Abstract
Over the last decade, mobile health applications (mHealth App) have evolved exponentially to assess and support our health and well-being. This paper presents an Artificial Intelligence (AI)-enabled mHealth app rating tool, called ACCU3RATE, 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. 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. ACCU3RATE 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 mHealth Apps to monitor and track their health. The performance evaluation shows that the proposed mHealth 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 ACCU3RATE, matches more closely to the rating performed by experts.
Highlights
The world population is rising rapidly which is causing the present healthcare facilities to fall short in meeting the need
This paper presents an Artificial Intelligence (AI)-enabled mHealth app rating tool, called ACCU3RATE, which takes multidimensional measures such as user star rating, user review and features declared by the developer to generate the rating of an app
The optimal functioning of the mHealth app can involve alert/ warning, connectivity via multiple interfaces of a mobile, well documented, stable output, customizable, ACCU3RATE: A Mobile health application rating scale based on user reviews low-cost/free, family-friendly, easy-to-use, recording for viewing recorded data
Summary
The world population is rising rapidly which is causing the present healthcare facilities to fall short in meeting the need. The massive deployment of the information and communication technology (ICT) infrastructure and enormous improvement of tele-density along with availability of relatively inexpensive smart gadgets, mobile-based therapies (called mHealth) have aimed to confront these issues [6, 7] These mHealth apps have facilitated medical research and practice in public health through the usage of mobile devices such as tablets and smartphones [8,9,10]. The mHealth app makes it easier for patients to gain quicker access to medical care, enables real-time and continuous patient monitoring, and facilitates medication intake accuracy to increase patient safety [18] This has been applied to promotes self-management of many chronic illnesses such as diabetes, hypertension and cardiovascular disease [19,20,21,22]. Mobile health applications (mHealth App) have evolved exponentially to assess and support our health and well-being
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