Acute pain impairs retention of locomotor learning.

Despite abundant evidence that pain alters movement performance, considerably less is known about the potential effects of pain on motor learning. Some of the brain regions involved in pain processing are also responsible for specific aspects of motor learning, indicating that the two functions have the potential to interact, yet it is unclear if they do. In experiment 1, we compared the acquisition and retention of a novel locomotor pattern in young, healthy individuals randomized to either experience pain via capsaicin and heat applied to the lower leg during learning or no stimulus. On day 1, participants learned a new asymmetric walking pattern using distorted visual feedback, a paradigm known to involve mostly explicit re-aiming processes. Retention was tested 24 h later. Although there were no differences in day 1 acquisition between groups, individuals who experienced pain on day 1 demonstrated reduced retention on day 2. Furthermore, the degree of forgetting between days correlated with pain ratings during learning. In experiment 2, we examined the effects of a heat stimulus alone, which served as a control for (nonpainful) cutaneous stimulation, and found no effects on either acquisition or retention of learning. Thus, pain experienced during explicit, strategic locomotor learning interferes with motor memory consolidation processes and does so most likely through a pain mechanism and not an effect of distraction. These findings have important implications for understanding basic motor learning processes and for clinical rehabilitation, in which painful conditions are often treated through motor learning-based interventions.NEW & NOTEWORTHY Pain is a highly prevalent and burdensome experience that rehabilitation practitioners often treat using motor learning-based interventions. Here, we showed that experimental acute pain, but not a heat stimulus, during locomotor learning impaired 24-h retention of the newly learned walking pattern. The degree of retention loss was related to the perceived pain level during learning. These findings suggest important links between pain and motor learning that have significant implications for clinical rehabilitation.

Fluid Cognition Relates to Locomotor Switching in Neurotypical Adults, Not Individuals After Stroke.

BACKGROUND AND PURPOSE: The ability to switch between walking patterns (ie, locomotor switching) is vital for successful community navigation and may be impacted by poststroke impairments. Thus, the purpose of this work was to examine locomotor switching and the relationship between locomotor switching and fluid cognition in individuals after stroke compared with neurotypical adults. METHODS: Twenty-nine individuals more than 6 months after stroke and 18 neurotypical adults participated in a 2-day study. On day 1, participants were taught a new walking pattern on the treadmill and then locomotor switching was assessed by instructing participants to switch between the new walking pattern and their usual walking pattern. The change between these 2 patterns was calculated as the switching index. On day 2, the NIH Toolbox Cognition Battery was administered to obtain the Fluid Cognition Composite Score (FCCS), which reflected fluid cognition. The switching index was compared between groups using an analysis of covariance, and the relationship between locomotor switching and fluid cognition was assessed with regression. RESULTS: Individuals after stroke had significantly lower switching indexes compared with neurotypical adults (P = 0.03). The regression showed a significant interaction between group and FCCS (P = 0.002), with the FCCS predicting the switching index in neurotypical adults but not in individuals after stroke. DISCUSSION AND CONCLUSIONS: Individuals after stroke appear to have deficits in locomotor switching compared with neurotypical adults. The relationship between fluid cognition and locomotor switching was significant in neurotypical adults but not in individuals after stroke. Future work to understand the relationship between specific cognitive domains and locomotor switching is needed (see the Video, Supplemental Digital Content 1, available at: http://links.lww.com/JNPT/A361).

Precision Rehabilitation: Optimizing Function, Adding Value to Health Care.

Precision medicine efforts are underway in many medical disciplines; however, the power of precision rehabilitation has not yet been explored. Precision medicine aims to deliver the right intervention, at the right time, in the right setting, for the right person, ultimately bolstering the value of the care that we provide. To date, precision medicine efforts have rarely focused on function at the level of a person, but precision rehabilitation is poised to change this and bring the focus on function to the broader precision medicine enterprise. To do this, subgroups of individuals must be identified based on their level of function via precise measurement of their abilities in the physical, cognitive, and psychosocial domains. Adoption of electronic health records, advances in data storage and analytics, and improved measurement technology make this shift possible. Here we detail critical components of the precision rehabilitation framework, including (1) the synergistic use of various study designs, (2) the need for standardized functional measurements, (3) the importance of precise and longitudinal measures of function, (4) the utility of comprehensive databases, (5) the importance of predictive analyses, and (6) the need for system and team science. Precision rehabilitation has the potential to revolutionize clinical care, optimize function for all individuals, and magnify the value of rehabilitation in health care; however, to reap the benefits of precision rehabilitation, the rehabilitation community must actively pursue this shift.

Use of explicit processes during a visually guided locomotor learning task predicts 24-h retention after stroke.

Implicit and explicit processes can occur within a single locomotor learning task. The combination of these learning processes may impact how individuals acquire/retain the task. Because these learning processes rely on distinct neural pathways, neurological conditions may selectively impact the processes that occur, thus, impacting learning and retention. Thus, our purpose was to examine the contribution of implicit and explicit processes during a visually guided walking task and characterize the relationship between explicit processes and performance/retention in stroke survivors and age-matched healthy adults. Twenty chronic stroke survivors and twenty healthy adults participated in a 2-day treadmill study. Day 1 included baseline, acquisition1, catch, acquisition2, and immediate retention phases, and day 2 included 24-h retention. During acquisition phases, subjects learned to take a longer step with one leg through distorted visual feedback. During catch and retention phases, visual feedback was removed and subjects were instructed to walk normally (catch) or how they walked during the acquisition phases (retention). Change in step length from baseline to catch represented implicit processes. Change in step length from catch to the end of acquisition2 represented explicit processes. A mixed ANOVA found no difference in the type of learning between groups (P = 0.74). There was a significant relationship between explicit processes and 24-h retention in stroke survivors (r = 0.47, P = 0.04) but not in healthy adults (r = 0.34, P = 0.15). These results suggest that stroke may not affect the underlying learning mechanisms used during locomotor learning, but that these mechanisms impact how well stroke survivors retain the new walking pattern.NEW & NOTEWORTHY This study found that stroke survivors used implicit and explicit processes similar to age-matched healthy adults during a visually guided locomotion learning task. The amount of explicit processes was related to how well stroke survivors retained the new walking pattern but not to how well they performed during the task. This work illustrates the importance of understanding the underlying learning mechanisms to maximize retention of a newly learned motor behavior.

Use-dependent plasticity explains aftereffects in visually guided locomotor learning of a novel step length asymmetry.

Studies of upper extremity reaching show that use-dependent plasticity, or learning from repetition, plays an important role in shaping motor behaviors. Yet the impact of repetition on locomotor learning is unclear, despite the fact that gait is developed and practiced over millions of repetitions. To test whether repetition alone can induce storage of a novel walking pattern, we instructed two groups of young healthy subjects to learn an asymmetric walking pattern through two distinct learning paradigms. The first group learned a new pattern through an established visual distortion paradigm, which provided both sensory prediction error and repetition of movement patterns to induce walking aftereffects, and the second received veridical feedback with a target change, which provided only repetition (use-dependent plasticity) to induce aftereffects. When feedback was removed, both groups demonstrated aftereffects in the primary outcome, step asymmetry index. Surprisingly, despite the different task demands, both groups produced similar aftereffect magnitudes, which also had similar rates of decay, suggesting that the addition of sensory prediction errors did not improve storage of learning beyond that induced by the use-dependent process alone. To further characterize the use-dependent process, we conducted a second experiment to quantify aftereffect size in a third group who practiced double the asymmetry magnitude. This new group showed a proportionately greater magnitude of the use-dependent aftereffect. Together, these findings show that the primary driver of storage of a new step length asymmetry during visually guided locomotor learning is repetition, not sensory prediction error, and this effect scales with the learning magnitude.NEW & NOTEWORTHY Use-dependent plasticity, or learning from repetition, is an important process for upper extremity reaching tasks, but its contribution to walking is not well established. Here, we demonstrate the existence of a dose-dependent, use-dependent process during visually guided treadmill walking. We also show that sensory prediction errors, previously thought to drive aftereffects in similar locomotor learning paradigms, do not appear to play a significant role in visually driven learning of a novel step asymmetry during treadmill walking.

A single high-intensity exercise bout during early consolidation does not influence retention or relearning of sensorimotor locomotor long-term memories.

A single exercise bout has been found to improve the retention of a skill-based upper extremity motor task up to a week post-practice. This effect is the greatest when exercise intensity is high and exercise is administered immediately after motor practice (i.e., early in consolidation). Whether exercise can affect other motor learning types (e.g., sensorimotor adaptation) and tasks (e.g., walking) is still unclear as previous studies have not optimally refined the exercise parameters and long-term retention testing. Therefore, we investigated whether a single high-intensity exercise bout during early consolidation would improve the long-term retention and relearning of sensorimotor adaptation during split-belt treadmill walking. Twenty-six neurologically intact adults attended three sessions; sessions 2 and 3 were 1 day and 7 days after session 1, respectively. Participants were allocated either to Rest (REST) or to Exercise (EXE) group. In session 1, all groups walked on a split-belt treadmill in a 2:1 speed ratio (1.5:0.75 m/s). Then, half of the participants exercised for 5 min (EXE), while the other half rested for 5 min (REST). A short exercise bout during early consolidation did not improve retention or relearning of locomotor memories one or seven days after session 1. This result reinforces previous findings that the effect of exercise on motor learning may differ between sensorimotor locomotor adaptation and skilled-based upper extremity tasks; thus, the utility of exercise as a behavioral booster of motor learning may depend on the type of motor learning and task.

A single exercise bout and locomotor learning after stroke: physiological, behavioural, and computational outcomes.

KEY POINTS: Previous work demonstrated an effect of a single high-intensity exercise bout coupled with motor practice on the retention of a newly acquired skilled arm movement, in both neurologically intact and impaired adults. In the present study, using behavioural and computational analyses we demonstrated that a single exercise bout, regardless of its intensity and timing, did not increase the retention of a novel locomotor task after stroke. Considering both present and previous work, we postulate that the benefits of exercise effect may depend on the type of motor learning (e.g. skill learning, sensorimotor adaptation) and/or task (e.g. arm accuracy-tracking task, walking). ABSTRACT: Acute high-intensity exercise coupled with motor practice improves the retention of motor learning in neurologically intact adults. However, whether exercise could improve the retention of locomotor learning after stroke is still unknown. Here, we investigated the effect of exercise intensity and timing on the retention of a novel locomotor learning task (i.e. split-belt treadmill walking) after stroke. Thirty-seven people post stroke participated in two sessions, 24 h apart, and were allocated to active control (CON), treadmill walking (TMW), or total body exercise on a cycle ergometer (TBE). In session 1, all groups exercised for a short bout (∼5 min) at low (CON) or high (TMW and TBE) intensity and before (CON and TMW) or after (TBE) the locomotor learning task. In both sessions, the locomotor learning task was to walk on a split-belt treadmill in a 2:1 speed ratio (100% and 50% fast-comfortable walking speed) for 15 min. To test the effect of exercise on 24 h retention, we applied behavioural and computational analyses. Behavioural data showed that neither high-intensity group showed greater 24 h retention compared to CON, and computational data showed that 24 h retention was attributable to a slow learning process for sensorimotor adaptation. Our findings demonstrated that acute exercise coupled with a locomotor adaptation task, regardless of its intensity and timing, does not improve retention of the novel locomotor task after stroke. We postulate that exercise effects on motor learning may be context specific (e.g. type of motor learning and/or task) and interact with the presence of genetic variant (BDNF Val66Met).

A locomotor learning paradigm using distorted visual feedback elicits strategic learning.

Distorted visual feedback (DVF) during locomotion has been suggested to result in the development of a new walking pattern in healthy individuals through implicit learning processes. Recent work in upper extremity visuomotor rotation paradigms suggest that these paradigms involve implicit and explicit learning. Additionally, in upper extremity visuomotor paradigms, the verbal cues provided appear to impact how a behavior is learned and when this learned behavior is used. Here, in two experiments in neurologically intact individuals, we tested how verbal instruction impacts learning a new locomotor pattern on a treadmill through DVF, the transfer of that pattern to overground walking, and what types of learning occur (i.e., implicit vs. explicit learning). In experiment 1, we found that the instructions provided impacted the amount learned through DVF, but not the size of the aftereffects or the amount of the pattern transferred to overground walking. Additionally, the aftereffects observed were significantly different from the baseline walking pattern, but smaller than the behavior changes observed during learning, which is uncharacteristic of implicit sensorimotor adaptation. Thus, experiment 2 aimed to determine the cause of these discrepancies. In this experiment, when VF was not provided, individuals continued using the learned walking pattern when instructed to do so and returned toward their baseline pattern when instructed to do so. Based on these results, we conclude that DVF during locomotion results in a large portion of explicit learning and a small portion of implicit learning. NEW & NOTEWORTHY The results of this study suggest that distorted visual feedback during locomotor learning involves the development of an explicit strategy with only a small component of implicit learning. This is important because previous studies using distorted visual feedback have suggested that locomotor learning relies primarily on implicit learning. This paradigm, therefore, provides a new way to examine a different form of learning in locomotion.

Self-efficacy Mediates the Relationship between Balance/Walking Performance, Activity, and Participation after Stroke.

BACKGROUND: Many outcome measures (OM) that assess individuals' ability or beliefs in their ability to perform tasks exist to evaluate activity and participation after stroke; however, the relationship between various OM and activity/participation is unclear. OBJECTIVE: The purpose of this study was to explore the relationships between different OM and activity and participation in people after stroke. METHODS: Fifty-nine subjects post stroke participated in an assessment including self-selected walking speed, 6 minute walk test, Timed "Up and Go" test, Berg Balance Scale, Functional Gait Assessment, Walk 12, and Activity-specific Balance Confidence Scale. Step Watch Activity Monitoring (SAM) was used as a measure of activity and Stroke Impact Scale-Participation (SIS-P) as a measure of participation. Exploratory Factor Analysis was performed including all measures except SAM and SIS-P. Two factors were extracted and termed performance based (PB) and self-efficacy (SE). A path analysis assessed the role of SE as a mediator in the relationships of PB and SAM/SIS-P. RESULTS: In the path analysis, PB significantly predicts SE (p < 0.001, b = 0.44), but not SAM or SIS-P (p > 0.05, b = 0.25, and b = 0.11, respectively). SE significantly predicts both SAM and SIS-P (p < 0.001, b = 0.46, and b = 0.59, respectively). The indirect effects of PB on SAM and SIS-P were significant (p < 0.001; b = 0.20, and b = 0.26, respectively). CONCLUSION: These results suggest that SE mediates the relationship between PB and activity and participation after stroke, reinforcing that improving activity and participation is more complicated than only targeting performance. Clinicians should administer SE and PB measures to determine the most accurate view of patients after stroke and seek to improve SE through interventions.

The effect of post-acute rehabilitation setting on 90-day mobility after stroke: A difference-in-difference analysis.

Background: After discharged from the hospital for acute stroke, individuals typically receive rehabilitation in one of three settings: inpatient rehabilitation facilities (IRFs), skilled nursing facilities (SNFs), or home with community services (i.e., home health or outpatient clinics). The initial setting of post-acute care (i.e., discharge location) is related to mortality and hospital readmission; however, the impact of this setting on the change in functional mobility at 90-days after discharge is still poorly understood. The purpose of this work was to examine the impact of discharge location on the change in functional mobility between hospital discharge and 90-days post-discharge. Methods: In this retrospective cohort study, we used the electronic health record to identify individuals admitted to Johns Hopkins Medicine with an acute stroke and who had measurements of mobility [Activity Measure for Post Acute Care Basic Mobility (AM-PAC BM)] at discharge from the acute hospital and 90-days post-discharge. Individuals were grouped by discharge location (IRF=190 [40%], SNF=103 [22%], Home with community services=182 [(38%]). We compared the change in mobility from time of discharge to 90-days post-discharge in each group using a difference-in-differences analysis and controlling for demographics, clinical characteristics, and social determinants of health. Results: We included 475 individuals (age 64.4 [14.8] years; female: 248 [52.2%]). After adjusting for covariates, individuals who were discharged to an IRF had a significantly greater improvement in AM-PAC BM from time of discharge to 90-days post-discharge compared to individuals discharged to a SNF or home with community services (ß=-3.5 (1.4), p=0.01 and ß=-8.2 (1.3), p=<0.001, respectively). Conclusions: These findings suggest that the initial post-acute rehabilitation setting impacts the magnitude of functional recovery at 90-days after discharge from the acute hospital. These findings support the need for high-intensity rehabilitation and for policies that facilitate the delivery of high-intensity rehabilitation after stroke.

Coverage of physical therapy assessments in the Observational Medical Outcomes Partnership Model common data model.

BACKGROUND: High-value care aims to enhance meaningful patient outcomes while reducing costs and is accelerated by curating data across healthcare systems through common data models (CDMs), such as Observational Medical Outcomes Partnership Model (OMOP). Meaningful patient outcomes, such as physical function, must be included in these CDMs. However, the extent that physical therapy assessments are covered in the OMOP CDM is unclear. OBJECTIVE: Examine the extent that physical therapy assessments used in neurologic and orthopaedic conditions are in the OMOP CDM. METHODS: After identifying assessments, two reviewer teams independently mapped the neurologic and orthopaedic assessments into the OMOP CDM. We quantified agreement within the reviewer team by the number of assessments mapped by both reviewers, one reviewer but not the other, or neither reviewer. The reviewer teams then reconciled disagreements, after which we examined agreement and the average number of concept ID numbers per assessment. RESULTS: Of the 81 neurologic assessments, 48.1% (39/81) were initially mapped by both reviewers, 9.9% (8/81) were mapped by one reviewer but not the other, and 42% (34/81) were unmapped. After reconciliation, 46.9% (38/81) were mapped by both reviewers and 53.1% (43/81) were unmapped. Of the 79 orthopaedic assessments, 46.8% (37/79) were initially mapped by both reviewers, 12.7% (10/79) were mapped by one reviewer but not the other, and 48.1% (38/79) were unmapped. After reconciliation, 48.1% (38/79) were mapped by both reviewers and 51.9% (41/79) were unmapped. Most assessments that were mapped had more than one concept ID number (2.2±1.3 and 4.3±4.4 concept IDs per neurologic and orthopaedic assessment, respectively). CONCLUSIONS: The OMOP CDM includes some assessments recommended for use in neurologic and orthopaedic conditions, but many have multiple concept IDs. Including more functional assessments in the OMOP CDM and creating guidelines for mapping would improve our ability to include functional data in large datasets.

Accuracy of Video-Based Gait Analysis Using Pose Estimation During Treadmill Walking Versus Overground Walking in Persons After Stroke.

OBJECTIVE: Video-based pose estimation is an emerging technology that shows significant promise for improving clinical gait analysis by enabling quantitative movement analysis with little costs of money, time, or effort. The objective of this study is to determine the accuracy of pose estimation-based gait analysis when video recordings are constrained to 3 common clinical or in-home settings (ie, frontal and sagittal views of overground walking and sagittal views of treadmill walking). METHODS: Simultaneous video and motion capture recordings were collected from 30 persons after stroke during overground and treadmill walking. Spatiotemporal and kinematic gait parameters were calculated from videos using an open-source human pose estimation algorithm and from motion capture data using traditional gait analysis. Repeated-measures analyses of variance were then used to assess the accuracy of the pose estimation-based gait analysis across the different settings, and the authors examined Pearson and intraclass correlations with ground-truth motion capture data. RESULTS: Sagittal videos of overground and treadmill walking led to more accurate measurements of spatiotemporal gait parameters versus frontal videos of overground walking. Sagittal videos of overground walking resulted in the strongest correlations between video-based and motion capture measurements of lower extremity joint kinematics. Video-based measurements of hip and knee kinematics showed stronger correlations with motion capture versus ankle kinematics for both overground and treadmill walking. CONCLUSION: Video-based gait analysis using pose estimation provides accurate measurements of step length, step time, and hip and knee kinematics during overground and treadmill walking in persons after stroke. Generally, sagittal videos of overground gait provide the most accurate results. IMPACT: Many clinicians lack access to expensive gait analysis tools that can help identify patient-specific gait deviations and guide therapy decisions. These findings show that video-based methods that require only common household devices provide accurate measurements of a variety of gait parameters in persons after stroke and could make quantitative gait analysis significantly more accessible.

Impact of automated data flow and reminders on adherence and resource utilization for remotely monitoring physical activity in individuals with stroke or chronic obstructive pulmonary disease.

As rehabilitation advances into the era of digital health, remote monitoring of physical activity via wearable devices has the potential to change how we provide care. However, uncertainties about patient adherence and the significant resource requirements needed create challenges to adoption of remote monitoring into clinical care. Here we aim to determine the impact of a novel digital application to overcome these barriers. The Rehabilitation Remote Monitoring Application (RRMA) automatically extracts data about physical activity collected via a Fitbit device, screens the data for adherence, and contacts the participant if adherence is low. We compare adherence and estimate the resources required (i.e., time and financial) to perform remote monitoring of physical activity with and without the RRMA in two patient groups. Seventy-three individuals with stroke or chronic obstructive pulmonary disease completed 28 days of monitoring physical activity with the RRMA, while 62 individuals completed 28 days with the data flow processes being completed manually. Adherence (i.e., the average percentage of the day that the device was worn) was similar between groups (p=0.85). However, the RRMA saved an estimated 123.8 minutes or $50.24 per participant month when compared to manual processes. These results demonstrate that automated technologies like the RRMA can maintain patient adherence to remote monitoring of physical activity while reducing the time and financial resources needed. Applications like the RRMA can facilitate the adoption of remote monitoring in rehabilitation by reducing barriers related to adherence and resource requirements.

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.

Effects of an Acute High Intensity Exercise Bout on Retention of Explicit, Strategic Locomotor Learning in Individuals With Chronic Stroke.

BACKGROUND: Exercise priming, pairing high intensity exercise with a motor learning task, improves retention of upper extremity tasks in individuals after stroke, but has shown no benefit to locomotor learning. This difference may relate to the type of learning studied. Upper extremity studies used explicit, strategic tasks; locomotor studies used implicit sensorimotor adaptation (split-belt treadmill). Since walking is an important rehabilitation goal, it is crucial to understand under which circumstances exercise priming may improve retention of a newly learned walking pattern. OBJECTIVE: Determine the impact of exercise priming on explicit, strategic locomotor learning task retention in chronic stroke survivors. METHODS: Chronic stroke survivors (>6 months) performed 2 treadmill walking sessions. Visual feedback was used to train increased step length. Participants were assigned to control group (no exercise), continuous exercise (5 minutes high intensity), or long-interval exercise (15 minutes high/moderate intervals). After day 1 learning, participants either rested or performed exercise. On day 2, retention of the learned walking pattern was tested. RESULTS: All groups learned on day 1 (P < .001). The 2 priming groups showed significant changes in blood lactate and heart rate after exercise priming, the resting control group did not (P < .001). On day 2, there was no significant between-group difference in cued or un-cued task retention (P = .963 and .287, respectively). CONCLUSIONS: Exercise priming did not affect retention of an explicit locomotor task in chronic stroke survivors. Further work should explore subgroups of individuals for whom priming may have selective clinical benefit to locomotor learning.ClinicalTrials.gov Identifier: NCT03726047.

Opportunities for Improving Motor Assessment and Rehabilitation After Stroke by Leveraging Video-Based Pose Estimation.

ABSTRACT: Stroke is a leading cause of long-term disability in adults in the United States. As the healthcare system moves further into an era of digital medicine and remote monitoring, technology continues to play an increasingly important role in post-stroke care. In this Analysis and Perspective article, opportunities for using human pose estimation-an emerging technology that uses artificial intelligence to track human movement kinematics from simple videos recorded using household devices (e.g., smartphones, tablets)-to improve motor assessment and rehabilitation after stroke are discussed. The focus is on the potential of two key applications: (1) improving access to quantitative, objective motor assessment and (2) advancing telerehabilitation for persons post-stroke.

Development and Implementation of a Standard Assessment Battery Across the Continuum of Care for Patients After Stroke.

ABSTRACT: Stroke rehabilitation occurs across the continuum of care starting in the acute hospital and through the inpatient and outpatient settings. Rehabilitation aims to minimize impairments and maximize function in individuals after stroke. Because patients often undergo rehabilitation for extended periods, longitudinal assessment of impairment, activity, and participation can facilitate the evaluation of patients' progress toward recovery, as well as communication and decision making to guide clinical practice regarding the intervention(s) to be used and may also be leveraged for clinical research. However, the clinical implementation of a standard assessment battery that spans the continuum of care for patients after stroke is challenging because of operational and time constraints. Here, we describe the development and implementation of a standard assessment battery across the continuum of care by physical therapists, occupational therapists, and speech-language pathologists at the Sheikh Khalifa Stroke Institute. We specifically describe our experience in (1) identifying the core team to lead the process, (2) selecting the measures for the standard assessment battery, and the timeframe for administration, and (3) implementing the standard assessment battery in routine clinical practice.

A Learning Health System Infrastructure for Precision Rehabilitation After Stroke.

ABSTRACT: Functional recovery and the response to rehabilitation interventions after stroke are highly variable. Understanding this variability will promote precision rehabilitation for stroke, allowing us to deliver targeted interventions to the right person at the right time. Capitalizing on large, heterogeneous data sets, such as those generated through clinical care and housed within the electronic health record, can lead to understanding of poststroke variability. However, accessing data from the electronic health record can be challenging because of data quality, privacy concerns, and the resources required for data extraction. Therefore, creating infrastructure that overcomes these challenges and contributes to a learning health system is needed to achieve precision rehabilitation after stroke. We describe the creation of a Precision Rehabilitation Data Repository that facilitates access to systematically collected data from the electronic health record as part of a learning health system to drive precision rehabilitation. Specifically, we describe the process of (1) standardizing the documentation of functional assessments, (2) obtaining regulatory approval, (3) defining the patient cohort, and (4) extracting data for the Precision Rehabilitation Data Repository. The development of similar infrastructures at other institutions can help generate large, heterogeneous data sets to drive poststroke care toward precision rehabilitation, thereby maximizing poststroke function within an efficient healthcare system.

The feasibility of remotely monitoring physical, cognitive, and psychosocial function in individuals with stroke or chronic obstructive pulmonary disease.

Objective: Clinical implementation of remote monitoring of human function requires an understanding of its feasibility. We evaluated adherence and the resources required to monitor physical, cognitive, and psychosocial function in individuals with either chronic obstructive pulmonary disease or stroke during a three-month period. Methods: Seventy-three individuals agreed to wear a Fitbit to monitor physical function and to complete monthly online assessments of cognitive and psychosocial function. During a three-month period, we measured adherence to monitoring (1) physical function using average daily wear time, and (2) cognition and psychosocial function using the percentage of assessments completed. We measured the resources needed to promote adherence as (1) the number of participants requiring at least one reminder to synchronize their Fitbit, and (2) the number of reminders needed for each completed cognitive and psychosocial assessment. Results: After accounting for withdrawals, the average daily wear time was 77.5 ± 19.9% of the day and did not differ significantly between months 1, 2, and 3 (p = 0.30). To achieve this level of adherence, 64.9% of participants required at least one reminder to synchronize their device. Participants completed 61.0% of the cognitive and psychosocial assessments; the portion of assessments completed each month didnot significantly differ (p = 0.44). Participants required 1.13 ± 0.57 reminders for each completed assessment. Results did not differ by disease diagnosis. Conclusions: Remote monitoring of human function in individuals with either chronic obstructive pulmonary disease or stroke is feasible as demonstrated by high adherence. However, the number of reminders required indicates that careful consideration must be given to the resources available to obtain high adherence.