Dataset Information

Supervised Exercise Training Improves 6 min Walking Distance and Modifies Gait Pattern during Pain-Free Walking Condition in Patients with Symptomatic Lower Extremity Peripheral Artery Disease.

ABSTRACT: This study aimed to investigate the effects of supervised exercise training (SET) on spatiotemporal gait and foot kinematics parameters in patients with symptomatic lower extremity peripheral artery disease (PAD) during a 6 min walk test. Symptomatic patients with chronic PAD (Fontaine stage II) following a 3 month SET program were included. Prior to and following SET, a 6 min walk test was performed to assess the 6 min walking distance (6MWD) of each patient. During this test, spatiotemporal gait and foot kinematics parameters were assessed during pain-free and painful walking conditions. Twenty-nine patients with PAD (65.4 ± 9.9 years.) were included. The 6MWD was significantly increased following SET (+10%; p ≤ 0.001). The walking speed (+8%) and stride frequency (+5%) were significantly increased after SET (p ≤ 0.026). The stride length was only significantly increased during the pain-free walking condition (+4%, p = 0.001), whereas no significant differences were observed during the condition of painful walking. Similarly, following SET, the relative duration of the loading response increased (+12%), the relative duration of the foot-flat phase decreased (-3%), and the toe-off pitch angle significantly increased (+3%) during the pain-free walking condition alone (p ≤ 0.05). A significant positive correlation was found between changes in the stride length (r = 0.497, p = 0.007) and stride frequency (r = 0.786, p ≤ 0.001) during pain-free walking condition and changes in the 6MWD. A significant negative correlation was found between changes in the foot-flat phase during pain-free walking condition and changes in the 6MWD (r = -0.567, p = 0.002). SET was found to modify the gait pattern of patients with symptomatic PAD, and many of these changes were found to occur during pain-free walking. The improvement in individuals' functional 6 min walk test was related to changes in their gait pattern.

SUBMITTER: Lanzi S

PROVIDER: S-EPMC8659842 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Neuromusculoskeletal disorders affecting walking ability are often difficult to manage, in part due to limited understanding of how a patient's lower extremity muscle excitations contribute to the patient's lower extremity joint moments. To assist in the study of these disorders, researchers have developed electromyography (EMG) driven neuromusculoskeletal models utilizing scaled generic musculoskeletal geometry. While these models can predict individual muscle contributions to lower extremity joint moments during walking, the accuracy of the predictions can be hindered by errors in the scaled geometry. This study presents a novel EMG-driven modeling method that automatically adjusts surrogate representations of the patient's musculoskeletal geometry to improve prediction of lower extremity joint moments during walking. In addition to commonly adjusted neuromusculoskeletal model parameters, the proposed method adjusts model parameters defining muscle-tendon lengths, velocities, and moment arms. We evaluated our EMG-driven modeling method using data collected from a high-functioning hemiparetic subject walking on an instrumented treadmill at speeds ranging from 0.4 to 0.8 m/s. EMG-driven model parameter values were calibrated to match inverse dynamic moments for five degrees of freedom in each leg while keeping musculoskeletal geometry close to that of an initial scaled musculoskeletal model. We found that our EMG-driven modeling method incorporating automated adjustment of musculoskeletal geometry predicted net joint moments during walking more accurately than did the same method without geometric adjustments. Geometric adjustments improved moment prediction errors by 25% on average and up to 52%, with the largest improvements occurring at the hip. Predicted adjustments to musculoskeletal geometry were comparable to errors reported in the literature between scaled generic geometric models and measurements made from imaging data. Our results demonstrate that with appropriate experimental data, joint moment predictions for walking generated by an EMG-driven model can be improved significantly when automated adjustment of musculoskeletal geometry is included in the model calibration process.

Dataset Information

Supervised Exercise Training Improves 6 min Walking Distance and Modifies Gait Pattern during Pain-Free Walking Condition in Patients with Symptomatic Lower Extremity Peripheral Artery Disease.

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