Prediction of ADHD in Children Using Pedobarographic and Postural Data
For patients and families
In plain language
An automatic summary of structured registry data. It is an orientation aid, not a substitute for the official protocol or a physician assessment.
- What is being studied
- This is an observational study: the protocol does not assign a study treatment.
- Who it may be relevant to
- Registry conditions: Attention Deficit Hyperactivity Disorder (ADHD). Basic parameters: 10 years — 14 years · All.
- What needs checking
- Age, condition and sex are only basic indicators. Prior treatment, laboratory values and other mandatory requirements appear in the eligibility criteria below.
- Where it takes place
- Turkey (Türkiye)
- Next step
- Save the trial, show it to the treating physician, and confirm current recruitment with the study center. Costs, documents and travel →
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Official title
Prediction of Attention Deficit Hyperactivity Disorder (ADHD) in Middle School Children Using Machine Learning With Pedobarographic Data
Overview
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.
Detailed 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.
Primary outcome measures
- Prediction of ADHD Diagnosis Using Biomechanical and Postural Parameters [Time frame: Baseline (Single assessment session)]
Secondary outcome measures (12)
- Postural Assessment via Mobile Posture App [Time frame: Baseline]
- Postural Assessment - New York Posture Rating Test (NYPRT) [Time frame: Baseline]
- Plantar Pressure Distribution [Time frame: Baseline]
- Foot Posture Assessment - Foot Posture Index (FPI-6) Total Score [Time frame: Baseline]
- Sway Path Length [Time frame: Baseline]
- Number of Sways [Time frame: Baseline]
- Anteroposterior Stability Index (APSI) [Time frame: Baseline]
- Mediolateral Stability Index (MLSI) [Time frame: Baseline]
- Stability Index (SI) [Time frame: Baseline]
- Balance Performance - Y-Balance Test (Dynamic Balance) [Time frame: Baseline]
- Balance Performance - Flamingo Balance Test (Static Balance) [Time frame: Baseline]
- Physical Activity Level - International Physical Activity Questionnaire, Short Form (IPAQ-SF) [Time frame: Baseline (within the last 7 days before assessment)]
Eligibility 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
Criteria are shown verbatim from the registry (in English). Final eligibility is always assessed by the study center.
Healthy volunteers: Yes
Study design
- Observational model
- Case-control
Study locations
Turkey (Türkiye) · 1 center
- Biruni University, Faculty of Health Sciences — Istanbul
Identifiers
NCT: NCT07180758 · Uni.Biruni