Within the USA military, monitoring body composition is an essential component of predicting physical performance and establishing soldier readiness. The purpose of this study was to explore mobile phone three-dimensional optical imaging (3DO), a user-friendly technology capable of rapidly obtaining reliable anthropometric measurements and to determine the validity of the new Army one-site body fat equations using 3DO-derived abdominal circumference. Ninety-six participants (51 F, 45 M; age: 23·7 ± 6·5 years; BMI: 24·7 ± 4·1 kg/m2) were assessed using 3DO, dual-energy X-ray absorptiometry (DXA) and a 4-compartment model (4C). The validity of the Army equations using 3DO abdominal circumference was compared with 4C and DXA estimates. Compared with the 4C model, the Army equation overestimated BF% and fat mass (FM) by 1·3 ± 4·8 % and 0·9 ± 3·4 kg, respectively, while fat-free mass (FFM) was underestimated by 0·9 ± 3·4 kg (P < 0·01 for each). Values from DXA and Army equation were similar for BF%, FM and FFM (constant errors between −0·1 and 0·1 units; P ≥ 0·82 for each). In both comparisons, notable proportional bias was observed with slope coefficients of −0·08 to −0·43. Additionally, limits of agreement were 9·5–10·2 % for BF% and 6·8–7·8 kg for FM and FFM. Overall, while group-level performance of the one-site Army equation was acceptable, it exhibited notable proportional bias when compared with laboratory criterion methods and wide limits of agreement, indicating potential concerns when applied to individuals. 3DO may provide opportunities for the development of more advanced, automated digital anthropometric body fat estimation in military settings.

Smartphone applications (SPA) now offer the ability to provide accessible in-home monitoring of relevant individual health biomarkers. Previous cross-sectional validations of similar technologies have reported acceptable accuracy with high-grade body composition assessments; this research assessed longitudinal agreement of a novel SPA across a self-managed weight loss intervention of thirty-eight participants (twenty-one males, seventeen females). Estimations of body mass (BM), body fat percentage (BF%), fat-free mass (FFM) and waist circumference (WC) from the SPA were compared with ground truth (GT) measures from a dual-energy X-ray absorptiometry scanner and expert technician measurement. Small mean differences (MD) and standard error of estimate (SEE) were observed between method deltas (ΔBM: MD = 0·12 kg, SEE = 2·82 kg; ΔBF%: MD = 0·06 %, SEE = 1·65 %; ΔFFM: MD = 0·17 kg, SEE = 1·65 kg; ΔWC: MD = 1·16 cm, SEE = 2·52 cm). Concordance correlation coefficient (CCC) assessed longitudinal agreement between the SPA and GT methods, with moderate concordance (CCC: 0·55–0·73) observed for all measures. The novel SPA may not be interchangeable with high-accuracy medical scanning methods yet offers significant benefits in cost, accessibility and user comfort, in conjunction with the ability to monitor body shape and composition estimates over time.

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:hip ratio (WHR) and waist: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 intraclass correlation coefficients ≥ 0·93 (all P < 0·001) and root mean square coefficient of variation between 0·5 and 2·5 %. Precision error for WC and HC was between 0·5 and 1·9 cm. 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 with 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.

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 (BAA) = 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 BAA 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 standardised, but convenient, VAT estimation protocol that can account for the relationship between SAT and VAT that differs by sex/race.