Discriminant Analysis of Anthropometric and Training Variables among Runners of Different Competitive Levels

• Published in: International journal of environmental research and public health 2021-04, Vol.18 (8), p.4248
• Main Authors: Thuany, Mabliny, Souza, Raphael F de, Hill, Lee, Mesquita, João Lino, Rosemann, Thomas, Knechtle, Beat, Pereira, Sara, Gomes, Thayse Natacha
• Format: Article
• Language: English
• Subjects: Adult; Anthropometry; Athletes; Body height; Body mass; Body mass index; Body measurements; Body size; Brazil; Classification; Discriminant Analysis; Female; Humans; Male; Marathons; Middle Aged; Motivation; Multivariate analysis; Questionnaires; Recreation; Running; Sexes; Social networks; Socioeconomic factors; Socioeconomics; Variables; Variance analysis; Women
• ISSN: 1660-4601; 1661-7827; 1660-4601
• DOI: 10.3390/ijerph18084248

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 athletes", "amateur athletes" and "recreational athletes". 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.