Nikolaidis Pantelis T, Knechtle Beat
School of Health and Caring Sciences, University of West Attica, Egaleo, Greece.
Exercise Physiology Laboratory, Nikaia, Greece.
EXCLI J. 2023 Jun 22;22:559-566. doi: 10.17179/excli2023-6198. eCollection 2023.
Few research has been conducted on predictors of recreational runners' performance, especially in half-marathon running. The purpose of our study was (a) to investigate the relationship of half-marathon race time with training, anthropometry and physiological characteristics, and (b) to develop a formula to predict half-marathon race time in male recreational runners. Recreational runners (n=134, age 44.2±8.7 years; half-marathon race time 104.6±16.2 min) underwent a physical fitness battery consisting of anthropometric and physiological tests. The participants were classified into five performance groups (fast, 73-92 min; above average, 93-99 min; average 100-107 min; below average, 108-117 min; slow group, 118-160 min). A prediction equation was developed in an experimental group (EXP, n=67), validated in a control group (CON, n=67) and prediction bias was estimated with 95 % confidence intervals (CI). Performance groups differed in half-marathon race time, training days, training distance, age, weight, (body mass index) BMI, body fat (BF) and maximum oxygen uptake (VOmax) (p≤0.001, η≥0.132), where faster groups had better scores than the slower groups. Half-marathon race time correlated with physiological, anthropometric and training characteristics, with the faster the runner, the better the score in these characteristics (, VOmax, r=0.59; BMI, r=-0.55; weekly running distance, r=-0.53, p<0.001). Race time in EXP might be calculated (R=0.63, standard error of the estimate=9.9) using the equation 'Race time (min)=80.056+2.498×BMI-0.594×VOmax-0.191×weekly training distance in km'. Validating this formula in CON, no bias was shown (difference between observed and predicted value 2.3±12.8 min, 95 % CI -0.9, 5.4, p=0.153). Half-marathon race time was related to and could be predicted by BMI, VOmax and weekly running distance. Based on these relationships, a prediction formula for race time was developed providing a practical tool for recreational runners and professionals working with them.
针对业余跑步者运动表现的预测因素,尤其是半程马拉松跑的相关研究较少。我们研究的目的是:(a)调查半程马拉松比赛时间与训练、人体测量学和生理特征之间的关系;(b)开发一个公式来预测男性业余跑步者的半程马拉松比赛时间。业余跑步者(n = 134,年龄44.2±8.7岁;半程马拉松比赛时间104.6±16.2分钟)接受了包括人体测量和生理测试在内的一系列体能测试。参与者被分为五个表现组(快组,73 - 92分钟;高于平均水平组,93 - 99分钟;平均水平组,100 - 107分钟;低于平均水平组,108 - 117分钟;慢组,118 - 160分钟)。在实验组(EXP,n = 67)中开发了一个预测方程,在对照组(CON,n = 67)中进行验证,并以95%置信区间(CI)估计预测偏差。各表现组在半程马拉松比赛时间、训练天数、训练距离、年龄、体重、体重指数(BMI)、体脂(BF)和最大摄氧量(VOmax)方面存在差异(p≤0.001,η≥0.132),速度较快的组在这些指标上的得分优于速度较慢的组。半程马拉松比赛时间与生理、人体测量学和训练特征相关,跑步者速度越快,这些特征的得分越高(VOmax,r = 0.59;BMI,r = -0.55;每周跑步距离,r = -0.53,p < 0.001)。使用公式“比赛时间(分钟)= 80.056 + 2.498×BMI - 0.594×VOmax - 0.191×每周跑步距离(公里)”可计算实验组的比赛时间(R = 0.63,估计标准误差 = 9.9)。在对照组中验证该公式时,未显示出偏差(观察值与预测值之差为2.3±12.8分钟,95% CI -0.9,5.4,p = 0.153)。半程马拉松比赛时间与BMI、VOmax和每周跑步距离相关且可由其预测。基于这些关系,开发了一个比赛时间预测公式,为业余跑步者及与之合作的专业人员提供了一个实用工具。