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Predicting objective physical activity from self-report surveys: a model validation study using estimated generalized least-squares regression

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  • Nicholas Beyler
  • Wayne Fuller
  • Sarah Nusser
  • Gregory Welk

Abstract

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.

Suggested Citation

  • Nicholas Beyler & Wayne Fuller & Sarah Nusser & Gregory Welk, 2015. "Predicting objective physical activity from self-report surveys: a model validation study using estimated generalized least-squares regression," Journal of Applied Statistics, Taylor & Francis Journals, vol. 42(3), pages 555-565, March.
    • Handle: RePEc:taf:japsta:v:42:y:2015:i:3:p:555-565
    DOI: 10.1080/02664763.2014.978271
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    References listed on IDEAS

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    1. Ham, S.A. & Ainsworth, B.E., 2010. "Disparities in data on healthy people 2010 physical activity objectives collected by accelerometry and self-report," American Journal of Public Health, American Public Health Association, vol. 100(S1), pages 263-268.
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