A Factorial Randomized Controlled Trial of Implementation-Intention-Based Self-Affirmation Interventions: Findings on...

This study builds on growing evidence on implementation-intention-based self-affirmation intervention effects on mental health. Using a factorial design, this pre-registered study aimed to further investigate whether (1) strengthening the element of specificity within body-related self-affirming implementation intention (BS-AII) intervention compared to general self-affirming implementation intention (S-AII) would provide greater improvements in mental health outcomes for adults with psoriasis, and (2) whether the addition of a booster component would result in enhancing effectiveness at follow-up. A total of 306 adults with psoriasis were assessed for eligibility and 222 (aged 18-71 years) were randomized and received S-AII, BS-AII, or MGI (mere goal intention-control condition). Within each group, participants were again randomized to booster (B) or no-booster condition in a 3 × 2 factorial design, resulting in six groups: S-AII; S-AII + B; BS-AII; BS-AII + B; MGI; and MGI + B. Data were collected over three-time points, at baseline, 2 weeks post-intervention, and at 1-month later. Three primary outcomes were defined as a reduction of anxiety and depressive symptoms and enhancement of well-being. In terms of secondary outcomes, positive other- and self-directed feelings and also an emotional attitude toward the body were evaluated. To fully estimate intervention effects through intention-to-treat analysis, linear mixed models were used. A significant effect of time was observed, but no evidence of time-by-group interactions and no three-way interactions were detected. Exploratory analyses revealed two significant moderating effects of age and self-esteem, pointing to boundary conditions of the interventions. These findings offer to gain deeper insights on null (or negative) effects also reported in past works and highlight that self-affirmation interventions should be more thoroughly investigated and optimized before they can be broadly implemented in real-life contexts, especially to prevent backfiring and negative-enhancing effects.