Children and Adolescents’ Anthropometrics Body Composition from 3‐D Optical Surface Scans
Year: 2019
Type: article
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.
Source: Obesity
DOI: 10.1002/oby.22637
Authors: Michael C. Wong, Bennett K. Ng, Samantha F. Kennedy, Phoenix Hwaung, En Y. Liu, Nisa N. Kelly, Ian S. Pagano, Andrea K. Garber, Dominic C. Chow, Steven B. Heymsfield, John A. Shepherd
Language: English
Subfield: Physiology
Field: Medicine
Domain: Health Sciences
Open Access status: green
Awards: G1100744920, G231574186, G6674982174
OpenAlex ID: W2985037072
