通过先进的营养策略优化运动表现:人工智能和数字平台能否在超级耐力运动中发挥作用?

IF 4.2 2区 医学 Q1 SPORT SCIENCES Biology of Sport Pub Date : 2024-10-01 Epub Date: 2024-07-23 DOI:10.5114/biolsport.2024.141063
Luca Puce, Halil İbrahim Ceylan, Carlo Trompetto, Filippo Cotellessa, Cristina Schenone, Lucio Marinelli, Piotr Zmijewski, Nicola Luigi Bragazzi, Laura Mori
{"title":"通过先进的营养策略优化运动表现:人工智能和数字平台能否在超级耐力运动中发挥作用?","authors":"Luca Puce, Halil İbrahim Ceylan, Carlo Trompetto, Filippo Cotellessa, Cristina Schenone, Lucio Marinelli, Piotr Zmijewski, Nicola Luigi Bragazzi, Laura Mori","doi":"10.5114/biolsport.2024.141063","DOIUrl":null,"url":null,"abstract":"<p><p>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.</p>","PeriodicalId":55365,"journal":{"name":"Biology of Sport","volume":null,"pages":null},"PeriodicalIF":4.2000,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11475005/pdf/","citationCount":"0","resultStr":"{\"title\":\"Optimizing athletic performance through advanced nutrition strategies: can AI and digital platforms have a role in ultraendurance sports?\",\"authors\":\"Luca Puce, Halil İbrahim Ceylan, Carlo Trompetto, Filippo Cotellessa, Cristina Schenone, Lucio Marinelli, Piotr Zmijewski, Nicola Luigi Bragazzi, Laura Mori\",\"doi\":\"10.5114/biolsport.2024.141063\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>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.</p>\",\"PeriodicalId\":55365,\"journal\":{\"name\":\"Biology of Sport\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":4.2000,\"publicationDate\":\"2024-10-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11475005/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Biology of Sport\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.5114/biolsport.2024.141063\",\"RegionNum\":2,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2024/7/23 0:00:00\",\"PubModel\":\"Epub\",\"JCR\":\"Q1\",\"JCRName\":\"SPORT SCIENCES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Biology of Sport","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.5114/biolsport.2024.141063","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/7/23 0:00:00","PubModel":"Epub","JCR":"Q1","JCRName":"SPORT SCIENCES","Score":null,"Total":0}
引用次数: 0

摘要

营养对运动成绩至关重要,尤其是在超耐力运动中,这对营养提出了独特的挑战。尽管营养非常重要,但运动员在营养知识方面仍存在差距,而新兴的数字工具有可能弥补这一差距。ULTRA-Q是专为超耐力运动员设计的运动营养问卷,我们使用它来评估ChatGPT-3.5、ChatGPT-4、Google Bard和Microsoft Copilot的营养知识。他们的表现与经验丰富的超耐力运动员、注册运动营养师和营养师以及普通人进行了比较。ChatGPT-4 的准确率最高(93%),其次是 Microsoft Copilot(92%)、Bard(84%)和 ChatGPT-3.5(83%)。平均人工智能模型的总体得分率为 88%,其中身体成分得分率最高(94%),营养素得分率最低(84%)。平均人工智能模型在总体知识方面比普通人高出 31%,比超耐力运动员高出 20%。人工智能模型在体液知识方面表现优异,比注册营养师高出 49%,比普通人高出 42%,比超级耐力运动员高出 32%。在身体成分方面,人工智能模型比普通人高出 31%,比超级耐力运动员高出 24%。在营养补充剂方面,AI 模型比注册营养师高出 58%,比普通人高出 55%。最后,在 "营养素 "和 "恢复 "方面,它只比普通人高出 24% 和 29%。人工智能模型在运动营养知识方面表现出很高的熟练程度,有可能成为营养教育和建议的重要工具。人工智能生成的见解可与人类专家的判断相结合,从而有效优化运动员的表现。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Optimizing athletic performance through advanced nutrition strategies: can AI and digital platforms have a role in ultraendurance sports?

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.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Biology of Sport
Biology of Sport 生物-运动科学
CiteScore
8.20
自引率
12.50%
发文量
113
审稿时长
>12 weeks
期刊介绍: Biology of Sport is the official journal of the Institute of Sport in Warsaw, Poland, published since 1984. Biology of Sport is an international scientific peer-reviewed journal, published quarterly in both paper and electronic format. The journal publishes articles concerning basic and applied sciences in sport: sports and exercise physiology, sports immunology and medicine, sports genetics, training and testing, pharmacology, as well as in other biological aspects related to sport. Priority is given to inter-disciplinary papers.
期刊最新文献
A new perspective on cardiovascular function and dysfunction during endurance exercise: identifying the primary cause of cardiovascular risk. Analysis and prediction of unforced errors in men's and women's professional padel. Balancing the load: A narrative review with methodological implications of compensatory training strategies for non-starting soccer players. Changes in muscle quality and biomarkers of neuromuscular junctions and muscle protein turnover following 12 weeks of resistance training in older men. Characterizing microcycles' workload when combining two days structure within single training sessions during congested fixtures in an elite male soccer team.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1