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Matching Task Difficulty to Patient Ability During Task Practice Improves Upper Extremity Motor Skill After Stroke

A Proof-of-Concept Study
Published:April 23, 2016DOI:https://doi.org/10.1016/j.apmr.2016.03.022

      Abstract

      Objective

      To test the feasibility of the Fugl-Meyer Assessment of the Upper Extremity “keyform,” derived from Rasch analysis, as a method for systematically planning and progressing rehabilitation.

      Design

      Feasibility study, single group design.

      Setting

      University rehabilitation research laboratory.

      Participants

      Participants (N=10; mean age, 59.70±9.96y; 24.1±30.54mo poststroke) with ischemic or hemorrhagic stroke >3 months prior, voluntarily shoulder flexion ≥30°, and simultaneous elbow extension ≥20°.

      Interventions

      The keyform method defined initial rehabilitation targets (goals) and progressed the rehabilitation program after every third session. Targets were repetitively practiced within the context of client-selected functional tasks not in isolation.

      Main Outcome Measures

      Feasibility was defined by subject's pain or fatigue, upper extremity motor function (Wolf Motor Function Test), and movement patterns (kinematics). Assessments were administered pre- and posttreatment and compared using paired t tests. Task-difficulty and patient-ability measures were calculated using Rasch analysis and compared using paired t tests (P<.05).

      Results

      Ten participants completed 9 sessions, 200 movement repetitions per session in <2 hours without pain or fatigue. Participants gained upper extremity motor function (Wolf Motor Function Test: pretreatment, 22.23±24.26s; posttreatment, 15.46±22.12s; P=.01), improved shoulder-elbow coordination (index of curvature: pretreatment, 1.30±0.15; posttreatment, 1.21±0.11; P=.01), and exhibited reduced trunk compensatory movement (trunk displacement: pretreatment, 133.97±74.15mm; posttreatment, 108.08±64.73mm; P=.02). Task-difficulty and patient-ability measures were not statistically different throughout the program (person-ability measures of 1.01±0.05, 1.64±0.45, and 2.22±0.65 logits and item-difficulty measures of 0.93±0.37, 1.70±0.20, and 2.06±0.24 logits at the 3 testing time points, respectively; P>.05).

      Conclusions

      The Fugl-Meyer Assessment of the Upper Extremity keyform is a feasible method to ensure that the difficulty of tasks practiced were well matched to initial and evolving levels of upper extremity motor ability.

      Keywords

      List of abbreviations:

      FMA-UE (Fugl-Meyer Assessment of the Upper Extremity), IOC (index of curvature), TD (trunk displacement), UE (upper extremity), WMFT (Wolf Motor Function Test)
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      References

        • Krakauer J.W.
        Motor learning: its relevance to stroke recovery and neurorehabilitation.
        Curr Opin Neurol. 2006; 19: 84-90
        • Askim T.
        • Indredavik B.
        • Vangberg T.
        • Haberg A.
        Motor network changes associated with successful motor skill relearning after acute ischemic stroke: a longitudinal functional magnetic resonance imaging study.
        Neurorehabil Neural Repair. 2009; 23: 295-304
        • Guadagnoli M.A.
        • Lee T.D.
        Challenge point: a framework for conceptualizing the effects of various practice conditions in motor learning.
        J Mot Behav. 2004; 36: 212-224
        • Adkins D.L.
        • Boychuk J.
        • Remple M.S.
        • Kleim J.A.
        Motor training induces experience-specific patterns of plasticity across motor cortex and spinal cord.
        J Appl Physiol (1985). 2006; 101: 1776-1782
        • Sanger T.D.
        Failure of motor learning for large initial errors.
        Neural Comput. 2004; 16: 1873-1886
        • McCrea P.H.
        • Eng J.J.
        • Hodgson A.J.
        Saturated muscle activation contributes to compensatory reaching strategies after stroke.
        J Neurophysiol. 2005; 94: 2999-3008
        • Alaverdashvili M.
        • Foroud A.
        • Lim D.H.
        • Whishaw I.Q.
        “Learned baduse” limits recovery of skilled reaching for food after forelimb motor cortex stroke in rats: a new analysis of the effect of gestures on success.
        Behav Brain Res. 2008; 188: 281-290
        • Nudo R.J.
        • Milliken G.W.
        • Jenkins W.M.
        • Merzenich M.M.
        Use-dependent alterations of movement representations in primary motor cortex of adult squirrel monkeys.
        J Neurosci. 1996; 16: 785-807
        • Plautz E.J.
        • Milliken G.W.
        • Nudo R.J.
        Effects of repetitive motor training on movement representations in adult squirrel monkeys: role of use versus learning.
        Neurobiol Learn Mem. 2000; 74: 27-55
        • Kleim J.A.
        • Barbay S.
        • Nudo R.J.
        Functional reorganization of the rat motor cortex following motor skill learning.
        J Neurophysiol. 1998; 80: 3321-3325
        • Boyd L.
        • Winstein C.
        Explicit information interferes with implicit motor learning of both continuous and discrete movement tasks after stroke.
        J Neurol Phys Ther. 2006; 30 (discussion 58-49): 46-57
        • Wright B.
        • Masters G.N.
        Rating scale analysis.
        Mesa Pr, Chicago1982
        • Hambleton R.K.
        • Swaminathan H.
        • Rogers H.J.
        Fundamentals of item response theory.
        Sage, Newbury Park1991
        • Bond T.G.
        • Fox C.M.
        Applying the Rasch model: Fundamental measurement in the human sciences.
        Erlbaum, Mahwah2001
        • Wright B.D.
        • Stone M.H.
        Best test design.
        Mesa Pr, Chicago1979
        • Fugl-Meyer A.R.
        • Jaasko L.
        • Leyman I.
        • Olsson S.
        • Steglind S.
        The post-stroke hemiplegic patient: a method for evaluation of physical performance.
        Scand J Rehabil Med. 1975; 7: 13-31
        • Woodbury M.L.
        • Velozo C.A.
        • Richards L.G.
        • Duncan P.W.
        • Studenski S.
        • Lai S.M.
        Dimensionality and construct validity of the Fugl-Meyer Assessment of the Upper Extremity.
        Arch Phys Med Rehabil. 2007; 88: 715-723
        • Velozo C.A.
        • Woodbury M.L.
        Translating measurement findings into rehabilitation practice: an example using the Fugl-Meyer Assessment of the Upper Extremity with clients following stroke.
        J Rehabil Res Dev. 2011; 48: 1211-1222
        • Woodbury M.L.
        • Velozo C.A.
        • Richards L.G.
        • Duncan P.W.
        • Studenski S.
        • Lai S.M.
        Longitudinal stability of the Fugl-Meyer Assessment of the Upper Extremity.
        Arch Phys Med Rehabil. 2008; 89: 1563-1569
        • Kielhofner G.
        • Dobria L.
        • Forsyth K.
        • Basu S.
        The construction of keyforms for obtaining instantaneous measures from the occupational performance history interview rating scales.
        Occup Ther J Res. 2005; 25: 1-10
        • Linacre J.M.
        Instantaneous measurement and diagnosis.
        in: Smith R.M. Physical medicine and rehabilitation state of the art reviews, vol. 11: outcome measurement. Hanley & Belfus, Philadelphia1997: 315-324
        • Waddell K.J.
        • Birkenmeier R.L.
        • Moore J.L.
        • Hornby T.G.
        • Lang C.E.
        Feasibility of high-repetition, task-specific training for individuals with upper-extremity paresis.
        Am J Occup Ther. 2014; 68: 444-453
        • Page S.J.
        • Boe S.
        • Levine P.
        What are the “ingredients” of modified constraint-induced therapy? An evidence-based review, recipe, and recommendations.
        Restor Neurol Neurosci. 2013; 31: 299-309
        • Birkenmeier R.L.
        • Prager E.M.
        • Lang C.E.
        Translating animal doses of task-specific training to people with chronic stroke in 1-hour therapy sessions: a proof-of-concept study.
        Neurorehabil Neural Repair. 2010; 24: 620-635
        • Linacre J.M.
        Winsteps Rasch measurement computer program [computer program]. Version 3.70. Winsteps.com, Beaverton2006
        • Blanton S.
        • Wolf S.L.
        An application of upper-extremity constraint-induced movement therapy in a patient with subacute stroke.
        Phys Ther. 1999; 79: 847-853
        • Cirstea M.C.
        • Levin M.F.
        Compensatory strategies for reaching in stroke.
        Brain. 2000; 123: 940-953
        • Rettig O.
        • Fradet L.
        • Kasten P.
        • Raiss P.
        • Wolf S.I.
        A new kinematic model of the upper extremity based on functional joint parameter determination for shoulder and elbow.
        Gait Posture. 2009; 30: 469-476
        • Michaelsen S.M.
        • Luta A.
        • Roby-Brami A.
        • Levin M.F.
        Effect of trunk restraint on the recovery of reaching movements in hemiparetic patients.
        Stroke. 2001; 32: 1875-1883
        • Van Peppen R.P.
        • Kwakkel G.
        • Wood-Dauphinee S.
        • Hendriks H.J.
        • Van der Wees P.J.
        • Dekker J.
        The impact of physical therapy on functional outcomes after stroke: what's the evidence?.
        Clin Rehabil. 2004; 18: 833-862