在不同的距离上,男性跑步者的人体形状和身体构成
Aleksandra Stachoń1, Jadwiga Pietraszewska1, Anna Burdukiewicz2
1Faculty of Physical Education and Sport Sciences, Wroclaw University of Health and Sport Sciences, al. Ignacego Jana Paderewskiego 35, 51-612, Wrocław, Poland.
Scientific reports
|October 25, 2023
概括
人类测量参数显著预测学术运动员的跑步表现. 身体组成和体型在短跑者,中距离和长距离跑者之间有明显的差异,有助于性能预测.
科学领域:
- 运动科学 运动科学 运动科学
- 人体生理学 人体生理学
- 生物力学 生物力学
背景情况:
- 人类测量参数对于跑步中的精英运动表现至关重要.
- 对于学术竞争水平的运动员来说,它们对运动员的相关性仍然不太了解.
- 了解这些差异可以帮助教练和选手识别人才.
研究的目的:
- 调查学术跑步者的人体变量和身体组成.
- 为了区分短跑者,中距离和长距离跑者之间的形态特征.
- 评估体质特征对学术水平跑步表现的预测价值.
主要方法:
- 这项研究涉及68名学术运动员:26名短跑选手,22名中距离选手和20名长距离选手.
- 进行了人体测量和身体成分分析.
- 主要组件分析 (PCA) 用于确定关键的差异化因素.
主要成果:
- 短跑者表现出更大质量的身体,具有更大的肌肉和更宽的肩膀.
- 长途跑步运动员的特点是身材苗条,脚较长,皮下脂肪较高.
- 中距离跑步运动员是最苗条的,体窄,皮下脂肪很少,表现出混合的半形形态-半形形态特征.
结论:
- 在不同距离的学术跑步者之间存在着不同的形态特征.
- 总体体积,四肢肌肉,下肢段长度和身体脂肪是关键预测因素.
- 身体特征可以有效地预测学术运动员的跑步表现.
相关概念视频
01:15Construction of Frequency Distribution
A frequency distribution table can be constructed using the steps given below.
First, make a table with two columns—one with the title of the data that needs to be organized, and the other column for frequency. [Draw a third column for tally marks if needed]. Then, take a look at the items given in the data set and decide if an ungrouped frequency distribution table or a grouped frequency distribution table would be more suitable. If there are large sets of different values, then it is best to...
First, make a table with two columns—one with the title of the data that needs to be organized, and the other column for frequency. [Draw a third column for tally marks if needed]. Then, take a look at the items given in the data set and decide if an ungrouped frequency distribution table or a grouped frequency distribution table would be more suitable. If there are large sets of different values, then it is best to...
01:17Wald-Wolfowitz Runs Test II
The Wald-Wolfowitz runs test, commonly referred to as the runs test, is a nonparametric test used to assess the randomness of ordered data. The test evaluates the number of runs, which are consecutive sequences of similar elements within the data. If the number of runs is significantly higher or lower than expected, the data is considered non-random, indicating a detectable pattern or structure.
For binary data, runs are identified using symbols such as + and −, or equivalently, 1s and 0s. In...
For binary data, runs are identified using symbols such as + and −, or equivalently, 1s and 0s. In...