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Design of a Computer Model for the Identification of Adolescent Swimmers at Risk of Low BMD
This paper aims to elaborate a decision tree for the early detection of adolescent swimmers at risk of presenting low bone mineral density (BMD), based on easily measurable fitness and performance variables. The BMD of 78 adolescent swimmers was determined using dual-energy X-ray absorptiometry (DXA...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9964481/ https://www.ncbi.nlm.nih.gov/pubmed/36834149 http://dx.doi.org/10.3390/ijerph20043454 |
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author | Marin-Puyalto, Jorge Gomez-Cabello, Alba Gomez-Bruton, Alejandro Matute-Llorente, Angel Castillo-Bernad, Sergio Lozano-Berges, Gabriel Gonzalez-Agüero, Alejandro Casajus, Jose A. Vicente-Rodriguez, German |
author_facet | Marin-Puyalto, Jorge Gomez-Cabello, Alba Gomez-Bruton, Alejandro Matute-Llorente, Angel Castillo-Bernad, Sergio Lozano-Berges, Gabriel Gonzalez-Agüero, Alejandro Casajus, Jose A. Vicente-Rodriguez, German |
author_sort | Marin-Puyalto, Jorge |
collection | PubMed |
description | This paper aims to elaborate a decision tree for the early detection of adolescent swimmers at risk of presenting low bone mineral density (BMD), based on easily measurable fitness and performance variables. The BMD of 78 adolescent swimmers was determined using dual-energy X-ray absorptiometry (DXA) scans at the hip and subtotal body. The participants also underwent physical fitness (muscular strength, speed, and cardiovascular endurance) and swimming performance assessments. A gradient-boosting machine regression tree was built to predict the BMD of the swimmers and to further develop a simpler individual decision tree. The predicted BMD was strongly correlated with the actual BMD values obtained from the DXA (r = 0.960, p < 0.001; root mean squared error = 0.034 g/cm(2)). According to a simple decision tree (74% classification accuracy), swimmers with a body mass index (BMI) lower than 17 kg/m(2) or a handgrip strength inferior to 43 kg with the sum of both arms could be at a higher risk of having a low BMD. Easily measurable fitness variables (BMI and handgrip strength) could be used for the early detection of adolescent swimmers who are at risk of suffering from low BMD. |
format | Online Article Text |
id | pubmed-9964481 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-99644812023-02-26 Design of a Computer Model for the Identification of Adolescent Swimmers at Risk of Low BMD Marin-Puyalto, Jorge Gomez-Cabello, Alba Gomez-Bruton, Alejandro Matute-Llorente, Angel Castillo-Bernad, Sergio Lozano-Berges, Gabriel Gonzalez-Agüero, Alejandro Casajus, Jose A. Vicente-Rodriguez, German Int J Environ Res Public Health Article This paper aims to elaborate a decision tree for the early detection of adolescent swimmers at risk of presenting low bone mineral density (BMD), based on easily measurable fitness and performance variables. The BMD of 78 adolescent swimmers was determined using dual-energy X-ray absorptiometry (DXA) scans at the hip and subtotal body. The participants also underwent physical fitness (muscular strength, speed, and cardiovascular endurance) and swimming performance assessments. A gradient-boosting machine regression tree was built to predict the BMD of the swimmers and to further develop a simpler individual decision tree. The predicted BMD was strongly correlated with the actual BMD values obtained from the DXA (r = 0.960, p < 0.001; root mean squared error = 0.034 g/cm(2)). According to a simple decision tree (74% classification accuracy), swimmers with a body mass index (BMI) lower than 17 kg/m(2) or a handgrip strength inferior to 43 kg with the sum of both arms could be at a higher risk of having a low BMD. Easily measurable fitness variables (BMI and handgrip strength) could be used for the early detection of adolescent swimmers who are at risk of suffering from low BMD. MDPI 2023-02-16 /pmc/articles/PMC9964481/ /pubmed/36834149 http://dx.doi.org/10.3390/ijerph20043454 Text en © 2023 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 Marin-Puyalto, Jorge Gomez-Cabello, Alba Gomez-Bruton, Alejandro Matute-Llorente, Angel Castillo-Bernad, Sergio Lozano-Berges, Gabriel Gonzalez-Agüero, Alejandro Casajus, Jose A. Vicente-Rodriguez, German Design of a Computer Model for the Identification of Adolescent Swimmers at Risk of Low BMD |
title | Design of a Computer Model for the Identification of Adolescent Swimmers at Risk of Low BMD |
title_full | Design of a Computer Model for the Identification of Adolescent Swimmers at Risk of Low BMD |
title_fullStr | Design of a Computer Model for the Identification of Adolescent Swimmers at Risk of Low BMD |
title_full_unstemmed | Design of a Computer Model for the Identification of Adolescent Swimmers at Risk of Low BMD |
title_short | Design of a Computer Model for the Identification of Adolescent Swimmers at Risk of Low BMD |
title_sort | design of a computer model for the identification of adolescent swimmers at risk of low bmd |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9964481/ https://www.ncbi.nlm.nih.gov/pubmed/36834149 http://dx.doi.org/10.3390/ijerph20043454 |
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