拼图中的许多部分:一种全新的整体工作负载方法来设计体育实践
Luke Champion1, Kane Middleton2, Clare MacMahon2
1Sport and Exercise Science (Allied Health, Human Services and Sport), La Trobe University, Melbourne, VIC, Australia. L.Champion@latrobe.edu.au.
Sports medicine - open
|May 31, 2023
概括
体育中的代表性学习设计 (RLD) 通过在训练中复制竞争来优化运动表现. 这项研究引入了一种平衡的方法,考虑训练忠诚度和运动员负载,以最大限度地提高效益并最大限度地降低风险.
科学领域:
- 运动科学 运动科学 运动科学
- 运动学习是指运动学习.
- 技能获取 技能获得 技能获取
背景情况:
- 代表性学习设计 (RLD) 旨在通过在训练期间模拟竞争环境来提高运动表现.
- 现有的研究缺乏对学习转移的最佳代表性水平及其可变性的全面了解.
- 与高度代表性训练和潜在的过度使用相关的认知和身体负担尚未得到充分探索.
研究的目的:
- 通过检查训练代表性 (忠实性) 和运动员工作量 (负载) 之间的相互作用来解决当前RLD研究的局限性.
- 为应用RLD提出一个框架,以考虑对运动员的认知和身体需求.
- 根据代表性,需求和与感知-行动周期相关的预期结果对培训活动进行分类.
主要方法:
- 概念性论文整合了RLD的现有理论和实践.
- 分析训练忠实度,认知负荷和身体负荷之间的关系.
- 通过结合忠实性和负载概念来开发RLD的新方法.
主要成果:
- 强调需要考虑训练模拟的忠实性以及对运动员施加的认知和身体负担.
- 根据代表性,需求和与感知-行动循环的联系,建议对培训活动进行分类.
- 提出了RLD的新框架,以平衡训练效益与运动员过度负载的风险.
结论:
- 需要对RLD进行细微的应用,既要考虑训练与竞争有多紧密,也要考虑训练对运动员的要求.
- 整合忠诚度和负载为设计代表性培训课程提供了更全面的方法.
- 这种平衡的方法可以优化运动发展,同时减轻与慢性过度使用和过度要求相关的风险.
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