Trials | |
Augmented visual feedback of movement performance to enhance walking recovery after stroke: study protocol for a pilot randomised controlled trial | |
关键词: Stroke; Gait; Rehabilitation; Visual feedback; Biomechanics; | |
DOI : 10.1186/1745-6215-13-163 | |
来源: DOAJ |
【 摘 要 】
Abstract
Background
Increasing evidence suggests that use of augmented visual feedback could be a useful approach to stroke rehabilitation. In current clinical practice, visual feedback of movement performance is often limited to the use of mirrors or video. However, neither approach is optimal since cognitive and self-image issues can distract or distress patients and their movement can be obscured by clothing or limited viewpoints. Three-dimensional motion capture has the potential to provide accurate kinematic data required for objective assessment and feedback in the clinical environment. However, such data are currently presented in numerical or graphical format, which is often impractical in a clinical setting. Our hypothesis is that presenting this kinematic data using bespoke visualisation software, which is tailored for gait rehabilitation after stroke, will provide a means whereby feedback of movement performance can be communicated in a more meaningful way to patients. This will result in increased patient understanding of their rehabilitation and will enable progress to be tracked in a more accessible way.
Methods
The hypothesis will be assessed using an exploratory (phase II) randomised controlled trial. Stroke survivors eligible for this trial will be in the subacute stage of stroke and have impaired walking ability (Functional Ambulation Classification of 1 or more). Participants (
Discussion
Results from the trial will explore whether the early provision of visual feedback of biomechanical movement performance during gait rehabilitation demonstrates improved mobility outcomes after stroke and increased patient understanding of their rehabilitation.
Trial registration
Current Controlled Trials ISRCTN79005974
【 授权许可】
Unknown