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

摘要 膝关节骨关节炎是一种与年龄相关的严重疾病，会损害活动能力。基于实验室的步态测量 确定了与膝关节骨关节炎严重程度和进展相关的特定步态力学。 然而，当在实验室中成功纠正异常步态力学的干预措施在临床上实施时， 在临床试验中，它们不能有意义地改善膝关节骨关节炎症状或减缓进展。这一差距 实验室成功和现实世界的有效性之间的差异可能是因为个人在日常生活中的行走方式与 实验室设置，实验室步态可能仅代表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识别与关节健康或干预相关的新因素 有效性，能够更好地预测膝关节骨关节炎的进展和改善干预设计。 在我们设计或执行对真实步态的纵向研究之前，我们需要更好地了解真实的- 世界步态数据。最近的研究表明，个人走得较慢，平均步幅较短 与实验室环境中相比，实验室外的情况。这些发现表明，步态措施，具体是 在日常生活中，与实验室环境相比，与关节健康相关的其他因素也可能有所不同。此外，症状如 已知在实验室内采集期间影响步态力学的疼痛和疲劳在 在现实世界的环境中（并且可能比在实验室环境中更大程度上）。尽管取得了进展， 惯性测量单元（伊穆斯）等可穿戴传感器以及使用伊穆斯进行步态分析的普及 在实验室中，由于分析困难，伊穆斯在实际步态分析中的应用受到了限制 未观察到的数据和解释新数据的结果。我们的团队已经成功实施了可靠的 用于检测和分类行走活动的方法（例如，水平行走与楼梯，直行与转弯）， 将数据定向到可识别的参考系（例如，重力或功能），并计算可解释的 对应于传统步态测量并且与关节功能相关的结果（例如，膝关节范围 的运动，推进踝关节角速度，段间协调）。在这项研究中，我们将计算我们的 在3组中连续3天收集真实世界步态期间的既定步态测量值， 参与者：患有膝关节骨关节炎的老年人、无症状的老年人和年轻人。我们将使用 每天发送5次电子信息，收集生态有效的疼痛和疲劳测量值。我们将使用 这些数据比较膝关节活动范围、踝关节推进速度和 组间、日间以及真实世界和实验室环境之间的下肢协调（目标1）。我们将 建立步态测量与参与者自我报告的疼痛和疲劳之间的关系模型（目标2）。 这些目标的完成将提供初步数据，我们可以据此设计更大规模的研究（R 01）， 评估真实步态在膝关节骨关节炎进展中的作用。从长远来看，这些知识将使 以便更早地发现行动能力下降，并改进干预措施的设计和实施。

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.