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

      Obesity (Silver Spring, Md.)

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

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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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          Journal
          17228048
          10.1038/oby.2007.536

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