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Physiological Performance Measures as Indicators of CrossFit(®) Performance
CrossFit(®) began as another exercise program to improve physical fitness and has rapidly grown into the “sport of fitness”. However, little is understood as to the physiological indicators that determine CrossFit(®) sport performance. The purpose of this study was to determine which physiological p...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6524377/ https://www.ncbi.nlm.nih.gov/pubmed/31013585 http://dx.doi.org/10.3390/sports7040093 |
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author | Dexheimer, Joshua D. Schroeder, E. Todd Sawyer, Brandon J. Pettitt, Robert W. Aguinaldo, Arnel L. Torrence, William A. |
author_facet | Dexheimer, Joshua D. Schroeder, E. Todd Sawyer, Brandon J. Pettitt, Robert W. Aguinaldo, Arnel L. Torrence, William A. |
author_sort | Dexheimer, Joshua D. |
collection | PubMed |
description | CrossFit(®) began as another exercise program to improve physical fitness and has rapidly grown into the “sport of fitness”. However, little is understood as to the physiological indicators that determine CrossFit(®) sport performance. The purpose of this study was to determine which physiological performance measure was the greatest indicator of CrossFit(®) workout performance. Male (n = 12) and female (n = 5) participants successfully completed a treadmill graded exercise test to measure maximal oxygen uptake (VO(2max)), a 3-minute all-out running test (3MT) to determine critical speed (CS) and the finite capacity for running speeds above CS (D′), a Wingate anaerobic test (WAnT) to assess anaerobic peak and mean power, the CrossFit(®) total to measure total body strength, as well as the CrossFit(®) benchmark workouts: Fran, Grace, and Nancy. It was hypothesized that CS and total body strength would be the greatest indicators of CrossFit(®) performance. Pearson’s r correlations were used to determine the relationship of benchmark performance data and the physiological performance measures. For each benchmark-dependent variable, a stepwise linear regression was created using significant correlative data. For the workout Fran, back squat strength explained 42% of the variance. VO(2max) explained 68% of the variance for the workout Nancy. Lastly, anaerobic peak power explained 57% of the variance for performance on the CrossFit(®) total. In conclusion, results demonstrated select physiological performance variables may be used to predict CrossFit(®) workout performance. |
format | Online Article Text |
id | pubmed-6524377 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-65243772019-06-05 Physiological Performance Measures as Indicators of CrossFit(®) Performance Dexheimer, Joshua D. Schroeder, E. Todd Sawyer, Brandon J. Pettitt, Robert W. Aguinaldo, Arnel L. Torrence, William A. Sports (Basel) Article CrossFit(®) began as another exercise program to improve physical fitness and has rapidly grown into the “sport of fitness”. However, little is understood as to the physiological indicators that determine CrossFit(®) sport performance. The purpose of this study was to determine which physiological performance measure was the greatest indicator of CrossFit(®) workout performance. Male (n = 12) and female (n = 5) participants successfully completed a treadmill graded exercise test to measure maximal oxygen uptake (VO(2max)), a 3-minute all-out running test (3MT) to determine critical speed (CS) and the finite capacity for running speeds above CS (D′), a Wingate anaerobic test (WAnT) to assess anaerobic peak and mean power, the CrossFit(®) total to measure total body strength, as well as the CrossFit(®) benchmark workouts: Fran, Grace, and Nancy. It was hypothesized that CS and total body strength would be the greatest indicators of CrossFit(®) performance. Pearson’s r correlations were used to determine the relationship of benchmark performance data and the physiological performance measures. For each benchmark-dependent variable, a stepwise linear regression was created using significant correlative data. For the workout Fran, back squat strength explained 42% of the variance. VO(2max) explained 68% of the variance for the workout Nancy. Lastly, anaerobic peak power explained 57% of the variance for performance on the CrossFit(®) total. In conclusion, results demonstrated select physiological performance variables may be used to predict CrossFit(®) workout performance. MDPI 2019-04-22 /pmc/articles/PMC6524377/ /pubmed/31013585 http://dx.doi.org/10.3390/sports7040093 Text en © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Dexheimer, Joshua D. Schroeder, E. Todd Sawyer, Brandon J. Pettitt, Robert W. Aguinaldo, Arnel L. Torrence, William A. Physiological Performance Measures as Indicators of CrossFit(®) Performance |
title | Physiological Performance Measures as Indicators of CrossFit(®) Performance |
title_full | Physiological Performance Measures as Indicators of CrossFit(®) Performance |
title_fullStr | Physiological Performance Measures as Indicators of CrossFit(®) Performance |
title_full_unstemmed | Physiological Performance Measures as Indicators of CrossFit(®) Performance |
title_short | Physiological Performance Measures as Indicators of CrossFit(®) Performance |
title_sort | physiological performance measures as indicators of crossfit(®) performance |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6524377/ https://www.ncbi.nlm.nih.gov/pubmed/31013585 http://dx.doi.org/10.3390/sports7040093 |
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