Identifying candidates for targeted gait rehabilitation after stroke: better prediction through biomechanics-informed characterization

Abstract

Background: Walking speed has been used to predict the efficacy of gait training; however, poststroke motor impairments are heterogeneous and different biomechanical strategies may underlie the same walking speed. Identifying which individuals will respond best to a particular gait rehabilitation program using walking speed alone may thus be limited. The objective of this study was to determine if, beyond walking speed, participants' baseline ability to generate propulsive force from their paretic limbs (paretic propulsion) influences the improvements in walking speed resulting from a paretic propulsion-targeting gait intervention.

Methods: Twenty seven participants >6 months poststroke underwent a 12-week locomotor training program designed to target deficits in paretic propulsion through the combination of fast walking with functional electrical stimulation to the paretic ankle musculature (FastFES). The relationship between participants' baseline usual walking speed (UWS_baseline), maximum walking speed (MWS_baseline), and paretic propulsion (prop_baseline) versus improvements in usual walking speed (∆UWS) and maximum walking speed (∆MWS) were evaluated in moderated regression models.

Results: UWS_baseline and MWS_baseline were, respectively, poor predictors of ΔUWS (R ² = 0.24) and ΔMWS (R ² = 0.01). Paretic propulsion × walking speed interactions (UWS_baseline × prop_baseline and MWS_baseline × prop_baseline) were observed in each regression model (R ² s = 0.61 and 0.49 for ∆UWS and ∆MWS, respectively), revealing that slower individuals with higher utilization of the paretic limb for forward propulsion responded best to FastFES training and were the most likely to achieve clinically important differences.

Conclusions: Characterizing participants based on both their walking speed and ability to generate paretic propulsion is a markedly better approach to predicting walking recovery following targeted gait rehabilitation than using walking speed alone.

Keywords: Biomechanics; Efficacy; Electrical stimulation; FES; Gait; Locomotion; Physical Therapy; Prediction; Prognostic; Rehabilitation; Stroke; Walking.

Fig. 1

a Changes in usual walking speed (UWS) observed following 12 weeks of FastFES locomotor training (* P < 0.05). b Relationship between baseline UWS (x-axis) and ΔUWS (y-axis). c Interaction between baseline UWS and baseline paretic propulsion when predicting ΔUWS. The simple slopes presented were calculated using unstandardized regression coefficients (see Table 3), with moderation by baseline propulsion probed using 10.30 %bw (High Propulsion) and 0.50 %bw (Low Propulsion), which were, respectively, one standard deviation above and below the average for baseline propulsion. Although evaluated using these two values, it should be noted that baseline propulsion is treated as a continuous variable in the moderated regression model (represented by the curved arrow between regression slopes). d ΔUWS for different propulsion-speed subgroups. Abbreviations: HP-slow: high propulsion and slow walking speed subgroup; HP-fast: high propulsion and fast walking speed subgroup; LP-slow: low propulsion and slow walking speed subgroup; LP-fast: low propulsion and fast walking speed subgroup

Fig. 2

a Changes in maximum walking speed (MWS) observed following 12 weeks of FastFES locomotor training (* P < 0.05). b Relationship between baseline MWS (x-axis) and ΔMWS (y-axis). c Interaction between baseline MWS and baseline paretic propulsion when predicting ΔMWS. The simple slopes presented were calculated using unstandardized regression coefficients (see Table 3), with moderation by baseline propulsion probed using 10.30 %bw (High Propulsion) and 0.50 %bw (Low Propulsion), which were, respectively, one standard deviation above and below the average for baseline propulsion. Although evaluated using these two values, it should be noted that baseline propulsion is treated as a continuous variable in the moderated regression model (represented by the curved arrow between regression slopes). d ΔMWS for different propulsion-speed subgroups. Abbreviations: HP-slow: high propulsion and slow walking speed subgroup; HP-fast: high propulsion and fast walking speed subgroup; LP-slow: low propulsion and slow walking speed subgroup; LP-fast: low propulsion and fast walking speed subgroup