## 1 Introduction  
Osteoarthritis (OA), a medical condition commonly associated with aging, involves the degeneration of joint structures, leading to pain and functional limitations [1, 2, 3, 4]. Acropinaclavicular (AC) joint OA is the most common source of shoulder pain in middle-aged individuals, especially with regular overhead and cross-body tasks [5, 6]. Further studies found that 54-57% of patients who are elderly show signs of degenerative arthritis at the AC joint [7, 8]. Despite its prevalence, AC joint OA diagnostics face significant limitations, including variability in the sensitivity and specificity of physical tests and the inability of standard imaging techniques to accurately capture early degenerative changes or functional impairments [9, 10].  
Healthcare providers and researchers continue to study AC joint OA's pathogenesis and effective clinical management to understand its pathogenesis and clinical management. Various tests have been used to detect OA of the AC joint, including imaging and physical examinations, but diagnostic accuracy remains challenging. Clinicians perform physical examinations by placing and moving the shoulder to induce pain at the AC joint and detect OA. The cross-body adduction stress test, the acromiodacivical resisted-extension test, the Buchberger test, the active compression test, and the Paxions test are pain-inducing physical examination techniques commonly used to detect pathology in the AC joint [7, 8, 11, 12, 13, 14, 15]. However, these tests often lack consistency, with false negatives or misdiagnoses frequently reported. For instance, the cross-body adduction (CBA) test is a primary clinical diagnostic procedure for OA in the AC joint [16]. Although the CBA test is commonly used in clinical settings, inconsistencies have been observed in its test results. In particular, some patients who test negative for pain with the CBA test later test positive and are diagnosed with AC joint OA through imaging or arthroscopic procedure. These limitations highlight the need for enhanced diagnostic tools integrating biomechanical insights to improve clinical reliability.  
A recent study by [PERSON] et al. [17] proposed a new clinical test, the hand-behind-the-back (HBB) test, as a potentially superior diagnostic tool for AC joint OA. It hypothesizes it could offer a better physical diagnosis for AC joint OA in clinical trials with greater sensitivity. The HBB test resulted in more than a five-fold reduction in the AC joint articulating surface compared to the CBA test, offering a unique mechanical perspective [17]. Given these findings, our study uses musculoskeletal modeling software to focus on understanding the muscle force requirements during the CBA and HBB tests. The decision to prioritize the CBA and HBB tests over other diagnostic methods stems from their distinct mechanical challenges, frequent clinical use, and potential to shed light on specific AC joint mechanics.  
The objective of this study is to predict muscle forces during the CBA and HBB tests using two musculoskeletal modeling software programs, OpenSim and AnyBody Modeling SystemTM, and to evaluate the outcomes between the two. This research addresses gaps in AC joint biomechanics by providing detailed muscle force predictions and identifying variations between the software platforms regarding force magnitude and activation patterns. A deeper understanding of these forces can enhance diagnostic accuracy, inform rehabilitation strategies targeting specific muscle groups, and underline the role of accessory muscles in improving patient outcomes.