Size UK

Wells, et. al. (2007), have published data from 3D body scans taken from a 9617 member, cross-sectional sample of UK adults (stratified by age and SES). The study seems intended to gather commercial (garment sizing information) and health related information, but does have some implications for beauty research.


  • Body shape correlates with age in females more obviously than in males.
  • Females more likely to become “apples” – or proportionally more similar to males – with age.
  • BMI is not a good predictor of body shape/proportion or waist circumference: at least at the border of the overweight range. Male subjects with a BMI between 24-25 had waist circumferences ranging from 29.7″ to 43.3″ while female subjects ranged from 28.6″ to 44.8″ (a 3.5 standard deviation range in waist circumference for a narrow range of BMI).
  • BMI is insensitive to age associated body weight redistributions.
  • Height and circumference explains most of the variance in weight in both men (91.7%) and women (94.8%). Thus, visual cues are strong predictors of weight.    

Beauty relevant findings:

  • Rank order of strongest predictors of weight in women: height, hip, bust, thigh, and waist.
  • Rank order of strongest predictors of weight in men: height, waist, chest, and thigh.
  • Thigh, arm, and waist girths are strongly(?) related to body fat – implied but not directly addressed in this article.
  • After age 30, mean male waist-hip-chest measurements maintain relatively constant ratios (see Fig. 2A).
  • After age 30, mean female waist circumference increases relative to hip, chest, and bust: leading to a decreased tendency toward hourglass figures with age (see Fig. 2B).

  • Average male waist circumference increases about 0.2″ per decade.
  • Average female waist circumference increases about 1.1″ per decade.


  • Total body measurements from 3D scans accurate to 0.2″ were used
  • Large sample
  • Point-cloud data may be accessible to future research on attractiveness


  • Sample may not be representative
  • Cross-sectional data do not identify individual developmental trajectories: some of the relationships in the data could be cohort specific


Wayne Hooke

ResearchBlogging.orgWells JC, Treleaven P, & Cole TJ (2007). BMI compared with 3-dimensional body shape: the UK National Sizing Survey. The American journal of clinical nutrition, 85 (2), 419-25 PMID: 17284738

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