Precision exercise medicine: predicting unfavourable status and development in the 20-m shuttle run test performance in adolescence with machine learning

OBJECTIVES: To assess the ability to predict individual unfavourable future status and development in the 20m shuttle run test (20MSRT) during adolescence with machine learning (random forest (RF) classifier). METHODS: Data from a 2-year observational study (2013‒2015, 12.4±1.3 years, n=633, 50% gir...

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Autores principales: Joensuu, Laura, Rautiainen, Ilkka, Äyrämö, Sami, Syväoja, Heidi J, Kauppi, Jukka-Pekka, Kujala, Urho M, Tammelin, Tuija H
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BMJ Publishing Group 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8144034/
https://www.ncbi.nlm.nih.gov/pubmed/34104475
http://dx.doi.org/10.1136/bmjsem-2021-001053
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author Joensuu, Laura
Rautiainen, Ilkka
Äyrämö, Sami
Syväoja, Heidi J
Kauppi, Jukka-Pekka
Kujala, Urho M
Tammelin, Tuija H
author_facet Joensuu, Laura
Rautiainen, Ilkka
Äyrämö, Sami
Syväoja, Heidi J
Kauppi, Jukka-Pekka
Kujala, Urho M
Tammelin, Tuija H
author_sort Joensuu, Laura
collection PubMed
description OBJECTIVES: To assess the ability to predict individual unfavourable future status and development in the 20m shuttle run test (20MSRT) during adolescence with machine learning (random forest (RF) classifier). METHODS: Data from a 2-year observational study (2013‒2015, 12.4±1.3 years, n=633, 50% girls), with 48 baseline characteristics (questionnaires (demographics, physical, psychological, social and lifestyle factors), objective measurements (anthropometrics, fitness characteristics, physical activity, body composition and academic scores)) were used to predict: (Task 1) unfavourable future 20MSRT status (identification of individuals in the lowest 20MSRT tertile after 2 years), and (Task 2) unfavourable 20MSRT development (identification of individuals with 20MSRT development in the lowest tertile among adolescents with baseline 20MSRT below median level). RESULTS: Prediction performance for future 20MSRT status (Task 1) was (area under the receiver operating characteristic curve, AUC) 83% and 76%, sensitivity 80% and 60%, and specificity 78% and 79% in girls and boys, respectively. Twenty variables showed predictive power in boys, 14 in girls, including fitness characteristics, physical activity, academic scores, adiposity, life enjoyment, parental support, social status in school and perceived fitness. Prediction performance for future development (Task 2) was lower and differed statistically from random level only in girls (AUC 68% and 40% in girls and boys). CONCLUSION: RF classifier predicted future unfavourable status in 20MSRT and identified potential individuals for interventions based on a holistic profile (14‒20 baseline characteristics). The MATLAB script and functions employing the RF classifier of this study are available for future precision exercise medicine research.
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spelling pubmed-81440342021-06-07 Precision exercise medicine: predicting unfavourable status and development in the 20-m shuttle run test performance in adolescence with machine learning Joensuu, Laura Rautiainen, Ilkka Äyrämö, Sami Syväoja, Heidi J Kauppi, Jukka-Pekka Kujala, Urho M Tammelin, Tuija H BMJ Open Sport Exerc Med Original Research OBJECTIVES: To assess the ability to predict individual unfavourable future status and development in the 20m shuttle run test (20MSRT) during adolescence with machine learning (random forest (RF) classifier). METHODS: Data from a 2-year observational study (2013‒2015, 12.4±1.3 years, n=633, 50% girls), with 48 baseline characteristics (questionnaires (demographics, physical, psychological, social and lifestyle factors), objective measurements (anthropometrics, fitness characteristics, physical activity, body composition and academic scores)) were used to predict: (Task 1) unfavourable future 20MSRT status (identification of individuals in the lowest 20MSRT tertile after 2 years), and (Task 2) unfavourable 20MSRT development (identification of individuals with 20MSRT development in the lowest tertile among adolescents with baseline 20MSRT below median level). RESULTS: Prediction performance for future 20MSRT status (Task 1) was (area under the receiver operating characteristic curve, AUC) 83% and 76%, sensitivity 80% and 60%, and specificity 78% and 79% in girls and boys, respectively. Twenty variables showed predictive power in boys, 14 in girls, including fitness characteristics, physical activity, academic scores, adiposity, life enjoyment, parental support, social status in school and perceived fitness. Prediction performance for future development (Task 2) was lower and differed statistically from random level only in girls (AUC 68% and 40% in girls and boys). CONCLUSION: RF classifier predicted future unfavourable status in 20MSRT and identified potential individuals for interventions based on a holistic profile (14‒20 baseline characteristics). The MATLAB script and functions employing the RF classifier of this study are available for future precision exercise medicine research. BMJ Publishing Group 2021-05-21 /pmc/articles/PMC8144034/ /pubmed/34104475 http://dx.doi.org/10.1136/bmjsem-2021-001053 Text en © Author(s) (or their employer(s)) 2021. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ. https://creativecommons.org/licenses/by-nc/4.0/This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/ (https://creativecommons.org/licenses/by-nc/4.0/) .
spellingShingle Original Research
Joensuu, Laura
Rautiainen, Ilkka
Äyrämö, Sami
Syväoja, Heidi J
Kauppi, Jukka-Pekka
Kujala, Urho M
Tammelin, Tuija H
Precision exercise medicine: predicting unfavourable status and development in the 20-m shuttle run test performance in adolescence with machine learning
title Precision exercise medicine: predicting unfavourable status and development in the 20-m shuttle run test performance in adolescence with machine learning
title_full Precision exercise medicine: predicting unfavourable status and development in the 20-m shuttle run test performance in adolescence with machine learning
title_fullStr Precision exercise medicine: predicting unfavourable status and development in the 20-m shuttle run test performance in adolescence with machine learning
title_full_unstemmed Precision exercise medicine: predicting unfavourable status and development in the 20-m shuttle run test performance in adolescence with machine learning
title_short Precision exercise medicine: predicting unfavourable status and development in the 20-m shuttle run test performance in adolescence with machine learning
title_sort precision exercise medicine: predicting unfavourable status and development in the 20-m shuttle run test performance in adolescence with machine learning
topic Original Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8144034/
https://www.ncbi.nlm.nih.gov/pubmed/34104475
http://dx.doi.org/10.1136/bmjsem-2021-001053
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