A Novel, Wearable Inertial Measurement Unit for Stroke Survivors: Validity, Acceptability, and Usability.

OBJECTIVE: To establish the concurrent validity, acceptability, and sensor optimization of a consumer-grade, wearable, multi-sensor system to capture quantity and quality metrics of mobility and upper limb movements in stroke survivors. DESIGN: Single-session, cross-sectional. SETTING: Clinical research laboratory. PARTICIPANTS: Thirty chronic stroke survivors (age 57 (10) years; 33% female) with mild to severe motor impairments participated. INTERVENTIONS: Not Applicable. MAIN OUTCOME MEASURES: Participants donned 5 sensors and performed standardized assessments of mobility and upper limb (UL) movement. True/false, positive/negative time in active movement for the UL were calculated and compared to criterion-standards using an accuracy rate. Bland-Altman plots and linear regression models were used to establish concurrent validity of UL movement counts, step counts, and stance time symmetry of MiGo against established criterion-standard measures. Acceptability and sensor optimization were assessed through an end-user survey and decision matrix. RESULTS: Mobility metrics showed excellent association with criterion-standards for step counts (video: r=0.988, P<.001, IMU: r=0.921, P<.001) and stance-time symmetry (r=0.722, P<.001). In the UL, movement counts showed excellent to good agreement (paretic: r=0.849, P<.001, nonparetic: r=0.672, P<.001). Accuracy of active movement time was 85.2% (paretic) and 88.0% (nonparetic) UL. Most participants (63.3%) had difficulty donning/doffing the sensors. Acceptability was high (4.2/5). CONCLUSIONS: The sensors demonstrated excellent concurrent validity for mobility metrics and UL movements of stroke survivors. Acceptability of the system was high, but alternative wristbands should be considered.

Wearable Technology to Capture Arm Use of People With Stroke in Home and Community Settings: Feasibility and Early Insights on Motor Performance.

OBJECTIVE: The objectives of this study were to establish the short-term feasibility and usability of wrist-worn wearable sensors for capturing the arm and hand activity of people with stroke and to explore the association between factors related to the use of the paretic arm and hand. METHODS: Thirty people with chronic stroke were monitored with wrist-worn wearable sensors for 12 hours per day for a 7-day period. Participants also completed standardized assessments to capture stroke severity, arm motor impairments, self-perceived arm use, and self-efficacy. The usability of the wearable sensors was assessed using the adapted System Usability Scale and an exit interview. Associations between motor performance and capacity (arm and hand impairments and activity limitations) were assessed using Spearman correlations. RESULTS: Minimal technical issues or lack of adherence to the wearing schedule occurred, with 87.6% of days procuring valid data from both sensors. The average sensor wear time was 12.6 (standard deviation [SD] = 0.2) hours per day. Three participants experienced discomfort with 1 of the wristbands, and 3 other participants had unrelated adverse events. There were positive self-reported usability scores (mean = 85.4/100) and high user satisfaction. Significant correlations were observed for measures of motor capacity and self-efficacy with paretic arm use in the home and the community (Spearman correlation coefficients = 0.44-0.71). CONCLUSIONS: This work demonstrates the feasibility and usability of a consumer-grade wearable sensor for capturing paretic arm activity outside the laboratory. It provides early insight into the everyday arm use of people with stroke and related factors, such as motor capacity and self-efficacy. IMPACT: The integration of wearable technologies into clinical practice offers new possibilities to complement in-person clinical assessments and to better understand how each person is moving outside of therapy and throughout the recovery and reintegration phase. Insight gained from monitoring the arm and hand use of people with stroke in the home and community is the first step toward informing future research with an emphasis on causal mechanisms with clinical relevance.

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.

A metric for characterizing the arm nonuse workspace in poststroke individuals using a robot arm.

An overreliance on the less-affected limb for functional tasks at the expense of the paretic limb and in spite of recovered capacity is an often-observed phenomenon in survivors of hemispheric stroke. The difference between capacity for use and actual spontaneous use is referred to as arm nonuse. Obtaining an ecologically valid evaluation of arm nonuse is challenging because it requires the observation of spontaneous arm choice for different tasks, which can easily be influenced by instructions, presumed expectations, and awareness that one is being tested. To better quantify arm nonuse, we developed the bimanual arm reaching test with a robot (BARTR) for quantitatively assessing arm nonuse in chronic stroke survivors. The BARTR is an instrument that uses a robot arm as a means of remote and unbiased data collection of nuanced spatial data for clinical evaluations of arm nonuse. This approach shows promise for determining the efficacy of interventions designed to reduce paretic arm nonuse and enhance functional recovery after stroke. We show that the BARTR satisfies the criteria of an appropriate metric for neurorehabilitative contexts: It is valid, reliable, and simple to use.

Understanding stroke survivors' preferences regarding wearable sensor feedback on functional movement: a mixed-methods study.

BACKGROUND: In stroke rehabilitation, wearable technology can be used as an intervention modality by providing timely, meaningful feedback on motor performance. Stroke survivors' preferences may offer a unique perspective on what metrics are intuitive, actionable, and meaningful to change behavior. However, few studies have identified feedback preferences from stroke survivors. This project aims to determine the ease of understanding and movement encouragement of feedback based on wearable sensor data (both arm/hand use and mobility) for stroke survivors and to identify preferences for feedback metrics (mode, content, frequency, and timing). METHODS: A sample of 30 chronic stroke survivors wore a multi-sensor system in the natural environment over a 1-week monitoring period. The sensor system captured time in active movement of each arm, arm use ratio, step counts and stance time symmetry. Using the data from the monitoring period, participants were presented with a movement report with visual displays of feedback about arm/hand use, step counts and gait symmetry. A survey and qualitative interview were used to assess ease of understanding, actionability and components of feedback that users found most meaningful to drive lasting behavior change. RESULTS: Arm/hand use and mobility sensor-derived feedback metrics were easy to understand and actionable. The preferred metric to encourage arm/hand use was the hourly arm use bar plot, and similarly the preferred metric to encourage mobility was the hourly steps bar plot, which were each ranked as top choice by 40% of participants. Participants perceived that quantitative (i.e., step counts) and qualitative (i.e., stance time symmetry) mobility metrics provided complementary information. Three main themes emerged from the qualitative analysis: (1) Motivation for behavior change, (2) Real-time feedback based on individual goals, and (3) Value of experienced clinicians for prescription and accountability. Participants stressed the importance of having feedback tailored to their own personalized goals and receiving guidance from clinicians on strategies to progress and increase functional movement behavior in the unsupervised home and community setting. CONCLUSION: The resulting technology has the potential to integrate engineering and personalized rehabilitation to maximize participation in meaningful life activities outside clinical settings in a less structured environment.

Data-driven biomarkers outperform theory-based biomarkers in predicting stroke motor outcomes.

Chronic motor impairments are a leading cause of disability after stroke. Previous studies have predicted motor outcomes based on the degree of damage to predefined structures in the motor system, such as the corticospinal tract. However, such theory-based approaches may not take full advantage of the information contained in clinical imaging data. The present study uses data-driven approaches to predict chronic motor outcomes after stroke and compares the accuracy of these predictions to previously-identified theory-based biomarkers. Using a cross-validation framework, regression models were trained using lesion masks and motor outcomes data from 789 stroke patients (293 female/496 male) from the ENIGMA Stroke Recovery Working Group (age 64.9±18.0 years; time since stroke 12.2±0.2 months; normalised motor score 0.7±0.5 (range [0,1]). The out-of-sample prediction accuracy of two theory-based biomarkers was assessed: lesion load of the corticospinal tract, and lesion load of multiple descending motor tracts. These theory-based prediction accuracies were compared to the prediction accuracy from three data-driven biomarkers: lesion load of lesion-behaviour maps, lesion load of structural networks associated with lesion-behaviour maps, and measures of regional structural disconnection. In general, data-driven biomarkers had better prediction accuracy - as measured by higher explained variance in chronic motor outcomes - than theory-based biomarkers. Data-driven models of regional structural disconnection performed the best of all models tested (R2 = 0.210, p < 0.001), performing significantly better than predictions using the theory-based biomarkers of lesion load of the corticospinal tract (R2 = 0.132, p< 0.001) and of multiple descending motor tracts (R2 = 0.180, p < 0.001). They also performed slightly, but significantly, better than other data-driven biomarkers including lesion load of lesion-behaviour maps (R2 =0.200, p < 0.001) and lesion load of structural networks associated with lesion-behaviour maps (R2 =0.167, p < 0.001). Ensemble models - combining basic demographic variables like age, sex, and time since stroke - improved prediction accuracy for theory-based and data-driven biomarkers. Finally, combining both theory-based and data-driven biomarkers with demographic variables improved predictions, and the best ensemble model achieved R2 = 0.241, p < 0.001. Overall, these results demonstrate that models that predict chronic motor outcomes using data-driven features, particularly when lesion data is represented in terms of structural disconnection, perform better than models that predict chronic motor outcomes using theory-based features from the motor system. However, combining both theory-based and data-driven models provides the best predictions.

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. Objective: 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 four 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 four 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.

Understanding preferences of stroke survivors for feedback provision about functional movement behavior from wearable sensors: a mixed-methods study.

Background: In stroke rehabilitation, wearable technology can be used as an intervention modality by providing timely, meaningful feedback on motor performance. Stroke survivors' preferences may offer a unique perspective on what metrics are intuitive, actionable, and meaningful to change behavior. However, few studies have identified feedback preferences from stroke survivors. This project aims to determine stroke survivors' satisfaction with feedback from wearable sensors (both mobility and arm/hand use) and to identify preferences for feedback type and delivery schedule. Methods: A sample of 30 chronic stroke survivors wore a multi-sensor system in the natural environment over a 1-week monitoring period. The sensor system captured time in active movement of each arm, arm use ratio, step counts and stance time symmetry. Using the data from the monitoring period, participants were presented with a movement report with visual displays of quantitative and qualitative feedback. A survey and qualitative interview were used to assess ease of understanding, actionability and components of feedback that users found most meaningful to drive lasting behavior change. Results: Arm/hand use and mobility sensor-derived feedback metrics were easy to understand and actionable. The preferred metric to encourage arm/hand use was the hourly arm use bar plot, and similarly the preferred metric to encourage mobility was the hourly steps bar plot, which were each ranked as top choice by 40% of participants. Participants perceived that quantitative (i.e., step counts) and qualitative (i.e., stance time symmetry) mobility metrics provided complementary information. Three main themes emerged from the qualitative analysis: 1) Motivation for behavior change, 2) Real-time feedback based on individual goals, and 3) Value of experienced clinicians for prescription and accountability. Participants stressed the importance of having feedback tailored to their own personalized goals and receiving guidance from clinicians on strategies to progress and increase functional movement behavior in the unsupervised home and community setting. Conclusion: The resulting technology has the potential to integrate engineering and personalized rehabilitation to maximize participation in meaningful life activities outside clinical settings in a less structured environment-one where stroke survivors live their lives.

Adaptive control is reversed between hands after left hemisphere stroke and lost following right hemisphere stroke.

Human motor adaptability is of utmost utility after neurologic injury such as unilateral stroke. For successful adaptive control of movements, the nervous system must learn to correctly identify the source of a movement error and predictively compensate for this error. The current understanding is that in bimanual tasks, this process is flexible such that errors are assigned to, and compensated for, by the limb that is more likely to produce those errors. Here, we tested the flexibility of the error assignment process in right-handed chronic stroke survivors using a bimanual reaching task in which the hands jointly controlled a single cursor. We predicted that the nondominant left hand in neurotypical adults and the paretic hand in chronic stroke survivors will be more responsible for cursor errors and will compensate more within a trial and learn more from trial to trial. We found that in neurotypical adults, the nondominant left hand does compensate more than the right hand within a trial but learns less trial-to-trial. After a left hemisphere stroke, the paretic right hand compensates more than the nonparetic left hand within-trial but learns less trial-to-trial. After a right hemisphere stroke, the paretic left hand neither corrects more within-trial nor learns more trial-to-trial. Thus, adaptive control of visually guided bimanual reaching movements is reversed between hands after the left hemisphere stroke and lost following the right hemisphere stroke. These results indicate that responsibility assignment is not fully flexible but depends on a central mechanism that is lateralized to the right hemisphere.

Corpus Callosal Microstructure Predicts Bimanual Motor Performance in Chronic Stroke Survivors: a Preliminary Cross-Sectional Study.

BACKGROUND: Microstructural changes in the corpus callosum (CC) are associated with more severe motor impairment in the paretic hand, poor recovery, and general disability. The purpose of this study was to determine if CC microstructure predicts bimanual motor performance in chronic stroke survivors. METHODS: We examined the relationship between the fractional anisotropy (FA) across the CC, in both the sensorimotor and non-sensorimotor regions, and movement times for two self-initiated and self-paced bimanual tasks in 41 chronic stroke survivors. Using publicly available control datasets (n = 52), matched closely for imaging acquisition parameters, we also explored the effect of stroke and age on callosal microstructure. RESULTS: In mild-to-moderate chronic stroke survivors with relatively localized lesions to the motor areas, lower callosal FA values, suggestive of a more disorganized microstructure, were associated with slower bimanual performance. Associations were strongest for the primary motor fibers (b = -2.19 ± 1.03, p = .035), followed closely by premotor/supplementary motor (b = -2.07 ± 1.07, p = .041) and prefrontal (b = -1.92 ± 0.97, p = .05) fibers of the callosum. Secondary analysis revealed that compared to neurologically age-similar adults, chronic stroke survivors exhibited significantly lower mean FA in all regions of the CC, except the splenium. CONCLUSION: Remote widespread changes in the callosal genu and body are associated with slower performance on cooperative bimanual tasks that require precise and interdependent coordination of the hands. Measures of callosal microstructure may prove to be a useful predictor of real-world bimanual performance in chronic stroke survivors.

Paired associative stimulation applied to the cortex can increase resting-state functional connectivity: A proof of principle study.

INTRODUCTION: There is emerging evidence that high Beta coherence (hBc) between prefrontal and motor corticies, measured with resting-state electroencephalography (rs-EEG), can be an accurate predictor of motor skill learning and stroke recovery. However, it remains unknown whether and how intracortical connectivity may be influenced using neuromodulation. Therefore, a cortico-cortico PAS (ccPAS) paradigm may be used to increase resting-state intracortical connectivity (rs-IC) within a targeted neural circuit. PURPOSE: Our purpose is to demonstrate proof of principle that ccPAS can be used to increase rs-IC between a prefrontal and motor cortical region. METHODS: Eleven non-disabled adults were recruited (mean age 26.4, sd 5.6, 5 female). Each participant underwent a double baseline measurement, followed by a real and control ccPAS condition, counter-balanced for order. Control and ccPAS conditions were performed over electrodes of the right prefrontal and motor cortex. Both ccPAS conditions were identical apart from the inter-stimulus interval (i.e ISI 5 ms: real ccPAS and 500 ms: control ccPAS). Whole brain rs-EEG of high Beta coherence (hBc) was acquired before and after each ccPAS condition and then analyzed for changes in rs-IC along the targeted circuit. RESULTS: Compared to ccPAS500 and baseline, ccPAS5 induced a significant increase in rs-IC, measured as coherence between electrodes over right prefrontal and motor cortex, (p <.05). CONCLUSION: These findings demonstrate proof of principle that ccPAS with an STDP derived ISI, can effectively increase hBc along a targeted circuit.

Genetic Factors, Brain Atrophy, and Response to Rehabilitation Therapy After Stroke.

OBJECTIVE: Patients show substantial differences in response to rehabilitation therapy after stroke. We hypothesized that specific genetic profiles might explain some of this variance and, secondarily, that genetic factors are related to cerebral atrophy post-stroke. METHODS: The phase 3 ICARE study examined response to motor rehabilitation therapies. In 216 ICARE enrollees, DNA was analyzed for presence of the BDNF val66met and the ApoE ε4 polymorphism. The relationship of polymorphism status to 12-month change in motor status (Wolf Motor Function Test, WMFT) was examined. Neuroimaging data were also evaluated (n=127). RESULTS: Subjects were 61±13 years old (mean±SD) and enrolled 43±22 days post-stroke; 19.7% were BDNF val66met carriers and 29.8% ApoE ε4 carriers. Carrier status for each polymorphism was not associated with WMFT, either at baseline or over 12 months of follow-up. Neuroimaging, acquired 5±11 days post-stroke, showed that BDNF val66met polymorphism carriers had a 1.34-greater degree of cerebral atrophy compared to non-carriers (P=.01). Post hoc analysis found that age of stroke onset was 4.6 years younger in subjects with the ApoE ε4 polymorphism (P=.02). CONCLUSION: Neither the val66met BDNF nor ApoE ε4 polymorphism explained inter-subject differences in response to rehabilitation therapy. The BDNF val66met polymorphism was associated with cerebral atrophy at baseline, echoing findings in healthy subjects, and suggesting an endophenotype. The ApoE ε4 polymorphism was associated with younger age at stroke onset, echoing findings in Alzheimer's disease and suggesting a common biology. Genetic associations provide insights useful to understanding the biology of outcomes after stroke.

Effort, success, and side of lesion determine arm choice in individuals with chronic stroke.

In neurotypical individuals, arm choice in reaching movements depends on expected biomechanical effort, expected success, and a handedness bias. Following a stroke, does arm choice change to account for the decreased motor performance, or does it follow a preinjury habitual preference pattern? Participants with mild-to-moderate chronic stroke who were right-handed before stroke performed reaching movements in both spontaneous and forced-choice blocks, under no-time, medium-time, and fast-time constraint conditions designed to modulate reaching success. Mixed-effects logistic regression models of arm choice revealed that expected effort predicted choices. However, expected success only strongly predicted choice in left-hemiparetic individuals. In addition, reaction times decreased in left-hemiparetic individuals between the no-time and the fast-time constraint conditions but showed no changes in right-hemiparetic individuals. Finally, arm choice in the no-time constraint condition correlated with a clinical measure of spontaneous arm use for right-, but not for left-hemiparetic individuals. Our results are consistent with the view that right-hemiparetic individuals show a habitual pattern of arm choice for reaching movements relatively independent of failures. In contrast, left-hemiparetic individuals appear to choose their paretic left arm more optimally: that is, if a movement with the paretic arm is predicted to be not successful in the upcoming movement, the nonparetic right arm is chosen instead.NEW & NOTEWORTHY Although we are seldom aware of it, we constantly make decisions to use one arm or the other in daily activities. Here, we studied whether these decisions change following stroke. Our results show that effort, success, and side of lesion determine arm choice in a reaching task: whereas left-paretic individuals modified their arm choice in response to failures in reaching the target, right-paretic individuals showed a pattern of choice independent of failures.

Corticospinal Tract Microstructure Predicts Distal Arm Motor Improvements in Chronic Stroke.

BACKGROUND AND PURPOSE: The corticospinal tract (CST) is a crucial brain pathway for distal arm and hand motor control. We aimed to determine whether a diffusion tensor imaging (DTI)-derived CST metric predicts distal upper extremity (UE) motor improvements in chronic stroke survivors. METHODS: We analyzed clinical and neuroimaging data from a randomized controlled rehabilitation trial. Participants completed clinical assessments and neuroimaging at baseline and clinical assessments 4 months later, postintervention. Using univariate linear regression analysis, we determined the linear relationship between the DTI-derived CST fractional anisotropy asymmetry (FAasym) and the percentage of baseline change in log-transformed average Wolf Motor Function Test time for distal items (ΔlnWMFT-distal_%). The least absolute shrinkage and selection operator (LASSO) linear regressions with cross-validation and bootstrapping were used to determine the relative weighting of CST FAasym, other brain metrics, clinical outcomes, and demographics on distal motor improvement. Logistic regression analyses were performed to test whether the CST FAasym can predict clinically significant UE motor improvement. RESULTS: lnWMFT-distal significantly improved at the group level. Baseline CST FAasym explained 26% of the variance in ΔlnWMFT-distal_%. A multivariate LASSO model including baseline CST FAasym, age, and UE Fugl-Meyer explained 39% of the variance in ΔlnWMFT-distal_%. Further, CST FAasym explained more variance in ΔlnWMFT-distal_% than the other significant predictors in the LASSO model. DISCUSSION AND CONCLUSIONS: CST microstructure is a significant predictor of improvement in distal UE motor function in the context of an UE rehabilitation trial in chronic stroke survivors with mild-to-moderate motor impairment.Video Abstract available for more insight from the authors (see the Video, Supplemental Digital Content 1, available at: http://links.lww.com/JNPT/A350).

Lost in Translation: Simple Steps in Experimental Design of Neurorehabilitation-Based Research Interventions to Promote Motor Recovery Post-Stroke.

Stroke continues to be a leading cause of disability. Basic neurorehabilitation research is necessary to inform the neuropathophysiology of impaired motor control, and to develop targeted interventions with potential to remediate disability post-stroke. Despite knowledge gained from basic research studies, the effectiveness of research-based interventions for reducing motor impairment has been no greater than standard of practice interventions. In this perspective, we offer suggestions for overcoming translational barriers integral to experimental design, to augment traditional protocols, and re-route the rehabilitation trajectory toward recovery and away from compensation. First, we suggest that researchers consider modifying task practice schedules to focus on key aspects of movement quality, while minimizing the appearance of compensatory behaviors. Second, we suggest that researchers supplement primary outcome measures with secondary measures that capture emerging maladaptive compensations at other segments or joints. Third, we offer suggestions about how to maximize participant engagement, self-direction, and motivation, by embedding the task into a meaningful context, a strategy more likely to enable goal-action coupling, associated with improved neuro-motor control and learning. Finally, we remind the reader that motor impairment post-stroke is a multidimensional problem that involves central and peripheral sensorimotor systems, likely influenced by chronicity of stroke. Thus, stroke chronicity should be given special consideration for both participant recruitment and subsequent data analyses. We hope that future research endeavors will consider these suggestions in the design of the next generation of intervention studies in neurorehabilitation, to improve translation of research advances to improved participation and quality of life for stroke survivors.

Young adults with recurrent low back pain demonstrate altered trunk coordination during gait independent of pain status and attentional demands.

Pain influences both attention and motor behavior. We used a dual-task interference paradigm to investigate (1) alterations in attentional performance, (2) the ability to switch task prioritization, and (3) the effect of attentional demand on trunk coordination during narrow-based walking in and out of a painful episode in individuals with recurrent low back pain (LBP). We tested twenty young adults with LBP both in and out of a painful episode and compared them to twenty matched back-healthy individuals. Participants simultaneously performed a narrow step width matching task and an arithmetic task, with and without instructions to prioritize either task. A motion capture system was used to record kinematic data, and frontal plane trunk coordination was analyzed using vector coding on the thorax and pelvis angles. Single-task performance, dual-task effect, dual-task performance variability, task prioritization switch, and trunk coordination were analyzed using paired t tests or repeated measures two-way ANOVAs. Results indicated that active pain has a detrimental effect on attentional processes, indicated by poorer single-task performance and increased dual-task performance variability for individuals with recurrent LBP. Individuals with LBP, regardless of pain status, were able to switch task prioritization to a similar degree as their back-healthy counterparts. Compared to the control group, individuals with recurrent LBP exhibited a less in-phase, more pelvis-dominated trunk coordination during narrow-based walking, independent of pain status and regardless of attentional manipulations. Thus, altered trunk coordination in persons with LBP appears to be habitual, automatic, and persists beyond symptom duration.

A perspective on the use of ecological momentary assessment and intervention to promote stroke recovery and rehabilitation.

Motivated by recent advances in technologies, ecological momentary assessment (EMA), and ecological momentary intervention (EMI) have seen a rise in behavioral medicine research that in real-time, informs the context for the behavior and prompts interventions to change that behavior in the natural setting when necessary. However, EMA and EMI have yet to be fully embraced in the field of stroke rehabilitation. Our objective is to provide a theoretically based perspective for the combined and synergistic use of EMA and EMI to promote person-centered, recovery-based durable changes in functional movement behaviors of stroke survivors. Research abounds for non-stroke populations with emerging evidence for the benefits of using real-time data capture techniques (i.e. EMA) coupled with EMI to better customize the content and timing of interventions to the inherent fluctuations in state and context that encompass the target behavior. We review existing EMA and EMI literature broadly in behavioral medicine and psychological science to identify how real-time repeated sampling technology has been used in the context of stroke rehabilitation and to delineate the pros and cons of this approach in general with non-stroke populations. We propose a coupled EMA and EMI strategy be used in conjunction with existing stroke recovery and rehabilitation practices. There is tremendous potential to effectively personalize recovery-promoting interventions to achieve durable behavior change, and importantly, shift the focus of rehabilitation practice from the health-care provider and clinical environment to the individual and their lived experience in the home and community.

Five Features to Look for in Early-Phase Clinical Intervention Studies.

Neurorehabilitation relies on core principles of neuroplasticity to activate and engage latent neural connections, promote detour circuits, and reverse impairments. Clinical interventions incorporating these principles have been shown to promote recovery and demote compensation. However, many clinicians struggle to find interventions centered on these principles in our nascent, rapidly growing body of literature. Not to mention the immense pressure from regulatory bodies and organizational balance sheets that further discourage time-intensive recovery-promoting interventions, incentivizing clinicians to prioritize practical constraints over sound clinical decision making. Modern neurorehabilitation practices that result from these pressures favor strategies that encourage compensation over those that promote recovery. To narrow the gap between the busy clinician and the cutting-edge motor recovery literature, we distilled 5 features found in early-phase clinical intervention studies-ones that value the more enduring biological recovery processes over the more immediate compensatory remedies. Filtering emerging literature through this lens and routinely integrating promising research into daily practice can break down practical barriers for effective clinical translation and ultimately promote durable long-term outcomes. This perspective is meant to serve a new generation of mechanistically minded and caring clinicians, students, activists, and research trainees, who are poised to not only advance rehabilitation science, but also erect evidence-based policy changes to accelerate recovery-based stroke care.