Spang Robert P, Haeger Christine, Mümken Sandra A, Brauer Max, Voigt-Antons Jan-Niklas, Gellert Paul
Quality and Usability Lab, Technische Universität Berlin, Berlin, Germany.
Institute of Medical Sociology and Rehabilitation Science - Charité, Universitätsmedizin Berlin, Berlin, Germany.
JMIR Rehabil Assist Technol. 2023 Mar 2;10:e42258. doi: 10.2196/42258.
As global positioning system (GPS) measurement is getting more precise and affordable, health researchers can now objectively measure mobility using GPS sensors. Available systems, however, often lack data security and means of adaptation and often rely on a permanent internet connection.
To overcome these issues, we aimed to develop and test an easy-to-use, easy-to-adapt, and offline working app using smartphone sensors (GPS and accelerometry) for the quantification of mobility parameters.
An Android app, a server backend, and a specialized analysis pipeline have been developed (development substudy). Parameters of mobility by the study team members were extracted from the recorded GPS data using existing and newly developed algorithms. Test measurements were performed with participants to complete accuracy and reliability tests (accuracy substudy). Usability was examined by interviewing community-dwelling older adults after 1 week of device use, followed by an iterative app design process (usability substudy).
The study protocol and the software toolchain worked reliably and accurately, even under suboptimal conditions, such as narrow streets and rural areas. The developed algorithms had high accuracy (97.4% correctness, F-score=0.975) in distinguishing dwelling periods from moving intervals. The accuracy of the stop/trip classification is fundamental to second-order analyses such as the time out of home, as they rely on a precise discrimination between the 2 classes. The usability of the app and the study protocol was piloted with older adults, which showed low barriers and easy implementation into daily routines.
Based on accuracy analyses and users' experience with the proposed system for GPS assessments, the developed algorithm showed great potential for app-based estimation of mobility in diverse health research contexts, including mobility patterns of community-dwelling older adults living in rural areas.
INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.1186/s12877-021-02739-0.
随着全球定位系统（GPS）测量变得更加精确且成本更低，健康研究人员现在可以使用GPS传感器客观地测量活动能力。然而，现有的系统往往缺乏数据安全性和适应性手段，并且常常依赖永久的互联网连接。
为克服这些问题，我们旨在开发并测试一款使用智能手机传感器（GPS和加速度计）来量化活动参数的易于使用、易于适应且可离线工作的应用程序。
已开发了一个安卓应用程序、一个服务器后端和一个专门的分析流程（开发子研究）。研究团队成员的活动参数通过使用现有算法和新开发的算法从记录的GPS数据中提取。对参与者进行测试测量以完成准确性和可靠性测试（准确性子研究）。在设备使用1周后，通过采访社区居住的老年人来检查可用性，随后进行迭代式应用程序设计过程（可用性子研究）。
即使在诸如狭窄街道和农村地区等次优条件下，研究方案和软件工具链也能可靠且准确地运行。所开发的算法在区分居住时段和移动时段方面具有很高的准确性（正确率97.4%，F值=0.975）。停止/行程分类的准确性对于诸如外出时间等二阶分析至关重要，因为这些分析依赖于对这两类的精确区分。该应用程序和研究方案的可用性在老年人中进行了试点，结果显示障碍较低且易于融入日常生活。
基于准确性分析以及用户对所提议的GPS评估系统的体验，所开发的算法在包括农村地区社区居住老年人的活动模式等各种健康研究背景下，在基于应用程序的活动能力估计方面显示出巨大潜力。
国际注册报告识别号（IRRID）：RR2-10.1186/s12877-021-02739-0。
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