Automated visual anthropometrics produced by mobile applications are accessible and cost-effective with the potential to assess clinically relevant anthropometrics without a trained technician present. Thus, the aim of this study was to evaluate the precision and agreement of smartphone-based automated anthropometrics against reference tape measurements. Waist and hip circumference (WC; HC), waist-to-hip ratio (WHR), and waist-to-height ratio (W:HT), were collected from 115 participants (69 F) using a tape measure and two smartphone applications (MeThreeSixty®, myBVI®) across multiple smartphone types. Precision metrics were used to assess test-retest precision of the automated measures. Agreement between the circumferences produced by each mobile application and the reference were assessed using equivalence testing and other validity metrics. All mobile applications across smartphone types produced reliable estimates for each variable with ICCs ≥0.93 (all p<0.001) and RMS-%CV between 0.5%-2.5%. PE for WC and HC were between 0.5cm-1.9cm. WC, HC, and W:HT estimates produced by each mobile application demonstrated equivalence with the reference tape measurements using 5% equivalence regions. Mean differences via paired t-tests were significant for all variables across each mobile application (all p<0.050) showing slight underestimation for WC and slight overestimation for HC which resulted in a lack of equivalence for WHR compared to the reference tape measure. Overall, the results of our study support the use of WC and HC estimates produced from automated mobile applications, but also demonstrates the importance of accurate automation for WC and HC estimates given their influence on other anthropometric assessments and clinical health markers.
Background: Digital imaging analysis (DIA) estimates collected from mobile applications comprise a novel technique that can collect body composition estimates remotely without the inherent restrictions of other research-grade devices. However, the accuracy of the artificial intelligence used in DIA is reliant on the accuracy of the developmental methods. Few DIA applications are trained by multicompartment models, but this developmental strategy may be most accurate. Thus, the aim of the present study was to assess the precision and agreement of a DIA application with developmental software trained by a four-compartment (4C) model using an actual 4C model as the criterion method. Methods: For this cross-sectional study, body composition estimations were collected from 102 participants (63 females, 39 males) using the methods necessary for a rapid 4C model and a DIA application using two different smartphones. Results: Intraclass correlation coefficients (0.96-0.99; all p < 0.001) and root mean square coefficients of variation (0.5%-3.0%) showed good reliability for body fat percentage, fat mass and fat-free mass. There were no significant mean differences between the 4C model or the DIA estimates for the total sample, by sex, and for non-Hispanic White (n = 61) and Black/African-American (n = 32) participants (all p > 0.050). DIA estimates demonstrated equivalence with the 4C model for all variables but revealed proportional biases that underestimated body fat percentage (both β = −0.25; p < 0.001) and fat mass (both β = −0.07; p < 0.010) at higher degrees of each variable. Conclusions: DIA applications trained by a 4C model are reliable and produce body composition estimates equivalent to an actual 4C model.
Assessments of visceral adipose tissue (VAT) are critical in preventing metabolic disorders; however, there are limited measurement methods that are accurate and accessible for VAT. The purpose of this cross-sectional study was to evaluate the association between VAT estimates from consumer grade devices and traditional anthropometrics and VAT and subcutaneous adipose tissue (SAT) from dual-energy x-ray absorptiometry (DXA). Data were collected from 182 participants (Female=114; White=127; Black/African-American=48) which included anthropometrics and indices of VAT produced by near-infrared reactance spectroscopy (NIRS), visual body composition (VBC), and multifrequency BIA (MFBIA). VAT and SAT were collected using DXA. Bivariate and partial correlations were calculated between DXAVAT and DXASAT and other VAT estimates. All VAT indices had positive moderate-strong correlations with VAT (all p<0.001) and SAT (all p<0.001). Only waist:hip (r=0.69), VATVBC (r=0.84), and VATMFBIA (r=0.86) had stronger associations with VAT than SAT (p<0.001). Partial associations between VATVBC and VATMFBIA were only stronger for VAT than SAT in White participants (r=0.67,p<0.001) but not female, male, or Black/African-American participants individually. Partial correlations for waist:hip were stronger for VAT than SAT, but only for male (r=0.40,p<0.010) or White participants (r=0.48,p<0.001). NIRS was amongst the weakest predictors of VAT which was highest in male participants (r=0.39,p<0.010) but non-existent in BAA participants (r=-0.02,p>0.050) after adjusting for SAT. Both anthropometric and consumer-grade VAT indices are consistently better predictors of SAT than VAT. These data highlight the need for a standardized, but convenient, VAT estimation protocol that can account for the relationship between SAT and VAT that differs by sex/race.
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