Philip Ben Joseph, Abdelrazek Mohamed, Bonti Alessio, Barnett Scott, Grundy John
Deakin University, Melbourne, Australia.
School of Information Technology, Faculty of Science, Engineering and Built Environment, Deakin University, Melbourne, Australia.
JMIR Mhealth Uhealth. 2022 Mar 9;10(3):e30468. doi: 10.2196/30468.
There has been a steady rise in the availability of health wearables and built-in smartphone sensors that can be used to collect health data reliably and conveniently from end users. Given the feature overlaps and user tendency to use several apps, these are important factors impacting user experience. However, there is limited work on analyzing the data collection aspect of mobile health (mHealth) apps.
This study aims to analyze what data mHealth apps across different categories usually collect from end users and how these data are collected. This information is important to guide the development of a common data model from current widely adopted apps. This will also inform what built-in sensors and wearables, a comprehensive mHealth platform should support.
In our empirical investigation of mHealth apps, we identified app categories listed in a curated mHealth app library, which was then used to explore the Google Play Store for health and medical apps that were then filtered using our selection criteria. We downloaded these apps from a mirror site hosting Android apps and analyzed them using a script that we developed around the popular AndroGuard tool. We analyzed the use of Bluetooth peripherals and built-in sensors to understand how a given app collects health data.
We retrieved 3251 apps meeting our criteria, and our analysis showed that 10.74% (349/3251) of these apps requested Bluetooth access. We found that 50.9% (259/509) of the Bluetooth service universally unique identifiers to be known in these apps, with the remainder being vendor specific. The most common health-related Bluetooth Low Energy services using known universally unique identifiers were Heart Rate, Glucose, and Body Composition. App permissions showed the most used device module or sensor to be the camera (669/3251, 20.57%), closely followed by location (598/3251, 18.39%), with the highest occurrence in the staying healthy app category.
We found that not many health apps used built-in sensors or peripherals for collecting health data. The small number of the apps using Bluetooth, with an even smaller number of apps using standard Bluetooth Low Energy services, indicates a wider use of proprietary algorithms and custom services, which restrict the device use. The use of standard profiles could open this ecosystem further and could provide end users more options for apps. The relatively small proportion of apps using built-in sensors along with a high reliance on manual data entry suggests the need for more research into using sensors for data collection in health and fitness apps, which may be more desirable and improve end user experience.
可用于从终端用户可靠且便捷地收集健康数据的健康可穿戴设备和内置智能手机传感器的可用性一直在稳步上升。鉴于功能重叠以及用户使用多个应用程序的倾向，这些都是影响用户体验的重要因素。然而，在分析移动健康（mHealth）应用程序的数据收集方面的工作有限。
本研究旨在分析不同类别的移动健康应用程序通常从终端用户收集哪些数据以及这些数据是如何收集的。这些信息对于从当前广泛采用的应用程序中指导通用数据模型的开发很重要。这也将告知一个全面的移动健康平台应该支持哪些内置传感器和可穿戴设备。
在我们对移动健康应用程序的实证研究中，我们确定了一个精心策划的移动健康应用程序库中列出的应用程序类别，然后使用该类别在谷歌应用商店中探索健康和医疗应用程序，随后使用我们的选择标准进行筛选。我们从托管安卓应用程序的镜像网站下载这些应用程序，并使用我们围绕流行的AndroGuard工具开发的脚本对其进行分析。我们分析了蓝牙外设和内置传感器的使用情况，以了解给定应用程序如何收集健康数据。
我们检索到3251个符合我们标准的应用程序，我们的分析表明，这些应用程序中有10.74%（349/3251）请求蓝牙访问权限。我们发现，在这些应用程序中，50.9%（259/509）的蓝牙服务通用唯一标识符是已知的，其余是特定于供应商的。使用已知通用唯一标识符的最常见的与健康相关的低功耗蓝牙服务是心率、血糖和身体成分。应用程序权限显示使用最多的设备模块或传感器是摄像头（669/3251，20.57%），紧随其后的是位置（598/3251，18.39%），在保持健康应用程序类别中出现频率最高。
我们发现，使用内置传感器或外设来收集健康数据的健康应用程序并不多。使用蓝牙的应用程序数量较少，使用标准低功耗蓝牙服务的应用程序数量更少，这表明专有算法和定制服务的使用更为广泛，这限制了设备的使用。使用标准配置文件可以进一步开放这个生态系统，并可以为终端用户提供更多的应用程序选择。使用内置传感器的应用程序比例相对较小，同时对手动数据输入的高度依赖表明，需要对在健康和健身应用程序中使用传感器进行数据收集进行更多研究，这可能更受欢迎并改善终端用户体验。
