A Multidomain Approach to Assessing the Convergent and Concurrent Validity of a Mobile Application When Compared to Conventional Methods of Determining Body Composition

Validity of non-traditional measures of obesity compared to total body fat across the life course: A systematic review and meta-analysis

IntroductionTraditional obesity measures including body mass index, waist circumference, waist-to-hip ratio, and waist-to-height ratio have limitations. The primary objective of this study was to identify and review the validity of non-traditional obesity measures, using measures of total body fat as the reference standard, that have been used across multiple life stages. MethodsWe conducted a systematic review and searched MEDLINE, Embase, and PsycINFO. We included observational studies published from 2013 to October 2023 among "the general population" for any life stage that reported the validity of non-traditional obesity measures compared to total body fat reference standards. Separate meta-analyses were performed to pool correlation coefficients and mean differences for non-traditional obesity measures that were evaluated at multiple life stages. ResultsA total of 123 studies were included, and 55 validated non-traditional obesity measures were identified. Of these, 13 were evaluated at multiple life stages. Two-dimensional (2D) digital imaging technologies, three-dimensional (3D) body scanners, relative fat mass (RFM), and mid-upper arm circumference had high or moderate validity (pooled correlation coefficient >0.70). Pooled mean differences were small (<6%) between total body fat percentage from reference standards and from RFM, 2D digital imaging technologies, 3D body scanners, and the body adiposity index. Heterogeneity (I2) was >75% in most meta-analyses. ConclusionNumerous validated non-traditional obesity measures were identified; relatively few were evaluated at multiple life stages and did not consider health risks associated with adiposity. More research is needed to define valid obesity measures across all life stages that assess health and adiposity.

Comparing digital anthropometrics from mobile applications to reference methods: a scoping review

This scoping review aimed to assess the repeatability and accuracy of Digital Anthropometry by Mobile Application (DAM) compared to reference methods for estimating anthropometric dimensions, body volume (BV), and body composition. A comprehensive search was conducted on December 8th, 2024, without restrictions on language, time, sex, ethnicity, age, or health condition. We found 14 different DAMs across the 23 included studies. Reference methods for each estimated variable were: (a) Body circumferences-tape measure; (b) body mass-calibrated scale; (c) body length-stadiometer; (d) BV-Underwater Weighing; (e) percentage of body fat-Dual energy x-ray absorptiometry (DXA), BOD POD, 3, 4, and 5-compartment models; (f) fat mass and fat-free mass-DXA, 3 and 4-compartment models; (g) appendicular Lean Mass-DXA. DAMs demonstrated high repeatability and accuracy at a mean level in most studies. However, their accuracy is lower at individual-level analysis and for tracking changes over time. Estimated BV showed high accuracy compared to UWW (SEE = 0.68; MD = 0.04 to 0.1; LoA = 2.86), including the BV-derived DAMs integrated into alternative multi-compartment models compared to reference methods. As relatively new methods, DAMs offer numerous possibilities and areas for exploration in future studies. However, caution is advised due to their potentially low or unknown accuracy at the individual level.

Combined use of the CLivD score and FIB-4 for prediction of liver-related outcomes in the population

BACKGROUND AND AIMS

A need exists for effective and practical tools to identify individuals at increased risk of liver-related outcomes (LROs) within the general population.

APPROACH AND RESULTS

We externally validated the chronic liver disease (CLivD) score for LROs in the UK Biobank cohort. We also investigated the sequential combined use of CLivD and fibrosis-4 (FIB-4) scores. Our analysis included 369,832 adults without baseline liver disease and with available data for CLivD and FIB-4 computation. LROs reflecting compensated or decompensated liver cirrhosis or HCC were ascertained through linkages with electronic health care registries. Discriminatory performance and cumulative incidence were evaluated with competing-risk methodologies. Over a 10-year follow-up, time-dependent AUC values for LRO prediction were 0.80 for CLivD lab (including gamma-glutamyltransferase), 0.72 for CLivD non-lab (excluding laboratory values), and 0.75 for FIB-4. CLivD lab demonstrated AUC values exceeding 0.85 for liver-related death and severe alcohol-associated liver outcomes. The predictive performance of FIB-4 increased with rising CLivD scores; 10-year FIB-4 AUC values ranged from 0.60 within the minimal-risk CLivD subgroup to 0.81 within the high-risk CLivD subgroup. Moreover, in the minimal-risk CLivD subgroup, the cumulative incidence of LRO varied from 0.05% to 0.3% across low-to-high FIB-4 strata. In contrast, within the high-risk CLivD subgroup, the corresponding incidence ranged from 1.7% to 21.1% (up to 33% in individuals with FIB-4 >3.25).

CONCLUSIONS

The CLivD score is a valid tool for LRO risk assessment and improves the predictive performance of FIB-4. The combined use of CLivD and FIB-4 identified a subgroup where 1 in 3 individuals developed LROs within 10 years.

Emergence of imaging technology beyond the clinical setting: Utilization of mobile health tools for at-home testing

Body composition assessment plays a pivotal role in understanding health, disease risk, and treatment efficacy. This narrative review explores two primary aspects: imaging techniques, namely ultrasound (US) and dual-energy x-ray absorptiometry (DXA), and the emergence of artificial intelligence (AI) and mobile health apps in telehealth for body composition. Although US is valuable for assessing subcutaneous fat and muscle thickness, DXA accurately quantifies bone mineral content, fat mass, and lean mass. Despite their effectiveness, accessibility and cost remain barriers to widespread adoption. The integration of AI-powered image analysis may help explain tissue differentiation, whereas mobile health apps offer real-time metabolic monitoring and personalized feedback. New apps such as MeThreeSixty and Made Health and Fitness offer the advantages of clinic-based imaging techniques from the comfort of home. These innovations hold the potential for individualizing strategies and interventions, optimizing clinical outcomes, and empowering informed decision-making for both healthcare professionals and patients/clients. Navigating the intricacies of these emerging tools, critically assessing their validity and reliability, and ensuring inclusivity across diverse populations and conditions will be crucial in harnessing their full potential. By integrating advancements in body composition assessment, healthcare can move beyond the limitations of traditional methods and deliver truly personalized, data-driven care to optimize well-being.

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: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.

Waist-hip ratio is superior to BMI in predicting liver-related outcomes and synergizes with harmful alcohol use

BACKGROUND

Obesity is associated with liver disease, but the best obesity-related predictor remains undefined. Controversy exists regarding possible synergism between obesity and alcohol use for liver-related outcomes (LRO). We assessed the predictive performance for LROs, and synergism with alcohol use, of abdominal obesity (waist-hip ratio, WHR), and compared it to overall obesity (body mass index, BMI).

METHODS

Forty-thousand nine-hundred twenty-two adults attending the Finnish health-examination surveys, FINRISK 1992-2012 and Health 2000 studies, were followed through linkage with electronic healthcare registries for LROs (hospitalizations, cancers, and deaths). Predictive performance of obesity measures (WHR, waist circumference [WC], and BMI) were assessed by Fine-Gray models and time-dependent area-under-the-curve (AUC).

RESULTS

There are 355 LROs during a median follow-up of 12.9 years (509047.8 person-years). WHR and WC emerge as more powerful predictors of LROs than BMI. WHR shows significantly better 10-year AUC values for LROs (0.714, 95% CI 0.685-0.743) than WC (0.648, 95% CI 0.617-0.679) or BMI (0.550, 95% CI 0.514-0.585) both overall and separately among men and women. WHR is predictive also in BMI strata. Absolute 10-year risks of LROs are more dependent on WHR than BMI. Moreover, WHR shows a significant supra-additive interaction effect with harmful alcohol use for liver-related outcomes (excess 10-year cumulative incidence of 2.8% from the interaction), which is not seen between BMI and harmful alcohol use.

CONCLUSIONS

WHR is a better predictor than BMI or WC for LROs, and WHR better reflects the synergism with harmful alcohol use. WHR should be included in clinical assessment when evaluating obesity-related risks for liver outcomes.

Changes over time in the Chronic Liver Disease risk score predict liver-related outcomes: longitudinal analysis of the Whitehall II study

BACKGROUND AND AIMS

The Chronic Liver Disease (CLivD) risk score was recently shown to predict future advanced liver disease in the general population. We here investigated the impact of individual CLivD-score changes over time.

METHODS

Participants of both phase 3 (baseline, 1991-1994) and phase 5 (follow-up, 1997-1999) examinations of the Whitehall II study were followed for liver-related outcomes (hospitalization, cancer, death) until December 2019 through linkage with electronic healthcare registers. The CLivD score, its modifiable components (alcohol use, waist-hip ratio [WHR], diabetes, and smoking), and their individual changes were studied.

RESULTS

Among 6590 adults (mean age 50 years, 30% women) with a median 21-year follow-up, there were 80 liver outcomes. A rise in the CLivD score between baseline and follow-up examinations significantly increased the risk for liver-related outcomes (adjusted hazard ratio [aHR] 1.62, 95% confidence interval [CI] 1.01-2.60), more so in subjects with baseline intermediate-high CLivD scores (HR 2.4 for a CLivD-change) compared to minimal-low CLivD scores. Adverse changes over time in alcohol use and WHR, and new-onset diabetes also predicted liver outcomes. In contrast to WHR, changes in body weight (kg) showed a U-shaped association with liver outcomes.

CONCLUSIONS

A change in the CLivD score over time corresponds to a true change in the risk for liver-related outcomes, suggesting the usefulness of the CLivD score for assessing response to liver-directed lifestyle interventions. Changes in WHR predicted liver outcomes better than changes in body weight or waist circumference, independent of body mass index, supporting the WHR in assessing risk for future liver disease.

Visual body composition assessment methods: A 4-compartment model comparison of smartphone-based artificial intelligence for body composition estimation in healthy 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 <±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.

Comparison of various strategies to define the optimal target population for liver fibrosis screening: A population-based cohort study

BACKGROUND & AIMS

Liver fibrosis screening is recommended in high-risk populations, but the optimal definition of "high risk" remains to be established. We compared the performance of several risk-stratification strategies in a population-based setting.

METHODS

Data were obtained from the Finnish population-based health examination surveys Health 2000 and FINRISK 2002-2012. The Chronic Liver Disease Risk Score (CLivD) was compared to previously published risk-stratification strategies based on elevated liver enzymes, alcohol use, diabetes, fatty liver index, body mass index, and/or metabolic risk factors for their ability to detect either advanced liver fibrosis or incident severe liver events. Advanced fibrosis was defined as an Enhanced Liver Fibrosis (ELF^TM ) score >9.8 in the Health 2000 study (n = 6084), and incident liver events were ascertained from registry linkage in the combined FINRISK 2002-2012 and Health 2000 cohort (n = 26,957).

RESULTS

Depending on the cohort, 53%-60% of the population was considered at risk using the CLivD strategy (low-intermediate-high risk, excluding the minimal-risk category), compared to 30%-32% according to the other risk-stratification strategies. The CLivD captured 85%-91% of cases in the population with advanced liver fibrosis and 90% of incident severe liver events within 10 years from baseline. This compares to 33%-44% and 56%-67% captured by the other risk-stratification strategies, respectively. The 10-year cumulative incidence of liver events varied by risk-stratification strategy (1.0%-1.4%).

CONCLUSIONS

Compared to previously reported traditional risk factor-based strategies, use of the CLivD captured substantially more cases with advanced liver disease in the population and may be superior for targeting further fibrosis screening.

Mobile applications for the sport and exercise nutritionist: a narrative review

Mobile technology is widespread in modern society, and the applications (apps) that they run can serve various purposes. Features such as portability, ease of communication, storage, and relative low cost may make such technology attractive to practitioners in several fields. This review provides a critical narrative on the existing literature for apps relevant to the field of sport and exercise nutrition. Three main areas are discussed: (1) dietary analysis of athletes, (2) nutrition education for athletes, (3) estimating body composition. The key purpose of the review was to identify what literature is available, in what areas apps may have a benefit over traditional methods, and considerations that practitioners should make before they implement apps into their practice or recommend their use to coaches and athletes.