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- Anoosha Pai S
- Six Skov
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Background
Note: During the final revision of this submission, it came to our attention that there is an error in our simulation. While the model’s muscle strengths and masses were doubled, the inertia values were not. This likely impacts the results presented in this report. This error is being addressed for a future report. An updated version of the code, which addresses this error, is included above.
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Hamstring strains are one of the most prevalent injuries that occur in sports involving high-speed running. Hamstring strain injuries account for 37% of all muscle injuries in sports [1, 3] and have increased by about 4% annually in the last decade [23]. Recently, eccentric Nordic Hamstring Exercise (NHE) has attracted a lot of attention in the world of biomechanics and sports science for its potential in reducing muscle injury risk among athletes and non-athletes alike [34]–[67]. To understand the mechanism behind the preventive action of NHE against injury, it is important to understand what physical stimuli NHE applies to the muscle, and how the muscle responds.
Many studies have focused on the second half of this question— how the muscle is responding. Several investigate the changes in bulk force and torque outputs after NHE training to characterize the muscle response [78], [89]. A few others have investigated muscle level adaptations through volume changes of individual hamstring muscles [89] and fascicle length changes in the biceps femoris muscles [910] after NHE training. However, there is little work that has emphasized the stimulus causing these changes. Van Hooren et al. characterized the force in the knee flexors during a rep of NHE [1011]. Therefore, with our work, we aim to recreate these findings and expand on them by characterizing the power and workloads experienced by the knee flexors during NHE
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Modeling
We used OpenSim 4.0 [1112] with a custom static optimization code in MATLAB R2021a (Mathworks, Inc., Natick MA, USA) for the simulation. We modeled the lower extremities and torso using the musculoskeletal model described by Arnold et. al., [1213] with 20 degrees of freedom and 42 musculotendon actuators spanning the pelvis and lower extremities. This model was linearly scaled to match the anthropometric measurements from the static Motion Capture trial. Model scaling automatically adjusted the muscle optimal fiber lengths and moment arms, and tendon slack lengths.
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- Single leg model does capture asymmetries between legs.
- EMG of all hamstring muscles are not available. Furthermore, EMG data during maximum voluntary contraction trials was not acquired. Hence exact comparison was not possible.
- Static optimization ignores muscle activation dynamics, which might vary the nature of muscle work and power as observed in our results, especially in the give-up region that involves rapid motion.
- Our model neglects tendon compliance, which may be a major contributor to the NHE.
- Minimizing activation squared may not fully capture the only cost terms relevant muscle coordination strategy during NHE.
Future Work
One area for future work is improving the accuracy of the model. Future work should include muscle activation dynamics and tendon compliance to give more realistic and accurate muscle activations, forces, and power during the NHE, especially post the give-up point. Dynamic optimization is a promising tool for this future direction. Furthermore, future studies could test whether MRI-informed subject-specific models can better explain the loading seen in these muscles during NHE.
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We thank Reed Gurchiek, Nicos Haralabidis, Nick Bianco, Jon Stingel, Carmichael Ong, and Scott Delp for providing countless hours of technical support and encouragement. We also thank Kristen Steudel and Katie Marusich for their experimental data.
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