23: Assessing Covariate Effects in Longitudinal Fréchet Regression for Physical Activity Distributions

Thomas M. Menino Convention & Exhibition Center 

Physical activity (PA) provides protective health benefits and helps inform the intervention design. Commonly used time-course PA summary metrics, such as the averaged step count, may sacrifice some key nuances of micro PA patterns that could serve as digital markers. In this project, we are interested in temporal associations between nutrient intake and daily PA distributions expressed in step counts. We extend Fréchet regression to longitudinal object data using working correlation matrices via the quadratic inference function method in Python, which enables correlation adjustment and bootstrap inference. Daily PA distributions are characterized by quantiles derived from empirical cumulative distribution functions. The method is illustrated using seven-day accelerometer data from 352 adolescents in the Early Life Exposure in Mexico to ENvironmental Toxicants (ELEMENT) Study and usual micronutrient intake from a validated food frequency questionnaire. Analyses were adjusted for the demographic and lifestyle variables. This presentation will report the effects of mean daily magnesium, vitamin C, and vitamin D intake on PA pattern distributions under different working correlations.

Frechet regression

Quadratic inference function

Generalized estimating equations

Longitudinal data analysis

Metric-spaced objects 

Section on Nonparametric Statistics