Symmetrical and bilateral robotic practice, combined with functional task training, can significantly improve motor function, arm activity, and self-perceived bilateral arm ability in patients late after stroke.

Higher intensity of RT that assists forearm and wrist movements may lead to greater improvement in motor ability and functional performance in stroke patients. A sample size of only 20 to 25 in each arm of a larger randomized controlled trial is needed to confirm the findings for similar subjects.

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.

Background: The timing of transcranial direct current stimulation (tDCS) with neurorehabilitation interventions may affect its modulatory effects. Motor function has been reported to be modulated by the timing of tDCS; however, whether the timing of tDCS would also affect restoration of daily function and upper extremity motor control with neurorehabilitation in stroke patients remains largely unexplored. Mirror therapy (MT) is a potentially effective neurorehabilitation approach for improving paretic arm function in stroke patients. This study aimed to determine whether the timing of tDCS with MT would influence treatment effects on daily function, motor function and motor control in individuals with chronic stroke. Methods: This study was a double-blinded randomized controlled trial. Twenty-eight individuals with chronic stroke received one of the following three interventions: (1) sequentially combined tDCS with MT (SEQ), (2) concurrently combined tDCS with MT (CON), and (3) sham tDCS with MT (SHAM). Participants received interventions for 90 min/day, 5 days/week for 4 weeks. Daily function was assessed using the Nottingham Extended Activities of Daily Living Scale. Upper extremity motor function was assessed using the Fugl-Meyer Assessment Scale. Upper extremity motor control was evaluated using movement kinematic assessments. Results: There were significant differences in daily function between the three groups. The SEQ group had greater improvement in daily function than the CON and SHAM groups. Kinematic analyses showed that movement time of the paretic hand significantly reduced in the SEQ group after interventions. All three groups had significant improvement in motor function from pre-intervention to post-intervention.

No abstract