Purpose: Physical activity, in particular moderate to vigorous physical activity (MVPA), sedentary behaviour (SB) are known to impact on health. Less well established is how patterning of activity affects health. Capturing the complex time pattern of activity using accelerometry remains a challenge. Occupational health research suggests Exposure Variation Analysis (EVA) could provide a useful tool. The purpose of this paper is to 1) explain the application of EVA to accelerometer data, 2) demonstrate how EVA thresholds, derivatives could be chosen, used to examine adherence to MVPA, SB guidelines, 3) explore the validity of EVA outputs.

Methods: EVA reduces a complex time-line of exposure into a two-dimensional matrix showing combinations of exposure level (in categories) and duration of uninterrupted sequences (in categories). Data from 4 individuals with different daily activity patterns were collected to demonstrate the applicability of EVA. EVA outputs were also compared for accelerometer data collected from 3 occupational groups with known different activity patterns: seated workstation office workers, standing workstation office workers and teachers. Standard accelerometer data collection procedures were used. Data processing by a custom LabVIEW program calculated EVA matrices and derivatives aligned to common guidelines.

Results: Data from one individual is presented in a time-based line graph form in conjunction with the resultant EVA matrix and EVA graph. Line graphs and related EVA graphs for 3 further individuals highlight the use of EVA derivatives for examining compliance with MVPA and SB guidelines. For the seated office workers, standing office workers and teachers, analyses confirm no difference in bouts of MVPA but very clear differences as expected in extended bouts of SB and brief bursts of activity, thus providing evidence of construct validity of the EVA approach.

Conclusion: The major advantage of EVA is its ability in a single analysis to simultaneously capture the time pattern of activity at various levels of intensity according to the choice of the researcher. Whilst presented here with four levels of intensity, sedentary, light, moderate and vigorous, EVA could be utilised with dichotomous data such as sitting vs standing, or adjusted to match future refinements of activity guidelines. EVA offers a unique and comprehensive generic method that is ideal for processing large quantities of accelerometer data. EVA is able, for the first time, to concisely capture the time pattern (both frequency and intensity) of activity, and can be tailored for both occupational and public health research.