title: Prediction of Body Mass Index Using Concurrently Self-Reported or Previously Measured Height and Weight.
abstract: 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 in-person collection of self-reported data need to be examined.
authors.affiliation: Department of Nutrition, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America.
authors.forename: Zhaohui
authors.initials: Z
authors.lastname: Cui
authors.affiliation: Department of Nutrition, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America.|Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America.
authors.forename: June
authors.initials: J
authors.lastname: Stevens
authors.affiliation: Department of Nutrition, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America.
authors.forename: Kimberly P
authors.initials: KP
authors.lastname: Truesdale
authors.affiliation: Department of Biostatistics, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America.
authors.forename: Donglin
authors.initials: D
authors.lastname: Zeng
authors.affiliation: Division of Epidemiology and Community Health, School of Public Health, University of Minnesota, Minneapolis, Minnesota, United States of America.
authors.forename: Simone
authors.initials: S
authors.lastname: French
authors.affiliation: Department of Nutrition, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America.
authors.forename: Penny
authors.initials: P
authors.lastname: Gordon-Larsen
mesh_terms: D000293:Adolescent; D000328:Adult; D001827:Body Height / Q000502:physiology*; D015992:Body Mass Index; D001835:Body Weight / Q000502:physiology*; D005260:Female; D006801:Humans; D008137:Longitudinal Studies; D008297:Male; D008962:Models, Theoretical; D057566:Self Report; D011795:Surveys and Questionnaires; D055815:Young Adult