When Workout Buddies Are Virtual: AI Agents and Human Peers in a Longitudinal Physical Activity Study
On the map: LLM agents and tool use → General LLM agent frameworks and benchmarks
Abstract (arXiv)
Physical inactivity remains a critical global health issue, yet scalable strategies for sustained motivation are scarce. Conversational agents designed as simulated exercising peers (SEPs) represent a promising alternative, but their long-term impact is unclear. We report a six-month randomized controlled trial (N=280) comparing individuals exercising alone, with a human peer, or with a large language model-driven SEP. Results revealed a partnership paradox: human peers evoked stronger social presence, while AI peers provided steadier encouragement and more reliable working alliances. Humans motivated through authentic comparison and accountability, whereas AI peers fostered consistent, low-stakes support. These complementary strengths suggest that AI agents should not mimic human authenticity but augment it with reliability. Our findings advance human-agent interaction research and point to hybrid designs where human presence and AI consistency jointly sustain physical activity.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in General LLM agent frameworks and benchmarks, one of 16 directions in LLM agents and tool use. Builds on 9 of its 88 references that are in the map. First posted on arXiv 2 Feb 2026.
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- 2019How should my chatbot interact? A survey on human-chatbot interaction designcited by 56
- 2024GPTCoach: Towards LLM-Based Physical Activity Coachingcited by 29
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- 2025RiskRAG: A Data-Driven Solution for Improved AI Model Risk Reportingcited by 9
- 2021Exercise with Social Robots: Companion or Coach?cited by 1
- 2025When motivation can be more than a message: designing agents to boost physical activitycited by 1
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