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Bennett JP, Prado CM, Heymsfield SB, Shepherd JA. Evaluation of visceral adipose tissue thresholds for elevated metabolic syndrome risk across diverse populations: A systematic review. Obes Rev 2024:e13767. [PMID: 38761009 DOI: 10.1111/obr.13767] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/09/2023] [Revised: 04/11/2024] [Accepted: 04/17/2024] [Indexed: 05/20/2024]
Abstract
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.
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Affiliation(s)
| | - Carla M Prado
- Department of Agricultural, Food and Nutritional Science, University of Alberta, Edmonton, Alberta, Canada
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Tinsley GM, Rodriguez C, Siedler MR, Tinoco E, White SJ, LaValle C, Brojanac A, DeHaven B, Rasco J, Florez CM, Graybeal AJ. Mobile phone applications for 3-dimensional scanning and digital anthropometry: a precision comparison with traditional scanners. Eur J Clin Nutr 2024:10.1038/s41430-024-01424-w. [PMID: 38454153 DOI: 10.1038/s41430-024-01424-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2023] [Revised: 02/19/2024] [Accepted: 02/20/2024] [Indexed: 03/09/2024]
Abstract
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.
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Affiliation(s)
- Grant M Tinsley
- Energy Balance & Body Composition Laboratory, Department of Kinesiology & Sport Management, Texas Tech University, Lubbock, TX, USA.
| | - Christian Rodriguez
- Energy Balance & Body Composition Laboratory, Department of Kinesiology & Sport Management, Texas Tech University, Lubbock, TX, USA
| | - Madelin R Siedler
- Energy Balance & Body Composition Laboratory, Department of Kinesiology & Sport Management, Texas Tech University, Lubbock, TX, USA
| | - Ethan Tinoco
- Energy Balance & Body Composition Laboratory, Department of Kinesiology & Sport Management, Texas Tech University, Lubbock, TX, USA
| | - Sarah J White
- Energy Balance & Body Composition Laboratory, Department of Kinesiology & Sport Management, Texas Tech University, Lubbock, TX, USA
| | - Christian LaValle
- Energy Balance & Body Composition Laboratory, Department of Kinesiology & Sport Management, Texas Tech University, Lubbock, TX, USA
| | - Alexandra Brojanac
- Energy Balance & Body Composition Laboratory, Department of Kinesiology & Sport Management, Texas Tech University, Lubbock, TX, USA
| | - Brielle DeHaven
- Energy Balance & Body Composition Laboratory, Department of Kinesiology & Sport Management, Texas Tech University, Lubbock, TX, USA
| | - Jaylynn Rasco
- Energy Balance & Body Composition Laboratory, Department of Kinesiology & Sport Management, Texas Tech University, Lubbock, TX, USA
| | - Christine M Florez
- Energy Balance & Body Composition Laboratory, Department of Kinesiology & Sport Management, Texas Tech University, Lubbock, TX, USA
| | - Austin J Graybeal
- School of Kinesiology and Nutrition, University of Southern Mississippi, Hattiesburg, MS, USA
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Bennett JP, Cataldi D, Liu YE, Kelly NN, Quon BK, Schoeller DA, Kelly T, Heymsfield SB, Shepherd JA. Development and validation of a rapid multicompartment body composition model using 3-dimensional optical imaging and bioelectrical impedance analysis. Clin Nutr 2024; 43:346-356. [PMID: 38142479 DOI: 10.1016/j.clnu.2023.12.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2023] [Revised: 11/21/2023] [Accepted: 12/11/2023] [Indexed: 12/26/2023]
Abstract
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.
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Affiliation(s)
- Jonathan P Bennett
- Graduate Program in Human Nutrition, University of Hawai'i at Manoa, Agricultural Science Building, 1955 East-West Rd, Honolulu, HI, 96822, USA; Department of Epidemiology, University of Hawai'i Cancer Center, 701 Ilalo St, Honolulu, HI, 96813, USA
| | - Devon Cataldi
- Department of Epidemiology, University of Hawai'i Cancer Center, 701 Ilalo St, Honolulu, HI, 96813, USA
| | - Yong En Liu
- Department of Epidemiology, University of Hawai'i Cancer Center, 701 Ilalo St, Honolulu, HI, 96813, USA
| | - Nisa N Kelly
- Department of Epidemiology, University of Hawai'i Cancer Center, 701 Ilalo St, Honolulu, HI, 96813, USA
| | - Brandon K Quon
- Department of Epidemiology, University of Hawai'i Cancer Center, 701 Ilalo St, Honolulu, HI, 96813, USA
| | - Dale A Schoeller
- Department of Nutritional Sciences, University of Wisconsin-Madison, 1415 Linden Drive, Madison, WI, 53706, USA
| | - Thomas Kelly
- Hologic Inc, 250 Campus Drive, Marlborough, MA, 01752, USA
| | - Steven B Heymsfield
- Pennington Biomedical Research Center, Louisiana State University, 6400 Perkins Rd, Baton Rouge, LA, 70808, USA
| | - John A Shepherd
- Graduate Program in Human Nutrition, University of Hawai'i at Manoa, Agricultural Science Building, 1955 East-West Rd, Honolulu, HI, 96822, USA; Department of Epidemiology, University of Hawai'i Cancer Center, 701 Ilalo St, Honolulu, HI, 96813, USA.
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Leong LT, Wong MC, Liu YE, Glaser Y, Quon BK, Kelly NN, Cataldi D, Sadowski P, Heymsfield SB, Shepherd JA. Generative deep learning furthers the understanding of local distributions of fat and muscle on body shape and health using 3D surface scans. COMMUNICATIONS MEDICINE 2024; 4:13. [PMID: 38287144 PMCID: PMC10824755 DOI: 10.1038/s43856-024-00434-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2023] [Accepted: 01/10/2024] [Indexed: 01/31/2024] Open
Abstract
BACKGROUND Body shape, an intuitive health indicator, is deterministically driven by body composition. We developed and validated a deep learning model that generates accurate dual-energy X-ray absorptiometry (DXA) scans from three-dimensional optical body scans (3DO), enabling compositional analysis of the whole body and specified subregions. Previous works on generative medical imaging models lack quantitative validation and only report quality metrics. METHODS Our model was self-supervised pretrained on two large clinical DXA datasets and fine-tuned using the Shape Up! Adults study dataset. Model-predicted scans from a holdout test set were evaluated using clinical commercial DXA software for compositional accuracy. RESULTS Predicted DXA scans achieve R2 of 0.73, 0.89, and 0.99 and RMSEs of 5.32, 6.56, and 4.15 kg for total fat mass (FM), fat-free mass (FFM), and total mass, respectively. Custom subregion analysis results in R2s of 0.70-0.89 for left and right thigh composition. We demonstrate the ability of models to produce quantitatively accurate visualizations of soft tissue and bone, confirming a strong relationship between body shape and composition. CONCLUSIONS This work highlights the potential of generative models in medical imaging and reinforces the importance of quantitative validation for assessing their clinical utility.
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Affiliation(s)
- Lambert T Leong
- Molecular Bioscience and Bioengineering at University of Hawaii, Honolulu, HI, USA
- Department of Epidemiology, University of Hawaii Cancer Center, Honolulu, HI, USA
| | - Michael C Wong
- Department of Epidemiology, University of Hawaii Cancer Center, Honolulu, HI, USA
| | - Yong E Liu
- Department of Epidemiology, University of Hawaii Cancer Center, Honolulu, HI, USA
| | - Yannik Glaser
- Information and Computer Science at University of Hawaii, Honolulu, HI, USA
| | - Brandon K Quon
- Department of Epidemiology, University of Hawaii Cancer Center, Honolulu, HI, USA
| | - Nisa N Kelly
- Department of Epidemiology, University of Hawaii Cancer Center, Honolulu, HI, USA
| | - Devon Cataldi
- Department of Epidemiology, University of Hawaii Cancer Center, Honolulu, HI, USA
| | - Peter Sadowski
- Information and Computer Science at University of Hawaii, Honolulu, HI, USA
| | - Steven B Heymsfield
- Pennington Biomedical Research Center, Louisiana State University, Baton Rouge, LO, USA
| | - John A Shepherd
- Molecular Bioscience and Bioengineering at University of Hawaii, Honolulu, HI, USA.
- Department of Epidemiology, University of Hawaii Cancer Center, Honolulu, HI, USA.
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Cataldi D, Bennett JP, Wong MC, Quon BK, Liu YE, Kelly NN, Kelly T, Schoeller DA, Heymsfield SB, Shepherd JA. Accuracy and precision of multiple body composition methods and associations with muscle strength in athletes of varying hydration: The Da Kine Study. Clin Nutr 2024; 43:284-294. [PMID: 38104490 DOI: 10.1016/j.clnu.2023.11.040] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2023] [Revised: 11/27/2023] [Accepted: 11/29/2023] [Indexed: 12/19/2023]
Abstract
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.
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Affiliation(s)
- Devon Cataldi
- Department of Epidemiology, University of Hawaii Cancer Center, 701 Ilalo Street, Honolulu, HI, 96813, USA
| | - Jonathan P Bennett
- Department of Epidemiology, University of Hawaii Cancer Center, 701 Ilalo Street, Honolulu, HI, 96813, USA
| | - Michael C Wong
- Department of Epidemiology, University of Hawaii Cancer Center, 701 Ilalo Street, Honolulu, HI, 96813, USA
| | - Brandon K Quon
- Department of Epidemiology, University of Hawaii Cancer Center, 701 Ilalo Street, Honolulu, HI, 96813, USA
| | - Yong En Liu
- Department of Epidemiology, University of Hawaii Cancer Center, 701 Ilalo Street, Honolulu, HI, 96813, USA
| | - Nisa N Kelly
- Department of Epidemiology, University of Hawaii Cancer Center, 701 Ilalo Street, Honolulu, HI, 96813, USA
| | - Thomas Kelly
- Hologic Inc, 250 Campus Dr, Marlborough, MA 01752, USA
| | - Dale A Schoeller
- Isotope Ratio Core Biotech Center and Nutritional Sciences, Henry Mall Madison, WI 53706, USA
| | - Steven B Heymsfield
- Pennington Biomedical Research Center, 6400 Perkins Road, Baton Rouge, LA, 7080, USA
| | - John A Shepherd
- Department of Epidemiology, University of Hawaii Cancer Center, 701 Ilalo Street, Honolulu, HI, 96813, USA.
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Salmón-Gómez L, Catalán V, Frühbeck G, Gómez-Ambrosi J. Relevance of body composition in phenotyping the obesities. Rev Endocr Metab Disord 2023; 24:809-823. [PMID: 36928809 PMCID: PMC10492885 DOI: 10.1007/s11154-023-09796-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 03/08/2023] [Indexed: 03/18/2023]
Abstract
Obesity is the most extended metabolic alteration worldwide increasing the risk for the development of cardiometabolic alterations such as type 2 diabetes, hypertension, and dyslipidemia. Body mass index (BMI) remains the most frequently used tool for classifying patients with obesity, but it does not accurately reflect body adiposity. In this document we review classical and new classification systems for phenotyping the obesities. Greater accuracy of and accessibility to body composition techniques at the same time as increased knowledge and use of cardiometabolic risk factors is leading to a more refined phenotyping of patients with obesity. It is time to incorporate these advances into routine clinical practice to better diagnose overweight and obesity, and to optimize the treatment of patients living with obesity.
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Affiliation(s)
- Laura Salmón-Gómez
- Metabolic Research Laboratory, Clínica Universidad de Navarra, Irunlarrea 1, Pamplona, 31008, Spain
- CIBER Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III, Pamplona, Spain
| | - Victoria Catalán
- Metabolic Research Laboratory, Clínica Universidad de Navarra, Irunlarrea 1, Pamplona, 31008, Spain
- CIBER Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III, Pamplona, Spain
- Obesity and Adipobiology Group, Instituto de Investigación Sanitaria de Navarra (IdiSNA) Pamplona, Pamplona, Spain
| | - Gema Frühbeck
- Metabolic Research Laboratory, Clínica Universidad de Navarra, Irunlarrea 1, Pamplona, 31008, Spain
- CIBER Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III, Pamplona, Spain
- Obesity and Adipobiology Group, Instituto de Investigación Sanitaria de Navarra (IdiSNA) Pamplona, Pamplona, Spain
- Department of Endocrinology & Nutrition, Clínica Universidad de Navarra, Pamplona, Spain
| | - Javier Gómez-Ambrosi
- Metabolic Research Laboratory, Clínica Universidad de Navarra, Irunlarrea 1, Pamplona, 31008, Spain.
- CIBER Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III, Pamplona, Spain.
- Obesity and Adipobiology Group, Instituto de Investigación Sanitaria de Navarra (IdiSNA) Pamplona, Pamplona, Spain.
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Ashby N, Jake LaPorte G, Richardson D, Scioletti M, Heymsfield SB, Shepherd JA, McGurk M, Bustillos B, Gist N, Thomas DM. Translating digital anthropometry measurements obtained from different 3D body image scanners. Eur J Clin Nutr 2023; 77:872-880. [PMID: 37165098 DOI: 10.1038/s41430-023-01289-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2022] [Revised: 03/31/2023] [Accepted: 04/25/2023] [Indexed: 05/12/2023]
Abstract
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.
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Affiliation(s)
- Nicholas Ashby
- Department of Mathematical Sciences, United States Military Academy, West Point, NY, USA
| | - G Jake LaPorte
- Department of Mathematical Sciences, United States Military Academy, West Point, NY, USA
| | - Daniel Richardson
- Department of Mathematical Sciences, United States Military Academy, West Point, NY, USA
| | - Michael Scioletti
- Department of Mathematical Sciences, United States Military Academy, West Point, NY, USA
| | | | | | - Michael McGurk
- Research and Analysis Directorate, U.S. Army Center for Initial Military Training (CIMT), U.S. Army Training & Doctrine Command (TRADOC), Fort Eustis, VA, USA
| | - Brenda Bustillos
- Research and Analysis Directorate, U.S. Army Center for Initial Military Training (CIMT), U.S. Army Training & Doctrine Command (TRADOC), Fort Eustis, VA, USA
| | - Nicholas Gist
- Department of Physical Education, United States Military Academy, West Point, NY, USA
| | - Diana M Thomas
- Department of Mathematical Sciences, United States Military Academy, West Point, NY, USA.
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