學院	醫學院
系所	職能治療學系
題名	Predicting arm nonuse in individuals with good arm motor function after stroke rehabilitation: A machine learning study.
作者	Chen, Y-W., Li, Y-C., Huang, C-Y., Lin, C-J., Tien, C-J., Chen, W-S., Chen, C-L., & Lin, K-C.*
期刊名稱	International Journal of Environmental Research and Public Health
發表日期	2023
著作性質	原著
語文	英文
關鍵字	stroke rehabilitation; arm nonuse; predictors; chronic stroke; machine learning
摘要	Abstract: Many stroke survivors demonstrate arm nonuse despite good arm motor function. This retrospective secondary analysis aims to identify predictors of arm nonusers with good arm motor function after stroke rehabilitation. 78 participants were categorized into two groups using the Fugl- Meyer Assessment Upper Extremity Scale (FMA-UE) and the Motor Activity Log Amount of Use (MAL-AOU). Group 1 comprised participants with good motor function (FMA-UE ≥31) and low daily upper limb use (MAL-AOU ≤2.5), and group 2 comprised all other participants. Feature selection analysis was performed on 20 potential predictors to identify the five most important predictors for group membership. Predictive models were built with the five most important predictors using four algorithms. The most important predictors were preintervention scores on the FMA-UE, MAL-Quality of Movement, Wolf Motor Function Test-Quality, MAL-AOU, and Stroke Self-Efficacy Questionnaire. Predictive models classified the participants with accuracies ranging from 0.75 to 0.94 and areas under the receiver operating characteristic curve ranging from 0.77 to 0.97. The result indicates that measures of arm motor function, arm use in activities of daily living, and self-efficacy could predict postintervention arm nonuse despite good arm motor function in stroke. These assessments should be prioritized in the evaluation process to facilitate the design of individualized stroke rehabilitation programs to reduce arm nonuse.