A semiparametric risk score for physical activity

We develop a generalized partially additive model to build a single semiparametric risk scoring system for physical activity across multiple populations. A score comprised of distinct and objective physical activity measures is a new concept that offers challenges due to the nonlinear relationship between physical behaviors and various health outcomes. We overcome these challenges by modeling each score component as a smooth term, an extension of generalized partially linear single-index models. We use penalized splines and propose two inferential methods, one using profile likelihood and a nonparametric bootstrap, the other using a full Bayesian model, to solve additional computational problems. Both methods exhibit similar and accurate performance in simulations. These models are applied to the National Health and Nutrition Examination Survey and quantify nonlinear and interpretable shapes of score components for all-cause mortality.

Automated summary

The study develops a generalized partially additive model for assessing physical activity across multiple populations, tackling challenges posed by the nonlinear relationship between physical behaviors and health outcomes. By modeling each score component as a smooth term and using penalized splines, two inferential methods are proposed to address computational problems, with both exhibiting accurate performance in simulations. Applied to a national survey, the models quantify nonlinear and interpretable shapes of score components for all-cause mortality.