Optimizing Hand Rehabilitation Post-Stroke Using Interactive Virtual Environments

October 6, 2015 updated by: Sergei V. Adamovich PhD, New Jersey Institute of Technology
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.

Study Overview

Status

Completed

Conditions

Intervention / Treatment

Detailed Description

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.

Study Type

Interventional

Enrollment (Actual)

55

Phase

  • Phase 1

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Locations

  • United States
    • New Jersey
      • Newark, New Jersey, United States, 07102
        • New Jersey Institute of Technology

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

16 years to 78 years (Adult, Older Adult)

Accepts Healthy Volunteers

No

Genders Eligible for Study

All

Description

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

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

  • Primary Purpose: Treatment
  • Allocation: Randomized
  • Interventional Model: Parallel Assignment
  • Masking: Double

Arms and Interventions

Participant Group / Arm
Intervention / Treatment
Active Comparator: Train Paretic Hand and Arm Separate
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
Robotically measured and facilitated training of the hemiparetic hand and arm in isolation, in a three dimensional haptically rendered virtual environment.
Other Names:
  • Isolated UE training
Experimental: Train Paretic Hand and Arm Together
Behavioral: HAT training
Robotically measured and facilitated training of the hemiparetic hand and arm as an integrated functional unit, in a three dimensional haptically rendered virtual environment
Other Names:
  • Integrated UE training
Experimental: Train Both Hands Together in VE
Behavioral: Bimanual Training
Robotically measured and facilitated training of the hemiparetic hand and non-hemiparetic hand together, in a three dimensional haptically rendered virtual environment
Other Names:
  • Bilateral UE training

What is the study measuring?

Primary Outcome Measures

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

Secondary Outcome Measures

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

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

New Jersey Institute of Technology

Collaborators

Rutgers, The State University of New Jersey

Investigators

  • Principal Investigator: Sergei V. Adamovich, PhD, New Jersey Institute of Technology

Publications and helpful links

The person responsible for entering information about the study voluntarily provides these publications. These may be about anything related to the study.

General Publications

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start

March 1, 2009

Primary Completion (Actual)

March 1, 2013

Study Completion (Actual)

March 1, 2015

Study Registration Dates

First Submitted

February 16, 2010

First Submitted That Met QC Criteria

February 19, 2010

First Posted (Estimate)

February 22, 2010

Study Record Updates

Last Update Posted (Estimate)

October 7, 2015

Last Update Submitted That Met QC Criteria

October 6, 2015

Last Verified

October 1, 2015

More Information

Terms related to this study

Keywords

Additional Relevant MeSH Terms

Other Study ID Numbers

  • 5R01HD058301-02 (U.S. NIH Grant/Contract)

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

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