國家衛生研究院 NHRI:Item 3990099045/14957
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    题名: Predicting arm nonuse in individuals with good arm motor function after stroke rehabilitation: A machine learning study
    作者: Chen, YW;Li, YC;Huang, CY;Lin, CJ;Tien, CJ;Chen, WS;Chen, CL;Lin, KC
    贡献者: Institute of Biomedical Engineering and Nanomedicine
    摘要: 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. A total of 78 participants were categorized into 2 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 5 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.
    日期: 2023-02-25
    關聯: International Journal of Environmental Research and Public Health. 2023 Feb 25;20(5):Article number 4123.
    Link to: http://dx.doi.org/10.3390/ijerph20054123
    Cited Times(Scopus): https://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85150040978
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