Robust Compositional Analysis of Physical Activity and Sedentary Behaviour Data

Abstract

Although there is an increasing awareness of the suitability of using compositional data methodology in public health research, classical methods of statistical analysis have been primarily used so far. The present study aims to illustrate the potential of robust statistics to model movement behaviour using Czech adolescent data. We investigated: (1) the inter-relationship between various physical activity (PA) intensities, extended to model relationships by age; and (2) the associations between adolescents’ PA and sedentary behavior (SB) structure and obesity. These research questions were addressed using three different types of compositional regression analysis—compositional covariates, compositional response, and regression between compositional parts. Robust counterparts of classical regression methods were used to lessen the influence of possible outliers. We outlined the differences in both classical and robust methods of compositional data analysis. There was a pattern in Czech adolescents’ movement/non-movement behavior—extensive SB was related to higher amounts of light-intensity PA, and vigorous PA ratios formed the main source of potential aberrant observations; aging is associated with more SB and vigorous PA at the expense of light-intensity PA and moderate-intensity PA. The robust counterparts indicated that they might provide more stable estimates in the presence of outlying observations. The findings suggested that replacing time spent in SB with vigorous PA may be a powerful tool against adolescents’ obesity.

Sedentary behavior patterns and adiposity in children: a study based on compositional data analysis

“…The means and standard deviations were calculated for the outcome measures. For compositional variables, the robust compositional means were calculated [19].…”

“…Specifically, compositional covariates were expressed through pivot coordinates which enable for interpretation in terms of dominance of a given compositional part with reference to the rest of components in the first coordinate (e.g., ilr 1 ) [30]. For this purpose, the compositional parts were permutated, as explained in detail in previous papers [19,22]. Linear models with robust estimators were used to eliminate the influence of possible outliers [19] that may occur in movement behavior data.…”

“…For this purpose, the compositional parts were permutated, as explained in detail in previous papers [19,22]. Linear models with robust estimators were used to eliminate the influence of possible outliers [19] that may occur in movement behavior data.…”

“…Differences in adiposity were considered significant when 95% CIs did not cover zero. A compressive description of robust CoDA is available in a previous paper [19] and in Supplementary files.…”

“…For this reason, analyses of the association between sedentary patterns and obesity should take into account individual PA levels. The amounts of time spent in SB and PA represent a sub-composition of the 24-h cycle [18,19]. Given that the time in a day is constrained to 24 h, a change in the duration of one time-use component (e.g., time spent in PA) inevitably results in a change in the duration of one or more of the remaining time-use components (e.g., time spent in SB) [20].…”