Predicting Objective Physical Activity from Self-Report Surveys: A Model Validation Study Using Estimated Generalized Least-Squares Regression

Predicting Objective Physical Activity from Self-Report Surveys: A Model Validation Study Using Estimated Generalized Least-Squares Regression

Published: Mar 30, 2015
Publisher: Journal of Applied Statistics, vol 42, no. 3 (subscription required)
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Authors

Nicholas Beyler

Wayne Fuller

Sarah Nusser

Gregory Welk

Physical activity measurements derived from self-report surveys are prone to measurement errors. Monitoring devices like accelerometers offer more objective measurements of physical activity, but are impractical for use in large-scale surveys. A model capable of predicting objective measurements of physical activity from self-reports would offer a practical alternative to obtaining measurements directly from monitoring devices. Using data from National Health and Nutrition Examination Survey 2003–2006, we developed and validated models for predicting objective physical activity from self-report variables and other demographic characteristics. The prediction intervals produced by the models were large, suggesting that the ability to predict objective physical activity for individuals from self-reports is limited.

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