Prediction of ADHD in Children Using Pedobarographic and Postural Data

NCT07180758 | Recruiting

• 1 other identifier: Uni.Biruni
• Study Type: observational
• Target: 100
• Locations: 1 country
• Sites: 1

Brief Summary

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.

Conditions

Attention Deficit Hyperactivity Disorder (ADHD)

Keywords

Attention Deficit Disorder with Hyperactivity; Artificial Intelligence; Posture; Machine Learning; child

Outcome Measures

Primary Outcomes (1)

• Prediction of ADHD Diagnosis Using Biomechanical and Postural Parameters

  Diagnostic accuracy (sensitivity, specificity, overall accuracy, AUC) of a machine learning model developed using postural, balance, pedobarographic, and anthropometric parameters will be evaluated in distinguishing ADHD and control children.

  Baseline (Single assessment session)

Secondary Outcomes (13)

• Postural Assessment via Mobile Posture App

  Baseline

• Postural Assessment - New York Posture Rating Test (NYPRT)

  Baseline

• Plantar Pressure Distribution

  Baseline

• Foot Posture Assessment - Foot Posture Index (FPI-6) Total Score

  Baseline

• Sway Path Length

  Baseline

Study Arms (2)

ADHD Group

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.

Healthy Control Group

This group includes age- and gender-matched children (10-14 years old) without a diagnosis of ADHD or other neurodevelopmental/psychiatric 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.

Eligibility Criteria

• Age: 10 Years - 14 Years
• Sex: all
• Healthy Volunteers: Yes
• Age Groups: Child (0-17)
• Sampling Method: Non-Probability Sample

Study Population

Children aged 10 to 14 years, including both those diagnosed with ADHD and healthy peers, attending a middle school in the Eyüpsultan district. Participants will be selected based on their eligibility criteria and categorized accordingly.

You may qualify if:

• 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.

You may not qualify if:

• 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

Sponsors & Collaborators

• Biruni University: lead

Study Sites (1)

Biruni University, Faculty of Health Sciences

Istanbul, Turkey (Türkiye)

RECRUITING

MeSH Terms

Conditions

Attention Deficit Disorder with Hyperactivity

Condition Hierarchy (Ancestors)

Attention Deficit and Disruptive Behavior Disorders; Neurodevelopmental Disorders; Mental Disorders

Study Officials

• Öykü Ak, MSc Candidate

  Biruni University, Faculty of Health Sciences

  PRINCIPAL INVESTIGATOR

Central Study Contacts

Güzin Kaya Aytutuldu, Asst prof.

CONTACT

+90 5366265884; guzinkaya14@gmail.com

Study Design

• Study Type: observational
• Observational Model: CASE CONTROL
• Time Perspective: PROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Assistant Professor

Study Record Dates

• First Submitted: July 7, 2025
• First Posted: September 18, 2025
• Study Start: May 9, 2025
• Primary Completion: December 1, 2025
• Study Completion: March 1, 2026
• Last Updated: September 18, 2025

Record last verified: 2025-09

Locations

TU (1)