Machines Assisting Recovery From Stroke (MARS)
Machines Assisting Recovery From Stroke: Robotic Activity Mobility Center in a Fitness Center for People With Neurologic Disability
調査の概要
詳細な説明
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.
研究の種類
入学 (実際)
段階
- 適用できない
連絡先と場所
研究場所
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Alabama
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Birmingham、Alabama、アメリカ、35210
- Locomotor Control Lab
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参加基準
適格基準
就学可能な年齢
健康ボランティアの受け入れ
受講資格のある性別
説明
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.
研究計画
研究はどのように設計されていますか?
デザインの詳細
- 主な目的:処理
- 割り当て:なし
- 介入モデル:単一グループの割り当て
- マスキング:なし(オープンラベル)
武器と介入
参加者グループ / アーム |
介入・治療 |
|---|---|
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実験的:Factor Targeted Walking Training
Individuals undergo 5x 2 week periods of targeted training based upon evaluation of walking factor results
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Individuals walk on a treadmill for 30 minutes while exposed to either endurance, balance, challenge, strength, or speed focused approaches
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この研究は何を測定していますか?
主要な結果の測定
結果測定 |
メジャーの説明 |
時間枠 |
|---|---|---|
|
10 m Walk Test
時間枠:Baseline (pre-intervention) and 10 weeks (post-intervention)
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Overground 10 meter walk test consisting of three trials administered at baseline (pre-intervention) and at 10 weeks (post-intervention).
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Baseline (pre-intervention) and 10 weeks (post-intervention)
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協力者と研究者
研究記録日
主要日程の研究
研究開始
一次修了 (実際)
研究の完了 (実際)
試験登録日
最初に提出
QC基準を満たした最初の提出物
最初の投稿 (見積もり)
学習記録の更新
投稿された最後の更新 (実際)
QC基準を満たした最後の更新が送信されました
最終確認日
詳しくは
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