Bloom: Designing for LLM-Augmented Behavior Change Interactions
Abstract
Large language models (LLMs) offer novel opportunities to support health behavior change, yet existing work has narrowly focused on text-only interactions. Building on decades of HCI research on effective behavior change interactions, we present Bloom, an application for physical activity promotion that integrates an LLM-based health coaching chatbot with existing design strategies and UI elements. As part of Bloom's development, we conducted a redteaming evaluation and contribute a safety benchmark dataset. In a four-week randomized field study (N=54) comparing Bloom to a no-LLM control, we observed important shifts in psychological outcomes: participants in the LLM condition reported stronger beliefs that activity was beneficial, greater enjoyment, and more self-compassion. Both conditions significantly increased physical activity levels, doubling the proportion of participants meeting recommended weekly guidelines, though descriptively, we observed no advantage for the LLM condition in short-term physical activity levels. Instead, our findings suggest that LLMs may be more effective at shifting mindsets that precede longer-term behavior change.
Cite
@article{arxiv.2510.05449,
title = {Bloom: Designing for LLM-Augmented Behavior Change Interactions},
author = {Matthew Jörke and Defne Genç and Valentin Teutschbein and Shardul Sapkota and Sarah Chung and Paul Schmiedmayer and Maria Ines Campero and Abby C. King and Emma Brunskill and James A. Landay},
journal= {arXiv preprint arXiv:2510.05449},
year = {2026}
}