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Background Stroke is a condition marked by considerable variability in lesions, recovery trajectories, and responses to therapy. Consequently, precision medicine in rehabilitation post-stroke, which aims to deliver the “right intervention, at the right time, in the right setting, for the right person,” is essential for optimizing stroke recovery. Although artificial intelligence (AI) has been effectively utilized in other medical fields, no current AI system is designed to tailor and continuously refine rehabilitation plans post-stroke. Methods We propose a novel AI-based decision-support system for precision rehabilitation that uses reinforcement learning (RL) to personalize the treatment plan. Specifically, our system iteratively adjusts the sequential treatment plan—timing, dosage, and intensity—to maximize long-term outcomes based on a patient model that includes covariate data (the context). The system collaborates with clinicians and people with stroke to customize the recommended plan based on clinical judgment, constraints, and preferences. To achieve this goal, we propose a contextual Markov decision process (CMDP) framework and a novel hierarchical Bayesian model-based RL algorithm, named posterior sampling for contextual RL (PSCRL), that discovers and continuously adjusts near-optimal sequential treatments by efficiently balancing exploitation and exploration while respecting constraints and preferences. Results We implemented and validated our precision rehabilitation system in simulations with 150 diverse, synthetic patients. Simulation results showed the system’s ability to continuously learn from both upcoming data from the current patient and a database of past patients via Bayesian hierarchical modeling. Specifically, the algorithm’s sequential treatment recommendations became increasingly more effective in improving functional gains for each patient over time and across the synthetic patient population. As a result, the algorithm’s treatments were superior to non-adaptive, “one-size-fits-all” dosing schedules (uniform, decreasing, and increasing). Conclusions Our novel AI-based precision rehabilitation system, based on contextual model-based RL, has the potential to play a key role in novel learning health systems in rehabilitation.

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.

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.

The priorities of individuals with chronic stroke are not always reflected in clinical practice. This study provides insight into meaningful factors related to long-term motor recovery in stroke survivors. Thirty individuals with chronic stroke participated in semi-structured interviews about movement, recovery, and barriers to and facilitators of mobility and paretic arm use. The interviews were analyzed using inductive thematic analysis. Three categories, the individual, environment, and task, defined five emergent themes. Individual: (1) mindset is a strong and consistent influencer of daily physical activity and overall recovery; (2) severe physical impairment limits physical activity and recovery, regardless of other factors; and (3) a negative perception of disability impacts mindset and willingness to move in public. Environment: (4) social and physical environments influence physical activity and recovery. Task: (5) participation in meaningful activities increases physical activity and promotes long-term recovery. Strategies to incorporate paretic arm use, exercise, and encouragement from others facilitate physical activity. Insufficient paretic limb function, environmental obstacles, and fear are barriers to physical activity. Neurorehabilitation must address the factors that are meaningful to stroke survivors. Building motor capacity is essential and must be integrated with factors such as a positive mindset and proper environment. Individual differences reinforce the need for personalized care.

Background: Evidence supports cortical reorganization in sensorimotor areas induced by constraint-induced movement therapy (CIMT). However, only a few studies examined the neural plastic changes as a function of task specificity. This retrospective exploratory analysis aims to evaluate the functional brain activation changes during a precision and a power grasp task in chronic stroke survivors who received two-weeks of CIMT compared to a no-treatment control group. Methods: Fourteen chronic stroke survivors, randomized to CIMT (n=8) or non-CIMT (n=6), underwent functional MRI (fMRI) before and after a two-week period. Two behavioral measures, the 6-item Wolf Motor Function Test (WMFT-6) and the Motor Activity Log (MAL), and fMRI brain scans were collected before and after a two-week period. During scan runs, participants performed two different grasp tasks (precision, power). Pre to post changes in laterality index (LI) were compared by group and task for two predetermined motor regions of interest: dorsal premotor cortex (PMd) and primary motor cortex (MI). Results: The CIMT group showed significant improvements in both the WMFT-6 and the MAL post-intervention, whereas the non-CIMT group showed improvements in only the MAL. Two weeks of CIMT resulted in a relative increase in activity in a key region of the motor network, PMd of the lesioned hemisphere, under precision grasp task conditions compared to the non-treatment control group. No changes in LI were observed in MI for either task or group. Conclusions: These findings provide evidence for task-specific effects of CIMT in the promotion of recovery-supportive cortical reorganization in chronic stroke survivors.

\"Use it and improve it, or lose it\" is one of the axioms of motor therapy after stroke. There is, however, little understanding of the interactions between arm function and use in humans post-stroke. Here, we explored putative non-linear interactions between upper extremity function and use by developing a first-order dynamical model of stroke recovery with longitudinal data from participants receiving constraint induced movement therapy (CIMT) in the EXCITE clinical trial. Using a Bayesian regression framework, we systematically compared this model with competitive models that included, or not, interactions between function and use. Model comparisons showed that the model with the predicted interactions between arm function and use was the best fitting model. Furthermore, by comparing the model parameters before and after CIMT intervention in participants receiving the intervention one year after randomization, we found that therapy increased the parameter that controls the effect of arm function on arm use. Increase in this parameter, which can be thought of as the confidence to use the arm for a given level of function, lead to increase in spontaneous use after therapy compared to before therapy.

The aim of constraint-induced movement therapy (CIMT) is to promote use of a limb that is functionally impaired after a stroke. In one form of CIMT to treat upper limb impairment, use of the less severely affected arm is restricted for many hours each weekday over 2 consecutive weeks. The EXCITE trial has previously shown the efficacy of this intervention for patients 3–9 months poststroke who were followed-up for the next 12 months. We assessed the retention of improvements 24 months after the intervention. In the EXCITE trial, 106 of 222 participants who had mild to moderate poststroke impairments were randomly assigned to receive CIMT rather than usual and customary care. We assessed this group of patients every 4 months for the primary outcome measure of impaired upper limb function, as measured with the Wolf motor function test (WMFT) and the motor activity log (MAL). Health-related quality of life, measured with the stroke impact scale (SIS), was a secondary outcome measure. Analysis was per protocol. This trial is registered with ClinicalTrials.gov, number NCT00057018. The effects at 24 months after treatment did not decline from those at 12 months for time taken to complete the WMFT (−0·32 s, 95% CI −3·70 to 3·06), for weight lifted in the WMFT (−1·39 kg, −2·74 to −0·04), for WMFT grip strength (−4·39 kg, −6·91 to −1·86), for amount of use in the MAL (−0·17, −0·38 to 0·04), or for how well the limb was used in the MAL (−0·14, −0·34 to 0·06). The additional changes were in the direction of increased therapeutic effect. For the strength components of the WMFT, p<0·0001. Patients who have mild to moderate impairments 3–9 months poststroke have substantial improvement in functional use of the paretic upper limb and quality of life 2 years after a 2-week CIMT intervention. Thus, this intervention has persistent benefits.

The authors find that disruption of primary motor cortex or dorsolateral prefrontal cortex with transcranial magnetic stimulation has differential effects on motor memory retention depending on whether training was done in blocks of trials or with different tasks interleaved. This suggests that the neural substrate for motor-memory consolidation depends on the practice structure used for training. Motor-skill practice drives subsequent offline activity in functionally related resting human brain networks. We investigated the manner in which offline neural networks are modulated by practice structures that affect motor-skill retention. Interference to dorsolateral-prefrontal cortex (DLPFC), but not to primary motor cortex (M1), after variable practice attenuated motor-skill retention, whereas interference to M1, but not to DLPFC, after constant practice attenuated motor-skill retention. We conclude that neural substrates of motor-memory consolidation are modulated by practice structure.

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.