Assessing adult physical activity and compliance with 2008 CDC guidelines using a Bayesian two-part measurement error model

TL;DR

This study develops a Bayesian two-part measurement error model to accurately assess adult compliance with CDC physical activity guidelines, accounting for measurement errors and activity bout requirements, using survey data.

Contribution

It introduces a novel Bayesian measurement error model specifically designed to evaluate physical activity compliance relative to CDC guidelines, including bout duration considerations.

Findings

Model accurately accounts for 10-minute activity bouts.

Corrects bias and accounts for day-to-day variation.

Applied successfully to Iowa and national survey data.

Abstract

While there is wide agreement that physical activity is an important component of a healthy lifestyle, it is unclear how many people adhere to public health recommendations on physical activity. The Physical Activity Guidelines (PAG), published by the CDC, provide guidelines to American adults, but it is difficult to assess compliance with these guidelines. The PAG further complicate adherence assessment by recommending activity to occur in at least 10 minute bouts. To better understand the measurement capabilities of various instruments to quantify activity, and to propose an approach to evaluate activity relative to the PAG, researchers at Iowa State University administered the Physical Activity Measurement Survey (PAMS) to over 1,000 participants in four different Iowa counties. In this paper, we develop a two-part Bayesian measurement error model and apply it to the PAMS data in order to assess compliance to the PAG in the Iowa adult population. The model accurately accounts for the 10 minute bout requirement put forth in the PAG. The measurement error model corrects biased estimates and accounts for day to day variation in activity. The model is also applied to the nationally representative National Health and Nutrition Examination Survey.