Literature DB >> 24113336

Predicting clinically significant changes in motor and functional outcomes after robot-assisted stroke rehabilitation.

Yu-wei Hsieh1, Keh-chung Lin2, Ching-yi Wu3, Hen-yu Lien4, Jean-lon Chen5, Chih-chi Chen5, Wei-han Chang6.   

Abstract

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 outpatients with stroke (N=55).
INTERVENTIONS: Patients with stroke received RT for 90 to 105min/d, 5d/wk, 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, Box and Block Test (BBT) score, and FMA distal score.
RESULTS: Statistical analysis showed that the BBT score (odds ratio[OR]=1.06; P=.04) was a significant predictor of clinically important changes in the FMA. Being a woman (OR=3.9; P=.05) and BBT score (OR=1.07; P=.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 (OR=1.07; P=.02). The R(2) values for the 3 logistic regression models were low (.114-.272).
CONCLUSIONS: The 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.
Copyright © 2014 American Congress of Rehabilitation Medicine. Published by Elsevier Inc. All rights reserved.

Entities:  

Keywords:  AOU; BBT; Box and Block Test; FMA; Fugl-Meyer Assessment; MAL; Motor Activity Log; Prognosis; QOM; RT; Rehabilitation; Stroke; VIF; amount of use; quality of movement; robot-assisted therapy; variance inflation factor

Mesh:

Year:  2013        PMID: 24113336     DOI: 10.1016/j.apmr.2013.09.018

Source DB:  PubMed          Journal:  Arch Phys Med Rehabil        ISSN: 0003-9993            Impact factor:   3.966


  10 in total

1.  Age is negatively associated with upper limb recovery after conventional but not robotic rehabilitation in patients with stroke: a secondary analysis of a randomized-controlled trial.

Authors:  Francesca Cecchi; Marco Germanotta; Claudio Macchi; Angelo Montesano; Silvia Galeri; Manuela Diverio; Catiuscia Falsini; Monica Martini; Rita Mosca; Emanuele Langone; Dionysia Papadopoulou; Maria Chiara Carrozza; Irene Aprile
Journal:  J Neurol       Date:  2020-08-25       Impact factor: 4.849

2.  Kinematic measures of Arm-trunk movements during unilateral and bilateral reaching predict clinically important change in perceived arm use in daily activities after intensive stroke rehabilitation.

Authors:  Hao-ling Chen; Keh-chung Lin; Rong-jiuan Liing; Ching-yi Wu; Chia-ling Chen
Journal:  J Neuroeng Rehabil       Date:  2015-09-21       Impact factor: 4.262

3.  Predictors of poststroke health-related quality of life in Nigerian stroke survivors: a 1-year follow-up study.

Authors:  Ashiru Mohammad Hamza; Nabilla Al-Sadat; Siew Yim Loh; Nowrozy Kamar Jahan
Journal:  Biomed Res Int       Date:  2014-05-28       Impact factor: 3.411

4.  Predictors of activities of daily living outcomes after upper limb robot-assisted therapy in subacute stroke patients.

Authors:  Marco Franceschini; Michela Goffredo; Sanaz Pournajaf; Stefano Paravati; Maurizio Agosti; Francesco De Pisi; Daniele Galafate; Federico Posteraro
Journal:  PLoS One       Date:  2018-02-21       Impact factor: 3.240

5.  Retrospective Robot-Measured Upper Limb Kinematic Data From Stroke Patients Are Novel Biomarkers.

Authors:  Michela Goffredo; Sanaz Pournajaf; Stefania Proietti; Annalisa Gison; Federico Posteraro; Marco Franceschini
Journal:  Front Neurol       Date:  2021-12-21       Impact factor: 4.003

6.  Do somatosensory deficits predict efficacy of neurorehabilitation using neuromuscular electrical stimulation for moderate to severe motor paralysis of the upper limb in chronic stroke?

Authors:  Keita Tsuzuki; Michiyuki Kawakami; Takuya Nakamura; Osamu Oshima; Nanako Hijikata; Mabu Suda; Yuka Yamada; Kohei Okuyama; Tetsuya Tsuji
Journal:  Ther Adv Neurol Disord       Date:  2021-08-25       Impact factor: 6.570

7.  The structure, processes, and outcomes of stroke rehabilitation in Ghana: A study protocol.

Authors:  Cosmos Yarfi; Gifty Gyamah Nyante; Anthea Rhoda
Journal:  Front Neurol       Date:  2022-08-24       Impact factor: 4.086

8.  tDCS and Robotics on Upper Limb Stroke Rehabilitation: Effect Modification by Stroke Duration and Type of Stroke.

Authors:  Sofia Straudi; Felipe Fregni; Carlotta Martinuzzi; Claudia Pavarelli; Stefano Salvioli; Nino Basaglia
Journal:  Biomed Res Int       Date:  2016-03-31       Impact factor: 3.411

9.  Does Upper Extremity Proprioceptive Training Have an Impact on Functional Outcomes in Chronic Stroke Patients?

Authors:  Numan Melik Ocal; Nuray Alaca; Mehmet Kerem Canbora
Journal:  Medeni Med J       Date:  2020-06-30

10.  Predicting Clinically Significant Improvement After Robot-Assisted Upper Limb Rehabilitation in Subacute and Chronic Stroke.

Authors:  Jae Joon Lee; Joon-Ho Shin
Journal:  Front Neurol       Date:  2021-07-01       Impact factor: 4.003

  10 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.