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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Detalles Bibliográficos
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:
Original Research
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

Internet

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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