Novel advancements in wearable technologies include continuous measurement of body composition via smart watches. The accuracy and stability of these devices are unknown.
This study evaluated smart ...watches with integrated bioelectrical impedance analysis (BIA) sensors for their ability to measure and monitor changes in body composition.
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.
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.
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.
Full text
Available for:
CMK, GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
The accurate assessment of total body and regional body circumferences, volumes, and compositions are critical to monitor physical activity and dietary interventions, as well as accurate disease ...classifications including obesity, metabolic syndrome, sarcopenia, and lymphedema. We assessed body composition and anthropometry estimates provided by a commercial 3-dimensional optical (3DO) imaging system compared to criterion measures.
Participants of the Shape Up! Adults study were recruited for similar sized stratifications by sex, age (18–40, 40–60, >60 years), BMI (under, normal, overweight, obese), and across five ethnicities (non-Hispanic NH Black, NH White, Hispanic, Asian, Native Hawaiian/Pacific Islander). All participants received manual anthropometry assessments, duplicate whole-body 3DO (Styku S100), and dual-energy X-ray absorptiometry (DXA) scans. 3DO estimates provided by the manufacturer for anthropometry and body composition were compared to the criterion measures using concordance correlation coefficient (CCC) and Bland–Altman analysis. Test-retest precision was assessed by root mean square error (RMSE) and coefficient of variation.
A total of 188 (102 female) participants were included. The overall fat free mass (FFM) as measured by DXA (54.1 ± 15.2 kg) and 3DO (55.3 ± 15.0 kg) showed a small mean difference of 1.2 ± 3.4 kg (95% limits of agreement −7.0 to +5.6) and the CCC was 0.97 (95% CI: 0.96–0.98). The CCC for FM was 0.95 (95% CI: 0.94–0.97) and the mean difference of 1.3 ± 3.4 kg (95% CI: −5.5 to +8.1) reflected the difference in FFM measures. 3DO anthropometry and body composition measurements showed high test-retest precision for whole body volume (1.1 L), fat mass (0.41 kg), percent fat (0.60%), arm and leg volumes, (0.11 and 0.21 L, respectively), and waist and hip circumferences (all <0.60 cm). No group differences were observed when stratified by body mass index, sex, or race/ethnicity.
The anthropometric and body composition estimates provided by the 3DO scanner are precise and accurate to criterion methods if offsets are considered. This method offers a rapid, broadly available, and automated method of body composition assessment regardless of body size. Further studies are recommended to examine the relationship between measurements obtained by 3DO scans and metabolic health in healthy and clinical populations.
Full text
Available for:
GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Objective
This study aimed to explore the accuracy and precision of three‐dimensional optical (3DO) whole‐body scanning for automated anthropometry and estimating total and regional body composition.
...Methods
Healthy children and adolescents (n = 181, ages 5‐17 years) were recruited for the Shape Up! Kids study. Each participant underwent whole‐body dual‐energy x‐ray absorptiometry and 3DO scans; multisite conventional tape measurements served as the anthropometric criterion measure. 3DO body shape was described using automated body circumference, length, and volume measures. 3DO estimates were compared with criterion measures using simple linear regression by the stepwise selection method.
Results
Of the 181 participants, 112 were used for the training set, 49 were used for the test set, and 20 were excluded for technical reasons. 3DO body composition estimates were strongly associated with dual‐energy x‐ray absorptiometry measures for percent body fat, fat mass, and fat‐free mass (R2: 0.83, 0.96, and 0.98, respectively). 3DO provided reliable measurements of fat mass (coefficient of variation, 3.30; root mean square error RMSE, 0.53), fat‐free mass (coefficient of variation, 1.34; RMSE, 0.53 kg), and percent body fat (RMSE = 1.2%).
Conclusions
3DO surface scanning provides accurate and precise anthropometric and body composition estimates in children and adolescents with high precision. 3DO is a safe, accessible, and practical method for evaluating body shape and composition in research and clinical settings.
Full text
Available for:
FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SAZU, SBCE, SBMB, UL, UM, UPUK
Objective
The aim of this study was to investigate whether digitally re‐posing three‐dimensional optical (3DO) whole‐body scans to a standardized pose would improve body composition accuracy and ...precision regardless of the initial pose.
Methods
Healthy adults (n = 540), stratified by sex, BMI, and age, completed whole‐body 3DO and dual‐energy X‐ray absorptiometry (DXA) scans in the Shape Up! Adults study. The 3DO mesh vertices were represented with standardized templates and a low‐dimensional space by principal component analysis (stratified by sex). The total sample was split into a training (80%) and test (20%) set for both males and females. Stepwise linear regression was used to build prediction models for body composition and anthropometry outputs using 3DO principal components (PCs).
Results
The analysis included 472 participants after exclusions. After re‐posing, three PCs described 95% of the shape variance in the male and female training sets. 3DO body composition accuracy compared with DXA was as follows: fat mass R2 = 0.91 male, 0.94 female; fat‐free mass R2 = 0.95 male, 0.92 female; visceral fat mass R2 = 0.77 male, 0.79 female.
Conclusions
Re‐posed 3DO body shape PCs produced more accurate and precise body composition models that may be used in clinical or nonclinical settings when DXA is unavailable or when frequent ionizing radiation exposure is unwanted.
Full text
Available for:
FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SAZU, SBCE, SBMB, UL, UM, UPUK
Reply to Y Lu et al Bennett, Jonathan P; Liu, Yong En; Kelly, Nisa N ...
The American journal of clinical nutrition,
03/2023, Volume:
117, Issue:
3
Journal Article
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.
This study aimed to evaluate 3DO’s accuracy and precision by subgroups of age, body mass index, and ethnicity.
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.
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).
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).
Background
Many predictors of morbidity caused by metabolic disease are associated with body shape. 3D optical (3DO) scanning captures body shape and has been shown to accurately and precisely ...predict body composition variables associated with mortality risk. 3DO is safer, less expensive, and more accessible than criterion body composition assessment methods such as dual‐energy X‐ray absorptiometry (DXA). However, 3DO scanning has not been standardized across manufacturers for pose, mesh resolution, and post processing methods.
Purpose
We introduce a scanner‐agnostic algorithm that automatically fits a topologically consistent human mesh to 3DO scanned point clouds and predicts clinically important body metrics using a standardized body shape model. Our models transform raw scans captured by any 3DO scanner into fixed topology meshes with anatomical consistency, standardizing the outputs of 3DO scans across manufacturers and allowing for the use of common prediction models across scanning devices.
Methods
A fixed‐topology body mesh template was automatically registered to 848 training scans from three different 3DO systems. Participants were between 18 and 89 years old with body mass index ranging from 14 to 52 kg/m2. Scans were registered by first performing a coarse nearest neighbor alignment between the template and the input scan with an anatomically constrained principal component analysis (PCA) domain deformation using a device and gender specific bootstrap basis trained on 70 seed scans each. The template mesh was then optimized to fit the target with a smooth per‐vertex surface‐to‐surface deformation. A combined unified PCA model was created from the superset of all automatically fit training scans including all three devices. Body composition predictions to DXA measurements were learned from the training mesh PCA coefficients using linear regression. Using this final unified model, we tested the accuracy of our body composition models on a withheld sample of 562 scans by fitting a PCA parameterized template mesh to each raw scan and predicting the expected body composition metrics from the principal components using the learned regression model.
Results
We achieved coefficients of determination (R2) above 0.8 on all nine fat and lean predictions except female visceral fat (0.77). R2 was as high as 0.94 (total fat and lean, trunk fat), and all root‐mean‐squared errors were below 3.0 kg. All predicted body composition variables were not significantly different from reference DXA measurements except for visceral fat and female trunk fat. Repeatability precision as measured by the coefficient of variation (%CV) was around 2–3x worse than DXA precision, with visceral fat %CV below 2x DXA %CV and female total fat mass at 5x.
Conclusions
Our method provides an accurate, automated, and scanner agnostic framework for standardizing 3DO scans and a low cost, radiation‐free alternative to criterion radiology imaging for body composition analysis. We published a web‐app version of this work at https://shapeup.shepherdresearchlab.org/3do‐bodycomp‐analyzer/ that accepts mesh file uploads and returns templated meshes with body composition predictions for demo purposes.
Full text
Available for:
BFBNIB, FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SAZU, SBCE, SBMB, UL, UM, UPUK
New recommendations for the assessment of malnutrition and sarcopenia include body composition, specifically reduced muscle mass. Three-dimensional optical imaging (3DO) is a validated, accessible, ...and affordable alternative to dual X-ray absorptiometry (DXA).
Identify strengths and weaknesses of 3DO for identification of malnutrition in participants with low body mass index (BMI) and eating disorders.
Participants were enrolled in the cross-sectional Shape Up! Adults and Kids studies of body shape, metabolic risk, and functional assessment and had BMI of <20 kg/m2 in adults or <85% of median BMI (mBMI) in children and adolescents. A subset was referred for eating disorders evaluation. Anthropometrics, scans, strength testing, and questionnaires were completed in clinical research centers. Lin’s Concordance Correlation Coefficient (CCC) assessed agreement between 3DO and DXA; multivariate linear regression analysis examined associations between weight history and body composition.
Among 95 participants, mean ± SD BMI was 18.3 ± 1.4 kg/m2 in adult women (N = 56), 19.0 ± 0.6 in men (N = 14), and 84.2% ± 4.1% mBMI in children (N = 25). Concordance was excellent for fat-free mass (FFM, CCC = 0.97) and strong for appendicular lean mass (ALM, CCC = 0.86) and fat mass (FM, CCC = 0.87). By DXA, 80% of adults met the low FFM index criterion for malnutrition, and 44% met low ALM for sarcopenia; 52% of children and adolescents were <−2 z-score for FM. 3DO identified 95% of these cases. In the subset, greater weight loss predicted lower FFM, FM, and ALM by both methods; a greater percentage of weight regained predicted a higher percentage of body fat.
3DO can accurately estimate body composition in participants with low BMI and identify criteria for malnutrition and sarcopenia. In a subset, 3DO detected changes in body composition expected with weight loss and regain secondary to eating disorders. These findings support the utility of 3DO for body composition assessment in patients with low BMI, including those with eating disorders.
This trial was registered at clinicaltrials.gov as NCT03637855.
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).
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.
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.
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.
Full text
Available for:
GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
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.
This study aimed to evaluate the ability of 3DO in monitoring body composition changes across multiple intervention studies.
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.
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.
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).
PDF
