TUTORES

after stroke

The term walking competency (Salbach et al., 2004) is used to describe an ensemble of abilities related to walking that enables the individual to navigate the community proficiently and safely. Elements of walking competency include being able to: walk fast enough to cross the street safely (Robinett and Vondran, 1988; Perry et al., 1995), walk far enough to accomplish activities of daily living (Lerner-Frankiel et al., 1986), negotiate sidewalk curbs inde-pendently, turn the head while walking without losing balance, react to unexpected perturbations while walking without loss of stability, and demon-strate anticipatory demon-strategies to avoid or accommo-date obstacles in the travel path (Shumway-Cook and Woollacott, 1995). Thus walking competency is linked to the accomplishment of basic every-day tasks, leisure activities and participation in life.

Reliability, validity and responsiveness of gait speed

Gait speed at a comfortable pace is likely the best-known measure of walking performance (Wade, 1992; see Volume II, chapter 3). Timed walking tests (over 5, 10 or 30 m) are easy to carry out and when standardized instructions are used, the inter-rater (Holden et al., 1984; Wade et al., 1987) and test–retest reliability (Holden et al., 1984; Evans et al., 1997) of measures of walking speed in persons with stroke are high. The construct validity of walking speed is also very good. In persons with stroke, comfortable walking speed has been shown to be positively cor-related to strength (r 0.25–0.67) of the lower extremity (Bohannon, 1986; Bohannon and Walsh, 1992), to balance (r 0.60; Richards et al., 1995), to motor recovery (r 0.62; Brandstater et al., 1983), to functional mobility (r 0.61; Podsiadlo and Richardson, 1991), and negatively correlated to spasticity of the lower extremity (Norton et al., 1975; Lamontagne et al., 2001; Hsu et al., 2003).

Moreover, subjects who walk faster tend to have a

better walking pattern (Wade et al., 1987; Richards et al., 1995).

Thus, in terms of reliability and validity, the 10-min walk (10mW) test at natural or free pace is a very good measure, but is it always the most responsive meas-ure? For example, should maximum gait speed also be tested to assess the capacity of persons with stroke to have a burst of speed to, for example cross a busy street (Nakamura et al., 1988; Suzuki et al., 1990;

Bohannon and Walsh, 1992). Others believe measur-ing gait speed over 5 m is enough. For the sake of argument, let us define “measure of choice” as the measure that is most responsive to change as deter-mined by the SRM. Salbach et al. (2001) exadeter-mined the responsiveness of four different timed gait tests in 50 persons, tested an average of 8 and 38 days after stroke. They found the 5-min walk (5mW) at comfort-able pace test was most responsive followed by the 5mW maximum pace, the 10mW comfortable and the 10mW maximum pace. Responsiveness of a measure of physical performance cannot be gener-alized because it is related to stroke severity. Table 1.1 compares data from two studies, one with subjects in the early (Salbach et al., 2001) and the other in a sub-acute phase (Richards et al., 2004) post-stroke. It gives estimates of the magnitude of change that can be expected in clinical measures over 8 weeks.

As demonstrated by the Salbach et al. data, the 5mW was more responsive than the timed up and go (TUG) (Podsiadlo and Richardson, 1991) or the balance scale (Berg et al., 1989, 1992a, b, 1995). On the other hand, in the Richards et al. data, the Barthel index (Mahoney and Barthel, 1954) ambulation subscore was the most sensitive measure, followed by the balance scale and the TUG. In this group of subjects, walking speed was less sensitive, likely due to the more severe disability as indicated by the walking speed and TUG time. The balance scale scores, however, are comparable between groups at both evaluations and the SRMs indicate that it is the second most responsive measure for both groups.

To further examine the relation between stroke severity, as gauged by walking speed, and respon-siveness, data from the sub-acute stroke subjects in the Richards et al. (2004) study were subdivided

according to whether the subjects walked0.3 or0.3 m/s at baseline. Figure 1.1 compares the SRM values of the different measures in the two groups. Although the Barthel ambulation subscore remains the most responsive, the SRM is closer to the TUG and gait speed values in the faster walking group and conversely, the balance scale is more responsive in the slower walking group. These results are similar to those reported by Salbach et al.

(2001) and Richards et al. (1995), in persons with acute stroke. Such results illustrate how floor and ceiling effects relate to responsiveness. When select-ing a locomotor-related outcome measure it is important to consider the locomotor abilities of the persons to be evaluated. The Fugl-Meyer leg (FM-L) (Fugl-Meyer et al., 1975) subscale and the balance scale which rate the achievement of movement tasks, have a ceiling effect when evaluating higher-performing subjects. Conversely, walking speed can have a floor effect when evaluating subjects who walk at very slow speeds and require assistance

(Richards et al., 1995). The TUG also has a floor effect because many subjects cannot complete the test 2 months after stroke (Richards et al., 1999).

We must now question the classical recovery curve that has been defined by plotting change over time in clinical measures that have a ceiling effect.

Thus, it is generally accepted that most recovery occurs in the first 6-week post-stroke when the effects of rehabilitation augment natural recovery.

Thereafter, recovery slows but continues up to about 6-month post-stroke (Skilbeck et al., 1983;

Richards et al., 1992; Jorgensen et al., 1995). With a continuous measure such as gait speed, however, recovery has been documented up to 2-year post-stroke (Richards et al., 1995). Moreover, a number of intervention studies in persons with chronic stroke have confirmed that recovery of function (Dean et al., 2000; Tangeman et al., 1990; Teixeira-Salmela et al., 1999, 2001; Salbach et al., 2004) and changes in brain organization (Liepert et al., 2000) occur beyond 6 months post-stroke.

Table 1.1. Magnitude of change over 8-week period in persons with acute and sub-acute stroke.

Acute stroke (n 50) Sub-acute stroke (n 62)

Measure (max. score) Baseline Post-therapy SRM Baseline Post-therapy Change Change (%) SRM

STREAM (100) 77
25 88
18 0.89

FM-L (34) 19.7
6.8 22.5
6.4 2.8
3.7 20
29 0.77

FM-A (66) 25.6
19.8 30.6
21.9 5.0
6.8 25
36 0.74

Barthel index (100) 75
26 90
17 0.99

Barthel ambulation (47) 19.4
8.6 37.7
7.8 18.3
8.5 120
86 2.14

Balance scale (56) 37
18 47
11 1.04 36.1
10.6 45.9
7.6 9.8
7.5 38
46 1.31 TUG (time, s) 32.3
29.1 19.6
17.5 0.88 53.5
24.2 31.3
17.9 22.2
18.3 40
22 1.22

5mWcom(cm/s) 59
34 88
38 1.22

5mWmax(cm/s) 83
50 1.16
55 1.00

10mW_com(cm/s) 59
34 84
36 0.92 26.3
14.1* 57
35.8 30.7
29.0 127
130 1.06

10mW_max(cm/s) 79
47 105
47 0.83

Data for subjects with acute stroke obtained from Salbach et al. (2001); subjects, undergoing regular rehabilitation were evaluated 1-week post-stroke and 8 weeks later. Data for subjects with sub-acute stroke taken from Richards et al. (2004); subjects who received task-oriented physical therapy, were evaluated on average 52-days post-stroke and 8 weeks later. SRM that represents the average change score over a set period of time divided by the SD of that change.

*Comfortable walking speed measured over 5, 10 or 30 m. Maximum (max.) score for each measure shown in first column; values give mean or mean
1 SD.

When examining change in outcome measures, it is important to question whether the amount of change is larger than the measurement error. For con-tinuous measures, such as walking speed, systematic and random error in repeated measures (measure-ment error) can be mathematically derived (Evans et al., 1997). It is also possible to calculate the stan-dard error of the mean of scale scores from pub-lished reliability studies (Stratford, 2004). Once it is established if the change is greater than the error estimation, it becomes important to decide the MID. For example, the MID of the balance scale is 6 points (Stevenson, 2001), for the 6-min walk (6 MINW) test it is 54 m (Redelmeier et al., 1997) and for the Stroke Impact Scale it is 10–15 points on a subscale (Duncan et al., 1999). One suggested MID

for scale scores is a change of about 11% (Iyer et al., 2003), another is the value one-half an SD of baseline scores (Norman et al., 2003).

Clearly, walking speed alone will not evaluate aspects of walking competency related to endu-rance, the ability to ascend or descend stairs, or navigate in different terrains under various environ-mental conditions (Malouin and Richards, 2005).

In real life, one usually must rise from a bed or a chair before beginning to walk, not easy tasks for persons with stroke, in part because the affected leg supports less than 50% of the body weight (Engardt and Olsson, 1992; Malouin et al., 2003, 2004a, b). The physical demands of rising from a chair, as meas-ured by the percent maximum muscle activation level (PMAL) of the vastus medialis, are more than triple the approximately 25% PMAL needed for walking, and larger than the 65% PMAL required for stair ascent in healthy subjects (Richards, 1985;

Richards et al., 1989). A mobility test like the TUG thus also assesses the ability to perform the sub-tasks of rising and sitting, walking initiation and walking.

Stair ascent and descent of a flight of 14 stairs can be added to the TUG to create the more difficult stair test (Perron et al., 2003). Although persons with a dynamic strength deficit of about 25% in the knee extensors (Moffet et al., 1993a, b) can walk without apparent disability, stair climbing will reveal the impairment. The recently developed rise-to-walk test (Dion et al., 2003; Malouin et al., 2003) com-bines the sit-to-rise test with walking initiation, thus combining two different motor programs while remaining an easier test than the TUG because it does not require the subject to walk 3 m. Subjects with more severe stroke are less able to smoothly transfer from one activity to another and tend to perform first one task and then the second (Dion et al., 2003). This decreased fluidity of task merging can be evaluated in the laboratory (Dion et al., 2003), or by a recently validated clinical method (Malouin et al., 2003).

Poor endurance (Potempa et al., 1995; Macko et al., 1997), largely ignored in clinical practice, has become the focus of much research and the 6 MINW test, that measures functional endurance, has been 2.6

Figure 1.1. Comparison of responsiveness for six outcome measures as determined by the SRM (y-axis) in subjects (n 62) with sub-acute stroke who walked at slow (0.30 m/s) or moderate speeds (0.30 m/s) at baseline. BA: Barthel index ambulation subscale; BB: balance scale; FM-A: Fugl-Meyer arm subscale; speed: walking speed at comfortable pace. Data from Richards et al. (2004).

selected as an outcome measure in a number of stroke trials (Visintin et al., 1998; Dean et al., 2000;

Nilsson et al., 2001; Duncan et al., 2003; Salbach et al., 2004). Moreover, the practice of calculating the dis-tance walked in 6 min from the walking speed over 10 m overestimates the actual distance (Dean et al., 2001). Even persons with chronic stroke who walk at near-normal speed (122–142 cm/s) may require functional endurance training (Richards et al., 1999), highlighting the importance of using tests with increasing physical demands.

“Walking competency” as a goal of therapy is rela-tively new, particularly those aspects related to cog-nitive processes such as anticipatory control and navigational skills. Clinicians and researchers alike are grappling to develop new approaches for both therapy and evaluation. The dynamic gait index evaluates the ability to modify gait in response to changing task demands. It is able to predict falls in the elderly (Shumway-Cook et al., 1997) and in persons with vestibular problems (Whitney et al., 2000), although the reliability of certain items has been questioned in this population (Wrisley et al., 2003). Others have investigated the dimensions of the physical environment that might impact on mobility. Understanding the relationship of envi-ronment to mobility is crucial to both prevention and rehabilitation of mobility problems in older adults (Shumway-Cook et al., 2002). One can argue that the best test of walking competency is to be able to participate in daily routines such as evalu-ated by the fitness, personal care, housing and mobility categories of the assessment of life habits (Life-H) instrument, based on the handicap cre-ation process model (Fougeyrollas et al., 1998). It has been validated to assess many aspects of life participation of people with disabilities, regardless of the type of underlying impairment (Fougeyrollas and Noreau, 2001). It is not surprising that Desrosiers et al. (2003) have reported high correla-tions between participation (handicap situacorrela-tions) measured by the Life-H, and impairment and dis-ability measures of the leg, supporting the impor-tance of mobility and gait speed (Perry et al., 1995) to promote social integration after stroke.

1.5 The use of laboratory-based gait