Faculty of Sport Sciences, University of Extremadura, Cáceres, 10003, Spain.
Department of Exercise Physiology, Faculty of Educational Sciences and Psychology, University of Mohaghegh Ardabili, Ardabil, 56199-11367, Iran.
BMC Pediatr. 2024 Apr 30;24(1):289. doi: 10.1186/s12887-024-04744-9.
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.
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]).
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.
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.
监测训练负荷是为了提高身体表现和最大限度地减少受伤的发生率。本研究考察了基于成熟期（即成熟偏移和峰值速度 -PHV- 以及健康变量 - 例如压力和睡眠质量 -）的累积训练负荷参数之间的相关性。第二个目的是分析上述指标之间的多元线性回归。
20 名精英年轻 U14 足球运动员（M = 13.26 ± 0.52 岁，95%置信区间 [13.02, 13.51]）在 26 周（早期、中期和赛季末）内进行评估，以获得压力、睡眠质量和赛季内的工作量测量值（累积急性工作量 [AW]、累积慢性工作量 [CW]、累积急性：慢性工作量比 [ACWLR]、累积训练单调 [TM]、累积训练应变 [TS]）。
分析显示，睡眠质量与训练单调之间存在中度、统计学上显著的负相关（r = -0.461，p < 0.05）。其他变量之间没有观察到显著相关性（p > 0.05）。在多元线性回归分析中，成熟度、PHV、睡眠和压力共同解释了 AW 的 17%、CW 的 17.1%、ACWLR 的 11%、TM 的 21.3%和 TS 的 22.6%的方差。然而，这些预测因子的个体回归系数没有统计学意义（p > 0.05），表明预测能力有限。
本研究强调了睡眠质量对训练单调的影响，突出了管理训练负荷以降低过度训练风险的重要性。非显著的回归系数表明，根据评估的变量预测训练结果的复杂性。这些见解强调了在训练负荷管理和运动员健康监测中采用整体方法的必要性。
