Evidence map›Paper›PMID 42205602›Full record

ArticleJMIR XR and spatial computing2024

Enhancing Mixed Reality Simulation Training Technology With Real-Time Performance Visualization: Mixed Methods Study With Medical First Responders.

Olivia Zechner, Helmut Schrom-Feiertag, Rafael Wespi, Daniele Pretolesi, Quynh Nguyen, Manfred Tscheligi

Abstract read
In one paragraph

Article in JMIR XR and spatial computing, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
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

6 authors.

Olivia ZechnerCenter for Technology Experience, Austrian Institute of Technology, Giefinggasse 4, Vienna, 1221, Austria, 43 505504506.ORCID https://orcid.org/0000-0001-9673-3716
Helmut Schrom-FeiertagCenter for Technology Experience, Austrian Institute of Technology, Giefinggasse 4, Vienna, 1221, Austria, 43 505504506.ORCID https://orcid.org/0000-0002-5327-2494
Rafael WespiDepartment of Emergency Medicine, Inselspital, University of Bern, Bern, Switzerland.ORCID https://orcid.org/0000-0002-3416-3938
Daniele PretolesiCenter for Technology Experience, Austrian Institute of Technology, Giefinggasse 4, Vienna, 1221, Austria, 43 505504506.ORCID https://orcid.org/0000-0001-9075-0187
Quynh NguyenCenter for Technology Experience, Austrian Institute of Technology, Giefinggasse 4, Vienna, 1221, Austria, 43 505504506.ORCID https://orcid.org/0000-0002-9231-2575
Manfred TscheligiDepartment for Artificial Intelligence and Human Interfaces, University of Salzburg, Salzburg, Austria.ORCID https://orcid.org/0000-0001-6056-7285

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Mixed reality (MR) simulation training is emerging in paramedical education as a way to practice responding to stress-intensive scenarios like mass casualty incidents in a safe and controlled environment. Current training platforms, however, lack real-time stress and human performance monitoring tools. Objective: The study aims to enhance MR training for medical first responders through real-time evaluation of performance and stress levels, leveraging biosignal monitoring and advanced analytics to allow instructors to tailor feedback and maintain optimal challenge and safety levels. Methods: The study includes a structured, multiphase approach including initial requirement gathering (structured interviews and cocreation workshops), an online design survey, iterative prototype development, and a field trial (including training observations and interviews). Data were collected from 5 end user consortium members across Europe. Quantitative data from checklists were analyzed using frequencies and percentages to understand feature usage and event occurrences. Qualitative data from semistructured interviews and cocreation workshops were transcribed, coded, and subjected to thematic analysis to identify patterns and insights into the usability and effectiveness of the enhanced features in the MR training. Results: The study identified a number of requirements that medical first responders have for an MR training system, including requirements not included in currently available solutions. A total of 80 performance metrics were initially identified and refined to a set of 54 metrics, which were categorized into key performance indicator groups such as scene safety, triage performance, and communication. Requirements for smart wearables to monitor stress levels are provided and highlight the importance of a user-centered design process to provide users with effective tools that fit their needs. Stress visualization preferences are described in the form of a dashboard as well as in virtual environments surrounding the avatar. Using an iterative design process and user feedback, a training system was developed, integrating real-time performance tracking and stress monitoring. The field trial provided insights into the practical use of these features during a real training exercise, showed interaction preferences between trainer and trainees, and highlighted further improvement opportunities. Conclusions: This research enhances MR training for paramedics by integrating real-time performance metrics and stress indicators based on a human-centered design approach that aligns with end user needs, thereby laying the foundation for developing more effective and immersive training solutions for high-stress professions.

Indexed as

human-centered designhuman performanceimmersive technologiesmedical first respondersmixed realityparamedicsimulationsimulation trainingstressstress monitoring

Identifiers

PMID42205602
PMCPMC13202515

What OpenQuestion holds

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LicenceCC BY
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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.