Generative Experiences for Digital Mental Health Interventions: Evidence from a Randomized Study

TL;DR

This paper introduces GUIDE, a system that dynamically generates personalized digital mental health interventions, significantly reducing stress and enhancing user experience in a randomized study.

Contribution

It presents a novel paradigm of generative experience for digital mental health, enabling runtime composition of intervention content and interaction structure.

Findings

GUIDE significantly reduced stress (p = .02).

GUIDE improved user experience (p = .04).

Supported diverse reflection and action forms.

Abstract

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.