METHODOLOGY OF MULTI-FACTOR ANALYSIS IN SOLVING RESERVOMETRY PROBLEMS IN THE SPORT OF THE HIGHEST ACHIEVEMENTS

А. А. Khadartsev, E. Minina, А. .. Lastovetskij, V. A. Khromushin
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Abstract

Sports of the highest achievements requires the fullest possible use of the psychophysical capabilities of an athlete, which requires a fundamentally different approach, compared to mass sports, in the preparation of athletes using modern scientific methods for diagnosing the functional state of the body (FSB). Conducting scientific research with the required quality needs the use of multivariate analysis of the obtained primary data characterizing the FSB. Analysis by individual factors or analysis of only the final result does not allow to identify weaknesses and assess the reserves of the athlete’s body. The indicated requirements for multivariate analysis are satisfied by the Algebraic Model of Constructive Logic Algorithm (AMCLA), which is used in healthcare. AMCLA as an analytical tool allows performing complex analytical calculations and building expert systems on its basis. The AMCLA is based on the logic of predicates, which fundamentally distinguishes this mathematical apparatus from neural networks. The use of AMCLA cannot be considered as an alternative to the use of others methods of multivariate analysis. The best is the result of the analysis, confirmed by fundamentally different methods. Comparative analytical calculations with neural network algorithms have shown coincidence in the fundamental components of the result. Nevertheless, AMCLA can also identify the most characteristic differences, evaluate restrictions in the choice of treatment and the correct choice of factors, which is important in analytical studies in biomedical research. To participate in the study, 182 qualified male athletes aged 19–22 years (20.5±1.5) were selected, with different levels of functional reserves. They were engaged in sports with a high dynamic and static component (football, basketball, volleyball, boxing, swimming, athletics), according to the classification by Mitchell JH and co-authors, who are at the stage of improving their sportsmanship. The dysfunctional orientation of regulatory mechanisms in qualified athletes in groups with rhythm disturbances and impaired repolarization processes against the background of a decrease in physical performance and stress of adaptation mechanisms was reliably classified by phasographic speed indicators of the heart electrical activity, which was determined using AMCLA. Innovation indicators averaged phase of the cardiac cycle suggests a lack of verified earlier values that characterize the different states. In this case, AMCLA made it possible to identify the most significant indicators of the phase averaged cardiac cycle to characterize various states and differentiate the ranges of their values. Multivariate analysis of the functional state indicators of the athlete’s body with the use of AMCLA can be a methodology for solving the problems of reserve measurement in the highest achievements sports.
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多因素分析方法学在解决储层测量问题中取得了最高成就
最高成就的体育运动需要尽可能充分利用运动员的心理物理能力,这需要一种根本不同的方法,与大众体育相比,在运动员的准备中使用现代科学方法来诊断身体的功能状态(FSB)。进行具有所需质量的科学研究需要对获得的表征FSB的原始数据进行多变量分析。通过个人因素分析或只分析最终结果,都不能确定运动员的弱点和评估运动员身体的储备。构造逻辑算法代数模型(AMCLA)在医疗卫生领域的应用满足了多元分析的要求。AMCLA作为一种分析工具,允许在其基础上执行复杂的分析计算和构建专家系统。AMCLA基于谓词逻辑,这从根本上区别了这种数学设备与神经网络。使用AMCLA不能被认为是使用其他多变量分析方法的替代方法。最好的是用完全不同的方法证实的分析结果。与神经网络算法的比较分析计算表明,结果的基本成分是一致的。然而,AMCLA还可以识别最具特征的差异,评估治疗选择的限制因素和正确选择因素,这在生物医学研究的分析研究中具有重要意义。参与研究的是182名年龄在19-22岁(20.5±1.5),具有不同功能储备水平的合格男性运动员。根据Mitchell JH和合著者的分类,他们从事的运动具有高度的动态和静态成分(足球,篮球,排球,拳击,游泳,田径),他们处于提高体育精神的阶段。采用AMCLA测定心电活动的相速指标,对节律障碍和复极过程受损的合格运动员在体能下降和适应机制应激背景下的调节机制的功能障碍取向进行了可靠的分类。创新指标的平均阶段的心脏周期表明,缺乏验证的早期值表征不同的状态。在这种情况下,AMCLA可以识别出最重要的期平均心周期指标,以表征各种状态并区分其值的范围。运用AMCLA对运动员身体功能状态指标进行多变量分析,可以为解决高成就运动中储备测量问题提供一种方法。
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