Author: Thuany, Mabliny; de Souza, Raphael F.; Hill, Lee; Mesquita, João Lino; Rosemann, Thomas; Knechtle, Beat; Pereira, Sara; Gomes, Thayse Natacha
Title: Discriminant Analysis of Anthropometric and Training Variables among Runners of Different Competitive Levels Cord-id: og6y7ul3 Document date: 2021_4_16
ID: og6y7ul3
Snippet: The purpose of this study was to investigate the multivariate profile of different types of Brazilian runners and to identify the discriminant pattern of the distinct types of runners, as a runners’ ability to self-classify well. The sample comprised 1235 Brazilian runners of both sexes (492 women; 743 men), with a mean age of 37.94 ± 9.46 years. Individual characteristics were obtained through an online questionnaire: Sex, age, body height (m) and body mass (kg), socioeconomic status, and tr
Document: The purpose of this study was to investigate the multivariate profile of different types of Brazilian runners and to identify the discriminant pattern of the distinct types of runners, as a runners’ ability to self-classify well. The sample comprised 1235 Brazilian runners of both sexes (492 women; 743 men), with a mean age of 37.94 ± 9.46 years. Individual characteristics were obtained through an online questionnaire: Sex, age, body height (m) and body mass (kg), socioeconomic status, and training information (i.e., self-classification, practice time, practice motivation, running pace, frequency and training volume/week). Multivariate analysis of variance was conducted by sex and the discriminant analysis was used to identify which among running pace, practice time, body mass index and volume/training could differentiate groups such as “professional athletesâ€, “amateur athletes†and “recreational athletesâ€. For both sexes, running pace was the variable that better discriminated the groups, followed by BMI and volume/week. The practice time is not a good indicator to differentiate runner’s types. In both sexes, semi-professional runners were those that better self-classify themselves, with amateur runners presenting the highest classification error. This information can be used to guide the long-term training, athlete’s selection programs, and to identify the strengths and weaknesses of athletes.
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