A wearable mHealth system for the longitudinal monitoring of joint function in patients with knee OA

项目摘要/摘要 膝关节骨关节炎(OA)是导致独立生活的成年人残疾的主要原因之一。 膝骨性关节炎患者会感到疼痛、僵硬和活动范围减小。尽管临床表现强劲 关于可以改善关节功能的康复建议，目前还没有找到确切的剂量范围。 研究表明，过多或过少的活动和运动强度都会增加疼痛， 这表明存在一个最佳的、个性化的强度和频率。缺乏能够 在时间线上收集足够的生物力学参数是发展的主要障碍 个性化的康复治疗，以应对动态变化的关节功能表现。 实验室研究发现，运动学和运动学参数，如膝关节内收力矩和 屈曲角度可以提供对身体功能的客观评估和对骨性关节炎严重程度和 进步。然而，临床环境中步态实验室的可用性往往是缺乏的，因此提供了一种 对病人病情的看法严重不足。最近，移动和可穿戴技术已经 在以临床为导向的研究方面取得进展。尽管他们有确凿的科学证据证明 临床益处，由于移动技术的局限性，尚未将移动技术应用于康复 当前的技术。目前，还没有能够实现长期可移动的工程解决方案 监测与膝关节骨性关节炎相关的生物力学参数和患者行为， 可靠、准确的态度。 本研究建议开发一套完整的mHealth系统，能够有效地评估生物力学 与OA症状相关的运动，并基于我们的 1)一种安装在膝关节袖子上测量膝关节运动学的新型柔性可穿戴传感器的最新发展 通过间接测量皮肤弹性和2)低成本、高能效的器械鞋垫 用膝盖袖子测量膝盖的运动能力。我们假设这项研究的成功完成将产生 关于生物力学的骨性关节炎症状及其与疼痛、生活质量和 患者家庭和社区环境中的康复依从性。为实现这一研究目标，目标1 将专注于开发一个集成的mHealth系统，可以收集相关的生物力学和 远程设置中的行为数据，并将其转发到云。目标2将评估其准确性和可靠性 在可穿戴系统中测量相关的生物力学参数。最后，目标3将探索 可穿戴系统在模拟中获取临床重要信息的稳健性和可用性 家居环境。这项研究将为我们未来的研究提供技术和科学基础 将测试我们的假设，即建议的可穿戴系统可以通过提供 客观措施从而提高临床疗效，为临床医生提供重要的临床信息。

Project Summary / Abstract Knee osteoarthritis (OA) is one of the leading causes of disability among independently living adults. Individuals with knee OA experience pain, stiffness, and decreased range of motion. Despite strong clinical recommendations for rehabilitation that can improve joint function, the exact dosing range has not been found. Studies have shown that both excessive and low amounts of activities and exercise intensity can increase pain, suggesting that there is an optimal, individualized intensity and frequency. The lack of technologies that can collect sufficient biomechanical parameters in the timeline serves as the primary barrier for the development of personalized rehabilitation treatment to cope with dynamically changing functional performances of the joints. Laboratory studies have found that kinetic and kinematic parameters, such as knee adduction moment and flexion angles, can provide objective assessments of physical function and an estimation of OA severity and progression. However, availability of gait laboratories in clinical settings is often lacking and thus provides a severely under-sampled view of the patients’ condition. More recently, mobile and wearable technologies have made inroads in clinically oriented research studies. Despite their strong scientific evidence of accuracy and clinical benefits, mobile technologies have not yet been deployed for rehabilitation because of the limitations of current technologies. At present, there exists no engineering solutions that enable long-term ambulatory monitoring of biomechanical parameters and patient behavior that are relevant to knee OA in an efficient, reliable, and accurate manner. This study proposes to develop an integrated mHealth system that can effectively assess the biomechanics of movement associated with OA symptoms and monitor motor activities in free-living conditions based on our recent development of 1) a novel flexible wearable sensor attached to a knee sleeve that measures knee kinematics through an indirect measure of skin stretch and 2) a low-cost, power-efficient instrumented insole that together with the knee sleeve measures knee kinetics. We hypothesize successful completion of this study will generate new knowledge regarding biomechanical OA symptoms and their correlations to the pain, quality of life, and rehabilitation compliance in patients’ home and community settings. To accomplish this research goal, Aim 1 will focus on the development of an integrated mHealth system that can collect relevant biomechanical and behavioral data in remote settings, and relay them to the cloud. Aim 2 will evaluate the accuracy and reliability of the wearable system in measuring the relevant biomechanical parameters. Lastly, Aim 3 will explore the robustness and usability of the wearable system in deriving clinically important information in the simulated home environment. This study will yield technological and scientific fundamentals for future studies where we will test our hypothesis that the proposed wearable system can improve rehabilitation adherence by providing objective measures thus improving clinical outcomes, and provide clinicians with important clinical information.