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. 2025 Apr 8;34(11):4547–4559. doi: 10.1111/jocn.17722

Mixed Reality in Nursing Practice: A Mixed Methods Systematic Review

Chandler H Moser 1, Changhwan Kim 2, Bindu Charles 3, Renilda Tijones 4, Elsa Sanchez 4, Jedry G Davila 4, Hemilla R Matta 5, Michael J Brenner 6, Vinciya Pandian 7,✉; CILDI‐CLABSI Study Team
PMCID: PMC12489439  PMID: 40200558

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.

Design

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

Methods

The 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.

Results

Eight 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.

Conclusion

While 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 Method

This review adheres to PRISMA guidelines.

Patient or Public Contribution

No Patient or Public Contribution.

Trial and Protocol Registration

PROSPERO registration: #CRD42022324066

Keywords: augmented reality, competency, ergonomics, healthcare technology, mixed reality, nursing efficiency, nursing practice, satisfaction, team effectiveness

Summary.

  • Impact
    • ○
      What Problem Did the Study Address?: Limited evidence on the effectiveness of MR technology in nursing practice.
    • ○
      What Were the Main Findings?: MR shows potential in improving efficiency, satisfaction and competency, but faces technical and ergonomic challenges.
    • ○
      Where and on Whom Will the Research Have an Impact?: Practicing nurses, educators and policymakers aim to integrate innovative technologies in healthcare.
  • What does this paper contribute to the wider global clinical community?
    • ○
      Highlights the potential of MR to enhance nursing practice and education.
    • ○
      Identifies key barriers and facilitators to MR implementation in healthcare.
    • ○
      Proposes directions for future research to establish clinical relevance and optimise MR applications.

1. Introduction

Among the most promising developments in digital healthcare technology is the rapidly evolving implementation of mixed reality (MR). MR blends the physical and digital worlds, creating environments where physical and digital objects co‐exist and interact in real time. This integration is achieved by overlaying computer‐generated images, animations, or data onto a user's view of the natural world, often using headsets or smart glasses. Augmented Reality (AR), a subset of MR, overlays digital information—such as images, sounds, or other data—onto the real world, enhancing the user's perception of their environment. In contrast, virtual reality (VR) creates a fully immersive experience separate from the real world. MR is becoming increasingly common in healthcare (Gerup et al. 2020). It transforms healthcare by enhancing training, procedural precision and remote collaboration, allowing for immersive simulation‐based learning, real‐time guidance and enhanced patient engagement.

Integrating MR into nursing education promises to accelerate learning and address the expected shortfall in nurses to care for the growing population of older adults (American Association of Colleges of Nursing 2022). Whereas extensive work has been conducted on VR in nursing, far less attention has been paid to AR and MR. The demographic shift of an aging population, accompanied by lower birth rates (GDB 2021 Fertility and Forecasting Collaborators 2024), requires innovative and scalable approaches to recruiting, training and retaining the next generation of nurses. Accelerated by the COVID‐19 pandemic, modern nursing didactic training is increasingly conducted online to reach a broader student base (Leaver et al. 2022). Current studies highlight VR and MR as potentially powerful adjuncts to traditional nursing education (Liu et al. 2023; Uymaz and Uymaz 2022). As such, training is often delivered in flexible, asynchronous formats, MR is a potential tool for enhancing individual learning or simulating hands‐on tasks remotely (Quqandi et al. 2023). However, implementing MR in distance learning environments presents unique pedagogical and technological challenges and cost barriers (Alzahrani 2020).

Compared with MR integration in nursing education, research into MR applications for nursing practice is relatively sparse. Nurses continuously adopt new technology in their workplaces (Mansour and Nogues 2022) in step with the fourth industrial revolution, marked by rapid advances in biotechnology, information technology and artificial intelligence. This evolution has widespread implications for the future of healthcare. Long‐term vision is essential for implementing advanced technologies; however, a significant barrier to implementing this technology is the reluctance to change within the healthcare system (Ćwiklicki et al. 2020). Resistance to change is an expected reaction (Cheraghi et al. 2023) to which nurses are not immune (Clark 2013; Copnell and Bruni 2006). Furthermore, poor technology user interface design, lack of proper integration into clinical workflow and increased complexity are well‐understood barriers to adopting technology (Gurses et al. 2009).

Despite these challenges, technical advances are essential for modern healthcare systems (Buonocore 2004). Wüller et al. conducted a scoping review of theoretical and actual MR interventions for nurses (2019), which found that much of the literature consisted of prototype evaluation, with only a few empirical studies. They also found grey literature on the topic but few peer‐reviewed journal articles (Wuller et al. 2019). This updated assessment of peer‐reviewed interventional studies aims to familiarise clinicians and researchers with the current state of the technology and future paths for MR in nursing practice.

2. Aim

This study aims to provide a current review of studies investigating the implementation of MR in nursing practice, focusing on nursing efficiency, ergonomics, satisfaction, competency and team effectiveness.

3. Methods

3.1. Eligibility Criteria

This mixed methods systematic review followed the Preferred Reporting Items for Systematic Reviews and Meta‐Analysis (PRISMA) guidelines (Page et al. 2021) and was registered with PROSPERO (registration #CRD42022324066) (Moser et al. 2022). Inclusion criteria included studies that evaluated nurse efficiency, satisfaction, ergonomics, competency and team effectiveness using AR or MR, either alone or combined with other interventions. Secondary outcomes included health equity. Exclusion criteria were reviews, conference proceedings, dissertations, book chapters, letters to the editor, studies focusing solely on virtual reality, studies involving only nursing students, studies without a nursing component, interventions used exclusively by patients for self‐care and studies without full‐text availability.

3.2. Information Sources

The search strategy was developed in collaboration with an informationist and covered literature published between January 2013 and January 2023. Databases searched included MEDLINE, Embase, CINAHL, Cochrane Library, Web of Science, Scopus, APA PsycINFO, Academic Search Premier, ACM Digital Library, AIS (Association of Information Science) e‐library, Compendex (Elsevier Engineering Village) and Elsevier ScienceDirect. The final search dates for each database are detailed in Table S1. This study includes both quantitative and qualitative data to provide important context‐specific information on the complex intersection of nursing and new technology (Noyes et al. 2019). The decision to exclude studies before 2013 was made to include only mature and modern implementations of mixed and augmented reality via headsets and smartphone applications.

3.3. Search Strategy

Search terms and Medical Subject Headings (MeSH) focused on AR/MR and their applications in healthcare, emphasising nursing‐related outcomes. Boolean operators (AND/OR) refined the search, with filters applied to limit results to English‐language studies published within the last decade. Detailed search terms are provided in Table S1.

3.4. Selection Process

The Covidence software program was used to manage the article selection process. Two independent reviewers screened titles and abstracts for relevance, with full‐text reviews conducted for potentially eligible studies. Disagreements were resolved by a third reviewer (VP). Studies meeting the inclusion criteria proceeded to data extraction.

3.5. Data Collection Process

Data were extracted using Covidence software and a standardised template hosted on Qualtrics. Two reviewers independently extracted data, with discrepancies resolved by a third reviewer. Data included study characteristics (country study was conducted, design, population, sample size and purpose of the study), intervention characteristics (type of device, description of intervention and function) and outcomes. Barriers and facilitators to implementing the intervention were recorded. Missing data or unclear information were resolved through reviewer consensus or by consulting the original study.

3.6. Study Risk of Bias Assessment

The Cochrane ROBINS‐I instrument for risk of bias in non‐randomised studies was used to evaluate non‐randomised studies in the following domains: confounding, selection of participants, classification of interventions, deviations from intended interventions, missing data, measurement of outcomes and selection of the reported result. Response options for bias with the ROBINS‐I instrument were low, moderate, severe or critical (Sterne et al. 2019).

We used the revised Cochrane RoB 2 instrument for risk of bias in randomised trials to appraise the quality of randomised controlled trials' study design and extent of potential bias by considering the following domains: randomisation, deviations from the intended intervention, missing data, outcome measurement and selection of the reported result (Sterne et al. 2019). Response options for the RoB 2 instrument were low, some concerns, or high.

Finally, one qualitative study was assessed using the Critical Appraisal Skills Programme (CASP) checklist (Critical Appraisal Skills Programme 2024). This CASP tool provided a framework for our team to judge the methodological rigour and validity of the results. Four reviewers independently used these criteria to appraise quality. An author (CK) settled disputes about the study quality that could not be arbitrated by consensus.

3.7. Effect Measures

Quantitative outcomes were reported as mean differences and standard deviations for interval data and counts and percentages for categorical variables. For qualitative data, themes were presented.

3.8. Synthesis Methods

A narrative summary was used to describe study designs, participant characteristics and outcome measures. The results of quantitative studies were examined for changes before and after AR/MR intervention. We reported percent and absolute changes by the experimental group (AR/MR versus standard practice). If any of the investigation's outcomes were similar enough to warrant a comparison between two or more studies, our team aggregated the percent change in the outcome alongside a narrative summary. We planned to conduct a random‐effects meta‐analysis when a test of heterogeneity was significant, and there were three or more studies of similar methodology to conduct the analysis. We conducted a narrative summary of any qualitative results. The mixed methods synthesis follows a parallel‐convergent design in which quantitative and qualitative results are combined in the Discussion section (Hong et al. 2017).

3.9. Reporting Bias Assessment

Potential reporting biases were assessed through a comparison of published outcomes against prespecified study protocols, where available, and evaluation of incomplete reporting within the studies.

3.10. Certainty Assessment

The Grading of Recommendations, Assessment, Development and Evaluations (GRADE) approach was used to assess the certainty of evidence across outcomes, incorporating the risk of bias, consistency and precision of effect estimates.

4. Results

4.1. Study Selection

Electronic database searches yielded 1805 potentially relevant titles. After removing 431 duplicates via Covidence software and excluding 1021 irrelevant articles based on the screening of titles and abstracts, 353 articles were considered for full‐text review. Eight studies met the predefined inclusion and exclusion criteria and were included in this review (Figure 1).

FIGURE 1.

FIGURE 1

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PRISMA flow chart. [Colour figure can be viewed at wileyonlinelibrary.com]

4.2. Study Characteristics

Table 1 summarises the characteristics of the eight studies included in this review. Publication years ranged from 2017 to 2021, with more publications in recent years. Most papers were from 2019 to 2021. Among these, a plurality of studies were conducted in the USA (Dias et al. 2021; Hanson et al. 2020; Kaylor et al. 2019; Leary et al. 2020). The review comprised three randomised studies (Dias et al. 2021; Fumagalli et al. 2017; Leary et al. 2020), three quasi‐experimental studies (Hanson et al. 2020; Herath et al. 2021; Klinker et al. 2020), one interrater reliability study (Kaylor et al. 2019) and one qualitative study (Romare et al. 2021). The review examined nurses from diverse backgrounds, including those specialising in wound care/management (Kaylor et al. 2019; Klinker et al. 2020), adult critical care (Fumagalli et al. 2017), neonatal critical care (Dias et al. 2021) and nursing anaesthesia (Romare et al. 2021). Other nurses studied were involved in interdisciplinary teams during hospital floor crash code drills (Hanson et al. 2020), including paediatric settings (Leary et al. 2020). There was a range of experience and expertise observed among the participants, spanning from advanced practice nurses exemplified by nurse anaesthetists (Romare et al. 2021) to individuals lacking formal training, such as untrained adult volunteers performing nursing tasks (Herath et al. 2021). Furthermore, the review acknowledged the presence of novice nurses (Dias et al. 2021; Fumagalli et al. 2017) within the studied cohorts. Due to the high heterogeneity in interventions and outcome measures, our team deemed meta‐analysis inappropriate.

TABLE 1.

Study characteristics.

Author, year Country Design Sample (n) Purpose
Fumagalli et al. (2017) Italy Randomised pilot

Critically ill older adults (n = 103)

Nurses (n = 3)

 Assess near‐infrared device for venipuncture safety/efficacy
Kaylor et al. (2019) United States Reliability Bedside nurse + wound nurse; 16 patients; wounds (n = 21) Test AR glasses telehealth technology for wound assessment
Klinker et al. (2020) Germany Quasi‐experimental Wound nurses (n = 45) Validate AR design for wound measurement and documentation tasks
Leary et al. (2020) United States Randomised pilot CPR‐trained health care providers, primarily nurses (n = 100) Assess AR device for CPR performance
Dias et al. (2021) United States Randomised controlled trial Novice neonatal intensive care unit nurses (n = 45) Compare intubation instruction methods
Hanson et al. (2020) United States Quasi‐experimental

Registered nurses (n = 22)

Physicians (n = 34)

 Test AR intervention for paediatric code cart education
Herath et al. (2021) Australia Pilot Untrained adult volunteers (n = 5) Evaluate AR for venipuncture tourniquet use
Romare et al. (2021) Sweden Content analysis Nurse anaesthetists (n = 7) Describe smart glasses use for vital sign monitoring
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Abbreviations: AR, augmented reality; CPR, cardiopulmonary resuscitation.

4.3. Risk of Bias Analysis

The results of ROBINS‐I are summarised in Figure S2 for four non‐randomised experimental studies. Three studies were rated as having a serious overall risk, with selection bias being a common domain (Hanson et al. 2020; Herath et al. 2021; Klinker et al. 2020); the fourth study was rated as moderate (Kaylor et al. 2019). In the present review, studies with a high risk of bias encompassed common problems such as bias in outcome measurement.

Table S2 reports the results of the CASP questionnaire for the included qualitative study. A majority of our reviewers agreed that the elements of methodological rigour were present.

4.4. Interventions

Table 2 summarises the different interventions used in the eight studies included in this review. Five studies used MR (‘smart’) glass headsets (Dias et al. 2021; Kaylor et al. 2019; Klinker et al. 2020; Leary et al. 2020; Romare et al. 2021). These glasses overlay digital objects or words onto the field of vision for the wearer. Two studies used an intervention delivered by a mobile phone application (Hanson et al. 2020; Herath et al. 2021). These interventions overlay digital images onto the video output from cell phone cameras. Finally, Fumagalli et al. (2017) used an MR representation device to visualise superficial veins, projecting a digital image of vein positions onto the participant's skin. Taken together, MR technology is being leveraged in diverse ways within the clinical setting, from training and education (Dias et al. 2021; Hanson et al. 2020; Herath et al. 2021; Leary et al. 2020) to direct patient care (Fumagalli et al. 2017; Kaylor et al. 2019; Klinker et al. 2020; Romare et al. 2021). The software used with the MR displays was either off‐the‐shelf or in‐house developed; no two studies reported using the same software.

TABLE 2.

Intervention characteristics.

Author, year Device Description Function
Fumagalli et al. (2017) Easy Vein (Near infrared radiation‐based Visualised veins as dark vessels Aid venipuncture, intravenous catheter insertion
Kaylor et al. (2019) Microsoft AR glasses Streamed video, accessed wound data Guide wound care, measure wounds
Klinker et al. (2020) Microsoft HoloLens Tested wound apps with hands‐free input (voice or eye blinking) Real‐time wound documentation
Leary et al. (2020) CPReality + Micosoft HoloLens Integrated CPR app with feedback manikin Improve CPR compression metrics
Dias et al. (2021) AR video laryngoscopy Streamed intubation video to supervisor for guidance Enhance intubation skills
Hanson et al. (2020) Paediatric code cart AR mobile app Simulated cart for training Teach cart use without physical carts or restocking
Herath et al. (2021) AR mobile app Simulated arm for tourniquet training Develop tourniquet skills
Romare et al. (2021) Google smart glasses Displayed vitals via minicomputer Real‐time monitoring of vitals without traditional monitors
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Abbreviations: App, application; AR, augmented reality; CPR, cardiopulmonary resuscitation.

The instruments used to measure outcomes were as varied as the tested interventions. No two studies used the same outcome measure. Only two studies used an instrument with documented validity and reliability (Fumagalli et al. 2017; Kaylor et al. 2019). One study conducted validity and reliability testing of an instrument as part of its study design (Klinker et al. 2020). The remainder used bespoke questionnaires or other unvalidated measurement methods.

4.5. Outcomes

Table 3 summarises the outcomes of the included studies. Due to the heterogeneity of study designs and outcomes, meta‐analysis was not appropriate.

TABLE 3.

Study outcomes.

Author, year Outcome Experimental Control p
Fumagalli et al. (2017) Completion time (minutes) 8.0 ± 5.8 7.0 ± 3.9 0.17
Attempts needed 1.2 ± 0.6 1.3 ± 0.6 0.36
Hematomas 8.5% 28.6% 0.01
Pain 51.1% 44.6% 0.56
Anxiety (HADS) 7.7 5.8 0.04
Depression (HADS) 7.5 5.7 0.04
Kaylor et al. (2019) Percent agreement for wound assessment 98% NA
Klinker et al. (2020) Voice commands Eye blinking
Performance expectancy 4.9 ± 1.6 5.5 ± 1.4 4.3 ± 1.3 < 0.001
Effort expectancy 5.4 ± 1.4 5.7 ± 1.2 5.1 ± 1.2 < 0.01
Patient influence 4.5 ± 1.7 5.1 ± 1.4 4.8 ± 1.3 > 0.05
Behavioural intention 4.9 ± 1.5 5.2 ± 1.4 4.2 ± 1.6 < 0.01
Satisfaction 5.1 ± 1.4 5.6 ± 1.3 3.9 ± 1.6 < 0.001
Completion time (seconds) NA 125 ± 90 86 ± 33 < 0.001
Leary et al. (2020) Compression rate pre‐simulation 121 ± 3 114 ± 1 0.006
Compression depth pre‐simulation (mm) 48 ± 1 52 ± 1 0.007
Compression rate post‐simulation 122 ± 15 117 ± 11 0.09
Compression depth post‐simulation (mm) 49 ± 8 52 ± 8 0.10
Realism satisfaction 79% 59% 0.07
Dias et al. (2021) Indirect VL AR VL DL
Intubation success rate 72% 71% 32% < 0.001
Median successful intubations 4 4 1 0.002
Success within two attempts 80% 73% 27% 0.008
Fastest intubation time (seconds) 11.6 10.9 17.3 0.02
Hanson et al. (2020) Accuracy 93.2% 81.8% < 0.001
Speed (seconds) 14.3 26.8 < 0.001
Herath et al. (2021) Tourniquet pressure (mmHg) 46.7 23.1 Not reported
Romare et al. (2021) Qualitative themes Facing and embracing responsibilities

  1. A new way of working

  2. Encountering side effects

  3. Obstacles and personal affect
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Abbreviations: AR, augmented reality; C, control; DL, direct laryngoscopy; I, intervention; VL, videolaryngoscopy.

4.5.1. MR Effects on Efficiency

The included studies provide mixed evidence for enhancing nursing efficiency by reducing wasted effort and resources (The results of Cochrane RoB 2 for identified RCT studies are depicted in Figure S1. Of the three included studies, two were rated overall high risk for bias (Dias et al. 2021; Leary et al. 2020), and one was rated as having some concerns (Fumagalli et al. 2017). Klinker et al. 2020). refer to a hypothetical increase in efficiency with MR‐assisted wound care documentation compared with standard electronic wound documentation. The study compared documentation completion times to a smart glasses application controlled by voice commands versus eye blinking. While they reported a mean decrease from 127 to 86 s (p < 0.001) with eye blink controls, the authors do not compare the method to standard non‐MR wound documentation. The study reports statistically significant improvements in effort and performance expectancy, comparing one or both MR control methods to standard documentation, suggesting only a subjective and hypothetical boost to nursing efficiency over traditional methods.

Similarly, Dias et al. (2021) found that during intubation of a manikin, airway identification was notably quicker in both the indirect video laryngoscopy and MR‐assisted video laryngoscopy groups, taking 7.2 and 5.9 s, respectively, compared to the direct laryngoscopy group, which took 10.9 s. Intubation times and success rates were similar between the video laryngoscopy and MR‐assisted groups. While these metrics showed significant improvements over the direct laryngoscopy method for novice users, MR assistance did not confer any additional benefits to efficiency over video assistance.

Furthermore, a study comparing standard venipuncture and the near‐infrared (NIR) electromagnetic radiation technique found that both approaches had comparable procedure durations of roughly 7–8 min and required roughly 1–1.3 attempts (Fumagalli et al. 2017). Although the incidence of hematoma was significantly lower with the NIR technique (8.5%) compared to the standard technique (8.5% vs. 28.6%, p = 0.012), the authors do not report any evidence to support an improvement in nursing efficiency.

Conversely, the use of an MR paediatric code cart training application significantly improved accuracy in locating code cart items from 81.8% to 93.2% (p < 0.001) (Hanson et al. 2020). Additionally, the mean time to locate items was significantly reduced after using the application (14.3 s vs. 26.8 s, p = 0.001). However, this study did not use a control group without exposure to the application, so the results may be attributable in part to test–retest learning effects.

4.5.2. MR Effects on Satisfaction

Only one study measured a nursing satisfaction outcome. Klinker et al. (2020) reported increased satisfaction associated with MR glasses for wound documentation from 3.9 to 5.1 and 5.6 points on a 7‐point Likert scale, comparing standard to MR‐based documentation with voice commands and eye blinks, respectively (p < 0.001). That increase in satisfaction was reflected in improved behavioural intention scores, which measured intent to use a particular method in the future. The authors reported that participants would be slightly more likely to use the device over standard documentation in the future, with mean scores of 4.9 and 5.2 points for the MR methods and 4.2 points for the standard. In addition, users of an MR‐augmented CPR training course showed a non‐statistically significant improvement in satisfaction compared with those using standard audiovisual feedback (79% vs. 59%, p = 0.07) (Leary et al. 2020).

4.5.3. MR Effects on Ergonomics

Romare et al. (2021) observed that technical issues were encountered by nurse anaesthetists using smart glasses to display patient vital signs in the operating room. These challenges included recurrent system malfunctions, menu switching errors and unexpected shutdowns, resulting in battery depletion and limiting clinical utility. Connectivity issues were also an issue that led to delays in vital signs display. Moreover, the unreliability of voice control, the learning curve and sensitivity to external voices caused frustration and distraction. Some participants reported that their attention would divert from patient care to the glasses, ultimately choosing to remove them to prioritise patient safety. For some, the glasses caused discomfort, fatigue or headaches from upward and rightward glances to operate them. Some of those with prescription glasses had difficulty wearing the device. Lastly, the weight of the glasses pressing against the temples caused some discomfort that led to ear pain.

Despite these challenges, many participating anaesthetists expressed willingness to continue using the MR glasses, considering them an overall improvement in anaesthesia care. One improvement was better attention to vital signs, which could be monitored more easily while performing other tasks. This was particularly helpful during induction and intubation.

Study participants praised the ergonomic designs of MR implementations, while others found devices to be uncomfortable or hindered their actions. Ergonomic barriers were interference with clinical roles or distraction (Dias et al. 2021), and technical limitations such as device malfunction and other visualisation difficulties (Herath et al. 2021; Kaylor et al. 2019). Ergonomic facilitators were devices that improved response time and situational awareness (Dias et al. 2021; Hanson et al. 2020).

4.5.4. MR Effects on Competency

This review has shown that MR technology, combined with kinesthetic learning, can improve the effectiveness of other training tools in healthcare. Herath et al. (2021) found an improvement in the average force sensor output value from 23.1 mmHg to 46.7 mmHg after training with an MR phone application, similar to the 44 mmHg reference value from experienced nurses. However, this small pilot study tested a prototype sensor and MR application, which did not assess statistical significance.

Dias et al. (2021) reported competency‐related outcomes that did not demonstrate any benefit of MR‐assisted laryngoscopy over video laryngoscopy for novice users. While the authors reported that 72% of participants successfully intubated within the first or second attempt, compared to only 27% in the DL group, this rate was exceeded by standard video laryngoscopy. Additionally, none of the participants using MR or video‐assisted laryngoscopy experienced oesophageal intubations, while 32% of participants in the direct laryngoscopy group did. Likewise, Leary et al. (2020) compared CPR refresher participants using an MR training package with those using the standard audiovisual feedback manikin. The authors did not report any improvement with the MR enhancement, with both groups mostly demonstrating chest compression rates and depths failing the training standards. When testing participants without any feedback assistance afterward, the study again found no statistically significant difference in the mean rate or depth of compressions, with both groups again failing the training standard on average.

Conversely, while Klinker et al. (2020) did not directly measure competency outcomes, they reported on the subjective expectancy of performance using MR smart glasses to perform wound assessments. The authors found improvements in how participants expected their future performance to improve. They compared standard wound documentation, voice‐controlled smart glasses, and eye blink‐controlled glasses, from 4.3 to 4.9 to 5.5 points, respectively, on a 7‐point scale where higher scores corresponded with higher performance expectancy (p < 0.001). Likewise, Kaylor et al. (2019) demonstrated that MR technology can allow a less experienced bedside nurse to be successfully coached by a remote wound care nurse. When comparing their assessments of the six items of a wound assessment tool with a second in‐person wound nurse, there was an overall agreement of 98% on all items in a wound assessment instrument.

4.5.5. MR Effects on Team Effectiveness

Through narrative synthesis, our team found only one study that discussed team effectiveness outcomes. Kaylor et al. (2019) reported that the wound nurse experts and the bedside nurses expressed comfort and satisfaction with the partnership brought about by a shared experience through the MR glass.

4.6. Health Equity Considerations

While some included studies have health equity implications, our narrative synthesis found that none directly or indirectly measured equity outcomes.

4.7. Barriers and Facilitators of MR Implementation

Our narrative synthesis identified key barriers and facilitators to implementing MR technology in healthcare (Table 4). Barriers included data security concerns requiring secure WiFi (Kaylor et al. 2019; Romare et al. 2021), technical issues such as voice recognition flaws, battery limitations and visualisation difficulties (Herath et al. 2021; Romare et al. 2021), as well as discomfort from non‐ergonomic designs (Klinker et al. 2020; Romare et al. 2021) and potential interference with clinical roles or workflows (Dias et al. 2021; Romare et al. 2021). Facilitators included MR's customizable and user‐friendly features with intuitive interfaces and multimodal interaction options, such as voice commands and gamification (Dias et al. 2021; Klinker et al. 2020; Romare et al. 2021). MR enhanced patient care by improving response times, situational awareness, documentation and hygiene practices (Hanson et al. 2020; Klinker et al. 2020; Romare et al. 2021). Additionally, MR was valued for its ability to deliver augmented information, boosting user confidence and accessibility (Hanson et al. 2020; Leary et al. 2020; Romare et al. 2021), while early adoption was driven by clinicians' interest in innovation (Romare et al. 2021).

TABLE 4.

Barriers and facilitators of MR implementation.

Barriers Facilitators
Data security: Requires secure WiFi for data transmission (Kaylor et al. 2019; Romare et al. 2021) Customizable: Facilities tailored MR data display (Romare et al. 2021)
Technical issues: Voice recognition flaws, shutdowns, battery limits (Romare et al. 2021); visualisation problems (Herath et al. 2021; Kaylor et al. 2019) User‐friendly: Minimal learning curve, intuitive features, multimodal interaction (e.g., voice, eye blinking) (Dias et al. 2021; Kaylor et al. 2019; Romare et al. 2021)
Clinical role interference: MR seen as distracting (Dias et al. 2021; Romare et al. 2021); steep learning curve (Leary et al. 2020; Romare et al. 2021) Patient care improvements: Enhanced safety, response times, situational awareness, documentation and hygiene (Hanson et al. 2020; Klinker et al. 2020; Romare et al. 2021)
Poor ergonomics: Uncomfortable glasses, incompatible with prescriptions, repetitive eye strain (Klinker et al. 2020; Romare et al. 2021) Enhanced information delivery: Boosted confidence, effectiveness and accessibility (Hanson et al. 2020; Leary et al. 2020; Romare et al. 2021)
Individual aptitude: Clinicians interested in innovation (Romare et al. 2021)
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5. Discussion

Our review identified several promising applications of mixed reality (MR) technology in clinical settings, including nursing education, clinical training and patient care. The eight studies reviewed employed various MR implementations, such as smart glasses and mobile applications, to enhance nursing functions. These studies comprised three randomised controlled trials, four non‐randomised studies and one qualitative study, each with distinct designs, objectives and outcomes. The findings present a nuanced picture: while MR technology can improve nursing efficiency, competency and patient care, the evidence remains mixed and highlights the need for further rigorous and well‐designed research to establish its efficacy and best practices in clinical settings. Unlike VR, which immerses users in a completely virtual environment, MR and AR blend digital elements with the real world, allowing nurses to interact with physical and virtual objects in real time. This integration enables nurses to refine skills on the job and improve care by overlaying information onto the nurse's field of view to enhance accuracy and efficiency during procedures.

One of the most promising roles of MR is to enhance nursing efficiency in clinical and educational settings. MR enables the integration of real‐time information through another screen (Dias et al. 2021; Fumagalli et al. 2017) or directly into one's field of vision via holo‐lenses (Klinker et al. 2020; Romare et al. 2021), which would otherwise be inaccessible simultaneously. This advancement could potentially contribute to the effectiveness of MR as a tool for nurses to enhance patient safety while concurrently maintaining or even enhancing procedural efficiency, particularly in terms of completion time (Dias et al. 2021; Fumagalli et al. 2017). Moreover, MR could eventually streamline necessary yet monotonous, time‐consuming tasks in nursing, such as reducing the time it takes to document (Klinker et al. 2020) and simplify training set‐up (Hanson et al. 2020).

Several studies examined outcomes related to nursing satisfaction. Satisfaction is a strong determinant of a healthcare organisation's success and factors heavily into nursing turnover (Han et al. 2015). As nurses are typically called on to perform many administrative tasks to support their clinical role, using MR to facilitate task performance may positively influence job satisfaction. While Kaylor et al. (2019) and Klinker et al. (2020) both acknowledge a potential increase in satisfaction from interacting with MR devices, the studies in our review only hint at potential gains in nurse satisfaction. The paucity of evidence to support increased satisfaction from using MR is not limited to nurses; a systematic review by Urlings et al. (2022) similarly found few MR studies of limited quality in the context of patient education.

This review included two studies measuring ergonomic outcomes. MR has some documented benefits to ergonomics in the healthcare setting, such as in minimally invasive surgery (Hussain et al. 2020). Though MR applications benefiting nursing ergonomics do not yet appear well developed, the use of smart glasses to measure and document wounds does show potential in converting some tasks to being hands‐free (Klinker et al. 2020). Future studies may explore this potential further with other observation‐based tasks. Conversely, only a few of the studies we examined critically addressed other physical ergonomic issues that could be worsened by MR, such as eye strain, headaches and neck strain. Following the trend of most technology, as MR glasses become smaller, lighter and more user‐friendly, we can expect some of these issues to improve.

The use of MR Technology has additionally shown some potential for enhancing nurse competence through innovative training models. For example, Herath et al. (2021) tested a prototype tourniquet‐tightening trainer, stating the potential to assist in training phlebotomy skills. While the clinical relevance of this particular concept is not well established, kinesthetic activity to improve cognitive learning is widely practiced and documented in childhood education (Mazzoli et al. 2021); some literature suggests that a similar approach is appreciated in nurse learners (Wittmann‐Price and Godshall 2009). Additionally, while the results of Dias et al. (2021) were mixed on the benefits of MR‐assisted versus standard video laryngoscopy for teaching novices to intubate, participants had a lesser likelihood of oesophageal intubation than those who did not use the MR technology. The studies in the present review together suggest a future path forward for MR as an adjunct for enhancing nursing competency.

One study in our review reported on the impact of MR use on ratings of team effectiveness. Kaylor et al. (2019) demonstrated collaboration between a nurse at the bedside and a wound care expert, who coached them remotely through the MR application. Participants in this study positively appraised the teamwork aspect of the intervention. However, this study stands alone in addressing teamwork applications of MR. Likewise, the health literature regarding potential collaborative benefits outside the educational environment is sparse. Having multiple members interacting with the same augmented environment adds an additional layer of complexity that likely acts as a barrier to exploration; we speculate that as MR technology matures, more research addressing teamwork in the healthcare setting will emerge.

5.1. Health Equity

A health equity lens is necessary to fully leverage the potential of technological innovations, including MR, to benefit all patients and healthcare providers. Despite the lack of equity outcome measures in the included studies, this review supports a role for MR technology in addressing potential health inequities resulting from sociodemographic constraints, such as geographical disparities and age, which could be effective in enhancing patient experiences and improving access to care (Fumagalli et al. 2017; Kaylor et al. 2019). Furthermore, the integration of MR and smart glasses in healthcare training could democratise access to high‐quality education, enabling healthcare providers from diverse professions, experiences, and proficiency to benefit in a feasible, provider‐acceptable and cost‐effective manner (Dias et al. 2021; Hanson et al. 2020; Herath et al. 2021; Kaylor et al. 2019; Leary et al. 2020).

The Agency for Healthcare Research and Quality (2024) recently developed the Digital Healthcare Equity Framework, which focuses on equity in healthcare solutions incorporating digital technologies. Their implementation guidelines focused on diverse stakeholders. The framework is structured around the digital healthcare lifecycle, with three overarching domains that should be considered when developing and applying digital technologies in healthcare: (1) patient and community characteristics, (2) health system characteristics and (3) health information technology characteristics. Given the potential scalability of MR technology in nursing practice, integrating care equity in the earliest stages of technology development can improve its application. A recent study about medical providers' perceptions of MR with telemedicine revealed that innovative applications could extend access into underserved areas (Dinh et al. 2023). However, the technology could also exacerbate structural and socioeconomic barriers, such as technology literacy, financial capacity, access to necessary equipment and reliable high‐speed internet. Of the MR headsets used in studies included in this review, the enterprise‐focused Microsoft HoloLens 2 starts at USD 3500 (Microsoft 2024), and the Google Glass headset was priced at USD 999 before being discontinued (Cable News Network 2023). Although competition from emerging MR headsets may pressure prices downward, access to affordable MR headsets is a near‐term barrier to widespread adoption. However, the ubiquity of smartphones may enable better access to MR technology in exchange for a lower level of immersion.

5.2. Strengths and Limitations

While the potential for future innovation with MR in nursing, the nascent state of the technology is reflected in the heterogeneity of the evidence base. The broad applications of MR have led to considerable variability in study design, interventions and outcomes examined in this review, precluding meta‐analysis. This heterogeneity limited direct comparisons and supported the inclusion of qualitative synthesis. Additionally, our review concurs with several of the conclusions of Wüller et al. in that included studies lacked critical exploration of the MR applications' clinical relevance (2019). Ultimately, clinical relevance drives the adoption of MR technology, which is still unclear. Therefore, our review cannot suggest the adoption of the interventions presented. Beyond the issue of heterogeneity, we found few studies from peer‐reviewed sources that attempted to test an intervention using nurses in actual or simulated care environments. Nearly all studies were piloting a new intervention or testing a concept that involved MR, and as a result, used study designs that were vulnerable to bias, particularly due to outcome measurement. Despite these limitations, this review represents a comprehensive summary of the comparative effectiveness of MR interventions versus standard practice. By investigating only peer‐reviewed studies with comparison groups, our conclusions reflect the best evidence demonstrating the possible benefits of MR technology in future nursing practice.

5.3. Implications for Practice and Future Research

The review findings highlight a shift from evaluating the development of MR applications to investigating the technology's impact on nursing practice (Wuller et al. 2019). This pivot reveals a knowledge gap due to the limited evidence surrounding MR and AR technology applications. To bridge this gap, staying abreast of emerging technologies while in pilot phases and conducting comprehensive follow‐up studies with an equity lens is necessary. Future research should be grounded in theoretical frameworks to ensure that interventions address real practice needs rather than allowing technology to dictate changes. Engaging methodology experts in study teams will be essential for achieving this goal. Additionally, developing and refining new MR applications will require comparative effectiveness studies against current standards of practice. Such comparisons can establish the clinical relevance of MR interventions and avoid the pitfall of creating ‘solutions in search of a problem’. Pilot testing MR interventions in simulation settings offers an opportunity to improve research control and reduce the risk of study bias. If the intervention demonstrates a statistically significant improvement in the dependent variables in a simulated setting, it can be further tested in the actual clinical environment. However, as MR technology matures, it is crucial to explore the reliability, durability and interoperability of MR systems within existing healthcare infrastructures. Finally, regular updates to the literature can keep the nursing scientific community informed of new developments and advances in MR technology to keep practitioners at the forefront of innovation.

6. Conclusion

Although the evidence suggests the potential benefits of MR in enhancing nursing efficiency, competency and teamwork, the mixed results and heterogeneity of the interventions and study designs preclude definitive conclusions about MR's effectiveness in nursing practice. The nascent state of MR technology in nursing is reflected in the limited number of high‐quality, comparative effectiveness studies available. Addressing health equity implications and involving diverse stakeholders throughout the digital lifecycle is crucial to prevent technological advancements from worsening healthcare quality disparities. Future research should focus on refining promising MR applications, conducting rigorous comparative studies against current standards of practice, and critically exploring MR interventions' clinical relevance, reliability, durability and interoperability.

Author Contributions

Chandler H. Moser: methodology, investigation, data curation, writing – original draft, writing – review and editing, visualisation, supervision; Changhwan Kim: investigation, data curation, writing – original draft; Bindu Charles: investigation, data curation, writing – original draft; Renilda Tijones: investigation, data curation, writing – original draft; Elsa Sanchez: investigation, data curation, writing – original draft; Jedry G. Davila: investigation, data curation, writing – original draft; Hemilla R. Matta: investigation, data curation, writing – original draft; Michael J. Brenner writing‐review and editing; CILDI‐CLABSI Study Team: conceptualisation, methodology, investigation; Vinciya Pandian: conceptualisation, methodology, investigation, writing – review and editing, supervision, project administration.

Conflicts of Interest

The authors declare no conflicts of interest.

Supporting information

Figure S1. Risk of bias in randomised studies (RoB2). Panel A displays individual risk of bias judgements, and Panel B shows each category’s overall risk of bias.

JOCN-34-4547-s002.png (1.7MB, png)

Figure S2. Risk of bias in non‐randomised studies (ROBINS‐I). Panel A displays individual risk of bias judgements, and Panel B shows each category’s overall risk of bias.

JOCN-34-4547-s003.png (1.2MB, png)

Table S1. Search Strategy.

JOCN-34-4547-s001.docx (26.4KB, docx)

Table S2. CASP evaluation results.

JOCN-34-4547-s004.docx (19.1KB, docx)

Acknowledgements

The authors thank Stella Seal, Elvin Odamtten, and Emmanuela Deng for their contributions during the literature review.

CILDI‐CLABSI Study Team: Lauren E. Allison, BSN, RN, CNML, Nurse Manager, Medical Intensive Care Unit, Penn State Milton S. Hershey Medical Center, Hershey, PA, lallison1@pennstatehealth.psu.edu, Diane E. Berish, PhD, Associate Research Professor, Ross and Carol Nese College of Nursing, Leadership Team Tressa Nese and Helen Diskevich CGNE, The Pennsylvania State University, University Park, PA, deb460@psu.edu, Kayleigh R. Cavender, MSN, RN, Doctoral Student, Johns Hopkins University School of Nursing, Baltimore, MD, kfoulke1@jhu.edu, Jason E. Farley PhD, MPH, ANP‐BC, AACRN, Professor, Center for Infections Disease and Nursing Innovation, Johns Hopkins University School of Nursing, Baltimore, MD, jfarley1@jhu.edu, Mariah Frederick, Simulation Operations Specialist, Center for Immersive Learning and Digital Innovation, Johns Hopkins University School of Nursing, Baltimore, MD, mfreder5@jhu.edu, Chris Garrison, PhD, RN, CNE, CHSE, Teaching Professor, Simulation Lab Director, Ross and Carol Nese College of Nursing, The Pennsylvania State University, University Park, PA, cmg35@psu.edu, Ayse P. Gurses PhD, MS, MPH, Professor, Johns Hopkins Medicine, Baltimore, MD, agurses1@jhmi.edu, David N. Hager MD, PhD, Director of Medical Intensive Care Unit, Johns Hopkins Medicine, Baltimore, MD, dhager1@jhmi.edu, Udeme Isang MSN, RN, CLABSI Champion, Lead Nurse Medical Intensive Care Unit, The Johns Hopkins Hospital, Baltimore, MD, uisang2@jhmi.edu, Axel Krieger PhD, Associate Professor, Whiting School of Engineering, Baltimore, MD, axel@jhu.edu, Yu‐Chun Ku, MS, Robotics, Center for Immersive Learning and Digital Innovation, Ross and Nese College of Nursing, The Pennsylvania State University, University Park, PA, yku4@alumni.jh.edu, Jessica Ockimey, Simulation Operations Specialist, Center for Immersive Learning and Digital Innovation, Johns Hopkins University School of Nursing, Baltimore, MD, jockime1@jhu.edu, MiKaela Olsen DNP, APRN‐CNS, AOCNS, FAAN, Clinical Program Director for Oncology at Johns Hopkins Hospital and Johns Hopkins Health System, Baltimore, MD, olsenmi@jhmi.edu, John C. Madara, MD, Assistant Professor of Medicine and Director, Medical ICU, Hershey Medical Center, Penn State Health, Hershey, PA, jmadara@pennstatehealth.psu.edu, Yury Malachevsky DNP, RN, Clinical Instructor, Johns Hopkins University School of Nursing, Baltimore, MD, ymalach1@jhu.edu, Lisa L. Maragakis MD, MPH, Senior Director of Infection Prevention, Johns Hopkins Health System; Hospital Epidemiologist, The Johns Hopkins Hospital, Baltimore, MD, lmaraga1@jhmi.edu, Alphonsa Rahman DNP, APRN, CNS, CCRN, Clinical Nurse Specialist, Medical Intensive Care Unit, The Johns Hopkins Hospital, Baltimore, MD, arahima1@jhmi.edu, Amanda Rohde DNP, MSN, BSN, BS, RN, AGPCNP, CNE, CRNP, TCP, Assistant Professor, Center for Immersive Learning and Digital Innovation, Johns Hopkins University School of Nursing, Baltimore, MD, arohde2@jhmi.edu, Andrew Sanabria, MSN, RN, Infection Prevention Coordinator, Penn State Milton S. Hershey Medical Center, Hershey, PA, asanabria@pennstatehealth.psu.edu, Rahn Snyder, BSN, RN, CIC, Infection Prevention Coordinator, Penn State Milton S. Hershey Medical Center, Hershey, PA, rsnyder1@pennstatehealth.psu.edu, Sandy Swoboda DNP, MS, RN, Instructor, Center for Immersive Learning and Digital Innovation, Johns Hopkins University School of Nursing, Baltimore, MD, sswoboda@jhmi.edu, Nancy Sullivan DNP, MSN, RN, Assistant Professor, Director of Immersive Learning, Center for Immersive Learning and Digital Innovation, Johns Hopkins University School of Nursing, Baltimore, MD, nsulliv@jhmi.edu, Polly Trexler MS, CIC, Director of Infection Control, Johns Hopkins Health System, Baltimore, MD, ptrexle1@jhmi.edu, Mathias Unberath PhD, Assistant Professor, Whiting School of Engineering, Baltimore, MD, unberath@jhu.edu, Erin Barker, BSPH, Infection Control Epidemiologist, Johns Hopkins Health System, Baltimore, MD; ebarker5@jhmi.edu David Goldenberg, MD, FACS, Vice President, Otolaryngology – Head and Neck Surgery Services, Penn State Health; Professor and Chair, Department of Otolaryngology – Head and Neck Surgery, Penn State College of Medicine, The Milton S. Hershey Medical Center, Hershey, PA dgoldenberg@pennstatehealth.psu.edu.

Funding: This study was funded by the Agency for Healthcare Research and Quality for the study, ‘Center for Immersive Learning and Digital Innovation: A Patient Safety Learning Lab advancing patient safety through design, systems engineering and health services research’. PI: Vinciya Pandian; 5R18HS029124.

Disclaimer: The information or content and conclusions do not necessarily represent the official position or policy of, nor should any official endorsement be inferred by the Department of the Army, Department of Defence, or U.S. Government.

CILDI‐CLABSI Study Team available in Appendix.

Contributor Information

Vinciya Pandian, Email: vpandian@psu.edu.

CILDI‐CLABSI Study Team:

Lauren E. Allison, Erin Barker, Diane E. Berish, Jason E. Farley, Mariah Frederick, Christopher Garrison, David Goldenberg, Ayse P. Gurses, David N. Hager, Udeme Isang, Axel Krieger, Jessica Ockimey, MiKaela Olsen, John C. Madara, Yury Malachevsky, Lisa L. Maragakis, Alphonsa Rahman, Amanda Rohde, Andrew Sanabria, Rahn Snyder, Sandra M. Swoboda, Nancy Sullivan, Polly Trexler, and Mathias Unberath

Data Availability Statement

The data that support the findings of this study are available from the corresponding author upon reasonable request.

References

  1. Agency for Healthcare Research and Quality . 2024. “Evidence‐ and Consensus‐Based Digital Healthcare Equity Framework.” (Prepared by Johns Hopkins University under Contract No. 75Q80120D00015.). AHRQ Publication No. 24–0020‐1‐EF.
  2. Alzahrani, N. M. 2020. “Augmented Reality: A Systematic Review of Its Benefits and Challenges in E‐Learning Contexts.” Applied Sciences 10, no. 16: 5660. [Google Scholar]
  3. American Association of Colleges of Nursing . 2022. “Fact Sheet: Nursing Shortage.” https://www.aacnnursing.org/Portals/0/PDFs/Fact‐Sheets/Nursing‐Shortage‐Factsheet.pdf.
  4. Buonocore, D. 2004. “Leadership in Action: Creating a Change in Practice.” AACN Clinical Issues 15, no. 2: 170–181. 10.1097/00044067-200404000-00002. [DOI] [PubMed] [Google Scholar]
  5. Cable News Network . 2023. “Google Will Stop Selling Glass as it Looks to Cut Costs.” https://www.cnn.com/2023/03/16/tech/google‐glass‐gone/index.html.
  6. Cheraghi, R. , Ebrahimi H., Kheibar N., and Sahebihagh M. H.. 2023. “Reasons for Resistance to Change in Nursing: An Integrative Review.” BMC Nursing 22, no. 1: 310. 10.1186/s12912-023-01460-0. [DOI] [PMC free article] [PubMed] [Google Scholar]
  7. Clark, C. S. 2013. “Resistance to Change in the Nursing Profession: Creative Transdisciplinary Solutions.” Creative Nursing 19, no. 2: 70–76. 10.1891/1078-4535.19.2.70. [DOI] [PubMed] [Google Scholar]
  8. Copnell, B. , and Bruni N.. 2006. “Breaking the Silence: Nurses' Understandings of Change in Clinical Practice.” Journal of Advanced Nursing 55, no. 3: 301–309. 10.1111/j.1365-2648.2006.03911.x. [DOI] [PubMed] [Google Scholar]
  9. Critical Appraisal Skills Programme . 2024. “CASP Qualitative Studies Checklist.” https://casp‐uk.net/casp‐tools‐checklists/.
  10. Ćwiklicki, M. , Klich J., and Chen J.. 2020. “The Adaptiveness of the Healthcare System to the Fourth Industrial Revolution: A Preliminary Analysis.” Futures 122: 102602. 10.1016/j.futures.2020.102602. [DOI] [Google Scholar]
  11. Dias, P. L. , Greenberg R. G., Goldberg R. N., Fisher K., and Tanaka D. T.. 2021. “Augmented Reality‐Assisted Video Laryngoscopy and Simulated Neonatal Intubations: A Pilot Study.” Pediatrics 147, no. 3: 5009. 10.1542/peds.2020-005009. [DOI] [PubMed] [Google Scholar]
  12. Dinh, A. , Tseng E., Yin A. L., Estrin D., Greenwald P., and Fortenko A.. 2023. “Perceptions About Augmented Reality in Remote Medical Care: Interview Study of Emergency Telemedicine Providers.” JMIR Formative Research 7: e45211. 10.2196/45211. [DOI] [PMC free article] [PubMed] [Google Scholar]
  13. Fumagalli, S. , Torricelli G., Massi M., et al. 2017. “Effects of a New Device to Guide Venous Puncture in Elderly Critically Ill Patients: Results of a Pilot Randomized Study.” Aging Clinical and Experimental Research 29, no. 2: 335–339. 10.1007/s40520-016-0547-0. [DOI] [PubMed] [Google Scholar]
  14. GDB 2021 Fertility and Forecasting Collaborators . 2024. “Global Fertility in 204 Countries and Territories, 1950–2021, With Forecasts to 2100: A Comprehensive Demographic Analysis for the Global Burden of Disease Study 2021.” Lancet 403, no. 10440: 2057–2099. 10.1016/S0140-6736(24)00550-6. [DOI] [PMC free article] [PubMed] [Google Scholar]
  15. Gerup, J. , Soerensen C. B., and Dieckmann P.. 2020. “Augmented Reality and Mixed Reality for Healthcare Education Beyond Surgery: An Integrative Review.” International Journal of Medical Education 11: 1–18. 10.5116/ijme.5e01.eb1a. [DOI] [PMC free article] [PubMed] [Google Scholar]
  16. Gurses, A. P. , Xiao Y., and Hu P.. 2009. “User‐Designed Information Tools to Support Communication and Care Coordination in a Trauma Hospital.” Journal of Biomedical Informatics 42, no. 4: 667–677. 10.1016/j.jbi.2009.03.007. [DOI] [PubMed] [Google Scholar]
  17. Han, K. , Trinkoff A. M., and Gurses A. P.. 2015. “Work‐Related Factors, Job Satisfaction and Intent to Leave the Current Job Among United States Nurses.” Journal of Clinical Nursing 24, no. 21–22: 3224–3232. 10.1111/jocn.12987. [DOI] [PubMed] [Google Scholar]
  18. Hanson, K. , Shaikh N., Wooldridge A., et al. 2020. “Use of an Augmented Reality Application for Paediatric Code Cart Training.” BMJ Innovations 7, no. 4: 632–636. 10.1136/bmjinnov-2020-000628. [DOI] [Google Scholar]
  19. Herath, S. , Yau C. D., Hoh P. C., Liew O. W., and Ng T. W.. 2021. “An Augmented Reality Tourniquet Tightening Trainer for Peripheral Venepuncture.” Sensors and Actuators A: Physical 332, no. 2: 113202. 10.1016/j.sna.2021.113202. [DOI] [Google Scholar]
  20. Hong, Q. N. , Pluye P., Bujold M., and Wassef M.. 2017. “Convergent and Sequential Synthesis Designs: Implications for Conducting and Reporting Systematic Reviews of Qualitative and Quantitative Evidence.” Systematic Reviews 6, no. 1: 61. 10.1186/s13643-017-0454-2. [DOI] [PMC free article] [PubMed] [Google Scholar]
  21. Hussain, R. , Lalande A., Guigou C., and Bozorg‐Grayeli A.. 2020. “Contribution of Augmented Reality to Minimally Invasive Computer‐Assisted Cranial Base Surgery.” IEEE Journal of Biomedical and Health Informatics 24, no. 7: 2093–2106. 10.1109/JBHI.2019.2954003. [DOI] [PubMed] [Google Scholar]
  22. Kaylor, J. , Hooper V., Wilson A., et al. 2019. “Reliability Testing of Augmented Reality Glasses Technology: Establishing the Evidence Base for Telewound Care.” Journal of Wound, Ostomy, and Continence Nursing 46, no. 6: 485–490. 10.1097/WON.0000000000000585. [DOI] [PubMed] [Google Scholar]
  23. Klinker, K. , Wiesche M., and Krcmar H.. 2020. “Digital Transformation in Health Care: Augmented Reality for Hands‐Free Service Innovation.” Information Systems Frontiers 22: 1419–1431. 10.1007/s10796-019-09937-7. [DOI] [Google Scholar]
  24. Leary, M. , McGovern S. K., Balian S., Abella B. S., and Blewer A. L.. 2020. “A Pilot Study of CPR Quality Comparing an Augmented Reality Application vs. a Standard Audio‐Visual Feedback Manikin.” Frontiers in Digital Health 2: 1. 10.3389/fdgth.2020.00001. [DOI] [PMC free article] [PubMed] [Google Scholar]
  25. Leaver, C. A. , Stanley J. M., and Goodwin Veenema T.. 2022. “Impact of the COVID‐19 Pandemic on the Future of Nursing Education.” Academic Medicine 97, no. 3S: S82–S89. 10.1097/ACM.0000000000004528. [DOI] [PMC free article] [PubMed] [Google Scholar]
  26. Liu, K. , Zhang W., Li W., Wang T., and Zheng Y.. 2023. “Effectiveness of Virtual Reality in Nursing Education: A Systematic Review and Meta‐Analysis.” BMC Medical Education 23, no. 1: 710. 10.1186/s12909-023-04662-x. [DOI] [PMC free article] [PubMed] [Google Scholar]
  27. Mansour, S. , and Nogues S.. 2022. “Advantages of and Barriers to Crafting New Technology in Healthcare Organizations: A Qualitative Study in the COVID‐19 Context.” International Journal of Environmental Research and Public Health 19, no. 16: 9951. 10.3390/ijerph19169951. [DOI] [PMC free article] [PubMed] [Google Scholar]
  28. Mazzoli, E. , Salmon J., Teo W. P., et al. 2021. “Breaking Up Classroom Sitting Time With Cognitively Engaging Physical Activity: Behavioural and Brain Responses.” PLoS One 16, no. 7: e0253733. 10.1371/journal.pone.0253733. [DOI] [PMC free article] [PubMed] [Google Scholar]
  29. Microsoft . 2024. “Microsoft HoloLens 2.” https://www.microsoft.com/en‐us/hololens/buy.
  30. Moser, C. , Rohde A., Matta H., et al. 2022. “Augmented and Mixed Reality Versus Standard Practice in Nursing: A Systematic Review.” https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42022324066.
  31. Noyes, J. , Booth A., Moore G., Flemming K., Tuncalp O., and Shakibazadeh E.. 2019. “Synthesising Quantitative and Qualitative Evidence to Inform Guidelines on Complex Interventions: Clarifying the Purposes, Designs and Outlining Some Methods.” BMJ Global Health 4, no. 1: e000893. 10.1136/bmjgh-2018-000893. [DOI] [PMC free article] [PubMed] [Google Scholar]
  32. Page, M. J. , McKenzie J. E., Bossuyt P. M., et al. 2021. “The PRISMA 2020 Statement: An Updated Guideline for Reporting Systematic Reviews.” BMJ 372: n71. 10.1136/bmj.n71. [DOI] [PMC free article] [PubMed] [Google Scholar]
  33. Quqandi, E. , Joy M., Drumm I., and Rushton M.. 2023. “Augmented Reality in Supporting Healthcare and Nursing Independent Learning: Narrative Review.” Computers, Informatics, Nursing 41, no. 5: 281–291. 10.1097/CIN.0000000000000910. [DOI] [PubMed] [Google Scholar]
  34. Romare, C. , Enlof P., Anderberg P., Jildenstal P., Sanmartin Berglund J., and Skar L.. 2021. “Nurse Anesthetists' Experiences Using Smart Glasses to Monitor Patients' Vital Signs During Anesthesia Care: A Qualitative Study.” PLoS One 16, no. 4: e0250122. 10.1371/journal.pone.0250122. [DOI] [PMC free article] [PubMed] [Google Scholar]
  35. Sterne, J. A. C. , Savovic J., Page M. J., et al. 2019. “RoB 2: A Revised Tool for Assessing Risk of Bias in Randomised Trials.” BMJ 366: l4898. 10.1136/bmj.l4898. [DOI] [PubMed] [Google Scholar]
  36. Urlings, J. , Sezer S., Ter Laan M., et al. 2022. “The Role and Effectiveness of Augmented Reality in Patient Education: A Systematic Review of the Literature.” Patient Education and Counseling 105, no. 7: 1917–1927. 10.1016/j.pec.2022.03.005. [DOI] [PubMed] [Google Scholar]
  37. Uymaz, P. , and Uymaz A. O.. 2022. “Assessing Acceptance of Augmented Reality in Nursing Education.” PLoS One 17, no. 2: e0263937. 10.1371/journal.pone.0263937. [DOI] [PMC free article] [PubMed] [Google Scholar]
  38. Wittmann‐Price, R. A. , and Godshall M.. 2009. “Strategies to Promote Deep Learning in Clinical Nursing Courses.” Nurse Educator 34, no. 5: 214–216. 10.1097/NNE.0b013e3181b2b576. [DOI] [PubMed] [Google Scholar]
  39. Wuller, H. , Behrens J., Garthaus M., Marquard S., and Remmers H.. 2019. “A Scoping Review of Augmented Reality in Nursing.” BMC Nursing 18: 19. 10.1186/s12912-019-0342-2. [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Figure S1. Risk of bias in randomised studies (RoB2). Panel A displays individual risk of bias judgements, and Panel B shows each category’s overall risk of bias.

JOCN-34-4547-s002.png (1.7MB, png)

Figure S2. Risk of bias in non‐randomised studies (ROBINS‐I). Panel A displays individual risk of bias judgements, and Panel B shows each category’s overall risk of bias.

JOCN-34-4547-s003.png (1.2MB, png)

Table S1. Search Strategy.

JOCN-34-4547-s001.docx (26.4KB, docx)

Table S2. CASP evaluation results.

JOCN-34-4547-s004.docx (19.1KB, docx)

Data Availability Statement

The data that support the findings of this study are available from the corresponding author upon reasonable request.

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