ArticleProceedings of the ACM on human-computer interaction2025
Large Language Model Agents for Improving Engagement with Behavior Change Interventions: Application to Digital Mindfulness.
Article in Proceedings of the ACM on human-computer interaction, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
3 citing papers in PubMed.
- A Behavioral Science-Informed Agentic Workflow for Personalized Nutrition Coaching: Development and Validation Study.JMIR formative research · 2025Article
- The benefits and dangers of anthropomorphic conversational agents.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Designing a just-in-time adaptive intervention with trigger detection and a generative chatbot: Smoking cessation use case.Digital healthArticle
Corrections and comments
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Authors and funding
9 authors.
Funding
Abstract
Although engagement in self-directed wellness exercises typically declines over time, integrating social support such as coaching can sustain it. However, traditional forms of support are often inaccessible due to the high costs and complex coordination. Large Language Models (LLMs) show promise in providing human-like dialogues that could emulate social support. Yet, in-depth, in situ investigations of LLMs to support behavior change remain underexplored. We conducted two randomized experiments to assess the impact of LLM agents on user engagement with mindfulness exercises. First, a single-session study, involved 502 crowdworkers; second, a three-week study, included 54 participants. We explored two types of LLM agents: one providing information and another facilitating self-reflection. Both agents enhanced users' intentions to practice mindfulness. However, only the information-providing LLM agent, featuring a friendly persona, significantly improved engagement with the exercises. Our findings suggest that specific LLM agents may bridge the social support gap in digital health interventions.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.