Running performance variability among runners from different brazilian states: A multilevel approach

Mabliny Thuany, Thayse Natacha Gomes, Lee Hill, Thomas Rosemann, Beat Knechtle, Marcos B. Almeida

Research output: Contribution to journalArticlepeer-review

Abstract

The ecological model theory highlights that human development (or a given behavior) is the result of the interaction of variables derived from different levels, comprising those directly related to the subjects and those related to the environment. Given that, the purpose of this study is to establish whether runners’ performance may vary among different Brazilian states, as the factors associated with this difference. The sample comprised 1151 Brazilian runners (61.8% men) that completed an online questionnaire, providing information about biological (sex, age, height, and weight), training (running pace, frequency and volume/week, and motivation), sociodemographic (place of residence and wage) aspects, and perceptions about the environmental influences on the practice. Information about state variables was obtained from official institutes, and comprised the human development index (HDI), athletics events, and violence index. Multilevel analysis was conducted in HLM software. State-level characteristics explained ≈3% of the total variance in running performance. Of the total variance explained for the individual level, 56.4% was associated with male sex (β = −54.98; p < 0.001), age (β = 1.09; p < 0.001), body mass index (β = 6.86; p < 0.001), economic status (β = 6.23; p = 0.003), the perception of the natural environment (β = 7.58; p = 0.02), training frequency (β = −16.64; p < 0.001), and weekly volume (β = −0.30; p < 0.001). At the state level, only athletics events presented a positive and significant influence on performance. There is a significant role of the environment on the explanation of running performance variability, and given the diversity across states, environmental variables should not be neglected, as they are relevant to the exploration of other variables possibly related to running performance.

Original languageEnglish
Article number3781
JournalInternational Journal of Environmental Research and Public Health
Volume18
Issue number7
DOIs
Publication statusPublished - 1 Apr 2021
Externally publishedYes

Keywords

  • Multilevel modeling
  • Performance
  • Predictors

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