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BackgroundBody composition is a key component of health in both individuals and populations, and excess adiposity is associated with an increased risk of developing chronic diseases. Body mass index (BMI) and other clinical or consumer-facing tools for quantifying body fat (BF) are often inaccurate, cost-prohibitive, or cumbersome to use. The aim of the current study was to evaluate the performance of a novel automated computer vision method, visual body composition (VBC), that uses two-dimensional photographs captured via a conventional smartphone camera to estimate percentage total body fat (%BF).Methods134 healthy adults ranging in age (21-76 years), sex (61.2% women), race (60.4% Caucasian; 23.9% Black), and body mass index (BMI, 18.5-51.6 kg/m2) were evaluated at two clinical sites. Each participant had %BF measured with VBC, three consumer and two professional bioimpedance analysis (BIA) systems, as well as air displacement plethysmography (ADP). %BF measured by dual-energy X-ray absorptiometry (DXA) was set as the reference against which all other estimates were compared.ResultsRelative to DXA, VBC had the lowest mean absolute error and standard deviation (2.34%±1.83%) compared to all other evaluated methods (p2=0.01; p=0.41; LOA −4.7% to +6.4%), whereas all other evaluated methods had significant (pConclusionIn this first validation study of a novel, accessible, and easy-to-use system, VBC body fat estimates were accurate and without significant bias compared to DXA as the reference; VBC performance exceeded those of all other BIA and ADP methods evaluated. The wide availability of smartphones suggests that the VBC method for evaluating %BF can play a major role in quantifying adiposity levels in a wide range of settings.TRIAL REGISTRATIONFunded by Amazon, Inc., Seattle, WA. |
