PSYCHOPHYSICAL RISK PROFILING IN EATHLETES USING BEHAVIORAL ANALYSIS AND MACHINE LEARNING METHODS
DOI:
https://doi.org/10.67034/2786-8354.2026.20.2.26Keywords:
physical activity, eSports, digital technologies, physical condition, self-regulation, gamification, behavioral predictors, adaptationAbstract
Introduction. To conduct psychophysical risk profiling in eSports athletes using behavioral analysis and machine learning methods, with particular emphasis on the role of self-regulation, perceived stress, body mass index, physical activity, and digital technologies. The professionalization of eSports has brought the issue of players’ psychophysical adaptation to prolonged cognitive and psycho-emotional strain to the forefront. Despite the well-documented dangers of chronic stress, impaired self-regulation and physical inactivity, comprehensive models of psychophysical risks in eSports are still in their infancy.
Materials and Methods. The study involved 50 eSports athletes (median age = 21 years; 92.0% males). It was found that 84.0% of participants had more than five years of eSports experience, and 28.0% were classified as overweight. The Brief Self-Control Scale (BSCS-5) and Perceived Stress Scale (PSS-4) were administered. Statistical analysis included the Shapiro–Wilk test, Mann–Whitney U test, χ² test, Spearman’s rank correlation, and Fisher’s exact test. Risk prediction was performed using the Classification and Regression Trees (C&RT) algorithm with cross-validation.
Results. The level of physical activity among eAthletes was found to be uneven (χ² = 12.16; p = 0.002): only 12.0% engage in physical activity regularly, while 52.0% do so frequently. Excessive body weight was observed in 28.0% of participants. Regular use of digital technologies to support physical activity was low among respondents: only 24.0% used them frequently or constantly, while 76.0% used them irregularly (χ² = 13.52; p < 0.001). Levels of perceived stress on the PSS-4 scale (α = 0.660) and self-regulation on the BSCS scale (α = 0.809) were independent of gender or the duration of gaming sessions, but were higher among more experienced eSports players. Negative correlations were found between self-regulation and stress levels (ρ = −0.317; p < 0.05) and between self-regulation and body mass index (BMI) (ρ = −0.280; p < 0.05). These results confirm the key role of self-control in maintaining the psychophysical well-being of esports athletes. The model constructed using the C&RT method demonstrated high predictive accuracy (84%; CV cost = 0.160). It confirmed the leading role of self-regulation as a root predictor of functional status and enabled risk groups for psychophysical maladjustment to be identified. Using digital technologies did not affect BMI (p = 0.562), stress levels (p = 0.625) or self-regulation (p = 0.125). However, regular physical activity was associated with lower BMI values (U = 180.0; p = 0.030; d = 0.307).
Conclusions. Psychophysical maladaptation in eAthletes demonstrates a multifactorial nature, with impaired self-regulation representing its central mechanism. A “digital paradox” was identified: despite high levels of technological engagement, digital tools predominantly serve passive monitoring functions and do not independently produce meaningful health-related benefits. These findings support the need for gamified self-regulation support systems and personalized approaches to psychophysical risk management in eSports.
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