Abstract
Background/ObjectivesBody mass index (BMI) is a proxy for obesity that is commonly used in spite of its limitation in estimating body fatness. Trained observers with repeated exposure to different body types can estimate body fat (BF) of individuals compared to criterion methods with reasonable accuracy. The purpose of this study was to develop and validate a computer algorithm to provide a valid estimate %BF using digital photographs.Subjects/MethodsOur sample included 97 children and 226 adults (age in years: 11.3±3.3; 38.1±11.6, respectively). Measured height and weight were used (BMI in kg/m2: 20.4±4.4; 28.7±6.6 for children and adults, respectively). Dual x-ray absorptiometry (DXA) was the criterion method. Body volume (BVPHOTO) and body shape (BSPHOTO) were derived from two digital images. Final support vector regression (SVR) models were trained using age, sex, race, BMI for % BFNOPHOTO, plus BVPHOTO and BSPHOTO for %BFPHOTO. Separate validation models were used to evaluate the learning algorithm in children and adults. The differences in correlations between %BFDXA, %BFNOPHOTO and %BFPHOTO were tested using the Fisher’s Z-score transformation.ResultsMean BFDXA and BFPHOTO were 27.0%±9.2 vs. 26.7%± 7.4 in children and 32.9± 10.4% vs. 32.8%±9.3 in adults. SVR models produced %BFPHOTO values strongly correlated with %BFDXA. Our final model produced correlations of rDP = 0.80 and rDP = 0.87 in children and adults, respectively for %BFPHOTO vs. %BFDXA. The correlation between %BFNOPHOTO and %BFDXA was moderate, yet statistically significant in both children rDB = 0.70; p <0.0001 and adults rDB = 0.86; p<0.0001. However, the correlations for rDP were statistically higher than rDB (%BFDXA vs. %BFNOPHOTO) in both children and adults (children: Z = 5.95, p<0.001; adults: Z = 3.27, p<0.0001).ConclusionsOur photographic method produced valid estimates of BF in both children and adults. Further research is needed to create norms for subgroups by sex, race/ethnicity, and mobility status.
Highlights
Assessment of body composition, fat and fat-free mass, is vital to understanding many health-related conditions including cachexia induced by HIV, cancer, and other diseases; multiple sclerosis; wasting in neurological disorders such as Parkinson’s, Alzheimer’s, and muscular dystrophy; sarcopenia; obesity; eating disorders; proper growth in children, and response to exercise[1,2,3,4,5,6,7]
support vector regression (SVR) models produced %BFPHOTO values strongly correlated with % BFDXA
Obesity, characterized by an excess of body fat (BF) and sarcopenia, defined as diminution of primarily skeletal muscle, remain significant public health problems [9, 10]. Both obesity and sarcopenia can be assessed using highly accurate techniques such as dual-energy x-ray absorptiometry (DXA) or magnetic resonance imaging (MRI) but are not widely used in large-scale epidemiologic studies or non-clinical settings due, in part, cost and size of the equipment used for these methods
Summary
Assessment of body composition, fat and fat-free mass, is vital to understanding many health-related conditions including cachexia induced by HIV, cancer, and other diseases; multiple sclerosis; wasting in neurological disorders such as Parkinson’s, Alzheimer’s, and muscular dystrophy; sarcopenia; obesity; eating disorders; proper growth in children, and response to exercise[1,2,3,4,5,6,7]. Obesity, characterized by an excess of body fat (BF) and sarcopenia, defined as diminution of primarily skeletal muscle, remain significant public health problems [9, 10] Both obesity and sarcopenia can be assessed using highly accurate techniques such as dual-energy x-ray absorptiometry (DXA) or magnetic resonance imaging (MRI) but are not widely used in large-scale epidemiologic studies or non-clinical settings due, in part, cost and size of the equipment used for these methods. Field methods such as multiple skinfold measurements depend heavily upon repeated training of research staff to obtain accurate and reliable assessments [11]. There is significant need for a simple, portable, and relatively inexpensive but accurate measurement of body composition that performs well across age, sex, and racial/ethnic groups
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