[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"health-study-detail:100555192":3},{"organization":4,"armGroups":7,"interventions":14,"overallOfficials":20,"centralContacts":25,"locations":35,"responsibleParty":60,"collaborators":63,"id":66,"slug":67,"hasResults":68,"nctId":69,"briefTitle":70,"officialTitle":70,"acronym":10,"eligibilityCriteria":71,"healthyVolunteers":72,"sex":73,"minAge":74,"maxAge":10,"enrollmentInfo":75,"targetDuration":10,"studyType":78,"phases":10,"briefSummary":79,"conditions":80,"keywords":82,"overallStatus":37,"whyStopped":10,"lastUpdateSubmitDate":84,"lastUpdatePostDateStruct":85,"startDateStruct":88,"completionDateStruct":90,"leadSponsor":92,"locationsCount":93},{"fullName":5,"class":6},"University of Michigan","OTHER",[8],{"label":9,"type":10,"description":11,"interventionNames":12},"Comparison of acceleration and 3D rotation during balance and movement",null,"Can consumer-grade sensors used in mobile phones provide an accurate and valid measure of balance and gait when compared to gold standard research-grade sensors? A computational model for risk of fall will be developed.",[13],"Behavioral: risk of fall",[15],{"type":16,"name":17,"description":18,"armGroupLabels":19,"otherNames":10},"BEHAVIORAL","risk of fall","Gather information that will assist in determining risk of fall. The researchers will ask the subjects to perform several motor tests and study-related questionnaires.",[9],[21],{"name":22,"affiliation":23,"role":24},"Jennifer Liao, PT, Ph.D.","University of Michigan-Flint","PRINCIPAL_INVESTIGATOR",[26,31],{"name":27,"role":28,"phone":29,"phoneExt":10,"email":30},"Nathan Miller, Ph.D.","CONTACT","810-762-3234","natmille@umich.edu",{"name":32,"role":28,"phone":33,"phoneExt":10,"email":34},"Cathy A Larson, PT, Ph.D.","8107623373","clarson@umich.edu",[36],{"facility":23,"status":37,"city":38,"state":39,"zip":40,"country":41,"countryCode":42,"cosmosGeoPoint":43,"geoPoint":48,"contacts":49},"RECRUITING","Flint","Michigan","48502","United States","US",{"type":44,"coordinates":45},"Point",[46,47],-83.68746,43.01253,{"lat":47,"lon":46},[50,51,53,56,58],{"name":27,"role":28,"phone":29,"phoneExt":10,"email":30},{"name":52,"role":28,"phone":33,"phoneExt":10,"email":34},"Cathy A Larson, Ph.D.",{"name":54,"role":55,"phone":10,"phoneExt":10,"email":10},"Linda Zhu, Ph.D.","SUB_INVESTIGATOR",{"name":57,"role":24,"phone":10,"phoneExt":10,"email":10},"Jennifer Liao, Ph.D.",{"name":59,"role":55,"phone":10,"phoneExt":10,"email":10},"Charlotte Tang, Ph.D.",{"type":24,"investigatorFullName":61,"investigatorTitle":62,"investigatorAffiliation":5,"oldNameTitle":10,"oldOrganization":10},"Jennifer Liao","Assistant Professor of Physical Therapy, College of Health Sciences, The University of Michigan-Flint and Adjunct Assistant Professor of Radiology, Medical School",[64],{"name":23,"class":65},"UNKNOWN","100555192","using-consumer-grade-wearable-devices-for-fall-risk-evaluation-and-alerts-100555192",false,"NCT06508892","Using Consumer-grade Wearable Devices for Fall Risk Evaluation and Alerts","Inclusion Criteria:\n\n* 65 years or older\n\nExclusion Criteria:\n\n* have been diagnosed with neurological conditions such as multiple sclerosis, Parkinson's disease, traumatic brain injury, Alzheimer's disease, or have had a stroke in the last year\n* have orthopedic or cardiopulmonary conditions and\u002For surgeries in the past year\n* have physical limitations that would make it difficult or uncomfortable for individuals to perform the experimental tasks.",true,"ALL","65 Years",{"count":76,"type":77},100,"ESTIMATED","OBSERVATIONAL","Creation and use of a smartphone application for older adults to assess the participants' risk of fall. Phase 1: Compare the accuracy and validity of accelerometer and gyroscopic data from a smartphone and gold-standard, wearable sensors gathered during balance and gait activities. Phase 2: Develop a model that integrates wearable sensor data and individual characteristics, such as age, medical conditions, exercises, previous falls, fear of falls, along with gait and balance outcome measurements, to evaluate fall risk in older adults. Phase 3: Integrate the computational model in the design of a mobile app for wearable devices for older adults to self-administer fall risk assessments and provide individualized risk of fall information.",[81],"Mass Screening",[83],"fall risk","2025-08-04",{"date":86,"type":87},"2025-08-06","ACTUAL",{"date":89,"type":87},"2024-07-29",{"date":91,"type":77},"2026-12-31",{"name":5,"class":6},1]