Anderegg Jan, Brefin Stefanie L, Nigg Claudio R, Koschnick David, Paul Claudia, Ketelhut Sascha
School of Health Professions, Institute of Physiotherapy, Zurich University of Applied Sciences, Winterthur, Switzerland.
Department of Health Science, Institute of Sport Science, University of Bern, Bern, Switzerland.
Eur J Sport Sci. 2025 Sep;25(9):e70031. doi: 10.1002/ejsc.70031.
This study investigates the relationship between a web application-based load and recovery score (LRS) and established load parameters. Seventy-eight elite youth soccer players were recruited from a single top-tier Swiss club. All participants were healthy and injury-free at baseline and actively competing at the highest national youth level, participating in five training sessions per week. Players with recent injuries or chronic health conditions were excluded. Seventy-one players (32.4% female) with an average age of 18 years (SD = 1.2) met the inclusion criteria and were monitored throughout ≥ 35 days, applying a repeated-measure design. Daily assessments of the self-reported LRS, along with measurements of player and trainer session ratings of perceived exertion, total distance covered, and total distance > 20 km/h, were collected. Linear mixed-effects models were used to analyze the influence of load parameters on the following day's LRS. All training and match load parameters demonstrated significant negative correlations with the subsequent day's LRS. Player and trainer session ratings of perceived exertion had similar fixed effects (-0.013, 95% CI [-0.017, -0.010] vs. -0.008, 95% CI [-0.011, -0.006]), whereas total distance covered exhibited stronger associations (-0.668, 95% CI [-0.979, -0.355]) than total distance > 20 km/h (-0.009, 95% CI [-0.012, -0.006]). The impact of the different load parameters varied across groups and individuals. The LRS provides an easy-to-use digital tool that summarizes multiple training and recovery factors into one score, helping coaches and staff monitor player readiness in daily field settings. By offering accessible daily feedback, the LRS may help tailor training loads, manage recovery, and reduce the risk of overtraining and injuries.
本研究调查了基于网络应用程序的负荷与恢复评分（LRS）和既定负荷参数之间的关系。从瑞士一家顶级俱乐部招募了78名精英青年足球运动员。所有参与者在基线时均健康且无伤病，并积极参加国内最高水平的青年比赛，每周参加五次训练课程。近期受伤或患有慢性健康问题的球员被排除在外。71名球员（女性占32.4%），平均年龄18岁（标准差=1.2）符合纳入标准，并在≥35天内进行监测，采用重复测量设计。收集了自我报告的LRS的每日评估数据，以及球员和教练对训练课的主观用力程度评分、总跑动距离和速度>20公里/小时的总距离测量值。使用线性混合效应模型分析负荷参数对次日LRS的影响。所有训练和比赛负荷参数与次日的LRS均呈现显著负相关。球员和教练对训练课的主观用力程度评分具有相似的固定效应（-0.013，95%置信区间[-0.017，-0.010]与-0.008，95%置信区间[-0.011，-0.006]），而总跑动距离的关联更强（-0.668，95%置信区间[-0.979，-0.355]），高于速度>20公里/小时的总距离（-0.009，95%置信区间[-0.012，-0.006]）。不同负荷参数的影响因组和个体而异。LRS提供了一个易于使用的数字工具，将多个训练和恢复因素汇总为一个分数，帮助教练和工作人员在日常训练环境中监测球员的准备情况。通过提供可获取的每日反馈，LRS可能有助于调整训练负荷、管理恢复，并降低过度训练和受伤的风险。
