Abstract
Background: Regression equations using anthropometric measurements to predict soft (fat mass [FM], lean mass [LM], wobbling mass [WM]) and rigid (bone mineral content [BMC]) tissue masses of the extremities and core body segments have been developed for younger adults (16-35 years), but not older adults (36-65 years). Tissue mass estimates such as these would facilitate biomechanical modeling and analyses of older adults following fall or collision-related impacts that might occur during sport and recreational activities. Purpose: The purpose of this study was to expand on the previously established tissue mass prediction equations of the head, neck, trunk, and pelvis for healthy, younger adults by generating a comparable set of equations for an older adult population. Methods: A generation sample (38 males, 38 females) was used to create head, neck, trunk, and pelvis tissue mass prediction equations via multiple linear stepwise regression. A validation sample (13 males, 12 females) was used to assess equation accuracy; actual tissue masses were acquired from manually segmented full body Dual-Energy X-ray Absorptiometry scans. Results: Adjusted R2 values for the prediction equations ranged from 0.326 to 0.949, where BMC equations showed the lowest explained variances overall. Mean relative errors between actual and predicted masses ranged from –2.6% to 6.1% for trunk LM and FM, respectively. All actual tissue masses except head BMC (R2 = 0.092) were significantly correlated to those predicted from the equations (R2 = 0.403 to 0.963). Conclusion: This research provides a simple and effective method for predicting head, neck, trunk, and pelvis tissue masses in older adults that can be incorporated into biomechanical models for analyzing sport and recreational activities. Future work with this population should aim to improve core segment BMC predictions and develop equations for the extremities.
Highlights
IntroductionThe movement of soft tissues (muscle, fat, skin) independent of the underlying bone (i.e., rigid tissue) has been shown to have significant force attenuating effects during highly dynamic human movements, especially those involving impacts (Pain & Challis, 2006; Schmitt & Günther, 2010; Bazrgari et al, 2011) that might be experienced during sport and recreational activities
The movement of soft tissues independent of the underlying bone has been shown to have significant force attenuating effects during highly dynamic human movements, especially those involving impacts (Pain & Challis, 2006; Schmitt & Günther, 2010; Bazrgari et al, 2011) that might be experienced during sport and recreational activities
Regression equations using anthropometric measurements to predict soft and rigid tissue masses of the extremities and core body segments have been developed for younger adults (16-35 years), but not older adults (36-65 years)
Summary
The movement of soft tissues (muscle, fat, skin) independent of the underlying bone (i.e., rigid tissue) has been shown to have significant force attenuating effects during highly dynamic human movements, especially those involving impacts (Pain & Challis, 2006; Schmitt & Günther, 2010; Bazrgari et al, 2011) that might be experienced during sport and recreational activities. Regression equations using anthropometric measurements to predict soft (fat mass [FM], lean mass [LM], wobbling mass [WM]) and rigid (bone mineral content [BMC]) tissue masses of the extremities and core body segments have been developed for younger adults (16-35 years), but not older adults (36-65 years) Tissue mass estimates such as these would facilitate biomechanical modeling and analyses of older adults following fall or collision-related impacts that might occur during sport and recreational activities. Conclusion: This research provides a simple and effective method for predicting head, neck, trunk, and pelvis tissue masses in older adults that can be incorporated into biomechanical models for analyzing sport and recreational activities Future work with this population should aim to improve core segment BMC predictions and develop equations for the extremities
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