Literature DB >> 23774125

A standardized approach to the Fugl-Meyer assessment and its implications for clinical trials.

Jill See1, Lucy Dodakian, Cathy Chou, Vicky Chan, Alison McKenzie, David J Reinkensmeyer, Steven C Cramer.   

Abstract

BACKGROUND: Standardizing scoring reduces variability and increases accuracy. A detailed scoring and training method for the Fugl-Meyer motor assessment (FMA) is described and assessed, and implications for clinical trials considered.
METHODS: A standardized FMA scoring approach and training materials were assembled, including a manual, scoring sheets, and instructional video plus patient videos. Performance of this approach was evaluated for the upper extremity portion.
RESULTS: Inter- and intrarater reliability in 31 patients were excellent (intraclass correlation coefficient = 0.98-0.99), validity was excellent (r = 0.74-0.93, P < .0001), and minimal detectable change was low (3.2 points). Training required 1.5 hours and significantly reduced error and variance among 50 students, with arm FMA scores deviating from the answer key by 3.8 ± 6.2 points pretraining versus 0.9 ± 4.9 points posttraining. The current approach was implemented without incident into training for a phase II trial. Among 66 patients treated with robotic therapy, change in FMA was smaller (P ≤ .01) at the high and low ends of baseline FMA scores.
CONCLUSIONS: Training with the current method improved accuracy, and reduced variance, of FMA scoring; the 20% FMA variance reduction with training would decrease sample size requirements from 137 to 88 in a theoretical trial aiming to detect a 7-point FMA difference. Minimal detectable change was much smaller than FMA minimal clinically important difference. The variation in FMA gains in relation to baseline FMA suggests that future trials consider a sliding outcome approach when FMA is an outcome measure. The current training approach may be useful for assessing motor outcomes in restorative stroke trials.

Entities:  

Keywords:  motor; outcome measures; reliability; stroke; training; validity

Mesh:

Year:  2013        PMID: 23774125     DOI: 10.1177/1545968313491000

Source DB:  PubMed          Journal:  Neurorehabil Neural Repair        ISSN: 1545-9683            Impact factor:   3.919


  76 in total

1.  Robotic therapy for chronic stroke: general recovery of impairment or improved task-specific skill?

Authors:  Tomoko Kitago; Jeff Goldsmith; Michelle Harran; Leslie Kane; Jessica Berard; Sylvia Huang; Sophia L Ryan; Pietro Mazzoni; John W Krakauer; Vincent S Huang
Journal:  J Neurophysiol       Date:  2015-07-15       Impact factor: 2.714

2.  Automated Forelimb Tasks for Rodents: Current Advantages and Limitations, and Future Promise.

Authors:  Anil Sindhurakar; Samuel D Butensky; Jason B Carmel
Journal:  Neurorehabil Neural Repair       Date:  2019-06-12       Impact factor: 3.919

3.  Corticospinal Tract Injury Estimated From Acute Stroke Imaging Predicts Upper Extremity Motor Recovery After Stroke.

Authors:  David J Lin; Alison M Cloutier; Kimberly S Erler; Jessica M Cassidy; Samuel B Snider; Jessica Ranford; Kristin Parlman; Fabio Giatsidis; James F Burke; Lee H Schwamm; Seth P Finklestein; Leigh R Hochberg; Steven C Cramer
Journal:  Stroke       Date:  2019-10-25       Impact factor: 7.914

4.  Neuroimaging Identifies Patients Most Likely to Respond to a Restorative Stroke Therapy.

Authors:  Jessica M Cassidy; George Tran; Erin B Quinlan; Steven C Cramer
Journal:  Stroke       Date:  2018-01-10       Impact factor: 7.914

5.  Machine-Based, Self-guided Home Therapy for Individuals With Severe Arm Impairment After Stroke: A Randomized Controlled Trial.

Authors:  Daniel K Zondervan; Renee Augsburger; Barbara Bodenhoefer; Nizan Friedman; David J Reinkensmeyer; Steven C Cramer
Journal:  Neurorehabil Neural Repair       Date:  2014-10-01       Impact factor: 3.919

6.  Choice of Human-Computer Interaction Mode in Stroke Rehabilitation.

Authors:  Hossein Mousavi Hondori; Maryam Khademi; Lucy Dodakian; Alison McKenzie; Cristina V Lopes; Steven C Cramer
Journal:  Neurorehabil Neural Repair       Date:  2015-07-02       Impact factor: 3.919

7.  Connectivity measures are robust biomarkers of cortical function and plasticity after stroke.

Authors:  Jennifer Wu; Erin Burke Quinlan; Lucy Dodakian; Alison McKenzie; Nikhita Kathuria; Robert J Zhou; Renee Augsburger; Jill See; Vu H Le; Ramesh Srinivasan; Steven C Cramer
Journal:  Brain       Date:  2015-06-11       Impact factor: 13.501

8.  Breaking Proportional Recovery After Stroke.

Authors:  Merav R Senesh; David J Reinkensmeyer
Journal:  Neurorehabil Neural Repair       Date:  2019-08-16       Impact factor: 3.919

9.  BDNF Val66Met Polymorphism Is Related to Motor System Function After Stroke.

Authors:  Dae Yul Kim; Erin B Quinlan; Robert Gramer; Steven C Cramer
Journal:  Phys Ther       Date:  2015-09-17

10.  Gains Across WHO Dimensions of Function After Robot-Based Therapy in Stroke Subjects.

Authors:  Jennifer Wu; Lucy Dodakian; Jill See; Erin Burke Quinlan; Lisa Meng; Jeby Abraham; Ellen C Wong; Vu Le; Alison McKenzie; Steven C Cramer
Journal:  Neurorehabil Neural Repair       Date:  2020-10-21       Impact factor: 3.919

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