Table 2

Brief description of approaches to analyse associations between physical behaviours and health outcomes

Statistical modelBrief descriptionExamples
Linear regression modellingTraditional models establishing the relationship between a set of explanatory variables and an outcome (ie, health outcome). Exposure is usually limited to a single time-use behaviour. Interpretation is in terms of increasing time in one behaviour. 59 60
Isotemporal substitution modelIsotemporal substitution models examine the theoretical effects of displacing a fixed duration of time between behaviours. Given the fixed and finite duration of a day, increasing time in one movement behaviour (eg, LPA) will result in a net equal and opposite change in other movement behaviours (eg, SB). Interpretation is in terms of substituting one behaviour for other behaviours. 61 62
Multivariate pattern analysisA regression approach/analysis that can handle an unlimited number of multicollinear explanatory variables by using latent variable modelling. Models are cross-validated to optimise predictive ability. Interpretation is based on the complete pattern of associations among the explanatory variables in relation to the outcome. 25 63–66
Functional data analysisFunctional data analysis is an extension of scalar regression where the exposure or outcome is defined as a function rather than a scalar variable. The function can describe the full distribution of intensity of acceleration or the time-series of acceleration over the day. The function can be included in linear regression analysis through dimensional reduction techniques. Interpretation is in terms of certain accelerometer trace shapes. 37 38 67–69
Machine learning (ML)ML entails a broad range of techniques to automate the learning of high-dimensional and/or non-linear patterns in data with predictive ability (supervised ML) or data reduction (unsupervised ML) as its core priority. 41 70 71
  • LPA, light physical activity; SB, sedentary behaviour.