健身疲劳运动表现预测模型的统计缺陷
Alexandre Marchal1, Othmène Benazieb1, Yisakor Weldegebriel1
1Seenovate, Paris, 75009, France.
Scientific reports
|January 29, 2025
概括
适应性疲劳模型 (FFM) 有统计上的缺陷,显示参数识别能力差,过度适应. 它的疲劳成分可能对运动训练优化缺乏生物相关性.
科学领域:
- 运动科学 运动科学 运动科学
- 生物统计学 生物统计学
- 运动生理学 运动生理学
背景情况:
- 优化运动训练依赖于预测模型和数据收集.
- 健身疲劳模型 (FFM) 是预测训练反应的基本模型.
- 之前的研究已经质疑了FFM的方法和扩展.
研究的目的:
- 用贝叶斯框架统计诊断FFM的适应性和预测能力.
- 评估FFM组件的生物相关性,特别是疲劳.
- 评估模型性能,区分适用性和预测能力.
主要方法:
- 有生物学意义的先验的贝叶斯框架.
- 交叉验证技术用于评估模型性能.
- 用于参数识别分析的马尔科夫链.
- 使用两个独立数据集进行验证.
主要成果:
- 联邦货币基金组织 (FFM) 显示出严重的统计缺陷,包括不良条件.
- 观察到适应性和疲劳参数的识别能力较差.
- 检测到过度拟合模式,疲劳参数没有显著改善预测能力 (p > 0.40).
结论:
- 联邦货币基金表现出主要的统计局限性,质疑其可靠性.
- FFM的疲劳成分的生物相关性是可疑的.
- 开发可解释和生物学上有意义的体育表现模型仍然是一个挑战.
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