Peer-reviewed article

Development, validation and effectiveness of a parents led neuromotor training module with AI-based movement analysis for improving motor proficiency and sports participation in children with developmental coordination disorder: a protocol for an assessor-blinded randomized superiority controlled trial.

Hullumani S, Qureshi I · 11 Sept 2026

Abstract

Introduction Developmental Coordination Disorder (DCD) is a common neurodevelopmental condition characterized by impaired motor coordination, poor balance, reduced participation in physical activities, and limitations in functional performance. Conventional rehabilitation often requires prolonged therapist supervision and may not be consistently accessible, particularly in resource-limited settings. Parent-led rehabilitation and artificial intelligence (AI)-based movement analysis have emerged as promising approaches to enhance motor outcomes; however, evidence integrating these strategies remains limited. This manuscript presents the protocol for a randomized controlled trial evaluating a structured Parent-Led Neuromotor Training Programme integrated with AI-based movement analysis in children with DCD. Methods and analysis This protocol describes an assessor-blinded, two-arm, parallel-group randomized superiority controlled trial involving 62 children aged 7-14 years diagnosed with DCD according to the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5) criteria and scoring below the age-expected range on the Movement Assessment Battery for Children-Second Edition (MABC-2). Participants will be randomly allocated (1:1) to either an intervention group receiving a Parent-Led Neuromotor Training Programme integrated with AI-based movement analysis in addition to conventional physiotherapy or a control group receiving conventional physiotherapy alone. Parents will deliver supervised home-based training for 30-45 min per session, 5 days per week, for 12 weeks. Movement analysis will be performed using Google MediaPipe Pose, a validated markerless pose-estimation framework, to quantify joint kinematics, movement symmetry, coordination, movement smoothness, movement accuracy, movement speed, and task-completion time from standardized video recordings. The primary outcome will be motor proficiency, assessed using the Bruininks-Oseretsky Test of Motor Proficiency, Second Edition (BOT-2). Secondary outcomes will include AI-based movement metrics such as symmetry index, movement variability, and coordination score, as well as parental adherence, engagement, satisfaction, balance, and agility outcomes, parental adherence and satisfaction, and AI-derived movement metrics. The sample size was calculated to detect a clinically meaningful between-group difference with 80% power at a 5% significance level. Statistical analyses will follow the intention-to-treat principle, with linear mixed-effects models used to compare repeated measurements between groups. Missing data will be handled using appropriate imputation methods where necessary. Clinical trial registration https://ctri.nic.in/Clinicaltrials/regtrial.php?trialid=164461&EncHid=98252.11023&modid=1&compid=19 Identifier [CTRI/2026/06/112632].