“Taking the next step” : whole-body biomechanical gait analysis, and user-perspectives on robotic-assisted gait training post-stroke

Abstract: Background: Stroke, and its subsequent motor function impairments may result in limited gait ability characterised by compensatory movement patterns that include deviations and asymmetries. How these movement patterns should be evaluated and quantified in order to be monitored and treated in the long term is not yet standardised. Limitations in walking quality and quantity negatively affect quality of life and lead to great costs for society if independence is lost. Improved walking ability is hence highly prioritised in stroke rehabilitation. Gait-assisting robots have been developed to enable favourable controlled, high-intensive and task-specific training. Studies evaluating the effects of robotic-assisted gait training (RAGT) have, however, shown inconsistent results. Identifying responders to treatment may facilitate further development of RAGT to improve outcomes. This requires in-depth knowledge of how specific gait movement patterns should best be identified, quantified and treated in rehabilitation. There is also a need for greater insight into how individuals experience gait training in general, and RAGT in particular, as this will likely affect the performance and outcomes of training.Aim: This thesis aims to contribute to the discussion on how to quantify gait movement patterns post-stroke from a whole-body perspective. It will also evaluate the effects of RAGT on biomechanical measures of gait and explore the experience of high-intensive and robotic-assisted gait training in persons with impaired walking ability due to stroke.Methods: A systematic review and meta-analysis consolidated the evidence for the effects of RAGT on biomechanical measures of gait in persons post-stroke. Two descriptive, cross-sectional studies based on kinematic gait data (31 persons post-stroke and 41 non-disabled controls) investigated potential variables to quantify post-stroke gait. The size and angular velocity of the inclination angles between the Centre of Mass (CoM) and the ankle or head, respectively, was investigated with curve analyses covering the entire gait cycle. Furthermore, misclassification rates were calculated based on leave-one-out cross-validation and logistic regression to address the combinations of kinematic variables that most correctly classify a person post-stroke when compared to controls. Finally, individual interviews were performed and analysed using qualitative content analysis to explore the experiences of high-intensive gait training, including RAGT, among persons post-stroke.Results: The systematic review included 13 studies with a total of 412 individuals. The meta-analyses did generally not reveal significant differences between RAGT and comparator groups for biomechanical parameters. Risk of bias assessments raised concerns for several of the studies and the general quality of evidence for these outcomes was very low. An important finding was an inconsistency of biomechanical outcome measures. Data from the primary cross-sectional studies included in this thesis indicated a bilateral lower body adaptation likely to increase the base of support and an upper body leaning towards the affected side during walking in persons post-stroke. Furthermore, core sets of 2-3 kinematic gait variables were identified from both the upper and lower body that, when combined, were most likely to differentiate post-stroke gait from gait in non-disabled controls. Finally, qualitative analysis of participants’ perspectives on high-intensive gait training including RAGT revealed four categories which described: 1) A generally positive mindset when starting the gait training intervention; 2) That engaging in a high-intensive gait training programme was appreciated although experienced as mentally and physically exhausting. The role of the physiotherapist was perceived as crucial; 3) Potential barriers during RAGT, such as discomfort and lost control during walking with the robot, but also facilitators like concrete feedback and the possibility to walk longer distances, and; 4) The participants’ feelings of confidence or concern for the future.      Conclusions: The systematic review demonstrated a very low certainty in current evidence for employing RAGT instead of non-robotic gait training to improve gait biomechanics post-stroke. In addition, it emphasized the lack of standardised guidelines as to which outcome measures most sufficiently quantify gait post-stroke. The cross-sectional studies included in this thesis, presenting upper and lower body kinematic variables to differentiate gait patterns between individuals with stroke and those without, highlight the advantages of adopting a whole-body perspective when evaluating gait post-stroke. Finally, interviews identified valuable aspects from the user’s perspective that should be considered during further development of RAGT devices and the design of high-intensive gait rehabilitation programmes post-stroke.