Aprendizaje combinado frente a realidad virtual y métodos tradicionales en la formación en reanimación cardiopulmonar: un ensayo aleatorizado en estudiantes universitarios
Open accessAbstract
OBJECTIVE: Out-of-hospital cardiac arrest is the leading cause of sudden death. Basic Life Support (BLS) training is an essential competency in Nursing. Active learning methodologies such as blended learning may improve educational outcomes. DESIGN: A randomized controlled trial with four parallel groups (n=160). PARTICIPANTS: was conducted among first-year undergraduate nursing students. Site: At a university in the Region of Murcia, Spain. INTERVENTIONS: Four methodologies were compared: traditional training, virtual reality (VR), blended learning, and a control group with no intervention. Practical competence was assessed using an 8-item checklist covering key steps in the chain of survival. Technical parameters were measured using Laerdal QCPR® manikins, including percentage of high-quality CPR, compression depth, rate, and full chest recoil. ANOVA, chi-square tests, and correlation models were applied (p<0.05). RESULTS: Practical competence differed significantly between groups (p<0.001). MAIN MEASUREMENTS: The blended and traditional groups achieved the highest scores (6.6±1.7 and 6.6±1.5, respectively), followed by the VR group (6.3±1.5) and the control group (3.1±1.5). Mean CPR quality also differed significantly (p=0.0045), with the blended group showing the highest percentage (72.3%), followed by traditional (65.4%), VR (63.4%), and control (46.9%). Regarding compression depth (p=0.0011), the blended and VR groups achieved similar results (6.1cm), outperforming the traditional (5.5cm) and control (4.8cm) groups. Perceived usability of the VR system was high (78/100). CONCLUSIONS: Blended learning showed overall better performance in most variables analyzed, with higher CPR quality and equal or superior results in practical competence and compression depth compared to the traditional and immersive models. These findings support its effectiveness as a teaching strategy for acquiring BLS competencies.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Comparative evidence on technology-enhanced BLS instruction is inconsistent.
“Comparative evidence, however, is inconsistent: self-directed learning has yielded skill performance comparable to traditional instruction (16); virtual reality has shown no clear superiority over conventional simulation, despite greater cognitive engagement (13); and blended learning integrating online and in-person components may deliver superior overall CPR quality (17).”
From Application effect of Miller’s pyramid teaching method based on HeartCode BLS in basic life support training for nursing students (2026) · cites this paper as [17]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
No references could be resolved yet.
Research works printed but not linked: 24
- [1]Berdowski J. Berg R.A. Tijssen J.G. Koster R.W. Global incidences of out-of-hospital cardiac arrest and survival rates: Systematic review of 67 prospective studies Resuscitation. 81 2010 1479 1487 20828914
- [2]Grasner J.-T. Herlitz J. Tjelmeland I.B.M. Wnent J. Masterson S. Lilja G. European Resuscitation Council Guidelines 2021: Epidemiology of cardiac arrest in Europe Resuscitation 161 2021 61 79 33773833
- [3]Greif R. Lockey A. Breckwoldt J. Carmona F. Conaghan P. Kuzovlev A. European Resuscitation Council Guidelines 2021: Education Resuscitation. 161 2021 388 407 33773831
- [4]Elgohary M. Palazzo F.S. Breckwoldt J. Cheng A. Pellegrino J. Schnaubelt S. Blended learning for accredited life support courses - A systematic review Resusc Plus. 10 2022 100240 10.1016/j.resplu.2022.100240 PMID: 35592876; PMCID: PMC9112020 35592876 PMC9112020 · DOI
- [5]Vallée A. Blacher J. Cariou A. Sorbets E. Blended learning compared to traditional learning in medical education: Systematic review and meta-analysis J Med Internet Res. 22 2020 e16504 PMC7445617 32773378
- [6]Liu Q. Peng W. Zhang F. Hu R. Li Y. Yan W. The effectiveness of blended learning in health professions: Systematic review and meta-analysis J Med Internet Res. 18 2016 e2 26729058 PMC4717286
- [8]Flaschenberger I. Lee D. Palmer E. Immersive Opportunities: A systematic review of virtual reality in the classroom Barbieri W. Palmer E. Risks and Opportunities in Using Educational Technologies. 2025 SpringerBriefs in Education 87 107
- [9]Finn Y. Smyth S. Twelve tips for integrating Virtual Reality Simulation into Health Professions Curricula MedEdPublish. 14 2024 233 39554244 PMC11568370
- [11]Cheng A. Nadkarni V.M. Mancini M.B. Hunt E. Sinz E. Merchant R. Circulation. 138 2018 e82 e122 29930020
- [12]ILCOR. Education, Implementation and Teams 2020 International Consensus on Cardiopulmonary Resuscitation and Emergency Cardiovascular Care Science with Treatment Recommendations Resuscitation 156 2020 A78 A106 33098918
- [13]Schulz K.F. Altman D.G. Moher D. CONSORT Group CONSORT 2010 Statement: Updated guidelines for reporting parallel group randomised trials BMJ. 340 2010 c332 20332509 PMC2844940
- [15]Schroeder D.C. Semeraro F. Greif R. Bray J. Morley P. Parr M. KIDS SAVE LIVES: Basic Life Support Education for Schoolchildren: A Narrative Review and Scientific Statement From the International Liaison Committee on Resuscitation Resuscitation. 188 2023 109772 10.1016/j.resuscitation.2023.109772 Epub 2023 May 17. PMID: 37190748 · DOI
- [16]Kim K.J. Kim G. Effectiveness of e-learning in medical education: a systematic review Korean J Med Educ. 31 2019 19 27
- [17]Means B. Toyama Y. Murphy R. Bakia M. Jones K. Evaluation of Evidence-Based Practices in Online Learning: A Meta-Analysis and Review of Online Learning Studies US Department of Education 2010
- [18]Hew K.F. Lo C.K. Flipped classroom improves student learning in health professions education: a meta-analysis BMC Med Educ. 18 2018 38 29544495 PMC5855972
- [19]Nishiyama C. Iwami T. Murakami Y. Kitamura T. Okamoto Y. Marukawa S. Effectiveness of a simplified chest compression–only CPR training program for children Resuscitation. 90 2015 56 60 25724354
- [26]Semeraro F. Greif R. Böttiger B. Burkart R. Cimpoesu D. Georgiou M. Resuscitation. 161 2021 80 97 33773834
- [27]Semeraro F. Imbriaco G. Del Giudice D. Antognoli M. Celin D. Cuttitta M. Empowering the next Generation: An innovative “Kids Save Lives” blended learning programme for schoolchildren training Resuscitation. 194 2024 110088 38101506
- [28]Cheng A. Lockey A. Bhanji F. Lin Y. Hunt E.A. Land E. The use of high-fidelity simulation for training in pediatric resuscitation: A systematic review and meta-analysis Resuscitation. 93 2015 142 149 25888241
- [30]Gaba D.M. The future vision of simulation in healthcare Simul Healthc. 2 2007 126 135 19088617
- [32]Issenberg S.B. McGaghie W.C. Petrusa E.R. Gordon D.L. Scalese R.J. Features and uses of high-fidelity medical simulations that lead to effective learning: a BEME systematic review Med Teach. 27 2005 10 28 16147767
- [33]Rowe M. Frantz J. Bozalek V. The role of blended learning in health professional education: an integrative review Med Teach. 34 2012 e216 e221 10.3109/0142159X. 2012.642831 22455712 · DOI
- [34]Li M. Hong Y. Wu A. Ou W. Yang J. Lin G. The effectiveness of blended learning in nursing and medical education: An umbrella review Nurse Educ Pract. 86 2025 104421 40483931
- [35]Makransky G. Mayer R.E. Benefits of immersive virtual reality in learning environments: a meta-analysis Educ Psychol Rev. 34 2022 2019 2048 PMC9023331 35475019
Cited by
Sorted by citations
Xu, W., Cheng, F., Li, Z., & Wang, Y. (2026) · Frontiers in Medicine · doi:10.3389/fmed.2026.1863479
- Authors
- Ocampo Cervantes, A. B., Greif, R., Castro Delgado, R., López López, C. A., Carrión García, E., & Pardo Ríos, M.
- Affiliations
- University of Bern (CH)
- Venue
- Atención Primaria
- Publisher
- Elsevier BV
- Published
- 2026-02
- Volume
- 58
- Issue
- 2
- Pages
- 103413
- ISSN
- 0212-6567 (ISSN-L)
- PMC
- PMC12811162
- OpenAlex
- W7118185856
- Cited by
- 2
- References
- 0
- Citations by year
- 2026: 2
- FWCI
- 13.43
- Type
- journal article
- Language
- es
- Open access
- gold
- doi.org
- Licence
- Locations
- Atención Primaria (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- PubMed Central (publishedVersion, cc-by-nc-nd)
- Funders
- Gobierno de Espana Ministerio de Ciencia e Innovacion
- Topics
- Health Sciences › Medicine › Emergency Medicine › Cardiac Arrest and Resuscitation
- Health Sciences › Medicine › Physiology › Simulation-Based Education in Healthcare
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Innovations in Medical Education
- Keywords
- CPR training, basic life support, virtual reality, blended learning, nursing education, out-of-hospital cardiac arrest, CPR quality, compression depth, chain of survival, practical skills, undergraduate nursing students
- MeSH terms
- Virtual Reality, Adult, Education, Nursing, Female, Humans, Male, Students, Nursing, Universities, Cardiopulmonary Resuscitation, Young Adult
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review