Multimodal Personalization with Shared Decision Making for StayFine: Individualized App-based Relapse Prevention for Anxiety and Depression in Young People

Although the majority of young people with anxiety and depression disorders initially respond to CBT, many relapse after remission. Our group developed StayFine, an accessible app-based intervention aimed at preventing relapse of currently remitted anxiety and depressive disorders in 13-to-21-year-olds. Given the diversity in the residual symptom profile of potential users, the intervention is modular, thereby shaping each intervention for personal use. In this presentation, we describe the novel data-driven 4-step personalization approach to assign modules to 74 participants who took part in the StayFine intervention. The intervention is comprised of 6 modules, of which the first two – psycho-education and cognitive restructuring – and the last – creation of a relapse prevention plan based on all modules – are fixed. In-between, 3 of 5 optional modules are assigned, choosing from enhancing positive affect, behavioral activation, exposure, sleep and wellness. The personalization approach contains four steps indicating specific modules: (1) prior diagnoses (diagnostic interview: past anxiety or depression disorders), (2) transdiagnostic psychological factors (online self-report questionnaires: sleep quality, affect and wellness), (3) individual symptom networks (ecological momentary assessment of affect, thought and behavior via a two-week diary with six time-points per day) and subsequently (4) patient preference based on shared decision-making with a trained expert by experience. The (in)congruence between data-driven advice and participant preference will be presented, as well as a critical evaluation of this multimodal approach.