Introduction:
Body fat (BF) content better predicts adiposity-related cardiovascular risk than the body mass index (BMI). Accurate and accepted methods to assess BF are complex, expensive, and accessible only in clinical settings. Multi-sensor 3D body volume (3D-BV) measurement technology has been shown to accurately estimate BF. We assessed the hypothesis that 3D models generated from biplane imaging (i.e., front and side facing photographs) using mobile devices (App), could be used to predict body volume measurements and derive BF.
Methods:
We prospectively enrolled 196 subjects, who underwent 3D-BV (gold standard for body volume) within 24 hours, dual-energy X-ray Absorptiometry (iDEXA) (gold standard for BF) and photographs taken with an Ipad® App at a predefined distance and pose. These photos were post-processed with a computer-assisted algorithm to estimate body length, girth, and volume. These were used to calculate body density (BD) using the bicompartmental principles of body composition (BD=Body Mass/Volume) and derive BF% using the Siri equation [(4.95/BD-4.50) X 100]. Correlation indexes and residual plots were created to compare 3D-BV and iDEXA with the App. Mean differences were compared using a paired t-test.
Results:
Mean ±SD age was 31.9±9.15 years, 53% were women, weight 72.8±14.1 kg, BMI was 25.5±4.5 kg/m
2
.The App volume correlated with 3D-BV (R
2
=0.95,95%CI 0.90,0.95, p<.0001,
Figure 1-A
)
;
average difference between App volume vs 3D-BV volume was -1.2 Liters, 95%CI,0.03, 2.42, p=0.06, (
Figure 1-B
). App BF% correlated with iDEXA BF% (R
2
=0.92, 95%CI 0.90,0.94, p<.0001,
Figure 1-C
)
;
while average difference between App BF% vs iDEXA BF% was -0.13 %, 95%CI -0.6,0.4, p=0.6, (
Figure 1-D
).
Conclusion:
BF can be estimated using volume measurements obtained by biplane imaging from mobile devices and could serve as a home-based, portable, scalable, cost-effective, and convenient measure to assess BF and track changes in body composition over time.
