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Factors used to Influence Mobile Health Application Rating

Citation

Biswas, Milon et al. (2021), Factors used to Influence Mobile Health Application Rating, Dryad, Dataset, https://doi.org/10.5061/dryad.jdfn2z3bf

Abstract

Over the last five years, mobile health applications (mHealthapp) have evolved exponentially to assess and support our health and well-being. This paper presents an Artificial Intelligence (AI)-enabledmHealth app rating tool which takes multidimensional measures such as starrating, user’s review and features declared by the developer to generate apprating. However, currently, there is very little conceptual understanding onhow users’ reviews affect app rating from a multi-dimensional perspective. This study applies artificial intelligence (AI)-based text mining technique to develop more comprehensive understanding of users’ feedback based on an array of 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’s star rating, user’s 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. Our proposed model concentrates on heart related apps found in the play store and app gallery. The findings indicate the efficacy of the model 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 been also found that the fuzzy based rating has a high variance compared to the conventional app rating whereas the fuzzy based rating shows high relationship in contrast to scoring based on expert opinion.

Methods

The data collection phase started with selecting appropriate apps to provide the proof-of-concept. The apps were searched using the keywords "heart disease", "heart related medical app", "healthy heart", "heart care" in the Google Play Store and Apple store to evaluate the proposed AI-based app rating scale. A total of 317 applications in different categories have been identified based on the search criteria. Each app was initially qualitatively assessed and screened with titles, descriptions, and associated screenshots provided in the Play Store. Out of the total  317 m-Heath apps, 278 are from the Google Play Store while, 39 are from the Apple Store. Then, we set some exclusion criteria for apps, such as duplicate apps, apps not related to heart disease, apps downloaded less than 5000 times, game and simulation apps, apps not updated in more than 12 months, and non-English apps. Consequently, after filtering based on all these criteria, we selected 43 apps (23 apps from Google Play Store and 20 apps from the Apple Store).

Usage Notes

Users’ Rating and Review

Star Rating

Text Mining and Sentiment Analysis

       Step 1: Selection of mHealth apps:

       Step 2: Collection of comments:

       Step 3: Extraction of Keywords:

Calculate approval and certification using questionaries.