Alessia Paglialonga1, Francesco Pinciroli1,2, Gabriella Tognola1. 1. Istituto di Elettronica e di Ingegneria dell'Informazione e delle Telecomunicazioni, Consiglio Nazionale delle Ricerche, Milan, Italy. 2. Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Italy.
Abstract
PURPOSE: The aim of the study was to analyze, by using the ALFA4Hearing model (At-a-Glance Labeling for Features of Apps for Hearing Health Care), a sample of apps over a wide range of services in the hearing health care (HHC) domain in order to take a first picture of the current scenario of apps for HHC. METHOD: We tested 120 apps, and we characterized them by using the ALFA4Hearing model, which includes 29 features in 5 components (Promoters, Services, Implementation, Users, and Descriptive Information). We analyzed (a) the distribution of the 29 features in the sample, (b) the relationship between the Implementation features and the Services provided by the apps, and (c) the distribution of the 29 features in apps for professional use. RESULTS: The analysis of our sample of apps by means of the ALFA4Hearing model highlighted interesting trends and emerging challenges. Also, results suggested many potential opportunities for research and clinical practice, such as greater involvement of stakeholders, improved evidence base, higher technical quality, and usability. CONCLUSIONS: The ALFA4Hearing model is able to represent, at a glance, a large amount of information about apps for HHC, highlighting trends and challenges. It might be useful to HHC professionals as a basis for app characterization and informed decision making.
PURPOSE: The aim of the study was to analyze, by using the ALFA4Hearing model (At-a-Glance Labeling for Features of Apps for Hearing Health Care), a sample of apps over a wide range of services in the hearing health care (HHC) domain in order to take a first picture of the current scenario of apps for HHC. METHOD: We tested 120 apps, and we characterized them by using the ALFA4Hearing model, which includes 29 features in 5 components (Promoters, Services, Implementation, Users, and Descriptive Information). We analyzed (a) the distribution of the 29 features in the sample, (b) the relationship between the Implementation features and the Services provided by the apps, and (c) the distribution of the 29 features in apps for professional use. RESULTS: The analysis of our sample of apps by means of the ALFA4Hearing model highlighted interesting trends and emerging challenges. Also, results suggested many potential opportunities for research and clinical practice, such as greater involvement of stakeholders, improved evidence base, higher technical quality, and usability. CONCLUSIONS: The ALFA4Hearing model is able to represent, at a glance, a large amount of information about apps for HHC, highlighting trends and challenges. It might be useful to HHC professionals as a basis for app characterization and informed decision making.