Web-collected height and weight are increasingly used in epidemiological studies; however, the validity has rarely been evaluated.
The aim of the study was to validate self-reported height, weight, and corresponding body mass index (BMI) among Swedish adolescents aged approximately 16 years. A secondary aim was to investigate possible prediction factors for validity of self-reported BMI.
The study included 1698 adolescents from the population-based cohort BAMSE. Height and weight were collected through a Web-based questionnaire and subsequently measured using standard procedures. Differences between reported and measured height, weight, and corresponding BMI were compared by t tests and agreement was evaluated by Pearson correlation and Bland-Altman plots. Multivariable linear regression analysis was used to investigate whether lifestyle and demographic factors predicted validity of self-reported BMI.
On average, weight was underestimated by 1.1 kg and height was overestimated by 0.5 cm, leading to an underestimation of BMI by 0.5 kg/m2. Correlation coefficients were .98 for height, .97 for weight, and .94 for BMI, and highly significant. Females underestimated weight to a higher extent than males and overweight and obese participants underestimated weight to a higher extent than normal-weight participants, which resulted in higher underestimation of BMI. Underweight participants, on the contrary, overestimated weight and correspondingly BMI. Overall, a high proportion of participants were classified into the correct BMI category; however, among overweight and obese participants, only 60.2% (139/231) and 46% (20/44) were correctly classified, respectively. In the multivariable prediction model, only gender and BMI status significantly predicted discrepancy between reported and measured BMI.
Web-collected BMI may be used as a valid, quick, and cost-effective alternative to measured BMI among Swedish adolescents. The accuracy of self-reported BMI declines with increasing BMI and self-reported BMI should not be used to estimate the prevalence of overweight or obesity.