In:
PLOS ONE, Public Library of Science (PLoS), Vol. 16, No. 12 ( 2021-12-16), p. e0258050-
Abstract:
Over the last decade, mobile health applications (mHealth 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 ACCU 3 RATE, 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 ACCU 3 RATE 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 ACCU 3 RATE, matches more closely to the rating performed by experts.
Type of Medium:
Online Resource
ISSN:
1932-6203
DOI:
10.1371/journal.pone.0258050
DOI:
10.1371/journal.pone.0258050.g001
DOI:
10.1371/journal.pone.0258050.g002
DOI:
10.1371/journal.pone.0258050.g003
DOI:
10.1371/journal.pone.0258050.g004
DOI:
10.1371/journal.pone.0258050.g005
DOI:
10.1371/journal.pone.0258050.g006
DOI:
10.1371/journal.pone.0258050.g007
DOI:
10.1371/journal.pone.0258050.g008
DOI:
10.1371/journal.pone.0258050.g009
DOI:
10.1371/journal.pone.0258050.g010
DOI:
10.1371/journal.pone.0258050.g011
DOI:
10.1371/journal.pone.0258050.t001
DOI:
10.1371/journal.pone.0258050.s001
DOI:
10.1371/journal.pone.0258050.s002
DOI:
10.1371/journal.pone.0258050.s003
DOI:
10.1371/journal.pone.0258050.s004
DOI:
10.1371/journal.pone.0258050.r001
DOI:
10.1371/journal.pone.0258050.r002
DOI:
10.1371/journal.pone.0258050.r003
DOI:
10.1371/journal.pone.0258050.r004
DOI:
10.1371/journal.pone.0258050.r005
DOI:
10.1371/journal.pone.0258050.r006
DOI:
10.1371/journal.pone.0258050.r007
Language:
English
Publisher:
Public Library of Science (PLoS)
Publication Date:
2021
detail.hit.zdb_id:
2267670-3
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