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ACCU³RATE: A mobile health application rating scale based on user reviews

Biswas, Milon and Tania, Marzia Hoque and Kaiser, M Shamim and Kabir, Russell and Mahmud, Mufti and Kemal, Atika (2021) 'ACCU³RATE: A mobile health application rating scale based on user reviews.' PLoS One, 16 (12). e0258050-e0258050. ISSN 1932-6203

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Over the last decade, mobile health applications (m-Health App) have evolved exponentially to assess and support our health and well-being. Objective: This paper presents an Artificial Intelligence (AI) enabled mHealth app rating tool, called ACCURATE, which takes multidimensional measures such as user star rating, user review and features declared by the developer to generate the rating of an app. However, currently, there is very little conceptual understanding on how user reviews affect app rating from a multi-dimensional perspective. This study applies AI-based text mining technique to develop more comprehensive understanding of user feedback based on several important factors, determining the mHealth app ratings. Method: Based on the literature, six variables were identified that influence the mHealth app rating scale. These factors are user star rating, user text review, user interface (UI) design, functionality, security and privacy, and clinical approval. Natural Language Toolkit package is used for interpreting text and to identify the App users’ sentiment. Additional considerations were accessibility, protection and privacy, UI design for people living with physical disability. Moreover, the details of clinical approval, if exists, were taken from the developer’s statement. Finally, we fused all the inputs using fuzzy logic to calculate the new app rating score. Results and Conclusions: ACCURATE concentrates on heart related Apps found in the play store and App gallery. The findings indicate the efficacy of the proposed method as opposed to the current device scale. This study has implications for both App developers and consumers who are using m-Health Apps to monitor and track their health. The performance evaluation shows that the proposed m-Health scale has shown excellent reliability as well as internal consistency of the scale, and high inter-rater reliability index. It has also been noticed that the fuzzy based rating scale, as in ACCURATE, matches more closely to the rating performed by experts.

Item Type: Article
Uncontrolled Keywords: m-Health application; Application rating; User reviews; Artificial intelligence; Text mining; m-Health
Divisions: Faculty of Social Sciences
Faculty of Social Sciences > Essex Business School
SWORD Depositor: Elements
Depositing User: Elements
Date Deposited: 20 Dec 2021 16:47
Last Modified: 18 Aug 2022 12:26

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