  The association between a person's physical activity and various health
outcomes is an area of active research. The National Health and Nutrition
Examination Survey (NHANES) data provide a valuable resource for studying these
associations. NHANES accelerometry data has been used by many to measure
individuals' activity levels. A common approach for analyzing accelerometry
data is functional principal component analysis (FPCA). The first part of the
paper uses Poisson FPCA (PFPCA), Gaussian FPCA (GFPCA), and nonnegative and
regularized function decomposition (NARFD) to extract features from the
count-valued NHANES accelerometry data. The second part of the paper compares
logistic regression, random forests, and AdaBoost models based on GFPCA, NARFD,
or PFPCA scores in the context of mortality prediction. The results show that
Poisson FPCA is the best FPCA model for the inference of accelerometry data,
and the AdaBoost model based on Poisson FPCA scores gives the best mortality
prediction results.
