Objectives To assess the ability to predict individual unfavourable future status and development in the 20m shuttle run test (20MSRT) during adolescence with machine learning (random forest (RF) classifier). Methods Data from a 2-year observational study (2013‒2015, 12.4±1.3 years, n=633, 50% girls), with 48 baseline characteristics (questionnaires (demographics, physical, psychological, social and lifestyle factors), objective measurements (anthropometrics, fitness characteristics, physical activity, body composition and academic scores)) were used to predict: (Task 1) unfavourable future 20MSRT status (identification of individuals in the lowest 20MSRT tertile after 2 years), and (Task 2) unfavourable 20MSRT development (identification of individuals with 20MSRT development in the lowest tertile among adolescents with baseline 20MSRT below median level). Results Prediction performance for future 20MSRT status (Task 1) was (area under the receiver operating characteristic curve, AUC) 83% and 76%, sensitivity 80% and 60%, and specificity 78% and 79% in girls and boys, respectively. Twenty variables showed predictive power in boys, 14 in girls, including fitness characteristics, physical activity, academic scores, adiposity, life enjoyment, parental support, social status in school and perceived fitness. Prediction performance for future development (Task 2) was lower and differed statistically from random level only in girls (AUC 68% and 40% in girls and boys). Conclusion RF classifier predicted future unfavourable status in 20MSRT and identified potential individuals for interventions based on a holistic profile (14‒20 baseline characteristics). The MATLAB script and functions employing the RF classifier of this study are available for future precision exercise medicine research. Raw is agreed not to be shared with third parties. In other cases, data are available upon reasonable request. Please contact THT for data sharing.

中文翻译：

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精密运动医学：通过机器学习预测青春期20米穿梭运动测试性能的不良状况和发展

目的通过机器学习（随机森林（RF）分类器）评估青春期20m穿梭试验（20MSRT）中预测个体不利的未来状态和发展的能力。方法一项为期2年的观察性研究（2013-2015，12.4±1.3岁，n = 633，女孩占50％）的数据，具有48个基线特征（问卷（人口统计学，身体，心理，社会和生活方式因素），客观测量（人体测量学，健身特征，身体活动，身体成分和学术成绩））用于预测：（任务1）不利的20MSRT未来状态（确定2年后处于最低20MSRT水平的个人），（任务2）不利于20MSRT发展（在基线20MSRT低于中位水平的青少年中，在最低三分位数中具有20MSRT发展的个体的识别）。结果未来20MSRT状态（任务1）的预测性能分别为（接受者工作特征曲线下的面积，AUC）83％和76％，敏感性80％和60％，特异性78％和79％。二十个变量显示出男孩的预测能力，女孩中的14个变量，包括健身特征，体育锻炼，学业成绩，肥胖，生活享受，父母支持，学校社会地位和知觉健身。未来发展的预测性能（任务2）较低，且统计学上仅与女孩的随机水平有所不同（AUC 68％，男孩和女孩为40％）。结论RF分类器预测了20MSRT未来的不利状态，并基于整体特征（14×20个基线特征）确定了可能的干预对象。使用本研究的RF分类器的MATLAB脚本和函数可用于将来的精密运动医学研究。Raw同意不与第三方共享。在其他情况下，可根据合理要求提供数据。请联系THT进行数据共享。