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.
| Subjects: | Human-Computer Interaction (cs.HC) |
| Cite as: | arXiv:2510.05449 [cs.HC] |
| (or arXiv:2510.05449v2 [cs.HC] for this version) | |
| https://doi.org/10.48550/arXiv.2510.05449 arXiv-issued DOI via DataCite |
Submission history
From: Matthew Jörke [view email]
[v1]
Mon, 6 Oct 2025 23:31:18 UTC (4,786 KB)
[v2]
Tue, 3 Mar 2026 22:50:43 UTC (6,022 KB)