Abstract
Accurate self-assessment of body shape and size plays a key role in the prevention, diagnosis, and treatment of both obesity and eating disorders. These chronic conditions cause significant health problems, reduced quality of life, and represent a major problem for health services. Variation in body shape depends on two aspects of composition: adiposity and muscularity. However, most self-assessment tools are unidimensional. They depict variation in adiposity only, typically quantified by the body mass index. This can lead to substantial, and clinically meaningful, errors in estimates of body shape and size. To solve this problem, we detail a method of creating biometrically valid body stimuli. We obtained high-resolution 3D body shape scans and composition measures from 397 volunteers (aged 18–45 years) and produced a statistical mapping between the two. This allowed us to create 3D computer-generated models of bodies, correctly calibrated for body composition (i.e., muscularity and adiposity). We show how these stimuli, whose shape changes are based on change in composition in two dimensions, can be used to match the body size and shape participants believe themselves to have, to the stimulus they see. We also show how multivariate multiple regression can be used to model shape change predicted by these 2D outcomes, so that participants’ choices can be explained by their measured body composition together with other psychometric variables. Together, this approach should substantially improve the accuracy and precision with which self-assessments of body size and shape can be made in obese individuals and those suffering from eating disorders.
Highlights
The accurate perception and indexing of body adiposity, whether it is too low or too high, is a vital health prevention and management goal
All identities’ measures of fat mass (FATM) and skeletal muscle mass (SMM) taken from Bioelectrical impedance analysis (BIA) were used to predict their locations along that specific dimension, with the values of the two coefficients and the constant subsequently allowing us to model shape change
In addition to our FATM/ SMM model, we separately modelled shape change using the body mass index (BMI) values of our identities. (As above, training identities’ measures of BMI were used to predict their locations along each principal component analysis (PCA) dimension, with the values of the coefficient and the constant allowing us to model shape change.)
Summary
The accurate perception and indexing of body adiposity, whether it is too low or too high, is a vital health prevention and management goal. This error may be as high as 5–7 BMI units (Groves et al, 2019), again significantly shifting the chosen body across the World Health Organization BMI categories for health risk (WHO, 2018) To solve this problem and accurately represent the variation that exists in body size and shape, it is necessary to combine body composition measurements with 3D body shape scanning techniques in a large sample of volunteers. Such a data set can be used to determine the statistical mapping between 3D body shape change as a function of muscle mass and adiposity independently, and these statistical models could be used in turn to create appropriately calibrated 3D computer-generated models of bodies. Between 3D body shape, muscle mass, and fat mass to generate computer-generated imagery (CGI) stimuli; (iii) the proposal of a new 2D method of adjustment task which allows participants to select a body size/shape they believe themselves to have, or would like to have, expressed as body composition (i.e., a 2D outcome variable comprising both muscle and fat mass); (iv) the presentation of a new analysis pipeline, illustrated with toy data sets, in which multivariate regression is used to map the measured body composition of the observer onto the body composition derived from our method of adjustment task
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