Muscle Recruitment of Upper Trapezius for Computer Workers with Chronic Neck Pain

Muscle Recruitment of Upper Trapezius for Computer Workers with Chronic Neck Pain aHannah Cline, aMarQiesha Stephens, bDavid Flynn, aScott Marzilli, aX. Neil Dong aBiomechanics Laboratory, Department of Health and Kinesiology, The University of Texas at Tyler, Tyler, Texas bEast Texas Doctors of Chiropractic, Tyler, Texas The lifetime prevalence of chronic neck pain has been reported to be greater than 80% for office computer workers with intensive computer work. Despite the fact that prevention strategies may have reduced the incidence of chronic neck pain, they don’t provide rehabilitation or prevent disease progression for computer workers who already have a symptomatic disease. By providing the means to detect tension that may otherwise go unnoticed of computer workers through the use of intervention strategies, the desire to prevent and reduce muscle tension for symptomatic computer workers can be accomplished. The central hypothesis is that changes in the motor response of symptomatic workers are manifested with abnormally higher muscle activities at rest postures and such modifications in muscle recruitment can be reversed by retraining muscles to be at a more relaxed state by attention of audio or visual biofeedback in motor learning. To test the central hypothesis, differences of muscle activation patterns between normal controls and symptomatic subjects were established by collecting EMG activity of bilateral upper trapezius muscles during a thirty-minute typing task. To meet the criteria for the symptomatic group, our subject has to have neck discomfort related to computer use which has lasted more than three months in the past year and is present in the past seven days. Muscle activities during the typing task were analyzed in terms of Amplitude Probability Distribution Function (APDF) for normalized percentages of reference voluntary contraction. By comparing average muscle activity (50% of APDF), preliminary data from this study indicated that symptomatic workers had higher muscle activities in upper trapezius muscles than asymptomatic workers. Such results may help to establish a preset threshold level of muscle activity to differentiate symptomatic and asymptomatic workers. Based on these preliminary results, a portable EMG-based biofeedback system may be developed to alleviate chronic neck pain of symptomatic computer workers by testing the latter part of our hypothesis that motor learning strategies can be used to reverse the changes in muscle recruitment of these patients

The influence of smoothness and speed of stand-to-sit movement on joint kinematics, kinetics, and muscle activation patterns

BackgroundStand-to-sit (StandTS) is an important daily activity widely used in rehabilitation settings to improve strength, postural stability, and mobility. Modifications in movement smoothness and speed significantly influence the kinematics, kinetics, and muscle activation patterns of the movement. Understanding the impact of StandTS speed and smoothness on movement control can provide valuable insights for designing effective and personalized rehabilitation training programs.Research questionHow do the smoothness and speed of StandTS movement affect joint kinematics, kinetics, muscle activation patterns, and postural stability during StandTS?MethodsTwelve healthy younger adults participated in this study. There were two StandTS conditions. In the reference condition, participants stood in an upright position with their feet positioned shoulder-width apart on the force plate. Upon receiving a visual cue, participants performed StandTS at their preferred speed. In the smooth condition, participants were instructed to perform StandTS as smoothly as possible, aiming to minimize contact pressure on the seat. Lower leg kinetics, kinematics, and coordination patterns of muscle activation during StandTS were measured: (1) angular displacement of the trunk, knee, and hip flexion; (2) knee and hip extensor eccentric work; (3) muscle synergy pattern derived from electromyography (EMG) activity of the leg muscles; and (4) postural sway in the anterior–posterior (A-P), medio-lateral (M-L), and vertical directions.ResultsCompared to the reference condition, the smooth condition demonstrated greater eccentric knee extensor flexion and increased joint work in both the knee and hip joints. Analysis of specific muscle synergy from EMG activity revealed a significant increase in the relative contribution of hip joint muscles during the smooth condition. Additionally, a negative correlation was observed between knee extensor and vertical postural sway, as well as hip extensor work and M-L postural sway.ConclusionSmooth StandTS facilitates enhanced knee eccentric control and increased joint work at both the hip and knee joints, along with increased involvement of hip joint muscles to effectively manage falling momentum during StandTS. Furthermore, the increased contributions of knee and hip joint work reduced postural sway in the vertical and M-L directions, respectively. These findings provide valuable insights for the development of targeted StandTS rehabilitation training

Effects Of A 4-week Vibration-induced Hamstrings Fatigue Intervention On Quadriceps Weakness After ACL Reconstruction

Arthrogenic muscle inhibition (AMI) results from an inability to voluntarily activate all motor units in the quadriceps due to ongoing neuronal inhibition. This may be due to changes in small diameter afferent activity that increase the excitability of the flexor withdrawal pathway, causing over-excitation of the hamstrings and reciprocal inhibition of the quadriceps. Reciprocal inhibition of the quadriceps from Ia afferents of the hamstrings may be reduced with prolonged muscle vibration of the hamstrings via fatigue of the intrafusal muscle fibers. PURPOSE: To determine the effects of vibration-induced hamstring fatigue on AMI after ACL reconstruction (ACLr). METHODS: Seven adults (28.7 ± 8.2 yrs) with unilateral ACLr (time since surgery: 19.4 ± 9.7 months) were recruited. Participants received a 4-week long (3x/week) training program. Vibration-induced fatigue of the hamstrings consisted of 20 minutes of prolonged vibration applied directly to the hamstrings. Then, a cuff was placed on the proximal thigh and inflated to 150 mmHg to trap the metabolites in the muscle, and maintain hamstrings fatigue; during which participants performed four sets of 15 reps at 30% RM unilateral knee extension (KE). Quadriceps strength and quadriceps inhibition were assessed before and after the intervention using KE 1-repitition maximum (RM) normalized to body weight, and the central activation ratio (CAR) measured by a superimposed burst. The co-activation of the hamstrings was assessed using hamstring EMG during KE. Paired t-tests were used to examine the effect of prolong vibration on KE strength, quadriceps CAR, and hamstrings co-activation before and after the intervention. RESULTS: KE strength increased significantly by 38.5% (from 0.45 ± 0.1 to 0.62 ± 0.2 %BW, P =0.004); quadriceps CAR also increased significantly by 5.8% (from 93 ± 0.1% to 98 ± 0.8%, P=0.02). Finally, co-activation decreased by 34% (from 12 ± 1.3% to 8 ± 0.9%, P=0.03). CONCLUSION: These results suggest that quadriceps weakness may be due to over excitation of the hamstrings which results in reciprocal inhibition of the quadriceps. Vibration-induced hamstrings fatigue can be used as a rehabilitation strategy to restore normal quadriceps function following ACLr by reducing the hamstrings over-excitability and restoring full quadriceps activation

Prediction of trabecular bone architectural features by deep learning models using simulated DXA images

Dual-energy X-ray absorptiometry (DXA) is widely used for clinical assessment of bone mineral density (BMD). Recent evidence shows that DXA images may also contain microstructural information of trabecular bones. However, no current image processing techniques could aptly extract the information. Inspired by the success of deep learning techniques in medical image analyses, we hypothesized in this study that DXA image-based deep learning models could predict the major microstructural features of trabecular bone with a reasonable accuracy. To test the hypothesis, 1249 trabecular cubes (6 mm × 6 mm × 6 mm) were digitally dissected out from the reconstruction of seven human cadaveric proximal femurs using microCT scans. From each cube, simulated DXA images in designated projections were generated, and the histomorphometric parameters (i.e., BV/TV, BS, Tb.Th, DA, Conn. D, and SMI) of the cube were determined using Image J. Convolutional neural network (CNN) models were trained using the simulated DXA images to predict the histomorphometric parameters of trabecular bone cubes. The results exhibited that the CNN models achieved high fidelity in predicting these histomorphometric parameters (from R = 0.80 to R = 0.985), showing that the DL models exhibited the capability of predicting the microstructural features using DXA images. This study also showed that the number and resolution of input simulated DXA images had considerable impacts on the prediction accuracy of the DL models. These findings support the hypothesis of this study and indicate a high potential of using DXA images in prediction of osteoporotic bone fracture risk