CC BY
4Physiological mechanisms determining the performance of passing control standards
for physical culture
UDC 796.01:612
I.M. Mazikin1
Dr. Med., Professor M.M. Lapkin1
Dr. Med., Professor R.A. Zorin1
Dr. Med., Professor A.L. Pokhachevsky1' 2
PhD, Associate Professor M.V. Akulina1
II.P. Pavlov Ryazan State Medical University, Ryazan
2I.M. Sechenov First Moscow State Medical University, Moscow
Corresponding author: sport_med@list.ru
Abstract
Objective of the study was to identify significant physiological mechanisms of performance formation when passing control standards in physical culture and to establish the relationship between performance indicators and individual psychophysiological characteristics.
Methods and structure of the study. 120 young men aged 18-20 years old belonging to the main health group were examined. With the help of cluster analysis, groups were identified with different performance in passing control standards in terms of speed endurance and speed-strength indicators. To solve the problem of classifying students, taking into account their psychophysiological characteristics, an artificial neural network (ANN) was created, distributing students into groups with specified characteristics.
Results and conclusions. When analyzing the motivational basis of behavior, it was revealed that the students of the first cluster were dominated by an internal motive and the motive of focusing on success, with at the same time high rates of assessing their potential. The students of the second cluster were dominated by the motive for assessing the significance of the result of activity. Statistically significant differences in personal psychophysiological characteristics and indicators of physical performance were established in the subjects of the identified clusters. On the basis of a set of data obtained, cluster analysis and ANN technology made it possible to predict the performance of students with a high probability, as well as to rank indicators according to their significance for the formation of unequal performance.
Keywords: psychophysiological characteristics, cluster analysis, artificial neural networks.
Introduction. The level of physical fitness as the ratio of the effectiveness of activity and physiological costs for its implementation is the key concept of sports physiology [2, 5, 12]. An important factor determining the effectiveness of purposeful human activity is the interaction of such physiological mechanisms as psychophysiological features [4, 7, 15], indicators of dynamic functional lateralization [1], physical performance, and the motivational basis of behavior [6, 10]. The functioning of the mechanisms that reflect the effectiveness of sports activities from the standpoint of systemic physiology is associated with the "physiological price" of the result of an activity [13, 16], which determines one of the aspects of its effectiveness.
Today, the formation of a person's physical fitness is considered as a complex systemic phenomenon. In accordance with the theory of functional systems, the effectiveness of purposeful human activity is ensured
by the interaction of various physiological mechanisms [11, 14]. At the same time, psychophysiological characteristics in conjunction with the results of sports activities can serve as markers of the systemic organization of purposeful activities carried out with different results [5].
Objective of the study was to identify significant physiological mechanisms of performance formation when passing control standards in physical culture and to establish the relationship between performance indicators and individual psychophysiological characteristics.
Methods and structure of the study. Statistical data processing was carried out using the Statistica 13.0 software package. Quantitative indicators were assessed for compliance with the normal distribution using the Shapiro-Wilk test. Cluster analysis (k-means method) was used to identify groups. The median
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Table 1. Characteristics of the implementation of control standards
Indicator Group 1 Group 2 U (Z) p
Me LQ UQ Me LQ UQ
100 m run, s 12,9 12,4 14,3 14,3 14,0 14,5 860,5 0,001
1000 m run, s 245,0 240,0 248,0 255,0 246,0 268,0 734,0 0,001
Standing long jump, cm 257,0 248,0 265,0 275,0 264,0 280,0 716,5 0,001
Pull-ups, times 12,0 10,0 12,0 15,0 14,0 17,0 264,5 0,001
(Me), upper (UQ), and lower quartile (LQ) were used to describe the study groups. A comparative analysis of the indicators was carried out using the nonpara-metric Mann-Whitney test (U) for paired independent samples [8].
The construction of an artificial neural network (ANN) was carried out in an automatic mode based on groups of a number of indicators: neuroenergy mapping (NEM) with registration of the level of constant potential (LCP) in leads Fz, Cz, Oz, Td, Ts; motivational basis of behavior (test for assessing the level of claims according to V.K. Gerbachevsky); psycho-dynamic characteristics (the general structure of V.M. Rusalov's temperament - ergicity, plasticity, pace and speed, emotionality, social ergicity, social plasticity, social tempo and social emotionality, as well as the type of behavioral activity); the level of basic physical performance with the calculation of the normalized indicator PWC170; asymmetry coefficients (Annette
questionnaire, lateral organization profile (PLO), motor and sensory asymmetries).
The selection of indicators was based on a consistent assessment of the improvement in the quality of the model. ANNs were characterized by a certain architecture, training performance, control and test performance [3]. The study was approved by the Local Ethical Committee at the Ryazan State Medical University of the Ministry of Health of Russia on April 11, 2021. All study participants signed an informed consent.
Results of the study and their discussion. By the method of cluster analysis, two groups of students were identified depending on the effectiveness of passing the control standards for physical culture (Table 1). Group 1 (70 people) was designated as "effective in running disciplines", and group 2 (50 people) - as "effective in strength disciplines".
The creation, training and testing of the ANN was carried out using the Statistica Basic Academic 13.0
Table 2. Ranked list of indicators used by the ANN to predict the effectiveness of passing control standards
Indicator Rank Sensitivity *
Internal motive 1 13,86
Cognitive motive 2 4,30
Significance of results 3 2,79
Assessment of the level of results achieved 4 1,74
PWC170 Test 5 1,48
The initial background (Td-Ts lead) of the NEM 6 1,41
Social tempo 7 1,26
Annette's questionnaire coefficient 8 1,14
Coefficient manual asymmetry 9 1,10
Anxiety level 10 1,08
Lateral Organization profile coefficient 11 1,05
Social plasticity 12 1,00
LCP Verbal fluency test (Ts lead) NEM 13 1,00
LCP Schulte-Platonov test (lead Td-Ts) NEM 14 1.00
LCP Verbal fluency test (Td-Ts lead) NEM 15 0,99
Source Background (Cz lead) NEM 16 0,96
LCP Test of fluency of verbal responses (lead Td) NEM 17 0,70
* - values are rounded to hundredths (differences in other digits are taken into account when ranking indicators by sensitivity).
Theory and Practice of Physical Culture I teoriya.ru I April I № 4 2023
(Ru) software package. Machine learning technology assumed the use of an automatic advanced algorithm for creating and training ANNs in the mode of solving classification problems. Initially, an ANN was created using the following data as predictors: NEM (the level of constant potential of abduction Fz, Cz, Oz, Td, Ts), the motivational basis of behavior (the test for assessing the level of claims according to V.K. Gerbachevs-ky), psychodynamic characteristics (general structure of temperament V.M. Rusalova - ergicity, plasticity, pace and speed, emotionality, social ergicity, social plasticity, social tempo and social emotionality, as well as the type of behavioral activity), the level of basic physical performance (PWC170 (wt/kg), asymmetry coefficients (questionnaire Annette, lateral organization profile, motor and sensory asymmetries). The selection of indicators was based on a sequential assessment of the improvement in the quality of the model. This neural network was a multilayer perceptron with 17 input neurons, 17 intermediate layer neurons and two output neurons. The performance of the training sample was 100%, control - 100%, test - 100% (MLP 17-1 7-2; 100:100:100). In table. 2 shows the predictors used to solve the forecasting problem.
The use of ANN technology made it possible to solve the problem of classifying students by the effectiveness of passing control standards in physical culture based on a set of indicators, which has applied practical significance, and also allows ranking groups of indicators according to their classification significance. The sensitivity of this ANN in determining the effective group of practically healthy individuals was 100%; specificity - 100%.
The indicators of the motivational basis of behavior, the level of basic physical performance, indicators of dynamic functional asymmetry of the brain according to NEM criteria, indicators of psychodynamic characteristics and behavioral phenotypic lateralization were of the greatest importance for solving this problem. The mutual assistance of the indicated indicators of the systemic organization of the purposeful activity of the subjects of the identified clusters reflects the features and reasons for the formation of its various effectiveness.
Conclusions. The formation of physical fitness is associated not only with physical performance, but also with a certain combination of psychophysiological characteristics: anxiety, ergicity, plasticity, speed indicators of behavior, emotionality, focus on results, functional lateralization.
The effectiveness of passing control standards can be successfully predicted on the basis of a complex of physiological and psychophysiological indicators using cluster analysis and ANN technology. The proposed algorithm for solving the problem of distributing subjects into groups with a different set of physi-
ological, physical indicators and psychophysiological characteristics suggests the possibility of its use in the practice of sports selection. The good classification ability of the algorithm allows solving the applied problem of predicting "effective in running disciplines" and "effective in strength disciplines".
The obtained facts in the future will allow us to describe the optimal "psycho-physiological portraits of a person" that contribute to a certain direction of his physical fitness.
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