Deficit Fields for Stroke Recovery

Error-enhanced Learning & Recovery in 2 & 3 Dimensions

This study investigates the potential of customized robotic and visual feedback interaction to improve recovery of movements in stroke survivors. While therapists widely recognize that customization is critical to recovery, little is understood about how take advantage of statistical analysis tools to aid in the process of designing individualized training. Our approach first creates a model of a person's own unique movement deficits, and then creates a practice environment to correct these problems. Experiments will determine how the deficit-field approach can improve (1) reaching accuracy, (2) range of motion, and (3) activities of daily living. The findings will not only shed light on how to improve therapy for stroke survivors, it will test hypotheses about fundamental processes of practice and learning. This study will help us move closer to our long-term goal of clinically effective treatments using interactive devices.

Study Overview

• Status: Completed
• Conditions: Stroke
• Intervention / Treatment: Behavioral: Deficit-fields to reduce error; Behavioral: Deficit-fields to expand range of motion; Behavioral: Deficit-fields to improve function

• Study Type: Interventional
• Enrollment (Actual): 45
• Phase: Not Applicable

Contacts and Locations

Study Locations

• United States
  • Illinois
    • Chicago, Illinois, United States, 60611
      • Rehabilitation Institute of Chicago

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: 18 years to 100 years (Adult, Older Adult)
• Accepts Healthy Volunteers: Yes
• Genders Eligible for Study: All

Description

Inclusion Criteria:

STROKE SURVIVORS:

• adult (age >18)
• Chronic stage stroke recovery (8+ months post)
• available medical records and radiographic information about lesion locations
• strokes caused by an ischemic infarct in the middle cerebral artery
• primary motor cortex involvement
• a Fugl-Meyer score (between 15-50) to evaluate arm motor impairment level

HEALTHY CONTROL PARTICIPANTS:

• adult (age >18)
• healthy individuals with no history of stroke or neural injury

Exclusion Criteria:

• bilateral paresis;
• severe sensory deficits in the limb
• severe spasticity (Modified Ashworth of 4) preventing movement
• aphasia, cognitive impairment or affective dysfunction that would influence the ability to perform the experiment
• inability to provide an informed consent
• severe current medical problems
• diffuse/multiple lesion sites or multiple stroke events
• hemispatial neglect or visual field cut that would prevent subjects from seeing the targets.

Study Plan

How is the study designed?

Design Details

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

Number of Arms

3

Arms and Interventions

Participant Group / Arm	Intervention / Treatment
Experimental: Deficit-fields to reduce error; We hypothesize that a deficit-field design, using the statistics of a patient's errors to customize training, will provide optimal augmentation that varies during motion as needed. We will compare the training effects of error deficit-fields with previous methods of error augmentation to improve reaching ability.	Behavioral: Deficit-fields to reduce error; Stroke survivors exhibit error in both reaching extent and abnormal curvatures of motion. Prior error augmentation techniques multiply error by a constant at each instant during movement. However, magnification of spurious errors may provoke over-compensation. We hypothesize that a deficit-field design, using the statistics of a patient's errors to customize training, will provide optimal augmentation that varies during motion as needed. We will compare the training effects of error deficit-fields with previous methods of error augmentation to improve reaching ability.
Experimental: Deficit-fields to expand range of motion; Amplifying augmentation can expand motor exploration and improve skill retention in patients. Using motor exploration patterns from each patient, we will form customized deficit-fields to recover normal joint workspace. We will compare augmentation training that either amplifies or diminishes the observed deficits (Expt-1). We also compare deficit-fields with our prior augmentation methods to determine the added value of increased customization (Expt-2).	Behavioral: Deficit-fields to expand range of motion; Motor deficits manifest in the workspace limitations of joints, i.e. reduced range of motion, uneven extension-flexion, inter-joint coupling, and unwanted synergies. Our work builds upon these ideas by augmenting self-directed movement for training coordination. We found that amplifying augmentation can expand motor exploration and improve skill retention in patients. Using motor exploration patterns from each patient, we will form customized deficit-fields to recover normal joint workspace. We will compare augmentation training that either amplifies or diminishes the observed deficits (Expt-1). We also compare deficit-fields with our prior augmentation methods to determine the added value of increased customization (Expt-2).
Experimental: Deficit-fields to improve function; Here we present visual distortion of whole body movement during manual tasks during standing, including reaching, grasping, and object manipulation. We compare the training effects of feedback based on deficit-fields versus practice with normal vision.	Behavioral: Deficit-fields to improve function; Clinicians have recognized the benefits of training on everyday tasks (Hubbard, Parsons et al. 2009), as well as practice with whole-body actions (Boehme 1988; Bohannon 1995). However, typical robotic systems have only a single contact point and cannot drive the multiple joints involved in functional tasks. Visual distortions (e.g. a shift, rotation or stretch) can promote adaptation even without forces. Here we present visual distortion of whole body movement during manual tasks during standing, including reaching, grasping, and object manipulation. We compare the training effects of feedback based on deficit-fields versus practice with normal vision.

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Arm motor recovery scores on the Fugl-Meyer	Change from baseline in arm motor recovery as measured by Fugl-Meyer	Baseline at beginning of week 1 and 3 prior to intervention; post-evaluation at end of week 4; follow-up evaluation at end of week 5

Secondary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Number of blocks transferred in Box and Blocks Test	Change from baseline in number of blocks transferred during Box and Blocks Test	Baseline at beginning of week 1 and 3 prior to intervention; post-evaluation at end of week 4; follow-up evaluation at end of week 5
Modified Ashworth Scale (MAS)	Change from baseline in amount of spasticity in elbow flexors and extensors	Baseline at beginning of week 1 and 3 prior to intervention; post-evaluation at end of week 4; follow-up evaluation at end of week 5
Elbow active range of motion (ROM)	Change from baseline measured in degrees for elbow flexion and extension	Baseline at beginning of week 1 and 3 prior to intervention; post-evaluation at end of week 4; follow-up evaluation at end of week 5
Chedoke McMaster Stroke Assessment for Hand	Change in baseline in amount of hand motor recovery as measured by Chedoke scale	Baseline at beginning of week 1 and 3 prior to intervention; post-evaluation at end of week 4; follow-up evaluation at end of week 5
Time and completion score for Action Research Arm Test (ARAT)	Change in baseline score and time for completion of functional measures as part of ARAT	Baseline at beginning of week 1 and 3 prior to intervention; post-evaluation at end of week 4; follow-up evaluation at end of week 5

Collaborators and Investigators

• Sponsor: Shirley Ryan AbilityLab
• Collaborators: National Institute of Neurological Disorders and Stroke (NINDS); National Institutes of Health (NIH)
• Investigators: Principal Investigator: James L Patton, PhD, Shirley Ryan AbilityLab

Publications and helpful links

General Publications

• Wright ZA, Majeed YA, Patton JL, Huang FC. Key components of mechanical work predict outcomes in robotic stroke therapy. J Neuroeng Rehabil. 2020 Apr 21;17(1):53. doi: 10.1186/s12984-020-00672-8.

Study record dates

Study Major Dates

• Study Start (Actual): May 1, 2013
• Primary Completion (Actual): June 30, 2019
• Study Completion (Actual): June 30, 2019

Study Registration Dates

• First Submitted: October 1, 2015
• First Submitted That Met QC Criteria: October 6, 2015
• First Posted (Estimate): October 7, 2015

Study Record Updates

• Last Update Posted (Actual): June 10, 2021
• Last Update Submitted That Met QC Criteria: June 8, 2021
• Last Verified: October 1, 2018

More Information

Terms related to this study

• Keywords: stroke; upper extremity; robotic rehabilitation; error augmentation; motor exploration
• Additional Relevant MeSH Terms: Cardiovascular Diseases; Vascular Diseases; Cerebrovascular Disorders; Brain Diseases; Central Nervous System Diseases; Nervous System Diseases; Stroke
• Other Study ID Numbers: RehabilitationIC; 2R01NS053606-05A1 (U.S. NIH Grant/Contract)