Evaluating sports nutrition advice provided by general-purpose large language models for recreational female marathon runners: a multidimensional descriptive audit
Study Details
This new audit tested 5 general-purpose LLMs across 15 sports-nutrition prompts, repeated 10 times each, producing 750 responses for a standardized 39-year-old, 58-kg recreational female marathon runner targeting a 4-hour finish 🔍
Key Findings
🤖 All five models produced structured and generally usable sports-nutrition advice, but performance varied massively by question and domain
🍞 Pre-race carbohydrate recommendations were generally within accepted ranges, with no hard scientific errors detected across models
🏃 In-race fueling was less consistent: ChatGPT ranged from 30–90 g/h carbohydrate, Gemini 50–60 g/h, DeepSeek 15–120 g/h, and Doubao 5–145 g/h
⚠️ DeepSeek and Doubao therefore produced recommendations both below and above the study’s 30–90 g/h comparison range
🔄 One Doubao response recommended 115–145 g/h, while its accompanying gel schedule delivered only 37.5–45 g/h, demonstrating an internal contradiction
♀️ Female-specific integration was inconsistent, although ChatGPT covered all 7 directly prompted female-specific indicators in 10/10 runs
🧪 Models differed in how consistently they maintained the “test supplements in training first” principle before race-day use
📖 Readability also varied: DeepSeek had the highest mean Flesch Reading Ease score, while ChatGPT had the lowest mean Flesch–Kincaid grade level, Gunning Fog and SMOG scores
Conclusion
General-purpose LLMs can be useful for explaining sports-nutrition concepts and generating practical ideas, but athletes should be cautious with precise fuelling doses, supplements and female-specific issues because outputs can vary considerably between repeated prompts ✅
Best practice? Probably best off trusting a qualified nutrition practitioner to help guide your marathon nutrition plan.
Reference
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