Prediction of ADHD in Children Using Pedobarographic and Postural Data

Biruni University

Status: Enrolling

Conditions: Attention Deficit Hyperactivity Disorder (ADHD)

Study type: Observational

Funder types: Other

Identifiers

NCT07180758

Uni.Biruni

Details and patient eligibility

About

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.

Full description

This study aims to predict Attention Deficit Hyperactivity Disorder (ADHD) in middle school children by utilizing pedobarographic and postural parameters in combination with machine learning techniques. The study will include approximately 100 children aged 10-14, consisting of 50 children clinically diagnosed with ADHD and 50 healthy controls. Participants will be selected with permissions from the Eyüpsultan District Directorate of National Education and relevant school administrations in Istanbul.

All participants will undergo anthropometric assessments, including height, weight, BMI, waist, neck, and hip circumferences, and skinfold thickness (triceps, subscapular, suprailiac, abdominal). Postural analysis will be conducted using the Mobile Posture Assessment App and the New York Posture Rating Test, while foot posture will be evaluated with the Foot Posture Index (FPI).

Static and dynamic balance will be evaluated using the Flamingo Balance Test and the Y Balance Test, respectively. For pedobarographic measurements, the Metisens Static Pedobarography and Stabilometry System will be used. Children will stand barefoot for 20 seconds, and parameters such as plantar pressure distribution, contact area ratios, and Center of Pressure (COP) sway metrics (length, area, AP/ML) will be recorded. In addition, physical activity levels will be assessed using the International Physical Activity Questionnaire - Short Form (IPAQ-SF), which measures walking, moderate, and vigorous activities as well as sedentary time during the previous 7 days. Data will be converted into MET-minutes/week and categorized as Inactive, Minimally Active, or Highly Active according to standardized scoring protocols.

ADHD symptoms will be assessed using the DSM-IV-based assessment scale developed by Atilla Turgay, with Parent and Teacher Forms.

Data will be analyzed using statistical software (SPSS) to evaluate group differences and data distributions. Subsequently, machine learning and artificial intelligence algorithms will be employed to develop predictive models. Performance metrics such as accuracy, sensitivity, and specificity will be used to evaluate the model's success.

This study represents a novel attempt to utilize foot biomechanics and postural parameters as input data for machine learning-based ADHD prediction. It aims to offer an accessible, cost-effective, and objective clinical support tool, potentially contributing to early diagnosis strategies in neurodevelopmental disorders.

Enrollment: 100 estimated patients

Sex: All

Ages: 10 to 14 years old

Volunteers: Accepts Healthy Volunteers

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

Trial design

100 participants in 2 patient groups

ADHD Group

Description:

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

Description:

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.