Development of predictive mathematical models for physical performance parameters in sports and sports medicine

Introduction. Predictive modeling in healthcare is a rapidly evolving field of scientific knowledge at the intersection of information technology and medicine. In sports medicine, the importance of accurate forecasting of physical performance parameters in response to changing environmental conditions cannot be overstated. For athletes, such information provides a crucial competitive advantage before major competitions.

Objective. Development of methods and approaches to analyze clinical data obtained through comprehensive medical examinations of athletes.

Materials and methods. An analysis of anonymized medical data from comprehensive medical examinations was conducted for 6222 world-class athletes (3792 males and 2430 females) with a mean age of 23.3 ± 5.1 years. The data were stratified by sex and according to sports categories: cyclic sports (1376 athletes, including 861 males and 515 females); complex coordination sports (1342 athletes, including 761 males and 581 females); team sports (1618 athletes, including 980 males and 638 females); and combat sports (1886 athletes, including 1190 males and 696 females). The analysis included both clinical data on the presence (or absence) of pathological conditions identified during specialist medical examinations and physiological parameters from bicycle ergometer stress testing. Statistical analysis was performed using the Stat-Tech v. 4.6.0 software (StatTech, Russia).

Results. Using regression analysis, statistically significant (p < 0.001) predictive models for a set of physical performance parameters were developed, which revealed over 40 associations with clinical diagnoses made by medical specialists. The strongest correlations were observed between physical performance indicators and dental diagnoses. Future research will focus on creating a mathematical model to predict performance decline in world-class athletes, based on an analysis of disease development risk factors.

Conclusions. The developed and implemented approaches for analyzing clinical data from comprehensive medical examinations of world-class athletes enabled the creation of effective predictive mathematical models of physical performance parameters using linear regression methodology, while accounting for the presence/absence of identified diagnoses. The proposed models provide a comprehensive assessment of athletes’ functional status, thus allowing accurate prediction of physical performance levels and optimization of professional training by minimizing risks of overtraining and sports-related injuries.

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