Associations Between Foot Placement Asymmetries and Metabolic Cost of Transport in Hemiparetic Gait

Abstract

Stroke survivors often have a slow, asymmetric walking pattern. They also walk with a higher metabolic cost than healthy, age-matched controls. It is often assumed that spatial-temporal asymmetries contribute to the increased metabolic cost of walking poststroke. However, elucidating this relationship is made challenging because of the interdependence between spatial-temporal asymmetries, walking speed, and metabolic cost. Here, we address these potential confounds by measuring speed-dependent changes in metabolic cost and implementing a recently developed approach to dissociate spatial versus temporal contributions to asymmetry in a sample of stroke survivors. We used expired gas analysis to compute the metabolic cost of transport (CoT) for each participant at 4 different walking speeds: self-selected speed, 80% and 120% of their self-selected speed, and their fastest comfortable speed. We also computed CoT for a sample of age- and gender-matched control participants who walked at the same speeds as their matched stroke survivor. Kinematic data were used to compute the magnitude of a number of variables characterizing spatial-temporal asymmetries. Across all speeds, stroke survivors had a higher CoT than controls. We also found that our sample of stroke survivors did not choose a self-selected speed that minimized CoT, contrary to typical observations in healthy controls. Multiple regression analyses revealed negative associations between speed and CoT and a positive association between asymmetries in foot placement relative to the trunk and CoT. These findings suggest that interventions designed to increase self-selected walking speed and reduce foot-placement asymmetries may be ideal for improving walking economy poststroke.

Keywords: asymmetry; locomotion; metabolic cost; stroke.

Conflict of interest statement

Declaration of Conflicting Interests The author(s) have no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Figures

Figure 1

Metabolic cost of transport for stroke survivors and age-matched controls for all walking speeds. Each data point represents a single bout of walking, and each line connects matched walking speeds between a stroke survivor and their control. There are four pair of data points for each stroke survivor and control, with the exception of those individuals who only completed three bouts of walking. Each color represents a different stroke survivor and control pair.

Figure 2

Speed-dependent differences in (A) metabolic cost of transport, (B) absolute step length difference, (C) absolute stance time difference, and (D) absolute double support time difference across walking speeds for our sample of stroke survivors. For the first three conditions, walking speed is expressed relative to each individual’s self-selected speed. Across the group, the fastest comfortable speed represented a variable percentage of each individual’s self-selected speed and is simply noted ‘Fastest’. This figure only includes cases when an individual’s fastest speed was greater than 120% of the self-selected. Four individuals were unable to walk faster than 120% of their self-selected speed, and therefore did not have data points for the fastest comfortable speed. Asterisks represent statistically significant differences at the p

Figure 3

Histograms of the behavioral variables recorded from all participants in our sample of stroke survivors. Cost of transport represented the dependent variable while the remaining variables were included as potential fixed effects in the regression model.

Figure 4

Individual linear fits between the cost of transport and each of the candidate fixed effects. Only the magnitudes of spatial-temporal variables are plotted here as there were no significant associations between the signed values of each variable and the cost of transport. Step length difference and the lower limb portion of the Fugl-Meyer assessment were the only independent variables whose correlation with the cost of transport was just marginally significant. Each panel includes data pooled across all walking speeds.

Figure 5

Fit of the full regression model including speed and the step position contribution to step length difference. The model explained nearly 90% of the variance in the cost of transport within and across individuals. Data points which lie on the diagonal black line indicate that the model predicted the exact cost of transport for that individual. Points above and below the line indicate under- and over-estimates respectively.