[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"health-study-detail:100637518":3},{"organization":4,"armGroups":7,"interventions":10,"overallOfficials":10,"centralContacts":10,"locations":18,"responsibleParty":77,"collaborators":79,"id":86,"slug":87,"hasResults":88,"nctId":89,"briefTitle":90,"officialTitle":91,"acronym":92,"eligibilityCriteria":93,"healthyVolunteers":94,"sex":95,"minAge":96,"maxAge":10,"enrollmentInfo":97,"targetDuration":10,"studyType":100,"phases":10,"briefSummary":101,"conditions":102,"keywords":10,"overallStatus":21,"whyStopped":10,"lastUpdateSubmitDate":108,"lastUpdatePostDateStruct":109,"startDateStruct":112,"completionDateStruct":114,"leadSponsor":116,"locationsCount":117},{"fullName":5,"class":6},"KU Leuven","OTHER",[8,12,15],{"label":9,"type":10,"description":11,"interventionNames":10},"Freezers",null,"Patients with Parkinson's disease who self-report to experience Freezing of Gait daily.",{"label":13,"type":10,"description":14,"interventionNames":10},"Non-freezers","Patients with Parkinson's disease who do not experience Freezing of Gait.",{"label":16,"type":10,"description":17,"interventionNames":10},"Healthy controls","Healthy older adults",[19,44,61],{"facility":20,"status":21,"city":22,"state":10,"zip":23,"country":24,"countryCode":25,"cosmosGeoPoint":26,"geoPoint":31,"contacts":32},"Department of Rehabilitation Sciences","RECRUITING","Leuven","3001","Belgium","BE",{"type":27,"coordinates":28},"Point",[29,30],4.70093,50.87959,{"lat":30,"lon":29},[33,38,42],{"name":34,"role":35,"phone":36,"phoneExt":10,"email":37},"Moran Gilat","CONTACT","+3216 32 94 27","moran.gilat@kuleuven.be",{"name":39,"role":35,"phone":40,"phoneExt":10,"email":41},"Margot Genbrugge","+3216 19 44 94","margot.genbrugge@kuleuven.be",{"name":34,"role":43,"phone":10,"phoneExt":10,"email":10},"PRINCIPAL_INVESTIGATOR",{"facility":45,"status":46,"city":47,"state":10,"zip":48,"country":49,"countryCode":50,"cosmosGeoPoint":51,"geoPoint":55,"contacts":56},"Sports Science and Neurorehabilitation","NOT_YET_RECRUITING","Hamburg","20457","Germany","DE",{"type":27,"coordinates":52},[53,54],9.99302,53.55073,{"lat":54,"lon":53},[57],{"name":58,"role":35,"phone":59,"phoneExt":10,"email":60},"Christian Schlenstedt","+4940.361 226 43206","christian.schlenstedt@medicalschool-hamburg.de",{"facility":62,"status":46,"city":63,"state":10,"zip":64,"country":65,"countryCode":66,"cosmosGeoPoint":67,"geoPoint":71,"contacts":72},"Center for the study of movement, cognition and mobility","Tel Aviv","64","Israel","IL",{"type":27,"coordinates":68},[69,70],34.78057,32.08088,{"lat":70,"lon":69},[73],{"name":74,"role":35,"phone":75,"phoneExt":10,"email":76},"Jeffrey M Hausdorff","+972-3-6973081","jhausdor@tlvmx.gov.il",{"type":43,"investigatorFullName":34,"investigatorTitle":78,"investigatorAffiliation":5,"oldNameTitle":10,"oldOrganization":10},"Prof",[80,82,84],{"name":81,"class":6},"Michael J. Fox Foundation for Parkinson's Research",{"name":83,"class":6},"Tel Aviv Medical Center",{"name":85,"class":6},"Medical School Hamburg","100637518","aid-fog-artificial-intelligence-driven-freezing-of-gait-detection-in-the-home-100637518",false,"NCT07580612","AID-FOG: Artificial Intelligence-Driven Freezing of Gait Detection in the Home","Artificial Intelligence-Driven Freezing Of Gait Detection in the Home: Investigating How Free-living Activities Affect the Algorithm","AID-FOG","Inclusion Criteria:\n\nFor all participants\n\n* Voluntary written informed consent of the participant has been obtained prior to any study-related procedures, except the non-recorded pre-screening questions;\n* At least 18 years of age at the time of signing the Informed Consent Form (ICF);\n* Person is cognitively able to follow and understand instructions and provide voluntary written informed consent;\n* Person is able to walk for short distances (± 10 meters) independently, with- or without use of a walking aid;\n* Person does not live in a temporary or permanent care facility.\n\nFor participants with PD:\n\n* Clinical diagnosis of Parkinson's disease (PD) made by a neurologist according to the Movement Disorders Society guidelines;\n* Person self-reports to experience daily FOG (for recruitment of freezers only);\n* Person is willing to temporarily delay the morning anti-Parkinsonian medication during the standardized assessment visit.\n\nExclusion criteria:\n\n* Occurrence of any of the following within 3 months prior to informed consent: myocardial infarction, hospitalization for unstable angina, stroke, coronary artery bypass graft (CABG), percutaneous coronary intervention (PCI), implantation of a cardiac resynchronization therapy device (CRTD), active treatment for cancer or other malignant disease, uncontrolled congestive heart disease (NYHA class \\>3), acute psychosis or major psychiatric disorders or continued substance abuse, other neurological (than PD) or orthopaedic impairment that significantly impacts on gait;\n* Participant self-reports daily falls;\n* Participation in another interventional study, with or without an investigational medicinal product (IMP) or device (IMD)",true,"ALL","18 Years",{"count":98,"type":99},126,"ESTIMATED","OBSERVATIONAL","Freezing of gait (FOG) is a debilitating symptom of Parkinson's disease increases the risk of falling. Despite being a common symptom, it is still difficult to evaluate freezing of gait quickly and accurately. Currently, the gold-standard method to determine the severity of FOG is a manual analysis of video footage by an experienced assessor, collected during standardized FOG-provoking walking tests. Because this is a very time-intensive process, where different assessors sometimes obtain different results, our team at KU Leuven have developed an artificial-intelligent (AI) algorithm trained to identify FOG episodes based on wearable inertial measurement unit (IMU) sensor data. The AI algorithm has already undergone initial validation during laboratory testing, yielding promising results. The aim of this study is to investigate whether the AI algorithm can accurately detect FOG episodes in a less controlled environment, namely the home environment. In a second phase, the investigators will also use the collected data to improve the AI algorithm for automated FOG detection in the home. Finally, the investigators want to explore whether the AI algorithm can detect FOG in real-time.",[103,104,105,106,107],"Parkinson Disease, Idiopathic","Freezing of Gait","Validation","Wearable Sensors","Artifical Intelligence","2026-05-05",{"date":110,"type":111},"2026-05-12","ACTUAL",{"date":113,"type":111},"2025-09-22",{"date":115,"type":99},"2027-06",{"name":5,"class":6},3]