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

Table 1

Details of articles investigating the usability of AI-enabled mobile apps published between 2015 and March 2021.

Studies (years)	Usability attributes	Summary
[15–19] (2015)	Efficiency	These studies illustrate the validity and efficiency of a healthcare mobile app assessment method. The assessment methods developed and tested are suitable and widely available tools for measuring the reliability and utility of healthcare smartphone applications
	Attractiveness
	Learnability
	Operability
	Satisfaction
[20–26] (2016)	Size	In these studies, eHealth applications are developed for diabetes, weight loss, HIV, and CVD. They examine factors that influence the usability of mobile applications to determine which mobile machines are excellent and which usability characteristics are highly significant
	Visibility
	Comprehensibility of buttons and symbols
[10, 27–35] (2017-2018)	Learnability	In these articles, the learnability and efficiency of Challenger app, fitness app, mobile learning apps, the MoomMae app, and new mobile apps are discussed
	Efficiency
[36–42] (2019)	User star ratings	These studies look at characteristics of common mobile health apps and methods such as iterative convergent and hierarchical usability methods. Moreover, usability of different mobile health apps was also examined
	Privacy policy
	Ability to delete data
	Costs
[43–47] (2020)	Ease of use	These studies compared content with the targeted users to assess ease of use and acceptability of mobile health apps using a rating scale
	Acceptability
[48–52] (2021)	Efficiency	These articles examined efficiency and offered developers some guidance on the consumer criteria that must be addressed when creating cardiopulmonary resuscitation (CPR) support applications by assessing the CprPrototype app and discussed how different authentication structures are meant to enhance security
	Learning
	Satisfaction