Prediction of Attention Deficit Hyperactivity Disorder (ADHD) in Middle School Children Using Machine Learning With Pedobarographic Data
试验速览
- 阶段
- 不适用
- 状态
- 招募中
- 发起方
- 入组人数
- 100
- 试验地点
- 1
- 主要终点
- Prediction of ADHD Diagnosis Using Biomechanical and Postural Parameters
研究概览
简要总结
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.
详细描述
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.
研究设计
- 研究类型
- Observational
- 观察模型
- Case Control
- 时间视角
- Prospective
入排标准
- 年龄范围
- 10 Years 至 14 Years（Child）
- 性别
- All
- 接受健康志愿者
- 是
入选标准
- •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.
排除标准
- •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
结局指标
主要结局
Prediction of ADHD Diagnosis Using Biomechanical and Postural Parameters
时间窗: Baseline (Single assessment session)
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.
次要结局
- 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)
- Number of Sways(Baseline)
- Anteroposterior Stability Index (APSI)(Baseline)
- Mediolateral Stability Index (MLSI)(Baseline)
- Stability Index (SI)(Baseline)
- Balance Performance - Y-Balance Test (Dynamic Balance)(Baseline)
- Balance Performance - Flamingo Balance Test (Static Balance)(Baseline)
- Physical Activity Level - International Physical Activity Questionnaire, Short Form (IPAQ-SF)(Baseline (within the last 7 days before assessment))
- ADHD Symptom Severity(Baseline)
研究者
Guzin Kaya Aytutuldu
Assistant Professor
Biruni University