A robot-based interception task to quantify upper limb impairments in proprioceptive and visual feedback after stroke

The effect of body weight-supported Tai Chi Yunshou on upper limb motor function in stroke survivors based on neurobiomechanical analysis: a four-arm, parallel-group, assessors-blind randomized controlled trial protocol

Introduction

A series of functional disorders commonly occur after stroke, of which upper limb dysfunction is the most difficult to recover. The upper limb rehabilitation effect of Tai Chi Yunshou(TCY) in the later stage of stroke has been confirmed by research. Body weight support-Tai Chi Yunshou (BWS-TCY) is based on TCY exercise and robotic exoskeletons offers most flexibility in deweighting and control strategy. This study is aimed to explore the effect of BWS-TCY on upper limb motor function in stroke based on neurobiomechanics.

Methods and analysis

A single-blind randomized controlled trial will be conducted on 36 stroke survivors who will be randomly assigned to three groups: experimental group, control group A and control group B. In addition, 12 healthy elderly people will be recruited into the healthy control group. Those in the experimental group will receive 20 min of CRT and 20 min of BWS-TCY training, while participants in the control group A will receive 20 min of CRT and 20 min of Robot-assisted training. Participants in the control group B will undergo 40 min of Conventional rehabilitation training (CRT) daily. All interventions will take place 5 days a week for 12 weeks, with a 12-week follow-up period. No intervention will be carried out for the healthy control group. Upper limb function will be assessed before and after the intervention using various rating scales (Fugl-Meyer Assessment, Wolf Motor Function Test, etc.), as well as neurobiomechanical analyses (surface electromyography, functional near-infrared brain function analysis system, and Xsens maneuver Capture System). Additionally, 10 healthy elderly individuals will be recruited for neurobiomechanical analysis, and the results will be compared with those of stroke survivors.

Discussion

The results of this study will offer initial evidence on the effectiveness and feasibility of BWS-TCY as an early intervention for stroke rehabilitation. Positive findings from this study could contribute to the development of guidelines for the use of BWS-TCY in the early stages of stroke.

Ethics and dissemination

This study has been approved by the Research Ethics Committees of the seventh People's Hospital Affiliated to Shanghai University of Traditional Chinese Medicine (Study ID: 2022-7th-HIRB-022). The results of the study will be published in a peer-reviewed journal and presented at scientific conferences.

Clinical trial registration

https://clinicaltrials.gov/, ChiCTR 2200063150.

Transforming modeling in neurorehabilitation: clinical insights for personalized rehabilitation

Practicing clinicians in neurorehabilitation continue to lack a systematic evidence base to personalize rehabilitation therapies to individual patients and thereby maximize outcomes. Computational modeling- collecting, analyzing, and modeling neurorehabilitation data- holds great promise. A key question is how can computational modeling contribute to the evidence base for personalized rehabilitation? As representatives of the clinicians and clinician-scientists who attended the 2023 NSF DARE conference at USC, here we offer our perspectives and discussion on this topic. Our overarching thesis is that clinical insight should inform all steps of modeling, from construction to output, in neurorehabilitation and that this process requires close collaboration between researchers and the clinical community. We start with two clinical case examples focused on motor rehabilitation after stroke which provide context to the heterogeneity of neurologic injury, the complexity of post-acute neurologic care, the neuroscience of recovery, and the current state of outcome assessment in rehabilitation clinical care. Do we provide different therapies to these two different patients to maximize outcomes? Asking this question leads to a corollary: how do we build the evidence base to support the use of different therapies for individual patients? We discuss seven points critical to clinical translation of computational modeling research in neurorehabilitation- (i) clinical endpoints, (ii) hypothesis- versus data-driven models, (iii) biological processes, (iv) contextualizing outcome measures, (v) clinical collaboration for device translation, (vi) modeling in the real world and (vii) clinical touchpoints across all stages of research. We conclude with our views on key avenues for future investment (clinical-research collaboration, new educational pathways, interdisciplinary engagement) to enable maximal translational value of computational modeling research in neurorehabilitation.