Distance-induced changes in walking speed after stroke: Relationship to community walking activity

Abstract

Background and purpose:

Physical inactivity is a major contributing factor to reduced health and quality of life. The total distance walked during the 6-minute walk test is a strong indicator of real world walking activity after stroke. The purpose of this study was to determine if measurement of distance-induced changes in walking speed during the 6-minute walk test improves the test’s ability to predict community walking activity.

Methods:

For 40 individuals poststroke, community walking activity (steps/day), the total distance walked during the 6-minute walk test (6MWT_total), and the difference between the distances walked during the final and first minutes of the test (Δ6MWT_min6–min1) were analyzed using moderated regression. Self-efficacy, assessed using the Activities-specific Balance Confidence (ABC) scale, was also included in the model.

Results:

Alone, 6MWT_total explained 41% of the variance in steps/day. The addition of Δ6MWT_min6–min1 increased explanatory power by 29% (ΔR²=0.29, p<0.001). The final model accounted for 71% of steps/day variance (F_(4,32)=19.52, p<0.001). Examination of a significant 6MWT_total × Δ6MWT_min6–min1 interaction revealed a positive relationship between 6MWT_total and steps/day, with individuals whose distances declined from minute 1 to minute 6 by 0.10m/s or more presenting with substantially less steps/day than those whose distances did not decline.

Discussion and Conclusions:

Co-assessment of distance-induced changes in walking speed during the 6-minute walk test and the total distance walked substantially improves the prediction of real world walking activity after stroke. This study provides new insight into how walking ability after stroke can be characterized to reduce heterogeneity and advance personalized treatments.

Introduction

Physical inactivity is a major contributor to reduced health and quality of life. Community-dwelling survivors of stroke indicate that a deficit in their ability to walk farther distances is a key factor limiting their engagement in home and community activities¹. Likewise, better performance on clinic-based, functional tests of long-distance walking capacity, such as the 6-minute walk test (6MWT), predict better community participation and reintegration after stroke². Indeed, the total distance walked during the 6MWT is a strong predictor of real world walking activity after stroke³ and has served as the primary metric of interest recorded from this popular test; however, this metric fails to account for heterogeneity in how individuals achieve such distances.

In persons with multiple sclerosis⁴, polio⁵, and spinal muscular atrophy⁶, reduced 6MWT total distances are largely due to an inability to maintain an initially fast walking speed—that is to say, poor endurance. This presentation is likely the result of the pathological fatigue characteristic of these populations. In contrast, recent work in persons with stroke suggests that their inability to walk fast, even early in the walking bout, is the key cause of their reduced 6MWT distance^7,8. Assessment of how speed changes over the duration of the 6MWT could provide insight into key factors that underlie long-distance walking impairment after stroke. Different rehabilitation targets may ultimately be necessary for those whose speed decreases during the test versus those who maintain a constant walking speed or increase their speed during the test.

This exploratory study evaluated whether distance-induced changes in speed during the 6MWT could specify patients’ real world walking activity better than their 6MWT total distance. Given that self-efficacy has recently been shown to play a role in mediating the relationship between physical capacity and walking activity^9,10, our analysis also accounted for self-efficacy

Methods

Forty community-dwelling individuals in the chronic phase poststroke completed all study procedures. All participants were able to walk six minutes without physical assistance. Cerebellar stroke, neglect or hemianopia, or comorbidities that limited walking ability were exclusion criteria. All procedures were approved by the institutional review board and written informed consent was obtained from all individuals.

All testing was conducted under the supervision of a licensed physical therapist. Assistive devices and orthoses were allowed if needed for safety. Participants’ community walking activity¹⁰ was measured using ≥2 days of recordings of steps walked per day (steps/day) made by a StepWatch Activity Monitor (Orthocare Innovations, Seattle Washington) worn on the non-paretic leg during all walking activities. An average of 3.95±0.19 days of walking activity were available across participants. The total distance walked during the 6MWT (6MWT_total), the difference between the distances walked during minute 6 and minute 1 of the test (Δ6MWT_min6–min1), and self-efficacy as measured using the Activities-specific Balance Confidence (ABC) scale¹⁰ served as independent variables. Participants completed the 6MWT without verbal encouragement and with instructions to “cover as much distance as possible”¹¹.

Data Analysis

All analyses were conducted using commercially available software (IBM SPSS Statistics for Windows, version 24 (IBM Corp., Armonk, N.Y., USA)). Averages ± Standard Error are reported. Moderated regression evaluated changes to a steps/day model containing 6MWT_total and ABC score—variables known as key indicators of community walking activity after stroke^3,10. ABC score and 6MWT_total were entered first, followed by Δ6MWT_min6–min1 and the 6MWT_total × Δ6MWT_min6–min1 interaction. All assumptions for regression were ensured. Centered variables were used to minimize multicollinearity. An examination of model residuals revealed three participants whose data were outliers that contributed to a violation of normality. Removal of these datapoints from the final analysis restored normality. It should be noted that Δ6MWT_min6–min1 was a major determinant of steps/d both with and without these outliers. Both the final model R² and the R² adjusted for sample size and the number of predictors (R²_adj) are reported¹². A significant 6MWT_total × Δ6MWT_min6–min1 interaction was examined within ±1 standard deviation of the moderator variables^13,14. Additionally, to facilitate clinical interpretation of the findings of the regression analysis, independent t-tests compared subgroups of “endurant” and “non-endurant” individuals dichotomized based on a distance-induced decline in long-distance walking speed of 0.10m/s. More specifically, individuals with a decline in walking speed from minute 1 to minute 6 of the 6MWT that was greater than or equal to 0.10m/s were considered “non-endurant”. All other individuals were considered “endurant”. A cut-off of 0.10m/s was selected based on the findings of a recent systematic review reporting a 28 to 42 m minimal detectable change (MDC) for the 6-minute walk test in people in the chronic phase of stroke recovery—or a 0.08 to 0.12 m/s change in long-distance walking speed. The 0.10 m/s cut-off is the midpoint of the MDC range reported¹⁵.

Results

Participants were 58.4±1.6 years old, 2.9±0.7 years poststroke, 35% right side paretic, and 38% female. Their average lower-extremity Fugl-Meyer assessment score was 23.5±0.9 and ABC score was 73±3. They walked an average 5892±513 steps/day, 292±21m during the 6MWT, and 3.5±0.9m less during minute 6 versus minute 1 of the 6MWT.

6MWT_total (R²=0.41, P<0.001) and ABC (R²=0.21, P=0.004) were each bivariately correlated with steps/day; however, in the final regression model, ABC was not a significant independent predictor (Table). The addition of Δ6MWT_min6–min1 and the 6MWT_total × Δ6MWT_min6–min1 interaction explained an additional 29% of the variance in real world community walking activity. The final model accounted for 71% of the variance (R²=0.71, F_(4,32)=19.52, P<0.001).

Table.

Regression Model of Real-World Walking Activity

Model Statistics	Model Predictors	Predictor Statistics
		B	p
R2=0.42
R2adj=0.39	6MWTtotal	0.56	0.00
F(2,34)=12.49	ABC Score	0.14	0.38
P=0.00
R2=0.71	6MWTtotal	0.55	0.00
R2adj=0.67	ABC Score	0.16	0.19
F(4,32)=19.52	Δ6MWT(min6-min1)	0.64	0.00
P=0.00	Δ6MWT(min6-min1) × 6MWTtotal	−0.46	0.00

In order of importance (based on an examination of βs, see Table), Δ6MWT_min6–min1 and 6MWT_total were independent predictors. The interaction between these variables was also significant, indicating that the relationship between 6MWT_total and steps/day was moderated by Δ6MWT_min6–min1 (Fig. 1). More specifically, as observed in figure 1, examination of the interaction revealed that lower 6MWT_total predicts less steps/day, with individuals whose distances decline during the test (i.e., non-endurant individuals) presenting with the least steps/day. In contrast, individuals with minimal slowing during the 6MWT (i.e., are more endurant) present with substantially more steps/day. Figure 2A presents 6MWT and steps/d data for two individuals who exemplify the endurant and non-endurant subgroups.

Figure 1.

Relationships between (A) total 6-minute walk test distance (6MWT_total) and community walking activity (steps/day) and (B) 6MWT_total and steps/day as moderated by distance-induced changes in speed, shown as the difference between the distances walked during minute 6 versus minute 1 of the 6MWT (Δ6MWT_min6–min1).

Figure 2.

(A) Distance walked per minute, total 6MWT distance (6MWT), and community walking activity (steps/d) for two participants that exemplify the (B) non-endurant (i.e., those with a reduction in speed ≥ 0.10m/s) and endurant (i.e., those with a reduction in speed < 0.10 m/s) subgroups. (C) Total 6MWT distance, self-efficacy (ABC), steps/d for each subgroup. *P<0.05.

Pairwise comparisons of the endurant (N=24) and non-endurant (N=13) subgroups (Fig. 2B) revealed substantial differences in steps/day (P=0.013), but not 6MWT_total or ABC (Ps>0.05) (Fig. 2C). The non-endurant subgroup walked 62% less steps/day compared to the endurant subgroup (4198±909 versus 6810±546 steps/day)—a difference that would lead to different functional classifications of “most limited community ambulator” for the non-endurant subgroup and “least limited community ambulator” for the endurant subgroup³.

Discussion

The total distance walked during the 6MWT has been shown to be a strong predictor of real world community walking activity after stroke^2,3. This study builds on this prior work, motivating the co-assessment of total 6MWT distance and distance-induced changes in walking speed to better explain the walking activity of community-dwelling individuals poststroke. Indeed, the model presented in this study explained 71% of variance in steps/day—a substantially higher percentage than other recent work examining other factors (e.g., single and dual task gait speed, self-efficacy, and balance) that has ranged from 36%-61% of variance explained^2,9,10,16.

An assessment of distance-induced changes in walking speed provides insight into locomotor deficits underlying physical inactivity that are not reflected in the total distance walked. Indeed, poststroke heterogeneity is a likely reason for the importance that distance-induced changes in speed has in our model. That is, an individual with a fast initial speed that declines over the duration of the test (i.e., fast but not endurant) and an individual with a slow initial speed that is maintained over time (i.e., slow and endurant) may each present with comparable 6MWT distances despite having inherently different impairments in gait mechanics, walking efficiency, or cardiovascular capacity—the variables that ultimately serve as intervention targets.

Non-physical factors may also explain distance-induced changes in walking speed during the 6MWT. Reduced motivation may lead to distance-induced slowing in a person who otherwise has the capacity to maintain their speed for the duration of the test. Likewise, a competitive person may be motivated to operate at a higher percentage of their physiological reserve over the duration of the test. In this vein, Danks et al⁹ show that another non-physical factor, balance self-efficacy, influences real world walking activity. Among patients with high physical capacity, those with high self-efficacy presented with more real world walking activity than those with low self-efficacy. Our finding that self-efficacy was not a significant predictor in our model is not necessarily suggestive of reduced importance in determining physical activity after stroke; rather, may be the result of substantial overlap in the variance explained with the 6MWT (e.g., Danks et al⁹ did not include 6MWT_total in their model). Further study into the mediating roles that physical and non-physical factors (e.g., motivation and self-efficacy) play in the relationship between distance-induced changes in walking speed and real world physical activity is highly warranted to elucidate mechanisms and identify specific treatment targets.

Limitations

The generalizability of this exploratory study is limited to higher functioning, community-dwelling individuals poststroke capable of completing a 6MWT without assistance. Moreover, these findings may not extend to studies administering the 6MWT without the instruction to “cover as much distance as possible”. Additionally, we only investigated the effect of differences in walking speed from minute 1 to minute 6 of the 6MWT. Assessment of differences in speed between other minutes may provide additional information that can characterize people poststroke. Finally, participants’ real world ambulatory activity was computed from an average of 4 days of measurement. Additional days of measurement may have provided a more stable assessment; however, recent work has shown that 2 to 3 days of measurement may be sufficient in persons poststroke^3,16.

Conclusions

This study demonstrates that assessment of distance-induced changes in speed during the 6MWT explains real world ambulatory activity after stroke better than the total distance walked—a finding of importance given the relationships between physical activity and health and quality of life. Given the relative ease by which assessment of distance-induced changes in walking speed during the 6MWT can be made, this study motivates consideration of this variable when the goal of intervention is to improve real world walking activity.