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Predicting the Sprint Performance of Adolescent Track Cyclists Using the 3-Minute All-out Test

Mark Waldron Orcid Logo, Adrian Gray, Nicola Furlan, Aron Murphy

Journal of Strength and Conditioning Research, Volume: 30, Issue: 8, Pages: 2299 - 2306

Swansea University Author: Mark Waldron Orcid Logo

Abstract

This study aimed to predict 500-m time trial (TT) and 2,000-m pursuit speed of adolescent cyclists (age range = 13–15 years) using mechanical parameters derived from a critical power (CP) test and anthropometric variables. Ten well-trained competitive cyclists were assessed for body composition, bod...

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Published in: Journal of Strength and Conditioning Research
ISSN: 1064-8011
Published: Wolters Kluwer 2016
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URI: https://cronfa.swan.ac.uk/Record/cronfa51599
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spelling 2019-09-04T12:01:07.7074376 v2 51599 2019-08-28 Predicting the Sprint Performance of Adolescent Track Cyclists Using the 3-Minute All-out Test 70db7c6c54d46f5e70b39e5ae0a056fa 0000-0002-2720-4615 Mark Waldron Mark Waldron true false 2019-08-28 STSC This study aimed to predict 500-m time trial (TT) and 2,000-m pursuit speed of adolescent cyclists (age range = 13–15 years) using mechanical parameters derived from a critical power (CP) test and anthropometric variables. Ten well-trained competitive cyclists were assessed for body composition, body mass, stature, and frontal surface area (FSA), as well as completing the CP test. The personal best speed (km·h−1) of each rider during competition in 500-m TT and 2,000-m pursuit races was predicted based on the CP test data and anthropometric profiles using multiple regression analysis. A combination of the CP·FSA−1 and internal (predicted) to external work ratio performed by the cyclists (Wint:Wext) predicted 500-m TT speed (R 2 = 0.97; standard error of the estimate (SEE) = 0.82, P ≤ 0.001), whereas a combination of mean power·FSA−1 (mean power) and body fat percentage predicted 2,000-m pursuit speed (R 2 = 0.90; SEE = 1.5, P < 0.001). Between 90 and 97% of the variance in the sprint performance of adolescent cyclists can be explained by mechanical and anthropometric parameters, derived from a single visit to the laboratory. The tests and equations provided can be adopted by coaches to predict performance and set appropriate training intensities. Journal Article Journal of Strength and Conditioning Research 30 8 2299 2306 Wolters Kluwer 1064-8011 anthropometry; children; critical power; cycling; time trials 1 8 2016 2016-08-01 10.1519/JSC.0000000000001311 https://doi.org/10.1519/JSC.0000000000001311 COLLEGE NANME Sport and Exercise Sciences COLLEGE CODE STSC Swansea University 2019-09-04T12:01:07.7074376 2019-08-28T09:56:57.6450322 Faculty of Science and Engineering School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Sport and Exercise Sciences Mark Waldron 0000-0002-2720-4615 1 Adrian Gray 2 Nicola Furlan 3 Aron Murphy 4 0051599-28082019095728.docx Mainms_JSCR_Predictingthesprintperformanceofadolescenttrack-cyclistsusingthethree-minall-outtest.docx 2019-08-28T09:57:28.1100000 Output 50943 application/vnd.openxmlformats-officedocument.wordprocessingml.document Accepted Manuscript true 2019-08-28T00:00:00.0000000 false eng
title Predicting the Sprint Performance of Adolescent Track Cyclists Using the 3-Minute All-out Test
spellingShingle Predicting the Sprint Performance of Adolescent Track Cyclists Using the 3-Minute All-out Test
Mark Waldron
title_short Predicting the Sprint Performance of Adolescent Track Cyclists Using the 3-Minute All-out Test
title_full Predicting the Sprint Performance of Adolescent Track Cyclists Using the 3-Minute All-out Test
title_fullStr Predicting the Sprint Performance of Adolescent Track Cyclists Using the 3-Minute All-out Test
title_full_unstemmed Predicting the Sprint Performance of Adolescent Track Cyclists Using the 3-Minute All-out Test
title_sort Predicting the Sprint Performance of Adolescent Track Cyclists Using the 3-Minute All-out Test
author_id_str_mv 70db7c6c54d46f5e70b39e5ae0a056fa
author_id_fullname_str_mv 70db7c6c54d46f5e70b39e5ae0a056fa_***_Mark Waldron
author Mark Waldron
author2 Mark Waldron
Adrian Gray
Nicola Furlan
Aron Murphy
format Journal article
container_title Journal of Strength and Conditioning Research
container_volume 30
container_issue 8
container_start_page 2299
publishDate 2016
institution Swansea University
issn 1064-8011
doi_str_mv 10.1519/JSC.0000000000001311
publisher Wolters Kluwer
college_str Faculty of Science and Engineering
hierarchytype
hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Sport and Exercise Sciences{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Sport and Exercise Sciences
url https://doi.org/10.1519/JSC.0000000000001311
document_store_str 1
active_str 0
description This study aimed to predict 500-m time trial (TT) and 2,000-m pursuit speed of adolescent cyclists (age range = 13–15 years) using mechanical parameters derived from a critical power (CP) test and anthropometric variables. Ten well-trained competitive cyclists were assessed for body composition, body mass, stature, and frontal surface area (FSA), as well as completing the CP test. The personal best speed (km·h−1) of each rider during competition in 500-m TT and 2,000-m pursuit races was predicted based on the CP test data and anthropometric profiles using multiple regression analysis. A combination of the CP·FSA−1 and internal (predicted) to external work ratio performed by the cyclists (Wint:Wext) predicted 500-m TT speed (R 2 = 0.97; standard error of the estimate (SEE) = 0.82, P ≤ 0.001), whereas a combination of mean power·FSA−1 (mean power) and body fat percentage predicted 2,000-m pursuit speed (R 2 = 0.90; SEE = 1.5, P < 0.001). Between 90 and 97% of the variance in the sprint performance of adolescent cyclists can be explained by mechanical and anthropometric parameters, derived from a single visit to the laboratory. The tests and equations provided can be adopted by coaches to predict performance and set appropriate training intensities.
published_date 2016-08-01T03:57:14Z
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