The predictive validity of body mass index based on self-reported weight and height

Int J Obes. 1989;13(4):441-53.


In the Western society where obesity is a less desired condition it is natural that individuals may tend to report values of weight and height that they believe conform with current norms. Although several previous studies have shown that for the individual the difference between reported and controlled data may be small, such differences may affect body mass index distributions in various populations. The predictive validity of self-reported weight and height and of body mass index (BMI) calculated from these measures was analysed by using data from a random sample including 182 women with a mean age of 62 years (20-84 years) and 119 men with a mean age of 56 years (16-84 years) from a health care centre. Multiple multivariate linear regression analysis was used to study the relationship between self-reported (subjective) and controlled (objective) values. The regression lines of subjective parameters on controlled ones were significantly different from the line of identity for both sexes for weight, height and BMI (P less than 0.001). These lines demonstrate a 'flat slope syndrome', i.e. there is a systematic tendency for high values to be underestimated and for low ones to be overestimated. There was one exception for height in women, which was always overestimated, even for tall subjects. A systematic tendency for overweight and obese subjects to underestimate their body size and conversely for underweight subjects to overestimate it resulted in an incorrect BMI category distribution of 30 per cent of these subjects. This fact may invalidate data and conclusions of population surveys based on self-reported measures.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Adolescent
  • Adult
  • Aged
  • Aged, 80 and over
  • Body Constitution*
  • Body Height*
  • Body Weight*
  • Female
  • Humans
  • Male
  • Middle Aged
  • Models, Statistical
  • Predictive Value of Tests*
  • Regression Analysis