Validation of a White-light 3D Body Volume Scanner to Assess Body Composition.

Introduction: Estimating body fat content has shown to be a better predictor of adiposity-related cardiovascular risk than the commonly used body mass index (BMI). The white-light 3D body volume index (BVI) scanner is a non-invasive device normally used in the clothing industry to assess body shapes and sizes. We assessed the hypothesis that volume obtained by BVI is comparable to the volume obtained by air displacement plethysmography (Bod-Pod) and thus capable of assessing body fat mass using the bi-compartmental principles of body composition.

Methods: We compared BVI to Bod-pod, a validated bicompartmental method to assess body fat percent that uses pressure/volume relationships in isothermal conditions to estimate body volume. Volume is then used to calculate body density (BD) applying the formula density=Body Mass/Volume. Body fat mass percentage is then calculated using the Siri formula (4.95/BD - 4.50) × 100. Subjects were undergoing a wellness evaluation. Measurements from both devices were obtained the same day. A prediction model for total Bod-pod volume was developed using linear regression based on 80% of the observations (N=971), as follows: Predicted Bod-pod Volume (L)=9.498+0.805*(BVI volume, L)-0.0411*(Age, years)-3.295*(Male=0, Female=1)+0.0554*(BVI volume, L)*(Male=0, Female=1)+0.0282*(Age, years)*(Male=0, Female=1). Predictions for Bod-pod volume based on the estimated model were then calculated for the remaining 20% (N=243) and compared to the volume measured by the Bod-pod.

Results: Mean age among the 971 individuals was 41.5 ± 12.9 years, 39.4% were men, weight 81.6 ± 20.9 kg, BMI was 27.8 ± 6.3kg/m. Average difference between volume measured by Bod-pod- predicted volume by BVI was 0.0 L, median: -0.4 L, IQR: -1.8 L to 1.5 L, R2=0.9845. Average difference between body fat measured-predicted was-1%, median: -2.7%, IQR: -13.2 to 9.9, R2=0.9236.

Conclusion: Volume and BFM can be estimated by using volume measurements obtained by a white- light 3D body scanner and the prediction model developed in this study.