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：

---

精准运动医学：用机器学习预测青春期 20 米穿梭跑测试成绩的不利状态和发展

目的 使用机器学习（随机森林 (RF) 分类器）评估青春期 20 米穿梭跑测试 (20MSRT) 预测个人不利未来状态和发展的能力。方法 来自一项为期 2 年的观察性研究（2013-2015 年，12.4±1.3 年，n=633，50% 的女孩）的数据，具有 48 个基线特征（问卷调查（人口统计、身体、心理、社会和生活方式因素）、客观测量（人体测量学、健康特征、身体活动、身体成分和学业成绩））用于预测：（任务 1）不利的未来 20MSRT 状态（识别 2 年后处于最低 20MSRT 三分位数的个体），和（任务 2）不利的 20MSRT 发展（在基线 20MSRT 低于中位数水平的青少年中识别出 20MSRT 发展处于最低三分位数的个体）。结果 对未来 20MSRT 状态（任务 1）的预测性能（接受者操作特征曲线下面积，AUC）在女孩和男孩中分别为 83% 和 76%，敏感性分别为 80% 和 60%，特异性分别为 78% 和 79%。20 个变量显示男孩的预测能力，14 个女孩的预测能力，包括健康特征、身体活动、学业成绩、肥胖、生活享受、父母支持、学校社会地位和感知健康。未来发展（任务 2）的预测性能较低，并且仅在女孩中与随机水平有统计学差异（女孩和男孩的 AUC 分别为 68% 和 40%）。结论 RF 分类器预测了 20MSRT 中未来的不利状态，并根据整体概况（14-20 个基线特征）确定了潜在的干预个体。使用本研究的 RF 分类器的 MATLAB 脚本和函数可用于未来的精准运动医学研究。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。