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      The validity of obesity based on self-reported weight and height: Implications for population studies.

      Obesity (Silver Spring, Md.)
      Adult, Age Factors, Aged, Algorithms, Body Height, physiology, Body Mass Index, Body Weight, Cross-Sectional Studies, Female, Humans, Male, Middle Aged, Obesity, classification, diagnosis, epidemiology, psychology, Predictive Value of Tests, Prevalence, Questionnaires, standards, Reproducibility of Results, Self Disclosure, Sensitivity and Specificity, Sex Factors, Sweden

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          Abstract

          To validate self-reported information on weight and height in an adult population and to find a useful algorithm to assess the prevalence of obesity based on self-reported information. This was a cross-sectional survey consisting of 1703 participants (860 men and 843 women, 30 to 75 years old) conducted in the community of Vara, Sweden, from 2001 to 2003. Self-reported weight, height, and corresponding BMI were compared with measured data. Obesity was defined as measured BMI > or = 30 kg/m2. Information on education, self-rated health, smoking habits, and physical activity during leisure time was collected by a self-administered questionnaire. Mean differences between measured and self-reported weight were 1.6 kg (95% confidence interval, 1.4; 1.8) in men and 1.8 kg (1.6; 2.0) in women (measured higher), whereas corresponding differences in height were -0.3 cm (-0.5; -0.2) in men and -0.4 cm (-0.5; -0.2) in women (measured lower). Age and body size were important factors for misreporting height, weight, and BMI in both men and women. Obesity (measured) was found in 156 men (19%) and 184 women (25%) and with self-reported data in 114 men (14%) and 153 women (20%). For self-reported data, the sensitivity of obesity was 70% in men and 82% in women, and when adjusted for corrected self-reported data and age, it increased to 81% and 90%, whereas the specificity decreased from 99% in both sexes to 97% in men and 98% in women. The prevalence of obesity based on self-reported BMI can be estimated more accurately when using an algorithm adjusted for variables that are predictive for misreporting.

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