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The Prediction of Running Velocity during the 30–15 Intermittent Fitness Test Using Accelerometry-Derived Metrics and Physiological Parameters: A Machine Learning Approach

Measuring exercise variables is one of the most important points to consider to maximize physiological adaptations. High-intensity interval training (HIIT) is a useful method to improve both cardiovascular and neuromuscular performance. The 30–15(IFT) is a field test reflecting the effort elicited b...

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Autores principales: Di Credico, Andrea, Perpetuini, David, Chiacchiaretta, Piero, Cardone, Daniela, Filippini, Chiara, Gaggi, Giulia, Merla, Arcangelo, Ghinassi, Barbara, Di Baldassarre, Angela, Izzicupo, Pascal
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8535824/
https://www.ncbi.nlm.nih.gov/pubmed/34682594
http://dx.doi.org/10.3390/ijerph182010854
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author Di Credico, Andrea
Perpetuini, David
Chiacchiaretta, Piero
Cardone, Daniela
Filippini, Chiara
Gaggi, Giulia
Merla, Arcangelo
Ghinassi, Barbara
Di Baldassarre, Angela
Izzicupo, Pascal
author_facet Di Credico, Andrea
Perpetuini, David
Chiacchiaretta, Piero
Cardone, Daniela
Filippini, Chiara
Gaggi, Giulia
Merla, Arcangelo
Ghinassi, Barbara
Di Baldassarre, Angela
Izzicupo, Pascal
author_sort Di Credico, Andrea
collection PubMed
description Measuring exercise variables is one of the most important points to consider to maximize physiological adaptations. High-intensity interval training (HIIT) is a useful method to improve both cardiovascular and neuromuscular performance. The 30–15(IFT) is a field test reflecting the effort elicited by HIIT, and the final velocity reached in the test is used to set the intensity of HIIT during the training session. In order to have a valid measure of the velocity during training, devices such as GPS can be used. However, in several situations (e.g., indoor setting), such devices do not provide reliable measures. The aim of the study was to predict exact running velocity during the 30–15(IFT) using accelerometry-derived metrics (i.e., Player Load and Average Net Force) and heart rate (HR) through a machine learning (ML) approach (i.e., Support Vector Machine) with a leave-one-subject-out cross-validation. The SVM approach showed the highest performance to predict running velocity (r = 0.91) when compared to univariate approaches using PL (r = 0.62), AvNetForce (r = 0.73) and HR only (r = 0.87). In conclusion, the presented multivariate ML approach is able to predict running velocity better than univariate ones, and the model is generalizable across subjects.
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spelling pubmed-85358242021-10-23 The Prediction of Running Velocity during the 30–15 Intermittent Fitness Test Using Accelerometry-Derived Metrics and Physiological Parameters: A Machine Learning Approach Di Credico, Andrea Perpetuini, David Chiacchiaretta, Piero Cardone, Daniela Filippini, Chiara Gaggi, Giulia Merla, Arcangelo Ghinassi, Barbara Di Baldassarre, Angela Izzicupo, Pascal Int J Environ Res Public Health Article Measuring exercise variables is one of the most important points to consider to maximize physiological adaptations. High-intensity interval training (HIIT) is a useful method to improve both cardiovascular and neuromuscular performance. The 30–15(IFT) is a field test reflecting the effort elicited by HIIT, and the final velocity reached in the test is used to set the intensity of HIIT during the training session. In order to have a valid measure of the velocity during training, devices such as GPS can be used. However, in several situations (e.g., indoor setting), such devices do not provide reliable measures. The aim of the study was to predict exact running velocity during the 30–15(IFT) using accelerometry-derived metrics (i.e., Player Load and Average Net Force) and heart rate (HR) through a machine learning (ML) approach (i.e., Support Vector Machine) with a leave-one-subject-out cross-validation. The SVM approach showed the highest performance to predict running velocity (r = 0.91) when compared to univariate approaches using PL (r = 0.62), AvNetForce (r = 0.73) and HR only (r = 0.87). In conclusion, the presented multivariate ML approach is able to predict running velocity better than univariate ones, and the model is generalizable across subjects. MDPI 2021-10-15 /pmc/articles/PMC8535824/ /pubmed/34682594 http://dx.doi.org/10.3390/ijerph182010854 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/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 (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Di Credico, Andrea
Perpetuini, David
Chiacchiaretta, Piero
Cardone, Daniela
Filippini, Chiara
Gaggi, Giulia
Merla, Arcangelo
Ghinassi, Barbara
Di Baldassarre, Angela
Izzicupo, Pascal
The Prediction of Running Velocity during the 30–15 Intermittent Fitness Test Using Accelerometry-Derived Metrics and Physiological Parameters: A Machine Learning Approach
title The Prediction of Running Velocity during the 30–15 Intermittent Fitness Test Using Accelerometry-Derived Metrics and Physiological Parameters: A Machine Learning Approach
title_full The Prediction of Running Velocity during the 30–15 Intermittent Fitness Test Using Accelerometry-Derived Metrics and Physiological Parameters: A Machine Learning Approach
title_fullStr The Prediction of Running Velocity during the 30–15 Intermittent Fitness Test Using Accelerometry-Derived Metrics and Physiological Parameters: A Machine Learning Approach
title_full_unstemmed The Prediction of Running Velocity during the 30–15 Intermittent Fitness Test Using Accelerometry-Derived Metrics and Physiological Parameters: A Machine Learning Approach
title_short The Prediction of Running Velocity during the 30–15 Intermittent Fitness Test Using Accelerometry-Derived Metrics and Physiological Parameters: A Machine Learning Approach
title_sort prediction of running velocity during the 30–15 intermittent fitness test using accelerometry-derived metrics and physiological parameters: a machine learning approach
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8535824/
https://www.ncbi.nlm.nih.gov/pubmed/34682594
http://dx.doi.org/10.3390/ijerph182010854
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