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A computational predictor of the anaerobic mechanical power outputs from a clinical exercise stress test
We previously were able to predict the anaerobic mechanical power outputs using features taken from a maximal incremental cardiopulmonary exercise stress test (CPET). Since a standard aerobic exercise stress test (with electrocardiogram and blood pressure measurements) has no gas exchange measuremen...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2023
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10162510/ https://www.ncbi.nlm.nih.gov/pubmed/37146031 http://dx.doi.org/10.1371/journal.pone.0283630 |
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author | Leopold, Efrat Tuller, Tamir Scheinowitz, Mickey |
author_facet | Leopold, Efrat Tuller, Tamir Scheinowitz, Mickey |
author_sort | Leopold, Efrat |
collection | PubMed |
description | We previously were able to predict the anaerobic mechanical power outputs using features taken from a maximal incremental cardiopulmonary exercise stress test (CPET). Since a standard aerobic exercise stress test (with electrocardiogram and blood pressure measurements) has no gas exchange measurement and is more popular than CPET, our goal, in the current paper, was to investigate whether features taken from a clinical exercise stress test (GXT), either submaximal or maximal, can predict the anaerobic mechanical power outputs to the same level as we found with CPET variables. We have used data taken from young healthy subjects undergoing CPET aerobic test and the Wingate anaerobic test, and developed a computational predictive algorithm, based on greedy heuristic multiple linear regression, which enabled the prediction of the anaerobic mechanical power outputs from a corresponding GXT measures (exercise test time, treadmill speed and slope). We found that for submaximal GXT of 85% age predicted HRmax, a combination of 3 and 4 variables produced a correlation of r = 0.93 and r = 0.92 with % error equal to 15 ± 3 and 16 ± 3 on the validation set between real and predicted values of the peak and mean anaerobic mechanical power outputs (p < 0.001), respectively. For maximal GXT (100% of age predicted HRmax), a combination of 4 and 2 variables produced a correlation of r = 0.92 and r = 0.94 with % error equal to 12 ± 2 and 14 ± 3 on the validation set between real and predicted values of the peak and mean anaerobic mechanical power outputs (p < 0.001), respectively. The newly developed model allows to accurately predict the anaerobic mechanical power outputs from a standard, submaximal and maximal GXT. Nevertheless, in the current study the subjects were healthy, normal individuals and therefore the assessment of additional subjects is desirable for the development of a test applicable to other populations. |
format | Online Article Text |
id | pubmed-10162510 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-101625102023-05-06 A computational predictor of the anaerobic mechanical power outputs from a clinical exercise stress test Leopold, Efrat Tuller, Tamir Scheinowitz, Mickey PLoS One Research Article We previously were able to predict the anaerobic mechanical power outputs using features taken from a maximal incremental cardiopulmonary exercise stress test (CPET). Since a standard aerobic exercise stress test (with electrocardiogram and blood pressure measurements) has no gas exchange measurement and is more popular than CPET, our goal, in the current paper, was to investigate whether features taken from a clinical exercise stress test (GXT), either submaximal or maximal, can predict the anaerobic mechanical power outputs to the same level as we found with CPET variables. We have used data taken from young healthy subjects undergoing CPET aerobic test and the Wingate anaerobic test, and developed a computational predictive algorithm, based on greedy heuristic multiple linear regression, which enabled the prediction of the anaerobic mechanical power outputs from a corresponding GXT measures (exercise test time, treadmill speed and slope). We found that for submaximal GXT of 85% age predicted HRmax, a combination of 3 and 4 variables produced a correlation of r = 0.93 and r = 0.92 with % error equal to 15 ± 3 and 16 ± 3 on the validation set between real and predicted values of the peak and mean anaerobic mechanical power outputs (p < 0.001), respectively. For maximal GXT (100% of age predicted HRmax), a combination of 4 and 2 variables produced a correlation of r = 0.92 and r = 0.94 with % error equal to 12 ± 2 and 14 ± 3 on the validation set between real and predicted values of the peak and mean anaerobic mechanical power outputs (p < 0.001), respectively. The newly developed model allows to accurately predict the anaerobic mechanical power outputs from a standard, submaximal and maximal GXT. Nevertheless, in the current study the subjects were healthy, normal individuals and therefore the assessment of additional subjects is desirable for the development of a test applicable to other populations. Public Library of Science 2023-05-05 /pmc/articles/PMC10162510/ /pubmed/37146031 http://dx.doi.org/10.1371/journal.pone.0283630 Text en © 2023 Leopold et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Leopold, Efrat Tuller, Tamir Scheinowitz, Mickey A computational predictor of the anaerobic mechanical power outputs from a clinical exercise stress test |
title | A computational predictor of the anaerobic mechanical power outputs from a clinical exercise stress test |
title_full | A computational predictor of the anaerobic mechanical power outputs from a clinical exercise stress test |
title_fullStr | A computational predictor of the anaerobic mechanical power outputs from a clinical exercise stress test |
title_full_unstemmed | A computational predictor of the anaerobic mechanical power outputs from a clinical exercise stress test |
title_short | A computational predictor of the anaerobic mechanical power outputs from a clinical exercise stress test |
title_sort | computational predictor of the anaerobic mechanical power outputs from a clinical exercise stress test |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10162510/ https://www.ncbi.nlm.nih.gov/pubmed/37146031 http://dx.doi.org/10.1371/journal.pone.0283630 |
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