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Maximal oxygen uptake prediction from submaximal bicycle ergometry using a differential model

The maximal oxygen uptake (VO(2)max) estimation has been a subject of research for many years. Cardiorespiratory measurements during incremental tests until exhaustion are considered the golden yard stick to assess VO(2)max. However, precise VO(2)max determination based on submaximal tests is attrac...

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Autores principales: Petelczyc, Monika, Kotlewski, Michał, Bruhn, Sven, Weippert, Matthias
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10338562/
https://www.ncbi.nlm.nih.gov/pubmed/37438405
http://dx.doi.org/10.1038/s41598-023-38089-7
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author Petelczyc, Monika
Kotlewski, Michał
Bruhn, Sven
Weippert, Matthias
author_facet Petelczyc, Monika
Kotlewski, Michał
Bruhn, Sven
Weippert, Matthias
author_sort Petelczyc, Monika
collection PubMed
description The maximal oxygen uptake (VO(2)max) estimation has been a subject of research for many years. Cardiorespiratory measurements during incremental tests until exhaustion are considered the golden yard stick to assess VO(2)max. However, precise VO(2)max determination based on submaximal tests is attractive for athlete as well for clinical populations. Here, we propose and verify such a method based on experimental data. Using a recently developed model of heart rate (HR) and VO(2) kinetics in graded exercise tests, we applied a protocol, which is terminated at 80% of the estimated maximal HR during ergometer cycling. In our approach, initially, formula for maximal HR is selected by retrospective study of a reference population (17 males, 23.5 ± 2.0 years, BMI: 23.9 ± 3.2 kg/m(2)). Next, the subjects for experimental group were invited (nine subjects of both sexes: 25.1 ± 2.1 years, BMI 23.2 ± 2.2 kg/m(2)). After calculation of maximal HR using cardiorespiratory recordings from the submaximal test, VO(2)max is predicted. Finally, we compared the prediction with the values from the maximal exercise test. The differences were quantified by relative errors, which vary from 1.2% up to 13.4%. Some future improvements for the procedure of VO(2)max prediction are discussed. The experimental protocol may be useful for application in rehabilitation assessment and in certain training monitoring settings, since physical exertion is not a prerequisite and the approach provides an acceptable VO(2)max estimation accuracy.
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spelling pubmed-103385622023-07-14 Maximal oxygen uptake prediction from submaximal bicycle ergometry using a differential model Petelczyc, Monika Kotlewski, Michał Bruhn, Sven Weippert, Matthias Sci Rep Article The maximal oxygen uptake (VO(2)max) estimation has been a subject of research for many years. Cardiorespiratory measurements during incremental tests until exhaustion are considered the golden yard stick to assess VO(2)max. However, precise VO(2)max determination based on submaximal tests is attractive for athlete as well for clinical populations. Here, we propose and verify such a method based on experimental data. Using a recently developed model of heart rate (HR) and VO(2) kinetics in graded exercise tests, we applied a protocol, which is terminated at 80% of the estimated maximal HR during ergometer cycling. In our approach, initially, formula for maximal HR is selected by retrospective study of a reference population (17 males, 23.5 ± 2.0 years, BMI: 23.9 ± 3.2 kg/m(2)). Next, the subjects for experimental group were invited (nine subjects of both sexes: 25.1 ± 2.1 years, BMI 23.2 ± 2.2 kg/m(2)). After calculation of maximal HR using cardiorespiratory recordings from the submaximal test, VO(2)max is predicted. Finally, we compared the prediction with the values from the maximal exercise test. The differences were quantified by relative errors, which vary from 1.2% up to 13.4%. Some future improvements for the procedure of VO(2)max prediction are discussed. The experimental protocol may be useful for application in rehabilitation assessment and in certain training monitoring settings, since physical exertion is not a prerequisite and the approach provides an acceptable VO(2)max estimation accuracy. Nature Publishing Group UK 2023-07-12 /pmc/articles/PMC10338562/ /pubmed/37438405 http://dx.doi.org/10.1038/s41598-023-38089-7 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Petelczyc, Monika
Kotlewski, Michał
Bruhn, Sven
Weippert, Matthias
Maximal oxygen uptake prediction from submaximal bicycle ergometry using a differential model
title Maximal oxygen uptake prediction from submaximal bicycle ergometry using a differential model
title_full Maximal oxygen uptake prediction from submaximal bicycle ergometry using a differential model
title_fullStr Maximal oxygen uptake prediction from submaximal bicycle ergometry using a differential model
title_full_unstemmed Maximal oxygen uptake prediction from submaximal bicycle ergometry using a differential model
title_short Maximal oxygen uptake prediction from submaximal bicycle ergometry using a differential model
title_sort maximal oxygen uptake prediction from submaximal bicycle ergometry using a differential model
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10338562/
https://www.ncbi.nlm.nih.gov/pubmed/37438405
http://dx.doi.org/10.1038/s41598-023-38089-7
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