Predicting ADHD in middle-school children from foot pressure and posture measurements
Official title: Prediction of Attention Deficit Hyperactivity Disorder (ADHD) in Middle School Children Using Machine Learning With Pedobarographic Data
Run by Biruni University · NCT07180758 · Data updated 2026-11-12
Researchers will try to see if foot pressure and posture measurements can help predict ADHD in middle-school-aged children.
Quick facts
| Study type | Observational |
|---|---|
| Ages | 10 Years to 14 Years |
| Sex | All |
| Enrollment | 100 (estimated) |
| Sponsor | Biruni University Academic / other |
| Locations | 1 site (Istanbul) |
| Status | Recruiting |
| Started | May 9, 2025 |
| Main results expected | December 2025 |
| Study ends | March 2026 |
| Registry record updated | September 18, 2025 |
| Trial ID | NCT07180758 on ClinicalTrials.gov |
Is this trial a fit for you?
✓Likely a good fit
Ideal participants are 10–14-year-old middle-school students enrolled full-time in Eyüpsultan schools who have parental consent and age-appropriate motor development, including both children with clinically diagnosed ADHD and healthy controls.
✕Probably not a fit
Children who have had lower-extremity or spinal orthopedic surgery, congenital or acquired neuromuscular disorders, major visual or hearing impairments, systemic diseases, or who live outside the study area are unlikely to benefit or be eligible.
A plain-English reading of the study's rules. Only the study team can confirm whether you qualify.
What this trial studies
This observational cross-sectional project will enroll about 100 children aged 10–14 (approximately 50 with clinical ADHD and 50 healthy controls) from middle schools in the Eyüpsultan district of Istanbul. Participants will undergo non-invasive biomechanical testing including static and dynamic pedobarography, mobile posture analysis, balance tests (Flamingo and Y Balance), and anthropometric measurements, while behavioral ratings based on DSM-IV scales are collected from parents, teachers, and caregivers. Physical activity will be recorded with the IPAQ-SF and foot posture assessed with the Foot Posture Index. Collected features will be used to train and compare machine-learning classifiers such as random forest, logistic regression, and support vector machines to develop predictive models for ADHD.
Why it matters
Potential benefit: If successful, this work could provide a simple, non-invasive set of objective measurements to help screen or support diagnosis of ADHD in school-aged children.
How similar studies have gone: Previous research has reported differences in postural control and sway in children with ADHD, and some small machine-learning studies using movement or posture data have shown promising but preliminary predictive results.
Who can join: the full rules
Show full inclusion / exclusion criteria
Inclusion Criteria: * Students attending a middle school located in Eyüpsultan district * Informed consent obtained from their parents * Students enrolled in full-time education * Children with age-appropriate motor development skills. Exclusion Criteria: * Children who have undergone orthopedic interventions due to lower extremity or spinal deformities * Children with congenital or acquired neuromuscular disorders * Children with significant visual or auditory impairments * Children with systemic diseases
Where it's running
-
Biruni University, Faculty of Health Sciences
Istanbul, Turkey (Türkiye) Recruiting
Study contacts
- Principal investigator: Öykü Ak, MSc Candidate — Biruni University, Faculty of Health Sciences
- Study coordinator: Güzin Kaya Aytutuldu, Asst prof.
- Email: guzinkaya14@gmail.com
- Phone: +90 5366265884
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How to take part
- Review the eligibility criteria above with your treating physician.
- Check the official listing on ClinicalTrials.gov for the latest contact details and recruitment status.
- Contact the study coordinator or principal investigator to ask for pre-screening. Pre-screening is free and never obligates you to enroll.