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A computational musculoskeletal arm model for assessing muscle dysfunction in chronic obstructive pulmonary disease

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Abstract

Computational models have been used extensively to assess diseases and disabilities effects on musculoskeletal system dysfunction. In the current study, we developed a two degree-of-freedom subject-specific second-order task-specific arm model for characterizing upper-extremity function (UEF) to assess muscle dysfunction due to chronic obstructive pulmonary disease (COPD). Older adults (65 years or older) with and without COPD and healthy young control participants (18 to 30 years) were recruited. First, we evaluated the musculoskeletal arm model using electromyography (EMG) data. Second, we compared the computational musculoskeletal arm model parameters along with EMG-based time lag and kinematics parameters (such as elbow angular velocity) between participants. The developed model showed strong cross-correlation with EMG data for biceps (0.905, 0.915) and moderate cross-correlation for triceps (0.717, 0.672) within both fast and normal pace tasks among older adults with COPD. We also showed that parameters obtained from the musculoskeletal model were significantly different between COPD and healthy participants. On average, higher effect sizes were achieved for parameters obtained from the musculoskeletal model, especially for co-contraction measures (effect size = 1.650 ± 0.606, p < 0.001), which was the only parameter that showed significant differences between all pairwise comparisons across the three groups. These findings suggest that studying the muscle performance and co-contraction, may provide better information regarding neuromuscular deficiencies compared to kinematics data. The presented model has potential for assessing functional capacity and studying longitudinal outcomes in COPD.

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Funding

This project was supported by an award from the National Institute of Aging (NIA) (award number: 5R21AG059202-02). The findings of this manuscript are those of the authors and do not necessarily represent the official views of NIA.

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Authors and Affiliations

  1. Department of Biomedical Engineering, University of Arizona, 1230 N Cherry Ave, Tucson, AZ, 85721, USA

    Mehran Asghari, Miguel Peña, Haley Johnson & Nima Toosizadeh

  2. Department of Public Health, University of Arizona, Tucson, AZ, USA

    Martha Ruiz

  3. Neuroscience and Cognitive Science Program, University of Maryland, College Park, USA

    Hossein Ehsani

  4. Department of Kinesiology, University of Maryland College Park, Maryland, MD, USA

    Hossein Ehsani

  5. Arizona Center On Aging (ACOA), Department of Medicine, College of Medicine, University of Arizona, Tucson, AZ, USA

    Nima Toosizadeh

  6. Division of Geriatrics, General Internal Medicine and Palliative Medicine, Department of Medicine, University of Arizona, Tucson, AZ, USA

    Nima Toosizadeh

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  1. Mehran Asghari
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Asghari, M., Peña, M., Ruiz, M. et al. A computational musculoskeletal arm model for assessing muscle dysfunction in chronic obstructive pulmonary disease. Med Biol Eng Comput 61, 2241–2254 (2023). https://doi.org/10.1007/s11517-023-02823-0

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