Visuospatial Skills Explain Differences in the Ability to Use Propulsion Biofeedback Post-stroke.

BACKGROUND AND PURPOSE: Visual biofeedback can be used to help people post-stroke reduce biomechanical gait impairments. Using visual biofeedback engages an explicit, cognitively demanding motor learning process. Participants with better overall cognitive function are better able to use visual biofeedback to promote locomotor learning; however, which specific cognitive domains are responsible for this effect are unknown. We aimed to understand which cognitive domains were associated with performance during acquisition and immediate retention when using visual biofeedback to increase paretic propulsion in individuals post-stroke. METHODS: Participants post-stroke completed cognitive testing, which provided scores for different cognitive domains, including executive function, immediate memory, visuospatial/constructional skills, language, attention, and delayed memory. Next, participants completed a single session of paretic propulsion biofeedback training, where we collected treadmill-walking data for 20 min with biofeedback and 2 min without biofeedback. We fit separate regression models to determine if cognitive domain scores, motor impairment (measured with the lower-extremity Fugl-Meyer), and gait speed could explain propulsion error and variability during biofeedback use and recall error during immediate retention. RESULTS: Visuospatial/constructional skills and motor impairment best-explained propulsion error during biofeedback use (adjusted R 2  = 0.56, P = 0.0008), and attention best-explained performance variability (adjusted R 2  = 0.17, P = 0.048). Language skills best-explained recall error during immediate retention (adjusted R 2  = 0.37, P = 0.02). DISCUSSION AND CONCLUSIONS: These results demonstrate that specific cognitive domain impairments explain variability in locomotor learning outcomes in individuals with chronic stroke. This suggests that with further investigation, specific cognitive impairment information may be useful to predict responsiveness to interventions and personalize training parameters to facilitate locomotor learning.

Gait speed and individual characteristics are related to specific gait metrics in neurotypical adults.

Gait biofeedback is a well-studied strategy to reduce gait impairments such as propulsion deficits or asymmetric step lengths. With biofeedback, participants alter their walking to reach the desired magnitude of a specific parameter (the biofeedback target) with each step. Biofeedback of anterior ground reaction force and step length is commonly used in post-stroke gait training as these variables are associated with self-selected gait speed, fall risk, and the energy cost of walking. However, biofeedback targets are often set as a function of an individual's baseline walking pattern, which may not reflect the ideal magnitude of that gait parameter. Here we developed prediction models based on speed, leg length, mass, sex, and age to predict anterior ground reaction force and step length of neurotypical adults as a possible method for personalized biofeedback. Prediction of these values on an independent dataset demonstrated strong agreement with actual values, indicating that neurotypical anterior ground reaction forces can be estimated from an individual's leg length, mass, and gait speed, and step lengths can be estimated from individual's leg length, mass, age, sex, and gait speed. Unlike approaches that rely on an individual's baseline gait, this approach provides a standardized method to personalize gait biofeedback targets based on the walking patterns exhibited by neurotypical individuals with similar characteristics walking at similar speeds without the risk of over- or underestimating the ideal values that could limit feedback-mediated reductions in gait impairments.

Speed-dependent biomechanical changes vary across individual gait metrics post-stroke relative to neurotypical adults.

BACKGROUND: Gait training at fast speeds is recommended to reduce walking activity limitations post-stroke. Fast walking may also reduce gait kinematic impairments post-stroke. However, it is unknown if differences in gait kinematics between people post-stroke and neurotypical adults decrease when walking at faster speeds. OBJECTIVE: To determine the effect of faster walking speeds on gait kinematics post-stroke relative to neurotypical adults walking at similar speeds. METHODS: We performed a secondary analysis with data from 28 people post-stroke and 50 neurotypical adults treadmill walking at multiple speeds. We evaluated the effects of speed and group on individual spatiotemporal and kinematic metrics and performed k-means clustering with all metrics at self-selected and fast speeds. RESULTS: People post-stroke decreased step length asymmetry and trailing limb angle impairment, reducing between-group differences at fast speeds. Speed-dependent changes in peak swing knee flexion, hip hiking, and temporal asymmetries exaggerated between-group differences. Our clustering analyses revealed two clusters. One represented neurotypical gait behavior, composed of neurotypical and post-stroke participants. The other characterized stroke gait behavior-comprised entirely of participants post-stroke with smaller lower extremity Fugl-Meyer scores than the post-stroke participants in the neurotypical gait behavior cluster. Cluster composition was largely consistent at both speeds, and the distance between clusters increased at fast speeds. CONCLUSIONS: The biomechanical effect of fast walking post-stroke varied across individual gait metrics. For participants within the stroke gait behavior cluster, walking faster led to an overall gait pattern more different than neurotypical adults compared to the self-selected speed. This suggests that to potentiate the biomechanical benefits of walking at faster speeds and improve the overall gait pattern post-stroke, gait metrics with smaller speed-dependent changes may need to be specifically targeted within the context of fast walking.

Changes in hip mechanics during gait modification to reduce knee abduction moment.

First peak knee abduction moment (KAM) has been associated with the severity and progression of knee osteoarthritis (KOA). Gait modifications, including lateral trunk lean (TL), medial knee thrust (MKT), and reduced foot progression (FP) have decreased KAM. However, their effects on the hip joint are poorly understood. Reduced hip abduction moment has been found to be predictive of KOA progression and has been hypothesized to represent a decreased demand on the hip musculature. Lack of studies investigating changes in hip mechanics as a result of gait modification limits our understanding of their cumulative benefit, therefore, we investigated the effects of TL, MKT, and FP on internal hip abduction moment as well as rate change in net joint reaction force. Using real-time visual biofeedback, five trials were completed for each modification. Each modification target range was individualized to 3-5 SD greater (TL and FP) or lesser (MKT) than the participants mean baseline value. Kinematics and kinetics at the hip and knee were calculated at first peak KAM. Trunk lean and MKT decreased hip abduction moment compared to baseline (p < 0.001). Trunk lean increased rate change in net joint reaction force at both the hip (p < 0.001) and knee (p < 0.001) compared to baseline. Additional research is needed to fully understand the effect of gait modifications in a clinical population, particularly the relationship between hip abduction moments and KOA progression. Although interventions such as MKT and TL can be successful in reducing KAM, their effects on hip abduction moment should be considered before clinical implementation.