2.7. Statistical Analyses
WL
REGRESSION, SECONDARY
Statistical analyses were conducted using IBM SPSS (version 28) [A priori sample size calculation (see study protocol by Küchler et al. [Analyses were performed on an intention-to-treat basis, employing multiple imputations by chained equations [General linear models (GLM) were employed to explore group differences between UG and WL, GoD and WL (both primary analyses), and between UG and GoD (secondary analysis) 4 weeks (t1), 8 weeks (t2), and 6 months (t3) after randomization. For each group comparison, outcome variable, and assessment time, a linear regression model was applied, corrected for baseline values of the respective outcome variable. Dichotomous variables were dummy coded and continuous variables were To calculate the number of participants achieving reliable improvement in mindfulness (FMI) from pre- (t0) to post-intervention (t2), participants were coded as responders and non-responders according to the Reliable Change Index (RCI) [To examine the influence of guidance- and adherence-associated variables on the dependent variables “number of modules completed at t2” and “FMI at t2”, additional exploratory regression analyses were conducted using complete case data of the two IGs together. For “number of modules completed at t2”, we explored the predictor “SMS Coach signup” for both IGs and “number of e-coach contacts” for GoD. For “FMI at t2”, we examined the predictors “SMS Coach signup”, “number of modules completed at t2”, “average days of mindfulness practice per week”, and “average minutes per mindfulness practice day” for both IGs together, and additionally “number of e-coach contacts” for GoD. First, univariate associations between potential predictors and the respective dependent variable were investigated. Afterwards, a final multivariate model was established including all significant predictors. 