Prediction of body mass index using concurrently self-reported or previously measured height and weight

Zhaohui Cui, June Stevens, Kimberly P. Truesdale, Donglin Zeng, Simone French, Penny Gordon-Larsen

Research output: Contribution to journalArticlepeer-review

6 Scopus citations


Objective: To compare alternative models for the imputation of BMIM (measured weight in kilograms/ measured height in meters squared) in a longitudinal study. Methods: We used data from 11,008 adults examined at wave III (2001-2002) and wave IV (2007-2008) in the National Longitudinal Study of Adolescent to Adult Health. Participants were asked their height and weight before being measured. Equations to predict wave IV BMIM were developed in an 80% random subsample and evaluated in the remaining participants. The validity of models that included BMI constructed from previously measured height and weight (BMIPM) was compared to the validity of models that used BMI calculated from concurrently self-reported height and weight (BMISR). The usefulness of including demographics and perceived weight category in those models was also examined. Results: The model that used BMISR, compared to BMIPM, as the only variable produced a larger R2 (0.913 vs. 0.693), a smaller root mean square error (2.07 vs. 3.90 kg/m2) and a lower bias between normal-weight participants and those with obesity (0.98 vs. 4.24 kg/m2). The performance of the model containing BMISR alone was not substantially improved by the addition of demographics, perceived weight category or BMIPM. Conclusions Our work is the first to show that concurrent self-reports of height and weight may be more useful than previously measured height and weight for imputation of missing BMIM when the time interval between measures is relatively long. Other time frames and alternatives to inperson collection of self-reported data need to be examined.

Original languageEnglish (US)
Article numbere0167288
JournalPloS one
Issue number11
StatePublished - Nov 2016

Bibliographical note

Funding Information:
This research uses data from Add Health, a program project directed by Kathleen Mullan Harris and designed by J. Richard Udry, Peter S. Bearman, and Kathleen Mullan Harris at the University of North Carolina at Chapel Hill, and funded by grant P01-HD31921 from the Eunice Kennedy Shriver National Institute of Child Health and Human Development, with cooperative funding from 23 other federal agencies and foundations. Special acknowledgment is due Ronald R. Rindfuss and Barbara Entwisle for assistance in the original design. Information on how to obtain the Add Health data files is available on the Add Health website ( ). No direct support was received from grant P01-HD31921 for this analysis.

Publisher Copyright:
© 2016 Cui et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.


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