Real-time gait biofeedback is a promising rehabilitation strategy for improving biomechanical deficits in walking patterns of post-stroke individuals. Because wearable sensor technologies are creating avenues for novel applications of gait biofeedback, including use in tele-health, there is a need to evaluate the state of the current evidence regarding the effectiveness of biofeedback for post-stroke gait training. The objectives of this review are to: (1) evaluate the current state of biofeedback literature pertaining to post-stroke gait training; and (2) determine future research directions related to gait biofeedback in context of evolving technologies. Our overall goal was to determine whether gait biofeedback is effective at improving stroke gait deficits while also probing why and for whom gait biofeedback may be an efficacious treatment modality. Our literature review showed that the effects of gait biofeedback on post-stroke walking dysfunction are promising but are inconsistent in methodology and therefore results. We summarize sources of methodological heterogeneity in previous literature, such as inconsistencies in feedback target, feedback mode, dosage, practice structure, feedback structure, and patient characteristics. There is a need for larger-sample studies that directly compare different feedback parameters, employ more uniform experimental designs, and evaluate characteristics of potential responders. However, as these uncertainties in existing literature are resolved, the application of gait biofeedback has potential to extend neurorehabilitation clinicians' cues to individuals with post-stroke gait deficits during ambulation in clinical, home, and community settings, thereby increasing the quantity and quality of skilled repetitions during task-oriented stepping training. In addition to identifying gaps in previous research, we posit that future research directions should comprise an amalgam of mechanism-focused and clinical research studies, to develop evidence-informed decision-making guidelines for gait biofeedback strategies that are tailored to individual-specific gait and sensorimotor impairments. Wearable sensor technologies have the potential to transform gait biofeedback and provide greater access and wider array of options for clinicians while lowering rehabilitation costs. Novel sensing technologies will be particularly valuable for telehealth and home-based stepping exercise programs. In summary, gait biofeedback is a promising intervention strategy that can enhance efficacy of post-stroke gait rehabilitation in both clinical and tele-rehabilitation settings and warrants more in-depth research.

Background Paretic propulsion [measured as anteriorly-directed ground reaction forces (AGRF)] and trailing limb angle (TLA) show robust inter-relationships, and represent two key modifiable post-stroke gait variables that have biomechanical and clinical relevance. Our recent work demonstrated that real-time biofeedback is a feasible paradigm for modulating AGRF and TLA in able-bodied participants. However, the effects of TLA biofeedback on gait biomechanics of post-stroke individuals are poorly understood. Thus, our objective was to investigate the effects of unilateral, real-time, audiovisual TLA versus AGRF biofeedback on gait biomechanics in post-stroke individuals. Methods Nine post-stroke individuals (6 males, age 63 ± 9.8 years, 44.9 months post-stroke) participated in a single session of gait analysis comprised of three types of walking trials: no biofeedback, AGRF biofeedback, and TLA biofeedback. Biofeedback unilaterally targeted deficits on the paretic limb. Dependent variables included peak AGRF, TLA, and ankle plantarflexor moment. One-way repeated measures ANOVA with Bonferroni-corrected post-hoc comparisons were conducted to detect the effect of biofeedback on gait biomechanics variables. Results Compared to no-biofeedback, both AGRF and TLA biofeedback induced unilateral increases in paretic AGRF. TLA biofeedback induced significantly larger increases in paretic TLA than AGRF biofeedback. AGRF biofeedback increased ankle moment, and both feedback conditions increased non-paretic step length. Both types of biofeedback specifically targeted the paretic limb without inducing changes in the non-paretic limb. Conclusions By showing comparable increases in paretic limb gait biomechanics in response to both TLA and AGRF biofeedback, our novel findings provide the rationale and feasibility of paretic TLA as a gait biofeedback target for post-stroke individuals. Additionally, our results provide preliminary insights into divergent biomechanical mechanisms underlying improvements in post-stroke gait induced by these two biofeedback targets. We lay the groundwork for future investigations incorporating greater dosages and longer-term therapeutic effects of TLA biofeedback as a stroke gait rehabilitation strategy. Trial registrationNCT03466372