The compositional isotemporal substitution model: A method for estimating changes in a health outcome for reallocation of time between sleep, physical activity and sedentary behaviour

Abstract

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.

Lean mass accretion in children born very low birth weight is significantly associated with estimated changes from sedentary time to light physical activity

“…Statistical analysis of all movement behaviours together (compositional data analysis) was conducted in R with the package, compositions , following published statistical guidelines . Briefly, a compositional variable was created for each participant in which the average time spent in each of the four movement behaviours (sedentary time, LPA, MVPA, and sleep) was expressed as an isometric log‐ratio coordinate (ILR).…”

“…The creation of ILRs enables all movement behaviours to be included in the model while avoiding issues with multicollinearity. Geometric means were calculated for each movement behaviour and were normalized to represent minutes spent in a 1440‐minute day . Traditional measures of dispersion (ie, SD) cannot capture the codependent nature of compositional data.…”

“…Dispersion values closer to zero indicate more codependence between the two movement behaviours. The estimated change in body composition from reallocating time spent in each movement behaviour to another was tested in linear regression models using the following approach . First, linear regression models were used to predict body composition outcomes for a baseline composition of movement behaviours (ie, average time all children spent in each movement behaviour).…”

“…Because of their increased risk for obesity and cardiometabolic disease, promoting appropriate amounts of daily physical activity, sedentary time, and sleep may be especially beneficial for children born VLBW. A significant challenge in assessing the impact of movement behaviours (physical activity, sedentary time, sleep) on adiposity and other health outcomes is that individual components over 24 hours are codependent and, as such, time spent on one movement behaviour (eg, more sleep) means less time is available to spend on another (eg, physical activity) . This means that it is unknown whether an increase in one movement behaviour (eg, sleep) or the corresponding decrease (eg, sedentary time) in another is associated with the health outcome.…”

“…This means that it is unknown whether an increase in one movement behaviour (eg, sleep) or the corresponding decrease (eg, sedentary time) in another is associated with the health outcome. In recognition of this issue, several countries have or are working on national guidelines that integrate all movement behaviours and research experts in the field are transitioning towards compositional and isotemporal substitution methods that allow for statistical testing of the combined effects of physical activity, sedentary time, and sleep on health outcomes . These analyses allow for investigation of how one movement behaviour independent of the others impacts a health outcome.…”