NewPneu: A Novel Cost Effective mHealth System for Diagnosing Childhood Pneumonia in Low-Resource Settings

Salti, Tarek El; Sykes, Edward R.; Zajac, Warren; Abdullah, Saad; Khoja, Shariq

doi:10.1109/iemcon.2019.8936160

Cited by 6 publications

(1 citation statement)

References 11 publications

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“…Such sensors provide an effective platform for collecting real time metrics conveniently and less intrusively, which may be useful in medical research [17]. Recently, they have also been suggested for use in low-cost mHealth systems such as those for diagnosing pneumonia [18]. Similar suggestions have also been made for physiological measurements such as heart and respiration rates, blood oxygen saturation, and blood pressure for application in health interventions [19].…”

Section: Introductionmentioning

confidence: 99%

Data Collection Mechanisms in Health and Wellness Apps: Review and Analysis

Philip¹,

Abdelrazek²,

Bonti³

et al. 2022

JMIR Mhealth Uhealth

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Background 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. Objective 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. Methods 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. Results 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. Conclusions 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.

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Section: Introductionmentioning

confidence: 99%