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Abstract

Objective:

Self-reported weight may underestimate measured weight. Researchers have tried to reduce the error using statistical models to predict weight from self-reported weight. We investigate whether deriving equations within separate BMI categories improves the prediction of weight compared with an equation derived regardless of an individual's BMI.

Design and Methods:

The analysis included self-reported and measured data from 20,536 individuals participating in the EPIC-Norfolk study. In a derivation set (n = 15,381) two approaches were used to predict weight from self-reported weight: (1) using a linear regression model with measured weight as outcome and self-reported weight and age as predictors, and (2) using the same model fit separately within 3 strata defined by BMI (< 25, 25-30, ≥30 kg m−2). The performance of these approaches was assessed in a validation set (n = 5,155). Measured weight was compared to self-reported weight and predicted weight.

Results:

Self-reported weight underestimated measured weight (P < 0.0001): mean difference −1.2 ± 3.1 kg (men), −1.3 ± 2.5 kg (women). Underestimation was greater in obese participants (P < 0.0001). Predicted weight using approach 1 was not significantly different from measured weight (P < 0.05). However, in individuals with BMI < 25 kg m−2, weight was overestimated in men (0.90 ± 3.87 kg) and women (0.57 ± 2.06 kg), but underestimated in overweight (−0.29 ± 3.58, −0.20 ± 2.62 kg) and obese (−1.46 ± 5.05 kg, −0.73 ± 3.54 kg) men and women.

Conclusions:

Using separate prediction equations in strata of BMI did not further improve prediction of weight. In conclusion, predicted weight was closer to measured weight compared with self-reported weight, but using equations derived in strata of BMI did not further improve the prediction and are not recommended for prediction of weight.