Centre of Research, Education, Innovation and Intervention in Sport (CIFI2D), Faculty of Sport, University of Porto, Porto, Portugal.
Department of Physical Education, Post-Graduation Program of Physical Education, Federal University of Sergipe, São Cristóvão, SE, Brazil.
PLoS One. 2023 Mar 30;18(3):e0283157. doi: 10.1371/journal.pone.0283157. eCollection 2023.
Sports performance is the result of a complex interaction between individual and environmental factors. The purpose of this paper is to explain the methods used in the InTrack Project, a cross-sectional and cross-cultural project developed to investigate the variance in the performance of runners from different countries and to understand whether the differences in the performance can be explained by micro-level (athletes characteristics and proximal environment), meso-level (the distal environment that plays a relevant role on the relationships established at micro-level), and the macro-level (environmental features that shape countries characteristics). The sample will be comprised of runners, of both sexes, from four countries. Data collection will be performed in two steps: i) Individual information and ii) Country-level information. At the individual level, data will be obtained from an online survey. At the country level, characteristics data will be obtained from the secondary data available (demographic, social, and economic variables). Statistical procedures expected to be used include multilevel analysis, latent class analysis, addictive and multiplicative interaction in regression models. This wealth of information is of relevance to fill gaps regarding the existence of variables to connect different levels of information, and to provide scientific support about environmental characteristics important to predict runners' performance within and between countries.
运动表现是个体和环境因素复杂相互作用的结果。本文旨在解释 InTrack 项目中使用的方法，该项目是一个横断面和跨文化项目，旨在研究来自不同国家的跑步运动员表现的差异，并了解这些表现的差异是否可以通过微观层面（运动员特征和近端环境）、中观层面（在微观层面建立的关系中起相关作用的远端环境）和宏观层面（塑造国家特征的环境特征）来解释。该样本将由来自四个国家的男女跑步运动员组成。数据收集将分两步进行：i）个体信息和 ii）国家层面信息。在个体层面，将通过在线调查获取数据。在国家层面，将从可用的二手数据（人口、社会和经济变量）中获取特征数据。预计将使用的统计程序包括多层次分析、潜在类别分析、回归模型中的加性和乘法交互作用。这些丰富的信息对于填补不同层次信息之间存在变量的差距以及提供有关连接环境特征的科学支持以预测运动员在国家内部和国家之间的表现非常重要。
