Predictors of half-marathon performance in male recreational athletes
DOI:
https://doi.org/10.17179/excli2023-6198Keywords:
body fat, endurance, ergometry, long-distance running, performance groupAbstract
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 (VO2max) (p≤0.001, η2≥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 (e.g., VO2max, r=0.59; BMI, r=-0.55; weekly running distance, r=-0.53, p<0.001). Race time in EXP might be calculated (R2=0.63, standard error of the estimate=9.9) using the equation ‘Race time (min)=80.056+2.498×BMI-0.594×VO2max-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, VO2max 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.
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Copyright (c) 2023 Pantelis T. Nikolaidis, Beat Knechtle
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