Proximal Fugl-Meyer Assessment Scores Predict Clinically Important Upper Limb Improvement After 3 Stroke Rehabilitative Interventions

Distal Versus Proximal Arm Improvement After Paired Vagus Nerve Stimulation Therapy After Chronic Stroke

OBJECTIVE

To evaluate differences in upper-extremity (UE) segment-specific (proximal or distal segment) recovery after vagus nerve stimulation (VNS) paired with UE rehabilitation (Paired-VNS) compared with rehabilitation with sham-VNS (Control). We also assessed whether gains in specific UE segments predicted clinically meaningful improvement.

DESIGN

This study reports on a secondary analysis of Vagus nerve stimulation paired with rehabilitation for UE motor function after chronic ischemic stroke (VNS-REHAB), a randomized, triple-blinded, sham-controlled pivotal trial. A Rasch latent regression was used to determine differences between Paired-VNS and Controls for distal and proximal UE changes after in-clinic therapy and 3 months later. Subsequently, we ran a random forest model to assess candidate predictors of meaningful improvement. Each item of the Fugl-Meyer Assessment-Upper Extremity (FMA-UE) and Wolf Motor Function Test (WMFT) was evaluated as a predictor of response to treatment.

SETTING

Nineteen stroke rehabilitation centers in the USA and UK.

PARTICIPANTS

Dataset included 108 participants (N=108) with chronic ischemic stroke and moderate-to-severe UE impairments.

INTERVENTIONS

Not applicable.

MAIN OUTCOME MEASURES

FMA-UE and WMFT.

RESULTS

Distal UE improvement was significantly greater in the Paired-VNS group than in Controls immediately after therapy (95% confidence interval, 0.27-0.73; P≤.001) and after 3 months (95% confidence interval, 0.16-0.75; P=.003). Both groups showed similar improvement in proximal UE at both time points. A subset of both distal and proximal items from the FMA-UE and WMFT were predictors of meaningful improvement.

CONCLUSIONS

Paired-VNS improved distal UE impairment in chronic stroke to a greater degree than intensive rehabilitation alone. Proximal improvements were equally responsive to either treatment. Given that meaningful UE recovery is predicted by improvements across both proximal and distal segments, Paired-VNS may facilitate improvement that is otherwise elusive.

Development and External Validation of a Motor Intention-Integrated Prediction Model for Upper Extremity Motor Recovery After Intention-Driven Robotic Hand Training for Chronic Stroke

OBJECTIVE

To derive and validate a prediction model for minimal clinically important differences (MCIDs) in upper extremity (UE) motor function after intention-driven robotic hand training using residual voluntary electromyography (EMG) signals from affected UE.

DESIGN

A prospective longitudinal multicenter cohort study. We collected preintervention candidate predictors: demographics, clinical characteristics, Fugl-Meyer assessment of UE (FMAUE), Action Research Arm Test scores, and motor intention of flexor digitorum and extensor digitorum (ED) measured by EMG during maximal voluntary contraction (MVC). For EMG measures, recognizing challenges for stroke survivors to move paralyzed hand, peak signals were extracted from 8 time windows during MVC-EMG (0.1-5s) to identify subjects' motor intention. Classification and regression tree algorithm was employed to predict survivors with MCID of FMAUE. Relationship between predictors and motor improvements was further investigated.

SETTING

Nine rehabilitation centers.

PARTICIPANTS

Chronic stroke survivors (N=131), including 87 for derivation sample, and 44 for validation sample.

INTERVENTIONS

All participants underwent 20-session robotic hand training (40min/session, 3-5sessions/wk).

MAIN OUTCOME MEASURES

Prediction efficacies of models were assessed by area under the receiver operating characteristic curve (AUC). The best effective model was final model and validated using AUC and overall accuracy.

RESULTS

The best model comprised FMAUE (cutoff score, 46) and peak activity of ED from 1-second MVC-EMG (MVC-EMG 4.604 times higher than resting EMG), which demonstrated significantly higher prediction accuracy (AUC, 0.807) than other time windows or solely using clinical scores (AUC, 0.595). In external validation, this model displayed robust prediction (AUC, 0.916). Significant quadratic relationship was observed between ED-EMG and FMAUE increases.

CONCLUSIONS

This study presents a prediction model for intention-driven robotic hand training in chronic stroke survivors. It highlights significance of capturing motor intention through 1-second EMG window as a predictor for MCID improvement in UE motor function after 20-session robotic training. Survivors in 2 conditions showed high percentage of clinical motor improvement: moderate-to-high motor intention and low-to-moderate function; as well as high intention and high function.

A Decision Support System to Provide an Ongoing Prediction of Robot-Assisted Rehabilitation Outcome in Stroke Survivors

Clinicians often deal with complex robotic platform and serious games in stroke patients rehabilitation contexts, and they face two main problems: 1) the interpretation of either the performance in game or measures of a robotic system from the motor recovery point of view, and 2) the duration and complexity of clinical scales administration that makes repetitive assessments during the therapy unpractical. In this paper, a Random Tree Forest based system was trained and tested to provide a prediction of different clinical outcomes (i.e. FMA, ARAT, and MI) along the whole therapy duration, having non-clinical measures only as inputs, acting as a simulated decision support system. The dataset includes 30 post-stroke patients, that underwent a 30-session robot-assisted rehabilitation treatment. Results have shown that the system is able to produce very accurate and reliable predictions about the motor recovery of the patient at the end of the therapy, already in the first phases of the rehabilitation (i40% of therapy execution), just using robotic platform measures. Such a tool would provide a great benefit in terms of rehabilitation objectives planning, as a decision support tool for highly personalized rehabilitation treatments.

Biofeedback interventions for short term upper limb function following stroke: A systematic review with meta-analysis

BACKGROUND

Biofeedback has been used by rehabilitation professionals in the treatment of poststroke function impairments.

PURPOSE

Investigate the efficacy of any type of biofeedback intervention for the treatment of upper limb function in individuals following stroke.

STUDY DESIGN

Systematic review of literature with meta-analysis.

METHODS

Literature searches were conducted using MESH terms and text words in PubMed, Lilacs, Scielo, Scopus, PEDro, and Web of Science databases. The main outcome was improvement in upper limb's motor function and motor function in activities of daily living. We calculated the Mean Difference and Standardized Mean Difference for the assessment scales reported as primary outcome. The methodological quality of included studies was assessed using PEDro scale. The overall quality of the evidence was assessed using GRADE system.

RESULTS

From 1360 articles identified, 16 were included in the review (09 in the meta-analysis). Three forest plots of hemiparesis and one of hemiplegia showed that biofeedback therapy associated with conventional therapy has a greater improvement in participants upper limb motor function when compared to isolated conventional therapy. Two forest plots of hemiparesis and one of hemiplegia showed no superiority in participants improvement for biofeedback associated with conventional therapy when compared to isolated conventional therapy.

CONCLUSION

Biofeedback therapy associated with conventional therapy showed a small clinical effect when associated to conventional therapy and very low quality of evidence. Although further research with higher quality evidence is needed.

Consensus-Based Evaluation of Outcome Measures in Pediatric Stroke Care: A Toolkit

Following a pediatric stroke, outcome measures selected for monitoring functional recovery and development vary widely. We sought to develop a toolkit of outcome measures that are currently available to clinicians, possess strong psychometric properties, and are feasible for use within clinical settings. A multidisciplinary group of clinicians and scientists from the International Pediatric Stroke Organization comprehensively reviewed the quality of measures in multiple domains described in pediatric stroke populations including global performance, motor and cognitive function, language, quality of life, and behavior and adaptive functioning. The quality of each measure was evaluated using guidelines focused on responsiveness and sensitivity, reliability, validity, feasibility, and predictive utility. A total of 48 outcome measures were included and were rated by experts based on the available evidence within the literature supporting the strengths of their psychometric properties and practical use. Only three measures were found to be validated for use in pediatric stroke: the Pediatric Stroke Outcome Measure, the Pediatric Stroke Recurrence and Recovery Questionnaire, and the Pediatric Stroke Quality of Life Measure. However, multiple additional measures were deemed to have good psychometric properties and acceptable utility for assessing pediatric stroke outcomes. Strengths and weaknesses of commonly used measures including feasibility are highlighted to guide evidence-based and practicable outcome measure selection. Improving the coherence of outcome assessment will facilitate comparison of studies and enhance research and clinical care in children with stroke. Further work is urgently needed to close the gap and validate measures across all clinically significant domains in the pediatric stroke population.

Predicting patient-reported outcome of activities of daily living in stroke rehabilitation: a machine learning study

BACKGROUND

Machine Learning is increasingly used to predict rehabilitation outcomes in stroke in the context of precision rehabilitation and patient-centered care. However, predictors for patient-centered outcome measures for activities and participation in stroke rehabilitation requires further investigation.

METHODS

This study retrospectively analyzed data collected for our previous studies from 124 participants. Machine Learning models were built to predict postintervention improvement of patient-reported outcome measures of daily activities (i.e, the Motor Activity Log and the Nottingham Extended Activities of Daily Living) and participation (i.e, the Activities of Daily Living domain of the Stroke Impact Scale). Three groups of 18 potential predictors were included: patient demographics, stroke characteristics, and baseline assessment scores that encompass all three domains under the framework of International Classification of Functioning, Disability and Health. For each target variable, classification models were built with four algorithms, logistic regression, k-nearest neighbors, support vector machine, and random forest, and with all 18 potential predictors and the most important predictors identified by feature selection.

RESULTS

Predictors for the four target variables partially overlapped. For all target variables, their own baseline scores were among the most important predictors. Upper-limb motor function and selected demographic and stroke characteristics were also among the important predictors across the target variables. For the four target variables, prediction accuracies of the best-performing models with 18 features ranged between 0.72 and 0.96. Those of the best-performing models with fewer features ranged between 0.72 and 0.84.

CONCLUSIONS

Our findings support the feasibility of using Machine Learning for the prediction of stroke rehabilitation outcomes. The study was the first to use Machine Learning to identify important predictors for postintervention improvement on four patient-reported outcome measures of activities and participation in chronic stroke. The study contributes to precision rehabilitation and patient-centered care, and the findings may provide insights into the identification of patients that are likely to benefit from stroke rehabilitation.

Prognosis prediction of the effect of botulinum toxin therapy and intensive rehabilitation on the upper arm function in post-stroke patients using hierarchical cluster analysis

PURPOSE

We analysed the effect of botulinum neurotoxin A therapy (BoNT-A) with intensive rehabilitation on the upper limb (UL) spasticity in post-stroke patients by classifying function by UL movement and examining differences in functional improvement.

MATERIALS AND METHODS

In this non-randomized, controlled study. The patient function was classified into groups from the score of the sub-categories of the Fugl-Meyer Assessment (FMA-UE) before treatment in the Intervention group by hierarchical cluster analysis.

RESULTS

A total of 139 patients in the Intervention group were classified into six groups. All groups showed a significant improvement in FMA-UE after the intervention. In the group scoring 19-31 points on the FMA-UE and with the voluntary movement of shoulder, elbow, forearm, and finger, a significant improvement was observed compared to the Control group. Further, in the group scoring 26-47 points on the FMA-UE and with the voluntary movement of shoulder, elbow, forearm, wrist, and finger, a significant improvement was observed compared to the Control group.

CONCLUSIONS

In this study, BoNT-A and intensive rehabilitation showed improvement in spasticity and UL function. A high therapeutic effect is expected in patients with moderate impairment levels who have voluntary movement in whole UL or in UL except for the wrist.IMPLICATIONS FOR REHABILITATIONHierarchical cluster analysis focusing on the Fugl-Meyer Assessment of the Upper Extremity sub-categories may be useful for studies aimed to improve the upper arm function.Botulinum Neurotoxin A therapy (BoNT-A) and intensive rehabilitation in post-stroke patients showed improvement in spasticity and upper arm function.The degree of the upper arm function before the intervention may affect the improvement effect of BoNT-A and intensive rehabilitation.In the motor function, the post-stroke patients with a moderate impairment level who have voluntary movement of the whole upper limb or upper limb except for the wrist are most likely to receive these therapeutic effects.

A Random Tree Forest decision support system to personalize upper extremity robot-assisted rehabilitation in stroke: a pilot study

Robotic-based rehabilitation administered by means of serious games certainly represents the frontier of rehabilitation treatments, offering a high degree of customization of therapy, to meet individual patients' needs and to tailor a proper rehabilitation therapy. Despite the rush on developing complex rehabilitation systems, they often do not provide clinicians with long-term information about the outcome of rehabilitation, thus, not supporting them in the initial set-up phase of the therapy. In this paper, a Random-Forest based system was trained and tested to provide a prediction at discharge of several clinical scales outcomes (i.e. FMA, ARAT, and MI), having clinical scale scores and measures from the robotic system at the enrollment as inputs. The dataset includes 25 post-stroke patients from different clinics, that underwent a variable number of days of rehabilitation with a robotic treatment. Results have shown that the system is able to predict the final outcome with an accuracy ranging from 60% to 73% on the selected scales. Also results provide information on which variables are more relevant for the prediction of outcome of therapy, in particular clinical scales scores such as FMA, ARAT, MI, NRS, PCS, and MCS and robotic automatically extracted measurements related to patient's work expenditure and time. This supports the idea of using such a system in a clinical environment in a decision support tool for clinicians.

Mirror therapy in upper limb motor recovery and activities of daily living, and its neural correlates in stroke individuals: A systematic review and meta-analysis

Available literature indicates that 30-66% of stroke survivors present persistent upper limb impairment. Considering the importance of upper limb function for activities of daily living, it is necessary to investigate neurorehabilitation therapies that could improve the upper limb function. Among stroke complementary therapies, mirror therapy has shown promising results. Thus, the aim of this systematic review and meta-analyses was to review and synthesize clinical evidence on the use of mirror therapy on motor recovery of the upper limb and activities of daily living, and its neural correlates in stroke patients. The literature search was carried out in PubMed, ISI Web of Science, and Scopus databases. Twenty-nine studies met all the inclusion criteria. Two meta-analyses were conducted to compare mirror therapy with sham therapy on two general measures, upper limb assessment and activities of daily living. Results suggest that mirror therapy was better than sham therapy, mainly in the subacute phase, but the meta-analyses were nonsignificant. In addition, mirror therapy and cortical reorganization showed potential neural correlates, such as the primary motor cortex, precuneus, and posterior cingulate cortex.

Do somatosensory deficits predict efficacy of neurorehabilitation using neuromuscular electrical stimulation for moderate to severe motor paralysis of the upper limb in chronic stroke?

Background:

Various neurorehabilitation programs have been developed to promote recovery from motor impairment of upper extremities. However, the response of patients with chronic-phase stroke varies greatly. Prediction of the treatment response is important to provide appropriate and efficient rehabilitation. This study aimed to clarify whether clinical assessments, such as motor impairments and somatosensory deficits, before treatment could predict the treatment response in neurorehabilitation.

Methods:

The data from patients who underwent neurorehabilitation using closed-loop electromyography (EMG)-controlled neuromuscular electrical stimulation were retrospectively analyzed. A total of 66 patients with chronic-phase stroke with moderate to severe paralysis were included. The changes from baseline in the Fugl-Meyer Assessment–Upper Extremity (FMA-UE) and the Motor Activity Log-14 (MAL-14) of amount of use (AOU) and quality of movement (QOM) were used to assess treatment response, and multivariate logistic regression analysis was performed using the extracted candidate predictors, such as baseline clinical assessments, to identify predictors of FMA-UE and MAL-14 improvement.

Results:

FMA-UE and MAL-14 scores improved significantly after the intervention (FMA-UE p < 0.01, AOU p < 0.01, QOM p < 0.01). On multivariate logistic regression analysis, tactile sensory (p = 0.043) and hand function (p = 0.030) were both identified as significant predictors of FMA-UE improvement, tactile sensory (p = 0.047) was a significant predictor of AOU improvement, and hand function (p = 0.026) was a significant predictor of QOM improvement. The regression equations explained 71.2% of the variance in the improvement of FMA-UE, 69.7% of AOU, and 69.7% of QOM.

Conclusion:

Both motor and tactile sensory impairments predict improvement in motor function, tactile sensory impairment predicts improvement in the amount of paralytic hand use, and motor impairment predicts improvement in the quality of paralytic hand use following neurorehabilitation treatment in patients with moderate to severe paralysis in chronic-phase stroke. These findings may help select the appropriate treatment for patients with more severe paralysis and to maximize the treatment effect.

Shoulder function after constraint-induced movement therapy assessed with 3D kinematics and clinical and patient reported outcomes: A prospective cohort study

INTRODUCTION

We hypothesised that reduced shoulder function post stroke improves during constraint-induced movement therapy and that improvement in scapula upward rotation measured with three-dimensional kinematics is associated with improvements in clinical and patient reported outcomes.

METHODS

Thirty-seven patients were tested pre and post constraint-induced movement therapy and again at three-month follow-up. Kinematic outcome measures - with scapula upward rotation as the primary outcome - during tasks 5 (ReachLow) and 6 (ReachHigh) from the Wolf Motor Function Test were included together with clinical and patient reported outcomes. Changes in outcome measures were analysed with linear mixed models and logistic regression analysis.

FINDINGS

Scapula upward rotation was reduced from 16.2° pre intervention through 15.9° post intervention to 15.6° at three-month follow-up during ReachHigh. Statistically significant reductions of <2° were also found for shoulder flexion during ReachLow and trunk lateral flexion during ReachHigh. The clinical and patient reported outcomes showed improvements post constraint-induced movement therapy, and at follow-up, the outcomes resembled post values.

INTERPRETATION

The minimal improvements in selected 3D kinematic measures of upper extremity movements did not reflect any clinically meaningful changes. Therefore, the clinical and patient reported improvements could not be related to restitution of shoulder function.

Sequentially applied myoelectrically controlled FES in a task-oriented approach and robotic therapy for the recovery of upper limb in post-stroke patients: A randomized controlled pilot study

BACKGROUND

Functional recovery of the plegic upper limb in post-stroke patients may be enhanced by sequentially applying a myoelectrically controlled FES (MeCFES), which allows the patient to voluntarily control the muscle contraction during a functional movement, and robotic therapy which allows many repetitions of movements.

OBJECTIVE

Evaluate the efficacy of MeCFES followed by robotic therapy compared to standard care arm rehabilitation for post-stroke patients.

METHODS

Eighteen stroke subjects (onset ⩾ 3 months, age 60.1 ± 15.5) were recruited and randomized to receive an experimental combination of MeCFES during task-oriented reaching followed by robot therapy (MRG) or same intensity conventional rehabilitation care (CG) aimed at the recovery of the upper limb (20 sessions/45 minutes). Change was evaluated through Fugl-Meyer upper extremity (FMA-UE), Reaching Performance Scale and Box and Block Test.

RESULTS

The experimental treatment resulted in higher improvement on the FMA-UE compared with CG (P= 0.04), with a 10-point increase following intervention. Effect sizes were moderate in favor of the MRG group on FMA-UE, FMA-UE proximal and RPS (0.37-0.56).

CONCLUSIONS

Preliminary findings indicate that a combination of MeCFES and robotic treatment may be more effective than standard care for recovery of the plegic arm in persons > 3 months after stroke. The mix of motor learning techniques may be important for successful rehabilitation of arm function.

Prediction of Motor Recovery in the Upper Extremity for Repetitive Transcranial Magnetic Stimulation and Occupational Therapy Goal Setting in Patients With Chronic Stroke: A Retrospective Analysis of Prospectively Collected Data

Recovery from motor paralysis is facilitated by affected patients' recognition of the need for and practice of their own exercise goals. Neurorehabilitation has been proposed and used for the treatment of motor paralysis in stroke, and its effect has been verified. If an expected score for the neurorehabilitation effect can be calculated using the Fugl-Meyer Motor Assessment (FMA), a global assessment index, before neurorehabilitation, such a score will be useful for optimizing the treatment application criteria and for setting a goal to enhance the treatment effect. Therefore, this study verified whether the responsiveness to a treatment method, the NovEl intervention using repetitive transcranial magnetic stimulation and occupational therapy (NEURO), in patients with post-stroke upper extremity (UE) motor paralysis could be predicted by the pretreatment FMA score. No control group was established in this study for NEURO treatment. To analyze the recovery of the motor function in the UE, delta-FMA was calculated from the pre- and post-FMA scores obtained during NEURO treatment. The probability of three levels of treatment responsiveness was evaluated in association with delta-FMA score (<5, 5 ≤ delta-FMA <10, and ≥10 as non-responders; responders; and hyper-responders, respectively) according to the reported minimal clinically important difference (MCID). The association of the initial FMA scores with post-FMA scores, from the status of the treatment responsiveness, was determined by multinomial logistic regression analysis. Finally, 1,254 patients with stroke, stratified by FMA scores were analyzed. About 45% of the patients who had FMA scores ranging from 30 to 40 before treatment showed improvement over the MCID by NEURO treatment (odds ratio = 0.93, 95% CI = 0.92–0.95). Furthermore, more than 25% of the patients with more severe initial values, ranging from 26 to 30, improved beyond the MCID calculated in the acute phase (odds ratio = 0.87, 95% CI = 0.85–0.89). These results suggest that the evaluated motor function score of the UE before NEURO treatment can be used to estimate the possibility of a patient recovering beyond MCID in the chronic phase. This study provided clinical data to estimate the effect of NEURO treatment by the pretreatment FMA-UE score.

Predicting clinically significant motor function improvement after contemporary task-oriented interventions using machine learning approaches

BACKGROUND

Accurate prediction of motor recovery after stroke is critical for treatment decisions and planning. Machine learning has been proposed to be a promising technique for outcome prediction because of its high accuracy and ability to process large volumes of data. It has been used to predict acute stroke recovery; however, whether machine learning would be effective for predicting rehabilitation outcomes in chronic stroke patients for common contemporary task-oriented interventions remains largely unexplored. This study aimed to determine the accuracy and performance of machine learning to predict clinically significant motor function improvements after contemporary task-oriented intervention in chronic stroke patients and identify important predictors for building machine learning prediction models.

METHODS

This study was a secondary analysis of data using two common machine learning approaches, which were the k-nearest neighbor (KNN) and artificial neural network (ANN). Chronic stroke patients (N = 239) that received 30 h of task-oriented training including the constraint-induced movement therapy, bilateral arm training, robot-assisted therapy and mirror therapy were included. The Fugl-Meyer assessment scale (FMA) was the main outcome. Potential predictors include age, gender, side of lesion, time since stroke, baseline functional status, motor function and quality of life. We divided the data set into a training set and a test set and used the cross-validation procedure to construct machine learning models based on the training set. After the models were built, we used the test data set to evaluate the accuracy and prediction performance of the models.

RESULTS

Three important predictors were identified, which were time since stroke, baseline functional independence measure (FIM) and baseline FMA scores. Models for predicting motor function improvements were accurate. The prediction accuracy of the KNN model was 85.42% and area under the receiver operating characteristic curve (AUC-ROC) was 0.89. The prediction accuracy of the ANN model was 81.25% and the AUC-ROC was 0.77.

CONCLUSIONS

Incorporating machine learning into clinical outcome prediction using three key predictors including time since stroke, baseline functional and motor ability may help clinicians/therapists to identify patients that are most likely to benefit from contemporary task-oriented interventions. The KNN and ANN models may be potentially useful for predicting clinically significant motor recovery in chronic stroke.

Age is negatively associated with upper limb recovery after conventional but not robotic rehabilitation in patients with stroke: a secondary analysis of a randomized-controlled trial

BACKGROUND

There is consistent evidence that robotic rehabilitation is at least as effective as conventional physiotherapy for upper extremity (UE) recovery after stroke, suggesting to focus research on which subgroups of patients may better respond to either intervention. In this study, we evaluated which baseline variables are associated with the response after the two approaches.

METHODS

This is a secondary analysis of a randomized-controlled trial comparing robotic and conventional treatment for the UE. After the assigned intervention, changes of the Fugl-Meyer Assessment UE score by ≥ 5 points classified patients as responders to treatment. Variables associated with the response were identified in a univariate analysis. Then, variables independently associated with recovery were investigated, in the whole group, and the two groups separately.

RESULTS

A sample of 190 patients was evaluated after the treatment; 121 were responders. Age, baseline impairment, and neglect were significantly associated with worse response to the treatment. Age was the only independently associated variable (OR 0.967, p = 0.023). Considering separately the two interventions, age remained negatively associated with recovery (OR 0.948, p = 0.013) in the conventional group, while none of the variables previously identified were significantly associated with the response to treatment in the robotic group.

CONCLUSIONS

We found that, in our sample, age is significantly associated with the outcome after conventional but not robotic UE rehabilitation. Possible explanations may include an enhanced positive attitude of the older patients towards technological training and reduced age-associated fatigue provided by robotic-assisted exercise. The possibly higher challenge proposed by robotic training, unbiased by the negative stereotypes concerning very old patients' expectations and chances to recover, may also explain our findings.

TRIAL REGISTRATION NUMBER

NCT02879279.

Electromyographic biofeedback improves upper extremity function: a randomized, single-blinded, controlled trial

OBJECTIVE

To examine the effects of a 6-week surface electromyographic biofeedback intervention on the re-learning of upper extremity motor function in subjects with paretic upper extremity after stroke.

DESIGN

A randomized controlled trial.

SETTING

State Centre of Attention to Brain Injury, Madrid, Spain.

PARTICIPANTS

Thirty-eight participants in the sub-acute post-stroke stage were recruited and randomly allocated into either the surface electromyographic biofeedback (sEMG-BFB) or sham biofeedback (BFB) groups.

INTERVENTIONS

The sEMG-BFB group (n=19) received the intervention focused on re-learning scapulothoracic control during arm-reaching tasks involving shoulder abduction. The sham BFB group (n=19) received a sham intervention.

OUTCOME MEASURES

Upper extremity motor function assessed using the Fugl-Meyer Assessment-Upper Extremity Scale (66 points), the glenohumeral active range of motion, and the electromyographic amplitude signal of the middle deltoid and upper trapezius muscles were collected at baseline, after the intervention, and at the one-month follow-up.

RESULTS

Compared with the sham BFB group, the sEMG-BFB group experienced significant increases in upper extremity motor function after the intervention. The mean differences between groups were as follows: 4.79 points (95% CI 2.92 to 6.66) after the intervention; 6.55 points (95% CI 3.75 to 9.34) at the one-month follow-up; improved active range of motion 15.75 points (95% CI 6 to 30) after the intervention and electromyographic activity in the upper trapezius muscle changed in favour of the sEMG-BFB.

CONCLUSIONS

In the short term, a 6-week sEMG-BFB intervention effectively improved paretic upper limb motor function. Future research is needed to determine if the sEMG-BFB intervention has any long-term effects. Clinical trial number registration: NCT02974465 (ClinicalTrials.gov).

Treatment Effects of Upper Limb Action Observation Therapy and Mirror Therapy on Rehabilitation Outcomes after Subacute Stroke: A Pilot Study

Background

Action observation therapy and mirror therapy, two promising rehabilitation strategies, are aimed at enhancing the motor learning and functional improvement of stroke patients through different patterns of visual feedback and observation.

Objective

This study investigated and compared the treatment effects of the action observation therapy, mirror therapy, and active control intervention on motor and functional outcomes of stroke patients.

Methods

Twenty-one patients with subacute stroke were recruited in this study. All patients were randomly assigned to the action observation therapy, mirror therapy, or active control intervention for 3 weeks. Outcome measures were conducted at baseline, immediately after treatment, and at 3-month follow-up. The primary outcome was the Fugl-Meyer Assessment, and secondary outcomes included the Box and Block Test, Functional Independence Measure, and Stroke Impact Scale. Descriptive analyses and the number of patients whose change score achieved minimal clinically important difference were reported.

Results

Both the action observation therapy and active control intervention showed similar improvements on the Fugl-Meyer Assessment, Box and Block Test, and Stroke Impact Scale. Moreover, the action observation therapy had a greater improvement on the Functional Independence Measure than the other 2 groups did. However, the mirror therapy group gained the least improvements on the outcomes.

Conclusion

The preliminary results found that the patients in the action observation therapy and active control intervention groups had comparable benefits, suggesting that the 2 treatments might be used as an alternative to each other. A further large-scale study with at least 20 patients in each group to validate the study findings is needed. This trial is registered with NCT02871700.

Can robot-based measurements improve prediction of motor performance after robot-assisted upper-limb rehabilitation in patients with moderate-to-severe sub-acute stroke?

PURPOSE

Patients with moderate-to-severe stroke-related upper limb impairment can benefit from repetitive robot-assisted training. However, predicting motor performance in these patients from baseline measurements, including robot-based parameters would help clinicians to provide optimal treatments for each individual.

METHODS

Forty-six patients with sub-acute stroke underwent a 16-session upper limb rehabilitation combining usual care and robotic therapy. Motor outcomes (Fugl-Meyer Assessment Upper Extremity (FMA) score) were retrospectively analysed and potential predictors of motor outcome (including baseline FMA scores, kinematics and number of repetitions performed in the first session etc.) were determined.

RESULTS

The 16-sessions upper limb combined training program led to significantly improved clinical outcomes (gains of 13.8±11.2 for total FMA score and 7.3±6.7 for FMA Shoulder/Elbow score). For the prediction model, time since stroke poorly explained the FMA total score (R2 < 35%). The model however found that time since stroke and initial value of FMA Shoulder/Elbow score were predictors of the FMA Shoulder/Elbow score: (R2 = 59.6%).

CONCLUSION

This study found that clinical prediction of motor outcomes after moderate-to-severe upper-limb paresis is limited. However, initial proximal motor impairment severity predicted proximal motor performance. The value of baselines kinematics and of the number of repeated movements at initiation in the prediction would need further studies.

The unsolved role of heightened connectivity from the unaffected hemisphere to paretic arm muscles in chronic stroke

OBJECTIVE

Ipsilateral connectivity from the non-stroke hemisphere to paretic arm muscles appears to play little role in functional recovery, which instead depends on contralateral connectivity from the stroke hemisphere. Yet the incidence of ipsilateral projections in stroke survivors is often reported to be higher than normal. We tested this directly using a sensitive measure of connectivity to proximal arm muscles.

METHOD

TMS of the stroke and non-stroke motor cortex evoked responses in pre-activated triceps and deltoid muscles of 17 stroke survivors attending reaching training. Connectivity was defined as a clear MEP or a short-latency silent period in ongoing EMG in ≥ 50% of stimulations. We measured reaching accuracy at baseline, improvement after training and upper limb Fugl-Meyer (F-M) score.

RESULTS

Incidence of ipsilateral connections to triceps (47%) and deltoid (58%) was high, but unrelated to baseline reaching accuracy and F-M scores. Instead, these were related to contralateral connectivity from the stroke hemisphere. Absolute but not proportional improvement after training was greater in patients with ipsilateral responses.

CONCLUSIONS

Despite enhanced ipsilateral connectivity, arm function and learning was related most strongly to contralateral pathway integrity from the stroke hemisphere.

SIGNIFICANCE

Further work is needed to decipher the role of ipsilateral connections.

Relief from intractable phantom pain by combining psilocybin and mirror visual-feedback (MVF)

AL's leg was amputated resulting in phantom-limb pain (PLP). (1) When a volunteer placed her foot on or near the phantom - touching it evoked organized sensations in corresponding locations on AL's phantom. (2) Mirror-visual-feedback (MVF) relieved PLP, as did, "phantom massage". (3) Psilocybin-MVF pairing produced synergistic effects, complete elimination of PLP, and reduction in paroxysmal episodes. (4) Touching the volunteer's leg where AL previously had external fixators, evoked sensation of nails boring through the leg. Using a "telescoping" nail, we created the illusion of a nail being removed with corresponding pain relief. (5) Artificial flames produced warmth in the phantom.

Effects of mental practice combined with electromyogram-triggered electrical stimulation for upper extremity function in stroke patients

[Purpose] To investigate the effect of mental practice combined with electromyogram-triggered electrical stimulation (MP-EMG ES) on the upper extremity of stroke patients. [Subjects and Methods] Participants were randomly assigned to experimental group or control group. The experimental group received MP-EMG ES plus conventional rehabilitation therapy for 5 days per week for 4 weeks. The control group received only conventional rehabilitation therapy. Outcome measure included the Fugl-Meyer Assessment (FMA) and Motor Activity Log (MAL). [Results] Experimental group showed more improved in the FMA, MAL-AOU, MAL-QOM compared with the control group. [Conclusion] These results suggest that MP-EMG ES improves the upper extremity of subacute stroke patients better than conventional rehabilitation therapy alone.

Gross motor ability predicts response to upper extremity rehabilitation in chronic stroke

The majority of rehabilitation research focuses on the comparative effectiveness of different interventions in groups of patients, while much less is currently known regarding individual factors that predict response to rehabilitation. In a recent article, the authors presented a prognostic model to identify the sensorimotor characteristics predictive of the extent of motor recovery after Constraint-Induced Movement (CI) therapy amongst individuals with chronic mild-to-moderate motor deficit using the enhanced probabilistic neural network (EPNN). This follow-up paper examines which participant characteristics are robust predictors of rehabilitation response irrespective of the training modality. To accomplish this, EPNN was first applied to predict treatment response amongst individuals who received a virtual-reality gaming intervention (utilizing the same enrollment criteria as the prior study). The combinations of predictors that yield high predictive validity for both therapies, using their respective datasets, were then identified. High predictive classification accuracy was achieved for both the gaming (94.7%) and combined datasets (94.5%). Though CI therapy employed primarily fine-motor training tasks and the gaming intervention emphasized gross-motor practice, larger improvements in gross motor function were observed within both datasets. Poorer gross motor ability at pre-treatment predicted better rehabilitation response in both the gaming and combined datasets. The conclusion of this research is that for individuals with chronic mild-to-moderate upper extremity hemiparesis, residual deficits in gross motor function are highly responsive to motor restorative interventions, irrespective of the modality of training.

Ischemic Stroke

Each year, more than 795,000 people in the United States suffer a stroke and by 2030, it is estimated that 4% of the U.S. population will have had a stroke. Home healthcare clinicians will be increasingly called upon to assist stroke survivors and their caregivers adjust to disability and assist the survivor during their reintegration into the community. Therapeutic modalities are changing with advanced technology. Great strides are being made in the treatment of acute stroke; particularly endovascular interventions. More patients are surviving the acute stroke event and therefore will need to learn how to live with various degrees of disability. It is important for home healthcare clinicians to understand the process from acute event to medical stabilization, and from rehabilitation to long-term adaptation.

Baseline predictors of treatment gains in peak propulsive force in individuals poststroke

Background

Current rehabilitation for individuals poststroke focuses on increasing walking speed because it is an indicator of community walking ability and quality of life. Propulsive force generated from the paretic limb is critical to walking speed and may reflect actual neural recovery that restores the affected neural systems. A wide variation across individuals in the improvements in paretic propulsive force was observed following an intervention that targeted paretic propulsive force. This study aimed to determine if specific baseline characteristics can be used to predict patients who would respond to the intervention.

Methods

Participants (N = 19) with chronic poststroke hemiparesis walked at their self-selected and maximal walking speeds on a treadmill before and after a 12-week gait training program. Propulsive forces from the paretic limb were analyzed. Pearson correlation coefficient was used to determine the relationships between (1) treatment gains in walking speed and propulsive force following intervention, and (2) treatment gains in propulsive force and baseline propulsive forces.

Results

Treatment gains in self-selected walking speed were correlated to treatment gains in paretic propulsive force following intervention. In addition, changes in paretic propulsive force between self-selected and maximal walking speeds at baseline were strongly correlated to treatment gains in paretic propulsive force.

Conclusions

The capacity to modulate paretic propulsive force, rather than the absolute propulsive force during self-selected or maximal walking speed, predicted treatment gains in propulsive force following the intervention. Findings from this research could help to inform clinicians and researchers to target the appropriate patient population for rehabilitation interventions.