Optimizing Hand Rehabilitation Post-Stroke Using Interactive Virtual Environments

Study Overview
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Brief Summary
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The complexity of sensorimotor control required for hand function as well as the wide range of recovery of manipulative abilities makes rehabilitation of the hand most challenging. The investigators past work has shown that training in a virtual environment (VE) using repetitive, adaptive algorithms has the potential to be an effective rehabilitation medium to facilitate motor recovery of hand function. These findings are in accordance with current neuroscience literature in animals and motor control literature in humans. The investigators are now in a position to refine and optimize elements of the training paradigms to enhance neuroplasticity. The investigators first aim tests if and how competition among body parts for neural representations stifles functional gains from different types of training regimens. The second aim tests the functional benefits of unilateral versus bilateral training regimens.The third aim tests whether functional improvements gained from training in a virtual environment transfer to other (untrained) skills in the real world.

Detailed Description
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The complexity of sensorimotor control required for hand function as well as the wide range of recovery of manipulative abilities makes rehabilitation of the hand most challenging. The investigators past work has shown that training in a virtual environment (VE) using repetitive, adaptive algorithms has the potential to be an effective rehabilitation medium to facilitate motor recovery of hand function. These findings are in accordance with current neuroscience literature in animals and motor control literature in humans. The investigators are now in a position to refine and optimize elements of the training paradigms to enhance neuroplasticity. The investigators first aim tests if and how competition among body parts for neural representations stifles functional gains from different types of training regimens. The second aim tests the functional benefits of unilateral versus bilateral training regimens.The third aim tests whether functional improvements gained from training in a virtual environment transfer to other (untrained) skills in the real world.

Official Title
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Optimizing Hand Rehabilitation Post-Stroke Using Interactive Virtual Environments

Conditions
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Cerebrovascular Accident, Hemiparesis, Hemiplegia

Intervention / Treatment
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* Behavioral: HAS Training
* Behavioral: HAT training
* Behavioral: Bimanual Training


Participation Criteria
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Eligibility Criteria
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Inclusion Criteria: Six months post cerebrovascular accident Residual upper extremity impairment that affects participation At least ten degrees of active finger extension Tolerate passive shoulder flexion to chest level Exclusion Criteria: Severe neglect Severe aphasia

Ages Eligible for Study
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Minimum Age: 18 Years
Maximum Age: 80 Years

Sexes Eligible for Study
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All

Accepts Healthy Volunteers
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No

Study Plan
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How is the study designed?
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Design Details

Primary Purpose: Treatment
Allocation: Randomized
Intervention Model: Parallel Assignment
Masking: Double


Arms and Interventions

| Participant Group/Arm | Intervention/Treatment |
| --- | --- |
| Active Comparator: Train Paretic Hand and Arm Separate<br>Eight three hour training sessions of robotically facilitated hand and arm training in complex virtual environments, using activities that train the fingers in isolation and other activities that train the arm in isolation. | Behavioral: HAS Training<br>* Robotically measured and facilitated training of the hemiparetic hand and arm in isolation, in a three dimensional haptically rendered virtual environment.<br>* Other names: Isolated UE training;|
| Experimental: Train Paretic Hand and Arm Together<br> | Behavioral: HAT training<br>* Robotically measured and facilitated training of the hemiparetic hand and arm as an integrated functional unit, in a three dimensional haptically rendered virtual environment<br>* Other names: Integrated UE training;|
| Experimental: Train Both Hands Together in VE<br> | Behavioral: Bimanual Training<br>* Robotically measured and facilitated training of the hemiparetic hand and non-hemiparetic hand together, in a three dimensional haptically rendered virtual environment<br>* Other names: Bilateral UE training;|

What is the study measuring?
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Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Change in Jebsen Test of Hand Function |  | Two Weeks Prior to Training, Immediately Prior to Training, Immediately After Training, 3 Months After Training | 

Secondary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Change in Wolf Motor Function Test |  | Two Weeks Prior to Training, Immediately Prior to Training, Immediately After Training, 3 Months After Training | 
| Change in 9 Hole Peg Test |  | Two Weeks Prior to Training, Immediately Prior to Training, Immediately After Training, 3 Months After Training | 
| Change in Box and Blocks Test |  | Two Weeks Prior to Training, Immediately Prior to Training, Immediately After Training, 3 Months After Training | 
| Change in Robotically Collected Kinematics |  | 1 day before training and 1 day after training | 
| Change in Reach to Grasp Test |  | 1 day before training and 1 day after training | 

 Terms related to the study
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Keywords Provided by Centre Hospitalier Valida
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Cerebrovascular Accident, Robotics, Virtual Reality, Rehabilitation, Neuroplasticity