## 4 Discussion
Additionally, even though this study does not consider age-related changes, there can also be significant differences in the muscle force prediction due to age factors. For instance, aging may result in reduced muscle strength, altered joint kinematics, and degenerative structural adaptations, further complicating AC joint OA diagnosis. To clinically correlate these muscle forces, a practitioner would also need to consider the morphology of the AC joint. This radiographic evaluation would involve decreased joint space, osteophytes, and surrounding bone sclerosis or remodeling that may elicit irritation to patients as the muscles evaluated in this simulation stress the joint's degenerative nature in these clinical studies. While these two tests may elicit symptomatic patients with AC joint OA, their discrepancies between muscle group activation may be a focus for rehabilitation in a clinical setting. Osteoarthritis is well studied in other joints, such as the hip and knee, and skeletal muscle wasting, proprioception, and muscle balance across joints have been factors noted to affect OA. [PERSON] et al. discuss that muscle weakness is not only an important determinant of pain and disability during OA, but it may also be linked with structural changes at the knee, leading to symptomatic pain in OA patients [33]. They further discuss that therapeutic exercise to strengthen periarticular muscle may be beneficial in decreasing the regulation of certain biomarkers and improving cartilage health. Another study by [PERSON] et al. found that patients with unilateral hip OA had lower muscle strength, proprioceptive accuracy, and poorer functional balance than controls (\(p<0.003\)) [36]. This indicates that a strong and balanced periarticular muscle group would be a rehabilitation treatment goal for patients with OA. For clinical treatment of patients with OA, whether in the hip, knee, or, in our case, the AC joint, rehabilitation protocols to strengthen the shoulder girdle muscles discussed and evaluated in our models could be a valuable option for patients to improve their pain and disability. A significant limitation of this study is its theoretical nature and the lack of direct validation for the predicted muscle forces during the CBA and HBB tests against experimental data or the existing literature. While the models used in this study have been validated in previous research, they were not validated using subject-specific data for this particular investigation. This may limit the generalizability of the findings to individual patients in clinical settings, as variations in anatomy and biomechanics across different patients are crucial for personalized diagnostics and treatment planning. Although the simulations accurately represent general biomechanical movements, they may not fully capture the nuances of patient-specific conditions, potentially impacting their relevance for clinical decision-making and real-world biomechanics research. Additionally, the discrepancies observed between OpenSim and AnyBody Modeling System(tm) predictions highlight the limitations of relying solely on computational models for clinical decision-making. These discrepancies stem from differences in anatomical definitions, assumptions, and model implementations between the two platforms, leading to divergent predictions of muscle forces and activation patterns. So, there remains a gap in understanding how well these models reflect real-world muscle dynamics without validation through clinical trials or empirical data, such as motion capture or electromyography (EMG). Future research should prioritize validating muscle force predictions and activation patterns through experimental methods, such as EMG and motion capture, to ensure alignment with actual physiological data. The first step in this process is to record the motion capture data for the CBA and HBB tests, which can then use these data as input in the musculoskeletal model to determine the kinematics and kinetics of the tests.