Assessment of clinical measures of total and regional body composition from a commercial 3-dimensional optical body scanner

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.

Effect of Strength Training on Body Composition, Volumetrics and Strength in Female Breast Cancer Survivors

BACKGROUND/AIMS

This cross-sectional study investigates body composition and strength in female breast cancer survivors, focusing on the effects of radical mastectomy and the presence of upper extremity lymphoedema. The main objective was to understand body composition, volumetry, and strength, as well as response to strength training in female breast cancer survivors.

METHODS

Twenty-three women (aged 42-74 years old) with radical mastectomy in the last five years were assessed by measuring body composition (weight, water percentage, fat, muscle, and lean mass), maximal strength, perimeters, and brachial volumes. Participants completed a 10-week strength training program of moderate intensity with 20 training sessions. No significant differences were found between the affected/healthy hemispheres in terms of composition, perimeters, and volumetrics. However, 11 women were found to have lymphoedema (47.8%). No statistically significant differences were found between hemibodies after the intervention, although improvements were obtained in pectoral strength and manual grip, as well as in muscle mass and lean mass [p = 0.002 each]. Cases with lymphoedema were reduced to 5 (21.73%).

CONCLUSIONS

While strength training is shown to benefit body composition, strength, and the incidence of lymphoedema in mastectomized women, further scientific evidence is needed with larger controlled trials and follow-up studies to validate these findings, as well as the impact on the quality of life of these survivors.

The applicability of a commercial 3DO body scanner in measuring body composition in Chinese adults with overweight and obesity: a secondary analysis based on a weight-loss clinical trial

BACKGROUND

A commercial three-dimensional optical (3DO) scanning system was reported to be used in body composition assessment. However, the applicability in Chinese adults has yet to be well-studied.

METHODS

This secondary analysis was based on a 16-week weight-loss clinical trial with an optional extension to 24 weeks. Waist and hip circumference and body composition were measured by 3DO scanning at each follow-up visit during the study. Bioelectrical impedance analysis (BIA) was also performed to confirm the reliability of 3DO scanning at each visit. We used Lin's concordance correlation coefficients (CCC) to evaluate the correlation between the two methods above-mentioned. Bland-Altman analysis was also performed to evaluate the agreement and potential bias between different methods.

RESULTS

A total number of 70 Chinese adults overweight and obese (23 men and 47 women, aged 31.8 ± 5.8 years) were included in the analysis, which resulted in 350 3DO scans and corresponding 350 BIA measurements. The percent body fat, fat mass, and fat-free mass were 33.9 ± 5.4%, 26.7 ± 4.6 kg, and 50.3 ± 8.7 kg before the trial by 3DO scanning. And they were 30.5 ± 5.8%, 22.5 ± 4.7 kg, and 49.4 ± 8.3 kg after 16 weeks of the trial. Compared with BIA, 3DO scanning performed best in the assessment of fat-free mass (CCC = 0.89, 95%CI: 0.86, 0.90), then followed by fat mass (CCC = 0.76, 95%CI: 0.71, 0.80) and percent body fat (CCC = 0.70, 95%CI: 0.64, 0.75). Subgroup analysis showed that 3DO scanning and BIA correlated better in women than that in men, and correlated better in measuring fat-free mass in participants with larger body weight (BMI ≥28.0 kg/m2) than those with smaller body weight (<28.0 kg/m2).

CONCLUSIONS

3DO scanning is an effective technology to monitor changes in body composition in Chinese adults overweight and obese. However its accuracy and reliability in different ethnicities needs further exploration.

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.

Biomechanical and physiological biomarkers are useful indicators of military personnel readiness: a multi-institutional, multinational research collaboration

A ubiquitous problem facing military organisations is musculoskeletal injury (MSKI) risk identification. Recently, two research groups, each with their own funding, collaborated to address this problem. Combining their respective areas of expertise in biomechanics and physiological biomarkers, the group explored this problem in the laboratory and in the field. They have developed a machine learning model in a US Marine Corps (USMC) officer cadet cohort that identifies MSKI risk from a single jump test, identified a minimum inertial measurement unit sensor array to quantity jump and squat performance and have identified sex differences in overuse, lower-limb injury risk. This machine learning model was able to correctly predict lift to place within 4 kg using a testing data set and less than 1 kg in the training set of data. Such collaborative approaches are encouraged to address complicated research problems. To assemble an effective team, consider forming groups that best complement each other's areas of expertise and prioritise securing separate funding to ensure each group can act independently. By doing this, the group has assessed the suitability and feasibility of various wearable technologies, used machine learning to gain insights into USMC physiological training adaptations, and developed an understanding of MSKI risk profiles within this cohort.

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.

Sex-Specific Associations of Five Serum Essential Metal Elements with Thyroid Nodules in Euthyroid Adults: a Cross‑sectional Study

The association between the serum essential metal elements (magnesium, iron, copper, zinc, and calcium) and thyroid nodules is still inconsistent. The current study aims to investigate the relationship of metal elements with thyroid nodules and their malignant tendency. A total of 6480 Chinese euthyroid adults were included in our study. We collect basic information through questionnaires and medical checkups. We diagnose thyroid nodules by ultrasound and detect serum trace metal concentrations by using an automatic biochemical analyzer. Binary and multinomial logistic regressions were used to investigate the associations. As a result, we found that serum copper concentrations were positively associated with thyroid nodules in the second, third, and fourth quartiles, compared to the first quartile (P = 0.024, P = 0.016, P = 0.032) in women and P for trend is 0.038. There is a significant sex-specific association between copper concentrations and thyroid nodules (P for interaction = 0.009). The results of the multinomial logistic regression analyses indicate high serum calcium and magnesium concentrations emerged as consistent risk factors for thyroid nodules in both genders, whereas low zinc was a sex-specific factor. We also observed significant sex interactions in the relationships of magnesium (P for interaction = 0.043) with thyroid nodules with malignant tendency among participants with thyroid nodules. In conclusion, our study suggests that gender is an important factor when studying the association between serum metals and thyroid nodules. The imbalance of selected metal elements (calcium, copper, zinc, and magnesium) may relate to thyroid nodules and their malignant tendency, and future prospective studies are needed to further confirm the associations.

Evaluation of visceral adipose tissue thresholds for elevated metabolic syndrome risk across diverse populations: A systematic review

Beyond obesity, excess levels of visceral adipose tissue (VAT) significantly contribute to the risk of developing metabolic syndrome (MetS), although thresholds for increased risk vary based on population, regions of interest, and units of measure employed. We sought to determine whether a common threshold exists that is indicative of heightened MetS risk across all populations, accounting for sex, age, BMI, and race/ethnicity. A systematic literature review was conducted in September 2023, presenting threshold values for elevated MetS risk. Standardization equations harmonized the results from DXA, CT, and MRI systems to facilitate a comparison of threshold variations across studies. A total of 52 papers were identified. No single threshold could accurately indicate elevated risk for both males and females across varying BMI, race/ethnicity, and age groups. Thresholds fluctuated from 70 to 165.9 cm2, with reported values consistently lower in females. Generally, premenopausal females and younger adults manifested elevated risks at lower VAT compared to their older counterparts. Notably, Asian populations exhibited elevated risks at lower VAT areas (70-136 cm2) compared to Caucasian populations (85.6-165.9 cm2). All considered studies reported associations of VAT without accommodating covariates. No single VAT area threshold for elevated MetS risk was discernible post-harmonization by technology, units of measure, and region of interest. This review summarizes available evidence for MetS risk assessment in clinical practice. Further exploration of demographic-specific interactions between VAT area and other risk factors is imperative to comprehensively delineate overarching MetS risk.

Digital Biometry as an Obesity Diagnosis Tool: A Review of Current Applications and Future Directions

Obesity is a chronic relapsing disease and a major public health concern due to its high prevalence and associated complications. Paradoxically, several studies have found that obesity might positively impact the prognosis of patients with certain existing chronic diseases, while some individuals with normal BMI may develop obesity-related complications. This phenomenon might be explained by differences in body composition, such as visceral adipose tissue (VAT), total body fat (TBF), and fat-free mass (FFM). Indirect measures of body composition such as body circumferences, skinfold thicknesses, and bioelectrical impedance analysis (BIA) devices are useful clinically and in epidemiological studies but are often difficult to perform, time-consuming, or inaccurate. Biomedical imaging methods, i.e., computerized tomography scanners (CT scan), dual-energy X-ray absorptiometry (DEXA), and magnetic resonance imaging (MRI), provide accurate assessments but are expensive and not readily available. Recent advancements in 3D optical image technology offer an innovative way to assess body circumferences and body composition, though most machines are costly and not widely available. Two-dimensional optical image technology might offer an interesting alternative, but its accuracy needs validation. This review aims to evaluate the efficacy of 2D and 3D automated body scan devices in assessing body circumferences and body composition.

Priorities for efficacy trials of gender-affirming hormone therapy with estrogen: collaborative design and results of a community survey

PURPOSE

Treatment guidelines for gender-affirming hormone therapy with estrogen (GAHT-E) recommend specific dosing regimens based on limited data. Well-controlled efficacy trials are essential to tailoring treatment to patient goals as the guidelines recommend. The goal of this study was to take a foundational step toward designing community-centered effectiveness trials for gender-diverse individuals seeking GAHT-E.

METHODS

Our team developed a cross-sectional survey based on broad clinical experience and consultation with our community advisory board. The survey included 60 items covering demographics, transition history, goals and priorities for treatment, indicators of treatment success, sexual function goals, and future research priorities. The survey was distributed during the summer of 2021, primarily through social networks designed for gender-expansive individuals seeking treatment with estrogen.

RESULTS

A total of 1270 individuals completed the survey. Overall treatment goals most frequently rated "extremely important" or "very important" were the following: (1) improved satisfaction with life (81%), (2) appearing more feminine (80%), (3) appearing less masculine (77%), (4) improved mental health (76%), and (5) being seen as your true gender by others (75%). The three body characteristics most frequently rated "highest priority" or "high priority" among changes were the following: (1) facial hair (85%), (2) breast shape or size (84%), and (3) body shape (80%). The highest-rated research priority was comparing feminization with different routes of estrogen administration.

CONCLUSION

The goals and experiences of individuals seeking GAHT-E are diverse. Future clinical trials of GAHT-E should be grounded in the needs and priorities of community stakeholders.

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.

Associations between 47 anthropometric markers derived from a body scanner and relative fat-free mass in a population-based study

BACKGROUND

Low relative fat free mass (FFM) is associated with a greater risk of chronic diseases and mortality. Unfortunately, FFM is currently not being measured regularly to allow for individuals therapy.

OBJECTIVE

One reason why FFM is not being used may be related to additional equipment and resources, thus we aimed to identify easily accessible anthropometric markers related with FFM.

MATERIALS AND METHODS

We analyzed data of 1,593 individuals (784 women; 49.2%, age range 28-88 years) enrolled in the population-based Study of Health in Pomerania (SHIP-TREND 1). Forty-seven anthropometric markers were derived from a 3D optical body-scanner. FFM was assessed by bioelectrical impedance analysis (FFMBIA) or air displacement plethysmography (FFMADP). In sex-stratified linear regression models, FFM was regressed on anthropometric measurements adjusted for body height and age. Anthropometric markers were ranked according to the coefficient of determination (R2) derived from these regression models.

RESULTS

Circumferences of high hip, belly, middle hip, waist and high waist showed the strongest inverse associations with FFM. These relations were stronger in females than in males. Associations of anthropometric markers with FFMAPD were greater compared to FFMBIA.

CONCLUSION

Anthropometric measures were more strongly associated with FFMADP compared to FFMBIA. Anthropometric markers like circumferences of the high or middle hip, belly or waist may be appropriate surrogates for FFM to aid in individualized therapy. Given that the identified markers are representative of visceral adipose tissue, the connection between whole body strength as surrogate for FFM and fat mass should be explored in more detail.

Quantitative imaging workshop XIX: Utilizing quantitative thoracic imaging to optimize population health final summary

Lung cancer screening involves the use of thoracic CT for both detection and measurements of suspicious lung nodules to guide the screening management. Since lung cancer screening eligibility typically requires age over 50 years along with >20 pack-year tobacco exposure, thoracic CT scans also frequently reveal evidence for pulmonary emphysema as well as coronary artery calcification. These three thoracic diseases are collectively three of the leading causes of premature death across the world. Screening for the major thoracic diseases in this heavily tobacco-exposed cohort is broadening the focus of lung cancer screening to a more comprehensive health evaluation including discussing the relevance of screen-detected findings of the heart and lung parenchyma. The status and implications of these emerging issues were reviewed in a multidisciplinary workshop focused on the process of quantitative imaging in the lung cancer screening setting to guide the evolution of this important new area of public health.

Patient perceptions of three-dimensional (3D) surface imaging technology and traditional methods used to assess anthropometry

Background

Obesity and overweight are commonplace, yet attrition rates in weight management clinics are high. Traditional methods of body measurement may be a deterrent due to invasive and time-consuming measurements and negative experiences of how data are presented back to individuals. Emerging new technologies, such as three-dimensional (3D) surface imaging technology, might provide a suitable alternative. This study aimed to understand acceptability of traditional and 3D surface imaging-based body measures, and whether perceptions differ between population groups.

Methods

This study used a questionnaire to explore body image, body measurement and shape, followed by a qualitative semi-structured interview and first-hand experience of traditional and 3D surface imaging-based body measures.

Results

49 participants responded to the questionnaire and 26 participants attended for the body measurements and interview over a 2-month period. There were 3 main themes from the qualitative data 1) Use of technology, 2) Participant experience, expectations and perceptions and 3) Perceived benefits and uses.

Conclusion

From this study, 3D-surface imaging appeared to be acceptable to patients as a method for anthropometric measurements, which may reduce anxiety and improve attrition rates in some populations. Further work is required to understand the scalability, and the role and implications of these technologies in weight management practice. (University Research Ethics Committee reference number ER41719941).

Development and validation of a rapid multicompartment body composition model using 3-dimensional optical imaging and bioelectrical impedance analysis

BACKGROUND & AIMS

The multicompartment approach to body composition modeling provides a more precise quantification of body compartments in healthy and clinical populations. We sought to develop and validate a simplified and accessible multicompartment body composition model using 3-dimensional optical (3DO) imaging and bioelectrical impedance analysis (BIA).

METHODS

Samples of adults and collegiate-aged student-athletes were recruited for model calibration. For the criterion multicompartment model (Wang-5C), participants received measures of scale weight, body volume (BV) via air displacement, total body water (TBW) via deuterium dilution, and bone mineral content (BMC) via dual energy x-ray absorptiometry. The candidate model (3DO-5C) used stepwise linear regression to derive surrogate measures of BV using 3DO, TBW using BIA, and BMC using demographics. Test-retest precision of the candidate model was assessed via root mean square error (RMSE). The 3DO-5C model was compared to criterion via mean difference, concordance correlation coefficient (CCC), and Bland-Altman analysis. This model was then validated using a separate dataset of 20 adults.

RESULTS

67 (31 female) participants were used to build the 3DO-5C model. Fat-free mass (FFM) estimates from Wang-5C (60.1 ± 13.4 kg) and 3DO-5C (60.3 ± 13.4 kg) showed no significant mean difference (-0.2 ± 2.0 kg; 95 % limits of agreement [LOA] -4.3 to +3.8) and the CCC was 0.99 with a similar effect in fat mass that reflected the difference in FFM measures. In the validation dataset, the 3DO-5C model showed no significant mean difference (0.0 ± 2.5 kg; 95 % LOA -3.6 to +3.7) for FFM with almost perfect equivalence (CCC = 0.99) compared to the criterion Wang-5C. Test-retest precision (RMSE = 0.73 kg FFM) supports the use of this model for more frequent testing in order to monitor body composition change over time.

CONCLUSIONS

Body composition estimates provided by the 3DO-5C model are precise and accurate to criterion methods when correcting for field calibrations. The 3DO-5C approach offers a rapid, cost-effective, and accessible method of body composition assessment that can be used broadly to guide nutrition and exercise recommendations in athletic settings and clinical practice.

The Precision, Inter-Rater Reliability, and Accuracy of a Handheld Scanner Equipped with a Light Detection and Ranging Sensor in Measuring Parts of the Body-A Preliminary Validation Study

BACKGROUND

Anthropometric measurements play a crucial role in medico-legal practices. Actually, several scanning technologies are employed in post-mortem investigations for forensic anthropological measurements. This study aims to evaluate the precision, inter-rater reliability, and accuracy of a handheld scanner in measuring various body parts.

METHODS

Three independent raters measured seven longitudinal distances using an iPad Pro equipped with a LiDAR sensor and specific software. These measurements were statistically compared to manual measurements conducted by an operator using a laser level and a meterstick (considered the gold standard).

RESULTS

The Friedman test revealed minimal intra-rater variability in digital measurements. Inter-rater variability analysis yielded an ICC = 1, signifying high agreement among the three independent raters. Additionally, the accuracy of digital measurements displayed errors below 1.5%.

CONCLUSIONS

Preliminary findings demonstrate that the pairing of LiDAR technology with the Polycam app (ver. 3.2.11) and subsequent digital measurements with the MeshLab software (ver. 2022.02) exhibits high precision, inter-rater agreement, and accuracy. Handheld scanners show potential in forensic anthropology due to their simplicity, affordability, and portability. However, further validation studies under real-world conditions are essential to establish the reliability and effectiveness of handheld scanners in medico-legal settings.

Accuracy and precision of multiple body composition methods and associations with muscle strength in athletes of varying hydration: The Da Kine Study

BACKGROUND

Athletes vary in hydration status due to ongoing training regimes, diet demands, and extreme exertion. With water being one of the largest body composition compartments, its variation can cause misinterpretation of body composition assessments meant to monitor strength and training progress. In this study, we asked what accessible body composition approach could best quantify body composition in athletes with a variety of hydration levels.

METHODS

The Da Kine Study recruited collegiate and intramural athletes to undergo a variety of body composition assessments including air-displacement plethysmography (ADP), deuterium-oxide dilution (D2O), dual-energy X-ray absorptiometry (DXA), underwater-weighing (UWW), 3D-optical (3DO) imaging, and bioelectrical impedance (BIA). Each of these methods generated 2- or 3-compartment body composition estimates of fat mass (FM) and fat-free mass (FFM) and was compared to equivalent measures of the criterion 6-compartment model (6CM) that accounts for variance in hydration. Body composition by each method was used to predict abdominal and thigh strength, assessed by isokinetic/isometric dynamometry.

RESULTS

In total, 70 (35 female) athletes with a mean age of 21.8 ± 4.2 years were recruited. Percent hydration (Body Water6CM/FFM6CM) had substantial variation in both males (63-73 %) and females (58-78 %). ADP and DXA FM and FF M had moderate to substantial agreement with the 6C model (Lin's Concordance Coefficient [CCC] = 0.90-0.95) whereas the other measures had lesser agreement (CCC <0.90) with one exception of 3DO FFM in females (CCC = 0.91). All measures of FFM produced excellent precision with %CV < 1.0 %. However, FM measures in general had worse precision (% CV < 2.0 %). Increasing quartiles (significant p < 0.001 trend) of 6CM FFM resulted in increasing strength measures in males and females. Moreover, the stronger the agreement between the alternative methods to the 6CM, the more robust their correlation with strength, irrespective of hydration status.

CONCLUSION

The criterion 6CM showed the best association to strength regardless of the hydration status of the athletes for both males and females. Simpler methods showed high precision for both FM and FFM and those with the strongest agreement to the 6CM had the highest strength associations.

SUMMARY BOX

This study compared various body composition analysis methods in 70 athletes with varying states of hydration to the criterion 6-compartment model and assessed their relationship to muscle strength. The results showed that accurate and precise estimates of body composition can be determined in athletes, and a more accurate body composition measurement produces better strength estimates. The best laboratory-based techniques were air displacement plethysmography and dual-energy x-ray absorptiometry, while the commercial methods had moderate-poor agreement. Prioritizing accurate body composition assessment ensures better strength estimates in athletes.

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.

Cost-effective 3D scanning and printing technologies for outer ear reconstruction: current status

Current 3D scanning and printing technologies offer not only state-of-the-art developments in the field of medical imaging and bio-engineering, but also cost and time effective solutions for surgical reconstruction procedures. Besides tissue engineering, where living cells are used, bio-compatible polymers or synthetic resin can be applied. The combination of 3D handheld scanning devices or volumetric imaging, (open-source) image processing packages, and 3D printers form a complete workflow chain that is capable of effective rapid prototyping of outer ear replicas. This paper reviews current possibilities and latest use cases for 3D-scanning, data processing and printing of outer ear replicas with a focus on low-cost solutions for rehabilitation engineering.

Longitudinal concordance of body composition and anthropometric assessment by a novel smartphone application across a 12-week self-managed weight loss intervention

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.

Associations between 3D surface scanner derived anthropometric measurements and body composition in a cross-sectional study

BACKGROUND

3D laser-based photonic scanners are increasingly used in health studies to estimate body composition. However, too little is known about whether various 3D body scan measures estimate body composition better than single standard anthropometric measures, and which body scans best estimate it. Furthermore, little is known about differences by sex and age.

METHODS

105 men and 96 women aged between 18 and 90 years were analysed. Bioelectrical Impedance Analysis was used to estimate whole relative fat mass (RFM), visceral adipose tissue (VAT) and skeletal muscle mass index (SMI). An Anthroscan VITUSbodyscan was used to obtain 3D body scans (e.g. volumes, circumferences, lengths). To reduce the number of possible predictors that could predict RFM, VAT and SMI backward elimination was performed. With these selected predictors linear regression on the respective body compositions was performed and the explained variations were compared with models using standard anthropometric measurements (Body Mass Index (BMI), waist circumference (WC) and waist-to-height-ratio (WHtR)).

RESULTS

Among the models based on standard anthropometric measures, WC performed better than BMI and WHtR in estimating body composition in men and women. The explained variations in models including body scan variables are consistently higher than those from standard anthropometrics models, with an increase in explained variations between 5% (RFM for men) and 10% (SMI for men). Furthermore, the explained variation of body composition was additionally increased when age and lifestyle variables were added. For each of the body composition variables, the number of predictors differed between men and women, but included mostly volumes and circumferences in the central waist/chest/hip area and the thighs.

CONCLUSIONS

3D scan models performed better than standard anthropometric measures models to predict body composition. Therefore, it is an advantage for larger health studies to look at body composition more holistically using 3D full body surface scans.

Accuracy and Precision of 3-dimensional Optical Imaging for Body Composition by Age, BMI, and Ethnicity

BACKGROUND

The obesity epidemic brought a need for accessible methods to monitor body composition, as excess adiposity has been associated with cardiovascular disease, metabolic disorders, and some cancers. Recent 3-dimensional optical (3DO) imaging advancements have provided opportunities for assessing body composition. However, the accuracy and precision of an overall 3DO body composition model in specific subgroups are unknown.

OBJECTIVES

This study aimed to evaluate 3DO's accuracy and precision by subgroups of age, body mass index, and ethnicity.

METHODS

A cross-sectional analysis was performed using data from the Shape Up! Adults study. Each participant received duplicate 3DO and dual-energy X-ray absorptiometry (DXA) scans. 3DO meshes were digitally registered and reposed using Meshcapade. Principal component analysis was performed on 3DO meshes. The resulting principal components estimated DXA whole-body and regional body composition using stepwise forward linear regression with 5-fold cross-validation. Duplicate 3DO and DXA scans were used for test-retest precision. Student's t tests were performed between 3DO and DXA by subgroup to determine significant differences.

RESULTS

Six hundred thirty-four participants (females = 346) had completed the study at the time of the analysis. 3DO total fat mass in the entire sample achieved R2 of 0.94 with root mean squared error (RMSE) of 2.91 kg compared to DXA in females and similarly in males. 3DO total fat mass achieved a % coefficient of variation (RMSE) of 1.76% (0.44 kg), whereas DXA was 0.98% (0.24 kg) in females and similarly in males. There were no mean differences for total fat, fat-free, percent fat, or visceral adipose tissue by age group (P > 0.068). However, there were mean differences for underweight, Asian, and Black females as well as Native Hawaiian or other Pacific Islanders (P < 0.038).

CONCLUSIONS

A single 3DO body composition model produced accurate and precise body composition estimates that can be used on diverse populations. However, adjustments to specific subgroups may be warranted to improve the accuracy in those that had significant differences. This trial was registered at clinicaltrials.gov as NCT03637855 (Shape Up! Adults).

Translating digital anthropometry measurements obtained from different 3D body image scanners

BACKGROUND

Body image scanners are used in industry and research to reliably provide a wealth of anthropometric measurements within seconds. The demonstrated utility of the scanners drives the current proliferation of more commercially available devices that rely on their own reference body sites and proprietary algorithms to output anthropometric measurements. Since each scanner relies on its own algorithms, measurements obtained from different scanners cannot directly be combined or compared.

OBJECTIVES

To develop mathematical models that translate anthropometric measurements between the three popular commercially available scanners.

METHODS

A unique database that contained 3D scanner measurements in the same individuals from three different scanners (Styku, Human Solutions, and Fit3D) was used to develop linear regression models that translate anthropometric measurements between each scanner. A limits of agreement analysis was performed between Fit3D and Styku against Human Solutions measurements and the coefficient of determination, bias, and 95% confidence interval were calculated. The models were then applied to normalized scanner data from four different studies to compare the results of a k-means cluster analysis between studies. A scree plot was used to determine the optimal number of clusters derived from each study.

RESULTS

Correlations ranged between R2 = 0.63 (Styku and Human Solutions mid-thigh circumference) to R2 = 0.97 (Human Solutions and Fit3D neck circumference). In general, Fit3D had better agreement with Human Solutions compared to Styku. The widest disagreement was found in chest circumference (Fit3D (bias = 2.30, 95% CI = [-3.83, 8.43]) and Styku (bias = -5.60, 95% CI = [-10.98, -0.22]). The optimal number of body shape clusters in each of the four studies was consistently 5.

CONCLUSIONS

The newly developed models that translate measurements between the scanners Styku and Fit3D to predict Human Solutions measurements make it possible to standardize data between scanners allowing for data pooling and comparison.

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

BACKGROUND

Skeletal muscle is a large and clinically relevant body component that has been difficult and impractical to quantify outside of specialized facilities. Advances in smartphone technology now provide the opportunity to quantify multiple body surface dimensions such as circumferences, lengths, surface areas, and volumes.

OBJECTIVES

This study aimed to test the hypothesis that anthropometric body measurements acquired with a smartphone application can be used to accurately estimate an adult's level of muscularity.

METHODS

Appendicular lean mass (ALM) measured by DXA served as the reference for muscularity in a sample of 322 adults. Participants also had digital anthropometric dimensions (circumferences, lengths, and regional and total body surface areas and volumes) quantified with a 20-camera 3D imaging system. Least absolute shrinkage and selection operator (LASSO) regression procedures were used to develop the ALM prediction equations in a portion of the sample, and these models were tested in the remainder of the sample. Then, the accuracy of the prediction models was cross-validated in a second independent sample of 53 adults who underwent ALM estimation by DXA and the same digital anthropometric estimates acquired with a smartphone application.

RESULTS

LASSO models included multiple significant demographic and 3D digital anthropometric predictor variables. Evaluation of the models in the testing sample indicated respective RMSEs in women and men of 1.56 kg and 1.53 kg and R2's of 0.74 and 0.90, respectively. Cross-validation of the LASSO models in the smartphone application group yielded RMSEs in women and men of 1.78 kg and 1.50 kg and R2's of 0.79 and 0.95; no significant differences or bias between measured and predicted ALM values were observed.

CONCLUSIONS

Smartphone image capture capabilities combined with device software applications can now provide accurate renditions of the adult muscularity phenotype outside of specialized laboratory facilities. Am J Clin Nutr 2023;x:xx. This trial was registered at clinicaltrials.gov as NCT03637855 (https://clinicaltrials.gov/ct2/show/NCT03637855), NCT05217524 (https://clinicaltrials.gov/ct2/show/NCT05217524), and NCT03771417 (https://clinicaltrials.gov/ct2/show/NCT03771417).

Monitoring body composition change for intervention studies with advancing 3D optical imaging technology in comparison to dual-energy X-ray absorptiometry

BACKGROUND

Recent 3-dimensional optical (3DO) imaging advancements have provided more accessible, affordable, and self-operating opportunities for assessing body composition. 3DO is accurate and precise in clinical measures made by DXA. However, the sensitivity for monitoring body composition change over time with 3DO body shape imaging is unknown.

OBJECTIVES

This study aimed to evaluate the ability of 3DO in monitoring body composition changes across multiple intervention studies.

METHODS

A retrospective analysis was performed using intervention studies on healthy adults that were complimentary to the cross-sectional study, Shape Up! Adults. Each participant received a DXA (Hologic Discovery/A system) and 3DO (Fit3D ProScanner) scan at the baseline and follow-up. 3DO meshes were digitally registered and reposed using Meshcapade to standardize the vertices and pose. Using an established statistical shape model, each 3DO mesh was transformed into principal components, which were used to predict whole-body and regional body composition values using published equations. Body composition changes (follow-up minus the baseline) were compared with those of DXA using a linear regression analysis.

RESULTS

The analysis included 133 participants (45 females) in 6 studies. The mean (SD) length of follow-up was 13 (5) wk (range: 3-23 wk). Agreement between 3DO and DXA (R2) for changes in total FM, total FFM, and appendicular lean mass were 0.86, 0.73, and 0.70, with root mean squared errors (RMSEs) of 1.98 kg, 1.58 kg, and 0.37 kg, in females and 0.75, 0.75, and 0.52 with RMSEs of 2.31 kg, 1.77 kg, and 0.52 kg, in males, respectively. Further adjustment with demographic descriptors improved the 3DO change agreement to changes observed with DXA.

CONCLUSIONS

Compared with DXA, 3DO was highly sensitive in detecting body shape changes over time. The 3DO method was sensitive enough to detect even small changes in body composition during intervention studies. The safety and accessibility of 3DO allows users to self-monitor on a frequent basis throughout interventions. This trial was registered at clinicaltrials.gov as NCT03637855 (Shape Up! Adults; https://clinicaltrials.gov/ct2/show/NCT03637855); NCT03394664 (Macronutrients and Body Fat Accumulation: A Mechanistic Feeding Study; https://clinicaltrials.gov/ct2/show/NCT03394664); NCT03771417 (Resistance Exercise and Low-Intensity Physical Activity Breaks in Sedentary Time to Improve Muscle and Cardiometabolic Health; https://clinicaltrials.gov/ct2/show/NCT03771417); NCT03393195 (Time Restricted Eating on Weight Loss; https://clinicaltrials.gov/ct2/show/NCT03393195), and NCT04120363 (Trial of Testosterone Undecanoate for Optimizing Performance During Military Operations; https://clinicaltrials.gov/ct2/show/NCT04120363).

Machine learning-based obesity classification considering 3D body scanner measurements

Obesity can cause various diseases and is a serious health concern. BMI, which is currently the popular measure for judging obesity, does not accurately classify obesity; it reflects the height and weight but ignores the characteristics of an individual's body type. In order to overcome the limitations of classifying obesity using BMI, we considered 3-dimensional (3D) measurements of the human body. The scope of our study was limited to Korean subjects. In order to expand 3D body scan data clinically, 3D body scans, Dual-energy X-ray absorptiometry, and Bioelectrical Impedance Analysis data was collected pairwise for 160 Korean subjects. A machine learning-based obesity classification framework using 3D body scan data was designed, validated through Accuracy, Recall, Precision, and F1 score, and compared with BMI and BIA. In a test dataset of 40 people, BMI had the following values: Accuracy: 0.529, Recall: 0.472, Precision: 0.458, and F1 score: 0.462, while BIA had the following values: Accuracy: 0.752, Recall: 0.742, Precision: 0.751, and F1 score: 0.739. Our proposed model had the following values: Accuracy: 0.800, Recall: 0.767, Precision: 0.842, and F1 score: 0.792. Thus, our accuracy was higher than BMI as well as BIA. Our model can be used for obesity management through 3D body scans.

Next-generation smart watches to estimate whole-body composition using bioimpedance analysis: accuracy and precision in a diverse, multiethnic sample

BACKGROUND

Novel advancements in wearable technologies include continuous measurement of body composition via smart watches. The accuracy and stability of these devices are unknown.

OBJECTIVES

This study evaluated smart watches with integrated bioelectrical impedance analysis (BIA) sensors for their ability to measure and monitor changes in body composition.

METHODS

Participants recruited across BMIs received duplicate body composition measures using 2 wearable bioelectrical impedance analysis (W-BIA) model smart watches in sitting and standing positions, and multiple versions of each watch were used to evaluate inter- and intramodel precision. Duplicate laboratory-grade octapolar bioelectrical impedance analysis (8-BIA) and criterion DXA scans were acquired to compare estimates between the watches and laboratory methods. Test-retest precision and least significant changes assessed the ability to monitor changes in body composition.

RESULTS

Of 109 participants recruited, 75 subjects completed the full manufacturer-recommended protocol. No significant differences were observed between W-BIA watches in position or between watch models. Significant fat-free mass (FFM) differences (P < 0.05) were observed between both W-BIA and 8-BIA when compared to DXA, though the systematic biases to the criterion were correctable. No significant difference was observed between the W-BIA and the laboratory-grade BIA technology for FFM (55.3 ± 14.5 kg for W-BIA versus 56.0 ± 13.8 kg for 8-BIA; P > 0.05; Lin's concordance correlation coefficient = 0.97). FFM was less precise on the watches than DXA {CV, 0.7% [root mean square error (RMSE) = 0.4 kg] versus 1.3% (RMSE = 0.7 kg) for W-BIA}, requiring more repeat measures to equal the same confidence in body composition changes over time as DXA.

CONCLUSIONS

After systematic correction, smart-watch BIA devices are capable of stable, reliable, and accurate body composition measurements, with precision comparable to but lower than that of laboratory measures. These devices allow for measurement in environments not accessible to laboratory systems, such as homes, training centers, and geographically remote locations.

analysis combined with sagittal abdominal diameter for abdominal subcutaneous fat measurement

Abdominal subcutaneous fat tissue (ASFT) is an independent predictor of mortality. This prospective observational study aimed to establish a rapid, safe, and convenient estimation equation for abdominal subcutaneous fat area (SFA) using bioimpedance analysis (BIA) combined with sagittal abdominal diameter (SAD). A total of 520 adult subjects were recruited and were randomly divided into 2/3 (n = 346) and 1/3 (n = 174) to form a modeling group (MG) and a validation group (VG), respectively. Each subject's abdomen was scanned using computed tomography to obtain target variables (SFA_CT). Predictor variables for all subjects included bioimpedance index (h²/Z), anthropometric parameters height (h), weight (W), waist circumference (WC), hip circumference (HC), and SAD, along with age and sex (male =1, female = 0). SFA estimation equation SFA_BIA+SAD was established for the MG using stepwise multiple regression analysis. Cross-validation was performed using VG to evaluate the performance of the SFA_BIA+SAD estimation equation. Stepwise multiple regression analysis was applied from the MG, including SFA_BIA+SAD = 49.89 + 1.09 SAD-29.90 Sex + 4.71 W-3.63 h²/Z-1.50 h (r = 0.92, SEE = 28.10 cm², n = 346, p < 0.001). Mean differences in SFA_BIA+SAD relative to SFA_CT were -1.21 ± 21.53, 2.85 ± 27.16, and -0.98 ± 36.6 cm² at different levels of obesity (eutrophic, overweight, obese), respectively. This study did not have a large number of samples in different fields, so it did not have completely external validity. Application of BIA combined with SAD in anthropometric parameters achieves fast, accurate and convenient SAF measurement. Results of this study provide a simple, reliable, and practical measurement that can be widely used in epidemiological studies and in measuring individual SFA.

Three-dimensional optical body shape and features improve prediction of metabolic disease risk in a diverse sample of adults

OBJECTIVE

This study examined whether body shape and composition obtained by three-dimensional optical (3DO) scanning improved the prediction of metabolic syndrome (MetS) prevalence compared with BMI and demographics.

METHODS

A diverse ambulatory adult population underwent whole-body 3DO scanning, blood tests, manual anthropometrics, and blood pressure assessment in the Shape Up! Adults study. MetS prevalence was evaluated based on 2005 National Cholesterol Education Program criteria, and prediction of MetS involved logistic regression to assess (1) BMI, (2) demographics-adjusted BMI, (3) 85 3DO anthropometry and body composition measures, and (4) BMI + 3DO + demographics models. Receiver operating characteristic area under the curve (AUC) values were generated for each predictive model.

RESULTS

A total of 501 participants (280 female) were recruited, with 87 meeting the criteria for MetS. Compared with the BMI model (AUC = 0.819), inclusion of age, sex, and race increased the AUC to 0.861, and inclusion of 3DO measures further increased the AUC to 0.917. The overall integrated discrimination improvement between the 3DO + demographics and the BMI model was 0.290 (p < 0.0001) with a net reclassification improvement of 0.214 (p < 0.0001).

CONCLUSIONS

Body shape measures from an accessible 3DO scan, adjusted for demographics, predicted MetS better than demographics and/or BMI alone. Risk classification in this population increased by 29% when using 3DO scanning.

Digital Anthropometry for Body Circumference Measurements: European Phenotypic Variations throughout the Decades

This review summarizes body circumference-based anthropometrics that are in common use for research and in some cases clinical application. These include waist and hip circumference-based central body indices to predict cardiometabolic risk: waist circumference, waist-to-hip ratio, waist-to-height ratio, waist-to-thigh ratio, body adiposity index, a body shape index (ABSI), hip index (HI), and body roundness index (BRI). Limb circumference measurements are most often used to assess sarcopenia and include: thigh circumference, calf circumference, and mid-arm circumference. Additionally, this review presents fascinating recent developments in optic-based imaging technologies that have elucidated changes over the last decades in average body size and shape in European populations. The classical apple and pear shape concepts of body shape difference remain useful, but novel and exciting 3-D optical "e-taper" measurements provide a potentially powerful new future vista in anthropometrics.