (structured, self-guided) on demographics and other variables using two-sample t-tests (continuous) and chi-square tests (categorical); Wilcoxon rank sum tests or Fisher’s exact tests will be used instead if assumptions are violated. If differences between intervention groups exist, follow-up analyses will include those variables as covariates to ensure any observed intervention effect is not due to other differences between the groups. Primary analyses will be intent-to-treat analyses, that estimate intervention effects based on groups as randomized. Due to the repeat assessments over time across individuals (baseline, immediately after the 6-month intervention, and at 6- and 12-months post-intervention), repeated measures, random effects models will be used to estimate differences in rate of change between the groups at different time points. The main intervention effect will be defined as the difference between groups from baseline to immediately after the 6-month intervention, although later time points will assess maintenance of those effects. These models will incorporate random intercepts and slopes to account for between-person variation in overall level and change over time. All model assumptions will be checked and transformations or repeated measures approaches for non-normal data, such as generalized estimating equation (GEE) approaches will be used if needed. List-wise deletion will be used if < 5% of the data are missing; multiple imputation will be used if > 5% of the data are missing. If data appear to be missing not at random or exceeds > 20% missingness (which we have put procedures in place to prevent), we will conduct sensitivity analyses using Wu-Carrol and Diggle-Kennward approaches that our team has previously applied in RCT trials [