Related Experiment Video
Updated: Oct 2, 2025

Comparative Analysis of Lower Limb Kinematics between the Initial and Terminal Phase of 5km Treadmill Running
Published on: July 17, 2020
Performance Determinants in Trail-Running Races of Different Distances
Purpose:
While the physiological determinants of road running have been widely studied, there is a lack of research in trail-running racing performance. The aim of our study was to determine the physiological predictors of trail-running performance in races of different distances in similar terrain and weather conditions.
Methods:
Seventy-five trail runners participating in one of the races of the Ultra-Trail du Mont-Blanc were recruited. Previous to the race, each runner was evaluated with (1) an incremental treadmill test to determine maximal oxygen uptake, ventilatory thresholds, cost of running, and substrate utilization; (2) a power-force-velocity profile on a cycle ergometer; (3) maximal voluntary contractions of the knee extensors and plantar flexors; and (4) anthropometric characteristics. Neuromuscular fatigue was evaluated after the races. Twenty-four runners finished a SHORT (<55 km), 16 finished a MEDIUM (101 km), and 14 finished a LONG (>145 km) race. Correlations and multiple linear regressions were used to find the determinants of performance in each race distance.
Results:
Performance in SHORT was explained by maximal oxygen uptake and lipid utilization at 10 km/h (r2 = .825, P < .001). Performance in MEDIUM was determined by maximal oxygen uptake, maximal isometric strength, and body fat percentage (r2 = .917, P < .001). A linear model could not be applied in LONG, but performance was correlated to peak velocity during the incremental test.
Conclusions:
Performance in trail running is mainly predicted by aerobic capacity, while lipid utilization also influences performance in races <60 km and performance in approximately 100 km is influenced by muscle strength and body composition.
More Related Videos
Related Concept Videos
Construction of Frequency Distribution
First, make a table with two columns—one with the title of the data that needs to be organized, and the other column for frequency. [Draw a third column for tally marks if needed]. Then, take a look at the items given in the data set and decide if an ungrouped frequency distribution table or a grouped frequency distribution table would be more suitable. If there are large sets of different values, then it is...
Wald-Wolfowitz Runs Test I
The test works...

