Improving Lateral Stepping Control to Reduce Falls in the Elderly

 描述(申请人提供)：对于老年人来说，跌倒是非常常见和极其危险的事件，是造成伤害的主要原因，到2020年，造成的损失可能超过680亿美元。超过95%的髋部骨折是由跌倒引起的，尤其是侧向跌倒(向一侧)导致的骨折占所有髋部骨折的76%。大多数跌倒发生在走路时，人类走路时天生更不稳定。此外，指导设计干预措施以改善老年人行走功能的科学证据也非常有限。因此，显然有必要确定老年人在行走时保持侧向平衡所使用的控制策略，并开发有效的循证治疗策略来改善高危跌倒老年人的侧向平衡控制，以降低他们跌倒的风险。这项研究的主要目标是开发有助于预防跌倒的干预措施。这需要在下跌发生之前进行干预。随着年龄的增长，多种生理变化增加了内在的生理(神经肌肉)噪音，降低了控制权威(有效调节运动的能力)。这两者中的一种或两种都会增加步行的可变性，这可能会导致跌倒。然而，并不是所有的可变性都是有害的。在运动康复中，增加可变性有时甚至可以促进适应性和改善恢复。我们最近开发了新的计算控制理论模型，将生理噪音与控制权威分开，以确定行走的人类如何利用冗余来调节矢状面的变异性。 飞机。我们现在已经扩展了这项工作，以确定人类如何控制额面上的横向踏步运动。现实世界中的行走任务要求人类既能对不断变化的任务目标做出反应，又能选择有效的策略。在这里，目标1将确定低跌倒风险或高跌倒风险的老年人如何应对外部施加的挑战(强制步幅和/或横向扰动)。我们将把我们的理论框架与计算模型和实验相结合，以区分控制的影响和变异性的影响。另外，目标2将决定老年人如何做出(认知)内部选择，以避免风险或加强自己对潜在风险的防御。我们将再次整合实验、模型和分析，以确定低风险和高风险老年人如何选择不同的风险敏感策略。目标3将确定基于虚拟现实的有针对性的干预措施是否可以改善高危老年人的侧步控制和行走平衡，该干预要求人们既对侧向位置的强加变化做出反应，又选择有效的策略来做到这一点。在一项随机的、积极的对照治疗试验中，我们将比较 干预前后在有和没有侧向扰动的情况下步行能力的变化，在一种新的真实世界一样的导航任务中的表现，并建立了步行和平衡功能的临床评估。这项研究将应用新颖的实验和严格的计算和分析方法，极大地提高我们对老年人如何行走的理解。我们将通过实施基于VR的新型干预措施将这些知识转化为临床实践，这些干预措施有望改善跌倒高危老年人的步行功能。

 DESCRIPTION (provided by applicant): Falls are very common and extremely dangerous events for the elderly, constituting the leading cause of injury and incurring costs likely to exceed $68 Billion by 2020. Falls cause more than 95% of hip fractures and lateral falls (to the side) in particular contribute to 76% of all hip fractures. Most falls occur while walking and humans are inherently more unstable laterally when walking. Also, very limited scientific evidence exists to guide de- sign of interventions to improve walking function in the elderly. Thus, there is a clear need to identify the control strategies elderly use to maintain lateral balance while walking and to develop effective, evidence-based treatment strategies to improve lateral balance control in high fall risk elderly to reduce their risk of falls. The primary goal f this study is to develop interventions to help prevent falls. This requires intervening before fall occur. As people age, multiple physiological changes increase intrinsic physiological (neuromuscular) noise and decrease control authority (the ability to effectively regulate movements). Either or both of these can increase walking variability, which may contribute to falls. However, not all variability is detrimental. Increasing variability can sometimes even facilitate adaptability and improve recovery in locomotor rehabilitation. We recently developed novel computational control theory models that separate physiological noise from control authority to identify how walking humans exploit redundancy to regulate variability in the sagittal plane. We have now extended this work to determine how humans control lateral stepping movements in the frontal plane. Real-world walking tasks require humans both to be able to respond to changing task goals and also to choose effective strategies. Here, Aim 1 will determine how elderly with Low Fall Risk or High Fall Risk respond to externally imposed challenges (enforced step width and/or lateral perturbations). We will integrate our theoretical framework with computational models and experiments to differentiate effects of control from those of variability. Separately, Aim 2 will determine how elderly make (cognitive) internal choices to either avoid risk or fortify themselves against potential risk. We will again integrate experiments, models, and analyses to identify how Low and High Fall Risk elderly choose different risk-sensitive strategies. Aim 3 will determine if a targeted virtual reality based intervention that challenges people to both respond to imposed changes in lateral position, and also to choose effective strategies for doing so, can improve lateral stepping control and walking balance in High Fall Risk elderly. In a randomized, active control treatment trial, we will compare pre- and post-intervention changes in walking ability both with and without lateral perturbations, performance in a novel real-world-like navigation task, and established clinical assessments of walking and balance function. This study will apply novel experimental and rigorous computational and analytical approaches to greatly improve our understanding of how elderly individuals walk. We will translate this knowledge into clinical practice by implementing novel VR-based interventions that promise to improve walking function in high fall risk elderly.