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

• Published in: International journal of kinesiology and sports science 2024-04, Vol.12 (2), p.11-20
• Main Authors: Almeida da Encarnação, Samuel Gonçalves, Hugo Santos Rezende, Vitor, Martins Gonçalves, Iarni, Oliveira Ramalho Prata, Patrícia de, Novais Mansur, Henrique, Sampaio, Tatiana, Forte, Pedro, Teixeira, José Eduardo, Miguel Monteiro, António, Paula Muniz Guttierres, Ana
• Format: Article
• Language: English
• Subjects: Adolescents; Algorithms; Bayesian analysis; Body composition; Body fat; Chi-square test; Exercise; Flexibility; High school basketball; Learning algorithms; Machine learning; Mathematical models; Observational learning; Observational studies; Physical activity; Players; Regression analysis; Regression models; Risk analysis; Root-mean-square errors; Teenagers
• ISSN: 2202-946X; 2202-946X
• DOI: 10.7575/aiac.ijkss.v.12n.2p.11

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 bo