Predicting Objective Physical Activity from Self-Report Surveys: Limitations Based on a Model Validation Study Using Estimated Generalized Least Squares Regression
AbstractThis working paper used measurements of accelerometer-based and self-reported physical activity from the National Health and Nutrition Examination Survey 2003â€“2006 to develop and validate a set of models for predicting objective moderate to vigorous physical activity from self-report variables and other demographic characteristics. The prediction intervals produced by the models were extremely large, suggesting that the ability to predict objective physical activity from self-reports is limited.
Download InfoIf you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
Bibliographic InfoPaper provided by Mathematica Policy Research in its series Mathematica Policy Research Reports with number 7811.
Date of creation: 30 Jun 2013
Date of revision:
Contact details of provider:
Postal: Mathematica Policy Research P.O. Box 2393 Princeton, NJ 08543-2393 Attn: Communications
Fax: (609) 799-0005
Web page: http://www.mathematica-mpr.com/
More information through EDIRC
Physical Activity; NHANES; accelerometry; validation study; estimated generalized least squares;
Find related papers by JEL classification:
- C - Mathematical and Quantitative Methods
This paper has been announced in the following NEP Reports:
- NEP-ALL-2013-07-15 (All new papers)
- NEP-FOR-2013-07-15 (Forecasting)
- NEP-HEA-2013-07-15 (Health Economics)
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Jared M. Tucker & Gregory J. Welk & Nicholas K. Beyler, 2011. "Physical Activity in U.S. Adults: Compliance with the Physical Activity Guidelines for Americans," Mathematica Policy Research Reports 6946, Mathematica Policy Research.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Joanne Pfleiderer) or (Joanne Lustig).
If references are entirely missing, you can add them using this form.