The health benefits of physical activity are well known. Various parameters of physical activity, such as time or energy expenditure, are often assessed in observational and experimental studies. This article highlights several methodologic issues concerning the analysis of physical activity. These include non-normality, presence of many zeros, and violation of the independence assumption. Application of the standard regression model to a (log-transformed) physical activity variable may lead to spurious associations and misleading conclusions. We developed an alternative 2-part generalized-estimating-equations (GEE) approach to analyze the heterogeneous and correlated physical activity data. We first estimated a logistic GEE model for the prevalence of physical activity and factors affecting physical activity participation. We then fit a gamma GEE model to assess the effects of predictors among persons engaging in physical activity. An empirical application to an epidemiologic study of physical activity of community-dwelling older adults illustrates the proposed methodology.
From the aDepartment of Epidemiology and Biostatistics, School of Public Health, Curtin University of Technology, Perth, Western Australia; and bDivision of Mathematical Sciences, SPMS, Nanyang Technological University, 21 Nanyang Link, Singapore.
Submitted 19 October 2009; accepted 23 January 2010; posted 29 June 2010.
Correspondence: Andy H. Lee, Department of Epidemiology and Biostatistics, School of Public Health, Curtin Health Innovation Research Institute, Curtin University of Technology, GPO Box U 1987, Perth, WA 6845, Australia. E-mail: Andy.Lee@curtin.edu.au.