Biology subjects

Kettlety, S. A.

Publications and source records attributed to Kettlety, S. A..

2 recordsLinked to original sources

bioRxiv2022

Gait speed and individual characteristics can be used to predict specific gait metric magnitudes in neurotypical adults.

BackgroundGait biofeedback is commonly used to reduce gait dysfunction in a variety of clinical conditions. In these studies, participants alter their walking to reach the desired magnitude of a specific gait parameter (the biofeedback target) with each step. Biofeedback of parameters such as anterior ground reaction force and step length have been well-studied. Yet, there is no standardized methodology to set the target magnitude of these parameters. Here we present an approach to predict the anterior ground reaction force and step length of neurotypical adults walking at different speeds as a potential method for personalized gait biofeedback. Research questionTo determine if anterior ground reaction force and step lengths achieved during neurotypical walking could be predicted using gait speed and participants demographic and anthropomorphic characteristics. MethodsWe analyzed kinetic and kinematic data from 51 neurotypical adults who walked on a treadmill at up to eight speeds. We calculated the average peak anterior ground reaction force and step length of the right lower extremity at each speed. We used linear mixed-effects models to evaluate the effect of speed, leg length, mass, and age on anterior ground reaction force and step length. We fit the model to data from 37 participants and validated predictions from the final models on an independent dataset from 23 participants. ResultsFinal prediction models for anterior ground reaction force and step length both included speed, speed squared, age, mass, and leg length. The models both showed strong agreement between predicted and actual values on an independent dataset. SignificanceAnterior ground reaction force and step length for neurotypical adults can be predicted given an individuals gait speed, age, leg length, and mass. This may provide a standardized method to personalize targets for individuals with gait dysfunction in future studies of gait biofeedback.

bioinformatics↗

bioRxiv2022

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

BackgroundGait training at fast speeds is recommended to reduce walking activity limitations post-stroke. Fast walking may also reduce gait kinematic impairments post-stroke. However, the magnitude of speed-dependent kinematic impairment reduction in people post-stroke relative to neurotypical adult walking patterns is unknown. ObjectiveTo determine the effect of faster walking speeds on gait kinematics post-stroke relative to neurotypical adults walking at similar speeds. MethodsWe 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 ResultsPeople 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. Cluster composition was largely consistent at both speeds, and the distance between clusters increased at fast speeds ConclusionsThe biomechanical effect of fast walking post-stroke varied across individual gait metrics. For participants within the stroke gait behavior cluster, speed-dependent changes did not lead to an overall gait pattern more similar to neurotypical adults. This suggests that combining fast walking with an approach to strategically target gait metrics with smaller speed-dependent changes may potentiate the biomechanical benefits of fast walking.

neuroscience↗