Real-Time Model-Based Gait Retraining for Knee Osteoarthritis Rehabilitation
基于实时模型的膝骨关节炎康复步态再训练
基本信息
- 批准号:7305926
- 负责人:
- 金额:$ 20.84万
- 依托单位:
- 依托单位国家:美国
- 项目类别:
- 财政年份:2007
- 资助国家:美国
- 起止时间:2007-09-12 至 2009-08-31
- 项目状态:已结题
- 来源:
- 关键词:Computer SimulationComputersDailyDisease ProgressionExperimental ModelsFeasibility StudiesFutureGaitKneeKnee OsteoarthritisMeasuresMedialModificationOperative Surgical ProceduresOsteotomyPainPatient EducationPatientsPatternRehabilitation therapyResearchSeveritiesTimeTorqueUnited StatesWalkingbasegait examinationimproved functioningknee replacement arthroplastynovelprogramsreal time modelvisual feedback
项目摘要
DESCRIPTION (provided by applicant): The long term objective of this application is to develop a novel rehabilitation approach to slow the progression of knee osteoarthritis, resulting in improved function and reduced pain for millions of patients. Unlike traditional rehabilitation approaches, the proposed approach is quantitative, combining patient-specific computer models with experimental gait analysis. The computer models are used to predict altered yet natural looking gait patterns that reduce detrimental knee loads. Subsequent gait analysis with real-time visual feedback is then used to train the patient to match the knee loads predicted by the computer model. The specific load targeted by gait retraining is the external knee adduction torque (i.e., the moment that contributes to medial compartment force), since this load is a strong indicator of knee osteoarthritis severity and progression. Initial application of the approach to two subjects but without real-time visual feedback yielded reductions as large as 50% in both peaks of the adduction torque curve, comparable to the results of high tibial osteotomy surgery but without surgical intervention. To investigate the feasibility of this long term objective, we propose to pursue three specific aims in a feasibility study study involving 10 patients with early knee osteoarthritis: Specific aim 1: Predict with a patient-specific computational model whether similar gait modifications exist for each patient that will significantly reduce both knee adduction torque peaks. Specific aim 2: Determine whether patients can achieve significant knee adduction torque reductions after eight gait retraining sessions using real-time model-based visual feedback. Specific aim 3: Determine whether significant knee adduction torque reductions can be maintained for at least one month following the completion of the gait retraining program. If successful, the proposed approach may permit customized gait retraining to be used to slow the progression of knee osteoarthritis or even as a preventative measure. The end result could be millions of patients with decreased pain and improved function who can delay or even avoid knee replacement surgery. Knee osteoarthritis afflicts millions of people in the United States, causing pain and limiting the ability to perform important daily activities such as walking and stair climbing. Using patient-specific computer models and experimental gait analysis, this research seeks to develop a novel rehabilitation approach to slow the progression of the disease. If successful, the approach would allow many patients to delay or possibly even avoid the need for knee replacement surgery in the future.
描述(由申请人提供):本申请的长期目标是开发一种新型康复方法,以减缓膝关节骨关节炎的进展,从而改善数百万患者的功能并减轻疼痛。与传统的康复方法不同,所提出的方法是定量的,将患者特定的计算机模型与实验步态分析相结合。计算机模型用于预测改变但看起来自然的步态模式,以减少有害的膝盖负荷。随后的步态分析与实时视觉反馈,然后用于训练患者,以匹配由计算机模型预测的膝关节负荷。步态再训练所针对的特定负荷是外部膝内收扭矩(即,对内侧间室力有贡献的力矩),因为该载荷是膝关节骨关节炎严重程度和进展的有力指标。在没有实时视觉反馈的情况下,将该方法初始应用于两名受试者,内收扭矩曲线的两个峰值均降低了50%,与胫骨高位截骨术但没有手术干预的结果相当。为了研究这一长期目标的可行性,我们建议在涉及10例早期膝关节骨关节炎患者的可行性研究中追求三个特定目标:特定目标1:使用患者特定的计算模型预测每例患者是否存在类似的步态修改,这将显著降低两个膝关节内收扭矩峰值。具体目标2:使用基于实时模型的视觉反馈,确定患者在8次步态再训练后是否能够显著降低膝关节内收扭矩。具体目标3:确定在步态再训练计划完成后,膝关节内收扭矩的显著降低是否可以维持至少一个月。如果成功的话,所提出的方法可以允许定制的步态再训练用于减缓膝关节骨关节炎的进展,甚至作为一种预防措施。最终结果可能是数百万患者的疼痛减轻,功能改善,可以延迟甚至避免膝关节置换手术。膝关节骨关节炎困扰着美国数百万人,导致疼痛并限制了进行重要日常活动的能力,如步行和爬楼梯。使用患者特定的计算机模型和实验步态分析,这项研究旨在开发一种新的康复方法来减缓疾病的进展。如果成功的话,这种方法将使许多患者推迟甚至可能避免未来的膝关节置换手术。
项目成果
期刊论文数量(0)
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