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Motriz (Online) ; 26(4): e10200140, 2020. tab, graf
Artigo em Inglês | LILACS | ID: biblio-1143312

RESUMO

Abstract Aims: to identify and describe the clustering of characteristics related to running amongst Brazilian runners using latent class analysis and to verify if there is a profile associated with better performance. Material and Methods: a sample of 1149 Brazilian runners answered an online questionnaire, that provided information about biological (sex, age, height, weight), training (running pace, frequency and volume/week, motivation), and socioeconomic factors, as well as a multidimensional questionnaire of fear of failure. Latent Class Analysis was used to identify subgroups of Brazilian runners, based on BMI, training volume and frequency/week, motivation, socioeconomic factors, and the fear of failure. Further, a χ2 test was computed to verify statistical differences in the frequency of the descriptive variables between classes. Finally, binary logistic regression analysis estimated factors associated with running performance, with running pace as the dependent variable. Results: It was possible to identify two different classes among Brazilian runners, which were called "amateur runners" and "recreational runners". Variables that highlighted classes' differences were: volume and frequency training/week, motivation for the practice, and BMI. Regarding the running performance, logistic regression analysis showed that men (OR=5.39; 95%CI=4.00-7.25), young runners (OR=0.38; 95%CI=0.28-0.51), and "amateur runners" (OR=4.19; 95%CI=2.95-5.94) were more prone to have higher performance. Conclusion: Two distinct classes were found among Brazilian runners that were linked to their performance, highlighting that even among non-professional runners, differences can be observed regarding their classification. Hence, future studies should consider using these classes to properly stratify or identify non-professional runners.


Assuntos
Humanos , Corrida/tendências , Desempenho Atlético/tendências , Brasil , Estudos Transversais , Análise de Classes Latentes
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