使用因子法对武术和电子竞技运动员的心理生理指标进行比较分析

S. Piatysotska, L. Podrіgalo, V. Romanenko, Yu. Petrenko, Y. Aleksienko
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摘要

文章根据测试结果研究了不同水平的电竞运动员和格斗运动员的心理生理指标的因子结构。心理生理测试包括评估简单反应时间、选择反应时间、对移动物体的反应时间、评估局部动作频率(30 秒敲击测试),并使用平板电脑专用程序进行。对于格斗运动员来说,最重要的指标是局部动作频率、选择反应时间和简单反应时间。在精英电竞运动员中,权重最高的因素包括局部动作频率、选择反应时间、短期视觉记忆系数、对移动物体的反应时间和简单反应时间。业余电竞运动员的因子包括局部运动频率、短期视觉记忆系数、对移动物体的反应时间、选择反应时间和简单反应时间。所确定的因子结构和每个心理生理指标的相对权重反映了每项运动固有的具体特征,也是感官运动反应发展的特点。这些结果为推荐用于监测电竞运动员状况和根据运动员的熟练程度对其进行区分的一系列测试提供了依据。根据所获得的结果,可以推荐使用一系列测试来监测电竞运动员的状况,并根据运动员的熟练程度对其进行区分。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Comparative analysis of psychophysiological indicators of martial arts and e-sports athletes using the factor method
The article investigates the factorial structure of psychophysiological indicators in esports athletes of different proficiency levels and combat athletes based on the results of testing. Psychophysiological testing encompassed the assessment of simple reaction time, choice reaction time, reaction time to a moving object, evaluation of the frequency of local movements (30-second tapping test), and was conducted using specialized programs for tablet computers. For combat athletes, the most significant indicators were identified as the frequency of local movements, choice reaction time, and simple reaction time. In elite esports athletes, the factors with the highest weight included the frequency of local movements, choice reaction time, the coefficient of short-term visual memory, reaction time to a moving object, and simple reaction time. Amateur esports athletes exhibited factors comprising the frequency of local movements, the coefficient of short-term visual memory, reaction time to a moving object, choice reaction time, and simple reaction time. The identified factorial structures and the relative weight of each psychophysiological indicator reflect the specific characteristics inherent in each sport and characterize the development of sensorimotor reactions. These results provide the basis for recommending the implemented battery of tests for monitoring the condition of esports athletes and differentiating athletes based on their proficiency levels. The obtained results allow recommending the utilized battery of tests for monitoring the condition of esports athletes and differentiating athletes based on their proficiency levels.
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