Abstract

Introduction: Knowing which physiological variables predict running performance could help coaches to optimize training prescription to improve running performance. Objective: The present study investigated which physiological respiratory responses could predict 3000-m running performance. Methods: Seventeen amateur runners (29.82±7.1years; 173.12±9.0cm; 64.59±9.3kg) performed a maximal graded running test on a treadmill. The ventilatory threshold (VT), respiratory compensation point (RCP), and maximal oxygen consumption (VO2max) were assessed, as well as the respective velocities (vVT, vRCP, vVO2max). After 72 to 96 hours the runners performed the 3000-m running field test. The relationships between variables were performed using Pearson product momentum correlations. Thereafter, simple and multiple regression models were applied. The significance level adopted was 5% (p<0.05). Results: The majority of physiological responses were positive and well related to each other (r≥0.70; p<0.05). Despite vVT, vRCP, and vVO2max demonstrating a higher and inverse relationship with 3000-m time (r=-0.92; r =-0.96; r =-0.89; p<0.05), the multiple regression model indicated that vRCP and vVO2max are the best variables to predict 3000-m performance in experienced amateur road runners (R2=0.94). The equation proposed by the model was: 3000-m(s)=1399.21–[31.65*vRCP(km.h-1)]–[12.06*vVO2max (km.h-1)]. Conclusion: The vRCP and vVO2max may be used to predict 3000-m performance using only a maximal running test on a treadmill. In practical terms, coaches and physical conditioners can use performance in the 3000-m to select different exercise running intensities to prescribe exercise training intensities.

Details

Title
Prediction of 3000-m Running Performance Using Classic Physiological Respiratory Responses
Author
Lourenço, Thiago F; Fernando O C da Silva; Tessutti, Lucas S; da Silva, Carlos E; Abad, Cesar C C
Pages
18-24
Section
Articles
Publication year
2018
Publication date
2018
Publisher
Australian International Academic Centre PTY. Ltd (AIAC)
e-ISSN
2202946X
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2121413352
Copyright
© 2018. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and conditions, you may use this content in accordance with the terms of the License.