Ignite Creation Date:
2025-12-25 @ 2:00 AM
Ignite Modification Date:
2025-12-27 @ 11:55 PM
Study NCT ID:
NCT05247294
Status:
COMPLETED
Last Update Posted:
2022-02-18
First Post:
2022-01-26
Is NOT Gene Therapy:
True
Has Adverse Events:
False
Brief Title:
Assessment of Fluctuating Parkinson's Disease With Sensor-based Home Monitoring
Sponsor:
Organization:
Raw JSON
{'hasResults': False, 'derivedSection': {'miscInfoModule': {'versionHolder': '2025-12-24'}, 'conditionBrowseModule': {'meshes': [{'id': 'D010300', 'term': 'Parkinson Disease'}], 'ancestors': [{'id': 'D020734', 'term': 'Parkinsonian Disorders'}, {'id': 'D001480', 'term': 'Basal Ganglia Diseases'}, {'id': 'D001927', 'term': 'Brain Diseases'}, {'id': 'D002493', 'term': 'Central Nervous System Diseases'}, {'id': 'D009422', 'term': 'Nervous System Diseases'}, {'id': 'D009069', 'term': 'Movement Disorders'}, {'id': 'D000080874', 'term': 'Synucleinopathies'}, {'id': 'D019636', 'term': 'Neurodegenerative Diseases'}]}}, 'protocolSection': {'designModule': {'studyType': 'OBSERVATIONAL', 'designInfo': {'timePerspective': 'PROSPECTIVE', 'observationalModel': 'COHORT'}, 'enrollmentInfo': {'type': 'ACTUAL', 'count': 24}, 'patientRegistry': False}, 'statusModule': {'overallStatus': 'COMPLETED', 'startDateStruct': {'date': '2019-11-03', 'type': 'ACTUAL'}, 'expandedAccessInfo': {'hasExpandedAccess': False}, 'statusVerifiedDate': '2022-02', 'completionDateStruct': {'date': '2021-02-24', 'type': 'ACTUAL'}, 'lastUpdateSubmitDate': '2022-02-15', 'studyFirstSubmitDate': '2022-01-26', 'studyFirstSubmitQcDate': '2022-02-15', 'lastUpdatePostDateStruct': {'date': '2022-02-18', 'type': 'ACTUAL'}, 'studyFirstPostDateStruct': {'date': '2022-02-18', 'type': 'ACTUAL'}, 'primaryCompletionDateStruct': {'date': '2021-02-24', 'type': 'ACTUAL'}}, 'outcomesModule': {'primaryOutcomes': [{'measure': 'Correlation between sensor data and MDS-UPDRS data', 'timeFrame': '2-3 weeks study period', 'description': 'To correlate severity of fluctuating motor symptoms in PD patients using Intel® Pharma Analytics Platform\'s derived passive sensor data (percentage of daily tremor time and percentage of daily dyskinesia time, percentage of daily "inactivity") in an exploratory manner with concomitant assessment of motor fluctuations and dyskinesia using the application\'s based electronic symptom diary and data of tremor, off time and dyskinesia using the MDS-UPDRS.'}], 'secondaryOutcomes': [{'measure': 'Correlation between passive sensor data and PDQ-39 questionnaire', 'timeFrame': '2-3 weeks study period', 'description': 'Correlate the results of passive sensor data regarding "level of activity" and "time of activity" with measures of Quality of life as captured by the PDQ-39'}, {'measure': 'Correlation between passive sensor data and electronic home diaries', 'timeFrame': '2-3 weeks study period', 'description': 'Correlate the results passive sensor data of "level of activity" and "time of activity" with "on" and "off" data obtained from electronic home diary (on the same days).'}, {'measure': 'Correlation between motor test results (TUG, Static postural tests and finger tapping,) and relevant MDS-UPDRS items', 'timeFrame': '2-3 weeks study period', 'description': 'Correlate the results of guided structured motor tests (TUG, Static postural tests and finger tapping,) during "off" and during "on" performed in the clinic (visit 1) and at home (twice in each condition) with the results of in-clinic MDS-UPDRS items: 3.1 walking, 3.11 freezing of gait, 3.12 postural stability, 3.4 finger taps during "off" and during "on"'}, {'measure': 'Assessing compliance of PD patients using wearable devices and adherence to assessment protocol.', 'timeFrame': '2-3 weeks study period', 'description': 'The measures consist of: overall use of watch, Diary report -delay, Number of skipped daily questionnaires, number of skipped assessments, number of skipped medication reports, number of delayed medication reports, number of skipped medications intake, delay in medication intake and more.'}, {'measure': 'Correlation between medication regimen as prescribed by neurologist and patient adherence in real life', 'timeFrame': '2-3 weeks study period', 'description': 'To correlate medication regimen as prescribed by neurologist and patient adherence in real life, using electronic medication intake reporting'}]}, 'oversightModule': {'oversightHasDmc': False, 'isFdaRegulatedDrug': False, 'isFdaRegulatedDevice': False}, 'conditionsModule': {'keywords': ['Parkinson Disease', 'Home based monitoring', 'Sensor-based data', 'Motor fluctuations'], 'conditions': ['Parkinson Disease']}, 'descriptionModule': {'briefSummary': "The aim of this study is to implement home-based monitoring (HBM) using remote-capture wearable devices and patient reported outcomes (PROs) in a rather homogeneous subgroup of advanced Parkinson's Disease (PD) patients, suffering from significant motor fluctuations (MF) and Levodopa-induced dyskinesia (LID), over a two-week period.\n\nThe investigators aim to provide a more comprehensive picture of patient symptoms, severity, and fluctuations and compare these data to interview-derived clinical data.", 'detailedDescription': 'Parkinson\'s Disease (PD) is a neuro-degenerative disorder affecting millions of people worldwide. PD is associated with both motor and non-motor symptoms that affect patients\' functioning and Quality of life.\n\nThe motor symptoms consist of tremor, rigidity, bradykinesia and gait impairments. Additional important motor symptoms that are associated with chronic Levodopa therapy, are levodopa-induced dyskinesia and motor fluctuations.\n\nCurrently, the accepted clinical measurement of PD symptom severity is the Movement Disorders Society-unified Parkinson\'s disease rating scale (MDS-UPDRS), which is based, in part, on subjective and potentially recall-based reports by the patients and on semi-objective observations by the clinician.\n\nOn average, PD patients see their treating neurologist for in-clinic visits twice a year. These visits are limited in time and may leave some issues unattended regarding all aspects of disease and overall health. This may adversely affect the decision making process and the prescribed treatment plan.\n\nIn order to understand the accurate clinical status of patients, particularly in the motor fluctuating stage of PD and to monitor results of intervention, the treating neurologist may need a more comprehensive picture of their patients\' symptoms and lives during protracted periods and real life in their home environment.\n\nThe HBM apparatus used in this study will consist of a smartwatch (Apple watch) and a smartphone (Apple iPhone 8). The phone is pre-installed with an application which is part of the Intel® Pharma Analytics Platform. The mobile app was designed by usability experts and was tested with patients to ensure ease of use by an elderly population with PD.\n\nThe Intel platform is enhanced by a compendium of algorithms to extract clinical insights from the raw sensor data. Passive sensor data is transferred from the study smartwatch. The data includes three measures based on the smartwatch data: activity level, dyskinesia and tremor.\n\nThe study will be conducted at Movement Disorders Institute at Sheba Medical Center and will include two clinic visits and a 2-week HBM phase.\n\nThe first clinic visit as well as the 2-week home period will include the following daily assessments (once in "off" state and once in "on" state):\n\n1. 3- meter Timed up and go test (3mTUG)\n2. Finger tapping on smartphone (10 seconds, both hands)\n3. Hand tremor assessment: postural tremor (outstretched, 30 sec), rest tremor (30 sec)\n4. Pronation-supination\n\nIn addition, ePROs are captured with electronic home diaries. Participants will report the severity of PD symptoms they are experiencing on a 5-point scale throughout the day. For the duration of waking hours, participants will receive a notification on their phone to input information about their ON/OFF state every 30 minutes.\n\nThe primary objective is to correlate severity of fluctuating motor symptoms in PD patients using the Intel® Pharma Analytics Platform\'s derived passive sensor data (percentage of daily tremor time and percentage of daily dyskinesia time, percentage of daily "inactivity") in an exploratory manner with concomitant assessment of motor fluctuations and dyskinesia using the application\'s based electronic symptom diary and data of tremor, off time and dyskinesia using the MDS-UPDRS scale.'}, 'eligibilityModule': {'sex': 'ALL', 'stdAges': ['ADULT', 'OLDER_ADULT'], 'minimumAge': '30 Years', 'samplingMethod': 'PROBABILITY_SAMPLE', 'studyPopulation': "Advanced Parkinson's Disease (PD) patients, suffering from significant motor fluctuations (MF) and Levodopa-induced dyskinesia (LID)", 'healthyVolunteers': False, 'eligibilityCriteria': "Inclusion Criteria:\n\n1. Diagnosis of idiopathic Parkinson's disease for at least 5 years\n2. Males or females over the age of 30\n3. Patients treated with oral levodopa (3 daily doses or more), reporting motor fluctuations and (preferably) with l-dopa-induced dyskinesia\n4. UPDRS-MDS 4.4 functional impact of fluctuations (mild + severe, 2-4)\n5. Hoehn \\&Yahr stage 1-3 while ON\n6. Ability to operate smartphone technology in the household\n7. Mini-Mental State Examination score \\>20\n\nExclusion Criteria:\n\n1. Cognitive or psychiatric impairment that would preclude study participation as determined by the principal investigator\n2. Additional major comorbidities\n3. Levodopa-resistant tremor (tremor during ON)\n4. Levodopa-resistant freezing (freezing during ON)\n5. Previous surgical treatment for PD"}, 'identificationModule': {'nctId': 'NCT05247294', 'briefTitle': "Assessment of Fluctuating Parkinson's Disease With Sensor-based Home Monitoring", 'organization': {'class': 'OTHER_GOV', 'fullName': 'Sheba Medical Center'}, 'officialTitle': "Assessment of Fluctuating Parkinson's Disease With Sensor-based Home Monitoring", 'orgStudyIdInfo': {'id': '5428-18'}}, 'contactsLocationsModule': {'locations': [{'city': 'Ramat Gan', 'country': 'Israel', 'facility': 'Movement Disorders Institution, Sheba Medical Center', 'geoPoint': {'lat': 32.08227, 'lon': 34.81065}}]}, 'sponsorCollaboratorsModule': {'leadSponsor': {'name': 'Sheba Medical Center', 'class': 'OTHER_GOV'}, 'collaborators': [{'name': 'AbbVie', 'class': 'INDUSTRY'}, {'name': 'Intel Electronics Ltd.', 'class': 'UNKNOWN'}], 'responsibleParty': {'type': 'PRINCIPAL_INVESTIGATOR', 'investigatorTitle': 'Prof. Sharon Hassin', 'investigatorFullName': 'Dr. Sharon Hassin', 'investigatorAffiliation': 'Sheba Medical Center'}}}}