PNF and Movement

Korea Proprioceptive Neuromuscular Facilitation Association (대한고유수용성신경근촉진법학회)
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Predictive Analyses for Activities of the Upper Extremity and Daily Living based on Impairment of the Upper Extremity in People with Stroke - Preliminary Study using Clinical Scales -

뇌졸중 환자의 위팔 손상 수준에 따른 위팔 활동과 일상생활 활동의 예측도 분석 - 임상적 평가를 이용한 예비 연구 -

  • Jung, Young-Il (Department of Physical Therapy, Drim Sol Hospital) ;
  • Woo, Young-Keun (Department of Physical Therapy, College of Medical Sciences, Jeonju University)
  • Received : 2018.10.02
  • Accepted : 2018.10.18
  • Published : 2018.12.31
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Abstract

Purpose: This study analyzes the predictive power of upper extremity activity and the activities of daily living in patients with stroke using an easy-to-use evaluation tool. Methods: The Fugl-Meyer assessment (FMA) of the upper extremity and action research arm test (ARAT) are performed, and the Korean modified Barthel index (K-MBI) is measured. The predictive power of the upper extremity activity level and the daily activity level are analyzed using regression analysis. The statistical significance level is 0.05. Results: The coefficient of determination, R2, for predicting the ARAT using FMA was high at 0.88, but the regression equation for predicting the K-MBI using the FMA and ARAT did not show a statistically significant difference. Conclusion: The assessment of the upper extremity should be performed at the activity level, as well as the impairment level. The assessment for predicting the activities of daily living should be carried out for each level of the international classification of functioning (ICF), disability, and health, which can be linked to daily life, in addition to the assessment of the upper arm. Future research should conduct more diverse analyses using the ICF assessment tools at various levels.

Keywords

Table 1. General characteristics of subjects

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Table 2. The equations for FMA on the ARAT by single regression analysis

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Table 3. The equations for FMA on the K-MBI by single regression analysis

GOSOBJ_2018_v16n3_495_t0003.png 이미지

Table 4. The equations for ARAT on the K-MBI by single regression analysis

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Table 5. The equations for FMA and ARAT on the K-MBI by multiple regression analysis

GOSOBJ_2018_v16n3_495_t0005.png 이미지

Table 6. Output for multiple regression analyses for the prediction of the KMBI from the FMA and ARAT

GOSOBJ_2018_v16n3_495_t0006.png 이미지

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