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耐力运动中的人工智能:代谢,恢复和营养观点

Gerasimos V Grivas¹, Kousar Safari²

¹Physical Education and Sports, Division of Humanities and Political Sciences, Hellenic Naval Academy, 18539 Piraeus, Greece.

|October 29, 2025

概括

人工智能 (AI) 通过使用运动员数据来个性化营养,恢复和表现来增强耐力运动. 解决数据质量,透明度和访问对于AI至关重要.

科学领域:

运动科学运动科学运动科学
生物技术是生物技术.
数据科学数据科学数据科学

背景情况:

人工智能 (AI) 越来越多地被整合到耐力运动中,以优化性能,增强恢复和个性化营养.
本综述综合了当前关于人工智能应用的知识,重点关注代谢健康,营养和恢复,同时考虑道德影响.

研究的目的:

对耐力运动中人工智能应用的当前知识进行审查和综合.
突出AI在优化代谢健康,营养策略和恢复方面的作用.
为解决AI在耐力运动中的伦理考虑和未来研究方向.

主要方法:

采用了叙事审查方法,利用主要科学数据库 (PubMed,Scopus,Web of Science) 的有针对性的搜索.
数据提取的重点是人工智能方法 (包括机器学习),数据源,验证类型,性能指标和确定的局限性.
审查综合了研究结果,以提供结构化的概述,承认研究的异质性和缺乏正式的元分析.

主要成果:

人工智能系统有效地整合各种数据 (生理,环境,行为) 以改善代谢监测,恢复预测和个性化的营养.
通过持续的葡萄糖监测和人工智能算法,人工智能驱动的碳水化合物管理可以提高长时间炼期间的表现.
考虑到遗传因素,人工智能引导的补充策略显示出增强的 ergogenic 效果,尽管数据质量,模型通用性和公平访问方面的挑战仍然存在.

结论:

在耐力运动中整合人工智能为个性化表现,恢复和营养改善带来了巨大的潜力.
克服模型通用性,数据质量,算法透明度和公平访问方面的局限性对于实现人工智能的全部潜力至关重要.
未来的研究必须优先考虑多样化的数据收集,严格的外部验证,透明的部署,以及在体育领域提供强大和道德的人工智能解决方案的公平可用性.

关键词:

运动员监测运动员监测机器学习是机器学习.业绩分析业绩分析生理学建模生理学建模运动技术运动技术穿戴式技术是一种可穿戴技术.

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