Literature DB >> 24149638

Longitudinal Study in 3,000 m Male Runners: Relationship between Performance and Selected Physiological Parameters.

José A Bragada, Paulo J Santos, José A Maia, Paulo J Colaço, Vítor P Lopes, Tiago M Barbosa.   

Abstract

The purpose of the present study was to analyze longitudinal changes in 3,000 m running performance and the relationship with selected physiological parameters. Eighteen well-trained male middle-distance runners were measured six times (x3 per year) throughout two consecutive competitive seasons. The following parameters were measured on each occasion: maximal oxygen uptake (VO2max), running economy (RE), velocity at maximal oxygen uptake (vVO2max), velocity at 4mmol L(-1) blood lactate concentration (V4), and performance velocity (km·h(-1)) in 3,000 m time trials. Values ranged from 19.59 to 20.16 km·h(-1), running performance; 197 to 207 mL·kg(-1)·km(-1). RE; 17.2 to 17.7 km·h(-1), V4; 67.1 to 72.5 mL·kg(-1)·min(-1), VO2max; and 19.8 to 20.2 km·h(-1), vVO2max. A hierarchical linear model was used to quantify longitudinal relationships between running performance and selected physiological variables. Running performance decreased significantly over time, between each time point the decrease in running velocity was 0.06 km·h(-1). The variables that significantly explained performance changes were V4 and vVO2max. Also, vVO2max and V4 were the measures most strongly correlated with performance and can be used to predict 3,000 m race velocity. The best prediction formula for 3,000 m running performance was: y = 0.646 + 0.626x + 0.416z (R(2)=0.85); where y = V3,000 m velocity (km·h(-1)), x = V4 (km·h(-1)) and z = vVO2max (km·h(-1)). The high predictive power of vVO2max and V4 suggest that both coaches and athletes should give attention to improving these two physiological variables, in order to improve running performance. Key pointsV4 and vVO2max are the most important physiological variables to explain longitudinal changes in 3000 m running performance;3000 m running performance prediction is better if one uses both V4 and vVO2max in the same formula: y = 0.646 + 0.626x + 0.416z; R(2)=0.85, where y is the Vrace (km/h), x is V4 (km/h) and z is vVO2max (km/h).The V4 and vVO2max can be used for training control purposes.

Entities:  

Keywords:  Tracking; blood lactate; maximal oxygen uptake; running economy; running performance

Year:  2010        PMID: 24149638      PMCID: PMC3761698     

Source DB:  PubMed          Journal:  J Sports Sci Med        ISSN: 1303-2968            Impact factor:   2.988


  23 in total

1.  Relationship of running economy, ventilatory threshold, and maximal oxygen consumption to running performance in high school females.

Authors:  L N Cunningham
Journal:  Res Q Exerc Sport       Date:  1990-12       Impact factor: 2.500

Review 2.  Use of blood lactate measurements for prediction of exercise performance and for control of training. Recommendations for long-distance running.

Authors:  L V Billat
Journal:  Sports Med       Date:  1996-09       Impact factor: 11.136

3.  A five year physiological case study of an Olympic runner.

Authors:  A M Jones
Journal:  Br J Sports Med       Date:  1998-03       Impact factor: 13.800

4.  The relationship between 3 km running performance and selected physiological variables.

Authors:  S Grant; I Craig; J Wilson; T Aitchison
Journal:  J Sports Sci       Date:  1997-08       Impact factor: 3.337

5.  Gender effect on the relationship of time limit at 100% VO2max with other bioenergetic characteristics.

Authors:  V Billat; J Beillot; J Jan; P Rochcongar; F Carre
Journal:  Med Sci Sports Exerc       Date:  1996-08       Impact factor: 5.411

6.  Reproducibility of running time to exhaustion at VO2max in subelite runners.

Authors:  V Billat; J C Renoux; J Pinoteau; B Petit; J P Koralsztein
Journal:  Med Sci Sports Exerc       Date:  1994-02       Impact factor: 5.411

7.  The validity and accuracy of blood lactate measurements for prediction of maximal endurance running capacity. Dependency of analyzed blood media in combination with different designs of the exercise test.

Authors:  P Foxdal; B Sjödin; A Sjödin; B Ostman
Journal:  Int J Sports Med       Date:  1994-02       Impact factor: 3.118

8.  A longitudinal assessment of anaerobic threshold and distance-running performance.

Authors:  K Tanaka; Y Matsuura; A Matsuzaka; K Hirakoba; S Kumagai; S O Sun; K Asano
Journal:  Med Sci Sports Exerc       Date:  1984-06       Impact factor: 5.411

9.  Longitudinal associations between anaerobic threshold and distance running performance.

Authors:  K Tanaka; H Watanabe; Y Konishi; R Mitsuzono; S Sumida; S Tanaka; T Fukuda; F Nakadomo
Journal:  Eur J Appl Physiol Occup Physiol       Date:  1986

10.  Time to exhaustion at VO2max and lactate steady state velocity in sub elite long-distance runners.

Authors:  V Billat; O Bernard; J Pinoteau; B Petit; J P Koralsztein
Journal:  Arch Int Physiol Biochim Biophys       Date:  1994 May-Jun
View more
  17 in total

1.  Higher Precision of Heart Rate Compared with VO2 to Predict Exercise Intensity in Endurance-Trained Runners.

Authors:  Victor M Reis; Roland Van den Tillaar; Mario C Marques
Journal:  J Sports Sci Med       Date:  2011-03-01       Impact factor: 2.988

2.  Predictive Variables of Half-Marathon Performance for Male Runners.

Authors:  Josué Gómez-Molina; Ana Ogueta-Alday; Jesus Camara; Christoper Stickley; José A Rodríguez-Marroyo; Juan García-López
Journal:  J Sports Sci Med       Date:  2017-06-01       Impact factor: 2.988

3.  Longitudinal study in male swimmers: a hierachical modeling of energetics and biomechanical contributions for performance.

Authors:  Mário J Costa; José A Bragada; Daniel A Marinho; Vitor P Lopes; António J Silva; Tiago M Barbosa
Journal:  J Sports Sci Med       Date:  2013-12-01       Impact factor: 2.988

4.  Rationale and design of the cardiorespiratory fitness and hospitalization events in armed forces study in Eastern Taiwan.

Authors:  Gen-Min Lin; Yi-Hwei Li; Chung-Jen Lee; Jeng-Chuan Shiang; Ko-Huan Lin; Kai-Wen Chen; Yu-Jung Chen; Ching-Fen Wu; Been-Sheng Lin; Yun-Shun Yu; Felicia Lin; Fung-Ying Su; Chih-Hung Wang
Journal:  World J Cardiol       Date:  2016-08-26

5.  Better economy in field running than on the treadmill: evidence from high-level distance runners.

Authors:  M Mooses; B Tippi; K Mooses; J Durussel; J Mäestu
Journal:  Biol Sport       Date:  2015-03-15       Impact factor: 2.806

6.  Multi-Stage 20-m Shuttle Run Fitness Test, Maximal Oxygen Uptake and Velocity at Maximal Oxygen Uptake.

Authors:  Giorgos P Paradisis; Elias Zacharogiannis; Dafni Mandila; Athanasia Smirtiotou; Polyxeni Argeitaki; Carlton B Cooke
Journal:  J Hum Kinet       Date:  2014-07-08       Impact factor: 2.193

7.  Lower Leg Length is Associated with Running Economy in High Level Caucasian Distance Runners.

Authors:  Rauno Laumets; Karel Viigipuu; Kerli Mooses; Jarek Mäestu; Priit Purge; Ando Pehme; Priit Kaasik; Martin Mooses
Journal:  J Hum Kinet       Date:  2017-03-15       Impact factor: 2.193

8.  Effects of 12 weeks of aerobic training on autonomic modulation, mucociliary clearance, and aerobic parameters in patients with COPD.

Authors:  Marceli Rocha Leite; Ercy Mara Cipulo Ramos; Carlos Augusto Kalva-Filho; Ana Paula Coelho Figueira Freire; Bruna Spolador de Alencar Silva; Juliana Nicolino; Alessandra Choqueta de Toledo-Arruda; Marcelo Papoti; Luiz Carlos Marques Vanderlei; Dionei Ramos
Journal:  Int J Chron Obstruct Pulmon Dis       Date:  2015-11-23

9.  Prediction of half-marathon race time in recreational female and male runners.

Authors:  Beat Knechtle; Ursula Barandun; Patrizia Knechtle; Matthias A Zingg; Thomas Rosemann; Christoph A Rüst
Journal:  Springerplus       Date:  2014-05-16

10.  Changes in Endurance Performance in Young Athletes During Two Training Seasons.

Authors:  Łukasz Tota; Marcin Maciejczyk; Ilona Pokora; Jerzy Cempla; Wanda Pilch; Tomasz Pałka
Journal:  J Hum Kinet       Date:  2015-12-30       Impact factor: 2.193

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.