Michael C Wong1,2, Bennett K Ng2, Samantha F Kennedy3, Phoenix Hwaung3, En Y Liu2, Nisa N Kelly2, Ian S Pagano2, Andrea K Garber4, Dominic C Chow5, Steven B Heymsfield3, John A Shepherd1,2. 1. Graduate Program in Human Nutrition, University of Hawai'i Manoa, Honolulu, Hawaii, USA. 2. Department of Epidemiology, University of Hawai'i Cancer Center, Honolulu, Hawaii, USA. 3. Pennington Biomedical Research Center, Louisiana State University, Baton Rouge, Louisiana, USA. 4. Division of Adolescent & Young Adult Medicine, University of California, San Francisco, California, USA. 5. John A. Burns School of Medicine, University of Hawai'i, Honolulu, Hawaii, USA.
Abstract
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.
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.
Authors: Abishek Stanley; John Schuna; Shengping Yang; Samantha Kennedy; Moonseong Heo; Michael Wong; John Shepherd; Steven B Heymsfield Journal: Am J Clin Nutr Date: 2020-10-01 Impact factor: 7.045
Authors: Michael C Wong; Cassidy McCarthy; Nicole Fearnbach; Shengping Yang; John Shepherd; Steven B Heymsfield Journal: Am J Clin Nutr Date: 2022-04-01 Impact factor: 7.045
Authors: Michael C Wong; Bennett K Ng; Isaac Tian; Sima Sobhiyeh; Ian Pagano; Marcelline Dechenaud; Samantha F Kennedy; Yong E Liu; Nisa N Kelly; Dominic Chow; Andrea K Garber; Gertraud Maskarinec; Sergi Pujades; Michael J Black; Brian Curless; Steven B Heymsfield; John A Shepherd Journal: Obesity (Silver Spring) Date: 2021-09-21 Impact factor: 5.002
Authors: Jonathan P Bennett; Yong En Liu; Brandon K Quon; Nisa N Kelly; Lambert T Leong; Michael C Wong; Samantha F Kennedy; Dominic C Chow; Andrea K Garber; Ethan J Weiss; Steven B Heymsfield; John A Shepherd Journal: Obesity (Silver Spring) Date: 2022-08 Impact factor: 9.298
Authors: Nicole E Logan; Daniel R Westfall; Lauren B Raine; Sheeba A Anteraper; Laura Chaddock-Heyman; Susan Whitfield-Gabrieli; Arthur F Kramer; Charles H Hillman Journal: Med Sci Sports Exerc Date: 2022-06-24
Authors: Sima Sobhiyeh; Nathan Borel; Marcelline Dechenaud; Clinten A Graham; Michael Wong; Peter Wolenski; John Shepherd; Steven B Heymsfield Journal: Annu Int Conf IEEE Eng Med Biol Soc Date: 2020-07
Authors: Isaac Y Tian; Bennett K Ng; Michael C Wong; Samantha Kennedy; Phoenix Hwaung; Nisa Kelly; En Liu; Andrea K Garber; Brian Curless; Steven B Heymsfield; John A Shepherd Journal: Med Phys Date: 2020-10-20 Impact factor: 4.071
Authors: Dympna Gallagher; Aline Andres; David A Fields; William J Evans; Robert Kuczmarski; William L Lowe; Julie C Lumeng; Emily Oken; John A Shepherd; Shumei Sun; Steven B Heymsfield Journal: Obes Rev Date: 2020-04-20 Impact factor: 9.213