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 novel 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 an original combined approach able to highlight, by using data visualization on the ALFA4Hearing model, the relevance of the apps' features and their relationships. Third, we propose an innovative 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 characterization of apps.

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

Paglialonga, Alessia;Barbieri, Riccardo;Caiani, Enrico Gianluca;Riboldi, Marco
2017

Abstract

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 novel 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 an original combined approach able to highlight, by using data visualization on the ALFA4Hearing model, the relevance of the apps' features and their relationships. Third, we propose an innovative 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 characterization of apps.
RTSI 2017 - IEEE 3rd International Forum on Research and Technologies for Society and Industry, Conference Proceedings
9781538639061
Apps; hearing healthcare; m-Health (mobile health); smartphones; tablets; Computer Networks and Communications; Computer Science Applications1707 Computer Vision and Pattern Recognition; Energy Engineering and Power Technology; Industrial and Manufacturing Engineering; Health (social science); Management of Technology and Innovation; Artificial Intelligence
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11311/1045096
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