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| Format: | Preprint |
| Veröffentlicht: |
2025
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| Online-Zugang: | https://arxiv.org/abs/2509.26593 |
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| _version_ | 1866908569320816640 |
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| author | Lee, Kichang |
| author_facet | Lee, Kichang |
| contents | Large language models (LLMs) are emerging as everyday assistants, but their role as longitudinal virtual coaches is underexplored. This two-month single subject case study documents LLM guided half marathon preparation (July-September 2025). Using text based interactions and consumer app logs, the LLM acted as planner, explainer, and occasional motivator. Performance improved from sustaining 2 km at 7min 54sec per km to completing 21.1 km at 6min 30sec per km, with gains in cadence, pace HR coupling, and efficiency index trends. While causal attribution is limited without a control, outcomes demonstrate safe, measurable progress. At the same time, gaps were evident, no realtime sensor integration, text only feedback, motivation support that was user initiated, and limited personalization or safety guardrails. We propose design requirements for next generation systems, persistent athlete models with explicit guardrails, multimodal on device sensing, audio, haptic, visual feedback, proactive motivation scaffolds, and privacy-preserving personalization. This study offers grounded evidence and a design agenda for evolving LLMs from retrospective advisors to closed-loop coaching companions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_26593 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Exploring Large Language Model as an Interactive Sports Coach: Lessons from a Single-Subject Half Marathon Preparation Lee, Kichang Human-Computer Interaction H.4, K.7 Large language models (LLMs) are emerging as everyday assistants, but their role as longitudinal virtual coaches is underexplored. This two-month single subject case study documents LLM guided half marathon preparation (July-September 2025). Using text based interactions and consumer app logs, the LLM acted as planner, explainer, and occasional motivator. Performance improved from sustaining 2 km at 7min 54sec per km to completing 21.1 km at 6min 30sec per km, with gains in cadence, pace HR coupling, and efficiency index trends. While causal attribution is limited without a control, outcomes demonstrate safe, measurable progress. At the same time, gaps were evident, no realtime sensor integration, text only feedback, motivation support that was user initiated, and limited personalization or safety guardrails. We propose design requirements for next generation systems, persistent athlete models with explicit guardrails, multimodal on device sensing, audio, haptic, visual feedback, proactive motivation scaffolds, and privacy-preserving personalization. This study offers grounded evidence and a design agenda for evolving LLMs from retrospective advisors to closed-loop coaching companions. |
| title | Exploring Large Language Model as an Interactive Sports Coach: Lessons from a Single-Subject Half Marathon Preparation |
| topic | Human-Computer Interaction H.4, K.7 |
| url | https://arxiv.org/abs/2509.26593 |