ABSTRACT
Background Analysis of physical activity usually focuses on a small number of summary statistics derived from accelerometer recordings: average counts per minute, and the proportion of time spent in moderate-vigorous physical activity or in sedentary behaviour. We show how bigrams, a concept from the field of text mining, can be used to describe how a person’s activity levels change across (brief) time points. These variables can, for instance, differentiate between two people with the same time in moderate activity, where one person often stays in moderate activity from one moment to the next and the other does not.
Methods We use data on 4810 participants of the Avon Longitudinal Study of Parents and Children (ALSPAC). We generate a profile of bigram frequencies for each participant and test the association of each frequency with body mass index (BMI), as an exemplar.
Results We found several associations between changes in bigram frequencies and BMI. For instance, a 1 standard deviation decrease in the number of adjacent minutes in sedentary then moderate activity (or vice versa), with a corresponding increase in the number of adjacent minutes in moderate then vigorous activity (or vice versa), was associated with a 2.36 kg/m2 lower BMI [95% CI: -3.47, -1.26], after accounting for the time spent at sedentary, low, moderate and vigorous activity.
Conclusions Activity bigrams are novel variables that capture how a person’s activity changes from one moment to the next. These variables can be used to investigate how sequential activity patterns associate with other traits.
Key Messages
Epidemiologists typically use only a small number of variables to analyse the association of physical activity with other traits, such as the average counts per minute and the proportion of time spent in moderate-vigorous physical activity or being sedentary.
We demonstrate how activity bigrams can be used as a set of interpretable variables describing how a person’s activity levels change from one moment to the next.
Testing the association of activity bigrams with exposures or outcomes can help us gain further understanding of how physical activity is associated with other traits; with further research they might provide evidence for more refined public health advice.
