Shu Dingbo, Wang Jianping, Zhou Tong, Chen Feng, Meng Fanjing, Wu Xiaoyin, Zhao Zhenhua, Dai Siyu
Department of Radiology, Shaoxing people's Hospital (Shaoxing Hospital of Zhejiang University), Shaoxing, China.
School of Clinical Medicine, Hangzhou Normal University, Hangzhou, China.
BMC Sports Sci Med Rehabil. 2024 Apr 29;16(1):97. doi: 10.1186/s13102-024-00889-3.
Long-distance running is a popular competitive sport. We performed the current research as to develop an easily accessible and applicable model to predict half-marathon performance in male recreational half-marathon runners by nomogram.
Male recreational half-marathon runners in Zhejiang Province, China were recruited. A set of literature-based and panel-reviewed questionnaires were used to assess the epidemiological conditions of the recruited runners. Descriptive and binary regression analyses were done for the profiling and identification of predictors related to higher half-marathon performance (completing time ≤ 105 min). Participants were assigned to the training set (n = 141) and the testing set (n = 61) randomly. A nomogram was used to visually predict the half-marathon performance, and the receiver operating characteristic (ROC) was used to evaluate the predictive ability of the nomogram.
A total of 202 participants (median age: 49 years; higher half-marathon performance: 33.7%) were included. After multivariate analysis, three variables remained as significant predictors: longer monthly running distance [adjusted odds ratio (AOR) = 0.992, 95% confidence interval (CI): 0.988 to 0.996, p < 0.001], faster mean training pace (AOR = 2.151, 95% CI: 1.275 to 3.630, p < 0.001), and better sleep quality [the Pittsburgh Sleep Quality Index (PSQI), AOR = 2.390, 95% CI: 1.164 to 4.907, p = 0.018]. The AUC of the training and testing sets in nomogram were 0.750 and 0.743, respectively. Further ternary and linear regression analyses corroborated the primary findings.
This study developed a nomogram with good potential to predict the half-marathon performance of recreational runners. Our results suggest that longer monthly running distance, faster mean training pace and better sleep quality notably contribute to better half-marathon performance.
长跑是一项广受欢迎的竞技运动。我们开展了本研究，旨在开发一种易于获取且适用的模型，通过列线图预测男性业余半程马拉松跑者的半程马拉松成绩。
招募了中国浙江省的男性业余半程马拉松跑者。使用一组基于文献且经专家评审的问卷来评估所招募跑者的流行病学状况。对与更高半程马拉松成绩（完赛时间≤105分钟）相关的预测因素进行描述性分析和二元回归分析以进行剖析和识别。参与者被随机分配到训练集（n = 141）和测试集（n = 61）。使用列线图直观地预测半程马拉松成绩，并使用受试者工作特征曲线（ROC）评估列线图的预测能力。
共纳入202名参与者（中位年龄：49岁；更高半程马拉松成绩者：33.7%）。多变量分析后，三个变量仍为显著预测因素：每月跑步距离更长[调整优势比（AOR）= 0.992，95%置信区间（CI）：0.988至0.996，p < 0.001]、平均训练配速更快（AOR = 2.151，95% CI：1.275至3.630，p < 0.001）以及睡眠质量更好[匹兹堡睡眠质量指数（PSQI），AOR = 2.390，95% CI：1.164至4.907，p = 0.018]。列线图中训练集和测试集的曲线下面积（AUC）分别为0.750和0.743。进一步的三元和线性回归分析证实了主要发现。
本研究开发了一种具有良好潜力的列线图，可预测业余跑者的半程马拉松成绩。我们的结果表明，每月跑步距离更长、平均训练配速更快以及睡眠质量更好对更好的半程马拉松成绩有显著贡献。
