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增强移动医疗数据采集应用的传感功能。

Enhancing mHealth data collection applications with sensing capabilities.

机构信息

DigiHealth Institute, Neu-Ulm University of Applied Sciences, Neu-Ulm, Germany.

Institute of Clinical Epidemiology and Biometry, University of Würzburg, Würzburg, Germany.

出版信息

Front Public Health. 2022 Sep 15;10:926234. doi: 10.3389/fpubh.2022.926234. eCollection 2022.

Abstract

Smart mobile devices such as smartphones or tablets have become an important factor for collecting data in complex health scenarios (e.g., psychological studies, medical trials), and are more and more replacing traditional pen-and-paper instruments. However, simply digitizing such instruments does not yet realize the full potential of mobile devices: most modern smartphones have a variety of different sensor technologies (e.g., microphone, GPS data, camera, ...) that can also provide valuable data and potentially valuable insights for the medical purpose or the researcher. In this context, a significant development effort is required to integrate sensing capabilities into (existing) data collection applications. Developers may have to deal with platform-specific peculiarities (e.g., Android vs. iOS) or proprietary sensor data formats, resulting in unnecessary development effort to support researchers with such digital solutions. Therefore, a cross-platform mobile data collection framework has been developed to extend existing data collection applications with sensor capabilities and address the aforementioned challenges in the process. This framework will enable researchers to collect additional information from participants and environment, increasing the amount of data collected and drawing new insights from existing data.

摘要

智能手机和平板电脑等智能移动设备已成为在复杂健康环境(例如心理研究、医学试验)中收集数据的重要因素,它们越来越多地取代了传统的纸笔仪器。然而,仅仅将这些仪器数字化并不能充分发挥移动设备的潜力:大多数现代智能手机都具有各种不同的传感器技术(例如麦克风、GPS 数据、摄像头等),这些技术也可以为医疗目的或研究人员提供有价值的数据和潜在的见解。在这种情况下,需要投入大量精力来将感知能力集成到(现有的)数据收集应用程序中。开发人员可能需要处理特定于平台的特性(例如,Android 与 iOS)或专有的传感器数据格式,这导致开发人员需要投入不必要的精力来支持研究人员使用此类数字解决方案。因此,我们开发了一个跨平台的移动数据收集框架,以扩展现有数据收集应用程序的感知能力,并在这个过程中解决上述挑战。该框架将使研究人员能够从参与者和环境中收集更多信息,增加收集到的数据量,并从现有数据中获得新的见解。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9b4d/9521646/8a4a842cfe46/fpubh-10-926234-g0001.jpg

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