DOI: 10.1101/19001792Jul 13, 2019Paper

Design of a computer model for the identification of adolescent swimmers with low BMD

MedRxiv : the Preprint Server for Health Sciences
J. Marin-PuyaltoGerman Vicente-Rodríguez

Abstract

Objectives: 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. Methods: Bone mineral status of 78 adolescent swimmers was determined using DXA scans at the hip and subtotal body. Participants also underwent physical fitness (upper and lower body strength, running speed and cardiovascular endurance) and performance (swimming history, speed and ranking) assessments. A gradient boosting machine regression tree was built in order to predict BMD of the swimmers and to further develop a simpler individual decision tree, using a subtotal BMD height-adjusted Z-score of -1 as threshold value. Results: The predicted BMD using the gradient boosted model was strongly correlated with the actual BMD values obtained from DXA (r=0.960, p<0.0001) with a root mean squared error of 0.034 g/cm2. According to a simple decision tree, that showed a 73.9% of classification accuracy, swimmers with a body mass index (BMI) lower than 17 kg/m2 or a handgrip strength inferior to 43kg with the sum of both arms could be at higher risk of having low BMD. Conclusion: Easily measurable fitness variables (BM...Continue Reading

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