Performance of Activities of Daily Living in Typically Developing Children in Korea: Normative Value of K-MBI.

Objective: To determine the normative values of the Korean version of the Modified Barthel Index (K-MBI) score for typically developing children in Korea and assess its suitability for use in children. Methods: Rehabilitation physicians and occupational therapists with children were invited through an online platform to participate in a survey assessing their children's performance of activity of daily living (ADL) using the K-MBI. The questionnaire encompassed queries on sociodemographic information of children and the assessment criteria outlined in the K-MBI. The standardized K-MBI scores by age were estimated using the nonlinear least squares method. Results: The analysis incorporated responses from a total of 206 individuals. K-MBI total scores showed a rapid increase over the first 8 years of life, with 99% of children achieving a score of 90 or higher by age 8. Mobility scores exhibited a swift increase during early childhood, surpassing 90% of the maximum score at 3 years of age and nearing 100% at 7 years of age. In contrast, self-care scores demonstrated a more gradual advancement, achieving approximately 100% of the maximum score by the age of 10 years. Conclusion: Age-specific normative values for K-MBI scores of typically developing children were established, which can be used as a reference in clinical care. While the K-MBI captured the overall trajectory of children's ADL development, it did not discern subtle differences across various developmental stages. There is a need for the development of more refined assessment tools tailored specifically to children. [ABSTRACT FROM AUTHOR]

Copyright of Annals of Rehabilitation Medicine is the property of Korean Academy of Rehabilitation Medicine and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)