Factors used to Influence Mobile Health Application Rating

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.