AUTHOR=Nikolaidis Pantelis T. , Rosemann Thomas , Knechtle Beat 

TITLE=Development and Validation of Prediction Equation of “Athens Authentic Marathon” Men’s Race Speed

JOURNAL=Frontiers in Physiology

VOLUME=12

YEAR=2021

URL=https://www.frontiersin.org/journals/physiology/articles/10.3389/fphys.2021.682359

DOI=10.3389/fphys.2021.682359

ISSN=1664-042X

ABSTRACT=<sec><title>Aim</title><p>Despite the increasing popularity of outdoor endurance running races of different distances, little information exists about the role of training and physiological characteristics of recreational runners. The aim of the present study was (a) to examine the role of training and physiological characteristics on the performance of recreational marathon runners and (b) to develop a prediction equation of men’s race time in the “Athens Authentic Marathon.”</p></sec><sec><title>Methods</title><p>Recreational male marathon runners (<italic>n</italic> = 130, age 44.1 ± 8.6 years)—who finished the “Athens Authentic Marathon” 2017—performed a series of anthropometry and physical fitness tests including body mass index (BMI), body fat percentage (BF), maximal oxygen uptake (VO<sub>2</sub>max), anaerobic power, squat, and countermovement jump. The variation of these characteristics was examined by quintiles (i.e., five groups consisting of 26 participants in each) of the race speed. An experimental group (EXP, <italic>n</italic> = 65) was used to develop a prediction equation of the race time, which was verified in a control group (CON, <italic>n</italic> = 65).</p></sec><sec><title>Results</title><p>In the overall sample, a one-way ANOVA showed a main effect of quintiles on race speed on weekly training days and distance, age, body weight, BMI, BF, and VO<sub>2</sub>max (<italic>p</italic> ≤ 0.003, η<sup>2</sup> ≥ 0.121), where the faster groups outscored the slower groups. Running speed during the race correlated moderately with age (<italic>r</italic> = −0.36, <italic>p</italic> &lt; 0.001) and largely with the number of weekly training days (<italic>r</italic> = 0.52, <italic>p</italic> &lt; 0.001) and weekly running distance (<italic>r</italic> = 0.58, <italic>p</italic> &lt; 0.001), but not with the number of previously finished marathons (<italic>r</italic> = 0.08, <italic>p</italic> = 0.369). With regard to physiological characteristics, running speed correlated largely with body mass (<italic>r</italic> = −0.52, <italic>p</italic> &lt; 0.001), BMI (<italic>r</italic> = −0.60, <italic>p</italic> &lt; 0.001), BF (<italic>r</italic> = −0.65, <italic>p</italic> &lt; 0.001), VO<sub>2</sub>max (<italic>r</italic> = 0.67, <italic>p</italic> &lt; 0.001), moderately with isometric muscle strength (<italic>r</italic> = 0.42, <italic>p</italic> &lt; 0.001), and small with anaerobic muscle power (<italic>r</italic> = 0.20, <italic>p</italic> = 0.021). In EXP, race speed could be predicted (<italic>R</italic><sup>2</sup> = 0.61, standard error of the estimate = 1.19) using the formula “8.804 + 0.111 × VO<sub>2</sub>max + 0.029 × weekly training distance in km −0.218 × BMI.” Applying this equation in CON, no bias was observed (difference between observed and predicted value 0.12 ± 1.09 km/h, 95% confidence intervals −0.15, 0.40, <italic>p</italic> = 0.122).</p></sec><sec><title>Conclusion</title><p>These findings highlighted the role of aerobic capacity, training, and body mass status for the performance of recreational male runners in a marathon race. The findings would be of great practical importance for coaches and trainers to predict the average marathon race time in a specific group of runners.</p></sec>