Comparison of Upper Extremity Muscle Activation Levels Between Isometric and Dynamic Maximum Voluntary Contraction Protocols

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

Head, Neck, Trunk, and Pelvis Tissue Mass Predictions for Older Adults using Anthropometric Measures and Dual-Energy X-Ray Absorptiometry

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

A Descriptive Video Analysis of Helmet Impact Cases in North American Youth Football Players

Background: Detailed characterization of on-field helmet impacts in football through video analysis has mostly been limited to professional games due to the availability of high quality, multi-view video (e.g., broadcast footage). Few studies have assessed youth football helmet impacts using video-based methods, often with only a single-camera view. Objective: A multi-camera approach was used in this observation-based study to describe the mechanisms and situational factors of in-game helmet impacts experienced by youth football players. Methods: A descriptive video analysis was performed in which video of three games from two old divisions (game A: 9–12 years; games B and C: 13–14 years) was reviewed and parameters related to all cases of observed helmet impact were documented. Results: Overall, 95 helmet impact cases were identified (single helmet contact: 81.1%; multiple helmet contacts: 18.9%), with 115 helmet contacts. Helmet-to-ground contacts were most common (59.1%), followed by helmet-to-helmet (24.3%) and helmet-to-body (16.5%). Helmet impact cases generally occurred during a rush play (67.4%) and were concentrated in the mid-field (81%). Helmet contact locations were predominantly distributed between the rear (upper) (28.7%) and side (upper) (27.8%) helmet regions. Tackling was the most frequent activity leading to helmet impact (41.1%). Conclusion: These findings offer detailed on-field helmet impact characteristics at the youth level that can help inform athlete safety improvement efforts

Quantifying Forearm Soft Tissue Motion from Massless Skin Markers following Forward Fall Hand Impacts

Background: Investigating soft tissue motion related to impact events is important for understanding how the body mitigates potentially injurious forces through shock attenuation. Objectives: The aims of this study were to: 1) quantify displacement and velocity of the forearm soft tissues following forward fall impacts; and 2) compare two massless skin marker designs (single layer, uniform (SLU) design; stacked, non-uniform (SNU) design) in terms of how well they could be tracked over varying skin pigmentations using automated motion capture software. Methods: Two participant groups (skin pigmentation: light – 9F, 8M; dark – 9F, 6M) underwent simulated forward fall hand impacts for each marker design using a torso-release apparatus. Marker positions associated with planar motion of forearm soft tissues during impact were automatically tracked (ProAnalyst®) in the proximal-distal and anterior-posterior axes from high speed recordings (5000 f/s). Mean peak displacements and velocities for eight forearm regions were then calculated (LabVIEW®). Results: Overall, soft tissue displacement and velocity increased from distal to proximal forearm regions. The greatest displacement (1.47 cm) and velocity (112.8 cm/s) occurred distally toward the wrist. Soft tissue impact responses between sexes did not differ, on average (p > 0.05). The SLU and SNU markers produced different kinematic values (p < 0.05); however, the magnitudes of, and consequently meaningfulness of these statistical differences for automatically tracking soft tissue motion, were negligible (displacement: ≤ 0.05 cm; velocity: ≤ 2.5 cm/s). Conclusions: Forearm soft tissue motion was successfully quantified for forward fall hand impacts; both marker designs were deemed functionally equivalent

Predicting soft tissue thicknesses overlying the iliac crests and greater trochanters of younger and older adults.

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