Cargando…

Anaerobic Capacity in Running: The Effect of Computational Method

INTRODUCTION: To date, no study has compared anaerobic capacity (AnC) estimates computed with the maximal accumulated oxygen deficit (MAOD) method and the gross energy cost (GEC) method applied to treadmill running exercise. PURPOSE: Four different models for estimating anaerobic energy supply durin...

Descripción completa

Detalles Bibliográficos
Autores principales: Andersson, Erik P., Björklund, Glenn, McGawley, Kerry
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8371633/
https://www.ncbi.nlm.nih.gov/pubmed/34421649
http://dx.doi.org/10.3389/fphys.2021.708172
_version_ 1783739684491362304
author Andersson, Erik P.
Björklund, Glenn
McGawley, Kerry
author_facet Andersson, Erik P.
Björklund, Glenn
McGawley, Kerry
author_sort Andersson, Erik P.
collection PubMed
description INTRODUCTION: To date, no study has compared anaerobic capacity (AnC) estimates computed with the maximal accumulated oxygen deficit (MAOD) method and the gross energy cost (GEC) method applied to treadmill running exercise. PURPOSE: Four different models for estimating anaerobic energy supply during treadmill running exercise were compared. METHODS: Fifteen endurance-trained recreational athletes performed, after a 10-min warm-up, five 4-min stages at ∼55–80% of peak oxygen uptake, and a 4-min time trial (TT). Two linear speed-metabolic rate (MR) regression models were used to estimate the instantaneous required MR during the TT (MR(TT_req)), either including (5+Y(LIN)) or excluding (5-Y(LIN)) a measured Y-intercept. Also, the average GEC (GEC(AVG)) based on all five submaximal stages, or the GEC based on the last submaximal stage (GEC(LAST)), were used as models to estimate the instantaneous MR(TT_req). The AnC was computed as the difference between the MR(TT_req) and the aerobic MR integrated over time. RESULTS: The GEC remained constant at ∼4.39 ± 0.29 J⋅kg(–1)⋅m(–1) across the five submaximal stages and the TT was performed at a speed of 4.7 ± 0.4 m⋅s(–1). Compared with the 5-Y(LIN), GEC(AVG), and GEC(LAST) models, the 5+Y(LIN) model generated a MR(TT_req) that was ∼3.9% lower, with corresponding anaerobic capacities from the four models of 0.72 ± 0.20, 0.74 ± 0.16, 0.74 ± 0.15, and 0.54 ± 0.14 kJ⋅kg(–1), respectively (F(1.07,42) = 13.9, P = 0.002). The GEC values associated with the TT were 4.22 ± 0.27 and 4.37 ± 0.30 J⋅kg(–1)⋅m(–1) for 5+Y(LIN) and 5-Y(LIN), respectively (calculated from the regression equation), and 4.39 ± 0.28 and 4.38 ± 0.27 J⋅kg(–1)⋅m(–1) for GEC(AVG) and GEC(LAST), respectively (F(1.08,42) = 14.6, P < 0.001). The absolute typical errors in AnC ranged between 0.03 and 0.16 kJ⋅kg(–1) for the six pair-wise comparisons and the overall standard error of measurement (SEM) was 0.16 kJ⋅kg(–1). CONCLUSION: These findings demonstrate a generally high disagreement in estimated anaerobic capacities between models and show that the inclusion of a measured Y-intercept in the linear regression (i.e., 5+Y(LIN)) is likely to underestimate the MR(TT_req) and the GEC associated with the TT, and hence the AnC during maximal 4-min treadmill running.
format Online
Article
Text
id pubmed-8371633
institution National Center for Biotechnology Information
language English
publishDate 2021
publisher Frontiers Media S.A.
record_format MEDLINE/PubMed
spelling pubmed-83716332021-08-19 Anaerobic Capacity in Running: The Effect of Computational Method Andersson, Erik P. Björklund, Glenn McGawley, Kerry Front Physiol Physiology INTRODUCTION: To date, no study has compared anaerobic capacity (AnC) estimates computed with the maximal accumulated oxygen deficit (MAOD) method and the gross energy cost (GEC) method applied to treadmill running exercise. PURPOSE: Four different models for estimating anaerobic energy supply during treadmill running exercise were compared. METHODS: Fifteen endurance-trained recreational athletes performed, after a 10-min warm-up, five 4-min stages at ∼55–80% of peak oxygen uptake, and a 4-min time trial (TT). Two linear speed-metabolic rate (MR) regression models were used to estimate the instantaneous required MR during the TT (MR(TT_req)), either including (5+Y(LIN)) or excluding (5-Y(LIN)) a measured Y-intercept. Also, the average GEC (GEC(AVG)) based on all five submaximal stages, or the GEC based on the last submaximal stage (GEC(LAST)), were used as models to estimate the instantaneous MR(TT_req). The AnC was computed as the difference between the MR(TT_req) and the aerobic MR integrated over time. RESULTS: The GEC remained constant at ∼4.39 ± 0.29 J⋅kg(–1)⋅m(–1) across the five submaximal stages and the TT was performed at a speed of 4.7 ± 0.4 m⋅s(–1). Compared with the 5-Y(LIN), GEC(AVG), and GEC(LAST) models, the 5+Y(LIN) model generated a MR(TT_req) that was ∼3.9% lower, with corresponding anaerobic capacities from the four models of 0.72 ± 0.20, 0.74 ± 0.16, 0.74 ± 0.15, and 0.54 ± 0.14 kJ⋅kg(–1), respectively (F(1.07,42) = 13.9, P = 0.002). The GEC values associated with the TT were 4.22 ± 0.27 and 4.37 ± 0.30 J⋅kg(–1)⋅m(–1) for 5+Y(LIN) and 5-Y(LIN), respectively (calculated from the regression equation), and 4.39 ± 0.28 and 4.38 ± 0.27 J⋅kg(–1)⋅m(–1) for GEC(AVG) and GEC(LAST), respectively (F(1.08,42) = 14.6, P < 0.001). The absolute typical errors in AnC ranged between 0.03 and 0.16 kJ⋅kg(–1) for the six pair-wise comparisons and the overall standard error of measurement (SEM) was 0.16 kJ⋅kg(–1). CONCLUSION: These findings demonstrate a generally high disagreement in estimated anaerobic capacities between models and show that the inclusion of a measured Y-intercept in the linear regression (i.e., 5+Y(LIN)) is likely to underestimate the MR(TT_req) and the GEC associated with the TT, and hence the AnC during maximal 4-min treadmill running. Frontiers Media S.A. 2021-08-04 /pmc/articles/PMC8371633/ /pubmed/34421649 http://dx.doi.org/10.3389/fphys.2021.708172 Text en Copyright © 2021 Andersson, Björklund and McGawley. https://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 Physiology
Andersson, Erik P.
Björklund, Glenn
McGawley, Kerry
Anaerobic Capacity in Running: The Effect of Computational Method
title Anaerobic Capacity in Running: The Effect of Computational Method
title_full Anaerobic Capacity in Running: The Effect of Computational Method
title_fullStr Anaerobic Capacity in Running: The Effect of Computational Method
title_full_unstemmed Anaerobic Capacity in Running: The Effect of Computational Method
title_short Anaerobic Capacity in Running: The Effect of Computational Method
title_sort anaerobic capacity in running: the effect of computational method
topic Physiology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8371633/
https://www.ncbi.nlm.nih.gov/pubmed/34421649
http://dx.doi.org/10.3389/fphys.2021.708172
work_keys_str_mv AT anderssonerikp anaerobiccapacityinrunningtheeffectofcomputationalmethod
AT bjorklundglenn anaerobiccapacityinrunningtheeffectofcomputationalmethod
AT mcgawleykerry anaerobiccapacityinrunningtheeffectofcomputationalmethod