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Artificial intelligence chatbots are the vanguard in technology-basedintervention to change people's behavior. To develop intervention chatbots, thefirst step is to understand natural language conversation strategies in humanconversation. This work introduces an intervention conversation datasetcollected from a real-world physical activity intervention program for women.We designed comprehensive annotation schemes in four dimensions (domain,strategy, social exchange, and task-focused exchange) and annotated a subset ofdialogs. We built a strategy classifier with context information to detectstrategies from both trainers and participants based on the annotation. Tounderstand how human intervention induces effective behavior changes, weanalyzed the relationships between the intervention strategies and theparticipants' changes in the barrier and social support for physical activity.We also analyzed how participant's baseline weight correlates to the amount ofoccurrence of the corresponding strategy. This work lays the foundation fordeveloping a personalized physical activity intervention bot. The dataset andcode are available athttps://github.com/KaihuiLiang/physical-activity-counseling

Evaluation of In-Person Counseling Strategies To Develop Physical Activity Chatbot for Women