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S. Heymsfield, J. Stevens (2017)
Anthropometry: continued refinements and new developments of an ancient method.The American journal of clinical nutrition, 105 1
Patrick Harty, B. Sieglinger, S. Heymsfield, J. Shepherd, David Bruner, Matthew Stratton, G. Tinsley (2020)
Novel Body Fat Estimation Using Machine Learning and 3-Dimensional Optical ImagingEuropean journal of clinical nutrition, 74
K. Norton, T. Olds (1996)
Anthropometrica : a textbook of body measurement for sports and health courses
J. Bland, D. Altman (1999)
Measuring agreement in method comparison studiesStatistical Methods in Medical Research, 8
Louise Goh, S. Dhaliwal, T. Welborn, Andy Lee, P. Della (2014)
Anthropometric measurements of general and central obesity and the prediction of cardiovascular disease risk in women: a cross-sectional studyBMJ Open, 4
(2022)
Mobile scanning improvements from Size Stream
(2007)
National Health and Nutrition Examination Survey (NHANES): Anthropometry Procedures Manual
Sima Sobhiyeh, Alexander Dunkel, Marcelline Dechenaud, A. Mehrnezhad, S. Kennedy, J. Shepherd, P. Wolenski, S. Heymsfield (2021)
Digital Anthropometric Volumes: Toward the Development and Validation of a Universal Software.Medical physics
Abid Haleem, M. Javaid (2018)
3D scanning applications in medical field: A literature-based reviewClinical Epidemiology and Global Health
S. Heymsfield, B. Bourgeois, B. Ng, M. Sommer, Xin Li, J. Shepherd (2018)
Digital anthropometry: a critical reviewEuropean Journal of Clinical Nutrition, 72
B. Bogin, R. Keep (1999)
Eight thousand years of economic and political history in Latin America revealed by anthropometry.Annals of human biology, 26 4
L. Pilgrim (1992)
History of photogrammetry in medicine.Australasian physical & engineering sciences in medicine, 15 1
S. Kennedy, Brooke Smith, Sima Sobhiyeh, Marcelline Dechenaud, M. Wong, N. Kelly, J. Shepherd, S. Heymsfield (2021)
Digital Anthropometric Evaluation of Young Children: Comparison to Results Acquired with Conventional AnthropometryEuropean journal of clinical nutrition, 76
B. Bourgeois, B. Ng, D. Latimer, C. Stannard, L. Romeo, X. Li, J. Shepherd, S. Heymsfield (2017)
Clinically applicable optical imaging technology for body size and shape analysis: comparison of systems differing in designEuropean Journal of Clinical Nutrition, 71
P. Treleaven, J. Wells (2007)
3D Body Scanning and Healthcare ApplicationsComputer, 40
M. Feinleib (2005)
National Center for Health Statistics (NCHS)
Ankit Shah, Malini Prasad, S. Devjani, P. Rai, M. Ashby-Thompson, Wen Yu, D. Gallagher, B. Laferrère (2020)
Anthropometrics by Three-Dimensional Photonic Scanner in Patients with Obesity Before and After Bariatric SurgeryObesity Surgery, 31
K. Krishan (2018)
Anthropometry in Forensic Medicine and Forensic Science- 'Forensic Anthropometry'
Sima Sobhiyeh, S. Kennedy, Alexander Dunkel, Marcelline Dechenaud, J. Weston, J. Shepherd, P. Wolenski, S. Heymsfield (2020)
Digital anthropometry for body circumference measurements: Toward the development of universal three‐dimensional optical system analysis softwareObesity Science & Practice, 7
J. Wells, P. Treleaven, S. Charoensiriwath (2012)
Body shape by 3-D photonic scanning in Thai and UK adults: comparison of national sizing surveysInternational Journal of Obesity, 36
P. Jones, G. West, D. Harris, J. Read (1989)
The Loughborough anthropometric shadow scanner (LASS).Endeavour, 13 4
Study ImportanceWhat is already known?►Three‐dimensional (3D) optical imaging systems for quantifying body size, body shape, and related body composition in professional health care settings are increasingly being validated against traditional manual anthropometric reference methods.►Smartphone applications are now being introduced that profess similar capabilities as conventional 3D optical systems, but how they compare with these more costly and less available methods and with manual anthropometric reference methods is unknown.What does this study add?►This study shows that a free downloadable smartphone application can acquire image data and generate 3D humanoid avatars and representative anthropometric (circumference) measurements similar in precision and accuracy to 3D imaging systems now used in professional settings.How might these results change the direction of research or the focus of clinical practice?►These observations combined with the ubiquitous availability of smartphones create the possibility of phenotyping adult body size and shape, with important clinical and research implications, on a global scale.INTRODUCTIONAnthropometric measurements have long been used to study human anatomic features related to body size and shape (1). The versatility and practicality of anthropometry led to the introduction of digital scanners in the 1980s designed to accurately capture the needed body dimensions for clothing manufacture (2‐4). Rapid technological advancements over
Obesity – Wiley
Published: Jun 1, 2022
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