  Physical activity has significant benefits to health, yet large portions of
the population remain physically inactive. Mobile health applications show
promising potential for low-cost, scalable physical activity promotion, but
existing approaches are often insufficiently personalized to a user's context
and life circumstances. In this work, we explore the potential for large
language model (LLM) based conversational agents to motivate physical activity
behavior change. Through formative interviews with 12 health professionals and
10 non-experts, we identify design considerations and opportunities for LLM
health coaching. We present GPTCoach, a chatbot that implements an
evidence-based health coaching program, uses counseling strategies from
motivational interviewing, and can query and visualize health data from a
wearable through tool use. We evaluate GPTCoach as a technology probe in a user
study with 16 participants. Through quantitive and qualitative analyses, we
find promising evidence that GPTCoach can adhere to a health coaching program
while adopting a facilitative, supportive, and non-judgmental tone. We find
more variable support for GPTCoach's ability to proactively make use of data in
ways that foster motivation and empowerment. We conclude with a discussion of
our findings, implications for future research, as well as risks and
limitations.
