Purpose: The use of uniaxial summary measures from accelerometry (i.e., counts per minute or minutes spent in moderate-to-vigorous intensity) substantially restricts information about physical activity (PA), and is probably a result of reliance on analytic approaches that cannot handle collinear variables. In the present study, we aimed to determine the multivariate triaxial PA intensity signature related to metabolic health in children, by investigating associations of the whole spectra of PA intensities from all axes using multivariate pattern analysis.
Methods: We included 841 children (age, 10.2 ± 0.3 yr; body mass index, 18.0 ± 3.0; 50% boys) from the Active Smarter Kids study conducted in Norway 2014 to 2015 providing valid data on accelerometry (ActiGraph GT3X+) and several indices of metabolic health (aerobic fitness, abdominal fatness, insulin sensitivity, lipid metabolism, blood pressure) that were used to create a composite metabolic health score. We created intensity spectra from 0 to 100 to ≥10,000 counts per minute for separate axes and used multivariate pattern analysis to analyze the data.
Results: The explained variance of metabolic health was 3.2% for counts per minute from the vertical axis, 17.0% for the vertical axis intensity spectrum, and 29.5% for the full model including all axes. Thus, including full triaxial intensity spectra improved the model for metabolic health tenfold compared with using overall PA (counts per minute) from the vertical axis only. The intensity signature associated with metabolic health differed across the axes.
Conclusions: Our findings show that the three different axes carry distinct information about children's PA and the relation of PA to their health and demonstrate a great potential for triaxial accelerometry and a multivariate analytic approach to advance the field of PA epidemiology.