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. 2018 Apr;25(3):186-193.
doi: 10.1080/10749357.2018.1436384. Epub 2018 Feb 19.

Effects of Real-Time Gait Biofeedback on Paretic Propulsion and Gait Biomechanics in Individuals Post-Stroke

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Free PMC article

Effects of Real-Time Gait Biofeedback on Paretic Propulsion and Gait Biomechanics in Individuals Post-Stroke

Katlin Genthe et al. Top Stroke Rehabil. .
Free PMC article

Abstract

Objectives Gait training interventions that target paretic propulsion induce improvements in walking speed and function in individuals post-stroke. Previously, we demonstrated that able-bodied individuals increase propulsion unilaterally when provided real-time biofeedback targeting anterior ground reaction forces (AGRF). The purpose of this study was to, for the first time, investigate short-term effects of real-time AGRF gait biofeedback training on post-stroke gait. Methods Nine individuals with post-stroke hemiparesis (6 females, age = 54 ± 12.4 years 39.2 ± 24.4 months post-stroke) completed three 6-minute training bouts on an instrumented treadmill. During training, visual and auditory biofeedback were provided to increase paretic AGRF during terminal stance. Gait biomechanics were evaluated before training, and during retention tests conducted 2, 15, and 30 minutes post-training. Primary dependent variables were paretic and non-paretic peak AGRF; secondary variables included paretic and non-paretic peak trailing limb angle, plantarflexor moment, and step length. In addition to evaluating the effects of biofeedback training on these dependent variables, we compared effects of a 6-minute biofeedback training bout to a non-biofeedback control condition. Results Compared to pre-training, significantly greater paretic peak AGRFs were generated during the 2, 15, and 30-minute retention tests conducted after the 18-minute biofeedback training session. Biofeedback training induced no significant effects on the non-paretic leg. Comparison of a 6-minute biofeedback training bout with a speed-matched control bout without biofeedback demonstrated a main effect for training type, with greater peak AGRF generation during biofeedback. Discussion Our results suggest that AGRF biofeedback may be a feasible and promising gait training strategy to target propulsive deficits in individuals post-stroke.

Keywords: Feedback; gait biomechanics; hemiparesis; locomotor training; motor learning; push-off; walking.

Figures

Figure 1
Figure 1
(A) Flowchart summarizing the experimental protocol. The arrows indicate time points when gait data were collected during 30-second treadmill walking trials at self-selected speed. Experimental time points when participants took rest breaks (seated or standing) are also indicated. During the training bouts, intermittent (alternating 1-minute epochs) real-time AGRF biofeedback was provided to study participants. (B) Schematic showing biofeedback paradigm (adapted from Schenck and Kesar, 2016). The symbol x indicates current antero-posterior GRF, and the bars indicate targeted AGRF. An auditory tone indicating successful achievement of ARGF every step cycle (B).
Figure 2
Figure 2
Average peak AGRF (A), peak trailing limb angle (B), peak ankle plantarflexor moment (C), and step length (D) for the study participants (N = 9). Each graph shows the values for the paretic leg (line graphs with filled symbols and bold lines), non-paretic leg (line graphs with open symbols and dashed lines), and the deficit or asymmetry between the non-paretic and paretic leg value (depicted in the bar plots). Error bars denote standard errors. For each plot, the symbol *to the left of a metric indicate a significant main effect of time detected by the 1-way repeated measures ANOVA. The repeated measures one-way ANOVA showed a significant main effect of time for each of the 4 secondary variables (peak AGRF, trailing limb angle, plantarflexor momen for the paretic leg, and step length of non-paretic leg) indicating an increase in these variables during the retention tests compared to pre-training. The symbol **indicates a significant difference at that time point compared to Pre (detected by pairwise post-hoc comparisons). No significant effect of time was observed for the non-paretic leg peak AGRF, trailing limb angle, plantarflexor moment, and paretic step length.
Figure 3
Figure 3
Average (N = 9) peak AGRF (A) and peak trailing limb angle (B) immediately before (Pre) and after (Post) a 6-minute training bout with biofeedback and a control training bout without biofeedback (speed- and duration-matched). The symbol *indicates a significant main effect of training type detected by the two-way repeated measures ANOVA. The two-way repeated measures ANOVA for paretic peak AGRF revealed a main effect of training type (F = 13.51, p = 0.006). A significant effect of type of training was also observed for paretic peak trailing limb angle (F = 0.80, p = 0.01) and plantarflexor moment (F = 6.72, p = 0.03).

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