生成体验用于数字心理健康干预：来自随机研究的证据
Generative Experiences for Digital Mental Health Interventions: Evidence from a Randomized Study
AI总结：
本文提出生成体验范式，通过动态构建干预体验提升数字心理健康干预效果，实验显示GUIDE在减压和用户体验方面显著优于基线对照。
摘要：
数字心理健康（DMH）工具已广泛探索针对用户需求和情境的个性化干预。然而，这种个性化通常关注提供的支持内容，而非体验方式。即使匹配良好内容，当交互格式与用户参与方式不匹配时仍可能失败。本文引入生成体验作为DMH支持的范式，干预体验在运行时动态构建。我们通过GUIDE系统实现，该系统通过评分指南生成模块化组件，生成个性化干预内容和多模态交互结构。在N=237的随机研究中，GUIDE相比基于LLM的认知重构对照组，显著降低了压力（p=0.02）并提升了用户体验（p=0.04）。GUIDE还通过多样的交互流程支持多样化的反思和行动，同时揭示了交互序列中的个性化张力。本工作为在数字环境中动态塑造支持体验和实施的干预奠定了基础。
英文摘要：
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 an approach to 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 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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
