Literature DB >> 33393434

10 km performance prediction by metabolic and mechanical variables: influence of performance level and post-submaximal running jump potentiation.

Sebastián Del Rosso1,2, Danilo Pinho Souza1, Fabián Muñoz3,4, David G Behm5,6, Carl Foster, Daniel Boullosa7,8.   

Abstract

We aimed to develop models to explain performance and pacing during a 10-km running trial.Well-trained runners (n = 27, VO2max = 62.3 ± 4.5 mL·kg-1·min-1) divided into High (HPG, T10km = 33.9 ± 1.2 min, n = 9) and Low (LPG, T10km = 37.9 ± 1.2 min, n = 18) performers completed, in different days, the half squat and loaded squat jump (LSJ) exercises (1st day), an incremental test and a submaximal running bout to induce jump potentiation (2nd day), and a 10-km time trial (3rd day). Pacing was significantly different between performance groups (p < 0.05). The inclusion of mechanical and metabolic variables increased the explained variance in performance (LPG, r2adj = 0.87, p < 0.001; HPG, r2adj = 0.99 p < 0.01). Analysis between potentiation and non-potentiation groups revealed significant differences for the speed in the last 400 m (p = 0.02), and in the final RPE (p = 0.03). Performance and pacing can be explained by combining metabolic and mechanical variables and should be controlled by performance level. The relationship between jump potentiation and speed during the last 400 m may suggest that post-activation performance enhancement could be involved in pacing regulation.

Keywords:  Endurance; aerobic thresholds; maximal aerobic speed; post-activation performance enhancement; strength

Mesh:

Year:  2021        PMID: 33393434     DOI: 10.1080/02640414.2020.1860361

Source DB:  PubMed          Journal:  J Sports Sci        ISSN: 0264-0414            Impact factor:   3.337


  1 in total

1.  An Improved Logistic Regression Method for Assessing the Performance of Track and Field Sports.

Authors:  Songling Zheng; Xi Man
Journal:  Comput Intell Neurosci       Date:  2022-08-02
  1 in total

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