Walking adaptability after a stroke and its assessment in clinical settings

Abstract

Control of walking has been described by a tripartite model consisting of stepping, equilibrium, and adaptability. This review focuses on walking adaptability, which is defined as the ability to modify walking to meet task goals and environmental demands. Walking adaptability is crucial to safe ambulation in the home and community environments and is often severely compromised after a stroke. Yet quantification of walking adaptability after stroke has received relatively little attention in the clinical setting. The objectives of this review were to examine the conceptual challenges for clinical measurement of walking adaptability and summarize the current state of clinical assessment for walking adaptability. We created nine domains of walking adaptability from dimensions of community mobility to address the conceptual challenges in measurement and reviewed performance-based clinical assessments of walking to determine if the assessments measure walking adaptability in these domains. Our literature review suggests the lack of a comprehensive well-tested clinical assessment tool for measuring walking adaptability. Accordingly, recommendations for the development of a comprehensive clinical assessment of walking adaptability after stroke have been presented. Such a clinical assessment will be essential for gauging recovery of walking adaptability with rehabilitation and for motivating novel strategies to enhance recovery of walking adaptability after stroke.

Figure 1

Neural control model of functional walking. Neural control of walking can be explained as a tripartite model consisting of stepping, equilibrium, and adaptability [11, 14]. All three are necessary for optimal walking function. This review focuses on walking adaptability, which is defined as the ability to adjust walking to behavioral task goals and environmental circumstances.

Figure 2

Conceptual illustration of the domains of adaptability. This figure illustrates the relative demands that may be placed on the nine domains of walking adaptability in different ambulatory environments. The nine domains of walking adaptability have been adapted from earlier work by Patla and Shumway-Cook [15]. In a less complex and predictable environment such as the home, the requirements for walking adaptability would be less demanding and encompass fewer domains relative to more challenging environments such as walking on a nature trail or on a busy city street. Abbreviations: ON—obstacle negotiation; TM—temporal demands; CT—cognitive dual-tasking; TR—terrain demands; AM—ambient demands; PT—postural transitions demands; MT—motor dual-tasking; PL—physical load; TF—maneuvering traffic.

Figure 3

Number of walking adaptability domains captured by clinical assessments of walking function. The number of domains captured by clinical assessments range from 2 to 7, with the SOMAI capturing the least number of domains and the MTT and WTT capturing the most number of domains. Abbreviations: DGI—dynamic gait index; FGA—functional gait assessment; mEFAP—modified Emory functional ambulation profile; SCI-FAP—spinal cord injury functional ambulation profile; Hi-MAT—high-level mobility assessment test; CB&M—community balance and mobility scale; SOMAI—sensory-oriented mobility assessment instrument; WIT—walking InCHIANTI toolkit; SWOC—standardized walking obstacle course developed by Rubenstein and colleagues [31]; Obstacle course—obstacle course developed by Means [32]; MTT—multiple task test.

Figure 4

An illustrated example of an IRT-based computer adaptive test for walking adaptability after stroke. Development of an IRT-based assessment requires identification of a relevant pool of assessment items, conceptualizing a hierarchy of difficulty levels, and testing these assumptions using IRT methods. The IRT-based assessment may be administered using a computer adaptive format or an IRT-based static short form. This figure illustrates an example of hierarchical assessment items measuring walking adaptability in the terrain domain and administration of these items in an IRT-based computer adaptive test. Additionally, this example demonstrates incorporating assessment of not only “task performance”, but also the “quality of task performance” (i.e., movement strategy).