Accurate prediction of wobbling mass (WM), fat mass (FM), lean mass (LM), and bone mineral content (BMC) of living people using regression equations developed from anthropometric measures (lengths, circumferences, breadths, skinfolds) has previously been reported, but only for the extremities. Multiple linear stepwise regression was used to generate comparable equations for the head, neck, trunk, and pelvis of young adults (38 males, 38 females). Equations were validated using actual tissue masses from an independent sample of 13 males and 13 females by manually segmenting full-body dual-energy x-ray absorptiometry scans. Prediction equations exhibited adjusted R values ranging from .249 to .940, with more explained variance for LM and WM than BMC and FM, especially for the head and neck. Mean relative errors between predicted and actual tissue masses ranged from -11.07% (trunk FM) to 7.61% (neck FM). Actual and predicted tissue masses from all equations were significantly correlated (R = .329 to .937), except head BMC (R = .046). These results show promise for obtaining in-vivo head, neck, trunk, and pelvis tissue mass estimates in young adults. Further research is needed to improve head and neck FM and BMC predictions and develop tissue mass prediction equations for older populations.

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.

Soft and rigid tissue mass prediction equations have been previously developed and validated for the segments of the upper and lower extremities in living humans using simple anthropometric measurements. The reliability of these measurements has been found to be good to excellent for all measurement types (segment lengths, circumferences, breadths, skinfolds). However, the reliability of the measurements needed to develop corresponding equations for the head, neck, and trunk has yet to be determined. The purpose of this study was to quantify the inter- and intrameasurer reliability of 34 surface anthropometric measurements of the head, neck, and trunk segments. Measurements (11 lengths, 7 circumferences, 11 breadths, 5 skinfolds) were taken twice separately on 50 healthy, university-age individuals using standard anthropometric tools. The mean inter- and intrameasurer measurement differences were fairly small overall, with 64.7% and 67.6% of the relative differences less than 5%, respectively. All measurements, except for the right lateral trunk, had intraclass correlation coefficients (ICCs) greater than 0.75, and coefficients of variation (CVs) less than 10%, indicating good reliability overall. These results are consistent with previous work for the extremities and provide support for the use of the defined surface measurements for future tissue mass prediction equation development.

Background: Muscle activations (MA) during maximum voluntary contractions (MVC) are commonly utilized to normalize muscle contributions. Isometric MVC protocols may not activate muscles to the same extent as during dynamic activities, such as falls on outstretched hands (FOOSH), that can occur during sport or recreational activities. Objective: The purpose of this study was to compare the peak MA of upper extremity muscles during isometric and dynamic MVC protocols. Methods: Twenty-four (12 M, 12 F) university-aged participants executed wrist and elbow flexion and extension actions during five-second MVC protocols targeting six upper extremity muscles (three flexors and three extensors). Each protocol [isometric (ISO); dynamic (eccentric (ECC), concentric (CON), elastic band (ELAS), un-resisted (UNRES)] consisted of three contractions (with one-minute rest periods between) during two sessions separated by one week. Muscle activation levels were collected using standard electromyography (EMG) preparations, electrode placements and equipment reported previously. Results: Overall, the ECC and CON dynamic protocols consistently elicited higher peak muscle activation levels than the ISO protocol for both males and females during both sessions. Over 95% of the CON trials resulted in mean and peak muscle activation ratios greater than ISO, with 56.3% being significantly greater than ISO (p < 0.05). Conclusion: Higher activation levels can be elicited in upper extremity muscles when resistance is applied dynamically through a full range of motion during MVC protocols.

Soft tissues overlying the hip play a critical role in protecting against fractures during fall-related hip impacts. Consequently, the development of an efficient and cost-effective method for estimating hip soft tissue thicknesses in living people may prove to be valuable for assessing an individual’s injury risk and need to adopt preventative measures. The present study used multiple linear stepwise regression to generate prediction equations from participant characteristics (i.e., height, sex) and anthropometric measurements of the pelvis, trunk, and thigh to estimate soft tissue thickness at the iliac crests (IC) and greater trochanters (GT) in younger (16–35 years of age: 37 males, 37 females) and older (36–65 years of age: 38 males, 38 females) adults. Equations were validated against soft tissue thicknesses measured from full body Dual-energy X-ray Absorptiometry scans of independent samples (younger: 13 males, 13 females; older: 13 males, 12 females). Younger adult prediction equations exhibited adjusted R2 values ranging from 0.704 to 0.791, with more explained variance for soft tissue thicknesses at the GT than the IC; corresponding values for the older adult equations were higher overall and ranged from 0.819 to 0.852. Predicted and actual soft tissue thicknesses were significantly correlated for both the younger (R2 = 0.466 to 0.738) and older (R2 = 0.842 to 0.848) adults, averaging ≤ 0.75cm of error. This research demonstrates that soft tissue thicknesses overlying the GT and IC can be accurately predicted from equations using anthropometric measurements. These equations can be used by clinicians to identify individuals at higher risk of hip fractures who may benefit from the use of preventative measures.