Smartphone prediction of skeletal muscle mass: model development and validation in adults

The Critical Role of Body Composition Assessment in Advancing Research and Clinical Health Risk Assessment across the Lifespan

Obesity and low muscle mass are major public health concerns, especially in older adults, due to their strong links to cardiovascular disease, cancer, and mortality. Beyond body mass index, body composition metrics including skeletal muscle, fat mass, and visceral adipose tissue offer deeper insights into nutrition and disease risk. These measures are essential for both cross-sectional assessments and longitudinal tracking, providing a clearer picture of health changes over time. Selecting body composition assessment tools requires balancing cost, practicality, accuracy, and data quality. The right tools enhance research, refine clinical assessments, and inform targeted interventions. Aligning methods with specific research or clinical goals improves disease risk stratification and advances personalized treatments. This review highlights the importance of integrating body composition assessment into research and clinical practice, addressing knowledge gaps across diverse populations and emphasizing its potential in advancing precision medicine. It also highlights recent advancements in body composition assessment techniques that warrant consideration when evaluating techniques for a specific application. Future efforts should focus on refining these tools, expanding their accessibility, and developing comprehensive risk models that incorporate body composition alongside behavioral, environmental, and genetic factors to improve disease prediction and prevention strategies.

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.

Critical analysis of dual-energy x-ray absorptiometry-measured body composition changes with voluntary weight loss

OBJECTIVE

When treated with a macronutrient-balanced hypocaloric diet, do male individuals who have overweight and obesity lose relatively more dual-energy x-ray absorptiometry (DXA)-measured lean soft tissue (LST) mass than female individuals? Are there changes in bone mineral content (BMC), and if so, how do they impact relative reductions in LST compared to fat-free mass (FFM; LST plus BMC)? Are decrements in fat, LST, and FFM predictable from the magnitude of weight loss or baseline body composition?

METHODS

To answer these questions, DXA studies were conducted before and after a 9- to 12-week calorie-restriction period in 43 male and 97 female individuals who lost a mean (SD) of 10.8% (2.2%) and 10.7% (1.6%) of their baseline weight, respectively.

RESULTS

The proportion of weight loss as LST was significantly (p < 0.001) larger in male (mean [SD], 0.33 [0.11] kg) than female individuals (0.25 [0.11] kg); BMC paradoxically increased, thereby leading to a significantly smaller reduction in FFM than LST in the male (-3.87 [1.73] kg vs. -3.92 [1.74] kg; p < 0.001) and female individuals (-2.22 [1.18] kg vs. -2.24 [1.18] kg; p < 0.001), and three different analyses showed that the composition of weight loss tracked as predicted a priori from weight change and baseline body composition.

CONCLUSIONS

These observations provide insights into and future guidance for analyzing the DXA-measured body composition changes associated with newer pharmacotherapies for weight loss.

The null need not be nil: Clarifying the parallel arbitrariness of difference testing and equivalence testing

In every statistical analysis, a critical step is to determine the smallest effect size of interest, namely, the arbitrary dividing line between meaningful and negligible results. Different tests address this in different ways, and the contrasting approaches can sometimes lead to confusion. We discuss a key example of such confusion, whereby equivalence testing is perceived to be more arbitrary than difference testing. Our comments are intended to clarify that the latter methods share parallel arbitrariness, and to show how the contrary perception is fueled by the habituated use of "nil null hypotheses" in difference testing. The main premise is that nil null hypotheses give an appearance of objectivity by making the smallest effect size of interest an implicit factor in the interpretation stage of difference testing. When contrasted with the requirements of equivalence testing (where the smallest effect size of interest must be explicitly declared and justified a priori, in the form of the equivalence zone), it is therefore understandable how the misperception of greater arbitrariness could emerge. By combating the latter misperception, our comments serve to promote good practice in both difference testing and equivalence testing.

D3BT: Dynamic 3D Body Transformer for Body Fat Percentage Assessment

3D body scan has been adopted for body composition assessment due to its ability to accurately capture body shape measurements. However, the complexity of mesh representation and the lack of fine-shape descriptors limit its applications in body fat percentage analysis. Most studies rely on algorithms applied to anthropometric values derived from 3D scans, such as multiple girth measurements, which fail to account for the body's detailed shape. To address these issues, we explore the feasibility of using point cloud representation. However, few existing point-based methods are aimed at the human body or regression tasks. In this study, we introduce a new model, D3BT, which utilizes a transformer-based network on the body point cloud to efficiently learn shape information for regional and global fat percentage regression tasks. The model dynamically divides the points into voxels for enhanced transformer training, providing higher density and better alignment across different subjects, which is more suitable for body shape learning. We evaluate various models for predicting body fat percentage from 3D body scans, using ground truth data from dual-energy X-ray absorptiometry (DXA) reports. Compared to traditional methods that depend on anthropometric measurements and other point-based approaches, the proposed model shows superior results. In extensive experiments, the model reduces the Root Mean Square Error (RMSE) by an average of 10.30% and achieves an average R-squared score of 0.86.

Predicting Bone Mineral Content from Smartphone Digital Anthropometrics: Evaluation of an Existing Application and the Development of New Prediction Models

INTRODUCTION/BACKGROUND

Bone mineral content (BMC) is most commonly evaluated using dual-energy X-ray absorptiometry (DXA), but there are several challenges that limit use of DXA during routine care. Breakthroughs in digital imaging now allow smartphone applications to automate important anthropometrics that can predict several body composition components. However, it is unknown whether the anthropometrics automated using smartphone applications can predict DXA-derived BMC.

METHODOLOGY

A total of 214 participants (129 F, 85 M) had BMC measurements collected from an existing proprietary prediction equation, embedded within a smartphone application (MeThreeSixty), and evaluated against DXA. LASSO regression was then used to develop a new BMC prediction equation using the anthropometric estimates produced by the smartphone application in a portion of the participants (n = 174), which was subsequently evaluated against DXA in the remaining sample (n = 40). BMC z-scores were calculated and used to identify the prevalence of low BMC for the existing and newly developed smartphone prediction equations and evaluated against DXA-derived z-scores.

RESULTS

Neither BMC estimates (R2: 0.72; RMSE: 376 g) nor BMC z-scores (R2: 0.55; RMSE: 1.09 SD) produced from the existing propriety prediction equation demonstrated equivalence with DXA in the combined sample. Moreover, the existing prediction equation had a 69.6 % accuracy of identifying low BMC. LASSO regression for the newly developed smartphone prediction model produced the following equation: BMC (g) = -2020.769 + 60.902(Black=1, 0=all other races) - 180.364(Asian=1, 0=all other races) + 24.433(height) + 1.702(weight) + 2.92(shoulder circumference) + 0.258(arm surface area) - 715.29(waist circumference/(BMI2/3 x height1/2)). BMC (R2: 0.91; RMSE: 209 g) and BMC z-scores (R2: 0.85; RMSE: 0.61) produced from the newly developed equation in the testing sample demonstrated equivalence with DXA and had a 92.5 % accuracy of identifying low BMC.

CONCLUSIONS

Smartphone anthropometrics provide accurate and clinically relevant BMC measurements outside of an advanced setting through the use of our newly-developed smartphone prediction model.

Accurate prediction of three-dimensional humanoid avatars for anthropometric modeling

OBJECTIVE

To evaluate the hypothesis that anthropometric dimensions derived from a person's manifold-regression predicted three-dimensional (3D) humanoid avatar are accurate when compared to their actual circumference, volume, and surface area measurements acquired with a ground-truth 3D optical imaging method. Avatars predicted using this approach, if accurate with respect to anthropometric dimensions, can serve multiple purposes including patient body composition analysis and metabolic disease risk stratification in clinical settings.

METHODS

Manifold regression 3D avatar prediction equations were developed on a sample of 570 adults who completed 3D optical scans, dual-energy X-ray absorptiometry (DXA), and bioimpedance analysis (BIA) evaluations. A new prospective sample of 84 adults had ground-truth measurements of 6 body circumferences, 7 volumes, and 7 surface areas with a 20-camera 3D reference scanner. 3D humanoid avatars were generated on these participants with manifold regression including age, weight, height, DXA %fat, and BIA impedances as potential predictor variables. Ground-truth and predicted avatar anthropometric dimensions were quantified with the same software.

RESULTS

Following exploratory studies, one manifold prediction model was moved forward for presentation that included age, weight, height, and %fat as covariates. Predicted and ground-truth avatars had similar visual appearances; correlations between predicted and ground-truth anthropometric estimates were all high (R2s, 0.75-0.99; all p < 0.001) with non-significant mean differences except for arm circumferences (%Δ ~ 5%; p < 0.05). Concordance correlation coefficients ranged from 0.80-0.99 and small but significant bias (p < 0.05-0.01) was present with Bland-Altman plots in 13 of 20 total anthropometric measurements. The mean waist to hip circumference ratio predicted by manifold regression was non-significantly different from ground-truth scanner measurements.

CONCLUSIONS

3D avatars predicted from demographic, physical, and other accessible characteristics can produce body representations with accurate anthropometric dimensions without a 3D scanner. Combining manifold regression algorithms into established body composition methods such as DXA, BIA, and other accessible methods provides new research and clinical opportunities.

Differences in metabolic syndrome severity and prevalence across nine waist circumference measurements collected from smartphone digital anthropometrics

BACKGROUND & AIMS

Given the technological advances in 3D smartphone (SP) anthropometry, this technique presents a unique opportunity to improve metabolic syndrome (MetS) screening through optimal waist circumference (WC) landmarking procedures. Thus, the purpose of this study was to evaluate the associations between individual MetS risk factors and nine independent WC sites collected using tape measurement or SP anthropometrics and to determine the differences in MetS severity and prevalence when using these different WC measurement locations.

METHODS

A total of 130 participants (F:74, M:56; age: 27.8 ± 11.1) completed this cross-sectional evaluation. Using traditional tape measurement, WC was measured at the lowest rib (WCRib), superior iliac crest (WCIliac), and between the WCRib and WCIliac (WCMid). Additionally, WC measurements were automated using a SP application at six sites along the torso. MetS risk factors were used to calculate MetS severity (MetSindex) and prevalence. Associations were evaluated using multiple linear regression, the effect of each WC site on MetSindex was analyzed using mixed-models ANCOVA, and differences in MetS prevalence using WCIliac as the current standard were determined using sensitivity, specificity, chi-squared tests, and odds ratios.

RESULTS

The reference SP-WC (SPRef) and WCRib demonstrated the largest associations (all p < 0.001) with HDL cholesterol (SPRef: -0.48; WCRib: -0.49), systolic (SPRef: 0.32; WCRib: 0.30) and diastolic blood pressure (SPRef: 0.34; WCRib: 0.32), and fasting blood glucose (SPRef: 0.38; WCRib: 0.37). SPRef and WCRib were the only WC without significantly different MetSindex; yet demonstrated lower MetSindex and sensitivity (SPRef: 77.8 %; WCRib: 74.1 %) relative to WCIliac, the conventional (or standard) WC measure.

CONCLUSIONS

Compared to the current standard, SPRef and WCRib protocols are more highly associated with individual MetS risk factors and produce different MetSindex and diagnoses; highlighting the need for new MetS WC protocols. Given the surge in remote/mobile healthcare, SPRef may be an alternative to traditional methods in this context but requires further investigation before implementation.

Prediction of total and regional body composition from 3D body shape

Accurate assessment of body composition is essential for evaluating the risk of chronic disease. 3D body shape, obtainable using smartphones, correlates strongly with body composition. We present a novel method that fits a 3D body mesh to a dual-energy X-ray absorptiometry (DXA) silhouette (emulating a single photograph) paired with anthropometric traits, and apply it to the multi-phase Fenland study comprising 12,435 adults. Using baseline data, we derive models predicting total and regional body composition metrics from these meshes. In Fenland follow-up data, all metrics were predicted with high correlations (r > 0.86). We also evaluate a smartphone app which reconstructs a 3D mesh from phone images to predict body composition metrics; this analysis also showed strong correlations (r > 0.84) for all metrics. The 3D body shape approach is a valid alternative to medical imaging that could offer accessible health parameters for monitoring the efficacy of lifestyle intervention programmes.

Body composition estimation from mobile phone three-dimensional imaging: evaluation of the USA army one-site method

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.

Detection of body shape changes in obesity monitoring using image processing techniques

Body measurements are primarily made with a tape measure. In measurements taken with a tape measure, the inability to take measurements from the same part of the body each time, incorrect positioning of the tape measure, the occurrence of incorrect measurements, and the need for a person to take the measurements are significant problems in the traditional measurement method. Due to the social distancing rule that must be followed during the Covid-19 pandemic, the close contact between the person to be measured and the person taking the measurement became the starting point of this study. This study focuses on the detecting body shape changes using image processing techniques with 2D imaging. The novelty of the work is that non-contact body measurements are taken more accurately and reliably using the cosine theorem. Regular monitoring of obese patients is important in combating obesity, which is also the source of many health problems. In the monitoring of obese patients, it is necessary to determine the rate of slimming in areas where fat accumulation is intense. The error margin between the real measurements of human models and the calculated measurements was calculated as an average of ± 5.16% for waistline and an average of ± 4.58% for hip size. The cosine theorem was used instead of the ellipse formula used in the literature, and it was observed that the cosine theorem obtained results closer to reality. It is also thought that the developed system will be beneficial not only for extracting body measurements but also for extracting body measurements contactless in the textile sector. The study demonstrates the feasibility of image processing for non-contact body anthropometry and shape tracking.

Trunk-to-leg volume and appendicular lean mass from a commercial 3-dimensional optical body scanner for disease risk identification

BACKGROUND & AIMS

Body shape expressed as the trunk-to-leg volume ratio is associated with diabetes and mortality due to the associations between higher adiposity and lower lean mass with Metabolic Syndrome (MetS) risk. Reduced appendicular muscle mass is associated with malnutrition risk and age-related frailty, and is a risk factor for poor treatment outcomes related to MetS and other clinical conditions (e.g.; cancer). These measures are traditionally assessed by dual-energy X-ray absorptiometry (DXA), which can be difficult to access in clinical settings. The Shape Up! Adults trial (SUA) demonstrated the accuracy and precision of 3-dimensional optical imaging (3DO) for body composition as compared to DXA and other criterion measures. Here we assessed whether trunk-to-leg volume estimates derived from 3DO are associated with MetS risk in a similar way as when measured by DXA. We further explored if estimations of appendicular lean mass (ALM) could be made using 3DO to further improve the accessibility of measuring this important frailty and disease risk factor.

METHODS

SUA recruited participants across sex, age (18-40, 40-60, >60 years), BMI (under, normal, overweight, obese), and race/ethnicity (non-Hispanic [NH] Black, NH White, Hispanic, Asian, Native Hawaiian/Pacific Islander) categories. Each participant had whole-body DXA and 3DO scans, and measures of cardiovascular health. The 3DO measures of trunk and leg volumes were calibrated to DXA to express equivalent trunk-to-leg volume ratios. We expressed each blood measure and overall MetS risk in quartile gradations of trunk-to-leg volume previously defined by National Health and Nutrition Examination Survey (NHANES). Finally, we utilized 3DO measures to estimate DXA ALM using ten-fold cross-validation of the entire dataset.

RESULTS

Participants were 502 (273 female) adults, mean age = 46.0 ± 16.5y, BMI = 27.6 ± 7.1 kg/m2 and a mean DXA trunk-to-leg volume ratio of 1.47 ± 0.22 (females: 1.43 ± 0.23; males: 1.52 ± 0.20). After adjustments for age and sex, each standard deviation increase in trunk-to-leg volume by 3DO was associated with a 3.3 (95% odds ratio [OR] = 2.4-4.2) times greater risk of MetS, with individuals in the highest quartile of trunk-to-leg at 27.4 (95% CI: 9.0-53.1) times greater risk of MetS compared to the lowest quartile. Risks of elevated blood biomarkers as related to high 3DO trunk-to-leg volume ratios were similar to previously published comparisons using DXA trunk-to-leg volume ratios. Estimated ALM by 3DO was correlated to DXA (r2 = 0.96, root mean square error = 1.5 kg) using ten-fold cross-validation.

CONCLUSION

Using thresholds of trunk-to-leg associated with MetS developed on a sample of US-representative adults, trunk-to-leg ratio by 3DO after adjustments for offsets showed significant associations to blood parameters and MetS risk. 3DO scans provide a precise and accurate estimation of ALM across the range of body sizes included in the study sample. The development of these additional measures improves the clinical utility of 3DO for the assessment of MetS risk as well as the identification of low muscle mass associated with poor cardiometabolic and functional health.

The Associations between Depression and Sugar Consumption Are Mediated by Emotional Eating and Craving Control in Multi-Ethnic Young Adults

BACKGROUND/OBJECTIVES

Individuals with mental health conditions such as depression are vulnerable to poor dietary habits, potentially due to the maladaptive eating behaviors often used to regulate negative emotion. However, the specific dietary components most associated with depression, as well as the mediating roles of emotion regulation and other eating behaviors, remains ambiguous in young adults.

METHODS

For this cross-sectional evaluation, a total of 151 (86 F, 65 M; BMI: 22.0 ± 5.1 kg/m2; age: 21.4 ± 2.5 y) multi-ethnic participants (50 White, 36 Black, 60 Asian, and 5 White Hispanic) completed a digital 24-h dietary recall and self-reported measures of depressive symptoms, emotional regulation, and eating behaviors. LASSO regression was used to identify the dietary variables most associated with each subscale and to remove extraneous dietary variables, and multiple regression and mediation analyses were conducted for the remaining variables.

RESULTS

Out of >100 dietary factors included, only added sugar in the combined sample (p = 0.043), and relative sugar in females (p = 0.045), were retained and positively associated with depressive symptoms. However, the relationships between depression and added and relative sugar intake were mediated by craving control and emotional eating, respectively. Individuals with higher added sugar intake (p = 0.012-0.037), and females with higher relative sugar intake (p = 0.029-0.033), had significantly higher odds of risk for major depression disorder and the use of mental health medications.

CONCLUSIONS

Added and relative sugar intake are significantly associated with depressive symptoms in young adults, but these relationships may be mediated by facets of emotional dysregulation, such as emotional eating and craving control.

Skeletal muscle estimation: A review of techniques and their applications

Quantifying skeletal muscle size is necessary to identify those at risk for conditions that increase frailty, morbidity, and mortality, as well as decrease quality of life. Although muscle strength, muscle quality, and physical performance have been suggested as important assessments in the screening, prevention, and management of sarcopenic and cachexic individuals, skeletal muscle size is still a critical objective marker. Several techniques exist for estimating skeletal muscle size; however, each technique presents with unique characteristics regarding simplicity/complexity, cost, radiation dose, accessibility, and portability that are important factors for assessors to consider before applying these modalities in practice. This narrative review presents a discussion centred on the theory and applications of current non-invasive techniques for estimating skeletal muscle size in diverse populations. Common instruments for skeletal muscle assessment include imaging techniques such as computed tomography, magnetic resonance imaging, peripheral quantitative computed tomography, dual-energy X-ray absorptiometry, and Brightness-mode ultrasound, and non-imaging techniques like bioelectrical impedance analysis and anthropometry. Skeletal muscle size can be acquired from these methods using whole-body and/or regional assessments, as well as prediction equations. Notable concerns when conducting assessments include the absence of standardised image acquisition/processing protocols and the variation in cut-off thresholds used to define low skeletal muscle size by clinicians and researchers, which could affect the accuracy and prevalence of diagnoses. Given the importance of evaluating skeletal muscle size, it is imperative practitioners are informed of each technique and their respective strengths and weaknesses.

Predicting Nonalcoholic Fatty Liver Disease in Obese Populations with 3D Body Scans

Nonalcoholic Fatty Liver Disease (NAFDL), characterized by the accumulation of fat in the liver, is considered a significant threat to public health, particularly in conjunction with obesity and the presence of Nonalcoholic Steatohepatitis (NASH). Traditional clinical methods for evaluating NAFDL, such as liver biopsy and MRI, are either invasive or costly. The utilization of 3D body scans offers a noninvasive and efficient approach to capture precise body shape information rapidly. This study explores the correlation between NAFLD and body shape information in the obese population applying various machine learning models over diverse 3D shape features. The results of the experiments indicate that NAFDL exhibits a stronger association with features containing more intricate shape details. Point cloud features extracted from the 3D trunk region outperform other shape descriptors, such as girth measurements, achieving the highest accuracy at 72% and the F1 score exceeding 0.8 in the classification. These findings suggest that 3D body scans present a promising and cost-effective alternative for the diagnosis of hepatic steatosis. 3D body scans could be valuable in identifying NAFDL and NASH at an early stage, offering a more accessible option for individuals at risk for fatty liver.

Mobile phone applications for 3-dimensional scanning and digital anthropometry: a precision comparison with traditional scanners

BACKGROUND

The precision of digital anthropometry through 3-dimensional (3D) scanning has been established for relatively large, expensive, non-portable systems. The comparative performance of modern mobile applications is unclear.

SUBJECTS/METHODS

Forty-six adults (age: 23.3 ± 5.3 y; BMI: 24.4 ± 4.1 kg/m2) were assessed in duplicate using: (1) a mobile phone application capturing two individual 2D images, (2) a mobile phone application capturing serial images collected during a subject's complete rotation, (3) a traditional scanner with a time of flight infrared sensor collecting visual data from a subject being rotated on a mechanical turntable, and (4) a commercial measuring booth with structured light technology using 20 infrared depth sensors positioned in the booth. The absolute and relative technical error of measurement (TEM) and intraclass correlation coefficient (ICC) for each method were established.

RESULTS

Averaged across circumferences, the absolute TEM, relative TEM, and ICC were (1) 0.9 cm, 1.5%, and 0.975; (2) 0.5 cm, 0.9%, and 0.986; (3) 0.8 cm, 1.5%, and 0.974; and (4) 0.6 cm, 1.1%, and 0.985. For total body volume, these values were (1) 2.2 L, 3.0%, and 0.978; (2) 0.8 L, 1.1%, and 0.997; (3) 0.7 L, 0.9%, and 0.998; and (4) 0.8 L, 1.1%, and 0.996, with segmental volumes demonstrating higher relative errors.

CONCLUSION

A 3D scanning mobile phone application involving full rotation of subjects in front of a smartphone camera exhibited similar reliability to larger, less portable, more expensive 3D scanners. In contrast, larger errors were observed for a mobile scanning application utilizing two 2D images, although the technical errors were acceptable for some applications.

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.

Advances in digital anthropometric body composition assessment: neural network algorithm prediction of appendicular lean mass

Currently available anthropometric body composition prediction equations were often developed on small participant samples, included only several measured predictor variables, or were prepared using conventional statistical regression methods. Machine learning approaches are increasingly publicly available and have key advantages over statistical modeling methods when developing prediction algorithms on large datasets with multiple complex covariates. This study aimed to test the feasibility of predicting DXA-measured appendicular lean mass (ALM) with a neural network (NN) algorithm developed on a sample of 576 participants using 10 demographic (sex, age, 7 ethnic groupings) and 43 anthropometric dimensions generated with a 3D optical scanner. NN-predicted and measured ALM were highly correlated (n = 116; R2, 0.95, p < 0.001, non-significant bias) with small mean, absolute, and root-mean square errors (X ± SD, -0.17 ± 1.64 kg and 1.28 ± 1.04 kg; 1.64). These observations demonstrate the application of NN body composition prediction algorithms to rapidly emerging large and complex digital anthropometric datasets. Clinical Trial Registration: NCT03637855, NCT05217524, NCT03771417, and NCT03706612.

Smartphone derived anthropometrics: Agreement between a commercially available smartphone application and its parent application intended for use at point-of-care

BACKGROUND & AIMS

Smartphone applications can now automate body composition and anthropometric measurements remotely, prompting applications intended for use at point-of-care to provide commercially available smartphone applications intended for personal use. However, the agreement between such anthropometrics remain unclear.

METHODS

A total of 123 apparently healthy participants (F: 69; M: 54; age: 28.1 ± 11.3; BMI: 26.9 ± 5.9) completed consecutive body composition scans using a 3D smartphone application intended for personal use (MeThreeSixty; MTS) and it stationary counterpart intended for use in practice (Mobile Fit Booth; MFB). Agreement between devices were evaluated using root mean square error (RMSE), Bland-Altman analyses, and linear regression for all measurements, and additional equivalence testing was conducted for all circumference and limb length comparisons.

RESULTS

When evaluated against the MFB, MTS significantly overestimated all measurements other than waist circumference (p = 0.670) using paired t-tests. RMSE was 2.5 % for body fat percentage (BF%), 0.64-3.74 cm for all body circumferences, 0.71-2.3 kg for all lean mass estimates, and 126-659 cm2 and 608-4672 cm3 across all body surface area and body volume estimates, respectively. BF% was the only body composition estimate that did not demonstrate proportional bias (p = 0.221). Circumferences of the chest, shoulder, biceps, forearm, and ankle all demonstrated proportional bias (all coefficients: p < 0.050), but only chest, shoulder, and arm circumferences did not demonstrate equivalence. Arm surface area (p < 0.001) and arm (p = 0.002) and leg volumes (p = 0.004) were the only body surface area and volume estimates to reveal proportional biases.

CONCLUSIONS

These findings demonstrate the agreement between 3D anthropometric applications intended for clinical and personal use, particularly for whole-body composition estimates and clinically meaningful body circumferences. Given the advantages of commercially available remote applications, practitioners and consumers may consider using this method in place of those intended for clinical practice, but should express caution when overestimation is a concern.

Equations for smartphone prediction of adiposity and appendicular lean mass in youth soccer players

Digital anthropometry by three-dimensional optical imaging systems and smartphones has recently been shown to provide non-invasive, precise, and accurate anthropometric and body composition measurements. To our knowledge, no previous study performed smartphone-based digital anthropometric assessments in young athletes. The aim of this study was to investigate the reproducibly and validity of smartphone-based estimation of anthropometric and body composition parameters in youth soccer players. A convenience sample of 124 male players and 69 female players (median ages of 16.2 and 15.5 years, respectively) was recruited. Measurements of body weight and height, one whole-body Dual-Energy X-ray Absorptiometry (DXA) scan, and acquisition of optical images (performed in duplicate by the Mobile Fit app to obtain two avatars for each player) were performed. The reproducibility analysis showed percent standard error of measurement values < 10% for all anthropometric and body composition measurements, thus indicating high agreement between the measurements obtained for the two avatars. Mobile Fit app overestimated the body fat percentage with respect to DXA (average overestimation of + 3.7% in males and + 4.6% in females), while it underestimated the total lean mass (- 2.6 kg in males and - 2.5 kg in females) and the appendicular lean mass (- 10.5 kg in males and - 5.5 kg in females). Using data of the soccer players, we reparameterized the equations previously proposed to estimate the body fat percentage and the appendicular lean mass and we obtained new equations that can be used in youth athletes for body composition assessment through conventional anthropometrics-based prediction models.