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

摘要 膝骨性关节炎是一种严重的与年龄相关的疾病，会损害活动能力。基于实验室的步态测量 确定了与膝骨性关节炎的严重程度和进展相关的特定步态机制。 然而，当在实验室中实施成功纠正异常步态机制的干预措施时 临床试验表明，它们并不能有效改善膝骨性关节炎症状或减缓病情进展。这一差距 实验室内的成功和现实世界的效果之间的差异可能是因为个人在日常生活中行走的方式与在实验室中的不同 实验室环境，实验室内的步态可能只代表一个人真实世界步态的一小部分。 对真实世界步态的纵向测量可以确定与关节健康或干预相关的新因素 有效性，能够更好地预测膝骨性关节炎的进展和改进的干预设计。 在我们设计或执行对真实世界步态的纵向研究之前，我们需要更好地了解真实世界的步态 世界步态数据。最近的研究表明，个人行走速度较慢，步幅平均较短 与在实验室环境中相比，在实验室以外的时候。这些发现表明，步态测量特别是 与实验室环境相比，日常生活中与关节健康相关的因素也可能有所不同。此外，症状如 已知在实验室采集过程中影响步态机制的疼痛和疲劳在 在现实世界的环境中(可能比在实验室环境中更大程度上)。尽管取得了进展，但 可穿戴传感器，如惯性测量单元(IMU)和使用IMU进行步态分析的流行 在实验室中，IMUS在真实世界的步态分析中的使用受到了限制，因为在分析方面存在挑战 未观察到的数据和从新数据中解释的结果。我们的团队已经成功地实现了可靠 用于检测和分类步行活动的方法(例如，水平步行与楼梯、直行与转弯)， 将数据定向到可识别的参考系(例如，重力或泛函)，并计算可解释性 符合传统步态测量并与关节功能相关的结果(例如，膝关节范围 运动、推进脚踝的角速度、节间协调性)。在这项研究中，我们将计算我们的 在真实世界步态中确定的步态测量收集了3组连续3天的完整数据 研究对象：患有膝骨性关节炎的老年人、无症状的老年人和年轻人。我们将使用 每天5次收集生态有效的疼痛和疲劳测量数据的电子信息。我们将使用 这些数据用于比较膝关节活动范围、推进踝关节速度和 小组之间、日期之间以及现实世界和实验室环境之间的肢体协调(目标1)。我们会 模拟我们的步态测量和参与者自我报告的疼痛和疲劳之间的关系(目标2)。 这些目标的完成将提供初步数据，我们可以利用这些数据设计更大的研究(R01)以 评估真实世界步态在膝骨性关节炎进展中的作用。从长远来看，这一知识将使 用于更早地检测行动能力下降并改进干预设计和实施。

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.