Stroke can severely limit a person's ability to move their arm, especially when trying to reach by extending the elbow. These challenges often persist long after the stroke and make everyday activities more difficult. The investigators are testing a feedback strategy called error augmentation (EA) feedback that intentionally exaggerates movement errors to promote motor learning. In this study, the investigators designed a virtual reality training program that uses EA feedback to encourage people with chronic stroke to use more elbow extension during reaching. The EA feedback makes it appear as though the elbow is more bent than it actually is, prompting the participant to extend their elbow further than they normally would. By having the patient practice movement with enhanced feedback, the investigators predict that the patient will increase the range of motion and improve reaching ability. This is a short, proof-of-concept study to evaluate whether EA feedback shows early promise for improving arm movement in people with upper limb motor impairment after stroke. Participants are randomly assigned to either an EA training group or a control group (no-EA feedback). Each person completes three 30-minute virtual reality training sessions over 1 week. The investigators assess arm movement and motor impairment before and after training, and again one hour after the training to determine if improvements are retained. Findings from this preliminary study will help determine whether this EA-based training approach should be used in a longer 9-week clinical trial aimed at promoting long-term recovery of arm function after stroke.
Study Type
INTERVENTIONAL
Allocation
RANDOMIZED
Purpose
TREATMENT
Masking
DOUBLE
Enrollment
28
Subjects will undergo reaching training that includes a 30-degree elbow flexion error 3 times in 1 week.
Subjects will undergo reaching training that does not include EA feedback 3 times in 1 week.
Jewish Rehabilitation Hospital
Montreal, Quebec, Canada
Change in whole arm active workspace area
The size of the active arm workspace area will be expressed as a ratio of the active workspace determined when the subject actively moves their arm through the horizontal workspace to the passive workspace that is defined by the examiner moving the arm through the same space.
Time frame: Prior to training.
Change in the trunk-based index of performance
Described as a measure of reaching precision accounting for accuracy and speed of reaching together with the amount of trunk compensation.
Time frame: Baseline, immediately post-training, 1 hour post intervention.
Change in elbow extension angle at reaching offset
Elbow extension during a functional Test Task is calculated based on vectors between the markers placed on the acromion and the lateral epicondyle and between the lateral epicondyle and ulnar head of the affected arm. Arm movement onset/offset will be determined as the times at which the velocity of the endpoint marker increases/decreases and remains above/below 10% of the peak velocity.
Time frame: Baseline, immediately post-training, 1 hour post intervention.
Change in endpoint accuracy
The x,y root mean square distance between the endpoint marker and the target at the end of a reaching movement.
Time frame: Baseline, immediately post-training, 1 hour post intervention.
Change in movement time
The time between the onset and offset of the movement.
Time frame: Baseline, immediately post-training, 1 hour post intervention.
Change in path smoothness
The number of peaks on a tangential velocity trace of each reaching trial.
Time frame: Baseline, immediately post-training, 1 hour post intervention.
Change in path straightness
Described using the index of curvature where the ratio between the actual movement path and a straight line path between the initial and final targets.
Time frame: Baseline, immediately post-training, 1 hour post intervention.
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