Among the neurophysiologic factors, transcranial magnetic stimulation (TMS)–related measures were found to be the most commonly used to examine upper extremity motor function after stroke (Table 4). They were used in 27% (15 of 56) of the studies reviewed. The predictors of physical factors included visual functions, speech functions, upper extremity sensorimotor functions, and lower extremity motor functions (Table 5). Not surprisingly, the most popular predictor measures were tests of motor impairment, sensory impairment, and arm and hand function. However, only three studies53,55,65 examined hand dexterity, which is very important for upper extremity function in daily life. Incorporating the contralesional arm into life activities is a realistic and important goal for poststroke persons.6 All sociocognitive factors fell into the body functions/structures category. The defining feature of each, including perceptions, expectations, and emotion-related measures, is that they reside in the body/mind-body of the individual; most could be sequelae of the neurologic insult or be premorbidly present but impairments all the same.86 Predictors that capture sociocognitive factors such as self-efficacy may also provide important insight into the recovery of voluntary arm use in daily life.87–89 However, we did not find any studies using self-efficacy as a predictor measure in their models. Cognition, memory, emotion, and motivation were the sociocognitive factors that were observed and used to predict upper extremity outcomes at follow-up (Table 6).
At follow-up, most outcome measures were found to be in the domain of body functions/structure. Motor capability of the contralesional limb in the domain of body functions/structure was described with measures of range of motion, muscle strength, arm movement-related functions, and kinematic analysis (Table 7). Muscle strength was found to be the most popular outcome measure in this review. Twenty-two studies (39%) considered hand or arm muscle strength using the manual muscle test,39,58 hand-held dynamometry,67,70,90 the Medical Research Council guidelines,40,43–45,52,59,80,81 the Motricity Index,22,41,46,47,66,76,85 the Canadian Neurological Scale (distal arm),48 or the NIH Stroke Scale (arm).57 Although the kinematic measures are not as convenient for clinical assessments, they provide specific information about qualitative features of upper extremity movements, such as accuracy, efficiency, and speed79 and may be sensitive to motor capacity changes.
Few outcome measures were found to be in the domains of activities and participation. Unlike the muscle strength measures, the Fugl-Meyer Assessment (FMA) upper extremity motor score includes both body functions/structures and activities domains. The FMA was widely used in the studies (n = 14) reviewed here.19,42,49–51,54,60,61,67,68,75,76,83,84 However, none of the measures were considered representative of the participation domain. In the context of activities, the MAL is the only measure useful to quantify how much and how well the contralesional arm is used voluntarily in daily life activities. However, the three studies that used the MAL were ranked weak in methodological quality evaluation.67,71,82
Across the 56 studies, the time points of capturing outcomes varied from a few weeks45,82 to more than a year (Table 8).49,81 A six-month observation period was considered sufficient for determination of functional outcomes.91 Approximately 41% (23 of 56) of the selected studies had chosen a six-month period for capturing outcomes.17,19,22,48,50,51,53,56,60,62–64,67–69,70,71,73,76–78,85,92 The quality evaluation of internal, statistical, and external validity is summarized in Table 8.
In the 56 included studies, more than 95% used reliable and valid instruments for predictors and outcome measures (54 and 56 studies, respectively). Approximately 35% (19 of 56) used raters who were unaware of individuals' pathologic information and treatment program arrangement. Use of blinded raters is intended to decrease potential observational bias in data collection.36 In 98% (55 of 56) of the sample, participant dropout information (eg, withdrawal because of death or migration) was provided.
In 45 of the 56 studies, the time point of first obtaining predictor measures was within three months of stroke onset. The timing of this initial evaluation varied from several days (17 studies)7,39,40,43–45,49,50–53,57,58,64,66,68,76 to several months or years (7 studies).17,62,67,69,71,72,74,82,92 Of the studies in which predictor measures were obtained during the first few days after stroke onset, 53% (9 of 17) used TMS-related measures,39,40,43–45,49–52 24% (4 of 17) used imaging-related measures,53,57,58,68 and 24% (4 of 17) employed functional examinations.7,64,66,76 In studies in which the first time predictor measures were obtained more than three months after stroke onset, the study population was likely in the subacute/chronic stage of recovery, and the primary study purpose was to measure responsiveness to various interventions.17,62,67,71,82,92
Eighty-four percent (47 of 56) of the studies provided rationales for the statistical methods of detecting the relationship strength between predictor and outcome measures. The remaining 16% limited analysis to descriptive plots of predictor-outcome relationships. Thirty-four percent (19 of 56) of the studies did not have a sample size sufficient to provide a valid test of the prediction model. Forty-eight percent (27 of 56) of the studies used regression analysis. A control for multicollinearity was observed in only 9 studies of the subgroup of the studies that established the regression models (33%, 9 of 27 studies).44,56,60,62,69,71,74,79,83
Twenty-eight studies provided information about stroke pathology. The inclusion and exclusion criteria were specified in most of the studies (47 of 56). Sixty-eight percent (38 of 56) reported additional medical or paramedical interventions during the period of study. Only one study used cross-validation to establish a prediction model in a second group of poststroke participants.72 Similarly, only one study provided information about the clinical meaningfulness of the predictor and outcome measures.69
Using the best evidence criteria, 20 studies (36%) were retained for further analysis of significant predictors.21,44–47,50,51,53,54,56,60,62,63,69,74,75,79–81,83 The best predictors of arm-specific outcome measures were the initial neurophysiologic factors44–47,50,51,53,54,80,81 and initial motor capability.44,53,56,60,62,63,69,74,75,79,83 Among the neurophysiologic factors, the presence of a motor evoked potential (MEP), MEP amplitude, and MEP latency were the most frequently used variables. All were TMS-related measures.44–47,50,51,84 Lesion location in the brain and neuronal activation in the motor cortex were the predictor measures captured by imaging techniques.53,54,80,81 The most commonly used predictor measures of initial motor capability were deep sensation,60 muscle tone,60,79 active range of motion,62,79 muscle strength,44,56,79 and performance-based measures (eg, the FMA).53,56,60,63,69,74,75,83
Fifty-six of 935 studies met criteria for inclusion in this systematic review. Between 1979 and April 2008, there was a 317% increase in the frequency of published studies designed to determine the critical predictors of voluntary arm recovery after stroke. The frequency of such studies rose from 1.05 per year before 2000 to nearly 4.38 per year after 2000. There has been a profound increase in the prevalence of this kind of clinical research during the last eight years. This increase may be due in part to recent developments of valid and reliable predictor measures of poststroke upper extremity motor function that should prove useful to inform appropriate therapeutic programs. However, it should be noted that for the most part, this review was based primarily on prognostic studies rather than clinical intervention studies. In addition the included research studies were selected from a limited set of databases that did not include dissertations, theses, or conference proceedings, and further, the citations from selected articles were not checked for inclusion. Although clinical practice in neurologic physical therapy is becoming more evidence-based, it is far from routine use of prognostic tests and indicators to prescribe therapeutic programs.
To optimize the predictive value of arm-specific outcomes, 90% of the stroke participants from the selected studies were investigated in the acute/subacute stage. This is likely the case because assessments conducted in later stages of recovery may not provide sufficient predictive value for functional outcomes.56 Most of the studies of stroke rehabilitation recruit persons with either ischemic or hemorrhagic stroke because the outcome response to intervention is somewhat different between these two groups.93 For the purpose of this review, we included both types of stroke as long as the primary lesion was isolated to one hemisphere and resulted in a hemiparetic syndrome. This approach would have been appropriate to test the hypothesis that predictors of upper extremity motor function recovery are different between these two stroke etiologies. However, it is not possible to separate the results by stroke pathology because 54% (30 of 56) of the studies did not report stroke pathology. Only three studies reported inclusion of people with hemorrhagic stroke. Although we did not obtain complete information about the prevalence of each stroke type, it is reasonable to estimate that those with an infarction are much more numerous than those with hemorrhage (approximately 70% and 30%, respectively). According to statistics published by the American Stroke Association in 2008, approximately 10% of ischemic strokes and 40% of hemorrhagic strokes result in death within one month after stroke onset among persons aged 45 to 64 years,94 indicating that a greater number of individuals with infarct strokes than those with hemorrhagic strokes survive the acute phase. Because the clinical symptoms and recovery time course between infarct and hemorrhagic-type strokes are different,27 this information is most helpful here and for future prognostic studies.
This review focused on measurement used to capture voluntary arm use. To better understand arm-specific functional recovery, the ICF was used to categorize both predictors and outcome measures because it has been used widely to characterize health and disability in clinical practice and research through a framework for delivering goal-oriented rehabilitation.4 Recently, Salter et al95–97 selected 20 popular outcome measures with acceptable reliability and validity and further classified them using the ICF. Based on whether these outcomes captured the context of body functions/structures, activities, or participation, they were categorized into only one ICF domain. However, a number of stroke-related measures are designed to capture basic motor capacity and/or general functional recovery. Therefore, to classify a given measure into a single ICF category may be insufficient for fully characterizing the measure. In addition, there are other limitations to this kind of single-domain approach. First, the boundaries between ICF levels are not clear-cut.28 Second, the testing items of an instrument may be used to measure motor capabilities at different ICF levels. For example, the FMA includes items both at the levels of body functions/structures (eg, reflex) and activities (eg, grasp). Therefore, for this review, we allowed a measure to be classified in more than one ICF level. In this way, it provides a more comprehensive analysis of the various measurement instruments used for prognostication of functional upper extremity motor recovery after stroke.
Together, neurophysiologic measures43–47,50,51,53,54,80,81 and initial sensorimotor abilities44,53,56,60,62,63,74,75,79,83 have been shown to be the best predictors of arm-specific outcomes. The neurophysiologic measures provide a useful metric of the integrity of motor pathways, which is a viable index of functional motor performance.42 Additionally, neurophysiologic measures can be gathered in the early poststroke stage and require minimal participant cooperation.98 Measures of initial sensorimotor function provide a direct evaluation of motor behavior and reveal fundamental motor capability consequent to the stroke. These sensorimotor measures may be more sensitive to the effects of specific interventions than the neurophysiologic measures.60 However, it is also the case that a majority of the voluntary sensorimotor function measures are more vulnerable to associated neurologic impairments such as aphasia, apraxia, and neglect than direct neurophysiologic measures. Although the majority of the predictor models tested included a number of predictor variables, only five of 26 included neuroimaging and clinical variables together in their models.42,44,53,61,68 In all cases, the combined predictor variables resulted in a higher percentage of the outcome variance explained.
Unfortunately, there is still considerable controversy pertaining to the magnitude of a clinically meaningful change in the majority of upper extremity motor ability outcome measures. This highlights the importance of MCID determination for studies related to stroke interventions, particularly those pertaining to upper extremity recovery. It is of special note that only one study reviewed here provided information about MCID.69
The general definition of participation in the ICF is “the involvement of an individual in a life situation.”96 More specifically, the participation level of functional upper extremity motor recovery is defined as the involvement of the arms in life situations. In this review, very few predictor or outcome measures were classified at the participation level. Most of the measures pertained to the body functions/structures level. Using measures at the body functions/structures level may gather sufficient information to examine predictors, but does not provide significant information to examine outcome measures. Because voluntary arm use in daily activities is the primary objective for poststroke individuals,5,6 outcome measures classified at the participation level would be expected to provide information about how individuals incorporate their paretic arms in life situations at home or in the community.
Other than outcome measures that capture motor capability level of arm-specific function in daily life,7,20,21,75 the MAL is a useful measure to quantify how much and how well the contralesional arm is used in daily life activities. The MAL has been shown to have high internal consistency (Cronbach α ≥ 0.88) and moderate construct validity (Spearman ρ = 0.63) in persons with chronic stroke.12 In addition, the MAL has good interrater reliability (interclass correlation coefficient = 0.90–0.94).12 This review identified three studies that used either raw or change scores for the MAL posttreatment as an outcome measure to investigate the predictors of functional upper extremity motor recovery after Constraint-induced movement therapy in individuals more than six months poststroke.67,71,82 The potential predictors investigated in these three studies were individual/stroke characteristics (eg, side of stroke location, time since stroke, hand dominance, age, sex, and ambulatory status);71 persons' cognitive functions (measured by the Mini-Mental State Examination, the short-form Token Test, the Sustained Attention to Response Task, Logical Memory and Visual Reproduction subtests from the Wechsler Memory Scale, and the Trail Making Test Form B);82 and upper extremity motor severity (measured by the FMA).67
Interestingly, measures of individual/stroke characteristics and cognitive functions did not reach significance when examining the prediction model of actual arm use measured by the MAL.71,82 However, motor severity classified by the FMA was correlated with the changed MAL score after a community-based upper extremity group exercise among individuals with chronic stroke (mean poststroke duration 5.1 years).67 Persons in the mildly impaired group gained 1 point on the MAL, whereas persons in the moderately/severely impaired groups gained 0.2 to 0.5 points on the MAL.67 It seems that motor severity classified by the FMA is a better discriminator than the MAL in predicting which person might benefit the most from the community-based group exercise. Yet it is not clear whether motor severity captured in the acute stroke stage has good predictive value in relation to outcome measures of voluntary arm use. More studies using measures at the activities and participation level are needed to establish cause-and-effect relationships between predictors and outcome measures of voluntary arm use in real-life situations.
This is the first systematic review focused on critical predictors of arm-specific motor recovery that incorporated the ICF for meaningful outcomes of voluntary arm recovery in hemiparetic stroke. Until recently, clinical research has paid little attention to the more distal outcomes represented by the activities and participation categories in the ICF. Not surprisingly, initial measures that capture the integrity of neural connections (eg, in the corticospinal tract) and voluntary motor behavior were the best predictors of functional arm recovery at follow-up. This finding supports the usefulness of a top-down approach in which task-oriented training programs are developed and aimed at reducing the functional limitations apparent in contralesional limb use.99 The fact that few outcome measures for voluntary arm use exist at the participation level of the ICF underscores the need to develop reliable and valid measures of arm use in real-life environments.
The authors thank Pamela Corley for her help in developing the keywords/databases search and Dr. Sharon Myers, Jill Stewart, and Hsiu-Chen Lin for their suggestions or comments on the preparation of the manuscript.
1. Rosamond W, Flegal K, Furie K, et al. Heart disease and stroke statistics–2008 update: a report from the American Heart Association Statistics Committee and Stroke Statistics Subcommittee. Circulation
2. Mayo NE, Wood-Dauphinee S, Ahmed S, et al. Disablement following stroke. Disabil Rehabil
3. Mayo NE, Wood-Dauphinee S, Cote R, et al. Activity, participation, and quality of life 6 months poststroke. Arch Phys Med Rehabil
4. World Health Organization. ICF: International Classification of Functioning, Disability and Health
. Geneva: World Health Organization; 2001.
5. Barker RN, Brauer SG. Upper limb recovery after stroke: the stroke survivors' perspective. Disabil Rehabil
6. Barker RN, Gill TJ, Brauer SG. Factors contributing to upper limb recovery after stroke: a survey of stroke survivors in Queensland Australia. Disabil Rehabil
7. Nakayama H, Jorgensen HS, Raaschou HO, et al. Recovery of upper extremity function in stroke patients: the Copenhagen Stroke Study. Arch Phys Med Rehabil
8. Uswatte G, Giuliani C, Winstein C, et al. Validity of accelerometry for monitoring real-world arm activity in patients with subacute stroke: evidence from the extremity constraint-induced therapy evaluation trial. Arch Phys Med Rehabil
9. Taub E, Miller NE, Novack TA, et al. Technique to improve chronic motor deficit after stroke. Arch Phys Med Rehabil
10. Taub E, Crago JE, Uswatte G. Constraint induced movement therapy: a new approach to treatment in physical rehabilitation. Rehabil Psvchol
11. Uswatte G, Taub E, Morris D, et al. The motor activity log-28: assessing daily use of the hemiparetic arm after stroke. Neurology
12. van der Lee JH, Beckerman H, Knol DL, et al. Clinimetric properties of the motor activity log for the assessment of arm use in hemiparetic patients. Stroke
13. Carod-Artal FJ, Gonzalez-Gutierrez JL, Herrero JA, et al. Functional recovery and instrumental activities of daily living: follow-up 1-year after treatment in a stroke unit. Brain Inj
14. Frankel MR, Morgenstern LB, Kwiatkowski T, et al. Predicting prognosis after stroke: a placebo group analysis from the National Institute of Neurological Disorders and Stroke rt-PA Stroke Trial. Neurology
15. Fullerton KJ, Mackenzie G, Stout RW. Prognostic indices in stroke. Q J Med
16. Kwakkel G, Wagenaar RC, Kollen BJ, et al. Predicting disability in stroke–a critical review of the literature. Age Ageing
17. Wolf SL, Baker MP, Kelly JL. EMG biofeedback in stroke: effect of patient characteristics. Arch Phys Med Rehabil
18. De Souza LH, Hewer RL, Miller S. Assessment of recovery of arm control in hemiplegic stroke patients, Part 1: Arm function tests. Int Rehabil Med
19. Kusoffsky A, Wadell I, Nilsson BY. The relationship between sensory impairment and motor recovery in patients with hemiplegia. Scand J Rehabil Med
20. La Joie WJ, Reddy NM, Melvin JL. Somatosensory evoked potentials: their predictive value in right hemiplegia. Arch Phys Med Rehabil
21. Dudgeon BJ, DeLisa JA, Miller RM. Optokinetic nystagmus and upper extremity dressing independence after stroke. Arch Phys Med Rehabil
22. Parker VM, Wade DT, Langton HR. Loss of arm function after stroke: measurement, frequency, and recovery. Int Rehabil Med
23. Kruger E, Teasell R, Salter K, et al. The rehabilitation of patients recovering from brainstem strokes: case studies and clinical considerations. Top Stroke Rehabil
24. Teasell R, Foley N, Doherty T, et al. Clinical characteristics of patients with brainstem strokes admitted to a rehabilitation unit. Arch Phys Med Rehabil
25. Mosch SC, Max JE, Tranel D. A matched lesion analysis of childhood versus adult-onset brain injury due to unilateral stroke: another perspective on neural plasticity and recovery of social functioning. Cogn Behav Neurol
26. Umphred D, ed. Neurological Rehabilitation
. 5th ed. St. Louis: Mosby; 2007.
27. Hier DB, Edelstein G. Deriving clinical prediction rules from stroke outcome research. Stroke
28. Wade DT. Measurement in Neurological Rehabilitation
. New York: Oxford University Press; 1992.
29. Keith RA, Granger CV, Hamilton BB, et al. The functional independence measure: a new tool for rehabilitation. Adv Clin Rehabil
30. Collin C, Wade DT, Davies S, et al. The Barthel ADL Index: a reliability study. Int Disabil Stud
31. Rose L, Bakal DA, Fung TS, et al. Tactile extinction and functional status after stroke. A preliminary investigation. Stroke
32. Woldag H, Gerhold LL, de Groot M, et al. Early prediction of functional outcome after stroke. Brain Inj
33. Arac N, Sagduyu A, Binai S, et al. Prognostic value of transcranial magnetic stimulation in acute stroke. Stroke
34. Chira-Adisai W, Yan K, Shahani BT. Changes in serial correlation coefficients and fractional parameters during functional recovery in stroke patients. Electromyogr Clin Neurophysiol
35. Lyden PD, Lau GT. A critical appraisal of stroke evaluation and rating scales. Stroke
36. Portney LG, Watkins MP. Foundations of Clinical Research: Applications to Practice
. 3rd ed. Upper Saddle River, NJ: Pearson Education; 2009.
37. Symposium recommendations for methodology in stroke outcome research. Task Force on Stroke Impairment, Task Force on Stroke Disability, and Task Force on Stroke Handicap. Stroke
. 1990;21(9 Suppl):II68–II73.
38. Hsieh YW, Wang CH, Wu SC, et al. Establishing the minimal clinically important difference of the Barthel Index in stroke patients. Neurorehabil Neural Repair
39. Nagao S, Kawai N. Prediction of motor function by magnetic brain stimulation in patients with intracerebral hematoma. Neurol Med Chir (Tokyo)
40. Pennisi G, Rapisarda G, Bella R, et al. Absence of response to early transcranial magnetic stimulation in ischemic stroke patients: prognostic value for hand motor recovery. Stroke
41. Keren O, Ring H, Solzi P, et al. Upper limb somatosensory evoked potentials as a predictor of rehabilitation progress in dominant hemisphere stroke patients. Stroke
42. Stinear CM, Barber PA, Smale PR, et al. Functional potential in chronic stroke patients depends on corticospinal tract integrity. Brain
. 2007;130(Part 1):170–180.
43. Delvaux V, Alagona G, Gerard P, et al. Post-stroke reorganization of hand motor area: a 1-year prospective follow-up with focal transcranial magnetic stimulation. Clin Neurophysiol
44. Nascimbeni A, Gaffuri A, Imazio P. Motor evoked potentials: prognostic value in motor recovery after stroke. Funct Neurol
45. Rapisarda G, Bastings E, de Noordhout AM, et al. Can motor recovery in stroke patients be predicted by early transcranial magnetic stimulation? Stroke
46. Dachy B, Biltiau E, Bouillot E, et al. Facilitation of motor evoked potentials in ischemic stroke patients: prognostic value and neurophysiologic correlations. Clin Neurophysiol
47. Dachy B, Denis L, Deltenre P. Usefulness of transcranial magnetic stimulation to predict the development of reflex sympathetic dystrophy poststroke: a pilot study. Arch Phys Med Rehabil
48. Cruz Martinez A, Tejada J, Diez Tejedor E. Motor hand recovery after stroke. Prognostic yield of early transcranial magnetic stimulation. Electromyogr Clin Neurophysiol
49. Hendricks HT, Hageman G, van Limbeek J. Prediction of recovery from upper extremity paralysis after stroke by measuring evoked potentials. Scand J Rehabil Med
50. Hendricks HT, Pasman JW, Merx JL, et al. Analysis of recovery processes after stroke by means of transcranial magnetic stimulation. J Clin Neurophysiol
51. Hendricks HT, Pasman JW, van Limbeek J, et al. Motor evoked potentials in predicting recovery from upper extremity paralysis after acute stroke. Cerebrovasc Dis
52. Tzvetanov P, Rousseff RT, Atanassova P. Prognostic value of median and tibial somatosensory evoked potentials in acute stroke. Neurosci Lett
53. Binkofski F, Seitz RJ, Hacklander T, et al. Recovery of motor functions following hemiparetic stroke: a clinical and magnetic resonance-morphometric study. Cerebrovasc Dis
54. Schiemanck SK, Kwakkel G, Post MWM, et al. Impact of internal capsule lesions on outcome of motor hand function at one year post-stroke. J Rehabil Med
55. Higgins J, Mayo NE, Desrosiers J, et al. Upper-limb function and recovery in the acute phase poststroke. J Rehabil Res Dev
56. Kwakkel G, Kollen BJ, van der Grond J, et al. Probability of regaining dexterity in the flaccid upper limb: impact of severity of paresis and time since onset in acute stroke. Stroke
57. Karibe H, Shimizu H, Tominaga T, et al. Diffusion-weighted magnetic resonance imaging in the early evaluation of corticospinal tract injury to predict functional motor outcome in patients with deep intracerebral hemorrhage. J Neurosurg
58. Yoshioka H, Horikoshi T, Aoki S, et al. Diffusion tensor tractography predicts motor functional outcome in patients with spontaneous intracerebral hemorrhage. Neurosurgery
. 2008;62:97–103, discussion 103.
59. Matteis M, Vernieri F, Troisi E, et al. Early cerebral hemodynamic changes during passive movements and motor recovery after stroke. J Neurol
60. Feys H, De Weerdt W, Nuyens G, et al. Predicting motor recovery of the upper limb after stroke rehabilitation: value of a clinical examination. Physiother Res Int
61. Feys H, Hetebrij J, Wilms G, et al. Predicting arm recovery following stroke: value of site of lesion. Acta Neurol Scand
62. Fritz SL, Light KE, Patterson TS, et al. Active finger extension predicts outcomes after constraint-induced movement therapy for individuals with hemiparesis after stroke. Stroke
63. Kwakkel G, van Dijk GM, Wagenaar RC. Accuracy of physical and occupational therapists' early predictions of recovery after severe middle cerebral artery stroke. Clin Rehabil
64. Meldrum D, Pittock SJ, Hardiman O, et al. Recovery of the upper limb post ischaemic stroke and the predictive value of the Orpington Prognostic Score. Clin Rehabil
65. Katrak P, Bowring G, Conroy P, et al. Predicting upper limb recovery after stroke: the place of early shoulder and hand movement. Arch Phys Med Rehabil
66. Roy CW, Sands MR, Hill LD, et al. The effect of shoulder pain on outcome of acute hemiplegia. Clin Rehabil
67. Pang MY, Harris JE, Eng JJ. A community-based upper-extremity group exercise program improves motor function and performance of functional activities in chronic stroke: a randomized controlled trial. Arch Phys Med Rehabil
68. Prabhakaran S, Zarahn E, Riley C, et al. Inter-individual variability in the capacity for motor recovery after ischemic stroke. Neurorehabil Neural Repair
69. Fritz SL, George SZ, Wolf SL, et al. Participant perception of recovery as criterion to establish importance of improvement for constraint-induced movement therapy outcome measures: a preliminary study. Phys Ther
70. Pandyan AD, Cameron M, Powell J, et al. Contractures in the post-stroke wrist: a pilot study of its time course of development and its association with upper limb recovery. Clin Rehabil
71. Fritz SL, Light KE, Clifford SN, et al. Descriptive characteristics as potential predictors of outcomes following constraint-induced movement therapy for people after stroke. Phys Ther
72. Barreca SR, Finlayson MA, Gowland CA, et al. Use of the Halstead Category Test as a cognitive predictor of functional recovery in the hemiplegic upper limb: a cross-validation study. Clin Neuropsychol
73. Canning CG, Ada L, Adams R, et al. Loss of strength contributes more to physical disability after stroke than loss of dexterity. Clin Rehabil
74. Loewen SC, Anderson BA. Predictors of stroke outcome using objective measurement scales. Stroke
75. Hatakenaka M, Miyai I, Sakoda S, et al. Proximal paresis of the upper extremity in patients with stroke. Neurology
76. Smania N, Paolucci S, Tinazzi M, et al. Active finger extension: a simple movement predicting recovery of arm function in patients with acute stroke. Stroke
77. Katrak PH. Shoulder shrug—a prognostic sign for recovery of hand movement after stroke. Med J Aust
78. Sunderland A, Tinson D, Bradley L, et al. Arm function after stroke. An evaluation of grip strength as a measure of recovery and a prognostic indicator. J Neurol Neurosurg Psychiatry
79. Wagner JM, Lang CE, Sahrmann SA, et al. Sensorimotor impairments and reaching performance in subjects with poststroke hemiparesis during the first few months of recovery. Phys Ther
80. Jang SH, Cho SH, Kim YH, et al. Diffusion anisotrophy in the early stages of stroke can predict motor outcome. Restor Neurol Neurosci
81. Wenzelburger R, Kopper F, Frenzel A, et al. Hand coordination following capsular stroke. Brain
. 2005;128(Part 1):64–74.
82. Mark VW, Woods AJ, Mennemeier M, et al. Cognitive assessment for CI therapy in the outpatient clinic. NeuroRehabilitation
83. Shelton FD, Volpe BT, Reding M. Motor impairment as a predictor of functional recovery and guide to rehabilitation treatment after stroke. Neurorehabil Neural Repair
84. Sonde L, Bronge L, Kalimo H, et al. Can the site of brain lesion predict improved motor function after low-TENS treatment on the post-stroke paretic arm? Clin Rehabil
85. Jang SH, Kim YH, Chang Y, et al. The predictive value of cortical activation by passive movement for motor recovery in stroke patients. Restor Neurol Neurosci
86. Wade D. Why physical medicine, physical disability and physical rehabilitation? We should abandon Cartesian dualism. Clin Rehabil
87. Allen DD. Proposing 6 dimensions within the construct of movement in the movement continuum theory. Phys Ther
. 2007;87:888–898, discussion 925–834.
88. Allen DD. Validity and reliability of the movement ability measure: a self-report instrument proposed for assessing movement across diagnoses and ability levels. Phys Ther
. 2007;87:899–916, discussion 925–834.
89. Allen DD. Responsiveness of the movement ability measure: a self-report instrument proposed for assessing the effectiveness of physical therapy intervention. Phys Ther
. 2007;87:917–924, discussion 925–934.
90. Bohannon RW, Smith MB. Upper extremity strength deficits in hemiplegic stroke patients: relationship between admission and discharge assessment and time since onset. Arch Phys Med Rehabil
91. Jorgensen HS, Nakayama H, Raaschou HO. Neurologic and functional recovery the Copenhagen Stroke Study. Phys Med Rehabil Clin N Am
92. Wolf SL, Binder-MacLeod SA. Electromyographic biofeedback applications to the hemiplegic patient. Changes in upper extremity neuromuscular and functional status. Phys Ther
93. Paolucci S, Antonucci G, Grasso MG, et al. Functional outcome of ischemic and hemorrhagic stroke patients after inpatient rehabilitation: a matched comparison. Stroke
94. Rosamond WD, Folsom AR, Chambless LE, et al. Stroke incidence and survival among middle-aged adults: 9-year follow-up of the Atherosclerosis Risk in Communities (ARIC) cohort. Stroke
95. Salter K, Jutai JW, Teasell R, et al. Issues for selection of outcome measures in stroke rehabilitation: ICF Body Functions. Disabil Rehabil
96. Salter K, Jutai JW, Teasell R, et al. Issues for selection of outcome measures in stroke rehabilitation: ICF Participation. Disabil Rehabil
97. Salter K, Jutai JW, Teasell R, et al. Issues for selection of outcome measures in stroke rehabilitation: ICF activity. Disabil Rehabil
98. Hendricks HT, Zwarts MJ, Plat EF, et al. Systematic review for the early prediction of motor and functional outcome after stroke by using motor-evoked potentials. Arch Phys Med Rehabil
99. Winstein CJ, Wolf SL. Task-oriented training to promote upper extremity recovery. In: Stein J, Harvey RL, Macko RF, et al, eds. Stroke Recovery & Rehabilitation
. New York: Demos Medical Publishing; 2009:267–290.
Keywords:© 2009 Neurology Section, APTA
voluntary arm use; prediction; clinical meaningfulness