Evidence map›Paper›PMID 40200558›Full record

SynthesisJournal of clinical nursing2025

Mixed Reality in Nursing Practice: A Mixed Methods Systematic Review.

Chandler H Moser, Changhwan Kim, Bindu Charles, Renilda Tijones, Elsa Sanchez, Jedry G Davila, Hemilla R Matta, Michael J Brenner, Vinciya Pandian, CILDI‐CLABSI Study Team

Abstract readSystematic Review
In one paragraph

Synthesis in Journal of clinical nursing, 2025. 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. Redefining Nursing in Otorhinolaryngology-Head & Neck Care: Implications of the ICN's 2025 Definitions for an Evolving Specialty.ORL-head and neck nursing : official journal of the Society of Otorhinolaryngology and Head-Neck Nurses · 2026
    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

10 authors.

Chandler H MoserNurse Scientist, Center for Nursing Science and Clinical Inquiry, Madigan Army Medical Center, Joint Base Lewis-McChord, Tacoma, WA, USA.
Changhwan KimDoctoral Student, Johns Hopkins School of Nursing, Baltimore, Maryland, USA.
Bindu CharlesPathway to PhD Fellow, Johns Hopkins School of Nursing; Baltimore, Maryland, USA; and Doctoral Student, Sri Ramachandra Institute of Higher Education and Research, Chennai, India.
Renilda TijonesPathway to PhD Fellow, Johns Hopkins School of Nursing, Baltimore, Maryland, USA.
Elsa SanchezPathway to PhD Fellow, Johns Hopkins School of Nursing, Baltimore, Maryland, USA.
Jedry G DavilaPathway to PhD Fellow, Johns Hopkins School of Nursing, Baltimore, Maryland, USA.
Hemilla R MattaEmergency Nurse, Massachusetts General Hospital, Boston, Massachusetts, USA.
Michael J BrennerAssociate Professor, University of Michigan Medical School, Ann Arbor, Michigan, USA.
Vinciya PandianAssociate Dean for Graduate Education and Professor of Nursing; Joint Appointment with Otolaryngology-Head and Neck Surgery, College of Medicine; Executive Director of Center for Immersive Learning and Digital Innovation, Ross and Carol Nese College of Nursing, Penn State University, University Park, Pennsylvania, USA.
CILDI‐CLABSI Study Team

Funding

AHRQ HHS
6 · The paper itself

Abstract

AIM(S): To review the current evidence on mixed reality (MR) applications in nursing practice, focusing on efficiency, ergonomics, satisfaction, competency, and team effectiveness.

designMixed methods systematic review of empirical studies evaluating MR interventions in nursing practice.

methodsThe systematic review adhered to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines and was registered with PROSPERO. Studies were included if they assessed nursing outcomes related to MR interventions. Exclusion criteria encompassed reviews, studies focusing solely on virtual reality, and those involving only nursing students. The Cochrane ROBINS-I, RoB 2, and CASP tools assessed the risk of bias and methodological quality. DATA SOURCES: A comprehensive search of 12 databases (MEDLINE, Embase, CINAHL, Cochrane Library, Web of Science, and others) covered literature published between January 2013 and January 2023.

resultsEight studies met inclusion criteria, exploring diverse MR implementations, including smart glasses and mobile applications, across various nursing specialisations. MR demonstrated potential benefits in efficiency, such as faster task completion and improved accuracy. Satisfaction outcomes were limited but indicated promise. Ergonomic challenges were identified, including discomfort and technical issues. Studies on competency showed mixed results, with some evidence of improved skill acquisition. Team effectiveness and health equity outcomes were underexplored.

conclusionWhile MR shows potential in enhancing nursing practice, evidence is heterogeneous and clinical relevance remains unclear. Further rigorous comparative studies are necessary to establish its utility and address barriers to adoption. IMPLICATIONS FOR THE PROFESSION AND/OR PATIENT CARE: MR technology may enhance nursing efficiency, competency and satisfaction. Addressing ergonomic and technical challenges could optimise adoption and benefit patient care. REPORTING

methodThis review adheres to PRISMA guidelines. PATIENT OR PUBLIC CONTRIBUTION: No Patient or Public Contribution. TRIAL AND PROTOCOL REGISTRATION: PROSPERO registration: #CRD42022324066.

Indexed as

Virtual RealityClinical CompetenceErgonomicsHumansaugmented realitycompetencyergonomicshealthcare technologymixed realitynursing efficiencynursing practicesatisfactionteam effectiveness

Identifiers

PMID40200558
PMCPMC12489439

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.