Evidence map›Paper›PMID 34970424›Full record

SynthesisJournal of healthcare engineering2021

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

Adel Saeed Alzahrani, Valerie Gay, Ryan Alturki, Mohammad J AlGhamdi

RetractedAbstract readSystematic ReviewRetracted Publication
In one paragraph

Synthesis in Journal of healthcare engineering, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It has been retracted, and should not be counted. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

6 citing papers in PubMed.

  1. Article
  2. Article
  3. Review
  4. Article
  5. Review
  6. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

4 authors.

Adel Saeed AlzahraniSchool of Electrical and Data Engineering, Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney 2007, Australia.ORCID 0000-0002-6569-6278
Valerie GaySchool of Electrical and Data Engineering, Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney 2007, Australia.ORCID 0000-0002-4301-1995
Ryan AlturkiDepartment of Information Science, College of Computer and Information Systems, Umm Al-Qura University, Makkah, Saudi Arabia.ORCID 0000-0002-0967-1885
Mohammad J AlGhamdiDepartment of Information Science, College of Computer and Information Systems, Umm Al-Qura University, Makkah, Saudi Arabia.ORCID 0000-0002-4341-2772

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

Mobile ApplicationsTelemedicineArtificial IntelligenceDelivery of Health CareHumansPrivacy

Identifiers

PMID34970424
PMCPMC8714331

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.