Which training load indicators are greater correlated with maturation and wellness variables in elite U14 soccer players? - Hadi Nobari; Özgür Eken; Utkarsh Singh; Armin Gorouhi; José Carlos Ponce Bordón; Pablo Prieto-González; Ahmet Kurtoğlu; Tomás García Calvo (BMC, 2024)
Abstract Background Monitoring of training load is done to improve physical performance and minimize the incidence of injuries. The study examined the correlation between accumulated training load parameters based on periods with maturity (i.e., maturity offset and peak height velocity -PHV- and wellness variables -e.g., stress and sleep quality-). The second aim was to analyze the multi-linear regression between the above indicators. Methods Twenty elite young U14 soccer players (M = 13.26 ± 0.52 years, 95% CI [13.02, 13.51]) were evaluated over 26 weeks (early, mid, and end-season) to obtain stress, sleep quality, and measures of workload in the season (accumulated acute workload [AW], accumulated chronic workload [CW], accumulated acute: chronic workload ratio [ACWLR], accumulated training monotony [TM], accumulated training strain [TS]). Results The analysis revealed a moderate, statistically significant negative correlation between sleep quality and training monotony (r = -0.461, p < 0.05). No significant correlations were observed between other variables (p > 0.05). In the multi-linear regression analysis, maturity, PHV, sleep, and stress collectively accounted for variances of 17% in AW, 17.1% in CW, 11% in ACWLR, 21.3% in TM, and 22.6% in TS. However, individual regression coefficients for these predictors were not statistically significant (p > 0.05), indicating limited predictive power. Conclusion The study highlights the impact of sleep quality on training monotony, underscoring the importance of managing training load to mitigate the risks of overtraining. The non-significant regression coefficients suggest the complexity of predicting training outcomes based on the assessed variables. These insights emphasize the need for a holistic approach in training load management and athlete wellness monitoring.