DOI: 10.5061/dryad.jdfn2z3bf
title: Factors used to Influence Mobile Health Application Rating

description: 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.
description: 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).
description: 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.