Optimize Motor Learning to Improve Neurorehabilitation

Stroke Clinical Trial

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Official title:

Optimize Motor Learning to Improve Neurorehabilitation

Clinical Trial Details — Status: Recruiting

Administrative data

• NCT number: NCT04759976
• Other study ID #: 2018-01179
• Status: Recruiting
• Phase: N/A
• Start date: January 25, 2019
• Est. completion date: December 31, 2023

Study information

• Verified date: March 2023
• Source: University of Bern
• Contact: Laura Marchal-Crespo, Prof. Dr.
• Phone: +41 31 632 93 44
• Email: laura.marchal[@]artorg.unibe.ch
• Is FDA regulated: No
• Study type: Interventional

Clinical Trial Summary

The objective of this study is to develop and evaluate novel robotic training strategies that modulate errors based on the subjects' individual motor and cognitive needs. For this purpose, healthy adults and neurologic patients will participate in robotic motor learning experiments. Patients have a diagnosis of a neurological disease (i.e., stroke, spinal cord injury, multiple sclerosis, Guillain-Barré syndrome) limiting arm motor function.

Description:

Neurological patients (e.g., after stroke) engage in intensive and expensive neurorehabilitation therapy to regain part of their former motor functional ability to perform everyday activities with often limited and unsatisfactory outcome. Robots became a promising supplement or even alternative for neurorehabilitation therapy, providing cost-effective, high repetition and task-oriented training. However, results of an initial body of work comparing the effectiveness of robotic training strategies are highly inconclusive. A possible explanation is that most current robotic systems cover only one neurorehabilitation strategy (e.g. reducing or augmenting movement errors) and may thus insufficiently address the subjects' individual needs and the characteristics of the task to be learned. In this study, Investigators will perform several motor learning experiments with healthy adult and neurological patients in order to evaluate the relative motor and cognitive benefits of newly developed robotic training strategies that modulate errors based on the subject's age, skill level and tasks characteristics. The effects of the new strategies will be compared to classical robotic assistance, and to non-robotic feedback approaches, such as visual feedback. The culmination of this work may help to optimize training benefits of already existing rehabilitation robots.

Recruitment information / eligibility

• Status: Recruiting
• Enrollment: 250
• Est. completion date: December 31, 2023
• Est. primary completion date: July 31, 2023
• Accepts healthy volunteers: Accepts Healthy Volunteers
• Gender: All
• Age group: 18 Years and older

Eligibility

Inclusion Criteria:

• Aged =18 years

• Informed Consent as documented by signature ("Informed Consent" form)

• Bodyweight <120 kg

• Ability to communicate effectively with the examiner so that the validity of the patient's data could not be compromised

Exclusion Criteria:

• Excessive spasticity of the affected arm (Ashworth Scale =3)

• Serious medical or psychiatric disorder

• Orthopaedic, rheumatological, or other disease restricting movements of the paretic arm

• Shoulder subluxation

• Skin ulcerations at the paretic arm

• Cyber-sickness (i.e., nausea when looking at a screen or playing computer games)

• Serious cognitive defects or aphasia preventing effective use of the robotic devices

• Severe visual and auditory impairments

Related Conditions & MeSH terms

• Nervous System Diseases
• Neurologic Disorder
• Stroke

Intervention

Behavioral:
• Robotic motor training
The experiments consist in performing motor tasks with upper-limb robotic devices.

Locations

Country	Name	City	State
Switzerland	University of Bern	Bern

Sponsors (1)

Lead Sponsor	Collaborator
University of Bern

Country where clinical trial is conducted

Switzerland, 

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	Change in kinematic performance assessed by the robot	Motion changes from baseline in the kinematic variables assessed by the robot and motion trackers during the motor learning task. The kinematic performance analysis consists of end-effector position in the x, y, and z-axis, in meters, and joint angles in degrees.	Baseline, training (immediately after baseline), retention (1-2 days after the training)
Primary	Change in kinetic performance assessed by the robot	Force changes from baseline in the kinetic variables assessed by the robot using force sensors during the motor learning task. Kinetic performance analysis consists of interaction forces in x, y, and z-axis, in N and applied robot joint torques by the motors, in Nm.	Baseline, training (immediately after baseline), retention (1-2 days after the training)
Primary	Spatial analysis of changes in evoked potentials as assessed by Electroencephalography (EEG) measurement	Electroencephalographical assessment of changes in evoked potentials i.e. the electrical activity of the brain in response to stimulation of specific sensory nerve pathways.	Baseline, training (immediately after baseline), retention (1-2 days after the training)
Secondary	Change in embodiment	Virtual Reality (VR) Embodiment Scale, Self administered Likert scale of 1-7 (Strongly Disagree to Strongly Agree)	Before Intervention, Immediately after the end of intervention
Secondary	Spatial analysis of changes in Task-Based Brain Connectivity as assessed by Electroencephalography (EEG) measurement	Changes in Task-Based Brain Connectivity from baseline in electroencephalography measurement	Baseline, training (immediately after baseline or 1-2 days after baseline), retention (1-2 days after the training)
Secondary	Change in Motivation as assessed by Intrinsic Motivation Inventory (IMI)	Intrinsic Motivation Inventory, Self administered. Likert scale of 1-7 (1: not at all true - 4: somewhat true - 7: very true)	Before Intervention, Immediately after the end of intervention, at the end of the session
Secondary	Change in Cognitive Load as assessed by National Aeronautics and Space Administration (NASA) (Raw) Task Load Index	Self-reported cognitive load during a task, Self-administered National Aeronautics and Space Administration (Raw) Task Load Index (TLX), analog scale mapped from 0 to 100 (Endpoints: Low/High, Good/Poor)	Immediately after the end of intervention, At the end of the session
Secondary	System Usability as assessed by System Usability Scale (SUS)	Self reported system usability assessed by System Usability Scale (SUS) Likert scale of 1-5 (Strongly agree to Strongly disagree)	Immediately after the end of intervention, At the end of the session