Precision exercise medicine : predicting unfavourable status and development in the 20-m shuttle run test performance in adolescence with machine learning
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
Julkaistu sarjassa
BMJ Open Sport & Exercise MedicineTekijät
Päivämäärä
2021Tekijänoikeudet
© Author(s) (or their
employer(s)) 2021. Published by
BMJ.
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.
...
Julkaisija
BMJ Publishing GroupISSN Hae Julkaisufoorumista
2055-7647Asiasanat
Julkaisu tutkimustietojärjestelmässä
https://converis.jyu.fi/converis/portal/detail/Publication/97546617
Metadata
Näytä kaikki kuvailutiedotKokoelmat
Lisätietoja rahoituksesta
This work was supported by the Juho Vainio Foundation (201410342) and the Finnish Ministry of Education and Culture (OKM/92/626/2013). IR and SÄ received funding from Business Finland and IR a grant from the Jenny and Antti Wihuri Fund.Lisenssi
Samankaltainen aineisto
Näytetään aineistoja, joilla on samankaltainen nimeke tai asiasanat.
-
Predicting Children's Myopia Risk : A Monte Carlo Approach to Compare the Performance of Machine Learning Models
Artiemjew, Piotr; Cybulski, Radosław; Emamian, Mohammad; Grzybowski, Andrzej; Jankowski, Andrzej; Lanca, Carla; Mehravaran, Shiva; Młyński, Marcin; Morawski, Cezary; Nordhausen, Klaus; Pärssinen, Olavi; Ropiak, Krzysztof (SCITEPRESS Science and Technology Publications, 2024)This study presents the initial results of the Myopia Risk Calculator (MRC) Consortium, introducing an innovative approach to predict myopia risk by using trustworthy machine-learning models. The dataset included approximately ... -
Impact of a multimodal exercise program on tibial bone health in adolescents with Development Coordination Disorder : an examination of feasibility and potential efficacy
Tan, Jocelyn L.; Siafarikas, Aris; Rantalainen, Timo; Hart, Nicolas H.; McIntyre, Fleur; Hands, Beth; Chivers, Paola (Hylonome, 2020)Objectives: Developmental coordination disorder (DCD) compromises bone health purportedly due to lower levels of physical activity. The potential of an exercise intervention to improve bone health parameters in adolescents ... -
New Machine Learning Approach for Detection of Injury Risk Factors in Young Team Sport Athletes
Jauhiainen, Susanne; Kauppi, Jukka-Pekka; Leppänen, Mari; Pasanen, Kati; Parkkari, Jari; Vasankari, Tommi; Kannus, Pekka; Äyrämö, Sami (Georg Thieme Verlag KG, 2021)The purpose of this article is to present how predictive machine learning methods can be utilized for detecting sport injury risk factors in a data-driven manner. The approach can be used for finding new hypotheses for ... -
Recent advances in machine learning for maximal oxygen uptake (VO2 max) prediction : A review
Ashfaq, Atiqa; Cronin, Neil; Müller, Philipp (Elsevier, 2022)Maximal oxygen uptake ( max) is the maximum amount of oxygen attainable by a person during exercise. max is used in different domains including sports and medical sciences and is usually measured during an incremental ... -
Haemoglobin, iron status and lung function of adolescents participating in organised sports in the Finnish Health Promoting Sports Club Study
Toivo, Kerttu; Kannus, Pekka; Kokko, Sami; Alanko, Lauri; Heinonen, Olli J.; Korpelainen, Raija; Savonen, Kai; Selänne, Harri; Vasankari, Tommi; Kannas, Lasse; Kujala, Urho M.; Villberg, Jari; Niemelä, Onni; Parkkari, Jari (BMJ Publishing Group Ltd; British Association of Sport and Exercise Medicine, 2020)Objectives: To compare laboratory test results and lung function of adolescent organised sports participants (SP) with non-participants (NP). Methods: In this cross-sectional study, laboratory tests (haemoglobin, iron ...
Ellei toisin mainittu, julkisesti saatavilla olevia JYX-metatietoja (poislukien tiivistelmät) saa vapaasti uudelleenkäyttää CC0-lisenssillä.