Evidence map›Paper›PMID 40483416›Full record

ArticleBMC emergency medicine2025

Effectiveness of extended reality technologies in cardiopulmonary resuscitation training: a bayesian network meta-analysis.

Xiangmin Li, Xinbo Yin, Guoqing Huang, Xiaokai Wang

Abstract readNetwork Meta-Analysis
PubMed Full text at PMC Publisher
In one paragraph

Article in BMC emergency medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Article
  2. 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

4 authors.

Xiangmin LiEmergency Department, Xiangya Hospital, Central South University, Changsha, Hunan, China.
Xinbo YinEmergency Department, Xiangya Hospital, Central South University, Changsha, Hunan, China.
Guoqing HuangEmergency Department, Xiangya Hospital, Central South University, Changsha, Hunan, China.
Xiaokai WangEmergency Department, Xiangya Hospital, Central South University, Changsha, Hunan, China. xiaokaiwang@csu.edu.cn.

Funding

Peking Union Medical Foundation Ruiyi Emergency Medical Research Fund 22322012016
6 · The paper itself

Abstract

backgroundHigh-quality cardiopulmonary resuscitation (CPR) is critical to cardiac arrest patients. Extended Reality (XR) technologies, including Augmented Reality (AR), Virtual Reality (VR), and Mixed Reality (MR), provide immersive and interactive training, potentially enhancing CPR outcomes. This network meta-analysis compared the effectiveness of XR-based CPR training to traditional face-to-face methods.

methodsA Bayesian network meta-analysis was conducted following PRISMA guidelines. We systematically searched PubMed, Cochrane Library, Web of Science, EMBASE, and CNKI for randomized controlled trials (RCTs) comparing XR-based and traditional CPR training. Primary outcomes included chest compression depth and rate; secondary outcomes assessed full chest wall recoil. The CINeMA tool (GRADE framework) was used to assess evidence quality. Statistical analyses were performed using Stata 15 SE and ADDIS software with random-effects models.

results11 RCTs (1,190 participants) were included. MR showed the improvement in chest compression depth (SMD = 10.96; 95% CI, 0.95 to 20.82) compared to VR and traditional methods. For full chest wall recoil, AR outperformed VR (SMD = 48.57; 95% CI, 19.56 to 79.75) and traditional methods (SMD = 52.95; 95% CI, 25.94 to 80.48). However, no significant differences were observed for chest compression rate. SUCRA rankings placed MR as most effective for compression depth (87.4%) and AR for full chest wall recoil (99.1%). Evidence quality was moderate to high, with minor downgrades for imprecision. No publication bias was detected.

conclusionsXR technologies, particularly MR and AR, significantly improve chest compression depth and full chest wall recoil in comparing with face to face CRP training, offering a flexible and engaging approach to CPR training. Further studies are needed to evaluate long-term skill retention and real-world impact. CLINICAL TRIAL NUMBER: Not applicable.

Indexed as

Augmented RealityCardiopulmonary ResuscitationVirtual RealityBayes TheoremHumansRandomized Controlled Trials as TopicAugmented realityCardiopulmonary resuscitation trainingExtended realityMixed realityVirtual reality

Identifiers

PMID40483416
PMCPMC12145595

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.