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Body composition, energy expenditure and physical activity

Optical imaging technology for body size and shape analysis: evaluation of a system designed for personal use

European Journal of Clinical Nutrition volume 74, pages 920–929 (2020)Cite this article

Subjects

Abstract

Background/Objectives

Three-dimensional optical (3DO) imaging systems that rapidly and accurately provide body shape and composition information are increasingly available in research and clinical settings. Recently, relatively low-cost and space efficient 3DO systems with the ability to report and track individual assessments were introduced to the consumer market for home use. This study critically evaluated the first 3DO imaging device intended for personal operation, the Naked Body Scanner (NBS), against reference methods.

Participants/Methods

Circumferences at six standardized anatomic sites were measured with a flexible tape in 90 participants ranging in age (5–74 years), ethnicity, and adiposity. Regression analysis and Bland-Altman plots compared these direct measurements and dual-energy X-ray absorptiometry (DXA) %fat estimates to corresponding NBS values. Method precision was analyzed from duplicate anthropometric and NBS measurements in a subgroup of 51 participants.

Results

The NBS exhibited greater variation in test–retest reliability (CV, 0.4–2.7%) between the six measured anatomic locations when compared with manually measured counterparts (0.2–0.4%). All six device-derived circumferences correlated with flexible tape references (R2s, 0.84–0.97; p < 0.0001). Measurement bias was apparent for three anatomic sites while mean differences were present for five. The NBS’s %fat estimates also correlated with DXA results (R2 = 0.73, p < 0.0001) with no significant bias.

Conclusions

This system opens a new era of digital home-based assessments that can be incorporated into weight loss or exercise interventions accessible to clinical investigators as well as individual users.

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References

  1. FerroLuzzi A, et al. Physical status: the use and interpretation of anthropometry - Introduction. Who Tech Rep Ser. 1995;854:1–3.

    Google Scholar 

  2. Heymsfield SB, Stevens J. Anthropometry: continued refinements and new developments of an ancient method. Am J Clin Nutr. 2017;105:1–2.

    Article  Google Scholar 

  3. Himes JH. Anthropometric assessment of nutritional status. New York: Wiley-Liss; 1991. xi, 431 p.

    Google Scholar 

  4. Cameron N. The Measurement of Human Growth. London: Croom Helm; 1984.

    Google Scholar 

  5. Madden AM, Smith S. Body composition and morphological assessment of nutritional status in adults: a review of anthropometric variables. J Hum Nutr Diet. 2016;29:7–25.

    Article  CAS  Google Scholar 

  6. Ulijaszek SJ, Mascie-Taylor CGN. Anthropometry: the individual and the population. 1st edn. Cambridge: Cambridge University Press; 1994.

    Book  Google Scholar 

  7. Frisancho AR. Anthropometric standards for the assessment of growth and nutritional status. Ann Arbor: University of Michigan Press; 1990.

    Book  Google Scholar 

  8. Roche AF, Mukherjee D, Guo SM, Moore WM. Head circumference reference data: birth to 18 years. Pediatrics. 1987;79:706–12.

    CAS  PubMed  Google Scholar 

  9. Paxton A, et al. Anthropometric equations for studying body fat in pregnant women. Am J Clin Nutr. 1998;67:104–10.

    Article  CAS  Google Scholar 

  10. Piers LS, et al. Changes in energy expenditure, anthropometry, and energy intake during the course of pregnancy and lactation in well-nourished Indian women. Am J Clin Nutr. 1995;61:501–13.

    Article  CAS  Google Scholar 

  11. Braganca S, Arezes PM, Carvalho M. An overview of the current three dimensional body scanners for anthropometric data collection. In: Arezes PM, Baptista JS, Barroso MP, Carneiro P, Cordeiro P, Costa N, et al., editors. Occupational safety and hygiene III. London: CRC Press: Taylor & Francis Group; 2015. p. 149–54.

  12. Ling Y, et al. Rational and design of an overfeeding protocol in constitutional thinness: Understanding the physiology, metabolism and genetic background of resistance to weight gain. Ann Endocrinol (Paris). 2016;77:563–9.

    Article  Google Scholar 

  13. Soileau L, et al. Automated anthropometric phenotyping with novel Kinect-based three-dimensional imaging method: comparison with a reference laser imaging system. Eur J Clin Nutr. 2016;70:475–81.

    Article  CAS  Google Scholar 

  14. Xie B, et al. Accurate body composition measures from whole-body silhouettes. Med Phys. 2015;42:4668–77.

    Article  Google Scholar 

  15. de Groot LC, Sette S, Zajkas G, Carbajal A, Amorim JA. Nutritional status: anthropometry. Euronut SENECA investigators. Eur J Clin Nutr. 1991;45(Suppl 3):31–42.

    PubMed  Google Scholar 

  16. Lohman TG, Roche AF, Martorell R. Anthropometric standardization reference manual. Champaign, IL: Human Kinetics Books; 1988.

    Google Scholar 

  17. Bourgeois B, et al. Clinically applicable optical imaging technology for body size and shape analysis: comparison of systems differing in design. Eur J Clin Nutr. 2017;71:1329–35.

    Article  CAS  Google Scholar 

  18. Centers for Disease Control and Prevention (CDC), National Center for Health Statistics (NCHS). National Health and Nutrition Examination Survey (NHANES): Anthropometry Procedures Manual. Hyattsville, MD: Centers for Disease Control and Prevention; 2007.

  19. Lu Y, et al. Dual X-ray absorptiometry quality control: comparison of visual examination and process-control charts. J Bone Min Res. 1996;11:626–37.

    Article  CAS  Google Scholar 

  20. Gluer CC, Blake G, Lu Y, Blunt BA, Jergas M, Genant HK. Accurate assessment of precision errors: how to measure the reproducibility of bonedensitometry techniques. Osteoporos Int.1995;5:262–70.

    Article  CAS  Google Scholar 

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Acknowledgements

The authors thank Shape Up! Adults and Shape Up! Kids participants and parents for the time and enthusiasm they contributed to this study. In addition, this work could not have been completed without the assistance, data, and cooperation from Samantha Winter, Thomas Ward, and Bennett Ng at Naked Labs as well as formatting assistance from Melanie Peterson at Pennington Biomedical Research Center.

Funding

This work was partially supported by two National Institutes of Health NORC Center Grants P30DK072476, Pennington/Louisiana; and P30DK040561, Harvard; and R01DK109008, Shape UP! Adults.

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Authors and Affiliations

  1. Pennington Biomedical Research, Louisiana State University, Baton Rouge, LA, USA

    Samantha Kennedy, Phoenix Hwaung, Sima Sobhiyeh & Steven B. Heymsfield

  2. University of Hawaii Cancer Center, Honolulu, HI, USA

    Nisa Kelly, Yong E. Liu & John A. Shepherd

  3. Department of Public Health Sciences, Clemson University, Clemson, SC, USA

    Moonseong Heo

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  1. Samantha Kennedy
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  8. Steven B. Heymsfield
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Contributions

S.K., P.H., J.A.S., and S.B.H. analyzed the data and drafted the paper; S.K., P.H., J.A.S., and S.B.H. designed the study; S.K., P.H., and S.B.H. directed implementation and data collection; S.K., P.H., Y.E.L., and N.K. collected the data; J.A.S. and S.B.H. provided necessary logistical support; S.K., P.H., Y.E.L., N.K., S.S., J.A.S., and S.B.H. edited the paper for intellectual content and provided critical comments on the paper.

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Correspondence to Steven B. Heymsfield.

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Kennedy, S., Hwaung, P., Kelly, N. et al. Optical imaging technology for body size and shape analysis: evaluation of a system designed for personal use. Eur J Clin Nutr 74, 920–929 (2020). https://doi.org/10.1038/s41430-019-0501-2

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