Brief description of approaches to analyse associations between physical behaviours and health outcomes
| Statistical model | Brief description | Examples |
| Linear regression modelling | Traditional 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 model | Isotemporal 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 analysis | A 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 analysis | Functional 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.