Evidence map›Paper›PMID 39834839›Full record

ArticleFrontiers in digital health2024

Augmented Reality for extremity hemorrhage training: a usability study.

Krishant Tharun, Alberto Drogo, Carmine Tommaso Recchiuto, Serena Ricci

Abstract read
In one paragraph

Article in Frontiers in digital health, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. Review
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

4 authors.

Krishant TharunDepartment of Informatics, Bioengineering, Robotics and Systems Engineering, University of Genoa, Genoa, Italy.
Alberto DrogoDepartment of Pathophysiology and Transplantation, University of Milan, Milan, Italy.
Carmine Tommaso RecchiutoDepartment of Informatics, Bioengineering, Robotics and Systems Engineering, University of Genoa, Genoa, Italy.
Serena RicciDepartment of Informatics, Bioengineering, Robotics and Systems Engineering, University of Genoa, Genoa, Italy.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: Limb massive hemorrhage is the first cause of potentially preventable death in trauma. Its prompt and proper management is crucial to increase the survival rate. To handle a massive hemorrhage, it is important to train people without medical background, who might be the first responders in an emergency. Among the possible ways to train lay rescuers, healthcare simulation allows to practice in a safe and controlled environment. In particular, immersive technologies such as Virtual Reality (VR) and Augmented Reality (AR) give the possibility to provide real time feedback and present a realistic and engaging scenario, even though they often lack personalization. Methods: This work aims to overcome the above-mentioned limitation, by presenting the design, development and usability test of an AR application to train non-experienced users on the use of antihemorrhagic devices. The application combines a Microsoft Hololens2 headset, with an AR application developed in Unity Game Engine. It includes a training scenario with a multimodal interactive system made of visual and audio cues, that would adapt to user's learning pace and feedback preference. Results: Usability tests on 20 subjects demonstrated that the system is well tolerated in terms of discomfort and workload. Also, the system has been high rated for usability, user experience, immersion and sense of presence. Discussion: These preliminary results suggest that the combination of AR with multimodal cues can be a promising tool to improve hemorrhage management training, particularly for unexperienced users. In the future, the proposed application might increase the number of people who know how to use an anti-hemorrhagic device.

Indexed as

anti-hemorrhage devicesaugmented realityhealthcare simulationhemorrhage managementmassive limb bleedingsimulation-based training

Identifiers

PMID39834839
PMCPMC11743514

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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.