Article Text

Precision exercise medicine: predicting unfavourable status and development in the 20-m shuttle run test performance in adolescence with machine learning
  1. Laura Joensuu1,2,
  2. Ilkka Rautiainen3,
  3. Sami Äyrämö3,
  4. Heidi J Syväoja2,
  5. Jukka-Pekka Kauppi3,
  6. Urho M Kujala1,
  7. Tuija H Tammelin2
  1. 1Faculty of Sport and Health Sciences, University of Jyväskylä, Jyväskylä, Finland
  2. 2LIKES Research Centre for Physical Activity and Health, Jyväskylä, Finland
  3. 3Faculty of Information Technology, University of Jyväskylä, Jyväskylä, Finland
  1. Correspondence to Mrs Laura Joensuu; laura.p.joensuu{at}jyu.fi

Abstract

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.

  • adolescent
  • physical fitness
  • children's health and exercise
  • sports & exercise medicine

Data availability statement

Raw is agreed not to be shared with third parties. In other cases, data are available upon reasonable request. Please contact THT for data sharing.

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Data availability statement

Raw is agreed not to be shared with third parties. In other cases, data are available upon reasonable request. Please contact THT for data sharing.

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Footnotes

  • LJ and IR contributed equally.

  • Contributors LJ, IR, SÄ, HJS, UMK and THT contributed to planning this work. IR, J-PK and SÄ contributed to analyses. All authors contributed to the interpretation of the data, drafting and reporting the work, and revising critically the intellectual content. All authors have given final approval for this version, agreed to be accountable and are committed to resolving possible questions related to its content.

  • Funding 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.

  • Competing interests None declared.

  • Patient and public involvement Patients and/or the public were not involved in the design, or conduct, or reporting, or dissemination plans of this research.

  • Provenance and peer review Not commissioned; externally peer reviewed.

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