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Lower-Extremity Strength Differences Predict Activity Limitations in People With Chronic Stroke

P Kluding, PT, PhD, is Assistant Professor, Department of Physical Therapy and Rehabilitation Science, School of Allied Health, University of Kansas Medical Center, 3056 Robinson Hall, Mailstop 2002, 3901 Rainbow Blvd, Kansas City, KS 66160 (USA)
B Gajewski, PhD, is Associate Professor, Department of Biostatistics, Schools of Medicine and Nursing, University of Kansas Medical Center

Address all correspondence to Dr Kluding at: pkluding{at}kumc.edu

Submitted August 15, 2007; Accepted September 14, 2008

	Abstract

 
Background: Body system impairments following stroke have a complex relationship with functional activities. Although gait and balance deficits are well-documented in people after stroke, the overlapping influence of body impairments makes it difficult to prioritize interventions.

Objective: This study examined the relationship between prospectively selected measures of body function and structure (body mass index, muscle strength, sensation, and cognition) and activity (gait speed, gait endurance, and functional balance) in people with chronic stroke.

Design: This was a cross-sectional, observational study.

Methods: Twenty-six individuals with mean (SD) age of 57.6 (11) years and time after stroke of 45.4 (43) months participated. Four variables (body mass index, muscle strength difference between the lower extremities, sensation difference between the lower extremities, and Mini-Mental Status Exam score) were entered into linear regression models for gait speed, Six-Minute Walk Test distance, and Berg Balance Scale score.

Results: Lower-extremity strength difference was a significant individual predictor for gait speed, gait endurance, and functional balance. Cognition significantly predicted only gait speed.

Limitations: The authors did not include all possible factors in the model that may have influenced gait and balance in these individuals.

Conclusions: Strength deficits in the hemiparetic lower extremity should be an important target for clinical interventions to improve function in people with chronic stroke.

	Introduction

Top Abstract Introduction Method Results Discussion and Conclusions References

 
The consequences of stroke can be understood in the context of the International Classification of Functioning, Disability and Health (ICF) model.^1,2 In this model, health condition represents both healthy body systems and disorders or disease. This concept includes the damage that occurs in the brain tissue as a result of an ischemic blockage or hemorrhagic stroke, as well as other comorbidities. This damage often affects performance at the level of body function and structures in the ICF model, including motor weakness in a hemiparetic pattern, hypertonicity, impaired motor control, sensory loss, decreased cognition, and the effects of deconditioning.³ Together, these problems in body function and structures interact to produce problems with execution of tasks, classified as activity in the ICF model. Activities such as walking and functional balance ability influence a person's participation or ability to partake fully in life situations in the complete environment.¹

Mobility is one of the subdomains of activity and participation in the ICF model that is of specific concern to physical therapists.¹ Gait deficits in people who have had a stroke are well-documented and include both decreased walking speed^4,5 and decreased walking endurance.⁶ Standing balance also can be affected by a stroke and may influence functional mobility and increase the risk for falling.⁷ The overlapping influence of impairments in different body systems following a stroke makes it difficult to identify interventions and determine the prognosis for improvements in function. Previous research has examined the influence of various impairments on gait speed, gait endurance, and balance in people with chronic stroke, as summarized below.

Gait speed has been found to be a strong determinant of community mobility. One study showed that 39.3% of people living at home after a stroke were not able to walk to shopping venues or other places of interest in the community,⁴ and gait speed has been found to discriminate among self-reported levels of community ambulation.^4,5 Several researchers^8–11 have found correlations among measures of muscle strength (force-generating capacity), balance, daily ambulatory activity, aerobic fitness, hypertonicity, and lower-extremity motor control with short-distance (7–10 m) walking speed in people who have survived a stroke. Regression models have identified several variables that may explain the amount of variation in gait speed in people with stroke. These factors include muscle strength of individual muscle groups in the paretic limb,^12–14 muscle power of the nonparetic knee extensors,¹² sensation,¹⁴ self-efficacy,¹³ and sex.¹³

The Six-Minute Walk Test (6MWT) is a standardized test of walking endurance that can be used as a test of submaximal exercise capacity in people with stroke.⁶ Reference equations for 6MWT distance in elderly people who are healthy have been established based on sex, body mass index (BMI), and age.^15,16 In people with stroke, predictive factors include knee extension strength of the paretic leg, hypertonicity, balance, fast-paced gait speed, and aerobic fitness.^9,17,18 Measures of strength in other muscle groups, level of motor recovery, sensation, or cognition have not been reported in a regression model for prediction of 6MWT distance in people with stroke.

In addition to impaired gait mobility, people may have decreased functional balance following a stroke, as measured by the Berg Balance Scale (BBS).^19–21 Measurements of walking speed, aerobic fitness, daily ambulatory activity, and cognitive status have been found to correlate with BBS scores in people with chronic stroke.^7,10 Some researchers have evaluated BBS scores in a regression analysis as a potential predictor for falls or gait function,^7,10 but limited information is available on what factors may predict BBS score as an indicator of functional balance.

These studies have consistently found that individual measures of lower-extremity strength are important predictors of function following stroke, although a measure of overall hemiparetic leg weakness in comparison with the strong side has not been evaluated. There is some evidence that measures of general strength deficits in the lower limb are more closely related to functional outcome than measures of individual muscle function,^22–24 although none of these studies used regression analysis.

Furthermore, although there is some indication that BMI, sensation, and cognition may be important, these measures have not been assessed as potential predictors of function in people with stroke. It is important to identify which impairments in body function and structures are the strongest contributors to functional loss for people with chronic stroke in order to provide an appropriately targeted intervention. Although formal rehabilitation, in our current health care system, commonly ends after the first few months following a stroke, recent research has indicated that intense practice opportunities (eg, several hours per day for 2 weeks) can induce functional recovery^25–29 and can even induce neural changes in people who had a stroke years previously.³⁰ The purpose of this study was to examine the relationship between prospectively selected measures of body function and structure (BMI, muscle strength, sensation, and cognition) and activity (gait speed, gait endurance, and functional balance) in people with chronic stroke. We hypothesized that differences in these body function and structure measurements would predict measurements of activity in a linear regression model.

	Method

Top Abstract Introduction Method Results Discussion and Conclusions References

 
An institutionally approved informed consent form was signed by all individuals prior to their participation in this study. All testing was performed in a single session for each participant, with rests provided during the testing as requested by the participants.

Participants

A convenience sample of 26 people with chronic stroke was recruited for this study from a local stroke support organization. Volunteers were included in this study if they had a chronic stroke (at least 6 months prior to the study) and were able to transfer from a sitting position to a standing position and walk 9.1 m (30 ft) without assistance. A description of participant characteristics (54% male, 50% right-side stroke) and activity-level measurements are provided in Table 1.

BMI.
Body weight (in pounds) was measured using a portable scale, and height (in inches) was measured using a tape measure taped to a wall. Pounds and inches were converted to kilograms and meters. Body mass index was calculated using the equation: weight (kg)/[height (m)]².³¹

Muscle strength.
Five major muscle groups in the bilateral lower extremities were tested using a handheld dynamometer (MicroFET^*): hip flexors, hip abductors, knee flexors, knee extensors, and ankle dorsiflexors. The force pad of the dynamometer was held perpendicular to the limb segment, and participants were instructed to push against the dynamometer with maximal force for a count of 5. The desired movement was demonstrated to the participants, and their understanding was confirmed before starting. The less-affected lower limb was tested first, followed by the more-affected limb. Each muscle group was tested twice, and the average was used for analysis. Reliability for this type of dynamometer has been established previously,^32,33 but we assessed the test-retest reliability for these individuals with stroke using our procedures. Each participant was tested twice within each session, with a short rest between tests. One primary tester (PK) performed the majority of strength tests, and she was assisted by 2 other physical therapists. The testing position, dynamometer placement, and reliability coefficient (intraclass correlation coefficient [3,1])³⁴ for each muscle group tested are described in Table 2. A composite strength score for each lower extremity was calculated by adding together strength values for hip flexion, hip abduction, knee extension, knee flexion, and ankle dorsiflexion for each extremity.^35,36 The composite value for the more-affected side was subtracted from that of the less-affected side to give an indication of the difference in strength between the 2 sides.

Cognition.
The Folstein Mini-Mental Status Exam (MMSE) was administered to each participant.³⁹ The MMSE is a general screen for dementia and tests orientation, memory, attention, language, and ability to follow instructions. The highest possible score is 30.

Activity Measurements

Three measures of functional mobility were used, as described below.

Gait speed.
Self-selected walking speed was measured by having the participants walk at a comfortable pace over a 10-m distance. Participants were permitted to use any assistive devices or orthoses they preferred. Time (in seconds) was measured with a stopwatch, and the average time for 2 trials was recorded.

Gait endurance.
The 6MWT was used as a measure of walking endurance, using a 30.48-m (100-ft) walkway. Participants were instructed to cover as much ground as possible during the 6 minutes and were permitted to stop and rest, if needed. They were permitted to use their typical assistive devices or orthoses. Standardized encouragement (eg, "You are doing well, keep up the good work.") was provided to each participant at 1-minute intervals. If the participant requested a rest, the timer was not stopped during the rest, and standardized statements (eg, "It has been ___ minutes. Rest as long as you need to, and let me know when we can get started again.") were read to the participant. Total distance walked (in meters) was recorded.

Functional balance.
The BBS was used as a measure of balance.¹⁹ On the BBS, performance of each of 14 items, ranging in difficulty from sitting unsupported to standing on one foot, is rated on a 4-point scale, for a maximum possible score of 56.

Data Analysis

We used SPSS 15.0 for Windows for analysis of all data. Histograms for each variable were analyzed for normal distributions, and scatterplots were analyzed for outlying scores. Correlations among variables were calculated with the Pearson correlation coefficient. Linear regression models with 4 predictors (BMI, strength difference, sensation difference, and MMSE) were calculated for 10-m walk time, 6MWT distance, and BBS scores. The validity of each model was assessed through analysis of colinearity statistics (variance inflation factor) and Q-Q plots of unstandardized residuals, as well as Cook's distance (influence points) values for each participant. Data for participants with any missing data were not entered into the regression analysis (case deletion). A .05 level of significance was used for all statistical tests.

Role of the Funding Source

This study was not funded.

	Results

Top Abstract Introduction Method Results Discussion and Conclusions References

 
Descriptive Statistics and Correlations

Twenty-one of the 26 participants completed all of the testing. Five participants did not complete the full assessment because of time constraints, and only the values of the tests that were completed were entered into the analysis. The values for each of the independent variables are presented in Table 3. Significant correlations were noted among several of the variables, as noted in Table 4. With regard to the relationship between activity limitations and body function impairments, gait speed (10-m walk time) was significantly correlated with strength difference and MMSE, gait endurance (6MWT distance) was significantly correlated with strength difference, and balance (BBS) was significantly correlated with strength difference.

Functional Balance

The linear regression model for the BBS score with 4 variables (BMI, strength difference, sensation difference, and MMSE) approached significance (P=.06), with strength difference as a significant independent factor. Difference in sensation did approach significance in this model (P=.06).

	Discussion and Conclusions

Top Abstract Introduction Method Results Discussion and Conclusions References

 
The difference in strength between the lower extremities and mental status (MMSE) were found to be significantly correlated to gait speed (10-m walk time), and both factors were significant individual predictors of gait speed in our regression model. The relationship between lower-extremity strength and gait speed is supported by previous studies that examined this relationship with the strength and power of individual muscle groups.^12–14 In these studies, strength or power of the paretic knee extensors,^12,13 hip flexors and plantar flexors,¹⁴ and the nonparetic knee extensors¹² were found to predict gait speed in people with stroke. The different muscles identified in each of these studies may be partially explained by the strong relationship in weakness among muscle groups in an individual participant. The likely correlation among strength values for individual muscle groups may have influenced the regression models. Furthermore, none of these previous studies used values that indicated the magnitude of interlimb differences in muscle strength. Our study showed that a single measure that was intended to capture the weakness in the entire lower limb compared with the nonparetic lower extremity was a strong independent predictor of gait speed after stroke.

The influence of mental status (MMSE) on gait speed has not been reported previously for people with stroke. However, various measures of mental status (ie, the MMSE, a depression symptom score, and measures of positive and negative affect and mood) have been found to predict 6MWT distance in elder people who were healthy.^40,41 Our study included participants with a wide range of MMSE scores, with several of the participants scoring below 24, which indicates risk of dementia⁴² but which also may have been due to the presence of aphasia. We are fairly confident that even participants with low MMSE scores were able to follow the very simple instructions for the gait speed tests (ie, walk at a comfortable pace). The MMSE is used primarily as a screening measure for dementia, and its usefulness to ascertain overall mental status is limited. Furthermore, the difficulty in distinguishing cognitive deficits from communication deficits in this population may complicate the interpretation of our findings. Future work should screen for aphasia and include more-comprehensive cognitive and psychological assessments to determine the true nature of the relationship between gait speed and mental status in people with stroke.

The difference in strength between the lower extremities was found to be significantly correlated with gait endurance (6MWT distance). Although the overall regression model for gait endurance was not significant, a 1-sided test was used to assess the individual predictors, which revealed a significant relationship for strength difference. Previous researchers^9,17 have found paretic knee extension strength to be a significant predictor in regression models of gait endurance in people with stroke. Improvements in motor recovery (Fugl-Meyer test score) also have been found to predict improvements in gait endurance over a 3-month period in people who are higher functioning following subacute stroke.⁴³

The strength difference between the lower extremities correlated with balance (BBS) and was found to be a significant individual predictor for BBS score. The difference in lower-extremity sensation approached significance as a predictor of functional balance as well as gait speed. However, the sensation variable did not demonstrate strong or significant correlations with any of these measures. The influence of sensation and strength on predicting BBS score in people with stroke has not been reported previously.

Our model included 4 independent variables that we hypothesized would influence gait and balance function in people with chronic stroke. Two of these independent variables (sensation and strength) were calculated by taking the difference in scores between the 2 lower limbs. An advantage of this difference score is that it may be a valid marker for the construct of hemiparetic severity. This between-limb comparison provides unique insight, as compared with other studies (eg, Pohl et al⁴³) that have utilized single-limb measures (such as Fugl-Meyer test scores) as an indication of hemiparetic severity after stroke. These single-limb scores do not allow for any comparison between sides. One limitation of this approach is the difficultly interpreting this score, as a lower difference score could mean that both limbs were equally weak or lacking in sensation.

Several different methods have been reported in the literature for the calculation of strength deficit scores.^22,24,44 Measurements of strength difference expressed as a percentage of body weight have been found to be valid,⁴⁴ although no change in correlation was apparent when comparing strength measurements normalized and not normalized to body weight.²² Other strategies have been used to express strength difference as a ratio or percentage of the strong side or of predicted normal reference values.^22,24,44 However, when values are expressed as ratios or percentages, an appreciation of the absolute numbers is lost. For example, a 10% difference could mean any range of values, depending on the baseline strength. The simple subtraction measure used in our study to describe strength deficit is similar to that used in the study by Pohl et al⁴⁵ to calculate "cost" in comparing 2 different conditions.

In calculating the total strength score of each limb, we did not include hip extension because of the difficulty obtaining a standard position for testing hip extension with the handheld dynamometer. Although other researchers also have excluded hip extension from calculation of composite leg strength scores,³⁶ certainly the inclusion of hip extension might influence the relationship of these scores to functional tasks. Another limitation of our study is that we did not include factors in our regression model that have been shown to correlate with gait and balance function in people with stroke, such as age, hypertonicity, aerobic fitness, self-efficacy, and sex.^9,11,13,17

We did not focus on the relationship between balance and gait in this study, although it is likely that balance has an influence on gait function, as previously reported in people with subacute and chronic stroke.^10,43 The relationship that we found between the gait and balance measures in our participants may have influenced our regression models.

We considered 3 tasks as components of activity using the ICF model: gait speed, gait endurance, and functional balance. Consequently, 3 regressions were fit. Another possible approach for future work would be to expand the database so that activity limitations can be treated as a latent variable. This would allow a structural equation modeling framework to be used in order to promote parsimony (one dependent variable) while reducing measurement error by incorporation of 3 manifest variables. Although our study demonstrated statistical significance with several of the predictors, only moderately strong correlations were found. These suggested modifications to the analysis should further clarify the factors that predict activity limitations. A larger sample size (N=100) would be required to take advantage of such an approach. A larger data set also would allow us to include more than 4 independent variables and test for interactions in the regression analyses.

The strong influence of the difference in lower-extremity strength on gait speed, gait endurance, and functional balance indicates that overall strength deficits in the hemiparetic lower extremity should be an important target for clinical interventions. Improvements in measures of isometric torque to make the values between the lower extremities more similar should lead to improvements in function. Several comprehensive rehabilitation approaches have been reported to improve strength of the hemiparetic lower extremity, such as home-based exercise,⁴⁶ general fitness training,^47,48 and task-oriented exercise.⁴⁹ Investigating whether strength training alone can improve muscle strength and function would be an important area of future research. The potential impact of cognitive status on gait function also is an interesting area to explore further.

	Footnotes

 
Dr Kluding provided concept/idea/research design, writing, data collection, and project management. Dr Gajewski provided consultation (including review of manuscript before submission). Both authors provided data analysis.

The authors thank the American Stroke Foundation for their assistance with recruitment and for use of their facilities for data collection.

This study was approved by the Human Subjects Committee of the University of Kansas Medical Center.

^* Hoggan Health Industries, 8020 S 1300 West, West Jordon, UT 84088.

SPSS Inc, 233 S Wacker Dr, Chicago, IL 60606.

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