Evidence map›Paper›PMID 42205235›Full record

ArticleJMIR XR and spatial computing2026

Perceived Usability, User Experience, and Technology Acceptance of Role-Specific Augmented Reality Decision Support Tools for Cardiac Arrest Resuscitation: Prospective Observational Pilot Study.

Ryan Kang, Adam Cheng, Yiqun Lin, Hyeongil Nam, Jennifer Davidson, Donovan Curtis Duncan, Johan N Siebert, Sergio Manzano, Alexandre De Masi, Ana Rajic and 3 more

Abstract read
In one paragraph

Article in JMIR XR and spatial computing, 2026. 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. 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

13 authors.

Ryan KangDepartment of Electrical and Software Engineering, Schulich School of Engineering, University of Calgary, 2500 University Drive NW, Calgary, AB, T2N 1N4, Canada, 1 403-220-5644.ORCID https://orcid.org/0009-0005-6737-1978
Adam ChengKidSIM Simulation Program, Alberta Children's Hospital, Calgary, AB, Canada.ORCID https://orcid.org/0000-0002-1039-7502
Yiqun LinKidSIM Simulation Program, Alberta Children's Hospital, Calgary, AB, Canada.ORCID https://orcid.org/0000-0002-4726-5992
Hyeongil NamDepartment of Electrical and Software Engineering, Schulich School of Engineering, University of Calgary, 2500 University Drive NW, Calgary, AB, T2N 1N4, Canada, 1 403-220-5644.ORCID https://orcid.org/0000-0002-6017-8869
Jennifer DavidsonKidSIM Simulation Program, Alberta Children's Hospital, Calgary, AB, Canada.ORCID https://orcid.org/0009-0009-9415-0598
Donovan Curtis DuncanKidSIM Simulation Program, Alberta Children's Hospital, Calgary, AB, Canada.ORCID https://orcid.org/0009-0007-5096-1095
Johan N SiebertDepartment of Pediatric Emergency Medicine, Geneva University Hospitals, Geneva, Switzerland.ORCID https://orcid.org/0000-0002-2871-3382
Sergio ManzanoDepartment of Pediatric Emergency Medicine, Geneva University Hospitals, Geneva, Switzerland.ORCID https://orcid.org/0000-0001-9603-5520
Alexandre De MasiFaculty of Medicine, University of Geneva, Geneva, Switzerland.ORCID https://orcid.org/0000-0002-8164-2931
Ana RajicDepartment of Pediatric Emergency Medicine, Geneva University Hospitals, Geneva, Switzerland.ORCID https://orcid.org/0009-0006-1475-8084
Sharleen Kayne OlankaDepartment of Pediatric Emergency Medicine, Geneva University Hospitals, Geneva, Switzerland.ORCID https://orcid.org/0009-0002-9677-0191
Frederic EhrlerFaculty of Medicine, University of Geneva, Geneva, Switzerland.ORCID https://orcid.org/0000-0001-9734-3242
Kangsoo KimDepartment of Electrical and Software Engineering, Schulich School of Engineering, University of Calgary, 2500 University Drive NW, Calgary, AB, T2N 1N4, Canada, 1 403-220-5644.ORCID https://orcid.org/0000-0002-0925-378X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Cardiac arrest is a critical medical emergency that requires strict adherence to clinical guidelines to achieve optimal outcomes. Deviations from these guidelines, often due to task complexity, can adversely affect patient outcomes. Augmented reality (AR) offers a way to deliver role-specific, in-view guidance, but evidence on its perceived usability, user experience, and acceptability in cardiac arrest resuscitation remains limited. Objective: This study aimed to design, develop, and evaluate a role-specific AR decision support system for resuscitation team leaders and medication nurses. In this observational study, we assessed clinicians' perceived usability, user experience, and technology acceptance of the new AR system in a high-fidelity simulated cardiac arrest scenario. Methods: We conducted a prospective observational pilot study using a high-fidelity simulated pediatric cardiac arrest scenario. A total of 10 clinicians were recruited from Alberta Children's Hospital, including 5 (50%) of 10 pediatric emergency physicians serving as team leaders (men: 3/5, 60%, and women: 2/5, 40%; median age 41, IQR: 40-42 y) and 5 (50%) of 10 emergency nurses serving as medication nurses (men: 1/5, 20%, and women: 4/5, 80%; median age 45, IQR: 42-46 y). Participants used role-specific AR decision support interfaces deployed on HoloLens 2 head-mounted displays. Following the simulation, perceived usability, user experience, and technology acceptance were assessed using validated questionnaires: the System Usability Scale, User Experience Questionnaire, and Technology Acceptance Model. Data were collected via postsimulation surveys and analyzed descriptively. Results: Descriptive analyses were performed without inferential statistical testing. The mean System Usability Scale scores were 75.5 (SD 9.25, 95% CI 64.0-87.0) for team leaders and 82.0 (SD 11.20, 95% CI 68.0-96.0) for medication nurses. User experience was positive across roles, with mean User Experience Questionnaire scores indicating favorable attractiveness (team leaders: 1.87, SD 1.14, 95% CI 0.45-3.28; medication nurses: 2.43, SD 0.52, 95% CI 1.79-3.08), pragmatic quality (team leaders: 1.88, SD 0.87, 95% CI 0.80-2.97; medication nurses: 1.80, SD 0.69, 95% CI 0.94-2.66), and hedonic quality (team leaders: 2.40, SD 0.89, 95% CI 1.30-3.50; medication nurses: 2.28, SD 0.69, 95% CI 1.42-3.13). Technology acceptance was high, with mean combined Technology Acceptance Model scores of 5.92 (SD 0.46, 95% CI 5.35-6.49) for team leaders and 6.02 (SD 0.56, 95% CI 5.32-6.71) for medication nurses. Conclusions: This study introduces a novel role-specific AR decision support system that delivers tailored, in-view guidance to resuscitation team leaders and medication nurses during cardiac arrest. Unlike prior cognitive aids that present uniform or device-agnostic information, this system explicitly adapts interface content and structure to distinct clinical roles and workflows. The findings contribute early empirical evidence on the perceived usability, user experience, and acceptability of role-tailored AR support in high-acuity team settings and yield transferable design principles for developing role-aware AR interfaces. In real-world contexts, such systems may support protocol adherence and team coordination during resuscitation training and early-stage clinical deployment, informing future evaluations that incorporate objective performance and workflow outcomes.

Indexed as

augmented realitycardiopulmonary resuscitationclinical guideline adherencedecision support systemsdigital healthsimulation trainingtechnology acceptanceuser-centered designuser-computer interfacewearable electronic devices

Identifiers

PMID42205235
PMCPMC13202500

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.