Int J Sports Med 2019; 40(06): 397-403
DOI: 10.1055/a-0858-9900
Training & Testing
© Georg Thieme Verlag KG Stuttgart · New York

Predictive Ability of a Laboratory Performance Test in Mountain Bike Cross-country Olympic Athletes

Patrick Schneeweiss
1  Department of Sports Medicine, Medical Clinic, University of Tuebingen, Tuebingen
,
Philipp Schellhorn
1  Department of Sports Medicine, Medical Clinic, University of Tuebingen, Tuebingen
,
Daniel Haigis
1  Department of Sports Medicine, Medical Clinic, University of Tuebingen, Tuebingen
,
Andreas Niess
1  Department of Sports Medicine, Medical Clinic, University of Tuebingen, Tuebingen
,
Peter Martus
2  Institute for Clinical Epidemiology and Applied Biometry, University of Tuebingen, Tuebingen
,
Inga Krauss
1  Department of Sports Medicine, Medical Clinic, University of Tuebingen, Tuebingen
› Author Affiliations
Further Information

Publication History



accepted 06 February 2019

Publication Date:
01 April 2019 (online)

Abstract

Mountain bike Cross-Country Olympic (XCO) has an intermittent performance profile, underlining the importance of anaerobic metabolism. Traditional performance tests in cycling primarily quantify aerobic metabolism and inadequately meet the demands in XCO. The aim was therefore to validate a specific test that quantifies these requirements by means of an XCO race.

Twenty-three competitive XCO athletes (17.9±3.6 years) performed a previously developed performance test and an XCO race within one week. Correlations between individual anaerobic threshold (IAT), 4 mmol lactate threshold (LT4), maximal aerobic power (MAP), maximal effort time trials (TT) for 10–300 s and mean power output of the race (POR) were calculated. In addition, a multiple regression model of the predictive value of the test was calculated.

Variables correlated significantly (p<.01) with POR: IAT (r=.81), LT4 (r=0.79), MAP (r=0.91), TT10 (r=0.75), TT30 (r=0.85), TT60 (r=0.84) and TT300 (r=0.86). In the regression model, sex and body mass were set influencing variables (R²adj.=0.70), whereby MAP had the highest correlation with POR and significantly improved the predictive value of the model (R²adj.=0.86).

The high correlation of collected performance variables with POR indicated the MTB-PT’s additional benefit for performance testing in XCO because it is specific but very feasible.