ArticleAmerican journal of audiology2018
Automated Characterization of Mobile Health Apps' Features by Extracting Information From the Web: An Exploratory Study.
Article in American journal of audiology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Comparison of Psychometric Functions Measured Using Remote Testing and Laboratory Testing.Audiology research · 2024Article
- Process and Information Needs When Searching for and Selecting Apps for Smoking Cessation: Qualitative Study Using Contextual Inquiry.JMIR human factors · 2022Article
- Goldilocks and the Three Bears: A Just-Right Hybrid Model to Synthesize the Growing Landscape of Publicly Available Health-Related Mobile Apps.Journal of medical Internet research · 2021Article
- Self-Management Apps for People With Epilepsy: Systematic Analysis.JMIR mHealth and uHealth · 2021Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
purposeThe aim of this study was to test the viability of a novel method for automated characterization of mobile health apps.
methodIn this exploratory study, we developed the basic modules of an automated method, based on text analytics, able to characterize the apps' medical specialties by extracting information from the web. We analyzed apps in the Medical and Health & Fitness categories on the U.S. iTunes store.
resultsWe automatically crawled 42,007 Medical and 79,557 Health & Fitness apps' webpages. After removing duplicates and non-English apps, the database included 80,490 apps. We tested the accuracy of the automated method on a subset of 400 apps. We observed 91% accuracy for the identification of apps related to health or medicine, 95% accuracy for sensory systems apps, and an average of 82% accuracy for classification into medical specialties.
conclusionsThese preliminary results suggested the viability of automated characterization of apps based on text analytics and highlighted directions for improvement in terms of classification rules and vocabularies, analysis of semantic types, and extraction of key features (promoters, services, and users). The availability of automated tools for app characterization is important as it may support health care professionals in informed, aware selection of health apps to recommend to their patients.
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