Understanding user concerns and choice of app architectures in designing Audio-based mHealth Apps

Abstract

Due to recent advancements in smartphone sensing and computing capabilities, artificially intelligent mobile health (mHealth) applications are getting popular to monitor a range of diseases, including coronavirus-caused COVID-19 disease, chronic obstructive pulmonary disease (COPD), asthma, bronchitis, emphysema, and sleep apnea, among many others utilizing the audio recordings obtained from the smartphone microphones. Compared to other mHealth apps, audio-based apps suffer from various user concerns, including the privacy of data and battery drain rate , among many other concerns, which can adversely affect the user compliance, and app utility and life cycle. To address user concerns, mHealth apps should provide users options to configure the app, i.e., choose an architecture from a set of options based on user concerns and preferences. However, there is a dearth of knowledge about audio-based health monitoring app design. In this work, we present a focused user-centric app design study to better understand various concerns and choices that users want to see in an audio-based mHealth app. From a detailed analysis of 60 subjects with varying backgrounds, we find that around 85% subjects are concerned about the privacy of data and 93% subjects prefer to pick an app architecture that will not send raw audio recordings to a server. Findings from this work can guide the design of future mHealth apps that utilizes privacy-sensitive audio data. • Determine the major user concerns about the audio-based health monitoring smartphone apps and user choice when provided with a set of app configurations, i.e., architectures, that a user can choose when using a health monitoring app that relies on continuous microphone sensing to obtain audio recordings. • People with diferent backgrounds have a similar level of concerns while reporting their concerns about audio-based health monitoring apps. • Most of the study paracipants (around 66%) choose either the complete on-phone architecture, i.e., architecture that performs all computations on the phone, or the non- speech data-driven architecture, i.e., architecture that removes all speech components from audio recordings in the phone before sending them to the server for additional computation. • Only 7% participants choose the raw data-driven architecture, i.e., architecture that sends raw audio recordings to the server for all computation. • These fndings could be used as guidelines for the future user-centric app design to address user concerns and provide app customization options with the goal to improve the utility and compliance of the mHealth apps that rely on audio data obtained from the continous microphone sensing.