Dynamic Treatment Regimens of Physical Activity for Persons with Osteoarthritis

项目摘要/摘要 对影响骨关节炎(OA)病程的因素的有限了解阻碍了骨性关节炎的发展和 优化战略，以减轻疼痛和减缓疾病进展。虽然办公自动化是一个机械的- 作为一种由疾病驱动的疾病，关于最常见的体力活动的影响，重要的知识差距仍然存在 关节结构机械刺激的原因。骨性关节炎疼痛主要是活动引起的(例如，爬楼梯， 步行)，但矛盾的是，步行养生法可以减轻疼痛。此外，一定程度的振荡 压缩应力引起软骨的营养变化，而静态或高速应力引起分解代谢。 回应。考虑到不同类型的活动对关节的不同影响，研究也就不足为奇了 关于体力活动量对骨性关节炎影响的研究结果相互矛盾。此外，目前还不确定 活动类型可以有益于缓解疼痛或改善功能，而不会造成伤害和加速 疾病的发展。该项目的长期目标是促进临床医生和患者的决策。 为缓解疼痛、维持功能表现和减速提供更理想的体力活动方案 骨关节炎的结构性损害。这一建议的中心假设是，体力活动对 骨关节炎的结果取决于个体特征，如疾病的阶段和 不同类型的活动对办公自动化结果有不同的影响。此外，我们假设最优活动 治疗方案可能会随着患者特征的变化而变化。因此，一项针对个人的体力活动 需要可调整并符合患者当前特征的治疗方案。我们建议 利用骨关节炎倡议(OAI)和多中心骨关节炎研究(MOST)队列， 两项关于骨关节炎的大规模纵向研究，使用体力活动数据，客观地测量 加速度计，以及广泛的临床、成像和用药历史数据。我们的目标是：1) 描述体力活动类型与膝骨性关节炎结构进展的时变关联，2)到 研究一种针对个人的动态体力活动疗法来减轻膝骨性关节炎患者的疼痛， 以及3)研究针对个人的动态体力活动方案，以实现基于绩效的体能 膝关节骨性关节炎患者的功能。这项工作的贡献是巨大的，因为更好地理解 体力活动的类型和强度如何改善或恶化骨性关节炎的结果是迫切需要的。我们的 新的分析方法是对传统的基于体力活动推荐的方法的背离 对人群的平均影响。与面向人群的方法不同的是，这些方法对个人的 独特的特点，我们因人而异的方法将允许我们调整体力活动养生作为 疾病会进化。我们的工作将为不同类型的体力活动提供新的证据，并可能 更好的办公自动化管理。

PROJECT SUMMARY/ABSTRACT Limited understanding of factors affecting the course of Osteoarthritis (OA) has hampered development and optimization of strategies that would lessen pain and slow disease progression. While OA is a mechanically- driven disease, important knowledge gaps remain regarding the effects of physical activity, the most common reason for mechanical stimulation of joint structures. OA pain is mainly activity-induced (e.g., stair climbing, walking), but paradoxically, walking regimens can reduce pain. Further, certain degrees of oscillating compressive stress cause trophic changes in cartilage, whereas static or high velocity stress causes catabolic responses. Given the contrasting effects of different types of activities on the joint, it is not surprising that studies of the effects of the amount of physical activity on OA have had conflicting results. Further, it is uncertain what types of activity could be beneficial for pain relief or function improvement without causing harm and accelerating disease progression. The long-term goal of this project is to facilitate decision making for clinicians and patients of a more optimal physical activity regimen for pain relief, maintaining functional performance, and decelerating structural damage in osteoarthritis. The central hypothesis of this proposal is that physical activity's influence on osteoarthritis outcomes differs depending on individual characteristics such as the stage of disease and that different types of activity have different effects on OA outcomes. Further, we hypothesize that the optimal activity regimen may evolve as the patient characteristics change. Therefore, a person-specific physical activity regimen that is adjustable and consistent with the patient's current characteristics is needed. We propose to take advantage of the Osteoarthritis Initiative (OAI) and Multicenter Osteoarthritis Study (MOST) cohorts, two large-scale longitudinal studies on OA with data on physical activity, measured objectively by accelerometer, in addition to extensive clinical, imaging, and medication history data. Our aims are: 1) to characterize the time-varying association of physical activity types with knee OA structural progression, 2) to examine a person-specific and dynamic physical activity regimen for pain reduction in persons with knee OA, and 3) to examine a person-specific and dynamic physical activity regimen for performance-based physical function in persons with knee OA. The contribution of this work is significant because a better understanding of how physical activity types and intensity may improve or worsen the outcomes of OA is urgently needed. Our novel analytic approach is a departure from the traditional approaches of physical activity recommendation based on average effect in the population. Unlike population-oriented approaches that were indifferent to an individual's unique characteristics, our person-specific approach will allow us to adjust the physical activity regimen as the disease evolves. Our work will provide new evidence about diverse types of physical activity and could inform better OA management.