Evidence map›Paper›PMID 39180710›Full record

ReviewJournal of medical systems2024

Mobile Apps for Wound Assessment and Monitoring: Limitations, Advancements and Opportunities.

Muhammad Ashad Kabir, Sabiha Samad, Fahmida Ahmed, Samsun Naher, Jill Featherston, Craig Laird, Sayed Ahmed

Abstract readReview
In one paragraph

Review in Journal of medical systems, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed, 1 pooled it
–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

9 citing papers in PubMed, 1 synthesis or guideline pooled it.

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

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Muhammad Ashad KabirSchool of Computing, Mathematics and Engineering, Charles Sturt University, Bathurst, 2795, NSW, Australia. akabir@csu.edu.au.
Sabiha Samad *Department of Computer Science and Engineering, Chittagong University of Engineering and Technology, Chattogram, 4349, Chattogram, Bangladesh.
Fahmida Ahmed *Department of Computer Science and Engineering, Chittagong University of Engineering and Technology, Chattogram, 4349, Chattogram, Bangladesh.
Samsun Naher *Department of Computer Science and Engineering, Chittagong University of Engineering and Technology, Chattogram, 4349, Chattogram, Bangladesh.
Jill FeatherstonSchool of Medicine, Cardiff University, Cardiff, CF14 4YS, Wales, United Kingdom.
Craig LairdPrincipal Pedorthist, Walk Easy Pedorthics Pty. Ltd., Tamworth, 2340, NSW, Australia.
Sayed AhmedPrincipal Pedorthist, Foot Balance Technology Pty Ltd, Westmead, 2145, NSW, Australia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

With the proliferation of wound assessment apps across various app stores and the increasing integration of artificial intelligence (AI) in healthcare apps, there is a growing need for a comprehensive evaluation system. Current apps lack sufficient evidence-based reliability, prompting the necessity for a systematic assessment. The objectives of this study are to evaluate the wound assessment and monitoring apps, identify limitations, and outline opportunities for future app development. An electronic search across two major app stores (Google Play store, and Apple App Store) was conducted and the selected apps were rated by three independent raters. A total of 170 apps were discovered, and 10 were selected for review based on a set of inclusion and exclusion criteria. By modifying existing scales, an app rating scale for wound assessment apps is created and used to evaluate the selected ten apps. Our rating scale evaluates apps' functionality and software quality characteristics. Most apps in the app stores, according to our evaluation, do not meet the overall requirements for wound monitoring and assessment. All the apps that we reviewed are focused on practitioners and doctors. According to our evaluation, the app ImitoWound got the highest mean score of 4.24. But this app has 7 criteria among our 11 functionalities criteria. Finally, we have recommended future opportunities to leverage advanced techniques, particularly those involving artificial intelligence, to enhance the functionality and efficacy of wound assessment apps. This research serves as a valuable resource for future developers and researchers seeking to enhance the design of wound assessment-based applications, encompassing improvements in both software quality and functionality.

Indexed as

Artificial IntelligenceMobile ApplicationsHumansMonitoring, PhysiologicReproducibility of ResultsWounds and InjuriesAppsArtificial intelligenceMobileSmartphoneUlcerWound

Identifiers

PMID39180710
PMCPMC11344716

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.