Digital tools like 3D laser-based photonic scanners, which can
assess external anthropometric measurements for population based
studies, and predict body composition, are gaining in importance.
Here we focus on a) systematic deviation between manually determined
and scanned standard measurements, b) differences regarding the
strength of association between these standard measurements and body
composition, and c) improving these predictions of body composition
by considering additional scan measurements.
We analysed 104 men aged 19-23. Bioelectrical Impedance Analysis was
used to estimate whole body fat mass, visceral fat mass and skeletal
muscle mass (SMM). For the 3D body scans, an Anthroscan
VITUSbodyscan was used to automatically obtain 90 body shape
measurements. Manual anthropometric measurements (height, weight,
waist circumference) were also taken.
Scanned and manually measured height, waist circumference,
waist-to-height-ratio, and BMI were strongly correlated (Spearman
Rho>0.96), however we also found systematic differences. When
these variables were used to predict body fat or muscle mass,
explained variation and prediction standard errors were similar
between scanned and manual measurements. The univariable predictions
performed well for both visceral fat (r2 up to 0.92) and absolute
fat mass (AFM, r2 up to 0.87) but not for SMM (r2 up to 0.54). Of
the 90 body scanner measures used in the multivariable prediction
models, belly circumference and middle hip circumference were the
most important predictors of body fat content. Stepwise forward
model selection using the AIC criterion showed that the best
predictive power (r2 up to 0.99) was achieved with models including
49 scanner measurements.
The use of a 3D full body scanner produced results that strongly
correlate to manually measured anthropometric measures. Predictions
were improved substantially by including multiple measurements,
which can only be obtained with a 3D body scanner, in the models.