Smart Steps to Growth: Unleashing AI for Assessing Motor Skills in School Children

N/AActive Not RecruitingNCT07083726

Chang Gung Memorial Hospital250 enrolled

Overview

The purpose of this study is to develop an AI-based automated motor function assessment system (AIMAS) to improve early identification of developmental coordination disorder (DCD) in school-age children. The main hypothesis for this study is: Integrating AI into motor skill assessments will enhance the reliability, validity, efficiency, and accuracy of evaluating motor performance in children aged 6 to 12.

Study Type

OBSERVATIONAL

Enrollment

250

Conditions

AIMAS Developmental Coordination Disorder

Eligibility

Sex: ALLMin age: 6 YearsMax age: 12 Years

Medical Language ↔ Plain English

Inclusion Criteria: * Children aged 6 to 12 years. * For DCD group: formal diagnosis of Developmental Coordination Disorder (DCD). * For typically developing group: no disabilities or developmental delays. Exclusion Criteria: * Acute illnesses (e.g., pneumonia, upper gastrointestinal hemorrhage). * Significant developmental delays or disabilities. * Genetic diseases or disorders. * Neurological disorders or injuries.

Locations (1)

Chang Gung Memorial Hospital

Taoyuan District, Taiwan

Outcomes

Primary Outcomes

Motor Function Assessment Score

AI-based Motor Function Test (Higher scores indicate better motor performance and lower risk of Developmental Coordination Disorder (DCD).)

Time frame: Baseline

DCD Risk Classification

AIMAS System Classification (System's ability to identify children at risk of DCD, compared to clinical diagnosis.)

Time frame: Baseline

Secondary Outcomes

The Bruininks-Oseretsky Test of Motor Proficiency, Second Edition (BOT-2)

Time frame: baseline

Data from ClinicalTrials.gov

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