Analysing time-use composition as dependent variables in physical activity and sedentary behaviour research: different compositional data analysis approaches

Philip von Rosen
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Abstract

Abstract Recently, there has been a paradigm shift from considering physical activity and sedentary behaviour as “independent” risk factors of health to acknowledging their co-dependency and compositional nature. The focus is now on how these behaviours relate to each other rather than viewing them in isolation. Compositional data analysis (CoDA) is a methodology that has been developed specifically for compositional data and the number of publications using CoDA in physical activity and sedentary behaviour research has increased rapidly in the past years. Yet, only a small proportion of the published studies in physical activity and sedentary behaviour research have investigated the time-use composition as dependent variables. This could be related to challenges regarding the interpretation of the results and the lack of guidelines for deciding which statistical approach to use. Therefore, in this paper, four different approaches for analysing the time-use composition as dependent variables are presented and discussed. This paper advocates that the aim of research should guide how the dependent variable is defined and which data analysis approach is selected, and it encourages researchers to consider analysing time-use components as dependent variables in physical activity and sedentary behaviour research.
在体力活动和久坐行为研究中将时间使用构成作为因变量分析:不同构成数据分析方法
最近,有一种范式转变,从认为身体活动和久坐行为是健康的“独立”风险因素,到承认它们的相互依赖性和组成性。现在的重点是这些行为如何相互关联,而不是孤立地看待它们。成分数据分析(CoDA)是一种专门针对成分数据开发的方法,在过去几年中,在身体活动和久坐行为研究中使用CoDA的出版物数量迅速增加。然而,在已发表的关于身体活动和久坐行为的研究中,只有一小部分将时间使用构成作为因变量进行了调查。这可能与解释结果方面的挑战和缺乏决定使用哪种统计方法的指导方针有关。因此,在本文中,四种不同的方法来分析时间使用组成作为因变量提出和讨论。本文主张研究目标应指导因变量的定义和数据分析方法的选择,并鼓励研究人员在体力活动和久坐行为研究中考虑分析时间使用成分作为因变量。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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