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Joint Range of Motion During Oppositions Task in Individuals with Carpometacarpal Osteoarthritis Fernanda Campos Honors Senior Thesis J. Crayton Pruitt Family Department of Biomedical Engineering, University of Florida Advisor: Jennifer A. Nichols, PhD Musculoskeletal Biomechanics Lab April 2023
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2 Table of Contents Abstract ................................ ................................ ................................ ................................ ......................... 3 Introduction ................................ ................................ ................................ ................................ .................. 3 Methods ................................ ................................ ................................ ................................ ........................ 4 Results ................................ ................................ ................................ ................................ ........................... 8 Discussion ................................ ................................ ................................ ................................ .................... 11 Conclusion ................................ ................................ ................................ ................................ ................... 13 References ................................ ................................ ................................ ................................ .................. 13
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3 Abstract The primary objective of this project was to conduct an in depth investigation into carpometacarpal osteoarthritis (CMC OA) and its influence on thumb range of motion and joint coordination. To accomplish this , kinematic data was collected from a sample of 20 participants, 10 of whom exhibited early onset CMC OA, while the other 10 suffered from severe CMC OA. The data was then analyzed to facilitate a comparison of thumb motion between the two cohorts. The res ults of the analysis indicate that the severe CMC OA cohort exhibited a restricted range of CMC flexion and abduction, coupled with an elevated level of flexion in neighboring joints, as opposed to the early CMC OA cohort. Conversely, joint coordination wa s found to be consistent between the two groups. Through the identification of motion compensation and joint coordination through kinematic data, it is believed that early diagnosis and detection of CMC OA can be facilitated, thus contributing to more effe ctive treatment and improved prognosis of this debilitating degenerative disease. Intro duction The carpometacarpal (CMC) joint is found at the base of the thumb, between the trapezium and the first metacarpal. This joint has two main ax e s of rotation that allow for extension flexion and abduction adduction [1] . These movements make the CMC joint essential for daily functioning, as it is particularly important in the completion of opposition tasks . Carpometacarpal osteoarthritis (CMC OA) is a degenerative disease that affects 15% of people over the age of 30 and 85% of people between 71 to 8 0 years of age [2] . It is most prevalent in women, and people with CMC OA may suffer reduced range of motion, stiffness, and pain [3] . Kinematic data of thumb motion can give quantitative information about range of motion (ROM), joint coordination, and motion limitations. Th ese data, however, are currently limited given the challenges of using motion capture systems to study small bones. Information on ROM and joint coordination of the thumb at different stages of CMC OA can provide an additional tool in the diagnosis of this disease as well as the treatment of this condition [1]. Current treatments for CMC OA include steroid injection, activity m odification , and orthos es . However, these treatments tend to be available only in early CMC OA , highlighting the importance of early detection and diagnosis. Other surgical interventions are available, however, there is a considerable risk of postoperative complications and adverse outcomes [2] . Current
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4 diagnostic standards most commonl y include a grind test and radiologic assessment. The grind test consists of rotation of the thumb metacarpal while the joint is compressed axially. It is considered positive if the patient feels pain at the base of the thumb. The radiologic assessment is done to determine the stage of arthrosis. While these two methods are most effective at diagnosing CMC OA, there is a large number of diseases that present similar symptoms , such as carpal tunnel carpi radialis tendonitis, among others. Therefore, it is recommended that CMC examination includes details on joint enlargement, tenderness, deformity, and motion limitation [4] . This project aims to use kinematic data to evaluate joint coordination and range of motion during an opposition task in patients with various stages of CMC OA. It is hypothesized that people with severe OA will have a reduced range of motion at the CMC, compensatory movement of the neighboring joints, and different joint coordination than subjects with early stage OA. Identification of motion trends in patien ts with CMC OA can help inform future work into early diagnosis, intervention, and patient specific therapies for the treatment of this disease. Methods This study is a sub analysis of an ongoing IRB approved study on the compensatory mechanism s associated with movement evoked pain and CMC OA. The ongoing study has a n all female subject pool of 32 adults , 20 of which were analyzed for the current study. The presence and stage of OA was determined by obtaining radiographs and following the Eaton Littler classification . This classification has a total of four stages (1 4) and is based on joint space structu ral changes. subsequent stages progress in arthritic sensitivity [ 5 ]. Figure 1 shows radiographic examples. In this study, subjects with stage 1 or 2 were placed s and subjects with stage 3 or 4 were placed group. Each group had a total of 10 subjects . The average age and standard deviation were 62 .00 ± 12.17 and 69 .00 ± 10.73 for th e EOA and SOA groups, respectively.
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5 Figure 1. Eat on Littler Classification (A I, B II, C III, D IV) . Image from [ 5 ] . Th e kinematics and j oint coordination data of all subjects w ere collected using a marker based motion capture system. The marker sets were designed to emphasize and capture the motion of the interphalangeal ( IP ) , metacarpophalangeal ( MP ) , and CMC joints. Figure 2 shows the location of the marker clusters placed on eac h subject . The markers reflect infrared light sent from multiple cameras around the room, which record kinematic movement and genera te a 3D . Figure 2 . Diagram of marker clusters used in motion capture During data collection sessions, range of motion ( ROM ) and functional ability of the subject w ere observed as they performed a variety of tasks. One of these t as ks w as thumb opposition base of pinky (BOP ), where the subject move d the distal phalanx of th e ir thumb to the proximal joint of their pinky. Each subject completed the motion 3 times at a self selected speed .
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6 Throughout this task, the thumb joint s undergo flexion, extension, adduction , and abduction. The directions of these motions and how they ar e defined can be seen in Figure 3 [ 6 ] and Figure 4 shows the BOP task. Figure 3 . Illustration of standard thumb motions . Image from [ 6 ] . Figure 4. Illustration of BOP Task performed by all subjects. Prior to analysis, data was post processed using standard motion capture methods. Specifically , Vicon Nexus was used to fill the gaps in the marker trajectories caused by line of sight issues . Nexus has pre existing gap filling algorithms that use neighbor markers or valid frames nea r the data gap to complete the marker trajectory. Spline, patter n , rigid body , and kinematic are the four gap filling options [ 7 ]. OpenSim, an open source software that creates graphics based models for the analysis of biomechanics, was used to run inverse kinematics and attain joint angle values. Once gap filling of the 20 subjects was completed, the data was exported to Open S im where data and weight were used to scale an upper extremity model (20 scaled models total) . Each model through each frame of the motion data and positions the model in a way that best f its the experimental data [ 8 ]. The calculations done by inverse kinematics is completed using a least squares equation, which allows marker weights and coordinate weights to be adjusted. For all models, the weight of the thumb markers was incr eased to 10 (originally each marker has a weight of 1) to generate more accurate thumb joint angles .
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7 The inverse kinematics analysis produces a motion fi le (csv) that specifies the thumb joint angles at each timeframe of the task . For this study we focused on the CMC flexion, CMC abduction, MP flexion, and IP flexion joint angles . The data from the 20 subjects was compiled and analyzed using Python . Given the non homogeneity of the data due to different joint coordination across people , as well as ti me differences completing the task, the beginning, end, and 50% completion time points of each task were selected. Each participant completed the task 3 times, meaning 9 time points were manually selected for each participant. These time points were used t o normalize the total length of each task into percent completion of the task, allowing for averaging of the trials per participant and per group in Matlab . T he normalization of the task based on percent completion allowed for easy identification of the l owest and highest degree of motion during the task. This is how the ROM was calculated for each joint (highest degree angle lowest degree angle ). Once the ROM for each joint and all participants were calculated, JMP, a statistical software, was used to c omplete a t tests comparing the ROM of the CMC flexion, CMC abduction, MP flexion, and IP flexion across cohorts. Figure 5 shows a summary of the methods.
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8 Figure 5 . Summary of the methods Results Average joint angles across task completion The average joint angles across task completion showed no significant difference between the EOA and SOA cohorts. In fact, the two cohorts demonstrated similar trends in joint coordination. For both cohorts , IP flexion had the highest recorded mean angle, followed by MCP flexion, CMC flexion, and CMC abduction. Interestingly, e ach joint reaches its maximum mean value at different percent completion of the task. For EOA: CMC a bduction was 4.55 ° at 8% , CMC f le xion was 21.85 ° at 67%, MCP f lexion was 26.42 ° at 51%, and IP f lexion was 31.55 ° at 57%. For SOA: CMC a bduction was 5.89 ° at 83%, CMC f lexion was 22.68 ° at 55%, MCP f lexion was 31.94 ° at 48%, and IP f lexion was 37.43 ° at 54%. Figure 6 show s the plots of the joint angles throughout the task for EOA and SOA , respectively.
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9 Figure 6: Plots of joint angles as percent completion of the task. Solid lines represent the average joint angle across all participants in the group . Dashed lines represent the 95% confidence intervals. EOA is on the left. SOA is on the right. Range of motion of thumb joint angles by cohort Across all participants, t he range of motion of the joints was not significantly different between the EOA and SOA groups (p > 0.05) (Fig. 7) . The EOA group had a mean (± 95% CI) CMC flexion of 36 .45 ° ± 9.02° , CMC abduction of 18.77° ± 9.78° , MCP f lexion of 33.17° ± 29.53° , and IP f lexion of 34.98° ± 25.44°. Similarly, t he SOA group had a mean (± 95% CI) CMC flexion of 33.92° ± 8.09° , CMC a bduction of 15.59° ± 8.56° , MCP f lexion of 41.26° ± 18.29° , and IP f lexion of 44 .0° ± 20.86° .
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10 Figure 7. Bar graph of average ROM for CMC flexion , CMC abduction , MCP flexion , and IP flexion by cohort . Purple dashed line s indicate limit of anatomically possible ranges. However, the ROM calculated for a few participants exceeded anatomically possible joint limits (c.f., Fig 7, data points above dashed purple lines). Thus, an outlier analysis was performed test. Figure 8 shows the boxplots used for the outlier test. Based on this analysis, f or the ROM analysis, 3 out of the 20 participants (2 with EOA, 1 with SOA) were excluded as outliers . . Figure 8 test . Three outliers were identified . The ROM analysis was repeated without the outliers (Fig. 9) . The EOA cohort had a CMC flexion of 35.67° ± 11.25° (average ± 95% CI), CMC abduction of 14.72° ± 4.53° , MCP flexion of 27.60° ± 29.54° , and an IP flexion of 24.72° ± 16.55° . The SOA cohort had a CMC flexion of 32 .15° ± 8.02° , CMC abduction of 12.29° ± 4.76° , MCP flexion of 34.19° ± 10.11° , and IP flexion of 47.43° ± 22.07° . There was no significant difference in the ROM of each of the four joints between cohorts (p > 0.05).
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11 Figure 9. Bar graph of average ROM for CMC flexion , CMC abduction , MCP flexion , and IP flexion by cohorts with outliers removed. Purple dashed lines indicate the limit of anatomically possible ranges. Discussion This project demonstrated the usefulness of kinematic data to calculate joint angles and joint range of motions between individuals with EOA and SOA. The hypothesis was partially correct given people in the SOA group had a trend toward lower ROM at the CMC joint and showed compensatory movements of the IP and MCP joints. The hypothesis was incorrect in stating that joint coordination would differ between cohorts. In fact, there was similar joint coordination throughout completion of the task with IP flexion and CMC abduction reaching the largest and lowest angles , respectively . These results make sense given IP flexion has the highest anatomically possible range out of the four joints (100°) and CMC abduction has the lowest (50°) . Even though there we re no significant differences between the cohorts, some interesting trends were evident. The average CMC flexion and abduction of the SOA group tended to be lower than that of the EOA group, and the MCP flexion and IP flexion of the SOA group tended to be higher than that of the EOA group. These trends indicate possible compensatory movements of the MCP and IP in response to the reduced range of motion of the CMC. Joint compensation and reduced range of motion could be signals of CMC onset, presenting kinem atic data as an additional tool in the diagnosis of this degenerative disease.
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12 A challenge with understanding CMC OA is the heterogeneity across subjects. This is evident by the large sizes of the 95% confidence intervals in Figure s 6 and 9 . For the joint coordination plots, this heterogeneity could be addressed by n by their anatomically possible range . This could highlight the relative weight that each of the joints has throughout the task and help identify posit ions at which a given joint could be compensating For the ROM plots, n ormalizing the ranges of motion by the and reduce confounding variables given by anatomical differences between subjects . The inclusion of additional opposition tasks could also help identify joint coordination differences between subjects. Subject specific analysis could also provide a n approach to understanding heterogeneity , compared to cohort based analysis, which uses average joint angles that may obscure subject specific trends. Analyzing joint angles per subject could reveal changes in joint coordination throughout the 3 BOP movements, especially since different kinematic strategies can be used to complete the task. It is unclear if these different strategies are a result of pain/discomfort caused by CMC OA or if they are due to natural dexterity differences between subjects. This differentiation is particularly challenging to identify given the heterogeneity of CMC OA and the lack of a direct relationship between pain and OA stage reported in the literature. By identifying joint compensations at specific positions during the BOP task, clinicians could potentially diagnose CMC OA earlier and develop tailored treatment plans for patients. Additional l imitations for this study stemmed from the small sample size and kinematic simulation challenges . In this study, t he small sample size (n=20) and female only participant pool are not inclusive nor large enough to generalize about the CMC OA population . However, the methods used in this paper allow us to identify differences as well as trends between cohorts. A limitation of the methods was the challenges encountered with the kinematic simulations. The inverse kinematic tool use d in OpenSim 4.3 would occasionally break and result in anatomically incorrect upper limb motions, as seen in Figure 10. Addressing th ese c hallen ge s includ ed locking the movement of the 4 digits to reduce the model complications. While this should not affect the joint values recorded in this paper, there were still data points that exceeded anatomically possible ranges (Figure 7), therefore, further
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13 study into the Open Sim 4.3 simulations errors and limitations are needed to ensure correct inverse kinematic results. Figure 10. Open Sim upper limb broken model during inverse kinematics run. Conclusion This project successfully compared thumb joint coordination and range of motion between subjects with early stage OA and severe OA during an opposition task. Calculating thumb joint angles and ranges of motion provide s an effective way to analyze thumb joint coordination and identify compensatory movements across the thumb joints. Further research with a larger sample size and joint coordination . Individual subject analysis, as well as data normalization by hand width and , could help identify relative ranges of m otion and account for anatomical difference between participants. The heterogeneity of the results indicate how individualized care could be beneficial in the diagnosis and care of CMC OA. References [1] kinematics of the thumb during flexion and adduction motion: Evidence for a screw home Journal of Orthopaedic Research , vol. 35, no. 7, pp. 1556 1564, Jul. 2017, doi: 10.1002/jor.23421. [ 2] Denervation for Painful Arthritis: Follow Up of Long J Hand Surg Glob Online , vol. 5, no. 1, pp. 108 111, Jan. 2023, doi: 10.1016/j.jhsg.2022.02.0 05.
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14 [3] W. Y. Kwok, M. Kloppenburg, M. Marshall, E. Nicholls, F. R. Rosendaal, and G. Peat, in symptomatic community Osteoarthritis Cartilage , vol . 22, no. 6, pp. 756 763, 2014, doi: 10.1016/j.joca.2014.03.012. [4] Journal of Hand Therapy , vol. 23, no. 3, pp. 261 268, 2010, doi: 10.1016/j.jht.2010.02.001. [ 5 ] pp. 2729 2733, Dec. 2016, doi: 10.1007/s11999 016 4864 6. [ 6 ] Kinematic synergies of hand grasps: A comprehensive study on a large publicly available dataset Sci entific Figure on ResearchGate. Available from: https://www.researchgate.net/figure/Sign criteria for the CMC joint of the thumb_fig3_333438886 [accessed 29 Mar, 2023] [ 7 ] [ 8 ] ow Inverse Kinematics Works OpenSim Documentation https://simtkconfluence.stanford.edu:8443/display/OpenSim/How+Inverse+Kinematics+ Works (accessed Mar. 27, 2023).
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mods:accessCondition Copyright Fernanda Lizbeth Campos Ramirez. Permission granted to the University of Florida to digitize, archive and distribute this item for non-profit research and educational purposes. Any reuse of this item in excess of fair use or other copyright exemptions requires permission of the copyright holder.
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