Evidence map›Paper›PMID 31406943›Full record

ArticleHeliyon2019

Feasibility of an augmented reality cardiopulmonary resuscitation training system for health care providers.

Steve Balian, Shaun K McGovern, Benjamin S Abella, Audrey L Blewer, Marion Leary

Abstract read
In one paragraph

Article in Heliyon, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
38citing papers in PubMed, 2 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

38 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Trial
  4. Article
  5. Article
  6. Extended reality in cardiovascular care: a systematic review.European heart journal. Digital health · 2025
    Review
  7. Article
  8. Review
  9. Article
  10. Review
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  17. Readiness of Bystander Cardiopulmonary Resuscitation (BCPR) during the COVID-19 Pandemic: A Review.International journal of environmental research and public health · 2022
    Review
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  19. 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

5 authors.

Steve BalianCenter for Resuscitation Science and Department of Emergency Medicine, University of Pennsylvania, Philadelphia, PA, USA.
Shaun K McGovernCenter for Resuscitation Science and Department of Emergency Medicine, University of Pennsylvania, Philadelphia, PA, USA.
Benjamin S AbellaCenter for Resuscitation Science and Department of Emergency Medicine, University of Pennsylvania, Philadelphia, PA, USA.
Audrey L BlewerDepartment of Family Medicine and Community Health, Duke University, Durham, NC, USA.
Marion LearyCenter for Resuscitation Science and Department of Emergency Medicine, University of Pennsylvania, Philadelphia, PA, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

aim of the studyAugmented reality (AR) has the potential to offer a novel approach to CPR training that supplements conventional training methods with gamification and a more interactive learning experience. This is done through computer-generated imagery superimposed on users' view of the real environment to simulate interactive training scenarios. We sought to test the feasibility of an AR CPR training system (CPReality) for health care providers (HCPs).

methodsIn this feasibility trial, a CPR training manikin was integrated with a commercial AR device (Microsoft HoloLens) to provide participants with real-time audio-visual feedback via a holographic overlay of blood flow to vital organs dependent on CC quality. In this system, higher quality CC visually improved virtual blood circulation. HCPs performed a 2-minute cycle of hands-only CPR using only the AR system, and CC parameters were recorded. Descriptive data on participants' demographics, CC quality, and satisfaction with the training environment were reported using quantitative and qualitative analysis.

resultsBetween 10/2018-11/2018, we enrolled a convenience sample of 51 HCPs. The median age of participants was 31 years (IQR 27-41), 71% (36/51) were female, and 67% (34/51) were registered nurses. CC rates (mean 126 ± 12.9 cpm), depths (median 53 mm, IQR 46-58), and percent with complete recoil (median 80%, IQR 12-100) were consistent with guideline recommendations for good quality CPR. Participants were predominantly satisfied with the system, with 82% perceiving the experience as realistic, 98% recognizing the visualizations as helpful for training, and 94% willing to use the application in future CPR training.

conclusionsAs AR is increasingly applied in the healthcare setting, integration in CPR training offers a novel and promising educational approach. In this convenience sample of trained HCPs, high quality CC delivery was feasible using the AR CPR training system which was received favorably by most participants.

Indexed as

Augmented realityCardiac arrestCardiologyCardiopulmonary resuscitationComputer scienceEmergency medicineHealth professionSimulation

Identifiers

PMID31406943
PMCPMC6684477

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.