The DOI matches its Crossref record; 65 of the 65 references it cites were found in a registry or an index.
Body Composition Methods: Comparisons and Interpretation
Open accessAbstract
The incidence of obesity in the United States and other developed countries is epidemic. Because the prevalence of comorbidities to obesity, such as type 2 diabetes, has also increased, it is clear there is a great need to monitor and treat obesity and its comorbidities. Body composition assessments vary in precision and in the target tissue of interest. The most common assessments are anthropometric and include weight, stature, abdominal circumference, and skinfold measurements. More complex methods include bioelectrical impedance, dual-energy X-ray absorptiometry, body density, and total body water estimates. There is no single universally recommended method for body composition assessment in the obese, but each modality has benefits and drawbacks. We present here the most common methods and provide guidelines by way of examples to assist the clinician/researcher in choosing methods appropriate to their situation.
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Citations
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Metadata
- Authors
- Duren, D. L., Sherwood, R. J., Czerwinski, S. A., Lee, M., Choh, A. C., Siervogel, R. M., & Chumlea, W. C.
- Venue
- Journal of Diabetes Science and Technology
- Publisher
- SAGE Publications
- Published
- 2008-11
- Volume
- 2
- Issue
- 6
- Pages
- 1139-1146
- ISSN
- 1932-2968 (ISSN-L)
- OpenAlex
- W2123211977
- Cited by
- 451
- References
- 65
- FWCI
- 0.80
- Type
- journal article
- Language
- en
- Open access
- green
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- Duren, D. L., Sherwood, R. J., Czerwinski, S. A., Lee, M., Choh, A. C., Siervogel, R. M., & Chumlea, W. C. (2008). Body Composition Methods: Comparisons and Interpretation. Journal of Diabetes Science and Technology. https://doi.org/10.1177/193229680800200623
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
