TOWARDS THE DEVELOPMENT OF A NOVEL PREDICTIVE MODEL AND BIOMARKER FOR RECURRENT FALLER PHENOCONVERSION IN PARKINSON’S DISEASE
Summary
The goal of this study is to uncover differences in brain wave patterns between people with Parkinson's disease that fall often (3 or more falls in the past 12 months), and those that do not (1 or less falls in the past 12 months). The results from this study will be used to help build a computer algorithm that is able to predict individuals with Parkinson's disease who currently do not fall often but are a future risk of multiple falls.
Eligibility
Eligible ages: 50 to 85
Inclusion criteria:
Participants will need to:
1) Have a confirmed diagnosis of Parkinson's
2) Are able to at a minimum stand and step-in-place for a few minutes
3) Are willing to wear a leg band with a stepping-in-place sensor on their leg and a head cap with conductive gel that measures their brain waves.
Exclusion criteria:
Participants will not be eligible if:
1) They have a neurologic condition other than idiopathic PD
2) Are wheelchair bound
3) Have dementia
4) Have symptomatic postural hypotension
5) Have a significant cardiovascular or musculoskeletal disorder limiting standing/stepping or balance.
Participate
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Additional information
Contact information
Dr. Taylor Chomiak, Staff Scientist & Adj. Assistant Professor Department of Clinical Neurosciences CSM Optogenetics Platform Hotchkiss Brain Institute Alberta Children’s Hospital Research Institute Cumming School of Medicine University of Calgary 3330 Hospital Drive N.W. Calgary, Alberta, Canada, T2N 4N1 E | tgchomia@ucalgary.ca
Principal investigator:
Taylor Chomiak
Clinical trial:
No
REB-ID:
REB19-0529