Design of a Computer Model for the Identification of Adolescent Swimmers at Risk of Low BMD

Jorge Marin-Puyalto, Alba Gomez-Cabello, Alejandro Gomez-Bruton, Angel Matute-Llorente, Sergio Castillo-Bernad, Gabriel Lozano-Berges, Alejandro Gonzalez-Agüero, Jose A. Casajus and German Vicente-Rodriguez ()
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Jorge Marin-Puyalto: GENUD “Growth, Exercise, Nutrition and Development” Research Group, Universidad de Zaragoza, 50009 Zaragoza, Spain
Alba Gomez-Cabello: GENUD “Growth, Exercise, Nutrition and Development” Research Group, Universidad de Zaragoza, 50009 Zaragoza, Spain
Alejandro Gomez-Bruton: GENUD “Growth, Exercise, Nutrition and Development” Research Group, Universidad de Zaragoza, 50009 Zaragoza, Spain
Angel Matute-Llorente: GENUD “Growth, Exercise, Nutrition and Development” Research Group, Universidad de Zaragoza, 50009 Zaragoza, Spain
Sergio Castillo-Bernad: Department of Physiatry and Nursing, Faculty of Health and Sport Sciences (FCSD), Universidad de Zaragoza, 22001 Huesca, Spain
Gabriel Lozano-Berges: GENUD “Growth, Exercise, Nutrition and Development” Research Group, Universidad de Zaragoza, 50009 Zaragoza, Spain
Alejandro Gonzalez-Agüero: GENUD “Growth, Exercise, Nutrition and Development” Research Group, Universidad de Zaragoza, 50009 Zaragoza, Spain
Jose A. Casajus: GENUD “Growth, Exercise, Nutrition and Development” Research Group, Universidad de Zaragoza, 50009 Zaragoza, Spain
German Vicente-Rodriguez: GENUD “Growth, Exercise, Nutrition and Development” Research Group, Universidad de Zaragoza, 50009 Zaragoza, Spain

IJERPH, 2023, vol. 20, issue 4, 1-10

Abstract: This paper aims to elaborate a decision tree for the early detection of adolescent swimmers at risk of presenting low bone mineral density (BMD), based on easily measurable fitness and performance variables. The BMD of 78 adolescent swimmers was determined using dual-energy X-ray absorptiometry (DXA) scans at the hip and subtotal body. The participants also underwent physical fitness (muscular strength, speed, and cardiovascular endurance) and swimming performance assessments. A gradient-boosting machine regression tree was built to predict the BMD of the swimmers and to further develop a simpler individual decision tree. The predicted BMD was strongly correlated with the actual BMD values obtained from the DXA (r = 0.960, p < 0.001; root mean squared error = 0.034 g/cm 2 ). According to a simple decision tree (74% classification accuracy), swimmers with a body mass index (BMI) lower than 17 kg/m 2 or a handgrip strength inferior to 43 kg with the sum of both arms could be at a higher risk of having a low BMD. Easily measurable fitness variables (BMI and handgrip strength) could be used for the early detection of adolescent swimmers who are at risk of suffering from low BMD.

Keywords: osteoporosis prevention; decision tree; physical fitness; screening (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2023
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