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

Area of Science:

• Biomechanics
• Computational modeling
• Respiratory medicine

Background:

• Musculoskeletal system dysfunction is a common complication in chronic obstructive pulmonary disease (COPD).
• Assessing upper-extremity function (UEF) is crucial for understanding the impact of COPD on daily activities.
• Existing methods may not fully capture the nuanced neuromuscular deficits associated with COPD.

Purpose of the Study:

• To develop and validate a subject-specific computational arm model for characterizing UEF in older adults with COPD.
• To compare model-derived parameters with electromyography (EMG) and kinematics data.
• To identify key biomechanical indicators of muscle dysfunction in COPD.

Main Methods:

• A two degree-of-freedom, second-order, task-specific arm model was created.
• Participants included older adults (≥65 years) with and without COPD, and young healthy controls (18-30 years).
• Model performance was evaluated against EMG data; model parameters were compared across groups alongside kinematic data (e.g., elbow angular velocity).

Main Results:

• The model demonstrated strong cross-correlation with biceps EMG and moderate correlation with triceps EMG in older adults with COPD.
• Significant differences in musculoskeletal model parameters were observed between COPD and healthy participants.
• Muscle co-contraction measures derived from the model showed significant differences across all three groups (effect size = 1.650 ± 0.606, p < 0.001).

Conclusions:

• Computational musculoskeletal modeling provides a sensitive method for assessing UEF and muscle dysfunction in COPD.
• Muscle co-contraction analysis using this model offers superior insights into neuromuscular deficiencies compared to kinematics alone.
• The developed model has potential for evaluating functional capacity and monitoring longitudinal outcomes in COPD patients.