Continuous Digital Monitoring of Walking Speed in Frail Elderly Patients: Noninterventional Validation Study and Longitudinal Clinical Trial (Data for independent validation study)

Digital technologies and advanced analytics have drastically improved our ability to capture and interpret health relevant data from patients. However, to date, limited data and results have been published detailing real-world patient compliance, demonstrating accuracy in target indications or examining what novel insights and clinical value can be derived. Here we present novel, digital mobility data from two studies: an independent, non-interventional validation study with elderly, naturally slow walking subjects, and a global, multi-site phase IIb clinical trial involving patients with age-related muscle loss and slow walking speed (sarcopenia). Based on these data, we validate the accuracy of a novel algorithm for capturing in-clinic and real-world gait speed in frail, slow-walking adults. We demonstrate the feasibility of continuous monitoring with a wearable inertial sensor in elderly adults in real-world settings, and propose minimum thresholds for compliance required for robust capture of gait behaviors in this population. We also show how simple, inferred contextual information, describing the length of a given walking bout, can explain some of the variation in real-world gait speed, and use this information to demonstrate for the first time a relationship between in-clinic performance and real-world gait speed behavior. This work lays a foundation for exploration of the clinical relevance and value of such measures and is a first step in building a more complete chain of evidence between standardized physical performance assessment, real-world behavior, and subjective perceptions of mobility, independence and health.

This dataset contains data collected during the independent validation study: derived data from raw accelerometry data, and summary performance data.

The full dataset, including raw accelerometry data, is available here: https://mueller-et-al-2019.s3.amazonaws.com/index.html

数字技术与先进分析手段极大提升了我们从患者身上获取并解读健康相关数据的能力。然而迄今为止，鲜有公开发表的研究详述真实世界中的患者依从性、验证目标适应症中的检测准确性，或是探讨可从中挖掘的新型研究洞察与临床价值。
本研究披露了两项研究中的新型数字化运动行为数据：一项针对老年自然步态缓慢受试者的独立非干预性验证研究，以及一项纳入年龄相关性肌肉流失（sarcopenia）且步态缓慢患者的全球性多中心IIb期临床试验。基于上述数据，我们验证了一款新型算法的准确性，该算法可用于捕捉虚弱且步态缓慢的成人在临床环境与真实世界中的步行速度。我们证实了可穿戴惯性传感器在真实世界场景中对老年人群进行持续监测的可行性，并提出了该人群中可靠捕捉步态行为所需的最低依从性阈值。此外，我们还证明了用于描述单次步行时长的简易推导型上下文信息，可解释真实世界步行速度的部分变异情况，并借助该信息首次证实了临床步行表现与真实世界步态行为之间的关联。本研究为探索此类检测手段的临床相关性与应用价值奠定了基础，同时也是在标准化体能评估、真实世界行为表现与活动能力、独立性及健康的主观感知之间构建完整证据链的第一步。
本数据集包含独立验证研究中采集的数据：原始加速度计数据衍生得到的处理后数据，以及汇总性能数据。
包含原始加速度计数据在内的完整数据集可通过以下链接获取：https://mueller-et-al-2019.s3.amazonaws.com/index.html