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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

2024Journal article Open access
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dc.contributor.authorEncarnação, Samuel
dc.contributor.authorRezende, Vitor Hugo Santos
dc.contributor.authorGonçalves, Iarni Martins
dc.contributor.authorPrata, Patrícia de Oliveira Ramalho
dc.contributor.authorMansur, Henrique Novais
dc.contributor.authorSampaio, Tatiana
dc.contributor.authorForte, Pedro
dc.contributor.authorTeixeira, José Eduardo
dc.contributor.authorMonteiro, A.M.
dc.contributor.authorGuttierres, Ana Paula Muniz
dc.date.accessioned2024-10-31T14:37:52Z
dc.date.available2024-10-31T14:37:52Z
dc.date.issued2024
dc.description.abstractIncreasing 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.pt_PT
dc.description.sponsorshipThis research complied with all the norms established by the National Health Council (Res. 466/12) involving research with human beings. This study was approved by the scientific board of the Higher Institute of Educational Sciences of the Douro under the number 31142.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationEncarnação, Samuel Gonçalves Almeida da; Rezende, Vitor Hugo Santos; Gonçalves, Iarni Martins; Prata, Patrícia de Oliveira Ramalho; Mansur, Henrique Novais; Sampaio, Tatiana; Forte, Pedro; Teixeira, José Eduardo; Monteiro, António Miguel; Guttierres, Ana Paula Muniz (2024). 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. International Journal of Kinesiology and Sports Science. ISSN 2202-946X. 12:2, p. 11-20pt_PT
dc.identifier.doi10.7575/aiac.ijkss.v.12n.2p.11pt_PT
dc.identifier.issn2202-946X
dc.identifier.urihttp://hdl.handle.net/10198/30494
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherAustralian International Academic Centre PTY LTDpt_PT
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/pt_PT
dc.subjectTeenagerspt_PT
dc.subjectAthleticpt_PT
dc.subjectSports Performancept_PT
dc.subjectPhysical Activity Levelpt_PT
dc.subjectResearch Subject Categories::INTERDISCIPLINARY RESEARCH AREAS::Sportspt_PT
dc.titleComparison 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 Analysispt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.citation.endPage20pt_PT
oaire.citation.issue2pt_PT
oaire.citation.startPage11pt_PT
oaire.citation.titleInternational Journal of Kinesiology and Sports Sciencept_PT
oaire.citation.volume12pt_PT
person.familyNameEncarnação
person.familyNameSampaio
person.familyNameForte
person.familyNameTeixeira
person.familyNameMonteiro
person.givenNameSamuel
person.givenNameTatiana
person.givenNamePedro
person.givenNameJosé Eduardo
person.givenNameAntónio M.
person.identifier.ciencia-id9416-E2F5-E660
person.identifier.ciencia-id351B-B16B-79C7
person.identifier.ciencia-idD11C-9591-7A8A
person.identifier.ciencia-idC41C-6CCD-A1F0
person.identifier.orcid0000-0003-2965-2777
person.identifier.orcid0000-0001-8548-2907
person.identifier.orcid0000-0003-0184-6780
person.identifier.orcid0000-0003-4612-3623
person.identifier.orcid0000-0003-4467-1722
rcaap.rightsopenAccesspt_PT
rcaap.typearticlept_PT
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relation.isAuthorOfPublication.latestForDiscovery04e290ae-a93f-4d0b-a577-badc8e1067af

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