Background and Purpose: Rehabilitation research for individuals with chronic stroke is increasingly addressing spatiotemporal asymmetries (STA). Understanding the reliability of STA between sessions is essential for determining whether treatment responses exceed day-to-day variation. The purpose of this study was to determine the minimal detectable change (MDC) and test-retest reliability in STA and gait speed measures for individuals with chronic stroke.
Methods: Twenty-six individuals with chronic (>6 months) stroke participated in 2 visits. At each visit, participants walked across a 14-ft (4.3-m) GAITRite mat at comfortable gait speed (CGS) and fast gait speed (FGS). Spatiotemporal asymmetries ratios (paretic/nonparetic limb) were calculated for step length, stance time, and swing time. Intraclass correlation coefficients (ICC, 2,1) were calculated to determine reliability between sessions. Minimal detectable change values were calculated to determine the smallest change that is considered “real.”
Results: Spatiotemporal asymmetry measures were consistent between sessions, with ICCs ranging from 0.93 to 0.98. Asymmetry ratio MDC values were calculated for step length (CGS: 0.15; FGS: 0.19), swing time (CGS: 0.26; FGS: 0.20), and stance time (CGS: 0.09; FGS: 0.10). Gait speed MDC was 0.20 m/s and 0.22 m/s, respectively, for the CGS and FGS walking conditions.
Discussion and Conclusion: There were considerable differences among MDC values for the various STA measures, suggesting that larger changes will be necessary to show improvement for certain measures (eg, swing time asymmetry). These data will assist with setting clinical goals for patients with chronic stroke and will be useful for evaluating interventions designed to minimize temporal and spatial interlimb asymmetries during walking.
Department of Allied Health Sciences, Division of Physical Therapy, University of North Carolina, Chapel Hill.
Correspondence: Michael D. Lewek, PT, PhD, 3043 Bondurant Hall, CB#7135, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599 (email@example.com).
Supported by grants from the Foundation for Physical Therapy, Inc. Geriatric Endowment Fund and from the American Heart Association.
This work was presented at the Combined Sections Meeting of the American Physical Therapy Association, New Orleans, Louisiana, February 2011.
BACKGROUND AND PURPOSE
Following stroke, approximately 70% of individuals older than 65 years regain the ability to walk independently by 6 months after stroke1; however, only 30% walk at speeds greater than 0.8 m/s following inpatient rehabilitation.2 These slower walking speeds are associated with marked temporal and spatial interlimb asymmetries in 48% and 44% of subjects, respectively.3 Patterns of temporal asymmetry are characterized by a shorter stance time and longer swing time of the paretic limb (ie, stance time asymmetry and swing time asymmetry, respectively),4–6 while spatial asymmetry is often characterized by a shorter step length of the nonparetic limb (ie, step length asymmetry)7 (however, see Reisman et al8). We believe that restoring gait symmetry is important to improve energy efficiency, gait speed,3 and balance control, and to decrease the risk of falls, lower extremity musculoskeletal injury,9 and loss of bone mineral density in the paretic limb.
Several studies involving treadmill-based locomotor training have demonstrated improved gait speed,11–15 but no effect on interlimb spatiotemporal (eg, stance time, swing time, or step length) symmetry.12,16,17 Nevertheless, there have been recent advances in therapeutic interventions featuring novel methods of improving gait symmetry in individuals poststroke.8,18,19 In order to determine whether changes in gait symmetry are “real,” knowledge of the day-to-day variations is required. The minimal detectable change (MDC) is the amount of change necessary to suggest that a change in the outcome measure in response to treatment exceeds normal day-to-day variations.20,21 The MDC for gait speed has been estimated to be between 0.15 and 0.30 m/s, for individuals performing subacute rehabilitation following stroke,22,23 and 0.20 m/s for community-dwelling individuals with stroke.24
Whereas MDC values for gait speed have been established,24 we are unaware of such values for spatiotemporal asymmetry (STA) for individuals with chronic stroke. As rehabilitation protocols are enhanced to elicit changes in STA, it will be vital to understand the between-session reliability of STA in chronic stroke. In particular, STA measures of step length asymmetry and stance and swing time asymmetry are commonly reported in individuals poststroke.3 While numerous methods for calculating asymmetry have been reported, Patterson and colleagues3 concluded that most of these methods are correlated with each other and show comparable distributions and discriminative ability. A simple ratio value, which has an intuitive understanding, has therefore been proposed to establish consistency among clinicians and researchers. Therefore, the purpose of this study was to establish test-retest reliability and estimate MDC scores for step length, stance time, and swing time asymmetry ratios in individuals with chronic stroke.
Participants who exhibited clinical symptoms consistent with an ischemic or hemorrhagic unilateral brain lesion resulting in sensorimotor dysfunction were recruited for this study. Potential subjects were excluded if they could not walk 10 m without therapist assistance, had a preexisting cardiovascular, metabolic, or musculoskeletal condition that prohibited strenuous activity, a concurrent neurologic condition that could affect walking ability (eg, Parkinson's disease), a history of balance deficits or unexplained falls that predated the stroke, or had impaired cognition that affected the ability to follow directions. All participants signed an informed consent form approved by the University of North Carolina at Chapel Hill prior to participation.
Procedures and Data Management
All individuals participated in 2 visits separated by 11 ± 8 days (range, 3–36 days) to record spatiotemporal gait parameters via an electronic walkway. At each visit, participants walked overground, wearing their typical shoes, across a 14-ft (4.3-m) GAITRite mat (CIR Systems, Havertown, Pennsylvania). The GAITRite mat is a pressure-sensitive mat with an active area that is 24 in (61 cm) wide and 168 in (427 cm) long containing a total of 16 128 sensors. Sensor data were sampled at 60 Hz. The GAITRite mat has shown excellent reliability25 and concurrent validity26 in healthy individuals and individuals following stroke. Participants completed three trials at their self-selected comfortable gait speed (CGS) and three trials at the fastest possible walking speed (fast gait speed [FGS]). For the comfortable speed, participants were instructed to walk at their normal, comfortable pace; for the fast speed, participants were instructed to walk as fast as they safely could. Participants began their approach approximately 5 ft (1.5 m) before the mat and continued walking for approximately 5 ft (1.5 m) beyond the end of the mat to allow for appropriate acceleration and deceleration. Participants were permitted to use their customary ankle foot orthosis or assistive devices but were not provided therapist assistance. The same assistive device and/or ankle foot orthosis were used for both sessions.
Partial steps (eg, beginning or end of the walkway) and marks from assistive devices were removed automatically using the GAITRite software or manually by the investigators. Following the editing process, there remained an average of 20 ± 6 steps per subject for the CGS condition, and 17 ± 7 steps per subject for the FGS condition. From these remaining steps, the software calculated the average step length, stance time, and swing time (ie, single support time for the contralateral side) separately for the CGS and FGS trials, for both the paretic and nonparetic limbs. We chose not to calculate double support time asymmetry because the percentage of subjects who exhibit asymmetry in this measure is relatively small.3 For each measure, symmetry was calculated as the ratio of paretic/nonparetic limb, with values inverted, if necessary, to be 1.0 or more.3 Other investigators have calculated spatiotemporal symmetry, using a variety of methods and equations.6,8,17,18 To avoid confusion with the myriad of approaches, Patterson and colleagues3 recently advocated for the use of the simple ratio measure (paretic/nonparetic) used here. Importantly, this ratio measure is highly correlated with the symmetry index, symmetry angle,27 and the log of the ratio,28 as well as demonstrating a similar distribution and discriminative ability.3 In addition to STA measures, we determined CGS and FGS using the GAITRite software.
All statistical analyses were performed with SPSS (version 15.0, Chicago, Illinois). Gait parameters (gait speed and asymmetry ratios for step length, stance time, and swing time) were compared between sessions for both CGS and FGS walking conditions using paired samples t tests. For all measures, we used intraclass correlation coefficients (ICC, 2,1) to determine the reliability between test sessions. These correlation coefficients were subsequently used in the determination of the standard error of the measurement (SEM), calculated as follows:
in which SD is the standard deviation from the first test session,29 calculated separately for CGS and FGS walking conditions, and r is the correlation coefficient (ie, ICC (2,1)) for the respective measure. The MDC at the 95% CI was calculated as follows:
for step length, stance time, and swing time asymmetries, as well as gait speed for both CGS and FGS walking conditions.
We recruited 26 individuals (11 women/15 men; age: 56 ± 11 years; height: 1.74 ± 0.10 m; weight: 85.3 ± 19.3 kg) with chronic (>6 months) hemiparesis resulting from a unilateral, noncerebellar stroke. Participants experienced their stroke a mean (SD) of 60 (76) months before testing and had a mean (SD) lower extremity Fugl-Meyer score of 25 (5) (out of a possible 34 points). Ten participants exhibited left-side hemiparesis, and 16 had right-side hemiparesis.
Comfortable Gait Speed
The mean (SD) CGS for all participants was 0.70 (0.25) m/s for session 1 and 0.78 (0.28) m/s for session 2 (see Table 1). Despite a change in gait speed between sessions (P < 0.001), STAs showed high ICC (2,1) values (stance time asymmetry: 0.945; swing time asymmetry: 0.962; step length asymmetry: 0.976), indicating high consistency between sessions (see Figure 1). The MDC for asymmetry ratios and gait speed during the CGS condition are given in Table 1.
Fast Gait Speed
During the FGS walking condition, participants walked at a mean (SD) speed of 0.98 (0.40) m/s during session 1 and at 1.05 (0.40) m/s during session 2 (see Table 1). Participants walked significantly faster during the second visit (P < 0.01); however, STAs did not change significantly between sessions (all P > 0.48). High ICC (2,1) values were observed for each STA ratio (stance time asymmetry: 0.925; swing time asymmetry: 0.964; step length asymmetry: 0.944) (Figure 2). Minimal detectable change values for STA and gait speed during FGS walking are given in Table 1.
Whereas MDCs have been proposed for gait speed in individuals in the subacute and chronic phases following stroke, information is lacking about what constitutes a “real” change in STA measures. The data reported here provide valuable information to clinicians and researchers to determine whether a change in STA exceeds typical day-to-day variations.
In recent years there has been an intensive effort to restore gait symmetry to individuals following stroke using novel forms of rehabilitation.8,12,16,18,30,31 Effective evaluation of these approaches requires knowledge of typical between-session variations in STA. These and other novel rehabilitation approaches can be considered successful only if the imposed change in STA exceeds such variation. As further advances in rehabilitation for individuals with chronic stroke are proposed and tested, the values reported here will be critical for effectively evaluating the success of these approaches.
It is interesting to note that the MDC for stance time asymmetry and swing time asymmetry appeared to be different. The MDC for stance time asymmetry is smaller than the MDC for swing time asymmetry, which initially might suggest that it is easier to elicit a real change in stance time asymmetry. However, the evidence refutes this assumption as therapeutic approaches often fail to elicit changes in stance time asymmetry.8,17,18 Stance time measures incorporate two double support times, whereas swing time is the single support time of the contralateral limb. The observation of a larger MDC for swing time may be due to the inherent challenge associated with the balance requirement during single limb stance (ie, contralateral swing time).32 Each STA measure, however, provides important information and should therefore be considered in the assessment of gait for an individual following stroke.33 Step length asymmetry, for instance, has been related to the paretic limb's propulsive force generation.34 In addition, stance time and swing time asymmetries, while related to each other, are believed to represent different properties of gait.3
A limitation of this work is that we recruited a fairly small number of participants for this study. Eleven of the 26 participants (42%) may be considered “higher-functioning” with CGS greater than 0.8 m/s.35 This may limit the generalizability of our findings and suggests that these data should be applied primarily for limited-community and unrestricted community ambulators. Interestingly, our cohort had nearly the same average gait speed as that of subjects in a report by Patterson et al,3 albeit with a slightly greater percentage of individuals exhibiting STA in our cohort. Nevertheless, participants with overt STA were not intentionally recruited; instead, participants were enrolled as they presented to our laboratory. Using established thresholds for “normal” symmetry for step length (1.08), swing time (1.06), and stance time (1.05) asymmetry, we obtained slightly greater proportions of individuals with stance time asymmetry (73%) and swing time asymmetry (81%), but not step length asymmetry (50%) than what has been reported previously from a much larger sample.3 A major methodological difference between the current work and the larger sample reported by Patterson and colleagues3 is the use of an assistive device. Because gait speed appears to be more consistent in individuals requiring an assistive device compared with those who do not, including subjects who use assistive devices may have contributed to lower gait speed MDC values in our cohort.23 Recently, it was also reported that the use of an assistive device by individuals not accustomed to using an assistive device can increase gait symmetry.36 If cane use also minimizes STA in individuals who routinely use assistive devices, our data would underestimate the extent of STA. Nevertheless, our subjects (who were allowed to use assistive devices) exhibited greater asymmetry than the cohort described by Patterson et al3 (who were not allowed to use assistive devices), making it unlikely that the use of assistive devices minimized STA in our cohort. We caution, however, that our data may not generalize to cases in which individuals who routinely use an assistive device are tested without their aid. By allowing our participants to continue to use their typical assistive devices, however, we intended to capture the way that participants walk in the manner that most closely represents their real-world performance.
A second limitation of the study was that we did not accommodate participants to the laboratory before testing. There is the potential for participants to perceive walking across the GaitRite as a novel situation. While participants were accustomed to walking across carpeting, an initial baseline session would have alleviated concerns about “learning” to walk across the GaitRite mat. Because no intervention was involved, the use of multiple baseline sessions was deemed unnecessary to determine the repeatability of walking function. Nevertheless, we observed a change in gait speed, but not STA, from the first to the second session, indicating that subjects may have felt more comfortable during the second session. We caution, therefore, that if some subjects intentionally slowed their gait during the first session, this would increase the variability between sessions, producing an inflated MDC value for gait speed. If, however, a systematic learning effect occurred, then the intersession correlation (r value) will remain high, and the MDC value will be accurate. That our MDC values for gait speed are comparable with previously reported values22–24 suggests that if a learning effect occurred between sessions, it was minimal.
Finally, we did not standardize the time between test sessions. This was necessary to accommodate the travel and scheduling limitations of some of our participants. When calculating MDC values, it is important to obtain repeated measurements at a time when the measure of interest is stable.21 While changes may have occurred to the gait pattern between testing sessions, we believe that this is highly unlikely given the length of time since the stroke (all participants were greater than 6 months poststroke). In the chronic stage following stroke, STA values should be more stable without an intervention, making MDC values smaller; however, changes to STA may also be more difficult to elicit in the chronic stage. Despite earlier assertions that 6 months poststroke represents a functional plateau in the recovery process,37 we now know that intensive locomotor training can elicit dramatic changes in gait, even years after the stroke.38 Given that none of our participants were participating in intensive rehabilitation, we believe that the time between testing sessions did not influence our results.
While improving gait speed and endurance have represented the major goals of locomotor training poststroke, STA is an important gait parameter that needs to be addressed during rehabilitation.39 Previous measures of MDC for gait speed have been provided for individuals with chronic stroke24 and for those undergoing subacute rehabilitation following stroke.22,23 Here we have provided MDC values for STA in the CGS and FGS walking conditions for individuals with chronic (>6 months) stroke. These data will assist with setting clinical goals for patients with chronic stroke and will be useful for evaluating interventions designed to minimize interlimb asymmetry.
1. Lloyd-Jones D, Adams RJ, Brown TM, et al. Heart disease and stroke statistics—2010 update: a report from the American Heart Association. Circulation. 2010;121:e46–e215.
2. Hill K, Ellis P, Bernhardt J, Maggs P, Hull S. Balance and mobility outcomes for stroke patients: a comprehensive audit. Aust J Physiother. 1997;43:173–180.
3. Patterson KK, Gage WH, Brooks D, Black SE, McIlroy WE. Evaluation of gait symmetry after stroke: a comparison of current methods and recommendations for standardization. Gait Posture. 2010;31:241–246.
4. Patterson KK, Parafianowicz I, Danells CJ, et al. Gait asymmetry in community-ambulating stroke survivors. Arch Phys Med Rehabil. 2008;89:304–310.
5. Mizelle C, Rodgers M, Forrester L. Bilateral foot center of pressure measures predict hemiparetic gait velocity. Gait Posture. 2006;24:356–363.
6. Kim CM, Eng JJ. Symmetry in vertical ground reaction force is accompanied by symmetry in temporal but not distance variables of gait in persons with stroke. Gait Posture. 2003;18:23–28.
7. Hsu AL, Tang PF, Jan MH. Analysis of impairments influencing gait velocity and asymmetry of hemiplegic patients after mild to moderate stroke. Arch Phys Med Rehabil. 2003;84:1185–1193.
8. Reisman DS, Wityk R, Silver K, Bastian AJ. Locomotor adaptation on a split-belt treadmill can improve walking symmetry post-stroke. Brain. 2007;130:1861–1872.
9. Norvell DC, Czerniecki JM, Reiber GE, Maynard C, Pecoraro JA, Weiss NS. The prevalence of knee pain and symptomatic knee osteoarthritis among veteran traumatic amputees and nonamputees. Arch Phys Med Rehabil. 2005;86:487–493.
10. Jorgensen L, Crabtree NJ, Reeve J, Jacobsen BK. Ambulatory level and asymmetrical weight bearing after stroke affects bone loss in the upper and lower part of the femoral neck differently: bone adaptation after decreased mechanical loading. Bone. 2000;27:701–707.
11. Visintin M, Barbeau H, Korner-Bitensky N, Mayo NE. A new approach to retrain gait in stroke patients through body weight support and treadmill stimulation. Stroke. 1998;29:1122–1128.
12. Hornby TG, Campbell DD, Kahn JH, Demott T, Moore JL, Roth HR. Enhanced gait-related improvements after therapist- versus robotic-assisted locomotor training in subjects with chronic stroke. A randomized controlled study. Stroke. 2008;39:1786–1792.
13. Ada L, Dean CM, Hall JM, Bampton J, Crompton S. A treadmill and overground walking program improves walking in persons residing in the community after stroke: a placebo-controlled, randomized trial. Arch Phys Med Rehabil. 2003;84:1486–1491.
14. Sullivan KJ, Brown DA, Klassen T, et al. Effects of task-specific locomotor and strength training in adults who were ambulatory after stroke: results of the STEPS randomized clinical trial. Phys Ther. 2007;87:1580–1602; discussion 1587–1603.
15. Sullivan KJ, Knowlton BJ, Dobkin BH. Step training with body weight support: effect of treadmill speed and practice paradigms on poststroke locomotor recovery. Arch Phys Med Rehabil. 2002;83:683–691.
16. Patterson SL, Rodgers MM, Macko RF, Forrester LW. Effect of treadmill exercise training on spatial and temporal gait parameters in subjects with chronic stroke: a preliminary report. J Rehabil Res Dev. 2008;45:221–228.
17. Silver KH, Macko RF, Forrester LW, Goldberg AP, Smith GV. Effects of aerobic treadmill training on gait velocity, cadence, and gait symmetry in chronic hemiparetic stroke: a preliminary report. Neurorehabil Neural Repair. 2000;14:65–71.
18. Kahn JH, Hornby TG. Rapid and long-term adaptations in gait symmetry following unilateral step training in people with hemiparesis. Phys Ther. 2009;89:474–483.
19. Reisman DS, McLean H, Bastian AJ. Split-belt treadmill training poststroke: a case study. Neurol Phys Ther. 2010;34:202–207.
20. Beckerman H, Roebroeck ME, Lankhorst GJ, Becher JG, Bezemer PD, Verbeek AL. Smallest real difference, a link between reproducibility and responsiveness. Qual Life Res. 2001;10:571–578.
21. Tilson JK, Sullivan KJ, Cen SY, et al. Meaningful gait speed improvement during the first 60 days poststroke: minimal clinically important difference. Phys Ther. 2010;90:196–208.
22. Evans MD, Goldie PA, Hill KD. Systematic and random error in repeated measurements of temporal and distance parameters of gait after stroke. Arch Phys Med Rehabil. 1997;78:725–729.
23. Fulk GD, Echternach JL. Test-retest reliability and minimal detectable change of gait speed in individuals undergoing rehabilitation after stroke. J Neurol Phys Ther. 2008;32:8–13.
24. Flansbjer UB, Holmback AM, Downham D, Patten C, Lexell J. Reliability of gait performance tests in men and women with hemiparesis after stroke. J Rehabil Med. 2005;37:75–82.
25. Ng SS, Hui-Chan CW. The Timed Up & Go Test: its reliability and association with lower-limb impairments and locomotor capacities in people with chronic stroke. Arch Phys Med Rehabil. 2005;86:1641–1647.
26. Stokic DS, Horn TS, Ramshur JM, Chow JW. Agreement between temporospatial gait parameters of an electronic walkway and a motion capture system in healthy and chronic stroke populations. Am J Phys Med Rehabil. 2009;88:437–444.
27. Zifchock RA, Davis I, Higginson J, Royer T. The symmetry angle: a novel, robust method of quantifying asymmetry. Gait Posture. 2008;27:622–627.
28. Plotnik M, Giladi N, Hausdorff JM. A new measure for quantifying the bilateral coordination of human gait: effects of aging and Parkinson's disease. Exp Brain Res. 2007;181:561–570.
29. Perera S, Mody SH, Woodman RC, Studenski SA. Meaningful change and responsiveness in common physical performance measures in older adults. J Am Geriatr Soc. 2006;54:743–749.
30. Roerdink M, Lamoth CJ, Kwakkel G, van Wieringen PC, Beek PJ. Gait coordination after stroke: benefits of acoustically paced treadmill walking. Phys Ther. 2007;87:1009–1022.
31. Chen CH, Lin KH, Lu TW, et al. Immediate effect of lateral-wedged insole on stance and ambulation after stroke. Am J Phys Med Rehabil. 2010;89:48–55.
32. Brandstater ME, de Bruin H, Gowland C, Clark BM. Hemiplegic gait: analysis of temporal variables. Arch Phys Med Rehabil. 1983;64:583–587.
33. Wall JC, Turnbull GI. Gait asymmetries in residual hemiplegia. Arch Phys Med Rehabil. 1986;67:550–553.
34. Balasubramanian CK, Bowden MG, Neptune RR, Kautz SA. Relationship between step length asymmetry and walking performance in subjects with chronic hemiparesis. Arch Phys Med Rehabil. 2007;88:43–49.
35. Perry J, Garrett M, Gronley JK, Mulroy SJ. Classification of walking handicap in the stroke population. Stroke. 1995;26:982–989.
36. Beauchamp MK, Skrela M, Southmayd D, et al. Immediate effects of cane use on gait symmetry in individuals with subacute stroke. Physiother Can. 2009;61:154–160.
37. Jorgensen HS, Nakayama H, Raaschou HO, Olsen TS. Recovery of walking function in stroke patients: the Copenhagen Stroke Study. Arch Phys Med Rehabil. 1995;76:27–32.
38. Moore JL, Roth EJ, Killian C, Hornby TG. Locomotor training improves daily stepping activity and gait efficiency in individuals poststroke who have reached a “plateau” in recovery. Stroke. 2010;41:129–135.
39. Patterson KK. Clinician's commentary. Physiother Can. 2009;61:161–162.
cerebrovascular accident; gait; reliability; symmetry© 2011 Neurology Section, APTA