Predictors of longterm treatment response and dropouts in two personalised and gamified self-help mobile based interventions for the prevention of mental illness

Growing evidence confirms the efficacy of e-health preventive interventions for mental illness. Among those, particularly self-guided interventions are advantageous due to potentially low-costs, scalability, and flexibility. However, they are also characterised by high dropout rates. Here, we analyse predictors of treatment response and dropouts from two personalised and gamified self-guided e-health preventive interventions. Data were obtained from a large cohort randomised controlled trial of the ECoWeB (PREVENT) project, in which two m-health interventions were evaluated to prevent mental disorders for adolescents and young adults aged 16-22 with potential deficits in emotional competences from Belgium, Germany, Spain, and the UK (N = 1262, 78% female). This secondary analysis focused on predictors of intervention beneficiaries and dropout, such as age, gender, psychopathology baseline symptoms, and intervention characteristics. Results showed that older age, more psychopathological symptoms, and higher worry predicted increased treatment benefits, whereas being male, higher depression symptoms, and lower worry were associated to higher dropout rates. Strategies to increase compliance should focus on personalising interventions for subgroups at risk of dropout, such as tailoring content and motivation techniques for males, a younger population, and individuals with depressive symptoms. Additionally, incorporating motivational features like deeper gamification, adaptive support, and blended care models could enhance engagement and reduce dropout rates.