1 School of Health Sciences, University of South Australia, Adelaide, Australia.
2 Institute of Sport, Exercise, and Active Living (ISEAL), Victoria University, Melbourne, Australia.
Stat Methods Med Res. 2019 Mar;28(3):846-857. doi: 10.1177/0962280217737805. Epub 2017 Nov 20.
How people use their time has been linked with their health. For example, spending more time being physically active is known to be beneficial for health, whereas long durations of sitting have been associated with unfavourable health outcomes. Accordingly, public health messages have advocated swapping strategies to promote the reallocation of time between parts of the time-use composition, such as "Move More, Sit Less", with the aim of achieving optimal distribution of time for health. However, the majority of research underpinning these public health messages has not considered daily time use as a composition, and has ignored the relative nature of time-use data. We present a way of applying compositional data analysis to estimate change in a health outcome when fixed durations of time are reallocated from one part of a particular time-use composition to another, while the remaining parts are kept constant, based on a multiple linear regression model on isometric log ratio coordinates. In an example, we examine the expected differences in Body Mass Index z-scores for reallocations of time between sleep, physical activity and sedentary behaviour.
人们如何利用时间与他们的健康有关。例如,更多地进行身体活动被认为对健康有益,而长时间坐着与不利的健康结果有关。因此,公共卫生信息已经提倡采取策略来促进在时间利用组成部分之间重新分配时间,例如“多运动,少坐”,以实现时间的最佳分配以促进健康。然而,这些公共卫生信息所依据的大多数研究并没有将日常时间利用作为一个组成部分来考虑,并且忽略了时间利用数据的相对性质。我们提出了一种应用组合数据分析的方法,即在特定时间利用组成部分的固定时间段从一部分重新分配到另一部分时,估计健康结果的变化,同时保持其余部分不变,这是基于等距对数比坐标上的多元线性回归模型。在一个示例中,我们检查了在睡眠、体育活动和久坐行为之间重新分配时间对体重指数 z 分数的预期差异。