Prediction of ADHD in Children Using Pedobarographic and Postural Data

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.

• Postural Assessment via Mobile Posture App [Срок оценки: Baseline]
• Postural Assessment - New York Posture Rating Test (NYPRT) [Срок оценки: Baseline]
• Plantar Pressure Distribution [Срок оценки: Baseline]
• Foot Posture Assessment - Foot Posture Index (FPI-6) Total Score [Срок оценки: Baseline]
• Sway Path Length [Срок оценки: Baseline]
• Number of Sways [Срок оценки: Baseline]
• Anteroposterior Stability Index (APSI) [Срок оценки: Baseline]
• Mediolateral Stability Index (MLSI) [Срок оценки: Baseline]
• Stability Index (SI) [Срок оценки: Baseline]
• Balance Performance - Y-Balance Test (Dynamic Balance) [Срок оценки: Baseline]
• Balance Performance - Flamingo Balance Test (Static Balance) [Срок оценки: Baseline]
• Physical Activity Level - International Physical Activity Questionnaire, Short Form (IPAQ-SF) [Срок оценки: Baseline (within the last 7 days before assessment)]