Predicting clinically significant changes in motor and functional outcomes after robot-assisted stroke rehabilitation

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.

Effects of an assist-as-needed equipped Tenodesis-Induced-Grip Exoskeleton Robot (TIGER) on upper limb function in patients with chronic stroke

BACKGROUND

The original version of the Tenodesis-Induced-Grip Exoskeleton Robot (TIGER) significantly improved the motor and functional performance of the affected upper extremity of chronic stroke patients. The assist-as-needed (AAN) technique in robot-involved therapy is widely favored for promoting patient active involvement, thereby fostering motor recovery. However, the TIGER lacked an AAN control strategy, which limited its use in different clinical applications. The present study aimed to develop and analyze the training effects of an AAN control mode to be integrated into the TIGER, to analyze the impact of baseline patient characteristics and training paradigms on outcomes for individuals with chronic stroke and to compare training effects on the upper limb function between using the AAN-equipped TIGER and using the original prototype.

METHODS

This was a single-arm prospective interventional study which was conducted at a university hospital. In addition to 20 min of regular task-specific motor training, each participant completed a 20-min robotic training program consisting of 10 min in the AAN control mode and 10 min in the functional mode. The training sessions took place twice a week for 9 weeks. The primary outcome was the change score of the Fugl-Meyer Assessment of the Upper Extremity (FMA-UE), and the secondary outcomes were the change score of the Box and Blocks Test (BBT), the amount of use (AOU) and quality of movement (QOM) scales of the Motor Activity Log (MAL), the Semmes-Weinstein Monofilament (SWM) test, and the Modified Ashworth Scale (MAS) for fingers and wrist joints. The Generalized Estimating Equations (GEE) and stepwise regression model were used as the statistical analysis methods.

RESULTS

Sixteen chronic stroke patients completed all steps of the study. The time from stroke onset to entry into the trial was 21.7 ± 18.9 months. After completing the training with the AAN-equipped TIGER, they exhibited significant improvements in movement reflected in their total score (pre/post values were 34.6 ± 11.5/38.5 ± 13.4) and all their sub-scores (pre/post values were 21.5 ± 6.0/23.3 ± 6.5, 9.5 ± 6.2/11.3 ± 7.2, and 3.6 ± 1.0/3.9 ± 1.0 for the shoulder, elbow, and forearm sub-category, the wrist and hand sub-category, and the coordination sub-category, respectively) on the FMA-UE (GEE, p < 0.05), as well as their scores on the BBT (pre/post values were 5.9 ± 6.5/9.5 ± 10.1; GEE, p = 0.004) and the AOU (pre/post values were 0.35 ± 0.50/0.48 ± 0.65; GEE, p = 0.02). However, the original TIGER exhibited greater improvements in their performance on the FMA-UE than the participants training with the AAN-equipped TIGER (GEE, p = 0.008). The baseline score for the wrist and hand sub-category of the FMA-UE was clearly the best predictor of TIGER-mediated improvements in hand function during the post-treatment assessment (adjusted R2 = 0.282, p = 0.001).

CONCLUSIONS

This study developed an AAN-equipped TIGER system and demonstrated its potential effects on improving both the function and activity level of the affected upper extremity of patients with stroke. Nevertheless, its training effects were not found to be advantageous to the original prototype. The baseline score for the FMA-UE sub-category of wrist and hand was the best predictor of improvements in hand function after TIGER rehabilitation. Clinical trial registration ClinicalTrials.gov, identifier NCT03713476; date of registration: October19, 2018. https://clinicaltrials.gov/ct2/show/NCT03713476.

Overview of the role of robots in upper limb disabilities rehabilitation: a scoping review

BACKGROUND

Neuromotor rehabilitation and improvement of upper limb functions are necessary to improve the life quality of patients who have experienced injuries or have pathological outcomes. Modern approaches, such as robotic-assisted rehabilitation can help to improve rehabilitation processes and thus improve upper limb functions. Therefore, the aim of this study was to investigate the role of robots in upper limb disability improvement and rehabilitation.

METHODS

This scoping review was conducted by search in PubMed, Web of Science, Scopus, and IEEE (January 2012- February 2022). Articles related to upper limb rehabilitation robots were selected. The methodological quality of all the included studies will be appraised using the Mixed Methods Appraisal Tool (MMAT). We used an 18-field data extraction form to extract data from articles and extracted the information such as study year, country, type of study, purpose, illness or accident leading to disability, level of disability, assistive technologies, number of participants in the study, sex, age, rehabilitated part of the upper limb using a robot, duration and frequency of treatment, methods of performing rehabilitation exercises, type of evaluation, number of participants in the evaluation process, duration of intervention, study outcomes, and study conclusions. The selection of articles and data extraction was made by three authors based on inclusion and exclusion criteria. Disagreements were resolved through consultation with the fifth author. Inclusion criteria were articles involving upper limb rehabilitation robots, articles about upper limb disability caused by any illness or injury, and articles published in English. Also, articles involving other than upper limb rehabilitation robots, robots related to rehabilitation of diseases other than upper limb, systematic reviews, reviews, and meta-analyses, books, book chapters, letters to the editor, and conference papers were also excluded. Descriptive statistics methods (frequency and percentage) were used to analyses the data.

RESULTS

We finally included 55 relevant articles. Most of the studies were done in Italy (33.82%). Most robots were used to rehabilitate stroke patients (80%). About 60.52% of the studies used games and virtual reality rehabilitate the upper limb disabilities using robots. Among the 14 types of applied evaluation methods, "evaluation and measurement of upper limb function and dexterity" was the most applied evaluation method. "Improvement in musculoskeletal functions", "no adverse effect on patients", and "Safe and reliable treatment" were the most cited outcomes, respectively.

CONCLUSIONS

Our findings show that robots can improve musculoskeletal functions (musculoskeletal strength, sensation, perception, vibration, muscle coordination, less spasticity, flexibility, and range of motion) and empower people by providing a variety of rehabilitation capabilities.

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.

Predictors for Upper-Limb Functional Recovery Trajectory in Individuals Receiving Stroke Rehabilitation: A Secondary Analysis of Data from Randomized Controlled Trials

BACKGROUND

The objective of the study was to determine predictors for upper-limb functional recovery trajectory after occupational therapy in a population with chronic stroke.

METHODS

In this retrospective secondary analysis, Fugl-Meyer Assessment-Upper Extremity (FMA-UE) scores before and after intervention and at the 3-month follow-up were used to divide 105 participants with chronic stroke into three groups of recovery trajectories: fast (participants who reached an improvement of 7 after intervention), extended (those who reached an improvement of 7 at follow-up), and limited (those who did not reach an improvement of 7) recovery. Comparisons among the three groups were made in demographics, stroke characteristics, and baseline assessment scores. Logistic regression analyses were performed to determine predictors for group membership.

RESULTS

Time after onset of stroke and the baseline scores of FMA-UE, Stroke Impact Scale-Hand (SIS-Hand), Wolf Motor Function Test (WMFT)-Quality, WMFT-Time scores, Motor Activity Log-Amount of Use (MAL-AOU), and Motor Activity Log-Quality of Movement (MAL-QOM) scores were significantly different among the three groups. Univariate logistic regressions confirmed that SIS-Hand, WMFT-Quality, WMFT-Time, MAL-AOU, and MAL-QOM were significant predictors for both the fast versus limited recovery group membership and the extended versus limited group membership. Time after stroke onset and baseline FMA-UE were additional predictors for the fast versus limited recovery group membership.

CONCLUSION

These findings may assist healthcare professionals in making optimal therapeutic decisions and in informing clients and caregivers about the outcomes of stroke recovery.

The structure, processes, and outcomes of stroke rehabilitation in Ghana: A study protocol

Background

Conventional and complementary treatments are often used in rehabilitation for persons with stroke. The conventional treatment makes use of medications, physiotherapy, occupational, speech, and diet therapies, while the complementary treatment makes use of homeopathy, naturopathy, massage, and acupuncture. The structure, process, and outcomes of stroke rehabilitation using conventional or complementary treatments have not been empirically investigated in Ghana.

Aims

This study aims to investigate the structure, process, and outcomes of stroke rehabilitation at the Korle Bu Teaching Hospital (KBTH) in Accra and Kwayisi Christian Herbal Clinic (KCHC) in Nankese-Ayisaa, Ghana, and to explore the experiences of persons with stroke.

Methods

This study involves a mixed methods approach. This study will utilize three study designs, namely, cross-sectional, hospital-based cohort, and qualitative exploratory study designs. The objectives of the study will be achieved using three phases, namely, phase one will recruit health professionals and gather information on the structure and process of stroke rehabilitation at a conventional and complementary hospital using adapted questionnaires; phase two will determine the outcomes of stroke patients attending a conventional and complementary hospital facility at baseline, 2-, 3-, and 6-month follow-up using outcome measures based on the International Classification of Functioning, Disability and Health (ICF) model; and phase three will explore the experiences of stroke patients who use complementary or conventional treatment using an interview guide.

Data analysis

IBM SPSS Statistics Version 27 will be used to analyze the data using descriptive and inferential statistics. Repeated measures of ANOVA will be used to determine the differences between variables at baseline, 2-, 3-, and 6-month post-stroke. The qualitative data will be transcribed and entered into Atlas Ti version 9.0. The data will be coded and analyzed using thematic areas that will be generated from the codes.

Conclusion

The study protocol will provide a comprehensive overview of the structure, process, and outcomes of stroke rehabilitation in Ghana, incorporating both conventional and complementary treatment and rehabilitation into the stroke recovery journey. It will also inform clinical practice, with new insights on the experiences of stroke patients based on their choice of rehabilitation pathway.

Retrospective Robot-Measured Upper Limb Kinematic Data From Stroke Patients Are Novel Biomarkers

Background: The efficacy of upper-limb Robot-assisted Therapy (ulRT) in stroke subjects is well-established. The robot-measured kinematic data can assess the biomechanical changes induced by ulRT and the progress of patient over time. However, literature on the analysis of pre-treatment kinematic parameters as predictive biomarkers of upper limb recovery is limited.

Objective: The aim of this study was to calculate pre-treatment kinematic parameters from point-to-point reaching movements in different directions and to identify biomarkers of upper-limb motor recovery in subacute stroke subjects after ulRT.

Methods: An observational retrospective study was conducted on 66 subacute stroke subjects who underwent ulRT with an end-effector robot. Kinematic parameters were calculated from the robot-measured trajectories during movements in different directions. A Generalized Linear Model (GLM) was applied considering the post-treatment Upper Limb Motricity Index and the kinematic parameters (from demanding directions of movement) as dependent variables, and the pre-treatment kinematic parameters as independent variables.

Results: A subset of kinematic parameters significantly predicted the motor impairment after ulRT: the accuracy in adduction and internal rotation movements of the shoulder was the major predictor of post-treatment Upper Limb Motricity Index. The post-treatment kinematic parameters of the most demanding directions of movement significantly depended on the ability to execute elbow flexion-extension and abduction and external rotation movements of the shoulder at baseline.

Conclusions: The multidirectional analysis of robot-measured kinematic data predicts motor recovery in subacute stroke survivors and paves the way in identifying subjects who may benefit more from ulRT.

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.

Predicting Clinically Significant Improvement After Robot-Assisted Upper Limb Rehabilitation in Subacute and Chronic Stroke

Prior studies examining predictors of favorable clinical outcomes after upper limb robot-assisted therapy (RT) have many shortcomings. Therefore, the aim of this study was to identify meaningful predictors and a prediction model for clinically significant motor improvement in upper limb impairment after RT for each stroke phase. This retrospective, single-center study enrolled patients with stroke who received RT using InMotion2 along with conventional therapy (CT) from January 2015 to September 2019. Demographic characteristics, clinical measures, and robotic kinematic measures were evaluated. The primary outcome measure was the Fugl-Meyer Assessment-Upper Extremity (FMA-UE) and we classified patients with improvement more than the minimal clinically important difference as responders for each stroke phase. Univariable and multivariable logistic regression analyses were performed to assess the relationship between potential predictors and RT responders and determine meaningful predictors. Subsequently, meaningful predictors were included in the final prediction model. One hundred forty-four patients were enrolled. The Hand Movement Scale and time since onset were significant predictors of clinically significant improvement in upper limb impairment (P = 0.045 and 0.043, respectively), as represented by the FMA-UE score after RT along with CT, in patients with subacute stroke. These variables were also meaningful predictors with borderline statistical significance in patients with chronic stroke (P = 0.076 and 0.066, respectively). Better hand movement and a shorter time since onset can be used as realistic predictors of clinically significant motor improvement in upper limb impairment after RT with InMotion2 alongside CT in patients with subacute and chronic stroke. This information may help healthcare professionals discern optimal patients for RT and accurately inform patients and caregivers about outcomes of RT.

The effect of Electromyography (EMG)-driven Robotic Treatment on the recovery of the hand Nine years after stroke

OBJECTIVE

To investigate the effect of electromyography (EMG)-driven robotic therapy on the recovery of the hand in a stroke case lasting 9 years.

CASE

An 18-year-old patient with hemiparesis due to the ischemic lesion was admitted to our clinic with hand impairment. Fifteen sessions (5 weeks x 3 times) of robotic rehabilitation were applied with the Hand of Hope. Average EMG (mV) of flexor digitorum superficialis (FDS) muscle, average force (N) and the rate of force development (RFD)(N/s) were also assessed before and after the treatment following the 5th and 10th sessions and at the end of treatment. Also, Fugl-Meyer Assessment of Upper Extremity Scale (FMU-UE), Motor Activity Log (MAL), Canadian Occupational Performance Score (COPM) and Visual Analog Scale (VAS) were used for assessment before and after the treatment.

RESULTS

The average EMG measured from FDS increased from 0.093-0.133 mV. The average force and average RFD increased from 45.6-97.7 and from 135.6-172.6 respectively. While affected and/or unaffected side force ratio increased dramatically from 54%-82%, the FMA-UE score increased from 56-59. The MAL quality of use score increased from 3.93-4.13. Performance and satisfaction scores of COPM changed from 5.25-7.25 and 4.5-8.25 respectively. VAS score for fatigue changed from 6 to 4.

DISCUSSION

The improvement achieved 9 years later with 15 sessions of rehabilitation suggests that improvement may be possible for chronic stroke patients.

Effects of Robot-Assisted Rehabilitation on Hand Function of People With Stroke: A Randomized, Crossover-Controlled, Assessor-Blinded Study

IMPORTANCE

The effects of robot-assisted task-oriented training with tangible objects among patients with stroke remain unknown.

OBJECTIVE

To investigate the effects of robot-assisted therapy (RT) with a Gloreha device on sensorimotor and hand function and ability to perform activities of daily living (ADLs) among patients with stroke.

DESIGN

Randomized, crossover-controlled, assessor-blinded study.

SETTING

Rehabilitation clinic.

PARTICIPANTS

Patients (N = 24) with moderate motor and sensory deficits.

INTERVENTION

Patients participated in 12 RT sessions and 12 conventional therapy (CT) sessions, with order counterbalanced, for 6 wk, with a 1-mo washout period.

OUTCOMES AND MEASURES

Performance was assessed four times: before and after RT and before and after CT. Outcomes were measured using the Fugl-Meyer Assessment-Upper Extremity (FMA-UE), Box and Block Test, electromyography of the extensor digitorum communis (EDC) and brachioradialis, and a grip dynamometer for motor function; Semmes-Weinstein hand monofilament and the Revised Nottingham Sensory Assessment for sensory function; and the Modified Barthel Index (MBI) for ADL ability.

RESULTS

RT resulted in significantly improved FMA-UE proximal (p = .038) and total (p = .046) and MBI (p = .030) scores. Participants' EDC muscles exhibited higher efficacy during the small-block grasping task of the Box and Block Test after RT than after CT (p = .050).

CONCLUSIONS AND RELEVANCE

RT with the Gloreha device can facilitate whole-limb function, leading to beneficial effects on arm motor function, EDC muscle recruitment efficacy, and ADL ability for people with subacute and chronic stroke.

WHAT THIS ARTICLE ADDS

The evidence suggests that a task-oriented approach combined with the Gloreha device can facilitate engagement in whole-limb active movement and efficiently promote functional recovery.

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.

Does Upper Extremity Proprioceptive Training Have an Impact on Functional Outcomes in Chronic Stroke Patients?

Objective

We aimed to investigate the effect of upper extremity proprioceptive training on spasticity and functional motor skills in patients with chronic hemiplegia occurring after stroke.

Method

Thirty chronic hemiplegic patients (17 females, mean age: 66.47±12.55 years) admitted to the Research Center with a diagnosis of chronic hemiplegia developed after stroke were included in the study. Patients were divided into two groups. The first group received a conventional physiotherapy program (PTR) for 5 days a week and the second group additionally received a proprioceptive training program (PTR-PT) for 5 days a week. Before and 6 weeks after the treatment modified Ashworth scale (MAS), Fugl-Meyer upper extremity motor evaluation scale (FMA) and action-research-arm-test (ARAT) and motor activity log-28 scale (MAL-28) were applied. SSPS-22.0 program was used for statistical evaluation and p <0.05 was considered as the level of statistical significance.

Results

There was no difference in MAS scores before and after treatment in the groups (p>0.05). There was a statistically significant improvement in both PTR (p<0.05) and PTR-PT groups (p<0.001) for the FMA, ARAT and MAL-28. scale scores. Although the results obtained in the PTR, and PT groups were more improved, there was a significant result in favor of PTR-PT only regarding the MAL-28 scale scores (p<0.05). It was determined that adding proprioception-based exercises had the greatest effect on FMA, ARAT and MAL-28 in the evaluation of the effect size (>0.3).

Conclusion

It was observed that upper extremity proprioceptive training yielded better results in patients with chronic hemiplegia developed after stroke than conventional therapy in increasing the frequency and quality of movement in upper extremity. This result shows that proprioceptive training programs should be added to stroke rehabilitation methods.

Predictors of functional and motor outcomes following upper limb robot-assisted therapy after stroke

Robot-assisted therapy is an effective treatment for stroke patients and has recently gained popularity. Clinicians and researchers are trying to identify predictors to stratify patients for ensuring better stroke rehabilitation outcomes. However, previous studies have reported controversial results regarding the predictors of upper limb recovery after robot-assisted therapy. Our objective was to determine whether the demographic and clinical characteristics of stroke patients influence the motor and functional outcomes after robot-assisted therapy. We conducted a retrospective analysis of 48 hemiplegic patients who performed upper limb goal-directed tasks using RAPAEL Smart Glove (Neofect, Gyeonggi-do, Republic of Korea). Robot-assisted therapy was administered for 5 days a week over 4 weeks, and each session was for 30 minutes. The parameters of the primary outcomes after robot-assisted therapy were measured with the manual function test and functional independence measure. Correlation analysis showed that age, initial cognitive function, and the initial manual function test and the Modified Ashworth Scale for upper extremity scores were significant factors for independently predicting functional outcomes after robot-assisted therapy. Linear regression analysis revealed that the initial Mini-Mental State Examination (P < 0.001) and initial manual function test (P < 0.001) scores were significant predictors of the primary outcomes. In conclusion, our study suggests that stroke patients presenting with less spasticity, better initial cognitive function, and better initial motor function have a significant correlation with the functional outcomes after robot-assisted therapy.

Predictors of Clinically Important Changes in Actual and Perceived Functional Arm Use of the Affected Upper Limb After Rehabilitative Therapy in Chronic Stroke

OBJECTIVE

To identify the predictors of minimal clinically important changes in actual and perceived functional arm use of the affected upper limb after rehabilitative therapy.

DESIGN

Retrospective, observational cohort study.

SETTING

Outpatient rehabilitation settings.

PARTICIPANTS

A cohort of 94 patients with chronic stroke.

INTERVENTIONS

Patients received robot-assisted therapy, mirror therapy, or combined therapy for 4 weeks.

MAIN OUTCOME MEASURES

The primary outcome measures, assessed pre- and post intervention, included actual functional arm use measured by an accelerometer and perceived functional arm use measured by the Motor Activity Log (MAL). Candidate predictors included age, sex, time after stroke, side of stroke, and scores on the Fugl-Meyer Assessment, Modified Ashworth Scale, Medical Research Council scale, Wolf Motor Function Test, MAL (quality of movement), and Nottingham Extended Activities of Daily Living.

RESULTS

Being male (odds ratio [OR], 3.17; 95% CI, 1.13-8.87) and having a higher than median Medical Research Council score (OR, 2.68; 95% CI, 1.12-6.41) significantly predicted minimal clinically important changes assessed by an accelerometer. Fugl-Meyer Assessment scores (odds ratio, 1.06; 95% CI, 1.02-1.11) were a significant predictor of achieving clinically important changes in MAL amount of use. Wolf Motor Function Test (quality) scores (OR, 3.05; 95% CI, 1.38-6.77) could predict clinically important improvements in MAL quality of movement.

CONCLUSIONS

Predictors of clinically important changes in the use of the affected upper limb after robot-assisted therapy, mirror therapy, or combined therapy in patients with chronic stroke for 4 weeks differ for actual vs perceived use. Further studies are recommended to validate these findings in a larger sample.

Support Vector Machine-Based Classifier for the Assessment of Finger Movement of Stroke Patients Undergoing Rehabilitation

Purpose

Traditionally, clinical evaluation of motor paralysis following stroke has been of value to physicians and therapists because it allows for immediate pathophysiological assessment without the need for specialized tools. However, current clinical methods do not provide objective quantification of movement; therefore, they are of limited use to physicians and therapists when assessing responses to rehabilitation. The present study aimed to create a support vector machine (SVM)-based classifier to analyze and validate finger kinematics using the leap motion controller. Results were compared with those of 24 stroke patients assessed by therapists.

Methods

A non-linear SVM was used to classify data according to the Brunnstrom recovery stages of finger movements by focusing on peak angle and peak velocity patterns during finger flexion and extension. One thousand bootstrap data values were generated by randomly drawing a series of sample data from the actual normalized kinematics-related data. Bootstrap data values were randomly classified into training (940) and testing (60) datasets. After establishing an SVM classification model by training with the normalized kinematics-related parameters of peak angle and peak velocity, the testing dataset was assigned to predict classification of paralytic movements.

Results

High separation accuracy was obtained (mean 0.863; 95% confidence interval 0.857–0.869; p = 0.006).

Conclusion

This study highlights the ability of artificial intelligence to assist physicians and therapists evaluating hand movement recovery of stroke patients.

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.

Relative Efficacy of Sensor Modalities for Estimating Post-Stroke Motor Impairment

Wearable inertial sensing has been beneficial in the development of measures of motor impairment after stroke. While most early work focused on the use of accelerometry, recent work has increasingly shown that rate gyroscopes may provide complementary information. Differences in performance of accelerometers and gyroscopes in activity recognition may be due to the nature of the impairment. The current approach seeks to investigate the relative sensitivity of these sensor modalities to impairment by evaluating their classification accuracy for tasks adapted from the Fugl-Meyer Assessment. Our findings indicated that, for upper-extremity motion, classifiers trained using a combination of accelerometer and rate gyroscope data performed the best (accuracy of 73.1%). Classifiers trained using accelerometer data alone and rate gyroscope data alone performed slightly worse than the combined data classifier (70.2% and 65.7%, respectively).

Predictors of activities of daily living outcomes after upper limb robot-assisted therapy in subacute stroke patients

BACKGROUND

Upper limb recovery is one of the main goals of post-stroke rehabilitation due to its importance for autonomy in Activities of Daily Living (ADL). Although the efficacy of upper limb Robot-assisted Therapy (RT) is well established in literature, the impact of the initial status of the patient on the effects of RT is still understudied. This paper aims to identify whether demographic, clinical and motor characteristics of stroke patients may influence the ability to independently perform ADL after RT.

METHODS

A retrospective study was conducted on sixty stroke patients who conducted planar upper limb goal-directed tasks with the InMotion 2.0 robot. The RT was administered 5 days/week for 4 weeks and each session lasted 45 minutes. The primary outcome measure was the Modified Barthel Index (BI), dichotomized into favourable (BI ≥75) and unfavourable (BI<75) outcomes. The potential predictors were the demographic and clinical records, and the following clinical assessment scores: Modified Ashworth Scale-Shoulder (MAS-S); Modified Ashworth Scale-Elbow (MAS-E); Fugl-Meyer Assessment Upper Extremity (FMA-UE); upper limb section of the Motricity Index (MIul); total passive Range Of Motion (pROM); and Box and Block Test (BBT).

RESULTS

Statistical analysis showed that the BBT, FMA-UE and MIul scores were significant predictors of a favourable outcome in ADL. The cut-off scores of the independent variables were calculated (FMA-UE = 32; MIul = 48; BBT = 3) with respect to the dichotomic BI outcome. Their robustness was assessed with the Fragility Index (FMA-UE = 2; MIul = 3; BBT = 7), showing that BBT is the most robust predictor of favourable BI outcome. Moreover, subjects with all predictors higher than the cut-off scores had higher probability to increase their independence in ADL at the end of the therapy. Demographic records, spasticity and pROM were not identified as predictors.

CONCLUSION

Stroke patients with greater manual dexterity and less impairment appear to have a higher probability of achieving clinically significant ADL outcomes after upper limb RT. The obtained results can help to optimise the management of RT treatment planning. Further studies on a larger number of patients with a long-term follow up are recommended in order to evaluate other potential predictors and to validate the results.

Robot-assisted post-stroke motion rehabilitation in upper extremities: a survey

Recent neurological research indicates that the impaired motor skills of post-stroke patients can be enhanced and possibly restored through task-oriented repetitive training. This is due to neuroplasticity – the ability of the brain to change through adulthood. Various rehabilitation processes have been developed to take advantage of neuroplasticity to retrain neural pathways and restore or improve motor skills lost as a result of stroke or spinal cord injuries (SCI). Research in this area over the last few decades has resulted in a better understanding of the dynamics of rehabilitation in post-stroke patients and development of auxiliary devices and tools to induce repeated targeted body movements. With the growing number of stroke rehabilitation therapies, the application of robotics within the rehabilitation process has received much attention. As such, numerous mechanical and robot-assisted upper limb and hand function training devices have been proposed. A systematic review of robotic-assisted upper extremity (UE) motion rehabilitation therapies was carried out in this study. The strengths and limitations of each method and its effectiveness in arm and hand function recovery were evaluated. The study provides a comparative analysis of the latest developments and trends in this field, and assists in identifying research gaps and potential future work.

Factors influencing the response to high-frequency repetitive transcranial magnetic stimulation in patients with subacute stroke

BACKGROUND AND PURPOSE

High-frequency repetitive transcranial magnetic stimulation (rTMS) aids motor recovery in patients with subacute stroke. However, the response to high-frequency rTMS is highly variable between patients. The aim of this study was to identify the factors associated with improved motor function in response to high-frequency rTMS in subacute stroke patients with moderate to severe upper extremity motor involvement.

METHODS

Sixty-two patients with subacute stroke were enrolled. rTMS was applied over the primary motor cortex of the affected hemisphere at 10 Hz with 1,000 pulses/day for 10 days. Upper limb motor function was scored with the upper limb of Fugl-Meyer Assessment (FMA-UL) score before intervention and immediately after intervention. All FMA-UL changes greater than or equal to 5 points were considered clinically significant. Potential influencing factors considered included patient characteristics, motor function, corticospinal tract (CST) integrity assessments, and genetics. Multivariate logistic regression analysis was used to identify the significance of each of these factors.

RESULTS

We found that the two factors with the greatest impact on the improvement in FMA-UL score were: 1) the functional integrity of the CST, and 2) the brain-derived neurotrophic factor (BDNF) genotype (p < 0.05). Age tended to influence clinically significant changes in the FMA-UL score, although this effect was not significant.

CONCLUSION

Our findings suggest that high-frequency rTMS interventions aimed at improving upper extremity motor function in patients with subacute stroke with moderate to severe motor involvement should be individually tailored according to functional CST status and BDNF genotype.

tDCS and Robotics on Upper Limb Stroke Rehabilitation: Effect Modification by Stroke Duration and Type of Stroke

Objective. The aim of this exploratory pilot study is to test the effects of bilateral tDCS combined with upper extremity robot-assisted therapy (RAT) on stroke survivors. Methods. We enrolled 23 subjects who were allocated to 2 groups: RAT + real tDCS and RAT + sham-tDCS. Each patient underwent 10 sessions (5 sessions/week) over two weeks. Outcome measures were collected before and after treatment: (i) Fugl-Meyer Assessment-Upper Extremity (FMA-UE), (ii) Box and Block Test (BBT), and (iii) Motor Activity Log (MAL). Results. Both groups reported a significant improvement in FMA-UE score after treatment (p < 0.01). No significant between-groups differences were found in motor function. However, when the analysis was adjusted for stroke type and duration, a significant interaction effect (p < 0.05) was detected, showing that stroke duration (acute versus chronic) and type (cortical versus subcortical) modify the effect of tDCS and robotics on motor function. Patients with chronic and subcortical stroke benefited more from the treatments than patients with acute and cortical stroke, who presented very small changes. Conclusion. The additional use of bilateral tDCS to RAT seems to have a significant beneficial effect depending on the duration and type of stroke. These results should be verified by additional confirmatory studies.

Kinematic measures of Arm-trunk movements during unilateral and bilateral reaching predict clinically important change in perceived arm use in daily activities after intensive stroke rehabilitation

Background

Kinematic analysis has been used to objectively evaluate movement patterns, quality, and strategies during reaching tasks. However, no study has investigated whether kinematic variables during unilateral and bilateral reaching tasks predict a patient’s perceived arm use during activities of daily living (ADL) after an intensive intervention. Therefore, this study investigated whether kinematic measures during unilateral and bilateral reaching tasks before an intervention can predict clinically meaningful improvement in perceived arm use during ADL after intensive poststroke rehabilitation.

Methods

The study was a secondary analysis of 120 subjects with chronic stroke who received 90–120 min of intensive intervention every weekday for 3–4 weeks. Reaching kinematics during unilateral and bilateral tasks and the Motor Activity Log (MAL) were evaluated before and after the intervention.

Results

Kinematic variables explained 22 and 11 % of the variance in actual amount of use (AOU) and quality of movement (QOM), respectively, of MAL improvement during unilateral reaching tasks. Kinematic variables also explained 21 and 31 % of the variance in MAL-AOU and MAL-QOM, respectively, during bilateral reaching tasks. Selected kinematic variables, including endpoint variables, trunk involvement, and joint recruitment and interjoint coordination, were significant predictors for improvement in perceived arm use during ADL (P < 0.05).

Conclusions

Arm–trunk kinematics may be used to predict clinically meaningful improvement in perceived arm use during ADL after intensive rehabilitation. Involvement of interjoint coordination and trunk control variables as predictors in bilateral reaching models indicates that a high level of motor control (i.e., multijoint coordination) and trunk stability may be important in obtaining treatment gains in arm use, especially for bilateral daily activities, in intensive rehabilitation after stroke.

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

OBJECTIVE

To identify the baseline motor characteristics of the patients who responded to 3 prominent intervention programs.

DESIGN

Observational cohort study.

SETTING

Outpatient rehabilitation clinics.

PARTICIPANTS

Individuals with chronic stroke (N=174).

INTERVENTIONS

Participants received 30 hours of constraint-induced movement therapy (CIMT), robot-assisted therapy, or mirror therapy (MT).

MAIN OUTCOME MEASURES

The primary outcome measure was the change score of the Upper Extremity Fugl-Meyer Assessment (UE-FMA). The potential predicting variables were baseline proximal, distal, and total UE-FMA and Action Research Arm Test scores. We combined polynomial regression analyses and the minimal clinically important difference to stratify the patients as responders and nonresponders for each intervention approach.

RESULTS

Baseline proximal UE-FMA scores significantly predicted clinically important improvement on the primary outcome measure after all 3 interventions. Participants with baseline proximal UE-FMA scores of approximately <30 benefited significantly from CIMT and robot-assisted therapy, whereas participants with scores between 21 and 35 demonstrated significant improvement after MT. Baseline distal and total UE-FMA and Action Research Arm Test scores could also predict upper limb improvement after CIMT and MT, but not after robot-assisted therapy.

CONCLUSIONS

This study could inform clinicians about the selection of suitable rehabilitation approaches to help patients achieve clinically meaningful improvement in upper extremity function.

Predictors of poststroke health-related quality of life in Nigerian stroke survivors: a 1-year follow-up study

This study aims to identify the predictors in the different aspects of the health-related quality of life (HRQoL) and to measure the changes of functional status over time in a cohort of Nigerian stroke survivors. A prospective observational study was conducted in three hospitals of Kano state of Nigeria where stroke survivors receive rehabilitation. The linguistic-validated Hausa versions of the stroke impact scale 3.0, modified Rankin scale, Barthel index and Beck depression inventory scales were used. Paired samples t-test was used to calculate the amount of changes that occur over time and the forward stepwise linear regression model was used to identify the predictors. A total of 233 stroke survivors were surveyed at 6 months, and 93% (217/233) were followed at 1 year after stroke. Functional disabilities were significantly reduced during the recovery phase. Motor impairment, disability, and level of depression were independent predictors of HRQoL in the multivariate regression analysis. The involvement of family members as caregivers is the key factor for those survivors with improved functional status. Thus, to enhance the quality of poststroke life, it is proposed that a holistic stroke rehabilitation service and an active involvement of family members are established at every possible level.

Determinants of subjective health-related quality of life (HRQoL) for patients with schizophrenia

PURPOSE

To identify the determinants of schizophrenia-specific HRQoL levels, five types of factors (i.e., sociodemographic, clinical, psychopathological, neurocognitive, and psychosocial factors) were simultaneously investigated in the same cross-sectional sample.

METHODS

A total of 120 patients with a diagnosis of schizophrenia but not spectrum conditions were recruited by convenience sampling. Subjective HRQoL levels were measured using the disease-specific S-QoL-C. After sociodemographic and clinical data were collected, psychopathological data were self-rated with the Beck Depression Inventory-II (BDI-II) and were assessed with the Positive and Negative Syndrome Scale (PANSS) by professionally trained raters. Two neurocognitive assessments were conducted by licensed occupational therapists (OTs). Psychosocial factors were assessed using self-reports measures, including the, General Self-Efficacy Scale (GSES), Rosenberg Self-Esteem Scale (RSES), and Social Impact Scale (SIS). All measures were administered in random order. OTs, PANSS raters, and participants were blinded to score computation, and multiple hierarchical regression with the stepwise method was conducted.

RESULTS

The S-QoL-C scores were most strongly affected by psychosocial factors and the psychopathological factors, followed by clinical and sociodemographic factors. Total scores on the BDI-II had the largest contributions to S-QoL-C index scores and seven of eight S-QoL-C subscales. In addition, the GSES, RSES, and SIS showed effects across the S-QoL-C subscales. The BDI-II, GSES, and RSES all influenced the S-QoL-C index scores, in addition to the number of hospitalizations.

CONCLUSION

Psychosocial factors and psychopathological factors measured by the BDI-II had the greatest impact on schizophrenia-specific HRQoL levels. Psychiatric treatment programs focusing on psychosocial status and depressive symptoms can improve schizophrenia-specific HRQoL levels.