Bayesian analysis of manual dexterity in children with cerebral palsy using a computerized rehabilitation tool

Aim: Cerebral palsy (CP) is one of the most common motor neurodevelopmental disorders, affecting approximately three in every thousand live births in North America. The study aims to investigate and identify the factors influencing manual dexterity performance among children with CP and typically developing (TD) children according to the Manual Ability Classification System (MACS) levels. Methods: A total of 100 children aged 4 to 12 years were enrolled, including 50 diagnosed with CP and 50 TD children. Manual dexterity performance was assessed across MACS levels. A Bayesian seemingly unrelated regression (BayesSUR) framework was applied to identify influential factors, explicitly accounting for interrelationships among multiple response variables. This probabilistic approach allowed for robust estimation under uncertainty while incorporating correlations across outcomes. Results: The BayesSUR analysis revealed distinct factor influences MACS levels. For children with mild CP (MACS level 1), object type had the strongest effect on response time. For moderately affected children (MACS level 2), direction most strongly influenced movement error, while age impacted both error and success rate. Among severely affected children (MACS level 3) and TD children, gender emerged as the dominant factor influencing response time. However, the low inclusion probabilities of other factors suggest that additional data and validation are warranted. Conclusions: The findings highlight the importance of considering both individual characteristics and task-specific factors when designing interventions to improve manual dexterity in children with CP. These results contribute to a better understanding of the key determinants influencing motor performance and may guide the development of more effective therapeutic and rehabilitation strategies. The Trial Registration Number: CTRI/2018/07/014900.