Understanding how age, knee osteoarthritis, and symptoms influence the structure and variance of real-world gait mechanics

了解年龄、膝骨关节炎和症状如何影响现实世界步态力学的结构和变化

• 批准号: 10428883
• 负责人: Jocelyn Frey Hafer
• 金额: $ 19.68万
• 依托单位: UNIVERSITY OF DELAWARE
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-05-15 至 2024-02-28
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10428883
• 关键词: Address; Adult; Affect; Age; Anatomy; Ankle; Arthritis; Articular Range of Motion; Behavior; Classification; Clinical; Clinical Trials; Collection; Data; Data Collection; Early Diagnosis; Effectiveness; Effectiveness of Interventions; Elderly; Fatigue; Female; Foundations; Gait; Gait speed; Gravitation; Health; Hour; Human Characteristics; Impairment; Individual; Intervention; Joints; Knee; Knee Osteoarthritis; Knee joint; Knowledge; Leg; Length; Life; Longitudinal Studies; Lower Extremity; Machine Learning; Measurement; Measures; Mechanics; Methods; Modeling; Movement; Musculoskeletal; Outcome; Pain; Participant; Pathology; Patient Self-Report; Patients; Pattern; Physical activity; Population; Predictive Factor; Rehabilitation therapy; Reproducibility; Role; Severities; Shapes; Structure; Symptoms; System; Text Messaging; Time; Walking; Woman; age related; ankle joint; associated symptom; base; design; effective intervention; foot; gait examination; implementation intervention; improved; joint function; male; novel; radiological imaging; recruit; sensor; success; therapy design; wearable sensor technology; young adult

Abstract Knee osteoarthritis is a significant age-related condition that impairs mobility. Lab-based gait measurement has identified specific gait mechanics that are associated with knee osteoarthritis severity and progression. However, when interventions that successfully correct aberrant gait mechanics in the lab are implemented in clinical trials, they do not meaningfully improve knee osteoarthritis symptoms or slow progression. This gap between in-lab success and real-world efficacy may be because individuals walk differently in daily life than in lab settings, with in-lab gait likely only being representative of a small portion of an individual’s real-world gait. Longitudinal measurement of real-world gait could identify new factors that relate to joint health or intervention effectiveness, enabling better prediction of knee osteoarthritis progression and improved intervention design. Before we can design or execute a longitudinal study of real-world gait, we need a better understanding of real- world gait data. Recent studies indicate that individuals walk slower and with shorter stride lengths on average when out of the lab compared to in lab settings. These findings suggest that gait measures that are specifically tied to joint health may also differ during daily life compared to the lab setting. Additionally, symptoms such as pain and fatigue that are known to affect gait mechanics during in-lab collection vary substantially within and between days in real-world settings (and likely to a greater extent than in lab settings). Despite advances in wearable sensors such as inertial measurement units (IMUs) and the popularity of using IMUs for gait analysis in the lab, the use of IMUs in real-world gait analysis has been limited because of challenges with analyzing unobserved data and interpreting outcomes from novel data. Our team has successfully implemented reliable methods for detecting and categorizing walking activity (e.g., level walking vs. stairs, straight walking vs. turns), orienting data to recognizable reference frames (e.g., gravitational or functional), and calculating interpretable outcomes that correspond to traditional gait measures and are relevant to joint function (e.g., knee joint range of motion, propulsive ankle angular velocity, inter-segment coordination). In this study, we will calculate our established gait measures during real-world gait collected over 3 full, consecutive days in 3 groups of participants: older adults with knee osteoarthritis, older asymptomatic adults, and young adults. We will use electronic messaging to collect ecologically valid measures of pain and fatigue 5 times each day. We will use these data to compare the magnitude and variance of knee range of motion, propulsive ankle joint velocity, and lower extremity coordination between groups, days, and between real-world and lab settings (Aim 1). We will model the relationships between our gait measures and participant self-reported pain and fatigue (Aim 2). Completion of these aims will provide preliminary data with which we can design a larger study (R01) to evaluate the role of real-world gait in knee osteoarthritis progression. In the long term, this knowledge will allow for earlier detection of mobility decline and improved intervention design and implementation.

抽象的 膝盖骨关节炎是损害活动能力的重要年龄相关疾病。基于实验室的聚集测量 确定了与膝关节骨关节炎的严重程度和进展有关的特定步态力学。 但是，当在实验室中成功纠正实验室中的异常步态力学的干预措施将在 临床试验，它们不能有意义地改善膝关节骨关节炎症状或缓慢的进展。这个差距 在实验室的成功和现实世界的效率之间可能是因为个人在日常生活中的行走与在 实验室设置，具有LAB步态可能仅代表一个人实际步态的一小部分。 现实世界中的纵向测量可以确定与联合健康或干预有关的新因素 有效性，可以更好地预测膝关节骨关节炎的进展并改善干预设计。 在我们设计或执行对现实世界的纵向研究之前，我们需要更好地了解现实 世界步态数据。最近的研究表明，个人平均行走速度较慢，步幅较短 与实验室设置相比，出门时。这些发现表明，达到专门的措施 与实验室环境相比，与联合健康有关的日常生活也可能有所不同。此外，症状，例如 已知会影响在LAB收集期间收集力学的疼痛和疲劳在内部差异很大， 在现实世界中的几天之间（可能比实验室设置更大的程度）。尽管进步 可穿戴传感器，例如惯性测量单元（IMU）和使用IMU进行步态分析的普及 在实验室中，由于分析的挑战，在现实世界中使用IMU的使用受到限制 未观察到的数据并解释了新数据的结果。我们的团队已成功实施了可靠的 检测和分类步行活动的方法（例如，步行与楼梯，直行与转弯）， 定向数据以识别参考帧（例如重力或功能），并计算可解释的 与传统符合措施相对应并与关节功能相关的结果（例如，膝盖关节范围 运动，推进踝角速度，段间协调）。在这项研究中，我们将计算我们的 在现实世界中，已建立的会议措施连续3天，在3组中收集了3组 参与者：患有膝盖骨关节炎的老年人，年长的无症状成年人和年轻人。我们将使用 电子消息传递每天收集5次疼痛和疲劳的生态有效测量。我们将使用 这些数据以比较膝关节运动范围，推进踝关节速度和 组，天数以及现实世界和实验室环境之间的下肢协调（AIM 1）。我们将 建模我们的满足措施与参与自我报告的疼痛和疲劳之间的关系（AIM 2）。 这些目标的完成将提供初步数据，我们可以通过这些数据设计更大的研究（R01） 评估现实世界中聚集在膝关节骨关节炎进展中的作用。从长远来看，这些知识将允许 为了早期检测出机动性下降和改善的干预设计和实施。