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

TL;DR

This paper presents a dataset and analysis of human counseling strategies in physical activity interventions for women, aiming to inform the development of effective AI chatbots for behavior change.

Contribution

It introduces a new annotated dataset of intervention dialogues, a strategy classifier, and insights into how strategies relate to behavior change and participant characteristics.

Findings

Identified key intervention strategies linked to behavior change.

Developed a classifier to detect strategies in conversation.

Analyzed correlations between strategies and participant baseline weight.

Abstract

Artificial intelligence chatbots are the vanguard in technology-based intervention to change people's behavior. To develop intervention chatbots, the first step is to understand natural language conversation strategies in human conversation. This work introduces an intervention conversation dataset collected 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 of dialogs. We built a strategy classifier with context information to detect strategies from both trainers and participants based on the annotation. To understand how human intervention induces effective behavior changes, we analyzed the relationships between the intervention strategies and the participants' changes in the barrier and social support for physical activity. We also analyzed how participant's baseline weight correlates to the amount of occurrence of the corresponding strategy. This work lays the foundation for developing a personalized physical activity intervention bot. The dataset and code are available at https://github.com/KaihuiLiang/physical-activity-counseling