學院 |
醫學院 |
系所 |
職能治療學系 |
題名 |
Predicting clinically significant changes in motor and
functional outcomes after robot-assisted stroke rehabilitation |
作者 |
Hsieh, Y-W., Lin, K-C., Wu, C-Y., Lien, H-Y., Chen, J-L.,
Chen, C-C., & Chang, W-H. |
期刊名稱 |
Archives of Physical
Medicine and Rehabilitation (SCI 期刊) |
發表日期 |
2014 |
著作性質 |
原著 |
語文 |
英文 |
關鍵字 |
Stroke; Robot-assisted
rehabilitation; Outcome prediction; Clinically important change. |
摘要 |
Objective: To investigate the
predictors of minimal clinically important changes on outcome measures after
robot-assisted therapy (RT). Design: Observational cohort study. Setting: Outpatient
rehabilitation clinics. Participants: A cohort of 55
outpatients with stroke. Interventions: Patients with stroke
received RT for 90 to 105 min/day, 5 days/week, for 4 weeks. Main Outcome Measures: Outcome measures,
including the Fugl-Meyer Assessment (FMA) and Motor Activity Log (MAL), were
measured before and after the intervention. Potential predictors include
age, sex, side of lesion, time since stroke onset, finger extension, the
Box and Block Test (BBT) score, and the FMA distal score. Results: The statistical
analysis showed the BBT score (odds ratio, 1.06; P = 0.04) was a
significant predictor of clinically important changes in the FMA. Female sex
(odds ratio, 3.90; P = 0.05) and the BBT score (odds ratio, 1.07; P
= 0.02) were the 2 significant predictors of clinically significant
changes in the MAL amount of use subscale. The BBT score was the significant
predictor of an increased probability of achieving clinically important
changes in the MAL quality of movement subscale (odds ratio, 1.07; P =
0.02). The R2
values for the 3 logistic regression models were low (0.114 to 0.272). Conclusions: Our results revealed
that patients with stroke who had greater manual dexterity measured by the
BBT appear to have a higher probability of achieving clinically significant
motor and functional outcomes after RT. Further
studies are needed to evaluate other potential predictors to improve the
models and validate the findings. |