Generative Experiences for Digital Mental Health Interventions: Evidence from a Randomized Study
This addresses the need for more engaging and effective digital mental health tools by dynamically personalizing how support is experienced, though it is incremental in building on existing personalization efforts.
The paper tackled the problem of digital mental health interventions failing due to misaligned interaction formats, by introducing a generative experience paradigm that composes interventions at runtime, resulting in significant stress reduction (p = .02) and improved user experience (p = .04) compared to a control in a study with 237 participants.
Digital mental health (DMH) tools have extensively explored personalization of interventions to users' needs and contexts. However, this personalization often targets what support is provided, not how it is experienced. Even well-matched content can fail when the interaction format misaligns with how someone can engage. We introduce generative experience as a paradigm for DMH support, where the intervention experience is composed at runtime. We instantiate this in GUIDE, a system that generates personalized intervention content and multimodal interaction structure through rubric-guided generation of modular components. In a preregistered study with N = 237 participants, GUIDE significantly reduced stress (p = .02) and improved the user experience (p = .04) compared to an LLM-based cognitive restructuring control. GUIDE also supported diverse forms of reflection and action through varied interaction flows, while revealing tensions around personalization across the interaction sequence. This work lays the foundation for interventions that dynamically shape how support is experienced and enacted in digital settings.