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A Comparison of the Functioning and Disability Levels of Children With Hemiplegic and Diplegic Cerebral Palsy Based on ICF-CY Components.

Hasan BingolDilan Demirtaş Karaoba
Published in: Perceptual and motor skills (2024)
We compared children with hemiplegic and diplegic cerebral palsy (CP) using the conceptual framework of the International Classification of Functioning, Disability and Health: Child and Youth version (ICF-CY). We enrolled 42 children with CP aged 5 - 13 years old ( M age = 9.57, SD = 2.8 years). We assessed their trunk control and dynamic balance with the Trunk Control Measurement Scale (TCMS) and the Timed Up and Go test (TUG), and we used ABILHAND-Kids and Assessment of Life Habits (Life-H) to assess their manual ability and participation with activities of daily living. We administered the European Child Environment Questionnaire (ECEQ) to identify relevant environmental factors. We employed structural equation modeling (SEM) to identify specific factors contributing to potential differences between these CP groups. Children with hemiplegic CP demonstrated significantly better outcomes in terms of trunk control, dynamic balance, and environmental factors compared to those with diplegic CP ( p < .05). In contrast, children with diplegic CP demonstrated superior outcomes regarding manual ability, compared to those with hemiplegic CP ( p < .001). In our structural equation models, trunk control strongly predicted both dynamic balance (0.75) and environmental factors (0.74). Moreover, the relationships between trunk control and participation in daily and social activities were 0.54 and 0.47, respectively. Impaired trunk control and dynamic balance were significant contributors to increased activity restrictions and environmental barriers in children with diplegic CP. This suggests that improving disability and functioning in children with diplegic CP requires a focus on trunk control training and dynamic balance exercises.
Keyphrases
  • cerebral palsy
  • young adults
  • mental health
  • healthcare
  • physical activity
  • magnetic resonance
  • machine learning
  • computed tomography
  • deep learning
  • weight loss
  • clinical evaluation