Introduction. Telerehabilitation enables patients to access remote rehabilitation services for patient-physiotherapist videoconferencing in their own homes. Home-based virtual reality (VR) balance training has been shown to reduce postural instability in patients with Parkinson’s disease (PD). The primary aim was to compare improvements in postural stability after remotely supervised in-home VR balance training and in-clinic sensory integration balance training (SIBT). Methods. In this multicenter study, 76 PD patients (modified Hoehn and Yahr stages 2.5–3) were randomly assigned to receive either in-home VR telerehabilitation (n=38) or in-clinic SIBT (n=38) in 21 sessions of 50 minutes each, 3 days/week for 7 consecutive weeks. VR telerehabilitation consisted of graded exergames using the Nintendo Wii Fit system; SIBT included exercises to improve postural stability. Patients were evaluated before treatment, after treatment, and at 1-month follow-up. Results. Analysis revealed significant between-group differences in improvement on the Berg Balance Scale for the VR telerehabilitation group (p=0.04) and significant Time × Group interactions in the Dynamic Gait Index (p=0.04) for the in-clinic group. Both groups showed differences in all outcome measures over time, except for fall frequency. Cost comparison yielded between-group differences in treatment and equipment costs. Conclusions. VR is a feasible alternative to in-clinic SIBT for reducing postural instability in PD patients having a caregiver.

While stroke is one of the leading causes of disability, the prediction of upper limb (UL) functional recovery following rehabilitation is still unsatisfactory, hampered by the clinical complexity of post-stroke impairment. Predictive models leading to accurate estimates while revealing which features contribute most to the predictions are the key to unveil the mechanisms subserving the post-intervention recovery, prompting a new focus on individualized treatments and precision medicine in stroke. Machine learning (ML) and explainable artificial intelligence (XAI) are emerging as the enabling technology in different fields, being promising tools also in clinics. In this study, we had the twofold goal of evaluating whether ML can allow to derive accurate predictions of UL recovery in sub-acute patients, and disentangling the contribution of the variables shaping the outcomes. To do so, Random Forest equipped with four XAI methods was applied to interpret the results and assess the feature relevance and their consensus. Our results revealed increased performance when using ML compared to conventional statistical approaches. Moreover, the features deemed as the most relevant were concordant across the XAI methods, suggesting a good stability of the results. In particular, the baseline motor impairment as measured by simple clinical scales had the largest impact, as expected. Our findings highlight the core role of ML not only for accurately predicting the individual follow-up outcome scores after rehabilitation, but also for making ML results interpretable when associated to XAI methods. This provides clinicians with robust predictions and reliable explanations that are key factors in therapeutic planning/monitoring of stroke patients.

This study aims at exploring disability, health-related quality of life (HrQoL), psychological distress, and psychological features in post-stroke patients with chronic pain. An observational cross-sectional study involving 50 post-stroke patients (25 with chronic pain and 25 without pain) was conducted. The primary outcome was the self-reported level of disability and HrQoL which were both assessed through the Stroke Impact Scale 3.0. Both psychological distress and specific psychological features (i.e., self-efficacy, coping strategies, psychological flexibility, perceived social support) were examined. Post-stroke patients with chronic pain reported statistically significant higher levels of disability and worse HrQoL, higher psychological distress and inflexibility, as well as a lower level of self-efficacy and problem-oriented coping strategies than patients without pain (p < 0.001). Finally, correlation analysis in the group of stroke survivors with pain showed that higher levels of disability were significantly related to higher psychological distress. This study confirms the negative influence of chronic pain on disability and HrQoL in post-stroke patients and presents preliminary insights on the association between chronic pain, disability, HrQoL, psychosocial distress, and the patient’s approach in dealing with personal difficulties and emotions. These findings carry further implications for multidisciplinary management of post-stroke patients with chronic pain.

Background: The combined use of Robot-assisted UL training and Botulinum toxin (BoNT) appear to be a promising therapeutic synergism to improve UL function in chronic stroke patients. Objective: To evaluate the effects of Robot-assisted UL training on UL spasticity, function, muscle strength and the electromyographic UL muscles activity in chronic stroke patients treated with Botulinum toxin. Methods: This single-blind, randomized, controlled trial involved 32 chronic stroke outpatients with UL spastic hemiparesis. The experimental group (n = 16) received robot-assisted UL training and BoNT treatment. The control group (n = 16) received conventional treatment combined with BoNT treatment. Training protocols lasted for 5 weeks (45 min/session, two sessions/week). Before and after rehabilitation, a blinded rater evaluated patients. The primary outcome was the Modified Ashworth Scale (MAS). Secondary outcomes were the Fugl-Meyer Assessment Scale (FMA) and the Medical Research Council Scale (MRC). The electromyographic activity of 5 UL muscles during the “hand-to-mouth” task was explored only in the experimental group and 14 healthy age-matched controls using a surface Electromyography (EMGs). Results: No significant between-group differences on the MAS and FMA were measured. The experimental group reported significantly greater improvements on UL muscle strength (p = 0.004; Cohen's d = 0.49), shoulder abduction (p = 0.039; Cohen's d = 0.42), external rotation (p = 0.019; Cohen's d = 0.72), and elbow flexion (p = 0.043; Cohen's d = 1.15) than the control group. Preliminary observation of muscular activity showed a different enhancement of the biceps brachii activation after the robot-assisted training. Conclusions: Robot-assisted training is as effective as conventional training on muscle tone reduction when combined with Botulinum toxin in chronic stroke patients with UL spasticity. However, only the robot-assisted UL training contributed to improving muscle strength. The single-group analysis and the qualitative inspection of sEMG data performed in the experimental group showed improvement in the agonist muscles activity during the hand-to-mouth task. Clinical Trial Registration: www.ClinicalTrials.gov, identifier: NCT03590314

Article: State of the art and challenges for the classification of studies on electromechanical and robotic devices in neurorehabilitation: a scoping review - European Journal of Physical and Rehabilitation Medicine 2021 October;57(5):831-40 - Minerva Medica - Journals