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Comparative performance of four large language models in generating evidence-based exercise prescriptions using
1College of Physical Education, Sichuan University, Chengdu, China.
Frontiers in Physiology
|June 10, 2026
Summary
Claude 3.7 demonstrated superior performance in generating exercise prescriptions using the FITT-VP framework compared to other large language models (LLMs). This AI shows promise for personalized exercise medicine when used collaboratively with healthcare professionals.
Area of Science:
- Artificial Intelligence in Medicine
- Exercise Science
- Digital Health
Background:
- Exercise prescription is vital for health management but faces limitations due to practitioner expertise and time constraints.
- Large language models (LLMs) present a potential solution for personalized exercise prescriptions, but their comparative effectiveness is not well-established.
Purpose of the Study:
- To evaluate and compare the performance of four advanced LLMs in generating exercise prescriptions.
- To assess LLM-generated prescriptions using the established FITT-VP framework.
Main Methods:
- Four LLMs (GPT-4o, Claude 3.7, DeepSeek R1, Grok-3) were tested.
- Thirty synthetic patient profiles were created based on epidemiological data and clinical guidelines.
- Certified exercise specialists rated generated prescriptions on FITT-VP dimensions (Frequency, Intensity, Time, Type, Volume, Progression).
Main Results:
- Claude 3.7 achieved the highest overall score (50.23 ± 1.75) out of a possible 60.
- Significant differences were found among LLMs (p < 0.001), with Claude 3.7 outperforming GPT-4o and DeepSeek R1.
- Claude 3.7 excelled in Time and Progression, while DeepSeek R1 performed poorly in Intensity and Type.
Conclusions:
- Claude 3.7 shows potential as a drafting tool for personalized exercise prescriptions within a human-AI collaborative framework.
- Further research is needed to assess multi-run variability and clinical validation for real-world application.
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