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。
