Differences in Geriatric Anthropometric Data Between DXA-Based Subject-Specific Estimates and Non-Age-Specific Traditional Regression Models
| Autor: | Jean L. McCrory, Rakié Cham, April J. Chambers, Alison L. Sukits |
|---|---|
| Rok vydání: | 2011 |
| Předmět: |
Male
Gerontology Population ageing Biophysics Sensitivity and Specificity Article Absorptiometry Photon Linear regression Humans Medicine Computer Simulation Orthopedics and Sports Medicine Obesity Sex Distribution Geriatric Assessment Aged Aged 80 and over Models Statistical Anthropometry Anthropometric data business.industry Subject specific Rehabilitation Reproducibility of Results Regression analysis Pennsylvania medicine.disease Age specific Body Composition Regression Analysis Female business |
| Zdroj: | Journal of Applied Biomechanics. 27:197-206 |
| ISSN: | 1543-2688 1065-8483 |
| DOI: | 10.1123/jab.27.3.197 |
| Popis: | Age, obesity, and gender can have a significant impact on the anthropometrics of adults aged 65 and older. The aim of this study was to investigate differences in body segment parameters derived using two methods: (1) a dual-energy x-ray absorptiometry (DXA) subject-specific method (Chambers et al., 2010) and (2) traditional regression models (de Leva, 1996). The impact of aging, gender, and obesity on the potential differences between these methods was examined. Eighty-three healthy older adults were recruited for participation. Participants underwent a whole-body DXA scan (Hologic QDR 1000/W). Mass, length, center of mass, and radius of gyration were determined for each segment. In addition, traditional regressions were used to estimate these parameters (de Leva, 1996). A mixed linear regression model was performed (α = 0.05). Method type was significant in every variable of interest except forearm segment mass. The obesity and gender differences that we observed translate into differences associated with using traditional regressions to predict anthropometric variables in an aging population. Our data point to a need to consider age, obesity, and gender when utilizing anthropometric data sets and to develop regression models that accurately predict body segment parameters in the geriatric population, considering gender and obesity. |
| Databáze: | OpenAIRE |
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