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      Towards Understanding the Usability Attributes of AI-Enabled eHealth Mobile Applications

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          Abstract

          Mobile application (app) use is increasingly becoming an essential part of our daily lives. Due to their significant usefulness, people rely on them to perform multiple tasks seamlessly in almost all aspects of everyday life. Similarly, there has been immense progress in artificial intelligence (AI) technology, especially deep learning, computer vision, natural language processing, and robotics. These technologies are now actively being implemented in smartphone apps and healthcare, providing multiple healthcare services. However, several factors affect the usefulness of mobile healthcare apps, and usability is an important one. There are various healthcare apps developed for each specific task, and the success of these apps depends on their performance. This study presents a systematic review of the existing apps and discusses their usability attributes. It highlights the usability models, outlines, and guidelines proposed in previous research for designing apps with improved usability characteristics. Thirty-nine research articles were reviewed and examined to identify the usability attributes, framework, and app design conducted. The results showed that satisfaction, efficiency, and learnability are the most important usability attributes to consider when designing eHealth mobile apps. Surprisingly, other significant attributes for healthcare apps, such as privacy and security, were not among the most indicated attributes in the studies.

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          The potential for artificial intelligence in healthcare

          The complexity and rise of data in healthcare means that artificial intelligence (AI) will increasingly be applied within the field. Several types of AI are already being employed by payers and providers of care, and life sciences companies. The key categories of applications involve diagnosis and treatment recommendations, patient engagement and adherence, and administrative activities. Although there are many instances in which AI can perform healthcare tasks as well or better than humans, implementation factors will prevent large-scale automation of healthcare professional jobs for a considerable period. Ethical issues in the application of AI to healthcare are also discussed.
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            The mHealth App Usability Questionnaire (MAUQ): Development and Validation Study

            Background After a mobile health (mHealth) app is created, an important step is to evaluate the usability of the app before it is released to the public. There are multiple ways of conducting a usability study, one of which is collecting target users’ feedback with a usability questionnaire. Different groups have used different questionnaires for mHealth app usability evaluation: The commonly used questionnaires are the System Usability Scale (SUS) and Post-Study System Usability Questionnaire (PSSUQ). However, the SUS and PSSUQ were not designed to evaluate the usability of mHealth apps. Self-written questionnaires are also commonly used for evaluation of mHealth app usability but they have not been validated. Objective The goal of this project was to develop and validate a new mHealth app usability questionnaire. Methods An mHealth app usability questionnaire (MAUQ) was designed by the research team based on a number of existing questionnaires used in previous mobile app usability studies, especially the well-validated questionnaires. MAUQ, SUS, and PSSUQ were then used to evaluate the usability of two mHealth apps: an interactive mHealth app and a standalone mHealth app. The reliability and validity of the new questionnaire were evaluated. The correlation coefficients among MAUQ, SUS, and PSSUQ were calculated. Results In this study, 128 study participants provided responses to the questionnaire statements. Psychometric analysis indicated that the MAUQ has three subscales and their internal consistency reliability is high. The relevant subscales correlated well with the subscales of the PSSUQ. The overall scale also strongly correlated with the PSSUQ and SUS. Four versions of the MAUQ were created in relation to the type of app (interactive or standalone) and target user of the app (patient or provider). A website has been created to make it convenient for mHealth app developers to use this new questionnaire in order to assess the usability of their mHealth apps. Conclusions The newly created mHealth app usability questionnaire—MAUQ—has the reliability and validity required to assess mHealth app usability.
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              Empirical studies on usability of mHealth apps: a systematic literature review.

              The release of smartphones and tablets, which offer more advanced communication and computing capabilities, has led to the strong emergence of mHealth on the market. mHealth systems are being used to improve patients' lives and their health, in addition to facilitating communication between doctors and patients. Researchers are now proposing mHealth applications for many health conditions such as dementia, autism, dysarthria, Parkinson's disease, and so on. Usability becomes a key factor in the adoption of these applications, which are often used by people who have problems when using mobile devices and who have a limited experience of technology. The aim of this paper is to investigate the empirical usability evaluation processes described in a total of 22 selected studies related to mHealth applications by means of a Systematic Literature Review. Our results show that the empirical evaluation methods employed as regards usability could be improved by the adoption of automated mechanisms. The evaluation processes should also be revised to combine more than one method. This paper will help researchers and developers to create more usable applications. Our study demonstrates the importance of adapting health applications to users' need.
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                Author and article information

                Contributors
                Journal
                J Healthc Eng
                J Healthc Eng
                JHE
                Journal of Healthcare Engineering
                Hindawi
                2040-2295
                2040-2309
                2021
                21 December 2021
                : 2021
                : 5313027
                Affiliations
                1School of Electrical and Data Engineering, Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney 2007, Australia
                2Department of Information Science, College of Computer and Information Systems, Umm Al-Qura University, Makkah, Saudi Arabia
                Author notes

                Academic Editor: Ayush Dogra

                Author information
                https://orcid.org/0000-0002-6569-6278
                https://orcid.org/0000-0002-4301-1995
                https://orcid.org/0000-0002-0967-1885
                https://orcid.org/0000-0002-4341-2772
                Article
                10.1155/2021/5313027
                8714331
                34970424
                90330423-6b55-4a2e-b73e-430102072c5c
                Copyright © 2021 Adel Saeed Alzahrani et al.

                This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 4 July 2021
                : 30 July 2021
                : 16 November 2021
                Categories
                Research Article

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