Abstract
Background
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 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.
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
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.
Publisher
Public Library of Science (PLoS)
Reference106 articles.
1. Valuable features in mobile health apps for patients and consumers: content analysis of apps and user ratings;MF Mendiola;JMIR mHealth and uHealth,2015
2. Effect of self-monitoring on long-term patient engagement with mobile health applications;K Lee;PloS one,2018
3. Forecasting major impacts of COVID-19 pandemic on country-driven sectors: challenges, lessons, and future roadmap;S Kumar;Personal and Ubiquitous Computing,2021
4. iWorkSafe: towards healthy workplaces during COVID-19 with an intelligent pHealth App for industrial settings;MS Kaiser;IEEE Access,2021
5. Frost D, Mahmud M. Strengthening Health Systems in Low-Income Countries: A Stakeholder Engagement Framework. EGOV-CeDEM-ePart 2020. 2020; p. 215.
Cited by
67 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献