Multi-Site Identification and Generalization of Clusters of Walking Behaviors in Individuals With Chronic Stroke and Neurotypical Controls

Background Walking patterns in stroke survivors are highly heterogeneous, which poses a challenge in systematizing treatment prescriptions for walking rehabilitation interventions. Objectives We used bilateral spatiotemporal and force data during walking to create a multi-site research sample to: (1) identify clusters of walking behaviors in people post-stroke and neurotypical controls and (2) determine the generalizability of these walking clusters across different research sites. We hypothesized that participants post-stroke will have different walking impairments resulting in different clusters of walking behaviors, which are also different from control participants. Methods We gathered data from 81 post-stroke participants across 4 research sites and collected data from 31 control participants. Using sparse K-means clustering, we identified walking clusters based on 17 spatiotemporal and force variables. We analyzed the biomechanical features within each cluster to characterize cluster-specific walking behaviors. We also assessed the generalizability of the clusters using a leave-one-out approach. Results We identified 4 stroke clusters: a fast and asymmetric cluster, a moderate speed and asymmetric cluster, a slow cluster with frontal plane force asymmetries, and a slow and symmetric cluster. We also identified a moderate speed and symmetric gait cluster composed of controls and participants post-stroke. The moderate speed and asymmetric stroke cluster did not generalize across sites. Conclusions Although post-stroke walking patterns are heterogenous, these patterns can be systematically classified into distinct clusters based on spatiotemporal and force data. Future interventions could target the key features that characterize each cluster to increase the efficacy of interventions to improve mobility in people post-stroke

Changes in Midline Tremor and Gait Following Deep Brain Stimulation for Essential Tremor

Background: Essential tremor (ET) is a common movement disorder characterized by kinetic and postural tremor in the upper extremities and frequently in the midline. Persons with ET often also exhibit gait ataxia. Previous studies have observed associations between midline tremor severity and gait ataxia in persons with ET, suggesting a common pathophysiology distinct from that of upper extremity tremor. However, a causal link between midline tremor and gait impairment has not been established. Methods: We investigated tremor and gait in 24 persons with ET before and after implantation of unilateral deep brain stimulation into the ventralis intermedius nucleus of the thalamus. Results: Stimulation significantly improved tremor in the targeted upper extremity and midline. However, gait was unaffected at the cohort level. Furthermore, improvement in midline tremor was not significantly associated with gait improvement. Discussion: These findings revealed that midline tremor and gait impairment may be dissociable in persons with ET

Simple within-stride changes in treadmill speed can drive selective changes in human gait symmetry.

Millions of people walk with asymmetric gait patterns, highlighting a need for customizable rehabilitation approaches that can flexibly target different aspects of gait asymmetry. Here, we studied how simple within-stride changes in treadmill speed could drive selective changes in gait symmetry. In Experiment 1, healthy adults (n = 10) walked on an instrumented treadmill with and without a closed-loop controller engaged. This controller changed the treadmill speed to 1.50 or 0.75 m/s depending on whether the right or left leg generated propulsive ground reaction forces, respectively. Participants walked asymmetrically when the controller was engaged: the leg that accelerated during propulsion (right) showed smaller leading limb angles, larger trailing limb angles, and smaller propulsive forces than the leg that decelerated (left). In Experiment 2, healthy adults (n = 10) walked on the treadmill with and without an open-loop controller engaged. This controller changed the treadmill speed to 1.50 or 0.75 m/s at a prescribed time interval while a metronome guided participants to step at different time points relative to the speed change. Different patterns of gait asymmetry emerged depending on the timing of the speed change: step times, leading limb angles, and peak propulsion were asymmetric when the speed changed early in stance while step lengths, step times, and propulsion impulses were asymmetric when the speed changed later in stance. In sum, we show that simple manipulations of treadmill speed can drive selective changes in gait symmetry. Future work will explore the potential for this technique to restore gait symmetry in clinical populations

Identifying Unique Subgroups of Individuals With Stroke Using Heart Rate and Steps to Characterize Physical Activity

Background Low physical activity (PA) is associated with poor health outcomes after stroke. Step counts are a common metric of PA; however, other physiologic signals (eg, heart rate) may help to identify subgroups of individuals poststroke at varying levels of risk of poor health outcomes. Here, we aimed to identify clinically relevant subgroups of individuals poststroke based on PA profiles that leverage multiple data sources, including step count and heart rate data, from wearable devices. Methods and Results Seventy individuals poststroke participated. Participants wore a Fitbit Inspire 2 for 1 year and completed clinical assessments. We defined a group‐based steps‐per‐minute threshold and an individual heart rate threshold to categorize each minute of PA into 1 of 4 states: high steps/high heart rate, low steps/low heart rate, high steps/low heart rate, and low steps/high heart rate. We used the proportion of time spent in each state along with steps per day, sedentary time, mean steps among minutes with high steps and high heart rate, and resting heart rate in a k‐means clustering algorithm to identify subgroups and compared Activity Measure for Post‐Acute Care Mobility T Score, Stroke Impact Scale, and gait speed among subgroups. We identified 3 subgroups, Active (n=8), Sedentary (n=29), and Deconditioned (n=33), which differed significantly on all clustering variables except resting heart rate. We observed significant differences in Activity Measure for Post‐Acute Care Mobility T scores between subgroups, with the Deconditioned subgroup exhibiting the lowest score. Conclusions Quantifying PA with heart rate and step count using readily available wearable devices can identify clinically meaningful subgroups of individuals poststroke