PREDICTION OF ONE-HOUR RUNNING PERFORMANCE USING CONSTANT DURATION TESTS

• Published in: Journal of strength and conditioning research Vol. 20; no. 4; pp. 735 - 739
• Main Authors: GAMELIN, FRANÇOIS XAVIER, COQUART, JÉRÉMY, FERRARI, NICOLAS, VODOUGNON, HUBERT, MATRAN, RÉGIS, LEGER, LUC, BOSQUET, LAURENT
• Format: Journal Article
• Language: English
• Published: United States National Strength and Conditioning Association 01.11.2006; Lippincott Williams & Wilkins Ovid Technologies
• Subjects: Adult; Biomechanical Phenomena; Humans; Male; Mathematical models; Models, Statistical; Physical Endurance - physiology; Physical fitness; Predictive Value of Tests; Running; Running - physiology; Space life sciences
• ISSN: 1064-8011; 1533-4287
• DOI: 10.1519/00124278-200611000-00002

Critical velocity (CV) represents, theoretically, the highest velocity that can be sustained without fatigue. The aim of this study was to compare CV computed from 5 mathematical models in order to determine which CV estimate is better correlated with 1-hour performance and which model provides the most accurate prediction of performance. Twelve trained middle- and long-distance male runners (29 ± 5 years) performed 3 randomly ordered constant duration tests (6, 9, and 12 minutes), a maximal running velocity test for the estimation of CV, and a 1-hour track test (actual performance). Two linear, 2 nonlinear, and 1 exponential mathematical models were used to estimate CV and to predict the highest velocity that could be sustained during 1 hour (predicted performance). Although all CV estimates were correlated with performance (0.80 < r < 0.93, p < 0.01), it appeared that CV estimated from the exponential model was more closely associated with performance than all other models (r = 0.93; p < 0.01). Analysis of the bias ± 95% interval of confidence between actual and predicted performance revealed that none of the models provided an accurate prediction of the 1-hour performance velocity. In conclusion, the estimation of CV allows us to rank middle- and long-distance runners with regard to their ability to perform well in long-distance running. However, no models provide an accurate prediction of performance that could be used as a reference for coaches or athletes.