Invisible Impact of Empathy on Behavioral Change: Isolating the Effect of Empathy in Long-term Physical Activity Coaching Chatbot Interactions

2026-06-25

AI Summary

This study investigates the implicit role of empathy in behavior-change-oriented conversational agents, specifically examining whether empathetic responses can enhance sustained physical activity engagement even when users are unaware of such influence. Through a within-subjects experiment over six weeks with 13 participants, three WhatsApp-based fitness coaching chatbots—identical except for their levels of empathy—were deployed, marking the first use of a controlled-variable design to isolate empathy as an independent factor. Integrating large language model–driven dialogue systems, behavioral intention scales, and step-count tracking, the findings reveal that while the high-empathy version did not elicit explicit user preference, it significantly increased average step-count growth and accelerated adherence to recommendations. These results suggest that empathy operates through peripheral cognitive pathways to subtly bolster motivation and compliance, thereby exerting a latent yet meaningful facilitative effect on long-term behavior change.

Abstract

Current dialogue systems, powered by large language models, often treat empathy as essential without assessing its true impact, especially in behavior change, where motivation and adherence often depend on subtle user-chatbot dynamics. We examine this assumption by building three WhatsApp physical-activity (PA) coaching chatbots that differ only in empathy level and evaluating them in a six-week within-subject study (N = 13). Participants struggled to distinguish between the empathy conditions, and the non-empathetic version was often rated as more engaging and useful. However, higher-empathy variants were still associated with a larger overall average increase in step counts and faster improvement in intention to follow advice. These results suggest empathy's role is nuanced: it may be hard for lay users to identify explicitly, but it can still shape motivation and trust that support sustained change. We interpret this pattern through the Elaboration Likelihood Model's peripheral route. We highlight design implications for building next-generation PA coaching chatbots that balance effectiveness with human-like connection.

Problem

• empathy
• behavior change
• chatbot
• physical activity coaching
• user-chatbot interaction

Innovation

• empathy
• behavior change
• chatbot
• physical activity coaching
• Elaboration Likelihood Model