A1 Journal article (refereed)
Precision exercise medicine : predicting unfavourable status and development in the 20-m shuttle run test performance in adolescence with machine learning (2021)
Joensuu, L., Rautiainen, I., Äyrämö, S., Syväoja, H. J., Kauppi, J.-P., Kujala, U. M., & Tammelin, T. H. (2021). Precision exercise medicine : predicting unfavourable status and development in the 20-m shuttle run test performance in adolescence with machine learning. BMJ Open Sport & Exercise Medicine, 7(2), Article e001053. https://doi.org/10.1136/bmjsem-2021-001053
JYU authors or editors
Publication details
All authors or editors: Joensuu, Laura; Rautiainen, Ilkka; Äyrämö, Sami; Syväoja, Heidi J; Kauppi, Jukka-Pekka; Kujala, Urho M; Tammelin, Tuija H
Journal or series: BMJ Open Sport & Exercise Medicine
eISSN: 2055-7647
Publication year: 2021
Volume: 7
Issue number: 2
Article number: e001053
Publisher: BMJ Publishing Group
Publication country: United Kingdom
Publication language: English
DOI: https://doi.org/10.1136/bmjsem-2021-001053
Publication open access: Openly available
Publication channel open access: Open Access channel
Publication is parallel published (JYX): https://jyx.jyu.fi/handle/123456789/76444
Abstract
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.
Keywords: young people; physical fitness; fitness tests; forecasts; machine learning
Free keywords: adolescent; physical fitness; children's health and exercise; sports & exercise medicine
Contributing organizations
Ministry reporting: Yes
Reporting Year: 2021
JUFO rating: 1
