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Body composition, energy expenditure and physical activity

Clinical anthropometrics and body composition from 3D whole-body surface scans

A Correction to this article was published on 25 November 2020

This article has been updated

Abstract

Background/Objectives:

Obesity is a significant worldwide epidemic that necessitates accessible tools for robust body composition analysis. We investigated whether widely available 3D body surface scanners can provide clinically relevant direct anthropometrics (circumferences, areas and volumes) and body composition estimates (regional fat/lean masses).

Subjects/Methods:

Thirty-nine healthy adults stratified by age, sex and body mass index (BMI) underwent whole-body 3D scans, dual energy X-ray absorptiometry (DXA), air displacement plethysmography and tape measurements. Linear regressions were performed to assess agreement between 3D measurements and criterion methods. Linear models were derived to predict DXA body composition from 3D scan measurements. Thirty-seven external fitness center users underwent 3D scans and bioelectrical impedance analysis for model validation.

Results:

3D body scan measurements correlated strongly to criterion methods: waist circumference R2=0.95, hip circumference R2=0.92, surface area R2=0.97 and volume R2=0.99. However, systematic differences were observed for each measure due to discrepancies in landmark positioning. Predictive body composition equations showed strong agreement for whole body (fat mass R2=0.95, root mean square error (RMSE)=2.4 kg; fat-free mass R2=0.96, RMSE=2.2 kg) and arms, legs and trunk (R2=0.79-0.94, RMSE=0.5–1.7 kg). Visceral fat prediction showed moderate agreement (R2=0.75, RMSE=0.11 kg).

Conclusions:

3D surface scanners offer precise and stable automated measurements of body shape and composition. Software updates may be needed to resolve measurement biases resulting from landmark positioning discrepancies. Further studies are justified to elucidate relationships between body shape, composition and metabolic health across sex, age, BMI and ethnicity groups, as well as in those with metabolic disorders.

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Acknowledgements

We acknowledge Dr Kathleen Mulligan for fostering collaboration with the UCSF Clinical and Translational Science Institute, Viva Tai and Caitlin Sheets for guiding participants through the clinical DXA protocols, as well as Leila Kazemi, Louise Marquino and Eboni Stephens for their contributions as study coordinators. We also thank Greg Moore and Tyler Carter of Fit3D Inc. for their support with the Fit3D Proscanner and processing of 3D scan data.

Author contributions

JAS and BKN designed and conducted the research; BKN, BJH and JAS analyzed data; BKN and JAS drafted the manuscript and had primary responsibility for final content. All authors reviewed and approved the final manuscript.

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Correspondence to J A Shepherd.

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The authors declare no conflict of interest.

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Ng, B., Hinton, B., Fan, B. et al. Clinical anthropometrics and body composition from 3D whole-body surface scans. Eur J Clin Nutr 70, 1265–1270 (2016). https://doi.org/10.1038/ejcn.2016.109

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  • DOI: https://doi.org/10.1038/ejcn.2016.109

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