Computer Science and Engineering, Bangladesh University of Business and Technology, Mirpur, Dhaka, Bangladesh.
Department of Engineering Science, Institute of Biomedical Engineering, University of Oxford, Oxford, United Kingdom.
PLoS One. 2021 Dec 16;16(12):e0258050. doi: 10.1371/journal.pone.0258050. eCollection 2021.
Over the last decade, mobile health applications (mHealth App) have evolved exponentially to assess and support our health and well-being.
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.
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.
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.
在过去的十年中，移动健康应用程序（mHealth App）呈指数级发展，用于评估和支持我们的健康和幸福。
本文提出了一种人工智能（AI）支持的 mHealth 应用程序评级工具，称为 ACCU3RATE，它采用多维措施，如用户星级评分、用户评论和开发者声明的功能，生成应用程序的评分。然而，目前，从多维角度来看，用户评论如何影响应用程序评分的概念理解非常有限。本研究应用基于人工智能的文本挖掘技术，基于几个重要因素，开发更全面的用户反馈理解，确定 mHealth 应用程序的评分。
根据文献，确定了六个影响 mHealth 应用程序评分的变量。这些因素是用户星级评分、用户文本评论、用户界面（UI）设计、功能、安全性和隐私以及临床批准。自然语言工具包用于解释文本并识别应用程序用户的情绪。此外，还考虑了身体残疾用户的可访问性、保护和隐私、UI 设计。此外，如果存在临床批准的详细信息，则从开发者声明中获取。最后，我们使用模糊逻辑融合所有输入，以计算新的应用评分。
ACCU3RATE 专注于在 Play 商店和 App 画廊中发现的与心脏相关的应用程序。研究结果表明，与当前设备评分相比，该方法是有效的。本研究对使用 mHealth 应用程序监测和跟踪健康的应用程序开发人员和消费者都具有重要意义。性能评估表明，所提出的 mHealth 量表具有出色的可靠性和量表的内部一致性，以及较高的评分者间可靠性指数。还注意到，基于模糊的评分量表，如 ACCU3RATE，与专家评分更接近。
