Evaluation of Automated Anthropometrics Produced By Smartphone-Based Machine Learning: A Comparison With Traditional Anthropometric Assessments

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

Associations Between Visceral Adipose Tissue Estimates Produced By Near-Infrared Spectroscopy, Mobile Anthropometrics, and Traditional Body Composition Assessments and Estimates Derived From Dual-Energy X-Ray Absorptiometry

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 \u3c 0·001) and SAT (all P \u3c 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 \u3c 0·001). Partial associations between VATVBC and VATMFBIA were only stronger for VAT than SAT in White participants (r = 0·67, P \u3c 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 \u3c 0·010) or White participants (r = 0·48, P \u3c 0·001). NIRS was amongst the weakest predictors of VAT which was highest in male participants (r = 0·39, P \u3c 0·010) but non-existent in BAA participants (r = –0·02, P \u3e 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

Validity and Reliability of a Mobile Digital Imaging Analysis Trained By a Four-Compartment Model

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 \u3c 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 \u3e 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 \u3c 0.001) and fat mass (both β = −0.07; p \u3c 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

Smartwatch-Based Bioimpedance Analysis For Body Composition Estimation: Precision and Agreement With a 4-Compartment Model

Given that the prevalence of smartwatches has allowed them to become a hallmark in health monitoring, this technology is primed to provide accessible body composition estimations. The purpose of this study was to evaluate the precision and agreement of smartwatch-based bioimpedance analysis and multifrequency bioimpedance analysis to a 4-compartment model criterion. A total of 186 participants (114 F) underwent body composition assessments necessary for a 4-compartment model and smartwatch and multifrequency bioimpedance analysis. Total body water from each device were also compared to bioimpedance spectroscopy. Precision was adequate though slightly lower for the smartwatch compared to other methods. No device demonstrated equivalence with the 4-compartment model. Specifically, the smartwatch overestimated and multifrequency underestimated body fat. Multifrequency bioimpedance analysis, but not smartwatch bioimpedance analysis, demonstrated equivalence for total body water. Overall error was higher for males using the smartwatch compared to females. While these findings do not invalidate the use of smartwatch-based estimates, clinicians should consider that there may be large errors relative to clinical measures. If this wearable device is intended to be used to monitor body composition change over time, these findings demonstrate the need for future research to evaluate its accuracy during follow-up testing

Visual Body Composition Assessment Methods: A 4-Compartment Model Comparison of Smartphone-Based Artificial Intelligence For Body Composition Estimation In Heatlhy Adults

Background & Aims: Visual body composition (VBC) estimates produced from smartphone-based artificial intelligence represent a user-friendly and convenient way to automate body composition remotely and without the inherent geographical and monetary restrictions of other body composition methods. However, there are limited studies that have assessed the reliability and agreement of this method and thus, the aim of this study was to evaluate VBC estimates compared to a 4-compartment (4C) criterion model. Methods: A variety of body composition assessments were conducted across 184 healthy adult participants (114 F, 70 M) including dual-energy X-ray absorptiometry and bioimpedance spectroscopy for utilization in the 4C model and automated assessments produced from two smartphone applications (Amazon Halo®, HALO; and myBVI®) using either Apple® or Samsung® phones. Body composition components were compared to a 4C model using equivalence testing, root mean square error (RMSE), and Bland–Altman analysis. Separate analyses by sex and racial/ethnic groups were conducted. Precision metrics were conducted for 183 participants using intraclass correlation coefficients (ICC), root mean squared coefficients of variation (RMS-%CV) and precision error (PE). Results: Only %fat produced from HALO devices demonstrated equivalence with the 4C model although mean differences for HALO were \u3c±1.0 kg for FM and FFM. RMSEs ranged from 3.9% to 6.2% for %fat and 3.1–5.2 kg for FM and FFM. Proportional bias was apparent for %fat across all VBC applications but varied for FM and FFM. Validity metrics by sex and specific racial/ethnic groups varied across applications. All VBC applications were reliable for %fat, fat mass (FM), and fat-free mass (FFM) with ICCs ≥0.99, RMS-%CV between 0.7% and 4.3%, and PEs between 0.3% and 0.6% for %fat and 0.2–0.5 kg for FM and FFM including assessments between smartphone types. Conclusions: Smartphone-based VBC estimates produce reliable body composition estimates but their equivalence with a 4C model varies by the body composition component being estimated and the VBC being employed. VBC estimates produced by HALO appear to have the lowest error, but proportional bias and estimates by sex and race vary across applications