Comparison of Physical Activity Level, Body Composition, Strength, and Flexibility of Teen Basketball Players and Adolescents Non-Practitioners of Sport: An Observational Study with Machine Learning Analysis

Samuel Gonçalves Almeida da Encarnação, Vitor Hugo Santos Rezende, Iarni Martins Gonçalves, Patrícia de Oliveira Ramalho Prata, Henrique Novais Mansur, Tatiana Sampaio, Pedro Forte, José Eduardo Teixeira, António Miguel Monteiro, Ana Paula Muniz Guttierres

Abstract


Background: Increasing youths’ physical activity is mandatory to reduce the risk of non-communicable diseases (NCCDs). Basketball is a team sport that is potentially positive in increasing teenagers’ physical performance, health indicators, and well-being. Objective: The objective was to compare the physical activity level (PAL), body composition, strength, and flexibility of teen male basketball players (BG) (n = 15) and adolescent non-practitioners of sport (NS: n = 14). Methodology: All participants were healthy and free from any health disability from a Brazilian high school. A linear regression machine learning algorithm was applied to predict the adolescent´s physical components. In a quasi-experimental analysis, data were extracted by PAL, body fat percentage (BF%), handgrip strength (HG), back extensor muscle’s’ strength (BMS), lower limb power (LLP), and lower limb flexibility (LLF). Parametric (independent T-test) and non-parametric (Mann-Whitney U test) were employed to compare the variable’s average and chi-square was applied to compare categorical data. Results: BG presented an upper number of adolescents classified with high PAL than the NS group (p = 0.0002, large ES, V = 0.73) and a lower number of adolescents classified with low PAL than the NS group (p = 0.0002, V = 0.73), less BF% (p = 0.02, r = 0.85), greater values of HGS (p = 0.005, r = 0.34), greater values of BMSLS (p = 0.005, r = 0.33), greater values of LLP (p = 0.007, r = 0.30), and greater values for LLF (p = 0.02, r = 0.17). Therefore, there was a positive effect of high PAL compared with low PAL in HG, (p = 0.005, r = 0.24) and also for high PAL in LLF, (High PAL: (p = 0.006, r = 0.23). Regarding machine learning analysis, the four models (linear regression, Ridge regression, random forest regression, and Bayesian regression) expressed good generalization performance, with a coefficient of determination (R2) ranging from 0.77 to 0.88, root mean square error (RMSE) from 1.01 to 3.92, with an average mean difference of four points between the predicted and real values. The worst model was random forest regression R2 = 0.77, RMSE = 3.92, and the best model was Bayesian regression (R2 = 0.88, RMSE = 1.01). Conclusion: The BG group presented better results than the NS group for PAL, BF%, HG, BMS, LLP, and LLF. Body fat percentage precisely predicted the player’s’ vertical jump (VJ). In addition to the physical superiority of the BG, this study revealed the importance of managing body composition for both health and performance improvements.

Keywords


Teenagers, Athletic, Sports Performance, Physical Activity Level

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References


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DOI: https://doi.org/10.7575/aiac.ijkss.v.12n.2p.11

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