Brain injury severity due to direct head contact from near-side motor vehicle collisions.

OBJECTIVE: The objective of this study was to generate functional forms of brain injury risk curves using the National Automotive Sample System Crashworthiness Data System's (NASS-CDS) database for the years of 2001-2015. The population of interest was near-side occupants who experienced a direct head impact with an injury source located lateral to a typical seated position. METHODS: Brain injuries were restricted to Abbreviated Injury Scale (AIS) 2005 Update 2008 defined concussions and internal organ injuries of the head. Near-side occupants comprised two major groups, both of which were required to have evidence of head contact (i.e., a head injury with DIRINJ = 1 and SOUCON = 1 or 2): brain injured occupants (MAIS1, MAIS2, MAIS3+) and non-brain injured occupants with some other direct contact head injury (MAIS0). Analyzed cases were required to have an indication of a reasonable crash reconstruction. Injury sources allowed within the final sample consisted of A-pillars, B-pillars, roof/roof rails, impacting vehicles/exterior objects, other components of the vehicle's side interior, and other occupants or otherwise unspecified interior objects. Risk curves for occupants with brain injury severities of MAIS0, MAIS1+, MAIS2+, and MAIS3+ were generated using multivariate stepwise logistic regressions. Investigated predictors involved vehicle change in velocity, seat belt use, principal direction of force (PDOF), and injury source type (B-pillar and side window). RESULTS: Multivariate stepwise logistic regressions identified significant predictors of lateral change in velocity (dvlat) for all injury severity categories, and side window injury source (INJSOU = 56, 57, 58, 106, and 107) for MAIS0 and MAIS1+ risk curves. Although model sensitivity decreased for more severe injury predictions, risk curves dependent on only dvlat yielded accuracies of 70% for all presented models. CONCLUSIONS: Real world crashes are often complex and lack the benefit of real time monitoring; however, NASS-CDS post-crash investigations provide data useful for injury risk prediction. Further analysis is needed to determine the effect of data confidence, injury source, and accident sequence restrictions on NASS-CDS sampling biases. The presented models likely favor a more conservative risk prediction due to the limitations of NASS-CDS data collection, AIS code conversion, and unweighted sample analysis.

Upper limb strength in individuals with spinal cord injury who use manual wheelchairs.

INTRODUCTION: Manual wheelchair users have been found to be at risk for secondary upper extremity injuries. PURPOSE: The primary goal of this study was to compare shoulder strength and muscle imbalance of individuals with paraplegia to case-wise matched unimpaired controls (UC). A secondary goal was to evaluate the impact of age and neurologic level of injury (NLI) on weight-normalized strength (WNS). METHODS: The SCI group (n = 28) and the UC group (n = 28) completed bilateral shoulder isokinetic strength testing in the sagittal, frontal, and horizontal plane at 60 degrees/second using the BioDex system. Strength ratios, an indicator of muscle imbalance, were also calculated. RESULTS: No significant difference was seen in shoulder strength or strength ratios between the SCI group and the UC group. However, NLI was significantly related to WNS on several planes in the SCI group. Therefore, we dichotomized the SCI group into equal groups based on an NLI. The Low-SCI group was significantly stronger than the High-SCI group in most planes (P < 0.05). The High-SCI group was significantly weaker than the UC in extension (P < 0.01) and a trend (P < 0.01) was seen in flexion, abduction, and external rotation. The Low-SCI group was significantly stronger in abduction than the UC. CONCLUSION: WNS at the shoulder correlated with NLI. It is likely that this is related to contributions of the trunk and abdominal muscles during testing, since proximal trunk strength aids in generating forces distally. This study and others of strength in individuals with paraplegia may overestimate shoulder strength.

Filter frequency selection for manual wheelchair biomechanics.

Wheelchair locomotion is an important form of mobility for many individuals with spinal cord injury. However, manual wheelchair propulsion can lead to upper-limb pain and can be very inefficient. This has led investigators to apply biomechanics to the study of wheelchair use. The objectives of this study were (1) to determine the frequency content of the motion of both hands during two speeds of wheelchair propulsion, (2) to obtain the filter frequencies necessary to remove noise from wheelchair motion data, and (3) to provide signal-to-noise ratio data for wheelchair kinematics. The participants in this study were a random sample of manual wheelchair users with paraplegia caused by spinal cord injury. Subjects propelled their personal wheelchairs on a computer-controlled dynamometer at speeds of 0.9 m/s and 1.8 m/s. Motion data were collected at 60 Hz with the use of a commercial infrared marker-based system. The main outcome measures were arm motions and noise frequency spectra, filter cutoff frequencies, and signal-to-noise ratio. Our results indicate that there is no useful signal power above 6 Hz during manual wheelchair propulsion at the speeds that we analyzed. In many cases, there was no useful signal power above 4 Hz. This would indicate that the frequency content of manual wheelchair propulsion is similar to that of human gait. The mean signal-to-noise ratio varied from a high of 91 dB to a low of 21.8 dB. The signal-to-noise ratio was greatest in the x direction (along the line of progression) and lowest in the z direction (medial-lateral). Manual wheelchair propulsion kinematic data should be low-pass filtered at approximately 6 Hz for speeds at or below 1.8 m/s. The data presented in the archival literature appear to have been filtered at an appropriate frequency.

Ground reaction forces during the power clean.

Identifying and understanding the key biomechanical factors that exemplify the power clean can provide athletes the proper tools needed to prevail at a competitive event. Therefore, the purpose of this study was to characterize and describe ground reaction forces (Fz) during the power clean lift. Three 60-Hz motion-detecting cameras and an AMTI force plate were used to collect data from 10 collegiate weightlifting men who performed a power clean at 60 and 70% of their last competitive maximum clean. The results revealed that a greater peak force (Fz) was produced during the second pull compared with the first pull and unweighted phases in both percentage lifts. As the system weight increased from 60 to 70%, the peak force (Fz) increased for the first pull and unweighted phases and decreased during the second pull phase. Learning the proper technique of the power clean may provide athletes the basic understanding needed to be competitive in a weightlifting or sporting event.

Biomechanical analysis of the knee during the power clean.

To our knowledge, no scientific literature has examined the 3-dimensional forces acting at the knee joint during a power clean. Ten male weightlifting subjects (25.9 years, SD 3.54) performed 1 set of the power clean at 60 and 70% of their maximal collegiate level for 5 repetitions. The subjects displayed a large compressive, moderate anterior, and a small degree of lateral and medial force at the knee during both percentage lifts. The majority of these forces occurred during the second pull phase or the catching phase of the lift. Lifters with decreased weight/system weight percentages displayed a more efficient lift that placed less stress on the knees. This analysis may provide invaluable information in the assessment of weight percentages used for Olympic weightlifters throughout the training year. the power clean.