Variations of estimated maximal aerobic speed in children soccer players and its associations with the accumulated training load: Comparisons between non, low and high responders

Abstract

The aim of this study was twofold: (i) to examine the variations of estimated maximal aerobic speed between non, low and high responders and (ii) to analyze the relationships between accumulated training load parameters and variations of maximal aerobic speed in children soccer players. Forty-four male soccer players were assessed three times during the early and mid-season (second to fifth month of the season) and were monitored daily over the period of analysis using the rating of perceived exertion (RPE), recording the training duration (in min) and calculating the session-RPE (sRPE). Pairwise comparisons revealed that maximal aerobic speed (MAS) was greater for the third assessment than the first (p-value [p] = 0.003; standardized effect of Cohen [d] = 0.355) and second (p = 0.013; d = 0.193) assessments. Large correlations were found between MAS and accumulated RPE, accumulated time, and accumulated sRPE. Moreover, non, low and high responders differed in ΔMAS (p<0.001) with the last group presenting the largest improvement in MAS. Results suggest that children with lower MAS baseline levels will improve more this capacity over the early and mid-season period compared to children with better baseline levels. Moreover, associations between accumulated training load and MAS were found, suggesting that the training effort can be related with aerobic capacity changes.

Associations Between Variations in Accumulated Workload and Physiological Variables in Young Male Soccer Players Over the Course of a Season

“…Moreover, VO 2max between EaS to MiS was significantly related to PHV. Following the present study, Clemente et al (2020a) also reported significant improvements in maximal aerobic speed over the season in young soccer players, with the greatest improvements at the EnS (4 months after baseline test). However, Dragijsky et al (2017) showed an improvement in endurance, with the highest levels reported in the EaS, followed by a partial fall in the MiS, as well as at the EnS.…”

“…However, Dragijsky et al (2017) showed an improvement in endurance, with the highest levels reported in the EaS, followed by a partial fall in the MiS, as well as at the EnS. Findings from this study can most likely be explained by the possible changes in anthropometric traits, heart and lung mass, and nervous system maturation associated with normal growth and maturation leading to improvement in aerobic capacity (Malina et al, 2015;Clemente et al, 2020a).…”

“…Although changes in physical fitness variables, preceded by specific training periods, are evident in soccer players, the association between such changes and the accumulated load needs to be further investigated (Clemente et al, 2019). There is only one study available (Clemente et al, 2020a) that has examined the relationship between cardiorespiratory fitness and the accumulated training load parameters over a 4-months in-season period. Results from Clemente et al (2020a) indicated that training effort is strongly associated with changes in aerobic power.…”

“…There is only one study available (Clemente et al, 2020a) that has examined the relationship between cardiorespiratory fitness and the accumulated training load parameters over a 4-months in-season period. Results from Clemente et al (2020a) indicated that training effort is strongly associated with changes in aerobic power. Indeed, no study, to the authors' knowledge, has examined the relationship between training load, maturity status, and different physiological variables (e.g., VO 2max , maximum and resting heart rate [HR max and HR rest ], and speed variables) in elite young male soccer players.…”

Mechanism for Green Development Behavior and Performance of Industrial Enterprises (GDBP-IE) Using Partial Least Squares Structural Equation Modeling (PLS-SEM)

“…It satisfies the criterion for the sample size in SEM of being at least five times the number of the measurable item [69]. In addition, according to Clemente et al [70] and Maccallum and Bryant [71], the minimum sample size was estimated using Gpower software (University of Dusseldorf, Dusseldorf, Germany). The size of the sample was calculated to ensure α of 0.05 and power at 1-β = 0.80, and the result indicated a sample of 279, which is less than we have included (N = 615).…”