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The Anaerobic Capacity of Cross-Country Skiers: The Effect of Computational Method and Skiing Sub-technique
Anaerobic capacity is an important performance-determining variable of sprint cross-country skiing. Nevertheless, to date, no study has directly compared the anaerobic capacity, determined using the maximal accumulated oxygen deficit (MAOD) method and gross efficiency (GE) method, while using differ...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Frontiers Media S.A.
2020
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7739726/ https://www.ncbi.nlm.nih.gov/pubmed/33345029 http://dx.doi.org/10.3389/fspor.2020.00037 |
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author | Andersson, Erik P. Noordhof, Dionne A. Lögdal, Nestor |
author_facet | Andersson, Erik P. Noordhof, Dionne A. Lögdal, Nestor |
author_sort | Andersson, Erik P. |
collection | PubMed |
description | Anaerobic capacity is an important performance-determining variable of sprint cross-country skiing. Nevertheless, to date, no study has directly compared the anaerobic capacity, determined using the maximal accumulated oxygen deficit (MAOD) method and gross efficiency (GE) method, while using different skiing sub-techniques. Purpose: To compare the anaerobic capacity assessed using two different MAOD approaches (including and excluding a measured y-intercept) and the GE method during double poling (DP) and diagonal stride (DS) cross-country skiing. Methods: After an initial familiarization trial, 16 well-trained male cross-country skiers performed, in each sub-technique on separate occasions, a submaximal protocol consisting of eight 4-min bouts at intensities between ~47–78% of [Formula: see text] O(2peak) followed by a 4-min roller-skiing time trial, with the order of sub-technique being randomized. Linear and polynomial speed-metabolic rate relationships were constructed for both sub-techniques, while using a measured y-intercept (8+Y(LIN) and 8+Y(POL)) or not (8–Y(LIN) and 8–Y(POL)), to determine the anaerobic capacity using the MAOD method. The average GE (GE(AVG)) of all eight submaximal exercise bouts or the GE of the last submaximal exercise bout (GE(LAST)) were used to calculate the anaerobic capacity using the GE method. Repeated measures ANOVA were used to test differences in anaerobic capacity between methods/approaches. Results: A significant interaction was found between computational method and skiing sub-technique (P < 0.001, η(2) = 0.51) for the anaerobic capacity estimates. The different methodologies resulted in significantly different anaerobic capacity values in DP (P < 0.001, η(2) = 0.74) and in DS (P = 0.016, η(2) = 0.27). The 8-Y(POL) model resulted in the smallest standard error of the estimate (SEE, 0.24 W·kg(−1)) of the MAOD methods in DP, while the 8-Y(LIN) resulted in a smaller SEE value than the 8+Y(LIN) model (0.17 vs. 0.33 W·kg(−1)) in DS. The 8-Y(LIN) and GE(LAST) resulted in the closest agreement in anaerobic capacity values in DS (typical error 2.1 mL O(2)eq·kg(−1)). Conclusions: It is discouraged to use the same method to estimate the anaerobic capacity in DP and DS sub-techniques. In DP, a polynomial MAOD method (8-Y(POL)) seems to be the preferred method, whereas the 8-Y(LIN), GE(AVG), and GE(LAST) can all be used for DS, but not interchangeable, with GE(LAST) being the least time-consuming method. |
format | Online Article Text |
id | pubmed-7739726 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-77397262020-12-17 The Anaerobic Capacity of Cross-Country Skiers: The Effect of Computational Method and Skiing Sub-technique Andersson, Erik P. Noordhof, Dionne A. Lögdal, Nestor Front Sports Act Living Sports and Active Living Anaerobic capacity is an important performance-determining variable of sprint cross-country skiing. Nevertheless, to date, no study has directly compared the anaerobic capacity, determined using the maximal accumulated oxygen deficit (MAOD) method and gross efficiency (GE) method, while using different skiing sub-techniques. Purpose: To compare the anaerobic capacity assessed using two different MAOD approaches (including and excluding a measured y-intercept) and the GE method during double poling (DP) and diagonal stride (DS) cross-country skiing. Methods: After an initial familiarization trial, 16 well-trained male cross-country skiers performed, in each sub-technique on separate occasions, a submaximal protocol consisting of eight 4-min bouts at intensities between ~47–78% of [Formula: see text] O(2peak) followed by a 4-min roller-skiing time trial, with the order of sub-technique being randomized. Linear and polynomial speed-metabolic rate relationships were constructed for both sub-techniques, while using a measured y-intercept (8+Y(LIN) and 8+Y(POL)) or not (8–Y(LIN) and 8–Y(POL)), to determine the anaerobic capacity using the MAOD method. The average GE (GE(AVG)) of all eight submaximal exercise bouts or the GE of the last submaximal exercise bout (GE(LAST)) were used to calculate the anaerobic capacity using the GE method. Repeated measures ANOVA were used to test differences in anaerobic capacity between methods/approaches. Results: A significant interaction was found between computational method and skiing sub-technique (P < 0.001, η(2) = 0.51) for the anaerobic capacity estimates. The different methodologies resulted in significantly different anaerobic capacity values in DP (P < 0.001, η(2) = 0.74) and in DS (P = 0.016, η(2) = 0.27). The 8-Y(POL) model resulted in the smallest standard error of the estimate (SEE, 0.24 W·kg(−1)) of the MAOD methods in DP, while the 8-Y(LIN) resulted in a smaller SEE value than the 8+Y(LIN) model (0.17 vs. 0.33 W·kg(−1)) in DS. The 8-Y(LIN) and GE(LAST) resulted in the closest agreement in anaerobic capacity values in DS (typical error 2.1 mL O(2)eq·kg(−1)). Conclusions: It is discouraged to use the same method to estimate the anaerobic capacity in DP and DS sub-techniques. In DP, a polynomial MAOD method (8-Y(POL)) seems to be the preferred method, whereas the 8-Y(LIN), GE(AVG), and GE(LAST) can all be used for DS, but not interchangeable, with GE(LAST) being the least time-consuming method. Frontiers Media S.A. 2020-04-15 /pmc/articles/PMC7739726/ /pubmed/33345029 http://dx.doi.org/10.3389/fspor.2020.00037 Text en Copyright © 2020 Andersson, Noordhof and Lögdal. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Sports and Active Living Andersson, Erik P. Noordhof, Dionne A. Lögdal, Nestor The Anaerobic Capacity of Cross-Country Skiers: The Effect of Computational Method and Skiing Sub-technique |
title | The Anaerobic Capacity of Cross-Country Skiers: The Effect of Computational Method and Skiing Sub-technique |
title_full | The Anaerobic Capacity of Cross-Country Skiers: The Effect of Computational Method and Skiing Sub-technique |
title_fullStr | The Anaerobic Capacity of Cross-Country Skiers: The Effect of Computational Method and Skiing Sub-technique |
title_full_unstemmed | The Anaerobic Capacity of Cross-Country Skiers: The Effect of Computational Method and Skiing Sub-technique |
title_short | The Anaerobic Capacity of Cross-Country Skiers: The Effect of Computational Method and Skiing Sub-technique |
title_sort | anaerobic capacity of cross-country skiers: the effect of computational method and skiing sub-technique |
topic | Sports and Active Living |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7739726/ https://www.ncbi.nlm.nih.gov/pubmed/33345029 http://dx.doi.org/10.3389/fspor.2020.00037 |
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