体育现象学:新兴的方法论能推动先进的见解吗?

Frontiers in network physiology Pub Date : 2022-11-24 eCollection Date: 2022-01-01 DOI:10.3389/fnetp.2022.1060858
Adam W Kiefer, David T Martin
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引用次数: 0

摘要

应用体育科学的方法论主要推动了对特定成分机制的简化论基础,以推动运动员的训练和护理。虽然线性机制方法提供了有用的见解,但它们阻碍了更复杂的网络生理学模型的开发进展,该模型考虑了系统和子系统内和跨系统的多个因素的时间和空间相互作用。为此,需要一种更复杂的方法,制定这样一个方法框架可以被视为体育大挑战。具体而言,基于跨学科现象学的科学和建模框架是有价值的。表型学是人类精准医学中一个相对较新的领域,但它也是植物和进化生物学科学中一个发达的研究领域。创新的精准医学、便携式无损测量技术的融合,以及复杂人类行为建模的进步,是将表型学融入体育科学的核心。该方法能够应用表型适应度、可塑性、剂量反应动力学、临界窗口和行为的多维网络模型等概念。此外,档案以变化指数为基础,模型将运动员的表现或恢复轨迹视为其动态环境的函数。这一新框架是在几个示例体育科学领域中引入的,用于潜在的整合。特定的重点因素被提供为潜在的候选适应度变量,示例概况为精确训练和护理提供了一种可推广的建模方法。最后,讨论了未来的考虑因素,包括从单个运动员到团队的规模,以及成功实施表型组学所需的其他因素。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Phenomics in sport: Can emerging methodology drive advanced insights?

Phenomics in sport: Can emerging methodology drive advanced insights?

Phenomics in sport: Can emerging methodology drive advanced insights?

Methodologies in applied sport science have predominantly driven a reductionist grounding to component-specific mechanisms to drive athlete training and care. While linear mechanistic approaches provide useful insights, they have impeded progress in the development of more complex network physiology models that consider the temporal and spatial interactions of multiple factors within and across systems and subsystems. For this, a more sophisticated approach is needed and the development of such a methodological framework can be considered a Sport Grand Challenge. Specifically, a transdisciplinary phenomics-based scientific and modeling framework has merit. Phenomics is a relatively new area in human precision medicine, but it is also a developed area of research in the plant and evolutionary biology sciences. The convergence of innovative precision medicine, portable non-destructive measurement technologies, and advancements in modeling complex human behavior are central for the integration of phenomics into sport science. The approach enables application of concepts such as phenotypic fitness, plasticity, dose-response dynamics, critical windows, and multi-dimensional network models of behavior. In addition, profiles are grounded in indices of change, and models consider the athlete's performance or recovery trajectory as a function of their dynamic environment. This new framework is introduced across several example sport science domains for potential integration. Specific factors of emphasis are provided as potential candidate fitness variables and example profiles provide a generalizable modeling approach for precision training and care. Finally, considerations for the future are discussed, including scaling from individual athletes to teams and additional factors necessary for the successful implementation of phenomics.

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