Machines Assisting Recovery From Stroke (MARS)
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About
Locomotor disability remains a major obstacle to community function in stroke survivors. This disability is best characterized by the reduced gait speed and enhanced risk of falls that is observed in the majority of stroke survivors. Current robotic systems have focused on repetitive stepping in constrained, less-challenging environments than overground training and have failed to produce results that can justify their use. In contrast to this approach, this study will use a combinatorial approach on a moving platform (KineAssist-Mobility Activity Center) that simulates and enhances the challenges of overground training. We focus on five critical factors that contribute to reduced speed and fall risk during mobility activities: 1) lower limb weakness; 2) slow lower limb movements; 3) reduced balance; 4) reduced ability to respond to challenges during walking; and 5) reduced aerobic capacity. The end product of this study is to develop a comprehensive and standardized system for assessing and prescribing specific training modalities that can be used by clinicians to help stroke survivors who are limited by slow walking speed and high fall risk, and can improve participation in mobility activities.
Full description
Impairment in muscle strength is an important limiting factor in determining walking speed after stroke. There is a positive correlation between muscle strength and maximum gait speed (i.e. as muscles become stronger, maximum gait speed increases). Also, most stroke survivors walk at speeds that range from approximately 0.2 m/s to 0.8 m/s when asked to walk at a comfortable pace. These velocities are significantly lower than age-matched individuals (1.3 m/s to 1.4 m/s). Moreover, when stroke survivors were encouraged to walk at their self-selected maximum walking speed they achieved walking speeds from 0.3 m/s to 1.3 m/s, suggesting that stroke survivors have limited capability to adapt comfortable gait in order to increase walking speed to reach higher function.
Additionally, individuals with post-stroke hemiplegia are at high risk for falls due to poor balance and inability to tolerate environmental challenges. We have selected specific environmental hazards by turning to the current literature related to why people fall in the home or nonclinical environment. Research has identified specific risk factors for falls in people with stroke. Fallers have shown poorer balance, lower physical function measures than non-fallers, greater standing sway, impulsivity, and slowed response times, in addition to greater postural sway and reduced force generation when standing up and sitting down. Forster and Young found that fallers were more depressed and less socially active that non-fallers. They found that most falls occurred in patients' homes while walking or during transfers. Individuals reported loss of balance, getting their foot stuck, and difficulty performing transfers as reasons why they fell. Hyndman et. al, found that repeat fallers had significantly reduced arm function and activities of daily living (ADL) ability compared with those who did not fall.
A review concludes that the evidence supports a mix of approaches as a means for improving lower limb function during walking post-stroke. They concluded " . . . there is a need for high quality randomized trials and systematic reviews to determine the efficacy of clearly described individual techniques and task-specific requirements." However, Duncan and Dobkin argue that past mobility training approaches that focused on using either body-weight support treadmill training or robotic assistive training have failed to generate results that can justify their use for the mainstream stroke survivor [6]. They cite two studies in particular, SCILT [7] and LEAPS [8], which produced conclusions that were not supportive of the extra effort and technology necessary to implement these protocols. One major suggestion from the authors was that a combinatorial approach should be implemented that incorporates strength training, aerobic training, and balance training. We agree with this suggestion and we propose to test this combinatorial approach in our study using a unique and innovative robotic system especially developed to combine exercises that target force, speed, balance, and locomotor challenge all within a single program.
As a result of previous funding, we have developed innovative protocols for assessing and treating mobility disability in chronic stroke survivors by using a unique robotic platform. The KineAssist- Mobility Activity Center (KA-MAC), developed by HDT Robotics (partners with this study), uses a patented force-sensing, pelvic support mechanism to sense the user's intended walking speed and direction to drive a moving surface, thus allowing a person to move at their own intended speed and pace. The device is sensitive enough to allow sudden starting and stopping movements, so that balance tasks and responses to sudden disturbances can be accommodated. This system is uniquely different compared to a treadmill, which only moves at a fixed speed and can only allow repetitive stepping protocols. In summary, we have developed a unique and innovative robotic system that can allow individuals to move at self-driven speeds against challenging conditions in order to implement a combinatorial approach to assessment and intervention.
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Inclusion criteria
- Community dwelling unilateral stroke survivors, aged 19 years or older, at least 4 months post incident, residual hemiplegia, who are able to ambulate at least 14m with an assistive device or the assistance of one person, with receptive and expressive communication capability, approval of physician, and voluntarily provided informed consent.
Exclusion criteria
- Significant and acute medical conditions, amputations, spasticity management that included phenol block injections within 12 months or botulinum toxin injections within 4 months of the study, any cognition involvement that impairs the ability to follow directions for, and plans to move out of the area within the next year or no transportation to the study area.
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Interventional model
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22 participants in 1 patient group
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Data sourced from clinicaltrials.gov
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