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The association between a person's physical activity and various healthoutcomes is an area of active research. The National Health and NutritionExamination Survey (NHANES) data provide a valuable resource for studying theseassociations. NHANES accelerometry data has been used by many to measureindividuals' activity levels. A common approach for analyzing accelerometrydata is functional principal component analysis (FPCA). The first part of thepaper uses Poisson FPCA (PFPCA), Gaussian FPCA (GFPCA), and nonnegative andregularized function decomposition (NARFD) to extract features from thecount-valued NHANES accelerometry data. The second part of the paper compareslogistic regression, random forests, and AdaBoost models based on GFPCA, NARFD,or PFPCA scores in the context of mortality prediction. The results show thatPoisson FPCA is the best FPCA model for the inference of accelerometry data,and the AdaBoost model based on Poisson FPCA scores gives the best mortalityprediction results.

A Comparison of Functional Principal Component Analysis Methods with Accelerometry Applications