Prediction of Upper and Lower Extremity Tissue Masses Using Surface Anthropometric Measures and DXA



This chapter reviews the few studies that have reported regression equations that enable the prediction of upper and lower extremity soft and rigid tissue masses of living people, including lean (muscle), fat, and bone mineral content, from surface anthropometric measures such as segment lengths, breadths, circumferences, and skinfolds. Predicted tissue masses from the upper extremity segments (arm, forearm, forearm and hand) and lower extremity segments (thigh, leg, leg and foot) were validated against those obtained from full body Dual Energy X-ray Absorptiometry (DXA) scans. Twenty-four tissue mass prediction equations for the upper and lower extremity segments have been compiled which are highly predictive of measured tissue masses from DXA, with adjusted R 2 values ranging from 0.67 to 0.97. The studies that have evaluated the reliability of the methods used to develop the prediction equations are also reviewed. Almost 90% of the between- and 100% of the within-measurer reliability coefficients for the segment anthropometric measurements needed as inputs to the tissue mass prediction equations were found to be greater than 0.75 in magnitude, signifying good to excellent reliability. The reliability of manually segmenting tissue masses of the upper and lower extremities from DXA scans using analyst-assigned regions of interest was also assessed, and values were very high, ranging in magnitude from 0.991 to 1.000 across all tissue types and segments. In general, the low errors in predicted tissue masses and high reliability coefficients for the measured anthropometric measurements reported herein indicate that the methods used to develop and validate the tissue mass prediction equations are robust and appropriate for a variety of applications involving living people.


Lower Extremity Bone Mineral Content Anthropometric Measurement Tissue Masse Prediction Equation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.





In the Anterior/Posterior direction


Bone Mineral Content (kg or grams)


Dual Energy X-Ray Absorptiometry


Fat Mass (kg or grams)


Intra-class Correlation Coefficient




Lean Mass (kg or grams)




In the Medial/Lateral direction






Region of Interest (from DXA scans)


Wobbling Mass (W = LM + FM) (kg or g)



The authors wish to thank the Natural Sciences and Engineering Research Council of Canada (NSERC) for funding this research. Thanks also to Diagnostic Imaging at Windsor Regional Hospital and Nuclear Medicine at McMaster University Medical Centre for the use of their facilities, equipment, and technical support, and to the people who agreed to participate in the studies described in this chapter.


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Copyright information

© Springer Science+Business Media, LLC 2012

Authors and Affiliations

  1. 1.Department of Kinesiology, Department of Industrial and Manufacturing Systems EngineeringUniversity of WindsorWindsorCanada

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