[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"health-study-detail:100606838":3},{"organization":4,"armGroups":7,"interventions":10,"overallOfficials":15,"centralContacts":20,"locations":26,"responsibleParty":42,"collaborators":10,"id":45,"slug":46,"hasResults":47,"nctId":48,"briefTitle":49,"officialTitle":50,"acronym":10,"eligibilityCriteria":51,"healthyVolunteers":52,"sex":53,"minAge":54,"maxAge":55,"enrollmentInfo":56,"targetDuration":10,"studyType":59,"phases":10,"briefSummary":60,"conditions":61,"keywords":63,"overallStatus":28,"whyStopped":10,"lastUpdateSubmitDate":69,"lastUpdatePostDateStruct":70,"startDateStruct":73,"completionDateStruct":75,"leadSponsor":77,"locationsCount":78},{"fullName":5,"class":6},"Biruni University","OTHER",[8,12],{"label":9,"type":10,"description":11,"interventionNames":10},"ADHD Group",null,"This group includes children aged 10-14 years who have been diagnosed with Attention Deficit Hyperactivity Disorder (ADHD) based on DSM-IV criteria. Parent and teacher rating scales developed by Atilla Turgay will be used to assess ADHD symptom severity. Participants will undergo a comprehensive evaluation including postural assessment, foot posture analysis, balance measurement, pedobarographic and stabilometric pressure analysis, and physical activity assessment using the International Physical Activity Questionnaire - Short Form (IPAQ-SF). Based on the data obtained from these assessments, an artificial intelligence (AI)-supported predictive model will be developed to estimate ADHD-related patterns and distinguish ADHD profiles from healthy controls.",{"label":13,"type":10,"description":14,"interventionNames":10},"Healthy Control Group","This group includes age- and gender-matched children (10-14 years old) without a diagnosis of ADHD or other neurodevelopmental\u002Fpsychiatric disorders. The same battery of physical assessments-postural, foot posture, balance, pedobarographic and stabilometric measurements, and physical activity assessment using the International Physical Activity Questionnaire - Short Form (IPAQ-SF)-will be conducted. These data will be used in conjunction with the ADHD group to develop and validate the AI-based predictive model.",[16],{"name":17,"affiliation":18,"role":19},"Öykü Ak, MSc Candidate","Biruni University, Faculty of Health Sciences","PRINCIPAL_INVESTIGATOR",[21],{"name":22,"role":23,"phone":24,"phoneExt":10,"email":25},"Güzin Kaya Aytutuldu, Asst prof.","CONTACT","+90 5366265884","guzinkaya14@gmail.com",[27],{"facility":18,"status":28,"city":29,"state":10,"zip":10,"country":30,"countryCode":10,"cosmosGeoPoint":31,"geoPoint":36,"contacts":37},"RECRUITING","Istanbul","Turkey (Türkiye)",{"type":32,"coordinates":33},"Point",[34,35],28.94966,41.01384,{"lat":35,"lon":34},[38,41],{"name":39,"role":23,"phone":40,"phoneExt":10,"email":25},"Güzin Kaya Aytutuldu, Asst. Prof.","+90 536 625 5884",{"name":17,"role":19,"phone":10,"phoneExt":10,"email":10},{"type":19,"investigatorFullName":43,"investigatorTitle":44,"investigatorAffiliation":5,"oldNameTitle":10,"oldOrganization":10},"Guzin Kaya Aytutuldu","Assistant Professor","100606838","prediction-of-adhd-in-children-using-pedobarographic-and-postural-data-100606838",false,"NCT07180758","Prediction of ADHD in Children Using Pedobarographic and Postural Data","Prediction of Attention Deficit Hyperactivity Disorder (ADHD) in Middle School Children Using Machine Learning With Pedobarographic Data","Inclusion Criteria:\n\n* Students attending a middle school located in Eyüpsultan district\n* Informed consent obtained from their parents\n* Students enrolled in full-time education\n* Children with age-appropriate motor development skills.\n\nExclusion Criteria:\n\n* Children who have undergone orthopedic interventions due to lower extremity or spinal deformities\n* Children with congenital or acquired neuromuscular disorders\n* Children with significant visual or auditory impairments\n* Children with systemic diseases",true,"ALL","10 Years","14 Years",{"count":57,"type":58},100,"ESTIMATED","OBSERVATIONAL","The aim of this study is to investigate the potential of postural control and plantar pressure data in predicting Attention Deficit Hyperactivity Disorder (ADHD) in middle school students using machine learning methods. A total of 100 students will participate, including those identified with symptoms of ADHD and healthy controls. Participants will undergo non-invasive biomechanical assessments, including pedobarographic foot pressure measurement and mobile posture analysis. Behavioral data will be collected using DSM-IV-based rating scales developed by Atilla Turgay, completed separately by parents, teachers, and caregivers. All data will be used to develop predictive models using algorithms such as random forest, logistic regression, and support vector machines. The study is observational and cross-sectional.",[62],"Attention Deficit Hyperactivity Disorder (ADHD)",[64,65,66,67,68],"Attention Deficit Disorder with Hyperactivity","Artificial Intelligence","Posture","Machine Learning","child","2025-09-16",{"date":71,"type":72},"2025-09-18","ACTUAL",{"date":74,"type":72},"2025-05-09",{"date":76,"type":58},"2026-03",{"name":5,"class":6},1]