Novartis Institutes for BioMedical Research, Basel, Switzerland.
Biostatistics and Pharmacometrics, Novartis Pharmaceuticals Corporation, East Hannover, NJ, United States.
JMIR Mhealth Uhealth. 2019 Nov 27;7(11):e15191. doi: 10.2196/15191.
Digital technologies and advanced analytics have drastically improved our ability to capture and interpret health-relevant data from patients. However, only limited data and results have been published that demonstrate accuracy in target indications, real-world feasibility, or the validity and value of these novel approaches.
This study aimed to establish accuracy, feasibility, and validity of continuous digital monitoring of walking speed in frail, elderly patients with sarcopenia and to create an open source repository of raw, derived, and reference data as a resource for the community.
Data described here were collected as a part of 2 clinical studies: an independent, noninterventional validation study and a phase 2b interventional clinical trial in older adults with sarcopenia. In both studies, participants were monitored by using a waist-worn inertial sensor. The cross-sectional, independent validation study collected data at a single site from 26 naturally slow-walking elderly subjects during a parcours course through the clinic, designed to simulate a real-world environment. In the phase 2b interventional clinical trial, 217 patients with sarcopenia were recruited across 32 sites globally, where patients were monitored over 25 weeks, both during and between visits.
We have demonstrated that our approach can capture in-clinic gait speed in frail slow-walking adults with a residual standard error of 0.08 m per second in the independent validation study and 0.08, 0.09, and 0.07 m per second for the 4 m walk test (4mWT), 6-min walk test (6MWT), and 400 m walk test (400mWT) standard gait speed assessments, respectively, in the interventional clinical trial. We demonstrated the feasibility of our approach by capturing 9668 patient-days of real-world data from 192 patients and 32 sites, as part of the interventional clinical trial. We derived inferred contextual information describing the length of a given walking bout and uncovered positive associations between the short 4mWT gait speed assessment and gait speed in bouts between 5 and 20 steps (correlation of 0.23) and longer 6MWT and 400mWT assessments with bouts of 80 to 640 steps (correlations of 0.48 and 0.59, respectively).
This study showed, for the first time, accurate capture of real-world gait speed in slow-walking older adults with sarcopenia. We demonstrated the feasibility of long-term digital monitoring of mobility in geriatric populations, establishing that sufficient data can be collected to allow robust monitoring of gait behaviors outside the clinic, even in the absence of feedback or incentives. Using inferred context, we demonstrated the ecological validity of in-clinic gait assessments, describing positive associations between in-clinic performance and real-world walking behavior. We make all data available as an open source resource for the community, providing a basis for further study of the relationship between standardized physical performance assessment and real-world behavior and independence.
数字技术和高级分析极大地提高了我们从患者身上捕获和解释与健康相关数据的能力。然而，只有有限的数据和结果被发表，证明了这些新方法在目标指标中的准确性、实际可行性、有效性和价值。
本研究旨在确定对患有肌少症的虚弱老年患者进行连续数字步行速度监测的准确性、可行性和有效性，并创建一个开源资源库，其中包含原始、派生和参考数据，作为社区的资源。
这里描述的数据是作为 2 项临床研究的一部分收集的：一项独立的、非干预性验证研究和一项针对肌少症老年人的 2b 期干预性临床试验。在这两项研究中，参与者都通过佩戴在腰部的惯性传感器进行监测。在横断面、独立验证研究中，在一个模拟真实环境的诊所路径课程中，从 26 名自然缓慢行走的老年受试者中收集了数据。在 2b 期干预性临床试验中，全球 32 个地点招募了 217 名肌少症患者，患者在 25 周内接受监测，包括就诊期间和就诊之间。
我们已经证明，我们的方法可以在虚弱的缓慢行走的成年人中捕捉到诊所内的步态速度，在独立验证研究中，残差标准误差为 0.08 米/秒，在干预性临床试验中，4 米步行测试（4mWT）、6 分钟步行测试（6MWT）和 400 米步行测试（400mWT）的标准步态速度评估中，分别为 0.08、0.09 和 0.07 米/秒。我们通过在干预性临床试验中从 192 名患者和 32 个地点捕获 9668 天的真实世界数据，证明了我们方法的可行性。我们推导出了描述给定行走回合长度的推断上下文信息，并发现短 4mWT 步态速度评估与 5 到 20 步之间的步态速度之间存在正相关（相关性为 0.23），6MWT 和 400mWT 评估与 80 到 640 步之间的步态速度之间存在正相关（相关性分别为 0.48 和 0.59）。
本研究首次在患有肌少症的缓慢行走的老年人中准确捕捉到真实世界的步态速度。我们证明了在老年人群中长期进行数字移动监测的可行性，表明可以收集到足够的数据，以便在没有反馈或激励的情况下，对诊所外的步态行为进行稳健监测。通过推断上下文，我们证明了诊所内步态评估的生态有效性，描述了诊所内表现与真实世界行走行为之间的正相关关系。我们将所有数据作为开源资源提供给社区，为进一步研究标准化身体表现评估与真实世界行为和独立性之间的关系奠定了基础。
