e-Health solutions for better care: Characterization of health apps to extract meaningful information and support users' choices

In a larger and larger market of health apps (over 259,000 available on major app stores), an urgent emerging need is to find ways to provide accurate, useful app information to users. The aim of our research is the development of user support tools for a more informed adoption of health apps. Specifically, in this study we will outline three recently developed approaches aimed at highlighting meaningful information about health apps. First, we outline a descriptive method able to characterize a large set of apps for hearing healthcare by using a core set of features: the "At-a-glance Labelling for Features of Apps for Hearing Healthcare" (ALFA4Hearing) model. Second, we describe a combined approach able to highlight, by using data visualization on the ALFA4Hearing model, the relevance of the apps' features as well as their relationships. Third, we propose a novel automated approach able to extract, by using text analytics, meaningful information about apps directly from the web, a preliminary step towards the development of user support tools for automated app selection and characterization.