Effects of Different Training Load Parameters on Physical Performance Adaptation in Soccer Players: How Complex...

The aim of this study was to examine how physiological, locomotor, and mechanical load parameters contribute to variations in aerobic, anaerobic, and neuromuscular adaptations in male soccer players. A 12-week cohort study was conducted involving 41 male under-17 soccer players (16.4 ± 0.5 years old). All training sessions and matches were monitored using heart rate (HR) monitors, ratings of perceived exertion (RPE), and a global positioning system (GPS). The following variables were recorded daily: training impulse (TRIMP), session-RPE, total distance, high speed running (14.0 to 19.9 km/h, HSR), and very high speed running (>20 km/h, VHSR), and the number of accelerations and decelerations. Physical fitness was assessed twice - at baseline and after the 12-week intervention. The assessments included aerobic capacity via the Yo-Yo Intermittent Recovery Test (YYIRT), anaerobic capacity via the mean sprint time at Running-Based Anaerobic Sprint Test (RSAmean), muscle power using the Countermovement Jump (CMJ), and sprint performance measured in a 30-meter sprint. Simple linear regressions showed that both accumulated session-RPE (R² = 0.446, β = 0.668, p < 0.001) and accumulated TRIMP (R² = 0.417, β = 0.646, p < 0.001) were significant positive predictors of YYIRT delta, although explain less than half of variance. A multiple regression analysis revealed that accumulated VHSR significantly predicted RSAmean delta, indicating that higher VHSR values are associated with smaller and improved RSAmean (B = -0.003, p = 0.002), while HSR was not a significant predictor (p = 0.291). These findings suggest that internal load measures (session-RPE, TRIMP) are more strongly associated with aerobic adaptations, while specific external load metrics (e.g., VHSR) better explain RSA changes, highlighting the importance of modifying load monitoring strategies to the specific physiological adaptations targeted. Incorporating individualized load management based on these measures may help maximize performance improvements in practical contexts.