Statistical Analysis
LME, CIM
All quantitative data analyses were conducted using SPSS Statistics (version 28, IBM Corp), apart from effect size calculations, which were performed in R (version 4.1.2, R Foundation for Statistical Computing) [To assess within-participant intervention efficacy at course completion and the maintenance of intervention effects after the booster session, linear mixed-effects (LME) analyses were performed for each outcome using the assessment time point (ie, baseline, after the course, and after the booster) as a fixed-effect predictor. Participants were treated as repeated effects to account for within-participant error term correlations. The baseline time point was set as the reference category in all the models. We used a first-order autoregressive covariance matrix and restricted maximum likelihood estimation in each model. In this approach, within-participant residual errors are correlated but are independent between participants. LME analysis was chosen over more traditional repeated measures methods, such as ANOVA, as LME allows for better handling of within-participant correlations across repeated measures and can better accommodate missing data [Independent content analysis was conducted by KP and CIM on participants’ written responses to “the most significant change in their life, if any, following OPTIMiSE.” The 2 researchers then discussed and reached a consensus on the major themes arising from the content analysis. These themes were confirmed through discussion with CB and MF. After this process, the themes were compared with those reported in 2 previous qualitative studies of face-to-face memory groups [