C

"O

Slow Moderate

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).

When examining change in outcome measures, it is important to question whether the amount of change is larger than the measurement error. For continuous measures, such as walking speed, systematic and random error in repeated measures (measurement error) can be mathematically derived (Evans et al., 1997). It is also possible to calculate the standard error of the mean of scale scores from published 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 (6MINW) 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 endurance, the ability to ascend or descend stairs, or navigate in different terrains under various environmental 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 measured 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) combines 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 6MINW test, that measures functional endurance, has been 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 distance 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 relatively new, particularly those aspects related to cognitive 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 environment 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 evaluated by the fitness, personal care, housing and mobility categories of the assessment of life habits (Life-H) instrument, based on the handicap creation 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 correlations between participation (handicap situations) measured by the Life-H, and impairment and disability measures of the leg, supporting the importance of mobility and gait speed (Perry et al., 1995) to promote social integration after stroke.

1.5 The use of laboratory-based gait assessments and measures of brain reorganization help explain changes in clinical scales and performance-based measures

Laboratory outcome measures

This section will briefly illustrate how laboratory outcome measures can be used to:

1 validate clinical measures,

2 explain the results of clinical outcomes,

3 develop new measures,

4 guide therapy.

In-depth gait studies (see Volume II, chapter 19) have elucidated the disturbed motor control during gait in persons after chronic stroke. Low muscle activations (paresis), hyperactive stretch reflexes, excessive coac-tivation of antagonist muscles and hypoextensibility of muscles and tendons (Knutsson and Richards, 1979; Dietz et al., 1981; Lamontagne et al., 2000a, b, 2001, 2002) may be present to a different extent across subjects. While analysis of gait movements and muscle activations recorded concomitantly during a laboratory gait study allow for the differentiation of the salient motor disturbance (Knutsson and Richards, 1979) to guide therapy, such analyses are not available to usual clinical practice. From studies of moments of force and mechanical power produced by the muscle activations during walking, we know that the main propulsive force comes from the "push-off" contraction of the ankle plantarflexors (generation of power) at the end of the stance phase aided by the "pull-off" contraction of the hip flexors (generation of power) at swing initiation and the contraction of the hip extensors in early stance (Olney et al., 1991; Winter, 1991; Olney and Richards, 1996). Moreover, in persons with chronic stroke, Olney et al. (1994) found the power generated by the hip flexors and ankle plantarflexors of the paretic lower extremity to be the best predictors of walking speed. These laboratory results point to the ankle plantarflexors and hip flexors as muscles to be targeted in therapy to promote better walking speed (Olney et al., 1997;

Richards et al., 1998; Dean et al., 2000; Teixeira-Salmela et al., 2001; Richards et al., 2004).

While gait speed can be used to discern a change in functional status, it does not explain why the person walks faster. An analysis of gait kinetics (muscle activations, moments, powers, work) is needed to pinpoint the source of the increased speed. For example, in a recent trial evaluating the effects of task-oriented physical therapy, Richards et al. (2004) were able to attribute 27% of the improvement in walking speed to a better plantarflexor power burst.

Many new therapeutic approaches and outcome measures are first developed in the laboratory. Much work, for example, has been done on obstacle avoidance in healthy persons (McFadyen andWinter, 1991; Gerin-Lajoie et al., 2002) and persons with stroke (Said et al., 1999, 2001). Virtual reality technology is now being applied to the development of training paradigms to enable persons with stroke to practice navigational skills safely in changing contextual environments (Comeau et al., 2003; McFadyen et al., 2004).

An example of the use of laboratory data to validate a clinical measure is recent work related to the rise-to-walk test. The clinical fluidity scale of the rise-to-walk test was validated by comparing clinical decisions to the smoothness of the momentum curve derived from a biomechanical analysis made in the laboratory (Dion et al., 2003) and led to the development of a fluidity scale (Malouin et al., 2003).

Neuroimaging techniques for studying changes in brain activation patterns and their relationship with functional recovery

With the rapid development of neuroimaging techniques (Volume II, chapter 5), such as positron-emitted tomography (PET), functional magnetic resonance imaging (fMRI) and transcranial magnetic stimulations (TMS; Volume I, Chapter 15), it has become possible to study neural organization associated with motor recovery after brain damage.

Numerous studies have looked at the predictive value of TMS (Hendricks et al., 2002; Liepert, 2003).

It provides important prognostic information in the early stage after stroke. For instance, the persistence of motor evoked potentials (MEPs) in paretic muscles has been correlated with good motor recovery, whereas the lack of TMS responses is predictive of poor motor recovery. Patterns of brain activation can also be used early after stroke for predicting functional outcomes. In a longitudinal fMRI study, where hand motor scores were compared to the whole sensorimotor network activation, the early recruitment and high activation of the supplementary motor area (SMA) was correlated with faster or better recovery (Loubinoux et al., 2003). Based on findings from a study combining fMRI and TMS, it has been proposed that the early bilateral activation of the motor networks seen in patients with rapid and good recovery may be a prerequisite to regain motor function rapidly, and thus, may be predictive of motor recovery (Foltys et al., 2003).

Functional imaging and electrophysiologic brain imaging techniques have provided substantial information about adaptive changes of cerebral networks associated with recovery from brain damage (Calautti and Baron, 2003). For example, in two rigorously controlled studies, the effects of task-oriented training for the upper limb on brain activation patterns were studied using fMRI (Carey et al., 2002) and PET (Nelles et al., 2002). Both studies found that, in contrast to patients in control groups whose brain activation patterns remained unchanged, patients in the treatment groups displayed enhanced activations in the lesioned sensorimotor cortex in parallel with improved motor function. Similar correlations between changes in brain activation patterns and motor recovery have also been reported after a single dose of fluoxetine (Pariente et al., 2001). TMS mapping studies (Liepert, 2003) provide further evidence of a relationship between training-induced cerebral changes and motor recovery. In these studies, TMS was used to map the motor output area (motor representation) of targeted muscles. Increased cortical excitability and a shift in the motor maps after active rehabilitation (Traversa et al., 1997) or constraint-induced therapy (Liepert et al.,

2000) are associated with improved motor function suggesting treatment-induced reorganization in the affected hemisphere (Liepert et al., 2000).

Recently, the laterality index (LI) has been proposed to quantify changes of brain activation patterns observed in functional neuroimaging studies of recovery post-stroke (Cramer et al., 1997). LI provides an estimate of the relative hemispheric activation in motor cortices. LI values range from +1 (activation exclusively ipsilesional or affected hemisphere) to — 1 (activation exclusively contralesional or unaffected hemisphere). These LIs are generally lower in patients, especially in poorly recovered chronic patients, indicating a relatively greater activation of the unaffected hemisphere consistent with the aforementioned general patterns of changes (Calautti and Baron, 2003). Dynamic changes in LI values over time have also been reported in a longitudinal study (Marshall et al., 2000; Calautti et al.,

2001). After specific finger-tracking training, Carey et al. (2002) found increases in LI values corresponding to a switch of activation to the affected hemisphere to be related to improved hand function, suggesting that the LI is a good marker of brain reorganization.

Likewise, inter-hemispheric motor reorganization can be quantified using TMS input/output (i/o) curves. The i/o curves, provide a reliable measure (Carroll et al., 2001) of the increase of MEP amplitudes against incrementing levels of TMS intensity (Devanne et al., 1997). Comparisons of the excitability of the motor cortex of the two hemispheres (Fig. 1.2), indicate that in a patient with good motor recovery, the excitability of the motor cortex contralateral to the paretic tibialis anterior (TA) muscle (LI of motor threshold = 0.78) is greater (lower motor threshold, steeper slope and higher plateau of MEP amplitude) compared to the ipsilateral motor cortex and resembles the pattern seen in a healthy individual (LI of motor threshold = 1.0). In contrast,

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