Predicting Trail-Running Performance With Laboratory Exercise Tests and Field-Based Results

Autor: Hans-Christian Heitkamp, Tanja I. Janssen, Volker Scheer, Solveig Vieluf
Rok vydání: 2019
Předmět:
Zdroj: International Journal of Sports Physiology and Performance. 14:130-133
ISSN: 1555-0273
1555-0265
DOI: 10.1123/ijspp.2018-0390
Popis: Purpose: Trail running is a complex sport, and performance prediction is challenging. The aim was to evaluate 3 standard laboratory exercise tests in trail runners and correlate measurements to the race time of a trail competition evaluating its predictive power. Methods: Nine competitive male trail runners (mean age: 31 [5.8] y) completed 3 different laboratory exercise tests (step, ramp, and trail tests) for determination of maximal oxygen uptake (VO2max), vVO2max, ventilatory (VT) and lactate thresholds (LT), mechanical power output, and running economy (RE), followed by a 31-km trail race. Runners had previously participated in the same race (previous year) and finished in the top 2%. Finishing times (dependent value) were tested in multiple-regression analysis with different independent value combinations. Results: Linear-regression analysis revealed that variables measured during step and ramp tests significantly predicted performance. Step-test variables (speed at individual anaerobic threshold 16.4 [1.7] km/h and RE 12 km/h in %VO2max 65.6% [5.4%]) showed the highest performance prediction (R2 = .651, F2,6 = 5.60, P = .043), followed by the ramp test (vVO2max 20.3 [1.3] km/h; R2 = .477, F1,7 = 6.39, P = .04) and trail test (maximal power 3.9 [0.5] W/kg, VO2max 63.0 [4.8] mL O2·kg−1·min−1, vVT1 11.9 [0.7] km/h; R2 = .68, F3,5 = 3.52, P = .11). Adding race time from the preceding year to the step test improved the predictive power of the model (R2 = .988, F3,5 = 66.51, P Conclusions: The graded exercise test (VO2max, individual anaerobic threshold, and RE) most accurately predicted a 31.1-km trail-running performance. Combining submaximal intensities (individual anaerobic threshold and RE) with the previous year’s race time of that specific event increased the predictive power of the model to 99%.
Databáze: OpenAIRE