Biology of Sport
eISSN: 2083-1862
ISSN: 0860-021X
Biology of Sport
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abstract:
Original paper

Optimizing athletic performance through advanced nutrition strategies: can AI and digital platforms have a role in ultraendurance sports?

Luca Puce
1
,
Halil İbrahim Ceylan
2
,
Carlo Trompetto
1, 3
,
Filippo Cotellessa
1
,
Cristina Schenone
1
,
Lucio Marinelli
1, 3
,
Piotr Zmijewski
4
,
Nicola Luigi Bragazzi
1
,
Laura Mori
1, 3

  1. Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health, University of Genoa, Italy
  2. Physical Education and Sports Teaching Department, Faculty of Kazim Karabekir Education, Atatürk University, Erzurum, Turkey
  3. Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS) Ospedale Policlinico San Martino, Genova, Italy
  4. Jozef Pilsudski University of Physical Education in Warsaw Poland
Biol Sport. 2024;41(4):305–313
DOI: https://doi.org/10.5114/biolsport.2024.141063
Online publish date: 2024/07/23
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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% pointsin 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.
keywords:

Nutrition, Athletes, Artificial Intelligence, Nutritional knowledge, Ultra-endurance sports

  
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