Correcting Self-Reported Estimates of Obesity: Can We More Closely Approximate Measured Values?1

This study examined the feasibility of developing correction factors to adjust self-reported measures of Body Mass Index to more closely approximate measured values. Data are from the 2005 Canadian Community Health Survey where respondents were asked to report their height and weight and were subsequently measured. Regression analyses were used to determine which socio-demographic and health characteristics were associated with the discrepancies between reported and measured values. The sample was then split into two groups. In the first, the self-reported BMI and the predictors of the discrepancies were regressed on the measured BMI. Correction equations were generated using all predictor variables that were significant at the p<0.05 level. These correction equations were then tested in the second group to derive estimates of sensitivity, specificity and of obesity prevalence. Logistic regression was used to examine the relationship between measured, reported and corrected BMI and obesity-related health conditions. Corrected estimates provided more accurate measures of obesity prevalence, mean BMI and sensitivity levels. Self-reported data exaggerated the relationship between BMI and health conditions, while in most cases the corrected estimates provided odds ratios that were more similar to those generated with the measured BMI