Deficit Fields for Stroke Recovery

NCT ID: NCT02570256

Last Updated: 2021-06-10

Basic Information

• Recruitment Status: COMPLETED
• Clinical Phase: NA
• Total Enrollment: 45 participants
• Study Classification: INTERVENTIONAL
• Study Start Date: 2013-05-01
• Study Completion Date: 2019-06-30

Brief Summary

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.

Conditions

Stroke

Study Design

• Allocation Method: RANDOMIZED
• Intervention Model: PARALLEL
• Primary Study Purpose: TREATMENT
• Blinding Strategy: DOUBLE

Study Groups

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.

Group Type: EXPERIMENTAL

Deficit-fields to reduce error

Intervention Type: BEHAVIORAL

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.

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).

Group Type: EXPERIMENTAL

Deficit-fields to expand range of motion

Intervention Type: BEHAVIORAL

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).

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.

Group Type: EXPERIMENTAL

Deficit-fields to improve function

Intervention Type: BEHAVIORAL

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.

Interventions

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.

Intervention Type: 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).

Intervention Type: 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.

Intervention Type: BEHAVIORAL

Eligibility Criteria

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.

• Minimum Eligible Age: 18 Years
• Maximum Eligible Age: 100 Years
• Eligible Sex: ALL
• Accepts Healthy Volunteers: Yes

Sponsors

National Institutes of Health (NIH)

NIH

Sponsor Role: collaborator

National Institute of Neurological Disorders and Stroke (NINDS)

NIH

Sponsor Role: collaborator

Shirley Ryan AbilityLab

OTHER

Sponsor Role: lead

Responsible Party

James Patton

Co-Director, Robotics Laboratory, Sensory Motor Performance Program, Rehabilitation Institute of Chicago

Responsibility Role: PRINCIPAL_INVESTIGATOR

Principal Investigators

James L Patton, PhD

Role: PRINCIPAL_INVESTIGATOR

Shirley Ryan AbilityLab

Locations

Rehabilitation Institute of Chicago

Chicago, Illinois, United States

Countries

United States

References

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.

Reference Type: DERIVED

PMID: 32316977 (View on PubMed)

Other Identifiers

2R01NS053606-05A1

Identifier Type: NIH

Identifier Source: secondary_id

View Link

RehabilitationIC

Identifier Type: -

Identifier Source: org_study_id