[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"health-study-detail:100641165":3},{"organization":4,"armGroups":7,"interventions":10,"overallOfficials":10,"centralContacts":18,"locations":25,"responsibleParty":37,"collaborators":10,"id":39,"slug":10,"hasResults":40,"nctId":41,"briefTitle":42,"officialTitle":43,"acronym":10,"eligibilityCriteria":44,"healthyVolunteers":40,"sex":45,"minAge":46,"maxAge":47,"enrollmentInfo":48,"targetDuration":51,"studyType":52,"phases":10,"briefSummary":53,"conditions":54,"keywords":10,"overallStatus":59,"whyStopped":10,"lastUpdateSubmitDate":60,"lastUpdatePostDateStruct":61,"startDateStruct":64,"completionDateStruct":66,"leadSponsor":68,"locationsCount":69},{"fullName":5,"class":6},"National Cheng-Kung University Hospital","OTHER",[8,12,15],{"label":9,"type":10,"description":11,"interventionNames":10},"Young Adults",null,"Healthy young adults (aged 30-39 years) with no history of neurological or psychiatric disorders. Participants will complete standardized virtual reality (VR) driving tasks with integrated eye-tracking and upper-limb motion recording to establish normative visuomotor and cognitive performance benchmarks.",{"label":13,"type":10,"description":14,"interventionNames":10},"Cognitively Healthy Older Adults","Community-dwelling older adults (≥65 years) with normal cognitive function based on standardized screening (e.g., MoCA within normal range). Participants will undergo the same VR-based assessments to examine age-related changes in visual attention, executive control, and visuomotor coupling.",{"label":16,"type":10,"description":17,"interventionNames":10},"Mild Cognitive Impairment (MCI)","Older adults clinically identified with mild cognitive impairment according to established diagnostic criteria. Participants will complete identical VR driving tasks to investigate alterations in visual search behavior, attentional control, and visuomotor integration, and to support development of predictive AI models for cognitive decline and driving risk.",[19],{"name":20,"role":21,"phone":22,"phoneExt":23,"email":24},"Hsiu-Yun Hsu, Ph.D","CONTACT","886-6-2353535","2669","hyhsu@mail.ncku.edu.tw",[26],{"facility":5,"status":10,"city":27,"state":28,"zip":29,"country":28,"countryCode":30,"cosmosGeoPoint":31,"geoPoint":36,"contacts":10},"Tainan","Taiwan","704","TW",{"type":32,"coordinates":33},"Point",[34,35],120.21333,22.99083,{"lat":35,"lon":34},{"type":38,"investigatorFullName":10,"investigatorTitle":10,"investigatorAffiliation":10,"oldNameTitle":10,"oldOrganization":10},"SPONSOR","100641165",false,"NCT07656389","An AI-Based Prediction of Cognitive Capacity in Older Adults and Individuals With Mild Cognitive Impairment During Virtual Reality Driving Tasks","From Eye Movements to Visuomotor Coupling: An AI-Based Prediction of Cognitive Capacity in Older Adults and Individuals With Mild Cognitive Impairment During Virtual Reality Driving Tasks","Inclusion Criteria:\n\n* (1) a score of 23 or higher on the Montreal Cognitive Assessment; (2) a Clinical Dementia Rating score of 0 for cognitively healthy participants or 0.5 for participants with mild cognitive impairment (MCI); (3) healthy young adults aged between 30 and 39 years, and cognitively healthy older adults and participants with MCI aged between 65 and 85 years; (4) right-hand dominance; and (5) adequate visual function to complete VR and eye-tracking tasks, defined as corrected binocular visual acuity of at least 0.5 without severe visual field deficits.\n\nExclusion Criteria:\n\n* (1) the presence or history of major psychiatric disorders or central nervous system diseases; (2) significant ocular diseases, such as untreated cataracts, active retinal diseases, moderate-to-severe or poorly controlled glaucoma, or marked visual field deficits beyond a specified level; (3) epilepsy; and (4) severe dizziness or VR-induced motion sickness.","ALL","30 Years","85 Years",{"count":49,"type":50},192,"ESTIMATED","12 Months","OBSERVATIONAL","Driving ability in older adults is essential for independent mobility and social participation, yet declines under high cognitive load or distraction often lead to visual attention failures such as \"look-but-fail-to-see,\" increasing crash risk. Older adults and individuals with mild cognitive impairment (MCI) show impairments in visual attention, executive control, and visuomotor integration, which are not adequately captured by conventional assessments. Virtual reality (VR) integrated with eye-tracking and upper-limb motion analysis enables ecologically valid simulation of driving scenarios and precise quantification of visuomotor behavior. However, current studies are limited by single-scenario designs, unimodal AI models, and insufficient integration of action-related data.\n\nThis study proposes a multi-phase framework: Year 1 develops an eye-movement-based AI model for MCI identification; Year 2 integrates multimodal data in VR driving tasks; and Year 3 establishes an explainable AI system with longitudinal validation. The study aims to advance cognitive assessment and develop a digital tool for early MCI detection and driving risk prediction.",[16,55,56,57,58],"Virtual Reality","Eye Movement Disorder","Multimodal Monitoring","Older Adults","NOT_YET_RECRUITING","2026-06-14",{"date":62,"type":63},"2026-06-18","ACTUAL",{"date":65,"type":50},"2026-06-20",{"date":67,"type":50},"2029-12-31",{"name":5,"class":6},1]