Literature DB >> 20737619

Comparison of body fat estimates using 3D digital laser scans, direct manual anthropometry, and DXA in men.

Todd N Garlie1, John P Obusek, Brian D Corner, Edward J Zambraski.   

Abstract

OBJECTIVES: The purpose of this study was to assess the feasibility of utilizing three dimensional whole body laser surface scanning (3DS) to obtain specific anthropometric measurements to estimate percent body fat (BF).
METHODS: Percent BF estimates from 37 male volunteers, of age 18-62 yr, were determined by inputting manual anthropometric (MA) and 3DS anthropometric measurements into the current Army BF prediction equation for males. The results were compared with each other and to BF values from Dual Energy X-ray Absorptiometry (DXA), employed as a reference method.
RESULTS: Mean percent BF estimates (+/-SD) derived from MA, 3DS and from DXA were 18.4(+/-3.8), 18.8(+/-3.9), and 18.9(+/-4.7), respectively. Analysis of Variance tests revealed no statistical difference between the mean values. Correlation analysis comparing MA and 3DS derived percent BF estimates to each other and to those measured by DXA revealed moderate to strong Pearson correlation coefficients (r), small to moderate standard errors of the estimate (SEE), and were statistically significant (p < 0.05).
CONCLUSIONS: Correlation coefficients and SEE results for this sample were: (1) DXA vs 3DS; r = 0.74, SEE = 3.2, (2) MA vs DXA; r = 0.82, SEE = 2.8, and (3) MA vs 3DS; r = 0.96, SEE = 1.0. Lin's concordance analysis, including Bland-Altman limits of agreement (LOA), revealed statistically significant measurement agreement among the three measurement modalities (p < 0.05). The application of 3DS scanning to estimate percent BF from commonly used anthropometric measurements are in close agreement with BF estimates derived from analogous MA measurements and from DXA scanning. Published 2010Wiley-Liss, Inc.

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Year:  2010        PMID: 20737619     DOI: 10.1002/ajhb.21069

Source DB:  PubMed          Journal:  Am J Hum Biol        ISSN: 1042-0533            Impact factor:   1.937


  7 in total

Review 1.  Current status of body composition assessment in sport: review and position statement on behalf of the ad hoc research working group on body composition health and performance, under the auspices of the I.O.C. Medical Commission.

Authors:  Timothy R Ackland; Timothy G Lohman; Jorunn Sundgot-Borgen; Ronald J Maughan; Nanna L Meyer; Arthur D Stewart; Wolfram Müller
Journal:  Sports Med       Date:  2012-03-01       Impact factor: 11.136

2.  Automated anthropometric phenotyping with novel Kinect-based three-dimensional imaging method: comparison with a reference laser imaging system.

Authors:  L Soileau; D Bautista; C Johnson; C Gao; K Zhang; X Li; S B Heymsfield; D Thomas; J Zheng
Journal:  Eur J Clin Nutr       Date:  2015-09-16       Impact factor: 4.016

3.  3D Shape-based Body Composition Prediction Model Using Machine Learning.

Authors:  Yao Lu; Scott McQuade; James K Hahn
Journal:  Annu Int Conf IEEE Eng Med Biol Soc       Date:  2018-07

4.  A Smartphone Application for Personal Assessments of Body Composition and Phenotyping.

Authors:  Gian Luca Farina; Fabrizio Spataro; Antonino De Lorenzo; Henry Lukaski
Journal:  Sensors (Basel)       Date:  2016-12-17       Impact factor: 3.576

5.  Perspective: Are We Ready to Measure Child Nutritional Status with Lasers?

Authors:  Joel Conkle; Reynaldo Martorell
Journal:  Adv Nutr       Date:  2019-01-01       Impact factor: 8.701

6.  A collaborative, mixed-methods evaluation of a low-cost, handheld 3D imaging system for child anthropometry.

Authors:  Joel Conkle; Kate Keirsey; Ashton Hughes; Jennifer Breiman; Usha Ramakrishnan; Parminder S Suchdev; Reynaldo Martorell
Journal:  Matern Child Nutr       Date:  2018-10-18       Impact factor: 3.092

7.  Illustration of Measurement Error Models for Reducing Bias in Nutrition and Obesity Research Using 2-D Body Composition Data.

Authors:  Anarina L Murillo; Olivia Affuso; Courtney M Peterson; Peng Li; Howard W Wiener; Carmen D Tekwe; David B Allison
Journal:  Obesity (Silver Spring)       Date:  2019-01-22       Impact factor: 5.002

  7 in total

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