Parkinson's Disease Clinical Trial
Official title:
Investigating Responsive Vibration Cueing Modalities From a Wearable Device to Overcome Gait Freezing in Parkinson's Disease
Verified date | July 2021 |
Source | University of Oxford |
Contact | n/a |
Is FDA regulated | No |
Health authority | |
Study type | Interventional |
The aim of this pilot/feasibility study is to test if delivering rhythmic vibration cues to the lower legs, specifically in response to gait defects (rather than continuously), can improve walking quality and overcome gait freezing in Parkinson's disease. During the study, people with Parkinson's disease that suffer from regular (daily) gait freezing will undertake a series of walking/activity circuits, receiving continuous cueing, responsive cueing (delivered in response to gait freezing), no cueing and no device. Vibration cueing is provided by a non-invasive wearable device prototyped at the University of Oxford, worn on the lower legs during 3 circuits. A series of walking metrics will be analysed.
Status | Completed |
Enrollment | 17 |
Est. completion date | July 22, 2019 |
Est. primary completion date | July 22, 2019 |
Accepts healthy volunteers | No |
Gender | All |
Age group | 18 Years to 90 Years |
Eligibility | Inclusion Criteria: - Participant is willing and able to give informed consent for participation in the study. - Participant is fluent in English. - Participant is aged 18-90 years inclusive - Patients in whom the clinical diagnosis is stated as idiopathic Parkinson's disease or Parkinson's disease (PD). - Participant self-reports a history of daily gait freezing (several times a day), but should be able to stand and walk freely for short periods with minimal assistance. - Participant has no evidence for significant cognitive impairment. - In the investigator's opinion, is willing and able to comply with all trial requirements. Exclusion Criteria: - Participant has a medical or psychiatric illness that would interfere with completing initial and follow up assessments. - Participant has severe mental impairment, dementia or psychosis determined either by a prior diagnosis by a medical professional with relevant expertise, or by subjective assessment by the research team during the pre-study questionnaire (e.g. an inability to comprehend questions). - Active participation in a clinical drug trial. - Female who is pregnant, lactating or planning pregnancy during the course of the investigation. This information will be provided to us by the patient during recruitment screening. - Any other significant disease or disorder which, in the opinion of the Investigator, may either put the participants at risk because of participation in the trial, or may influence the result of the trial, or the participant's ability to participate in the trial. - Patients with frequent (daily) falls at home or out of home, or who demonstrate a high fall risk during the study. - Patients with atypical parkinsonism (eg MSA, PSP, CBD). - Patients who have had Deep Brain Stimulation for their PD. |
Country | Name | City | State |
---|---|---|---|
United Kingdom | The Bosworth Clinic | Cassington | Oxfordshire |
Lead Sponsor | Collaborator |
---|---|
University of Oxford |
United Kingdom,
Type | Measure | Description | Time frame | Safety issue |
---|---|---|---|---|
Primary | Step frequency | The primary objective is to test if gait-freeze initiated vibration cues, provided at the lower-leg, can improve gait/walking quality in PD patients. The primary outcome measure will be cadence (step frequency) with other gait measurements including frequency and duration of gait freeze events, continuous walking time, left/right rhythm and stride length. Measurements extracted from video data collected during 1 hour study sessions. Change in these measurements assessed between cued (intervention) and no-cue (no intervention) walking circuits for each participant. | Data collected during 1 hour sessions up to study completion. | |
Secondary | Automated gait freeze detection | Generation of algorithms capable of recognising gait freeze events from device movement data with >80% accuracy. Algorithm development to identify changes in accelerometer (g) and gyroscope (rsp) data from leg-worn devices indicative of gait freeze events. Algorithm accuracy expressed as % sensitivity and % specificity for identifying gait freeze events from movement data relative to observer time stamps. | Movement data collected during 1 hour sessions up to study completion. |
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