Predicting Arm Nonuse in Individuals with Good Arm Motor Function after Stroke Rehabilitation: A Machine Learning Study

Yu-Wen Chen; Yi-Chun Li; Chien-Yu Huang; Chia-Jung Lin; Chia-Jui Tien; Wen-Shiang Chen; Chia-Ling Chen; Keh-Chung Lin

doi:10.3390/ijerph20054123

Predicting Arm Nonuse in Individuals with Good Arm Motor Function after Stroke Rehabilitation: A Machine Learning Study

Int J Environ Res Public Health. 2023 Feb 25;20(5):4123. doi: 10.3390/ijerph20054123.

Authors

Yu-Wen Chen^{1

2}, Yi-Chun Li^{1

3}, Chien-Yu Huang¹, Chia-Jung Lin¹, Chia-Jui Tien¹, Wen-Shiang Chen^{4

5

6}, Chia-Ling Chen^{7

8}, Keh-Chung Lin^{1

9}

Affiliations

¹ School of Occupational Therapy, National Taiwan University College of Medicine, 17, F4, Xu-Zhou Road, Taipei 100, Taiwan.
² Department of Speech Language Pathology and Audiology, National Taipei University of Nursing and Health Sciences, 365, Mingde Road, Taipei 112, Taiwan.
³ Department of Occupational Therapy, I-Shou University College of Medicine, 8, Yida Road, Jiaosu Village, Yanchao District, Kaohsiung 824, Taiwan.
⁴ Department of Physical Medicine and Rehabilitation, College of Medicine, National Taiwan University, Taipei 10048, Taiwan.
⁵ Department of Physical Medicine and Rehabilitation, National Taiwan University Hospital, Taipei 10048, Taiwan.
⁶ Institute of Biomedical Engineering and Nanomedicine, National Health Research Institutes, 35, Keyan Road, Zhunan District, Miaoli 350, Taiwan.
⁷ Department of Physical Medicine and Rehabilitation, Chang Gung Memorial Hospital at Linkou, 5 Fusing Street, Gueishan District, Taoyuan 333, Taiwan.
⁸ Graduate Institute of Early Intervention, College of Medicine, Chang Gung University, 259 Wenhua 1st Road, Gueishan District, Taoyuan 333, Taiwan.
⁹ Division of Occupational Therapy, Department of Physical Medicine and Rehabilitation, National Taiwan University Hospital, 7 Chung-Shan South Road, Taipei 100, Taiwan.

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. 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.

Keywords: arm nonuse; chronic stroke; machine learning; predictors; stroke rehabilitation.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Activities of Daily Living
Arm
Humans
Recovery of Function
Retrospective Studies
Stroke Rehabilitation*
Stroke*
Upper Extremity

Grants and funding

This research was funded by the National Health Research Institutes (grant numbers NHRI-EX109-10929PI, NHRI-EX110-10929PI, NHRI-EX111-10929PI) and the Ministry of Science and Technology (grant numbers MOST-108-2314-B-002-165-MY3 and MOST-111-2314-B-002-168-MY3) in Taiwan.