- Email: [email protected]
Determinants of Walking Function After Stroke: Differences by Deficit Severity
115
ORIGINAL ARTICLE
Determinants of Walking Function After Stroke: Differences by Deficit Severity Shawnna L. Patterson, MD, PhD, Larry W. Forrester, PhD, Mary M. Rodgers, PhD, Alice S. Ryan, PhD, Frederick M. Ivey, PhD, John D. Sorkin, MD, PhD, Richard F. Macko, MD ABSTRACT. Patterson SL, Forrester LW, Rodgers MM, Ryan AS, Ivey FM, Sorkin JD, Macko RF. Determinants of walking function after stroke: differences by deficit severity. Arch Phys Med Rehabil 2007;88:115-9. Objectives: To investigate the relationship of cardiovascular fitness (VO2peak), neurologic deficits in balance and leg strength, and body composition to ambulatory function after stroke and to determine whether these relationships differ between those with milder versus more severe gait deficits. Design: Cross-sectional correlation study. Setting: Outpatient clinic of an academic medical center. Participants: Seventy-four people (43 men, 31 women; mean age ⫾ standard deviation, 64⫾10y) with chronic hemiparetic stroke. Interventions: Not applicable. Main Outcome Measures: Thirty-foot (9.1-m) walk velocity, 6-minute walk distance, VO2peak, Berg Balance Scale score, bilateral quadriceps eccentric torque, total and regional lean mass, and percentage of fat mass. Results: Short-distance walking correlated significantly with cardiovascular fitness, balance, paretic leg strength, nonparetic leg strength, percentage of body fat, and paretic lean mass but not with nonparetic lean mass. Long-distance walking correlated significantly with cardiovascular fitness, balance, paretic leg strength, nonparetic leg strength, and paretic lean mass but not with percentage of body fat or nonparetic lean mass. Stepwise regression showed that cardiovascular fitness, balance, and paretic leg strength were independently associated with long-distance walking (r2⫽.60, P⬍.001). Variance in long-distance walking was largely explained by balance for those who walked more slowly (⬍.48m/s) for short distances (r2⫽.42, P⬍.001) and by cardiovascular fitness for those who walked more quickly (⬎.48m/s) for short distances (r2⫽.26, P⫽.003).
From the Departments of Physical Therapy and Rehabilitation Science (Forrester, Rodgers), Neurology (Forrester, Macko) and Medicine, Division of Gerontology (Ryan, Ivey, Sorkin, Macko), University of Maryland School of Medicine, Baltimore, MD; Department of Neurology (Patterson, Forrester, Macko), Baltimore Veterans Affairs Maryland Health Care System, Baltimore, MD, and Baltimore VA Geriatrics Research, Education, and Clinical Center, Baltimore, MD (Patterson, Forrester, Ryan, Ivey, Sorkin, Macko). Supported by the National Institute of Aging Claude D. Pepper Older Americans Independence Center (grant no. P60AG12583); Department of Veterans Affairs Medical Center Baltimore Geriatric Research, Education and Clinical Center; VA Medical Center Rehabilitation Research and Development Center of Excellence in Exercise and Robotics Rehabilitation; VA Rehabilitation Research and Development Career Development Award (grant no. B2375V); VA Research Enhancement Award Program in Stroke; and National Institutes of Health (grant no. K01 AG019242). No commercial party having a direct financial interest in the results of the research supporting this article has or will confer a benefit upon the author(s) or upon any organization with which the author(s) is/are associated. Reprint requests to Shawnna L. Patterson, MD, PhD, Baltimore Veterans Administration Maryland Health Care System, GRECC (BT/GR/18), 10 N Greene St, Baltimore, MD 21201, e-mail: [email protected]. 0003-9993/07/8801-10949$32.00/0 doi:10.1016/j.apmr.2006.10.025
Conclusions: Short-distance walking after stroke is related to balance, cardiovascular fitness, and paretic leg strength. Long-distance walking ability differs by gait deficit severity, with balance more important in those who walk more slowly and cardiovascular fitness playing a greater role in those who walk more quickly. Improved understanding of the factors that predict ambulatory function may assist the design of individualized rehabilitation strategies across the spectrum of gait deficit severity in those with hemiparetic stroke. Key Words: Balance; Body composition; Gait; Hemiplegia; Physical fitness; Rehabilitation; Stroke. © 2007 by the American Congress of Rehabilitation Medicine and the American Academy of Physical Medicine and Rehabilitation MPROVING AMBULATORY FUNCTION is a common goal for people with gait deficits due to stroke. However, the Ifundamental determinants of home- and community-based walking performance capacity have not been systematically studied across the spectrum of gait deficit severities frequently encountered after stroke. In healthy older subjects, reference equations incorporate age, sex, height, weight, health, and physical activity status to explain substantial variability in the 6-minute walk distance (6MWD).1-4 Whether such reference equations apply to stroke has not been established. Neurologic deficits that lead to loss of leg strength and impaired balance are 2 factors that correlate to walking ability. One recent report5 describing stroke subjects with mild deficits suggests that balance, strength, lower-extremity spasticity, and body fat, but not cardiovascular fitness, contribute to ambulatory function. However, we and others6-10 have previously shown that subjects with chronic hemiparetic stroke have profoundly diminished cardiovascular fitness, muscular atrophy in the hemiparetic extremity, and altered body composition that are related to gait deficit severity. Recent studies show that long walking tasks11 and cardiovascular fitness12 can be improved long after traditional rehabilitation for stroke has been completed. Few studies have evaluated the physiologic, body composition, and neurologic factors that predict walking function across short and longer distances in chronic stroke subjects with hemiparetic gait. To our knowledge, no prior studies have differentiated the factors related to walking function in subjects with mild versus severe hemiparetic gait deficits. We studied the hypotheses that neurologic deficits, cardiovascular fitness, and body composition are related to ambulatory performance after stroke and that the relative contribution of these factors differs for short and long walking tasks. METHODS Participants This study was a cross-sectional analysis of baseline data obtained from consecutive subjects with chronic stroke who enrolled in a continuing aerobic exercise training study. SubArch Phys Med Rehabil Vol 88, January 2007
116
WALKING FUNCTION AFTER STROKE, Patterson
Table 1: Subject Demographics and Stroke Characteristics Demographics (N⫽74)
Values
Range
Age (y) Sex (M/F) Race (white/black/Hispanic) Body mass index (kg/m2) Time since stroke (mo) NIHSS score13 Spasticity Device use, n (%) AFO Assistive devices Single-point cane Quad cane Walker
64⫾10 43/31 31/36/2 28.1⫾5.3 48⫾59 3⫾3 1⫾1
42–84 NA NA 18.1–45.1 6–252 0–16 0–3 NA
39 61 38 17 6
(53) (82) (50) (23) (8)
NOTE. Values are mean ⫾ standard deviation (SD) or as otherwise indicated. Abbreviations: AFO, ankle-foot orthosis; F, female; M, male; NA, not applicable; NIHSS, National Institutes of Health Stroke Scale.
jects were older than 40 years of age and had chronic (⬎6mo) stroke with residual hemiparetic gait (table 1). People with severe or active renal, cardiac, pulmonary, or hematologic illness were excluded. Subjects were recruited from the Baltimore Veterans Affairs Hospital and the University of Maryland Medical Systems Hospitals. The study was approved by the University of Maryland institutional review board, and all subjects provided written informed consent. Testing was performed during 7 separate visits over a period of 4 to 8 weeks. Measurements The National Institutes of Health Stroke Scale13 score was determined during an initial physical examination. Balance was evaluated using the 14-item Berg Balance Scale (BBS) score for which a maximum score of 56 indicates good balance.9,10 Lower-extremity spasticity was quantified by a clinician during a neurologic physical examination using the Modified Ashworth Scale.14 Values for spasticity at the knee extensors and knee flexors were averaged to provide a composite score for the paretic leg. Use of an ankle-foot orthosis (AFO) or assistive device (none, single-point cane, quad cane, walker) was documented. The short walking test was the 30-foot walk.15 Subjects were asked to walk down a walkway at a self-selected comfortable walking speed. The time required to cover 30ft (9.1m) was noted, as was the number of steps taken. This test was repeated 3 times with a short rest period between each trial, and the average of the 3 values was used in the data analysis. Subjects were allowed to use their usual assistive devices, but no further assistance was provided. The 6-minute walk was the long walking task.16 Subjects were instructed to cover as much distance as possible during 6 minutes while walking up and down a 30.5-m hallway marked with orange cones. The distance covered during 6 minutes was recorded, along with baseline and final heart rates. The 6-minute walk measures endurance17 and may correlate to community activities.9 Subjects also underwent a treadmill tolerance test and a treadmill exercise stress test as described elsewhere.18 Those able to walk for 3 or more minutes at .09m/s or faster (.04m/s higher than the minimum treadmill speed) during the treadmill exercise stress test returned for a peak treadmill exercise test with open spirometry from which cardiovascular fitness (VO2peak) was determined.6,19 Treadmill tests were terminated Arch Phys Med Rehabil Vol 88, January 2007
for fatigue, gait instability, or American College of Sports Medicine criteria.20 Strength in the paretic and nonparetic knee extensors was measured by isokinetic dynamometrya at maximum and mean torques (between 35° and 60°) for concentric and eccentric movements made through a range of 20° to 70° across 3 speeds (30°, 90°, 120°/s). Eccentric movements were chosen for analysis because they represent maximum force output.21 For each leg, a composite score defined as the average of the maximum eccentric torque across all 3 speeds was used in the statistical evaluations. Body composition measurements were assessed using dual x-ray absorptiometry scans. Total mass, fat mass, and lean tissue mass8 were determined using Lunar Prodigy software.b The leg regions were defined by the anterior superior iliac crest as the superior aspect and divided at the midline into right and left. Data Analysis Descriptive statistics were used to generate mean and standard deviation (SD). Univariate correlations were determined using Pearson (parametric) or Spearman (nonparametric) correlation coefficients. Parametric variables with significant univariate correlations (P⬍.05) were entered into the regression analyses. Comparisons between groups were performed with Student t tests (parametric) and Mann-Whitney U tests (nonparametric). Analyses were performed on the entire cohort and after a median split analysis based on the median 30-foot walk velocity (.48m/s), which divided subjects into slower and faster walkers. Data analyses were performed with SPSS.c RESULTS Eighty-five subjects had their 6MWD and VO2peak measured. Eight subjects were excluded from this analysis because they had not completed both the 6-minute walk and peak treadmill exercise stress tests. One subject was excluded from this analysis secondary to a BBS score of 3 by outlier analysis (⬎4.25 SDs). Demographics and descriptive parameters of the subjects are listed in table 1. Balance, short and long walking tasks, cardiovascular fitness, leg strength, and body composition results are presented in table 2. Age, sex, race, and body mass index (BMI) did not significantly correlate to 30-foot walk velocity (r⫽⫺.17, r⫽⫺.12, r⫽.23, r⫽.13, respectively) or 6MWD (r⫽⫺.20, r⫽⫺.10, r⫽.20, r⫽.03, respectively). Balance, paretic eccentric leg strength, and cardiovascular fitness strongly correlated to both of these functional ambulatory measures (see table 2). Nonparetic leg strength and lean mass of the leg also correlated to these functional measures. Percentage of body fat showed a negative relationship with both short and long walking tasks. In this cohort the median speed was .48m/s, and this value was used to define 2 groups, slower walkers (those with a 30-ft walking velocity of .13–.48m/s) and faster walkers (those whose velocity was 0.49 –1.17m/s). There was no significant difference in age (P⫽.56), sex (P⫽.48), race (P⫽.13), or BMI (P⫽.51) in the slower versus faster walkers. The use of an AFO (73% vs 32%, P⫽.001) or assistive device (95% vs 70%, P⫽.005) was more common in slower walkers than in faster walkers. Faster walkers had significantly higher values on the BBS (41⫾7 vs 34⫾7, P⬍.001) and VO2peak measurements (14.8⫾4.4mL·kg⫺1·min⫺1 vs 11.3⫾2.6mL·kg⫺1·min⫺1, P⬍.001) and covered more distance during the 6-minute walk (305⫾ 99m vs 128⫾57m, P⬍.001). In addition, the strength measures in the paretic leg (87.9⫾34.4Nm vs 43.3⫾26.9Nm, P⬍.001) and nonparetic leg (128.3⫾38.4Nm vs 104.7⫾43.3Nm, P⫽.02)
117
WALKING FUNCTION AFTER STROKE, Patterson Table 2: Correlations of Neurologic Deficits, Cardiovascular Fitness, and Body Composition to Ambulatory Function Mean ⫾ SD
Variable
Function (N⫽74) BBS score 30-foot walk velocity (m/s) 6MWD (m) VO2peak (mL·kg⫺1·min⫺1) Quadriceps eccentric strength (n⫽62) Composite paretic strength (Nm) Composite nonparetic strength (Nm) Dual x-ray absorptiometry (n⫽65) Percentage fat (%) Paretic leg lean mass (kg) Nonparetic leg lean mass (kg)
Range
Correlation to 30-Foot Walk Velocity
Correlation to 6MWD
38⫾8 0.51⫾0.26 216⫾120 13.1⫾4.0
20–54 0.13–1.17 35–528 6.5–28.3
.64*
.69*
.88* .54*
.64*
66.3⫾38.1 117.1⫾42.2
3.1–165.2 19.4–192.3
.60* .38†
.57* .41*
34.7⫾8.8 76.2⫾20.3 79.8⫾19.7
18.1–50.0 43.5–120.4 47.9–119.1
⫺.26† .25† .24
⫺.24 .26† .24
*P⬍.001. P⬍.05.
†
were higher in faster walkers. There was no significant difference in percentage of body fat or lean mass of the legs between groups. When divided into slower and faster walkers, the correlations among balance, paretic eccentric leg strength, cardiovascular fitness, and 6MWD differed (table 3). In both groups, the BBS score highly correlated to the 30-foot walk velocity and the 6MWD. In slower walkers, cardiovascular fitness was associated with the 6MWD, although less strongly than in faster walkers. In contrast, in faster walkers, cardiovascular fitness and balance were strongly associated but age (r⫽⫺.43, P⬍.01), leg strength, and body fat also had significant correlation. In a stepwise linear regression model predicting 6MWD of all subjects, VO2peak, BBS score, and the eccentric quadriceps strength of the paretic limb were significant predictors of ambulatory function (table 4). Actual 6MWDs covered by subjects with chronic hemiparetic stroke are better predicted by the model than by published equations1 for healthy older subjects (fig 1). DISCUSSION The results of this study support the hypothesis that cardiovascular fitness, strength, balance, and body composition are Table 3: Neurologic Deficits, Cardiovascular Fitness, and Body Composition Correlations to 6MWD for Slower and Faster Walkers Correlation to 6MWD Variable
Function BBS score VO2peak (mL·kg⫺1·min⫺1) Quadriceps strength Composite paretic eccentric strength (Nm) Composite nonparetic eccentric strength (Nm) Dual x-ray absorptiometry Percentage fat (%) Paretic leg lean mass (kg) Nonparetic leg lean mass (kg) *P⬍.001. † P⬍.01. ‡ P⬍.05.
Slower (ⱕ.48m/s, n⫽37)
Faster (⬎.48m/s, n⫽37)
.66* .43†
.56* .57*
.12
.28†
.14
.45†
⫺.18 .24 .23
⫺.43‡ .29 .35
related to ambulatory function as measured by the 30-foot and 6-minute walk tests. These subjects with stroke had a large variation in 30-ft walking velocity (0.13–1.17m/s). Gait speed can be used as a measure of gait deficit severity, with the fastest speeds approaching normal (1.4m/s).22 The determinants of long-distance walking function differ by gait deficit severity, as defined by 30-ft walking velocity, with balance more important in those with more severe gait deficits and cardiovascular fitness, balance, and quadriceps strength representing stronger determinants in those with milder deficits. The differing predictors of walking function as they relate to deficit severity may have implications for rehabilitative therapy prescriptions. Peak VO2 was strongly related to long-distance walking specifically in people with milder deficits. In contrast to our findings, Pang et al5 recently reported that balance, leg strength, and spasticity, but not cardiovascular fitness, were related to 6MWD in stroke patients. One explanation for this difference may be the different methods used to determine VO2peak. Pang used cycle ergometry, which underestimates cardiovascular fitness compared with treadmill walking. Another possible explanation for the discrepancy between our data and those of Pang is that their subjects had an average gait speed of .88m/s, which is almost normal, suggesting very mild stroke deficits. Our findings are in agreement with those of Ryan et al,7 who found that both lean mass of the thighs and self-selected walking speed had strong correlations to VO2peak in people with mild to moderate gait deficit severity (selfselected walking velocity of .63⫾.31m/s), which is further evidence that gait speed is correlated to cardiovascular fitness. Collectively these findings support the hypothesis that fitness is related to walking function, at least in mild to moderately affected people. Table 4: Stepwise Linear Regression Models of 6MWD for All Subjects, Slower Walkers, and Faster Walkers Variable
All subjects Model 1: VO2peak Model 2: VO2peak, BBS score Model 3: VO2peak, BBS score, paretic leg strength Slower walkers Model 1: BBS score Faster walkers Model 2: VO2peak
Adjusted R2
P
.48 .57
⬍.001 ⬍.001
.60
⬍.001
.42
⬍.001
.26
.003
Arch Phys Med Rehabil Vol 88, January 2007
118
WALKING FUNCTION AFTER STROKE, Patterson
treadmill training show that although slower walkers have a smaller relative improvement in cardiovascular fitness than faster walkers, they still respond to progressive aerobic training.12 Strength and balance as well as cardiovascular fitness are necessary for both short and long distances. The importance of these parameters in the evaluation and treatment of patients with varying degrees of deficits differ. Our results suggest that balance training should be addressed in all stroke patients. After this, cardiovascular fitness and leg strength need to be targeted.
Fig 1. Comparison of observed, modeled, and predicted 6MWD for all subjects across range of gait speed. Observed 6MWDs are shown with filled circles with a solid regression line (r2ⴝ.78). 6MWDs calculated with the resultant equation from the stepwise linear regression model 3 are shown as a dashed line (r2ⴝ.52). The dotted line represents the values predicted by standard reference equations1 (r2ⴝ.03ⴛ10ⴚ3).
Balance was the strongest predictor of 30-foot walk velocity and 6MWD in stroke subjects with moderate to severe impairments. This relationship was also seen in another study of stroke subjects5 and in a study of older women with severe walking disability.23 A recent study24 of the rehabilitation of people with stroke showed that a walking intervention improved balance self-efficacy. Hence, our findings support a strong association between balance and walking, particularly in those with more severe walking disability. It is difficult to separate the effects of strength and balance on walking function. Unilateral weakness is a common characteristic of people with stroke.25 Numerous cross-sectional studies show that strength is another important variable in short- and long-distance ambulatory function.26,27 Our data further show that balance and cardiovascular fitness also play important roles. Spasticity has been targeted as a treatable factor affecting gait speed and function after hemiparetic stroke. Spasticity was not correlated to the 30-foot walk velocity. The model published by Pang et al5 includes spasticity as a significant factor related to walking; however, spasticity only explains 2% of the variation in 6MWD. There continues to be debate regarding the validity of measures of spasticity.14 Normative equations for people without neurologic deficit have found age, sex, fitness, strength, and balance to be predictors of walking function. Our data show that these equations are not applicable to subjects with stroke. Our subjects walked only 47% of the distance predicted by published reference equations for the 6MWD.1 The reference equations are poor predictors of the 6MWD for slower walkers and are better for faster walkers. The study by Dean et al28 included only 3 subjects with gait speeds less than 0.5m/s, and 6MWD values were 84.4% of those predicted. Stroke patients with slower gait speeds have severe deficits that are stroke specific, and function cannot be adequately predicted by age, sex, height, and weight. The correlation of cardiovascular fitness with 6MWD was stronger in faster walkers than in slower walkers, suggesting that fitness should be considered as a possible target for therapy in subjects with stroke-induced ambulation limitations. The lower correlation of VO2peak to gait speed in slower walkers does not imply that cardiovascular fitness is not important in these patients and, in fact, may suggest a stronger rationale for addressing this deficit domain. Recent results of 6 months of Arch Phys Med Rehabil Vol 88, January 2007
Study Limitations Limitations of this study include the absence of details regarding stroke type and location. In addition, the use of an AFO or assistive devices was not entered into the model. Although correlated with ambulatory activity, these may be self-prescribed29 and may be used for much longer than necessary. Some limitations in the strength data analysis are that no encouragement was given—therefore, values may be underestimates of true strength—and that the test is not routinely available in clinics. There may also be a floor effect in the VO2peak of the slower walkers, as evidenced by the universally poor fitness levels with a small amount of variability. Furthermore, this analysis does not include psychosocial factors that may affect functional recovery.30 CONCLUSIONS Balance, cardiovascular fitness, and paretic leg strength are all important factors involved in determining a person’s longand short-distance walking function after stroke. A continuum exists: balance is more important in those with more severe gait deficits, whereas cardiovascular fitness plays a greater role in those with milder deficits. Reference equations for the 6MWD are not applicable to the stroke population. Improved understanding of the factors that predict ambulatory function may assist the design of individualized rehabilitation strategies across the spectrum of gait deficit severity in those with hemiparetic stroke. Future studies could include evaluation of different combinations and order of interventions in slower- and faster-walking stroke subjects. References 1. Enright P, Sherrill D. Reference equations for the six-minute walk in healthy adults. Am J Respir Crit Care Med 1998;158(5 Pt 1): 1384-7. 2. Enright P, McBurnie M, Bittner V, et al; Cardiovascular Health Study. The 6-min walk test: a quick measure of functional status in elderly adults. Chest 2003;123:387-98. 3. Bautmans I, Lambert M, Mets T. The six-minute walk in community dwelling elderly: influence of health status. BMC Geriatr 2004;4:6-14. 4. Troosters T, Grosselink R, Decramer M. Six minute walking distance in healthy elderly subjects. Eur Respir J 1999;14:270-4. 5. Pang M, Eng J, Dawson A. Relationship between ambulatory capacity and cardiorespiratory fitness in chronic stroke: influence of stroke-specific impairments. Chest 2005;127:495-501. 6. Macko RF, DeSouza C, Tretter L, et al. Treadmill aerobic exercise training reduces the energy expenditure and cardiovascular demands of hemiparetic gait in chronic stroke patients: a preliminary report. Stroke 1997;28:326-30. 7. Ryan A, Dobrovolny C, Silver K, Smith G, Macko R. Cardiovascular fitness after stroke: role of muscle mass and gait deficit severity. J Stroke Cerebrovasc Dis 2000;9:185-91. 8. Ryan A, Dobrovolny C, Smith G, Silver K, Macko R. Hemiparetic muscle atrophy and increased intramuscular fat in stroke patients. Arch Phys Med Rehabil 2002;83:1703-7.
WALKING FUNCTION AFTER STROKE, Patterson
9. Steffen T, Hacker T, Mollinger L. Age- and gender-related test performance in community dwelling elderly people: six-minute walk test, Berg Balance Scale, timed up & go test, and gait speeds. Phys Ther 2002;82:128-37. 10. Smith P, Hembree J, Thompson M. Berg Balance Scale and functional reach: determining the best clinical tool for individuals post acute stroke. Clin Rehabil 2004;18:811-8. 11. Bassile C, Dean C, Boden-Albala B, Sacco R. Obstacle training programme for individuals post stroke: a feasibility study. Clin Rehabil 2003;17:130-6. 12. Macko R, Ivey F, Forrester L, et al. Treadmill exercise rehabilitation improves ambulatory function and cardiovascular fitness in patients with chronic stroke: a randomized, controlled trial. Stroke 2005;36:2206-11. 13. NIH Stroke Scale. Available at: http://www.ninds.nih.gov/ doctors/NIH_Stroke_Scale.pdf. Accessed October 15, 2006. 14. Pandyan A, Johnson G, Price C, Curless R, Rogers H. A review of the properties and limitations of the Ashworth and modified Ashworth scales as measures of spasticity. Clin Rehabil 1999;13: 373-83. 15. Rossier P, Wade D. Validity and reliability comparison of 4 mobility measures in patients presenting with neurologic impairment. Arch Phys Med Rehabil 2001;82:9-13. 16. Enright P. The six-minute walk test. Respir Care 2003;48:783-5. 17. Wade D. Measurement in neurological rehabilitation. New York: Oxford Univ Pr; 1992. 18. Macko R, Katzel L, Yataco A, et al. Low-velocity graded treadmill stress testing in hemiparetic stroke patients. Stroke 1997;28: 988-92. 19. Michael K, Allen J, Macko R. Reduced ambulatory activity after stroke: the role of balance, gait, and cardiovascular fitness. Arch Phys Med Rehabil 2005;86:1552-6. 20. ACSM. ACSM’s guidelines for exercise testing and prescription. 6th ed. Baltimore: Lippincott Williams & Wilkins; 2000. 21. Smith G, Silver KH, Goldberg A, Macko R. “Task-oriented” exercise improves hamstring strength and spastic reflexes in chronic stroke patients. Stroke 1999;30:2112-8.
119
22. Neumann D. Kinesiology of the musculoskeletal system: foundations for physical rehabilitation. St. Louis: Mosby; 2002. 23. Rantanen T, Guralnik J, Ferrucci L, et al. Coimpairments as predictors of severe walking disability in older women. J Am Geriatr Soc 2001;49:21-7. 24. Salbach N, Mayo N, Robichaud-Ekstrand S, Hanley J, Richards C, Wood-Dauphinee S. The effect of a task-oriented walking intervention on improving balance self-efficacy poststroke: a randomized, controlled trial. J Am Geriatr Soc 2005;53:576-82. 25. Thom T, Haase N, Rosamond W, et al; American Heart Association Statistics Committee and Stroke Statistics Subcommittee. Heart disease and stroke statistics—2006 update: a report from the American Heart Association Statistics Committee and Stroke Statistics Subcommittee [published erratum in: Circulation 2006;113: e696]. Circulation 2006;113:e85-151. 26. Bohannon R, Walsh S. Nature, reliability, and predictive value of muscle performance measures in patients with hemiparesis following stroke. Arch Phys Med Rehabil 1992;73:721-5. 27. Ostchega Y, Dillon C, Lindle R, Carroll M, Hurley B. Isokinetic leg muscle strength in older Americans and its relationship to a standardized walk test: data from the National Health and Nutrition Examination Survey 1999-2000. J Am Geriatr Soc 2004;52: 977-82. 28. Dean C, Richards C, Malouin F. Walking speed over 10 metres overestimates locomotor capacity after stroke. Clin Rehabil 2001; 15:415-21. 29. Dean E, Ross J. Relationships among cane fitting, function, and falls. Phys Ther 1993;73:494-504. 30. Glass T, Maddox G. The quality and quantity of social support: stroke recovery as psycho-social transition. Soc Sci Med 1992; 34:1249-61. Suppliers a. KinCom; Chattecx Corp, 4717 Adams Rd, Hixson, TN 37343. b. GE Healthcare, N25W 23255 Paul Rd, Pewaupee, WI 53072. c. Version 13.0; SPSS Inc, 233 S Wacker Dr, 11th Fl, Chicago, IL 60606.
Arch Phys Med Rehabil Vol 88, January 2007
ORIGINAL ARTICLE
Determinants of Walking Function After Stroke: Differences by Deficit Severity Shawnna L. Patterson, MD, PhD, Larry W. Forrester, PhD, Mary M. Rodgers, PhD, Alice S. Ryan, PhD, Frederick M. Ivey, PhD, John D. Sorkin, MD, PhD, Richard F. Macko, MD ABSTRACT. Patterson SL, Forrester LW, Rodgers MM, Ryan AS, Ivey FM, Sorkin JD, Macko RF. Determinants of walking function after stroke: differences by deficit severity. Arch Phys Med Rehabil 2007;88:115-9. Objectives: To investigate the relationship of cardiovascular fitness (VO2peak), neurologic deficits in balance and leg strength, and body composition to ambulatory function after stroke and to determine whether these relationships differ between those with milder versus more severe gait deficits. Design: Cross-sectional correlation study. Setting: Outpatient clinic of an academic medical center. Participants: Seventy-four people (43 men, 31 women; mean age ⫾ standard deviation, 64⫾10y) with chronic hemiparetic stroke. Interventions: Not applicable. Main Outcome Measures: Thirty-foot (9.1-m) walk velocity, 6-minute walk distance, VO2peak, Berg Balance Scale score, bilateral quadriceps eccentric torque, total and regional lean mass, and percentage of fat mass. Results: Short-distance walking correlated significantly with cardiovascular fitness, balance, paretic leg strength, nonparetic leg strength, percentage of body fat, and paretic lean mass but not with nonparetic lean mass. Long-distance walking correlated significantly with cardiovascular fitness, balance, paretic leg strength, nonparetic leg strength, and paretic lean mass but not with percentage of body fat or nonparetic lean mass. Stepwise regression showed that cardiovascular fitness, balance, and paretic leg strength were independently associated with long-distance walking (r2⫽.60, P⬍.001). Variance in long-distance walking was largely explained by balance for those who walked more slowly (⬍.48m/s) for short distances (r2⫽.42, P⬍.001) and by cardiovascular fitness for those who walked more quickly (⬎.48m/s) for short distances (r2⫽.26, P⫽.003).
From the Departments of Physical Therapy and Rehabilitation Science (Forrester, Rodgers), Neurology (Forrester, Macko) and Medicine, Division of Gerontology (Ryan, Ivey, Sorkin, Macko), University of Maryland School of Medicine, Baltimore, MD; Department of Neurology (Patterson, Forrester, Macko), Baltimore Veterans Affairs Maryland Health Care System, Baltimore, MD, and Baltimore VA Geriatrics Research, Education, and Clinical Center, Baltimore, MD (Patterson, Forrester, Ryan, Ivey, Sorkin, Macko). Supported by the National Institute of Aging Claude D. Pepper Older Americans Independence Center (grant no. P60AG12583); Department of Veterans Affairs Medical Center Baltimore Geriatric Research, Education and Clinical Center; VA Medical Center Rehabilitation Research and Development Center of Excellence in Exercise and Robotics Rehabilitation; VA Rehabilitation Research and Development Career Development Award (grant no. B2375V); VA Research Enhancement Award Program in Stroke; and National Institutes of Health (grant no. K01 AG019242). No commercial party having a direct financial interest in the results of the research supporting this article has or will confer a benefit upon the author(s) or upon any organization with which the author(s) is/are associated. Reprint requests to Shawnna L. Patterson, MD, PhD, Baltimore Veterans Administration Maryland Health Care System, GRECC (BT/GR/18), 10 N Greene St, Baltimore, MD 21201, e-mail: [email protected]. 0003-9993/07/8801-10949$32.00/0 doi:10.1016/j.apmr.2006.10.025
Conclusions: Short-distance walking after stroke is related to balance, cardiovascular fitness, and paretic leg strength. Long-distance walking ability differs by gait deficit severity, with balance more important in those who walk more slowly and cardiovascular fitness playing a greater role in those who walk more quickly. Improved understanding of the factors that predict ambulatory function may assist the design of individualized rehabilitation strategies across the spectrum of gait deficit severity in those with hemiparetic stroke. Key Words: Balance; Body composition; Gait; Hemiplegia; Physical fitness; Rehabilitation; Stroke. © 2007 by the American Congress of Rehabilitation Medicine and the American Academy of Physical Medicine and Rehabilitation MPROVING AMBULATORY FUNCTION is a common goal for people with gait deficits due to stroke. However, the Ifundamental determinants of home- and community-based walking performance capacity have not been systematically studied across the spectrum of gait deficit severities frequently encountered after stroke. In healthy older subjects, reference equations incorporate age, sex, height, weight, health, and physical activity status to explain substantial variability in the 6-minute walk distance (6MWD).1-4 Whether such reference equations apply to stroke has not been established. Neurologic deficits that lead to loss of leg strength and impaired balance are 2 factors that correlate to walking ability. One recent report5 describing stroke subjects with mild deficits suggests that balance, strength, lower-extremity spasticity, and body fat, but not cardiovascular fitness, contribute to ambulatory function. However, we and others6-10 have previously shown that subjects with chronic hemiparetic stroke have profoundly diminished cardiovascular fitness, muscular atrophy in the hemiparetic extremity, and altered body composition that are related to gait deficit severity. Recent studies show that long walking tasks11 and cardiovascular fitness12 can be improved long after traditional rehabilitation for stroke has been completed. Few studies have evaluated the physiologic, body composition, and neurologic factors that predict walking function across short and longer distances in chronic stroke subjects with hemiparetic gait. To our knowledge, no prior studies have differentiated the factors related to walking function in subjects with mild versus severe hemiparetic gait deficits. We studied the hypotheses that neurologic deficits, cardiovascular fitness, and body composition are related to ambulatory performance after stroke and that the relative contribution of these factors differs for short and long walking tasks. METHODS Participants This study was a cross-sectional analysis of baseline data obtained from consecutive subjects with chronic stroke who enrolled in a continuing aerobic exercise training study. SubArch Phys Med Rehabil Vol 88, January 2007
116
WALKING FUNCTION AFTER STROKE, Patterson
Table 1: Subject Demographics and Stroke Characteristics Demographics (N⫽74)
Values
Range
Age (y) Sex (M/F) Race (white/black/Hispanic) Body mass index (kg/m2) Time since stroke (mo) NIHSS score13 Spasticity Device use, n (%) AFO Assistive devices Single-point cane Quad cane Walker
64⫾10 43/31 31/36/2 28.1⫾5.3 48⫾59 3⫾3 1⫾1
42–84 NA NA 18.1–45.1 6–252 0–16 0–3 NA
39 61 38 17 6
(53) (82) (50) (23) (8)
NOTE. Values are mean ⫾ standard deviation (SD) or as otherwise indicated. Abbreviations: AFO, ankle-foot orthosis; F, female; M, male; NA, not applicable; NIHSS, National Institutes of Health Stroke Scale.
jects were older than 40 years of age and had chronic (⬎6mo) stroke with residual hemiparetic gait (table 1). People with severe or active renal, cardiac, pulmonary, or hematologic illness were excluded. Subjects were recruited from the Baltimore Veterans Affairs Hospital and the University of Maryland Medical Systems Hospitals. The study was approved by the University of Maryland institutional review board, and all subjects provided written informed consent. Testing was performed during 7 separate visits over a period of 4 to 8 weeks. Measurements The National Institutes of Health Stroke Scale13 score was determined during an initial physical examination. Balance was evaluated using the 14-item Berg Balance Scale (BBS) score for which a maximum score of 56 indicates good balance.9,10 Lower-extremity spasticity was quantified by a clinician during a neurologic physical examination using the Modified Ashworth Scale.14 Values for spasticity at the knee extensors and knee flexors were averaged to provide a composite score for the paretic leg. Use of an ankle-foot orthosis (AFO) or assistive device (none, single-point cane, quad cane, walker) was documented. The short walking test was the 30-foot walk.15 Subjects were asked to walk down a walkway at a self-selected comfortable walking speed. The time required to cover 30ft (9.1m) was noted, as was the number of steps taken. This test was repeated 3 times with a short rest period between each trial, and the average of the 3 values was used in the data analysis. Subjects were allowed to use their usual assistive devices, but no further assistance was provided. The 6-minute walk was the long walking task.16 Subjects were instructed to cover as much distance as possible during 6 minutes while walking up and down a 30.5-m hallway marked with orange cones. The distance covered during 6 minutes was recorded, along with baseline and final heart rates. The 6-minute walk measures endurance17 and may correlate to community activities.9 Subjects also underwent a treadmill tolerance test and a treadmill exercise stress test as described elsewhere.18 Those able to walk for 3 or more minutes at .09m/s or faster (.04m/s higher than the minimum treadmill speed) during the treadmill exercise stress test returned for a peak treadmill exercise test with open spirometry from which cardiovascular fitness (VO2peak) was determined.6,19 Treadmill tests were terminated Arch Phys Med Rehabil Vol 88, January 2007
for fatigue, gait instability, or American College of Sports Medicine criteria.20 Strength in the paretic and nonparetic knee extensors was measured by isokinetic dynamometrya at maximum and mean torques (between 35° and 60°) for concentric and eccentric movements made through a range of 20° to 70° across 3 speeds (30°, 90°, 120°/s). Eccentric movements were chosen for analysis because they represent maximum force output.21 For each leg, a composite score defined as the average of the maximum eccentric torque across all 3 speeds was used in the statistical evaluations. Body composition measurements were assessed using dual x-ray absorptiometry scans. Total mass, fat mass, and lean tissue mass8 were determined using Lunar Prodigy software.b The leg regions were defined by the anterior superior iliac crest as the superior aspect and divided at the midline into right and left. Data Analysis Descriptive statistics were used to generate mean and standard deviation (SD). Univariate correlations were determined using Pearson (parametric) or Spearman (nonparametric) correlation coefficients. Parametric variables with significant univariate correlations (P⬍.05) were entered into the regression analyses. Comparisons between groups were performed with Student t tests (parametric) and Mann-Whitney U tests (nonparametric). Analyses were performed on the entire cohort and after a median split analysis based on the median 30-foot walk velocity (.48m/s), which divided subjects into slower and faster walkers. Data analyses were performed with SPSS.c RESULTS Eighty-five subjects had their 6MWD and VO2peak measured. Eight subjects were excluded from this analysis because they had not completed both the 6-minute walk and peak treadmill exercise stress tests. One subject was excluded from this analysis secondary to a BBS score of 3 by outlier analysis (⬎4.25 SDs). Demographics and descriptive parameters of the subjects are listed in table 1. Balance, short and long walking tasks, cardiovascular fitness, leg strength, and body composition results are presented in table 2. Age, sex, race, and body mass index (BMI) did not significantly correlate to 30-foot walk velocity (r⫽⫺.17, r⫽⫺.12, r⫽.23, r⫽.13, respectively) or 6MWD (r⫽⫺.20, r⫽⫺.10, r⫽.20, r⫽.03, respectively). Balance, paretic eccentric leg strength, and cardiovascular fitness strongly correlated to both of these functional ambulatory measures (see table 2). Nonparetic leg strength and lean mass of the leg also correlated to these functional measures. Percentage of body fat showed a negative relationship with both short and long walking tasks. In this cohort the median speed was .48m/s, and this value was used to define 2 groups, slower walkers (those with a 30-ft walking velocity of .13–.48m/s) and faster walkers (those whose velocity was 0.49 –1.17m/s). There was no significant difference in age (P⫽.56), sex (P⫽.48), race (P⫽.13), or BMI (P⫽.51) in the slower versus faster walkers. The use of an AFO (73% vs 32%, P⫽.001) or assistive device (95% vs 70%, P⫽.005) was more common in slower walkers than in faster walkers. Faster walkers had significantly higher values on the BBS (41⫾7 vs 34⫾7, P⬍.001) and VO2peak measurements (14.8⫾4.4mL·kg⫺1·min⫺1 vs 11.3⫾2.6mL·kg⫺1·min⫺1, P⬍.001) and covered more distance during the 6-minute walk (305⫾ 99m vs 128⫾57m, P⬍.001). In addition, the strength measures in the paretic leg (87.9⫾34.4Nm vs 43.3⫾26.9Nm, P⬍.001) and nonparetic leg (128.3⫾38.4Nm vs 104.7⫾43.3Nm, P⫽.02)
117
WALKING FUNCTION AFTER STROKE, Patterson Table 2: Correlations of Neurologic Deficits, Cardiovascular Fitness, and Body Composition to Ambulatory Function Mean ⫾ SD
Variable
Function (N⫽74) BBS score 30-foot walk velocity (m/s) 6MWD (m) VO2peak (mL·kg⫺1·min⫺1) Quadriceps eccentric strength (n⫽62) Composite paretic strength (Nm) Composite nonparetic strength (Nm) Dual x-ray absorptiometry (n⫽65) Percentage fat (%) Paretic leg lean mass (kg) Nonparetic leg lean mass (kg)
Range
Correlation to 30-Foot Walk Velocity
Correlation to 6MWD
38⫾8 0.51⫾0.26 216⫾120 13.1⫾4.0
20–54 0.13–1.17 35–528 6.5–28.3
.64*
.69*
.88* .54*
.64*
66.3⫾38.1 117.1⫾42.2
3.1–165.2 19.4–192.3
.60* .38†
.57* .41*
34.7⫾8.8 76.2⫾20.3 79.8⫾19.7
18.1–50.0 43.5–120.4 47.9–119.1
⫺.26† .25† .24
⫺.24 .26† .24
*P⬍.001. P⬍.05.
†
were higher in faster walkers. There was no significant difference in percentage of body fat or lean mass of the legs between groups. When divided into slower and faster walkers, the correlations among balance, paretic eccentric leg strength, cardiovascular fitness, and 6MWD differed (table 3). In both groups, the BBS score highly correlated to the 30-foot walk velocity and the 6MWD. In slower walkers, cardiovascular fitness was associated with the 6MWD, although less strongly than in faster walkers. In contrast, in faster walkers, cardiovascular fitness and balance were strongly associated but age (r⫽⫺.43, P⬍.01), leg strength, and body fat also had significant correlation. In a stepwise linear regression model predicting 6MWD of all subjects, VO2peak, BBS score, and the eccentric quadriceps strength of the paretic limb were significant predictors of ambulatory function (table 4). Actual 6MWDs covered by subjects with chronic hemiparetic stroke are better predicted by the model than by published equations1 for healthy older subjects (fig 1). DISCUSSION The results of this study support the hypothesis that cardiovascular fitness, strength, balance, and body composition are Table 3: Neurologic Deficits, Cardiovascular Fitness, and Body Composition Correlations to 6MWD for Slower and Faster Walkers Correlation to 6MWD Variable
Function BBS score VO2peak (mL·kg⫺1·min⫺1) Quadriceps strength Composite paretic eccentric strength (Nm) Composite nonparetic eccentric strength (Nm) Dual x-ray absorptiometry Percentage fat (%) Paretic leg lean mass (kg) Nonparetic leg lean mass (kg) *P⬍.001. † P⬍.01. ‡ P⬍.05.
Slower (ⱕ.48m/s, n⫽37)
Faster (⬎.48m/s, n⫽37)
.66* .43†
.56* .57*
.12
.28†
.14
.45†
⫺.18 .24 .23
⫺.43‡ .29 .35
related to ambulatory function as measured by the 30-foot and 6-minute walk tests. These subjects with stroke had a large variation in 30-ft walking velocity (0.13–1.17m/s). Gait speed can be used as a measure of gait deficit severity, with the fastest speeds approaching normal (1.4m/s).22 The determinants of long-distance walking function differ by gait deficit severity, as defined by 30-ft walking velocity, with balance more important in those with more severe gait deficits and cardiovascular fitness, balance, and quadriceps strength representing stronger determinants in those with milder deficits. The differing predictors of walking function as they relate to deficit severity may have implications for rehabilitative therapy prescriptions. Peak VO2 was strongly related to long-distance walking specifically in people with milder deficits. In contrast to our findings, Pang et al5 recently reported that balance, leg strength, and spasticity, but not cardiovascular fitness, were related to 6MWD in stroke patients. One explanation for this difference may be the different methods used to determine VO2peak. Pang used cycle ergometry, which underestimates cardiovascular fitness compared with treadmill walking. Another possible explanation for the discrepancy between our data and those of Pang is that their subjects had an average gait speed of .88m/s, which is almost normal, suggesting very mild stroke deficits. Our findings are in agreement with those of Ryan et al,7 who found that both lean mass of the thighs and self-selected walking speed had strong correlations to VO2peak in people with mild to moderate gait deficit severity (selfselected walking velocity of .63⫾.31m/s), which is further evidence that gait speed is correlated to cardiovascular fitness. Collectively these findings support the hypothesis that fitness is related to walking function, at least in mild to moderately affected people. Table 4: Stepwise Linear Regression Models of 6MWD for All Subjects, Slower Walkers, and Faster Walkers Variable
All subjects Model 1: VO2peak Model 2: VO2peak, BBS score Model 3: VO2peak, BBS score, paretic leg strength Slower walkers Model 1: BBS score Faster walkers Model 2: VO2peak
Adjusted R2
P
.48 .57
⬍.001 ⬍.001
.60
⬍.001
.42
⬍.001
.26
.003
Arch Phys Med Rehabil Vol 88, January 2007
118
WALKING FUNCTION AFTER STROKE, Patterson
treadmill training show that although slower walkers have a smaller relative improvement in cardiovascular fitness than faster walkers, they still respond to progressive aerobic training.12 Strength and balance as well as cardiovascular fitness are necessary for both short and long distances. The importance of these parameters in the evaluation and treatment of patients with varying degrees of deficits differ. Our results suggest that balance training should be addressed in all stroke patients. After this, cardiovascular fitness and leg strength need to be targeted.
Fig 1. Comparison of observed, modeled, and predicted 6MWD for all subjects across range of gait speed. Observed 6MWDs are shown with filled circles with a solid regression line (r2ⴝ.78). 6MWDs calculated with the resultant equation from the stepwise linear regression model 3 are shown as a dashed line (r2ⴝ.52). The dotted line represents the values predicted by standard reference equations1 (r2ⴝ.03ⴛ10ⴚ3).
Balance was the strongest predictor of 30-foot walk velocity and 6MWD in stroke subjects with moderate to severe impairments. This relationship was also seen in another study of stroke subjects5 and in a study of older women with severe walking disability.23 A recent study24 of the rehabilitation of people with stroke showed that a walking intervention improved balance self-efficacy. Hence, our findings support a strong association between balance and walking, particularly in those with more severe walking disability. It is difficult to separate the effects of strength and balance on walking function. Unilateral weakness is a common characteristic of people with stroke.25 Numerous cross-sectional studies show that strength is another important variable in short- and long-distance ambulatory function.26,27 Our data further show that balance and cardiovascular fitness also play important roles. Spasticity has been targeted as a treatable factor affecting gait speed and function after hemiparetic stroke. Spasticity was not correlated to the 30-foot walk velocity. The model published by Pang et al5 includes spasticity as a significant factor related to walking; however, spasticity only explains 2% of the variation in 6MWD. There continues to be debate regarding the validity of measures of spasticity.14 Normative equations for people without neurologic deficit have found age, sex, fitness, strength, and balance to be predictors of walking function. Our data show that these equations are not applicable to subjects with stroke. Our subjects walked only 47% of the distance predicted by published reference equations for the 6MWD.1 The reference equations are poor predictors of the 6MWD for slower walkers and are better for faster walkers. The study by Dean et al28 included only 3 subjects with gait speeds less than 0.5m/s, and 6MWD values were 84.4% of those predicted. Stroke patients with slower gait speeds have severe deficits that are stroke specific, and function cannot be adequately predicted by age, sex, height, and weight. The correlation of cardiovascular fitness with 6MWD was stronger in faster walkers than in slower walkers, suggesting that fitness should be considered as a possible target for therapy in subjects with stroke-induced ambulation limitations. The lower correlation of VO2peak to gait speed in slower walkers does not imply that cardiovascular fitness is not important in these patients and, in fact, may suggest a stronger rationale for addressing this deficit domain. Recent results of 6 months of Arch Phys Med Rehabil Vol 88, January 2007
Study Limitations Limitations of this study include the absence of details regarding stroke type and location. In addition, the use of an AFO or assistive devices was not entered into the model. Although correlated with ambulatory activity, these may be self-prescribed29 and may be used for much longer than necessary. Some limitations in the strength data analysis are that no encouragement was given—therefore, values may be underestimates of true strength—and that the test is not routinely available in clinics. There may also be a floor effect in the VO2peak of the slower walkers, as evidenced by the universally poor fitness levels with a small amount of variability. Furthermore, this analysis does not include psychosocial factors that may affect functional recovery.30 CONCLUSIONS Balance, cardiovascular fitness, and paretic leg strength are all important factors involved in determining a person’s longand short-distance walking function after stroke. A continuum exists: balance is more important in those with more severe gait deficits, whereas cardiovascular fitness plays a greater role in those with milder deficits. Reference equations for the 6MWD are not applicable to the stroke population. Improved understanding of the factors that predict ambulatory function may assist the design of individualized rehabilitation strategies across the spectrum of gait deficit severity in those with hemiparetic stroke. Future studies could include evaluation of different combinations and order of interventions in slower- and faster-walking stroke subjects. References 1. Enright P, Sherrill D. Reference equations for the six-minute walk in healthy adults. Am J Respir Crit Care Med 1998;158(5 Pt 1): 1384-7. 2. Enright P, McBurnie M, Bittner V, et al; Cardiovascular Health Study. The 6-min walk test: a quick measure of functional status in elderly adults. Chest 2003;123:387-98. 3. Bautmans I, Lambert M, Mets T. The six-minute walk in community dwelling elderly: influence of health status. BMC Geriatr 2004;4:6-14. 4. Troosters T, Grosselink R, Decramer M. Six minute walking distance in healthy elderly subjects. Eur Respir J 1999;14:270-4. 5. Pang M, Eng J, Dawson A. Relationship between ambulatory capacity and cardiorespiratory fitness in chronic stroke: influence of stroke-specific impairments. Chest 2005;127:495-501. 6. Macko RF, DeSouza C, Tretter L, et al. Treadmill aerobic exercise training reduces the energy expenditure and cardiovascular demands of hemiparetic gait in chronic stroke patients: a preliminary report. Stroke 1997;28:326-30. 7. Ryan A, Dobrovolny C, Silver K, Smith G, Macko R. Cardiovascular fitness after stroke: role of muscle mass and gait deficit severity. J Stroke Cerebrovasc Dis 2000;9:185-91. 8. Ryan A, Dobrovolny C, Smith G, Silver K, Macko R. Hemiparetic muscle atrophy and increased intramuscular fat in stroke patients. Arch Phys Med Rehabil 2002;83:1703-7.
WALKING FUNCTION AFTER STROKE, Patterson
9. Steffen T, Hacker T, Mollinger L. Age- and gender-related test performance in community dwelling elderly people: six-minute walk test, Berg Balance Scale, timed up & go test, and gait speeds. Phys Ther 2002;82:128-37. 10. Smith P, Hembree J, Thompson M. Berg Balance Scale and functional reach: determining the best clinical tool for individuals post acute stroke. Clin Rehabil 2004;18:811-8. 11. Bassile C, Dean C, Boden-Albala B, Sacco R. Obstacle training programme for individuals post stroke: a feasibility study. Clin Rehabil 2003;17:130-6. 12. Macko R, Ivey F, Forrester L, et al. Treadmill exercise rehabilitation improves ambulatory function and cardiovascular fitness in patients with chronic stroke: a randomized, controlled trial. Stroke 2005;36:2206-11. 13. NIH Stroke Scale. Available at: http://www.ninds.nih.gov/ doctors/NIH_Stroke_Scale.pdf. Accessed October 15, 2006. 14. Pandyan A, Johnson G, Price C, Curless R, Rogers H. A review of the properties and limitations of the Ashworth and modified Ashworth scales as measures of spasticity. Clin Rehabil 1999;13: 373-83. 15. Rossier P, Wade D. Validity and reliability comparison of 4 mobility measures in patients presenting with neurologic impairment. Arch Phys Med Rehabil 2001;82:9-13. 16. Enright P. The six-minute walk test. Respir Care 2003;48:783-5. 17. Wade D. Measurement in neurological rehabilitation. New York: Oxford Univ Pr; 1992. 18. Macko R, Katzel L, Yataco A, et al. Low-velocity graded treadmill stress testing in hemiparetic stroke patients. Stroke 1997;28: 988-92. 19. Michael K, Allen J, Macko R. Reduced ambulatory activity after stroke: the role of balance, gait, and cardiovascular fitness. Arch Phys Med Rehabil 2005;86:1552-6. 20. ACSM. ACSM’s guidelines for exercise testing and prescription. 6th ed. Baltimore: Lippincott Williams & Wilkins; 2000. 21. Smith G, Silver KH, Goldberg A, Macko R. “Task-oriented” exercise improves hamstring strength and spastic reflexes in chronic stroke patients. Stroke 1999;30:2112-8.
119
22. Neumann D. Kinesiology of the musculoskeletal system: foundations for physical rehabilitation. St. Louis: Mosby; 2002. 23. Rantanen T, Guralnik J, Ferrucci L, et al. Coimpairments as predictors of severe walking disability in older women. J Am Geriatr Soc 2001;49:21-7. 24. Salbach N, Mayo N, Robichaud-Ekstrand S, Hanley J, Richards C, Wood-Dauphinee S. The effect of a task-oriented walking intervention on improving balance self-efficacy poststroke: a randomized, controlled trial. J Am Geriatr Soc 2005;53:576-82. 25. Thom T, Haase N, Rosamond W, et al; American Heart Association Statistics Committee and Stroke Statistics Subcommittee. Heart disease and stroke statistics—2006 update: a report from the American Heart Association Statistics Committee and Stroke Statistics Subcommittee [published erratum in: Circulation 2006;113: e696]. Circulation 2006;113:e85-151. 26. Bohannon R, Walsh S. Nature, reliability, and predictive value of muscle performance measures in patients with hemiparesis following stroke. Arch Phys Med Rehabil 1992;73:721-5. 27. Ostchega Y, Dillon C, Lindle R, Carroll M, Hurley B. Isokinetic leg muscle strength in older Americans and its relationship to a standardized walk test: data from the National Health and Nutrition Examination Survey 1999-2000. J Am Geriatr Soc 2004;52: 977-82. 28. Dean C, Richards C, Malouin F. Walking speed over 10 metres overestimates locomotor capacity after stroke. Clin Rehabil 2001; 15:415-21. 29. Dean E, Ross J. Relationships among cane fitting, function, and falls. Phys Ther 1993;73:494-504. 30. Glass T, Maddox G. The quality and quantity of social support: stroke recovery as psycho-social transition. Soc Sci Med 1992; 34:1249-61. Suppliers a. KinCom; Chattecx Corp, 4717 Adams Rd, Hixson, TN 37343. b. GE Healthcare, N25W 23255 Paul Rd, Pewaupee, WI 53072. c. Version 13.0; SPSS Inc, 233 S Wacker Dr, 11th Fl, Chicago, IL 60606.
Arch Phys Med Rehabil Vol 88, January 2007