ArticleBiology of sport2024
Optimizing athletic performance through advanced nutrition strategies: can AI and digital platforms have a role in ultraendurance sports?
Article in Biology of sport, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Review
- Generative artificial intelligence and large language models in sports medicine: a scoping review of applications, accuracy, and ethical implications.Frontiers in public health · 2026Article
- The AI recommendation paradox: a systematic review evaluating the promise, peril, and path forward for large language models in exercise recommendation.Biology of sport · 2026Review
- The role of artificial intelligence in sports training: opportunities, challenges and future applications for competitive swimming.Biology of sport · 2026Article
- Nutrition as a missing piece in the development of youth male soccer players: a scoping review and future directions.Biology of sport · 2026Review
- Longitudinal cohort study on subsequent injury risk in professional football players in the Qatar Stars League: a probabilistic approach using basic learning.Biology of sport · 2026Article
- A professional assessment of training plans for muscle hypertrophy and maximal strength developed by generative artificial intelligence.Biology of sport · 2025Article
- The role of artificial intelligence in enhancing sports education and public health in higher education: innovations in teaching models, evaluation systems, and personalized training.Frontiers in public health · 2025Article
- Enhancing adolescents' exercise motivation management with generative AI anthropomorphism: a chain-mediated model of technology acceptance and self-efficacy.Frontiers in psychology · 2025Article
- Article
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Authors and funding
9 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Nutrition is vital for athletic performance, especially in ultra-endurance sports, which pose unique nutritional challenges. Despite its importance, there exist gaps in the nutrition knowledge among athletes, and emerging digital tools could potentially bridge this gap. The ULTRA-Q, a sports nutrition questionnaire adapted for ultra-endurance athletes, was used to assess the nutritional knowledge of ChatGPT-3.5, ChatGPT-4, Google Bard, and Microsoft Copilot. Their performance was compared with experienced ultra-endurance athletes, registered sports nutritionists and dietitians, and the general population. ChatGPT-4 demonstrated the highest accuracy (93%), followed by Microsoft Copilot (92%), Bard (84%), and ChatGPT-3.5 (83%). The averaged AI model achieved an overall score of 88%, with the highest score in Body Composition (94%) and the lowest in Nutrients (84%). The averaged AI model outperformed the general population by 31% points and ultra-endurance athletes by 20% points in overall knowledge. The AI model exhibited superior knowledge in Fluids, outperforming registered dietitians by 49% points, the general population by 42% points, and ultra-endurance athletes by 32% points. In Body Composition, the AI model surpassed the general population by 31% points and ultraendurance athletes by 24% points. In Supplements, it outperformed registered dietitians by 58% points and the general population by 55% points. Finally, in Nutrients and in Recovery, it outperformed the general population only, by 24% and 29% points, respectively. AI models show high proficiency in sports nutrition knowledge, potentially serving as valuable tools for nutritional education and advice. AI-generated insights could be integrated with expert human judgment for effective athlete performance optimization.
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Registered trials
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