Assessing the role of ankle and hip joint proprioceptive information in balance recovery using vibratory stimulation.

Background: Previous work suggests that proprioceptive information from ankle and hip are crucial in maintaining balance during upright standing; however, the contribution of these proprioceptive information during stepping balance recovery in not clear. The goal of the current study was to assess the role of ankle and hip proprioceptive information on balance recovery performance by manipulating type 1a afferent in muscle spindles using vibratory stimulation. Methods: Twenty healthy young participants were recruited (age = 22.2 ± 2.7 years) and were randomly assigned to balance recovery sessions with either ankle or hip stimulation. Trip-like perturbations were imposed using a modified treadmill setup with a protecting harness. Vibratory stimulation was imposed bilaterally on ankle and hip muscles to expose participants to three condition of no-vibration, 40Hz vibration, and 80Hz vibration. Kinematics of the trunk and lower-extremities were measured using wearable sensors to characterize balance recovery performance. Outcomes were response time, recovery step length, trunk angle during toe-off and heel-strike of recovery stepping, and required time for full recovery. Findings: Ankle vibratory stimulation elicited main effects on reaction time and recovery step length (p < 0.002); reaction time and recovery step length increased by 23.0% and 21.2%, respectively, on average across the conditions. Hip vibratory stimulation elicited significant increase in the full recovery time (p = 0.019), with 55.3% increase on average across the conditions. Interpretation: Current findings provided evidence that vibratory stimulation can affect the balance recovery performance, causing a delayed recovery initiation and an impaired balance refinement after the recovery stepping when applied to ankle and hip muscles, respectively.

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

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.

Frailty assessment using a novel approach based on combined motor and cardiac functions: a pilot study.

BACKGROUND: Previous research showed association between frailty and an impaired autonomic nervous system; however, the direct effect of frailty on heart rate (HR) behavior during physical activity is unclear. The purpose of the current study was to determine the association between HR increase and decrease with frailty during a localized upper-extremity function (UEF) task to establish a multimodal frailty test. METHODS: Older adults aged 65 or older were recruited and performed the UEF task of rapid elbow flexion for 20 s with the right arm. Wearable gyroscopes were used to measure forearm and upper-arm motion, and electrocardiography were recorded using leads on the left chest. Using this setup, HR dynamics were measured, including time to peak HR, recovery time, percentage increase in HR during UEF, and percentage decrease in HR during recovery after UEF. RESULTS: Fifty-six eligible participants were recruited, including 12 non-frail (age = 76.92 ± 7.32 years), and 40 pre-frail (age = 80.53 ± 8.12 years), and four frail individuals (age = 88.25 ± 4.43 years). Analysis of variance models showed that the percentage increase in HR during UEF and percentage decrease in HR during recovery were both 47% smaller in pre-frail/frail older adults compared to non-frails (p < 0.01, effect size = 0.70 and 0.62 for increase and decrease percentages). Using logistic models with both UEF kinematics and HR parameters as independent variables, frailty was predicted with a sensitivity of 0.82 and specificity of 0.83. CONCLUSION: Current findings showed evidence of strong association between HR dynamics and frailty. It is suggested that combining kinematics and HR data in a multimodal model may provide a promising objective tool for frailty assessment.

Nonlinear analysis of the movement variability structure can detect aging-related differences among cognitively healthy individuals.

Studying the dynamics of nonlinear systems can provide additional information about the variability structure of the system. Within the current study, we examined the application of regularity and local stability measures to capture motor function alterations due to dual-tasking using a previously validated upper-extremity function (UEF). We targeted young (ages 18 and 30 years) and older adults (65 years or older) with normal cognition based on clinical screening. UEF involved repetitive elbow flexion without counting (ST) and while counting backward by one (DT1) or three (DT3). We measured the regularity (measured by sample entropy (SE)), local stability (measured by the largest Lyapunov exponent (LyE)), as well as conventional peak-dependent variability measures (coefficient of variation of kinematics parameters) to capture motor dynamic alterations due to dual-tasking. Within both groups, only SE showed significant differences between all pairs of UEF condition comparisons, even ST vs DT1 (p = 0.007, effect size = 0.507), for which no peak-dependent parameter showed significant difference. Among all measures, the only parameter that showed a significant difference between young and older adults was LyE (p < 0.001, effect size = 0.453). Current findings highlight the potential of nonlinear analysis to detect aging-related alterations among cognitively healthy participants.

A planar neuro-musculoskeletal arm model in post-stroke patients.

Mathematical modeling of the neuro-musculoskeletal system in healthy subjects has been pursued extensively. In post-stroke patients, however, such models are very primitive. Besides improving our general understanding of how stroke affects the limb motions, they can be used to evaluate rehabilitation strategies by computer simulations before clinical evaluations. A planar neuro-musculoskeletal arm model for post-stroke patients is developed. The main idea is to use a set of new coefficients, Muscle Significance Factors (MSF), to incorporate the effects of stroke in the muscle control performance. The model uses the optimal control theory to mimic the performance of the CNS and a two-link skeletal model with six muscles for the biomechanical part. The model was developed and evaluated using experimental data from six post-stroke patients with Brunnstrom levels of 4-6. The results show that MSFs are relatively distinct and independent from the arm motion which is used to determine their values. Its variation is in the range of 0-2.58% and decreases in higher Brunnstrom levels. The mean error of the model in predicting the path of motion varies from 0.9% in level 6 to 5.58% in level 4 subjects which can be considered a promising level of accuracy. Using the proposed model and the MSF to customize the model for each individual stroke patient seems a promising approach. It shows a reasonable level of robustness, i.e., independence from the type of motions and correlated with the severity of stroke, and accuracy in predicting the shape of the motion path.