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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; 78:509-514. [PMID: 38454153 DOI: 10.1038/s41430-024-01424-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [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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2
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Nield L, Thelwell M, Chan A, Choppin S, Marshall S. Patient perceptions of three-dimensional (3D) surface imaging technology and traditional methods used to assess anthropometry. OBESITY PILLARS (ONLINE) 2024; 9:100100. [PMID: 38357215 PMCID: PMC10865393 DOI: 10.1016/j.obpill.2024.100100] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/17/2023] [Revised: 01/16/2024] [Accepted: 01/17/2024] [Indexed: 02/16/2024]
Abstract
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).
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Affiliation(s)
- Lucie Nield
- Advanced Wellbeing Research Centre, Sheffield Hallam University, Olympic Legacy Park, Sheffield, S9 3TU, UK
| | - Michael Thelwell
- Advanced Wellbeing Research Centre, Sheffield Hallam University, Olympic Legacy Park, Sheffield, S9 3TU, UK
| | - Audrey Chan
- Sheffield Business School, City Campus, Sheffield Hallam University, S1 1WB, UK
| | - Simon Choppin
- Advanced Wellbeing Research Centre, Sheffield Hallam University, Olympic Legacy Park, Sheffield, S9 3TU, UK
| | - Steven Marshall
- Sheffield Business School, City Campus, Sheffield Hallam University, S1 1WB, UK
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García Flores FI, Klünder Klünder M, López Teros MT, Muñoz Ibañez CA, Padilla Castañeda MA. Development and Validation of a Method of Body Volume and Fat Mass Estimation Using Three-Dimensional Image Processing with a Mexican Sample. Nutrients 2024; 16:384. [PMID: 38337669 PMCID: PMC10856961 DOI: 10.3390/nu16030384] [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: 11/06/2023] [Revised: 12/12/2023] [Accepted: 12/27/2023] [Indexed: 02/12/2024] Open
Abstract
Body composition assessment using instruments such as dual X-ray densitometry (DXA) can be complex and their use is often limited to research. This cross-sectional study aimed to develop and validate a densitometric method for fat mass (FM) estimation using 3D cameras. Using two such cameras, stereographic images, and a mesh reconstruction algorithm, 3D models were obtained. The FM estimations were compared using DXA as a reference. In total, 28 adults, with a mean BMI of 24.5 (±3.7) kg/m2 and mean FM (by DXA) of 19.6 (±5.8) kg, were enrolled. The intraclass correlation coefficient (ICC) for body volume (BV) was 0.98-0.99 (95% CI, 0.97-0.99) for intra-observer and 0.98 (95% CI, 0.96-0.99) for inter-observer reliability. The coefficient of variation for kinetic BV was 0.20 and the mean difference (bias) for BV (liter) between Bod Pod and Kinect was 0.16 (95% CI, -1.2 to 1.6), while the limits of agreement (LoA) were 7.1 to -7.5 L. The mean bias for FM (kg) between DXA and Kinect was -0.29 (95% CI, -2.7 to 2.1), and the LoA was 12.1 to -12.7 kg. The adjusted R2 obtained using an FM regression model was 0.86. The measurements of this 3D camera-based system aligned with the reference measurements, showing the system's feasibility as a simpler, more economical screening tool than current systems.
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Affiliation(s)
| | - Miguel Klünder Klünder
- Research Subdirectorate, Children’s Hospital of Mexico Federico Gómez, Dr. Marquez St. 162, Colonia Doctores, Mexico City 06720, Mexico
| | - Miriam Teresa López Teros
- Health Department, Santa Fe Campus, Iberoamerican University, Prol. Paseo de la Reforma, Zedec Sta Fé, Álvaro Obregón, Mexico City 01219, Mexico;
| | - Cristopher Antonio Muñoz Ibañez
- Institute of Advanced Materials for Sustainable Manufacturing, Tecnológico de Monterrey, Canal de Miramontes, Tlalpan, Mexico City 14380, Mexico;
| | - Miguel Angel Padilla Castañeda
- Applied Science and Technology Institute (ICAT), National Autonomous University of Mexico (UNAM), Mexico City 04510, Mexico
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4
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Wong MC, Bennett JP, Quon B, Leong LT, Tian IY, Liu YE, Kelly NN, McCarthy C, Chow D, Pujades S, Garber AK, Maskarinec G, Heymsfield SB, Shepherd JA. Accuracy and Precision of 3-dimensional Optical Imaging for Body Composition by Age, BMI, and Ethnicity. Am J Clin Nutr 2023; 118:657-671. [PMID: 37474106 PMCID: PMC10517211 DOI: 10.1016/j.ajcnut.2023.07.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2023] [Revised: 07/03/2023] [Accepted: 07/13/2023] [Indexed: 07/22/2023] Open
Abstract
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).
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Affiliation(s)
- Michael C Wong
- Department of Epidemiology, University of Hawaii Cancer Center, Honolulu, HI, United States; Department of Human Nutrition, Food and Animal Sciences, University of Hawaii at Manoa, Honolulu, HI, United States
| | - Jonathan P Bennett
- Department of Epidemiology, University of Hawaii Cancer Center, Honolulu, HI, United States; Department of Human Nutrition, Food and Animal Sciences, University of Hawaii at Manoa, Honolulu, HI, United States
| | - Brandon Quon
- Department of Epidemiology, University of Hawaii Cancer Center, Honolulu, HI, United States
| | - Lambert T Leong
- Department of Epidemiology, University of Hawaii Cancer Center, Honolulu, HI, United States
| | - Isaac Y Tian
- Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, WA, United States
| | - Yong E Liu
- Department of Epidemiology, University of Hawaii Cancer Center, Honolulu, HI, United States
| | - Nisa N Kelly
- Department of Epidemiology, University of Hawaii Cancer Center, Honolulu, HI, United States
| | - Cassidy McCarthy
- Pennington Biomedical Research Center, Baton Rouge, LA, United States
| | - Dominic Chow
- John A. Burns School of Medicine, University of Hawaii at Manoa, Honolulu, HI, United States
| | - Sergi Pujades
- Inria, Université Grenoble Alpes, CNRS, Grenoble INP, LJK, Grenoble, France
| | - Andrea K Garber
- Department of Pediatrics, University of California San Francisco, San Francisco, CA, United States
| | - Gertraud Maskarinec
- Department of Epidemiology, University of Hawaii Cancer Center, Honolulu, HI, United States
| | | | - John A Shepherd
- Department of Epidemiology, University of Hawaii Cancer Center, Honolulu, HI, United States; Department of Human Nutrition, Food and Animal Sciences, University of Hawaii at Manoa, Honolulu, HI, United States.
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5
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Dong H, Cheng H, Liu J, Yan Y, Zhao X, Shan X, Huang G, Mi J. Overfat cutoffs and the optimal combination of body fat indices for detecting cardiometabolic risk among school-aged children. Obesity (Silver Spring) 2023; 31:802-810. [PMID: 36746769 DOI: 10.1002/oby.23651] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/29/2022] [Revised: 10/10/2022] [Accepted: 10/21/2022] [Indexed: 02/08/2023]
Abstract
OBJECTIVE This study aimed to develop cutoffs and the optimal combination for body fat indices for screening cardiometabolic risk (CMR) among the pediatric population. METHODS This cross-sectional study consisted of 8710 (50.3% boys) Chinese children aged 6 to 18 years. Body fat indices, including fat mass index (FMI), body fat percentage, trunk to leg fat ratio (TLR), and android to gynoid fat ratio, were derived from dual-energy x-ray absorptiometry scans. The area under the receiver operating characteristic curve was used to determine the best combination and optimal cutoffs of body fat indices to identify CMR. RESULTS Compared with anthropometry-based obesity measures, i.e., BMI and waist circumference, the FMI + TLR combination presented statistically higher area under the receiver operating characteristic curve values for discriminating CMR and its clustering. The optimal overfat cutoffs of FMI and TLR were respectively determined at the 75th percentile in boys and at the 80th percentile of FMI and the 75th percentile of TLR in girls. Moreover, simplified thresholds derived from age-group-merged cutoffs showed similar performance as optimal cutoffs in detecting CMR. CONCLUSIONS Both the optimal and simplified overfat cutoffs were provided for the Chinese pediatric population. The use of FMI and TLR together allows for adequate screening of CMR and its clustering.
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Affiliation(s)
- Hongbo Dong
- Center for Non-communicable Disease Management, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing, China
| | - Hong Cheng
- Department of Epidemiology, Capital Institute of Pediatrics, Beijing, China
| | - Junting Liu
- Department of Epidemiology, Capital Institute of Pediatrics, Beijing, China
| | - Yinkun Yan
- Center for Non-communicable Disease Management, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing, China
| | - Xiaoyuan Zhao
- Department of Epidemiology, Capital Institute of Pediatrics, Beijing, China
| | - Xinying Shan
- Department of Epidemiology, Capital Institute of Pediatrics, Beijing, China
| | - Guimin Huang
- Department of Epidemiology, Capital Institute of Pediatrics, Beijing, China
| | - Jie Mi
- Center for Non-communicable Disease Management, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing, China
- Department of Epidemiology, Capital Institute of Pediatrics, Beijing, China
- Key Laboratory of Major Diseases in Children, Ministry of Education, Beijing, China
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Digital Anthropometry: A Systematic Review on Precision, Reliability and Accuracy of Most Popular Existing Technologies. Nutrients 2023; 15:nu15020302. [PMID: 36678173 PMCID: PMC9864001 DOI: 10.3390/nu15020302] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2022] [Revised: 12/27/2022] [Accepted: 12/30/2022] [Indexed: 01/11/2023] Open
Abstract
Digital anthropometry (DA) has been recently developed for body composition evaluation and for postural analysis. The aims of this review are to examine the current state of DA technology, as well as to verify the methods for identifying the best technology to be used in the field of DA by evaluating the reliability and accuracy of the available technologies on the market, and lay the groundwork for future technological developments. A literature search was performed and 28 studies met the inclusion criteria. The reliability and accuracy of DA was high in most studies, especially in the assessment of patients with obesity, although they varied according to the technology used; a good correlation was found between DA and conventional anthropometry (CA) and body composition estimates. DA is less time-consuming and less expensive and could be used as a screening tool before more expensive imaging techniques or as an alternative to other less affordable techniques. At present, DA could be useful in clinical practice, but the heterogeneity of the available studies (different devices used, laser technologies, population examined, etc.) necessitates caution in the interpretation of the obtained results. Furthermore, the need to develop integrated technologies for analyzing body composition according to multi-compartmental models is increasingly evident.
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7
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Silhouette images enable estimation of body fat distribution and associated cardiometabolic risk. NPJ Digit Med 2022; 5:105. [PMID: 35896726 PMCID: PMC9329470 DOI: 10.1038/s41746-022-00654-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2022] [Accepted: 07/06/2022] [Indexed: 11/09/2022] Open
Abstract
Inter-individual variation in fat distribution is increasingly recognized as clinically important but is not routinely assessed in clinical practice, in part because medical imaging has not been practical to deploy at scale for this task. Here, we report a deep learning model trained on an individual’s body shape outline—or “silhouette” —that enables accurate estimation of specific fat depots of interest, including visceral (VAT), abdominal subcutaneous (ASAT), and gluteofemoral (GFAT) adipose tissue volumes, and VAT/ASAT ratio. Two-dimensional coronal and sagittal silhouettes are constructed from whole-body magnetic resonance images in 40,032 participants of the UK Biobank and used as inputs for a convolutional neural network to predict each of these quantities. Mean age of the study participants is 65 years and 51% are female. A cross-validated deep learning model trained on silhouettes enables accurate estimation of VAT, ASAT, and GFAT volumes (R2: 0.88, 0.93, and 0.93, respectively), outperforming a comparator model combining anthropometric and bioimpedance measures (ΔR2 = 0.05–0.13). Next, we study VAT/ASAT ratio, a nearly body-mass index (BMI)—and waist circumference-independent marker of metabolically unhealthy fat distribution. While the comparator model poorly predicts VAT/ASAT ratio (R2: 0.17–0.26), a silhouette-based model enables significant improvement (R2: 0.50–0.55). Increased silhouette-predicted VAT/ASAT ratio is associated with increased risk of prevalent and incident type 2 diabetes and coronary artery disease independent of BMI and waist circumference. These results demonstrate that body silhouette images can estimate important measures of fat distribution, laying the scientific foundation for scalable population-based assessment.
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8
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Wong MC, McCarthy C, Fearnbach N, Yang S, Shepherd J, Heymsfield SB. Emergence of the obesity epidemic: 6-decade visualization with humanoid avatars. Am J Clin Nutr 2022; 115:1189-1193. [PMID: 35030235 PMCID: PMC8971009 DOI: 10.1093/ajcn/nqac005] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2021] [Accepted: 01/10/2022] [Indexed: 01/16/2023] Open
Abstract
BACKGROUND Visualizations of the emerging obesity epidemic, such as with serial US color prevalence maps, provide graphic images that extend informative public health messages beyond those in written communications. Advances in low-cost 3D optical technology now allow for development of large image databases that include participants varying in race/ethnicity, body mass, height, age, and circumferences. When combined with contemporary statistical methods, these data sets can be used to create humanoid avatar images with prespecified anthropometric features. OBJECTIVES The current study aimed to develop a humanoid avatar series with characteristics of representative US adults extending over the past 6 decades. METHODS 3D optical scans were conducted on a demographically diverse sample of 570 healthy adults. Image data were converted to principal components and manifold regression equations were then developed with body mass, height, age, and waist circumference as covariates. Humanoid avatars were generated for representative adults with these 4 characteristics as reported in CDC surveys beginning in 1960-1962 up to 2015-2018. RESULTS There was a curvilinear increase in adult US population body mass, waist circumference, and BMI in males and females across the 9 surveys spanning 6 decades. A small increase in average adult population age was present between 1960 and 2018; height changes were inconsistent. A series of 4 avatars developed at ∼20-y intervals for representative males and females reveal the changes in body size and shape consistent with the emergence of the obesity epidemic. An additional series of developed avatars portray the shapes and sizes of males and females at key BMI cutoffs. CONCLUSIONS New mathematical approaches and accessible 3D optical technology combined with increasingly available large and diverse data sets across the life span now make unique visualization of body size and shape possible on a previously unattainable scale. This study is registered at https://clinicaltrials.gov/ct2/show/NCT03637855 as NCT03637855.
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Affiliation(s)
- Michael C Wong
- University of Hawaii Cancer Center, Honolulu, HI, USA
- Graduate Program in Nutritional Sciences, University of Hawaii at Manoa, Honolulu, HI, USA
| | - Cassidy McCarthy
- Pennington Biomedical Research Center, Louisiana State University System, Baton Rouge, LA, USA
| | - Nicole Fearnbach
- Pennington Biomedical Research Center, Louisiana State University System, Baton Rouge, LA, USA
| | - Shengping Yang
- Pennington Biomedical Research Center, Louisiana State University System, Baton Rouge, LA, USA
| | - John Shepherd
- University of Hawaii Cancer Center, Honolulu, HI, USA
| | - Steven B Heymsfield
- Pennington Biomedical Research Center, Louisiana State University System, Baton Rouge, LA, USA
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9
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Wong MC, Ng BK, Tian I, Sobhiyeh S, Pagano I, Dechenaud M, Kennedy SF, Liu YE, Kelly NN, Chow D, Garber AK, Maskarinec G, Pujades S, Black MJ, Curless B, Heymsfield SB, Shepherd JA. A pose-independent method for accurate and precise body composition from 3D optical scans. Obesity (Silver Spring) 2021; 29:1835-1847. [PMID: 34549543 PMCID: PMC8570991 DOI: 10.1002/oby.23256] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/22/2021] [Revised: 06/21/2021] [Accepted: 06/22/2021] [Indexed: 12/12/2022]
Abstract
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.
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Affiliation(s)
- Michael C Wong
- Graduate Program in Human Nutrition, University of Hawai'i Manoa, Honolulu, Hawaii, USA
- Department of Epidemiology, University of Hawai'i Cancer Center, Honolulu, Hawaii, USA
| | - Bennett K Ng
- Department of Emerging Growth and Incubation, Intel Corp., Santa Clara, California, USA
| | - Isaac Tian
- Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, Washington, USA
| | - Sima Sobhiyeh
- Pennington Biomedical Research Center, Louisiana State University, Baton Rouge, Louisiana, USA
| | - Ian Pagano
- Department of Epidemiology, University of Hawai'i Cancer Center, Honolulu, Hawaii, USA
| | - Marcelline Dechenaud
- Pennington Biomedical Research Center, Louisiana State University, Baton Rouge, Louisiana, USA
| | - Samantha F Kennedy
- Pennington Biomedical Research Center, Louisiana State University, Baton Rouge, Louisiana, USA
| | - Yong E Liu
- Department of Epidemiology, University of Hawai'i Cancer Center, Honolulu, Hawaii, USA
| | - Nisa N Kelly
- Department of Epidemiology, University of Hawai'i Cancer Center, Honolulu, Hawaii, USA
| | - Dominic Chow
- John A. Burns School of Medicine, University of Hawai'i, Honolulu, Hawaii, USA
| | - Andrea K Garber
- School of Medicine, University of California, San Francisco, California, USA
| | - Gertraud Maskarinec
- Department of Epidemiology, University of Hawai'i Cancer Center, Honolulu, Hawaii, USA
| | - Sergi Pujades
- Inria, Université Grenoble Alpes, CNRS, Grenoble INP, LJK, Grenoble, France
| | - Michael J Black
- Max Planck Institute for Intelligent Systems, Tübingen, Germany
| | - Brian Curless
- Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, Washington, USA
| | - Steven B Heymsfield
- Pennington Biomedical Research Center, Louisiana State University, Baton Rouge, Louisiana, USA
| | - John A Shepherd
- Graduate Program in Human Nutrition, University of Hawai'i Manoa, Honolulu, Hawaii, USA
- Department of Epidemiology, University of Hawai'i Cancer Center, Honolulu, Hawaii, USA
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10
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Brown JC, Carson TL, Thompson HJ, Agurs-Collins T. The Triple Health Threat of Diabetes, Obesity, and Cancer-Epidemiology, Disparities, Mechanisms, and Interventions. Obesity (Silver Spring) 2021; 29:954-959. [PMID: 34029445 PMCID: PMC8152945 DOI: 10.1002/oby.23161] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/12/2021] [Revised: 02/10/2021] [Accepted: 02/13/2021] [Indexed: 12/12/2022]
Abstract
Obesity and type 2 diabetes are both chronic, relapsing, progressive diseases that are recognized as risk factors for the development of multiple types of cancer. In a recent symposium titled "Hitting A Triple-Diabetes, Obesity, and the Emerging Links to Cancer Risk," convened by The Obesity Society during ObesityWeek 2019, experts in the field presented the current science and highlighted existing research gaps. Topics included (1) the epidemiology of obesity and diabetes and their links to cancer risk; (2) racial and ethnic differences in obesity, diabetes, and cancer risk; (3) biological mechanisms common to obesity and diabetes that may increase cancer risk; and (4) innovative interventions that can be used to prevent the development of cancers related to obesity and diabetes. This report provides an overview of the symposium and describes key research gaps and pressing questions in need of answers to advance the field. The collective burden of obesity, diabetes, and cancer represents one of the largest public health challenges of the century. Although the symposium was titled "hitting a triple," it was recognized that being able to disrupt the linkages among obesity, diabetes, and cancer would be a "grand slam" for public health and medicine.
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Affiliation(s)
- Justin C. Brown
- Pennington Biomedical Research Center, 6400 Perkins Rd, Baton Rouge, LA 70808, USA
- LSU Health Sciences Center New Orleans School of Medicine, 1901 Perdido St, New Orleans, LA 70112, USA
- Stanley S. Scott Cancer Center, Louisiana State University Health Sciences Center, 533 Bolivar St, New Orleans, LA, 70112, USA
| | - Tiffany L. Carson
- H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, 33617, USA
| | | | - Tanya Agurs-Collins
- National Cancer Institute, National Institutes of Health, Rockville, MD, 20850, USA
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Digital anthropometric evaluation of young children: comparison to results acquired with conventional anthropometry. Eur J Clin Nutr 2021; 76:251-260. [PMID: 34040201 PMCID: PMC8617044 DOI: 10.1038/s41430-021-00938-x] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2020] [Revised: 04/21/2021] [Accepted: 04/30/2021] [Indexed: 11/08/2022]
Abstract
OBJECTIVE Three-dimensional optical (3DO) imaging devices for acquiring anthropometric measurements are proliferating in healthcare facilities, although applicability in young children has not been evaluated; small body size and movement may limit device accuracy. The current study aim was to critically test three commercial 3DO devices in young children. METHODS The number of successful scans and circumference measurements at six anatomic sites were quantified with the 3DO devices in 64 children, ages 5-8 years. Of the scans available for processing, 3DO and flexible tape-measure measurements made by a trained anthropometrist were compared. RESULTS Sixty of 181 scans (33.1%) could not be processed for technical reasons. Of processed scans, mean 3DO-tape circumference differences tended to be small (~1-9%) and varied across systems; correlations and bias estimates also varied in strength across anatomic sites and systems (e.g., regression R2s, 0.54-0.97, all p < 0.01). Overall findings differed across devices; best results were for a multi-camera stationary system and less so for two rotating single- or dual-camera systems. CONCLUSIONS Available 3DO devices for quantifying anthropometric dimensions in adults vary in applicability in young children according to instrument design. These findings suggest the need for 3DO devices designed specifically for small and/or young children.
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Dechenaud ME, Kennedy S, Sobhiyeh S, Shepherd J, Heymsfield SB. Total body and regional surface area: Quantification with low-cost three-dimensional optical imaging systems. AMERICAN JOURNAL OF PHYSICAL ANTHROPOLOGY 2021; 175:865-875. [PMID: 33543784 DOI: 10.1002/ajpa.24243] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/26/2020] [Revised: 12/17/2020] [Accepted: 01/19/2021] [Indexed: 01/25/2023]
Abstract
OBJECTIVES Body surface area (SA) is a widely used physical measure incorporated into multiple thermophysiology and evolutionary biology models currently estimated in humans either with empirical prediction equations or costly whole-body laser imaging systems. The introduction of low-cost 3D scanners provides a new opportunity to quantify total body (TB) and regional SA, although a critical question prevails: can these devices acquire the quality of depth information and process this initial data to form a mesh that has the fidelity needed to generate accurate SA estimates? MATERIALS AND METHODS This question was answered by comparing SA estimates calculated using images from four commercial 3D scanners in 108 adults to corresponding estimates acquired with a whole-body laser system. This was accomplished by processing initial mesh data from all devices, including the laser system, with the same universal software adapted specifically for repairing mesh gaps, identifying landmarks, and generating SA measurements. RESULTS TB SA measured on all four 3D scanners was highly correlated with corresponding laser system estimates (R2 s, 0.98-0.99; all p < 0.001) with some small but significant mean differences (-0.19 to 0.06 m2 ); root-mean square errors (RMSEs) were small (0.02-0.03 m2 ); and significant bias was present for one device. Qualitatively similar results (e.g., R2 s, 0.78-0.95; mean Δs, -0.05 to 0.02 m2 ; RMSEs, 0.01-0.03 m2 ) were present for trunk, arm, and leg SA comparisons. DISCUSSION The current study observations demonstrate that low-cost and practical 3D optical scanners are capable of accurately quantifying TB and regional SA, thus opening new opportunities for evaluating human phenotypes and related physiological characteristics.
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Affiliation(s)
- Marcelline E Dechenaud
- Pennington Biomedical Research Center, Louisiana State University System, Baton Rouge, Louisiana, USA.,Louisiana State University, Baton Rouge, Louisiana, USA
| | - Samantha Kennedy
- Pennington Biomedical Research Center, Louisiana State University System, Baton Rouge, Louisiana, USA
| | - Sima Sobhiyeh
- Pennington Biomedical Research Center, Louisiana State University System, Baton Rouge, Louisiana, USA
| | - John Shepherd
- University of Hawaii Cancer Center, Honolulu, Hawaii, USA
| | - Steven B Heymsfield
- Pennington Biomedical Research Center, Louisiana State University System, Baton Rouge, Louisiana, USA
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Sobhiyeh S, Kennedy S, Dunkel A, Dechenaud ME, Weston JA, Shepherd J, Wolenski P, Heymsfield SB. Digital anthropometry for body circumference measurements: Toward the development of universal three-dimensional optical system analysis software. Obes Sci Pract 2020; 7:35-44. [PMID: 33680490 PMCID: PMC7909596 DOI: 10.1002/osp4.467] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2020] [Revised: 09/30/2020] [Accepted: 10/21/2020] [Indexed: 01/07/2023] Open
Abstract
Background/Objective Digital anthropometric (DA) assessments are increasingly being administered with three‐dimensional (3D) optical devices in clinical settings that manage patients with obesity and related metabolic disorders. However, anatomic measurement sites are not standardized across manufacturers, precluding use of published reference values and pooling of data across research centers. Subjects/Methods This study aimed to develop universal 3D analysis software by applying novel programming strategies capable of producing device‐independent DA estimates that agree with conventional anthropometric (CA) measurements made at well‐defined anatomic sites. A series of technical issues related to proprietary methods of 3D geometrical reconstruction and image analysis were addressed in developing major software components. To evaluate software accuracy, comparisons were made to CA circumference measurements made with a flexible tape at eleven standard anatomic sites in up to 35 adults scanned with three different commercial 3D optical devices. Results Overall, group mean CA and DA values across the three systems were in good agreement, with ∼2 cm systematic differences; CA and DA estimates were highly correlated (all p‐values <0.01); root‐mean square errors were low (0.51–3.27 cm); and CA‐DA bias tended to be small, but significant depending on anatomic site and device. Conclusions Availability of this software, with future refinements, has the potential to facilitate clinical applications and creation of large pooled uniform anthropometric databases.
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Affiliation(s)
- Sima Sobhiyeh
- Metabolism-Body-Composition Pennington Biomedical Research Center LSU System Baton Rouge Louisiana USA
| | - Samantha Kennedy
- Metabolism-Body-Composition Pennington Biomedical Research Center LSU System Baton Rouge Louisiana USA
| | - Alexander Dunkel
- Department of Mathematics Louisiana State University Baton Rouge Louisiana USA
| | | | - Jerome A Weston
- Department of Mathematics Louisiana State University Baton Rouge Louisiana USA
| | - John Shepherd
- Cancer Center University of Hawaii Honolulu Hawaii USA
| | - Peter Wolenski
- Department of Mathematics Louisiana State University Baton Rouge Louisiana USA
| | - Steven B Heymsfield
- Metabolism-Body-Composition Pennington Biomedical Research Center LSU System Baton Rouge Louisiana USA
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