Users' experiences of intensive robotic-assisted gait training post-stroke - "a push forward or feeling pushed around?"

PURPOSE: Robotic-assisted gait training (RAGT) is suggested to improve walking ability after stroke. The purpose of this study was to describe experiences of robotic-assisted gait training as part of a gait training intervention among persons in the chronic phase after stroke. MATERIALS AND METHODS: Semi-structured interviews were performed with 13 participants after a 6-week intervention including treadmill gait training with the Hybrid Assistive Limb® (HAL) exoskeleton. Data were analysed using qualitative content analysis. RESULTS: Four categories emerged: (1) A rare opportunity for potential improvements describes the mindset before the start of the intervention; (2) Being pushed to the limit represents the experience of engaging in intensive gait training; (3) Walking with both resistance and constraints reveals barriers and facilitators during HAL training; (4) Reaching the end and taking the next step alone illustrates feelings of confidence or concern as the intervention ended. CONCLUSIONS: The gait training intervention including RAGT was considered demanding but appreciated. Support and concrete, individual feedback was crucial for motivation, whilst the lack of variation was a barrier. Results encourage further development of exoskeletons that are comfortable to wear and stimulate active participation by enabling smoothly synchronised movements performed during task-specific activities in different environments. IMPLICATIONS FOR REHABILITATIONWhen provided in a suitable context, the mental and physical challenges of intensive robotic-assisted gait training can be both inspiring and motivating.Support and engagement along with informative feedback from therapists are suggested crucial for motivation.Intensive task-specific gait training may preferably be performed in an enriched environment and combined with other physiotherapy treatments to stimulate engagement.

Effect of robotic-assisted gait training on objective biomechanical measures of gait in persons post-stroke: a systematic review and meta-analysis.

BACKGROUND: Robotic-Assisted Gait Training (RAGT) may enable high-intensive and task-specific gait training post-stroke. The effect of RAGT on gait movement patterns has however not been comprehensively reviewed. The purpose of this review was to summarize the evidence for potentially superior effects of RAGT on biomechanical measures of gait post-stroke when compared with non-robotic gait training alone. METHODS: Nine databases were searched using database-specific search terms from their inception until January 2021. We included randomized controlled trials investigating the effects of RAGT (e.g., using exoskeletons or end-effectors) on spatiotemporal, kinematic and kinetic parameters among adults suffering from any stage of stroke. Screening, data extraction and judgement of risk of bias (using the Cochrane Risk of bias 2 tool) were performed by 2-3 independent reviewers. The Grading of Recommendations Assessment Development and Evaluation (GRADE) criteria were used to evaluate the certainty of evidence for the biomechanical gait measures of interest. RESULTS: Thirteen studies including a total of 412 individuals (mean age: 52-69 years; 264 males) met eligibility criteria and were included. RAGT was employed either as monotherapy or in combination with other therapies in a subacute or chronic phase post-stroke. The included studies showed a high risk of bias (n = 6), some concerns (n = 6) or a low risk of bias (n = 1). Meta-analyses using a random-effects model for gait speed, cadence, step length (non-affected side) and spatial asymmetry revealed no significant differences between the RAGT and comparator groups, while stride length (mean difference [MD] 2.86 cm), step length (affected side; MD 2.67 cm) and temporal asymmetry calculated in ratio-values (MD 0.09) improved slightly more in the RAGT groups. There were serious weaknesses with almost all GRADE domains (risk of bias, consistency, directness, or precision of the findings) for the included outcome measures (spatiotemporal and kinematic gait parameters). Kinetic parameters were not reported at all. CONCLUSION: There were few relevant studies and the review synthesis revealed a very low certainty in current evidence for employing RAGT to improve gait biomechanics post-stroke. Further high-quality, robust clinical trials on RAGT that complement clinical data with biomechanical data are thus warranted to disentangle the potential effects of such interventions on gait biomechanics post-stroke.

Core Sets of Kinematic Variables to Consider for Evaluation of Gait Post-stroke.

Background: Instrumented gait analysis post-stroke is becoming increasingly more common in research and clinics. Although overall standardized procedures are proposed, an almost infinite number of potential variables for kinematic analysis is generated and there remains a lack of consensus regarding which are the most important for sufficient evaluation. The current aim was to identify a discriminative core set of kinematic variables for gait post-stroke. Methods: We applied a three-step process of statistical analysis on commonly used kinematic gait variables comprising the whole body, derived from 3D motion data on 31 persons post-stroke and 41 non-disabled controls. The process of identifying relevant core sets involved: (1) exclusion of variables for which there were no significant group differences; (2) systematic investigation of one, or combinations of either two, three, or four significant variables whereby each core set was evaluated using a leave-one-out cross-validation combined with logistic regression to estimate a misclassification rate (MR). Results: The best MR for one single variable was shown for the Duration of single-support (MR 0.10) or Duration of 2nd double-support (MR 0.11) phase, corresponding to an 89-90% probability of correctly classifying a person as post-stroke/control. Adding Pelvis sagittal ROM to either of the variables Self-selected gait speed or Stride length, alternatively adding Ankle sagittal ROM to the Duration of single-stance phase, increased the probability of correctly classifying individuals to 93-94% (MR 0.06). Combining three variables decreased the MR further to 0.04, suggesting a probability of 96% for correct classification. These core sets contained: (1) a spatial (Stride/Step length) or a temporal variable (Self-selected gait speed/Stance time/Swing time or Duration of 2nd double-support), (2) Pelvis sagittal ROM or Ankle plantarflexion during push-off, and (3) Arm Posture Score or Cadence or a knee/shoulder joint angle variable. Adding a fourth variable did not further improve the MR. Conclusion: A core set combining a few crucial kinematic variables may sufficiently evaluate post-stroke gait and should receive more attention in rehabilitation. Our results may contribute toward a consensus on gait evaluation post-stroke, which could substantially facilitate future diagnosis and monitoring of rehabilitation progress.

Inclination angles of the ankle and head relative to the centre of mass identify gait deviations post-stroke.

BACKGROUND: Whole-body movement adjustments during gait are common post-stroke, but comprehensive ways of quantifying and evaluating gait from a whole-body perspective are lacking. RESEARCH QUESTION: Can novel kinematic variables related to Center of Mass (CoM) position discriminate side asymmetries as well as coordination between the upper and lower body during gait within persons post-stroke and compared to non-disabled controls? METHODS: Thirty-one persons post-stroke and 41 age-matched non-disabled controls walking at their self-selected speed were recorded by 3D motion capture. The Ankle-CoM Inclination Angle (A-CoMIA) and the Head-CoM Inclination Angle (H-CoMIA) defined the angle between the CoM and the ankle and the head, respectively, in the frontal plane. These angles and their angular velocities were compared between groups, and with regard to motor impairment severity during all phases of the gait cycle (GC) using a functional interval-wise testing analysis suitable for curve data. Upper and lower body coordination was assessed using cross- correlation. RESULTS: The A-CoMIA was symmetrical between body sides in persons post-stroke but larger compared to controls. The angular velocity of A-CoMIA also differed when compared to controls. The H-CoMIA was consistently asymmetrical in persons post-stroke and larger than in controls throughout the stance phase. There were only minor group differences in the angular velocity of H-CoMIA, with some side asymmetry in persons post-stroke. The A-CoMIA of the non-affected side, and the H- CoMIA, discriminated between persons with more severe impairments compared to those with milder impairments post-stroke. The variables showed strong cross- correlations in both groups. SIGNIFICANCE: The A-CoMIA and Head-CoMIA discriminated post-stroke gait from non-disabled, as well as motor impairment severity. These variables with the advantageous curve analysis during the entire GC add valuable whole-body information to existing parameters of post-stroke gait analysis through assessment of symmetry and upper and lower body coordination.