Attention Deficit Hyperactivity Disorder (ADHD)NCT07180758
Prediction of ADHD in Children Using Pedobarographic and Postural Data
An observational study of Attention Deficit Hyperactivity Disorder (ADHD), run by Biruni University at 1 site in Turkey (Türkiye).
Trial 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. Read more about this study
- Who is it for?
- Ages 10 to 14 yearsHealthy volunteers can take part
- Conditions the study is for
- Attention Deficit Hyperactivity Disorder (ADHD)
- Type of treatment
- Injection or infusion
- How many people
- 100 participants
- Where
- 1 siteTurkey (Türkiye)
- Study dates
- 9 May 2025to Mar 2026
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 10 to 14 years
- All sexes
- Healthy volunteers accepted
- Child
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.
Who may be able to join
- 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.
Who cannot join
- 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 locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
Turkey (Türkiye)1 site, 1 recruiting
- Recruiting
Biruni University, Faculty of Health Sciences
Istanbul
Güzin Kaya Aytutuldu, Asst. Prof., +90 536 625 5884, guzinkaya14@gmail.com
Öykü Ak, MSc Candidate
About this study
Official title: Prediction of Attention Deficit Hyperactivity Disorder (ADHD) in Middle School Children Using Machine Learning With Pedobarographic Data
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.
Read the 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.
- Keywords
- Attention Deficit Disorder with Hyperactivity, Artificial Intelligence, Posture, Machine Learning, child
- Medical subject headings
- Attention Deficit Disorder with Hyperactivity
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
| Study group | Role | What they receive |
|---|---|---|
| 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. |
What is being measured
The primary outcome is the main question the study is designed to answer.
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.
Measured: Baseline (Single assessment session)
Secondary outcomes (13)
Postural Assessment via Mobile Posture App
Postural alignment will be assessed using the Mobile Posture App in four views (frontal, sagittal-right, sagittal-left, and posterior). The application provides angular deviations in degrees (°) for multiple anatomical landmarks, including:
Frontal view: ears, shoulders, hips, and trunk inclination (tilt from vertical midline).
Sagittal views (right and left): trunk inclination (forward/backward tilt relative to vertical).
Posterior view: ears, shoulders, hips, and trunk inclination (tilt from vertical midline).
Higher values indicate greater postural deviation or asymmetry, while lower values indicate closer alignment to neutral posture.
Measured: Baseline
Postural Assessment - New York Posture Rating Test (NYPRT)
Postural alignment will be evaluated with the New York Posture Rating Test. The test yields a total score from 13 to 65 based on visual ratings of multiple body segments in standing posture. Higher scores indicate better postural alignment; lower scores indicate poorer posture.
Measured: Baseline
Plantar Pressure Distribution
Plantar pressure distribution will be assessed using the Metisens Static Pedobarography system. The outcome is expressed as % of total load distributed across left vs right foot and forefoot, midfoot, hindfoot regions. Higher asymmetry indicates poorer postural control.
Measured: Baseline
Foot Posture Assessment - Foot Posture Index (FPI-6) Total Score
Foot posture will be assessed using the Foot Posture Index (FPI-6), which evaluates six criteria including talar head palpation, supra/inframalleolar curvature, calcaneal frontal plane position, talonavicular bulging, medial arch height, and forefoot abduction/adduction. Each item is scored on a 5-point scale (-2 to +2), yielding a total score from -12 to +12.
Higher positive scores indicate increased pronation.
Negative scores indicate supination.
Scores around 0 indicate neutral foot posture.
Measured: Baseline
Sway Path Length
Sway path length of the Center of Pressure (COP) will be measured using the Metisens Stabilometry system during 20-second quiet stance. Greater path length indicates poorer postural stability.
Measured: Baseline
Number of Sways
Number of COP oscillations will be recorded using the Metisens Stabilometry system during quiet stance. Higher values indicate reduced stability.
Measured: Baseline
Anteroposterior Stability Index (APSI)
APSI will be calculated using the Metisens Stabilometry system, reflecting variability of COP in the anteroposterior direction. Higher values indicate poorer stability.
Measured: Baseline
Mediolateral Stability Index (MLSI)
MLSI will be calculated using the Metisens Stabilometry system, reflecting variability of COP in the mediolateral direction. Higher values indicate poorer stability.
Measured: Baseline
Stability Index (SI)
Overall Stability Index (SI) will be calculated using the Metisens Stabilometry system, combining both AP and ML variability. Higher values indicate poorer overall balance.
Measured: Baseline
Balance Performance - Y-Balance Test (Dynamic Balance)
Dynamic balance will be assessed using the Y-Balance Test. Participants will perform reach trials in the anterior, posteromedial, and posterolateral directions for both right and left legs. Each direction will be tested three times, and the mean reach distance (cm) of the three trials will be recorded for each leg and direction. Longer reach distances indicate better dynamic balance.
Measured: Baseline
Balance Performance - Flamingo Balance Test (Static Balance)
Static balance will be assessed using the Flamingo Balance Test, validated for use in children. Participants will perform the test on both the right and left legs. Each trial lasts 30 seconds, and the number of balance errors (e.g., touching the floor, losing balance) will be recorded. The test will be performed three times per leg, and the mean error count will be calculated. Higher number of errors indicates poorer static balance.
Measured: Baseline
Physical Activity Level - International Physical Activity Questionnaire, Short Form (IPAQ-SF)
Physical activity levels will be assessed using the International Physical Activity Questionnaire - Short Form (IPAQ-SF). The questionnaire records the frequency (days per week) and duration (minutes per day) of walking, moderate-intensity, and vigorous-intensity physical activity, as well as sedentary time, during the last 7 days. Data are converted into MET-minutes/week scores using standardized coefficients (walking: 3.3 METs, moderate: 4.0 METs, vigorous: 8.0 METs, sitting: 1.5 METs).
Higher total MET-min/week indicates higher physical activity level.
Participants are categorized as Inactive (Category 1), Minimally Active (Category 2), or Highly Active (Category 3) based on scoring guidelines.
Measured: Baseline (within the last 7 days before assessment)
ADHD Symptom Severity
Measured using the DSM-IV-Based Assessment and Screening Scale for Behavioral Disorders in Children and Adolescents (Atilla Turgay), completed by both parents and teachers.
Measured: Baseline
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted18 Sep 2025
- Study start9 May 2025
- Primary completionDec 2025 (estimated)
- Study completionMar 2026 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Attention Deficit Hyperactivity Disorder (ADHD)Studies of the same condition, open and under way.
- More trials run by Biruni UniversityThe sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Case-control
- Time perspective
- Prospective
- Enrolment
- 100 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Biruni University
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Guzin Kaya Aytutuldu, Biruni University
- Investigators
- Öykü Ak, MSc Candidate, Principal investigator, Biruni University, Faculty of Health Sciences
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07180758
- Sponsor's protocol ID
- Uni.Biruni
- Status last verified
- Sep 2025
- First submitted
- 7 Jul 2025
- Last updated
- 18 Sep 2025
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
Study at a glance
From the registry record, last updated by the sponsor on 18 Sep 2025. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Prediction of Attention Deficit Hyperactivity Disorder (ADHD) in Middle School Children Using Machine Learning With Pedobarographic Data
- ClinicalTrials.gov ID
- NCT07180758
- Acronym / sponsor ID
- Uni.Biruni
- Keywords
- Attention Deficit Disorder with Hyperactivity, Artificial Intelligence, Posture, Machine Learning, child
- Study type
- Observational
- Recruitment status
- Recruiting
- Enrolment
- 100 participants (estimated)
- Lead sponsor
- Biruni UniversityUniversity, hospital or other
- Study start
- 9 May 2025 (actual)
- Primary completion
- Dec 2025 (estimated)
- Study completion
- Mar 2026 (estimated)
- First posted
- 18 Sep 2025 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Study design
- Observational model
- Case-control
- Time perspective
- Prospective
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| 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. |
Who may qualify
- Ages
- 10 to 14 years
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Child
- 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.
Key inclusion criteria (4 listed)
- 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.
Key exclusion criteria (4 listed)
- 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
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Primary outcome
Prediction of ADHD Diagnosis Using Biomechanical and Postural Parameters
Time frame: 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.
13 secondary outcomes
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)
ADHD Symptom Severity
Time frame: Baseline
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 18 Sep 2025.
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
Öykü Ak, MSc Candidate principal investigator
Biruni University, Faculty of Health Sciences
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Attention Deficit Hyperactivity Disorder (ADHD), run by Biruni University at 1 site in Turkey (Türkiye).
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 4 inclusion and 4 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: looking for participants now, as of the sponsor's last update on 18 Sep 2025. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in Turkey (Türkiye). Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Güzin Kaya Aytutuldu, Asst prof. (+90 5366265884, guzinkaya14@gmail.com), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Prediction of ADHD Diagnosis Using Biomechanical and Postural Parameters (time frame: Baseline (Single assessment session)). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 18 Sep 2025.