压力驱动足球运动员的健康和运动:使用融合交叉映射在动态环境中识别因果关系
Benjamin D Stern1,2,3, Ethan R Deyle4, Eric J Hegedus1
1Department of Rehabilitation Sciences, School of Medicine, Tufts University, Boston, MA, USA.
International journal of sports physiology and performance
|August 8, 2024
概括
运动员的压力显著影响健康,可以通过训练负载数据来预测. 非线性方法揭示了这些复杂的,动态的关系,对于预测运动员在一个赛季的福祉至关重要.
科学领域:
- 运动科学 运动科学 运动科学
- 生物力学 生物力学
- 数据分析 数据分析
背景情况:
- 由于变量的复杂,动态的相互作用,预测运动员的健康是具有挑战性的.
- 传统的数据收集和统计方法往往无法捕捉运动员健康的因果关系.
- 现有的方法侧重于相关关系,而不是动态相互作用.
研究的目的:
- 通过检查动态,非线性关系来探索预测运动员健康的新方法.
- 调查外部训练负载,压力和其他健康指标之间的潜在因果关系.
- 在体育科学中超越相关性分析,以更深入地了解运动员的幸福感.
主要方法:
- 使用吸引力重建方法分析了两支足球队的数据.
- 检查GPS/加速度计测量的外部训练负载和健康变量之间的潜在因果关系.
- 专注于非线性动力学,以揭示复杂的相互作用.
主要成果:
- 运动员自我评估的压力被确定为运动员健康的基本驱动变量.
- 压力被发现可以预测运动员健康的其他组成部分.
- 玩家负载数据被证明可以预测自我评估的压力,表明反循环.
结论:
- 非线性方法可以有效地识别变量间的相互作用,以预测未来的运动员压力.
- 确定的关系表明了影响运动员健康的潜在因果机制.
- 这种方法为整个赛季的运动员幸福感提供了更具动态性和预测性的理解.
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