Effect of experiential preoperative instruction of virtual reality-based intensive care unit for neurosurgical patients: a study protocol for a randomised controlled trial in China

Abstract

Introduction

Preoperative anxiety is prevalent among neurosurgical patients and is associated with adverse clinical outcomes. Virtual reality (VR) technology offers an innovative approach to delivering immersive preoperative education, particularly in familiarising patients with the intensive care unit (ICU) environment. This study aims to evaluate whether a VR-based ICU orientation can reduce perioperative anxiety and improve psychological preparedness in adult neurosurgical patients.

Methods and analysis

This single-centre randomised controlled trial plans to enrol 108 patients at Xuanwu Hospital. Using a computer-generated random sequence, participants will be randomly assigned in a 1:1 ratio to two groups: a control group receiving standard preoperative guidance, and an experimental group receiving standard guidance plus a VR-based ICU experience tour conducted 1 day before surgery. The primary outcome measure is the incidence of anxiety within 24 hours before discharge from the ICU. Secondary outcome measures include the incidence of depression, cognitive impairment and delirium, duration of delirium, safety events and other clinical outcomes. Data collection points include baseline (T0), 24 hours before surgery (T1), during ICU stay (T2) and 30 days after discharge (T3). All data analyses will be performed using SPSS V.26.0 software and will follow the intention-to-treat principle. This study seeks to determine the effectiveness of a VR-based ICU experience tour in reducing perioperative psychological stress and improving postoperative clinical outcomes.

Ethics and dissemination

This study was approved by the Ethics Committee of Xuanwu Hospital, Capital Medical University (Approval ID: (2024) NO.152-002). The initial approval was obtained on 4 July 2024, and remains valid through 4 July 2026. All participants will provide written informed consent before any data collection takes place. The research findings are intended to be disseminated through publication in peer-reviewed scientific journals.

Trial registration number

ChiCTR2400093170.

STRENGTHS AND LIMITATIONS OF THIS STUDY.

• Adopts a randomised controlled trial design to rigorously compare virtual reality-based experiential instruction with conventional preoperative education.

• Uses computer-generated randomisation sequences for participant allocation to reduce selection bias and ensure group comparability.

• Calculates sample size based on statistical power analysis to detect clinically significant differences in the primary outcome.

• Collects comprehensive data across multiple time points and outcomes to provide a holistic view of intervention effects.

• Employs a single-centre design, which may limit the external validity and generalisability of findings to broader populations.

Introduction

Neurosurgical lesions occur within the central nervous system, which controls and coordinates the body’s response to external stimuli. Given the anatomical complexity of this region and the constraints imposed by the rigid skull, neurosurgical procedures are often characterised by a narrow operative field, deep location and technical difficulty. Consequently, these surgeries typically require prolonged general anaesthesia, extended mechanical ventilation and substantial anaesthetic use, making subsequent intensive care unit (ICU) care essential for patient stabilisation. Close monitoring and advanced life support in the ICU help ensure patients’ safety.¹ However, the ICU is an environment that contains numerous stressors and potential traumatic exposures.² Critically ill patients frequently experience distressing sensations, psychological stress and restricted mobility during their ICU stay. Growing evidence indicates that critical illness can lead to declines in mental, emotional and physical functioning. Hence, interventions aimed at improving the ICU experience and posthospitalisation outcomes are needed.³

Preoperative anxiety is common among patients undergoing surgical procedures,⁴ affecting approximately 27%–80% of surgical patients.⁵ Evidence suggests such anxiety often emerges once a procedure is scheduled and peaks on the day of the surgery.⁶ This anxiety may be exacerbated when patients encounter the unfamiliar and high-stress ICU environment. Previous studies indicate that negative emotional states in neurosurgery patients are associated with poorer quality of life, deficits in cognitive function including memory and attention, longer hospital stays, depression and increased physical disability.⁷ These factors can adversely affect surgical outcomes and postoperative recovery⁸ and may lead to higher doses of anaesthetics and sedatives during surgery and ICU stay, thereby elevating the risk of adverse events and drug interactions.⁹ Anxiety and depression during hospitalisation have been linked to psychiatric symptoms and delusional memories of the ICU experience.¹⁰ Reviews indicated that higher anxiety and depression scores on the first day of ICU admission correlated with increased risk of delirium.¹¹ Delirium in critically ill patients is associated with a 4.8% rise in unplanned extubation rates, extended duration of mechanical ventilation by 66 hours, prolonged ICU stay by 7 days, longer hospital stay by 8 days, lower Mini–Mental State Examination (MMSE) scores by 6 points, and a 25% increase in 6-month mortality.¹² Therefore, attention to the patients’ psychological state throughout hospitalisation, along with evaluation and optimisation of nursing protocols, is important to prevent or reduce anxiety and depression, mitigate psychiatric symptoms and improve both short-term and long-term clinical outcomes.

A randomised controlled trial demonstrated that incorporating ICU-related education into routine preoperative care can help patients understand ICU settings, treatments and nursing routines, correct misconceptions about stressors, improve stress-coping abilities, reduce anxiety and depression, and ultimately decrease the incidence of adverse clinical events.¹³ With the diversification of new media, health education now extends beyond traditional verbal and written communication to include images, audio and video, making standardised health instruction more vivid and comprehensible.¹⁴ Nevertheless, these methods often lack effective human-computer interaction, limiting patients’ ability to realistically anticipate and emotionally prepare for various postoperative scenarios in the ICU.

Advances in information technology, particularly virtual reality (VR), offer new possibilities for preoperative preparation. VR can immerse patients in a simulated ICU environment, allowing them to experience and learn how to respond to potential situations in a realistic yet controlled setting.¹⁵ VR refers to computer-generated simulations that enable immersive interaction within a digital environment,¹⁶ often delivered via head-mounted displays, motion tracking and interactive devices to stimulate perceptual and emotional responses.¹⁷ In recent years, VR has been widely adopted in clinical contexts. Reviews support its efficacy in treating mental disorders such as phobias, anxiety and post-traumatic stress disorder,¹⁸ managing pain¹⁹ and addressing sleep disturbances.²⁰ It has also been effectively integrated into perioperative care, to alleviate psychological and physiological stress.^{21 22}

Emerging evidence highlights VR’s potential to improve neurological and psychological outcomes in patients with neural vulnerabilities. A meta-analysis on brain injury rehabilitation reported that VR interventions significantly enhanced cognitive function and reduced symptoms of depression.²³ These benefits are attributed to VR’s capacity to deliver multisensory, interactive environments that may promote neuroplasticity and provide immersive distractions.²⁴ Moreover, research on simulation training emphasises the importance of user preparation. A recent study suggests that practising familiar procedures in VR simulations is an important component of trainee education.²⁵ This principle may also apply to other patient populations, including neurosurgical patients, indicating that allowing patients to pre-familiarise themselves with the ICU environment and relevant processes through VR can reduce their anxiety and confusion about the unfamiliar ICU setting, thereby enhancing the acceptability and effectiveness of the intervention.

Research on VR for perioperative in neurosurgical patients remains limited. A meta-analysis indicated VR can reduce preoperative anxiety, particularly in paediatric populations.²⁶ However, its effect on anxiety in adult patients is still debated. While one study confirmed VR’s efficacy in reducing preoperative anxiety in adults,²⁷ others have reached contrasting conclusions.²⁸ Currently, VR interventions for ICU patients are typically delivered 1–2 days postoperatively or during postdischarge follow-up. Neurosurgery patients often have head wounds, altered consciousness, limited mobility and multiple medical tubes, making postoperative VR implementation burdensome and less feasible. Therefore, preoperative VR intervention appears more practical. Additionally, existing VR-based preoperative guidance often focuses on procedural steps rather than realistic ICU settings. Cultural and departmental differences across regions also mean that currently available VR ICU scenarios may not align with the experiences of Chinese neurosurgical patients. To our knowledge, no studies have evaluated the effect of immersive, VR-based ICU orientation for neurosurgical patients before surgery. This study aims to develop a VR ICU environment and assess its application in preoperative education for neurosurgical patients, with the goal of reducing perioperative negative emotions and improving clinical outcomes.

Methods and analysis

Study design and setting

This single-centre, randomised controlled trial adopts a parallel-group design, with participants allocated to either an experimental group (EG) or a control group (CG). The EG will receive conventional preoperative guidance plus experiential instruction via a VR-based ICU environment, while the CG will only receive conventional preoperative guidance. Participants will be recruited from the Department of Neurosurgery, Xuanwu Hospital, Capital Medical University, China. Assessments will be performed at four predefined time points: preintervention (T0), 24 hours before surgery (T1), during ICU stay (T2) and 30 days postdischarge (T3). The study design is illustrated in figure 1.

Figure 1. The Consolidated Standards of Reporting Trials flow diagram of the participants’ recruitment and progress through the phases of the trial. ICU, intensive care unit.

This protocol has been developed in accordance with the Standard Protocol Items: Recommendations for Interventional Trials (SPIRIT) guidelines,²⁹ and the completed SPIRIT checklist is provided in the Additional File. The trial will also be reported following the Consolidated Standards of Reporting Trials statement.³⁰

Participants and recruitment

The trial was initially planned to commence in December 2024. However, due to necessary development and debugging of the VR equipment, the study initiation was postponed. Following approval from the Ethics Committee of Xuanwu Hospital, Capital Medical University, the trial timeline has been extended. Patient recruitment is now projected to begin in January 2026. Consequently, data collection is currently underway. Those who choose to participate and fulfil the eligibility criteria will be requested to provide written informed consent (see online supplemental material).

Inclusion criteria

1. Patients undergoing neurosurgery and planning to enter the ICU after surgery, with no restrictions on diagnosis.

2. Age ≥18 years.

3. No severe visual or hearing disorders (eg, diplopia, low vision caused by macular degeneration, retinopathy, severe hearing loss or deafness).

4. The expected stay in the ICU is between 1 and 3 days.

5. Sign the informed consent form.

Exclusion criteria

1. Emergency surgery.

2. With delirium with inclusion.

3. Patients with mental illness or cognitive disorders in the 6 months before inclusion.

4. Recent history of severe anxiety and depression.

5. Drug abusers/dependents in the past 6 months.

6. Pregnant or lactating women.

7. Other situations that the researchers believe are inappropriate for participation in this study.

Randomisation, concealment allocation and blinding

The randomisation will be conducted by an investigator not involved in the recruitment, assessment, intervention or data analysis. Participants will be randomly assigned to either EG or CG in a 1:1 ratio using the random sequence generated by the computer, with odd numbers for the CG and even numbers for the EG. An independent researcher will monitor the randomisation process and put the distribution of participants in an opaque sealed envelope. At enrolment, the project implementation staff will take the sealed envelope, check the allocation and provide written informed consent. Due to the nature of the intervention, blinding patients is impossible. The data analyst will be unaware of the randomisation allocation.

Sample size calculation

The hypothesis of this study is that the clinical effect of the EG will be better than that of the CG; that is, set as a superiority trial, the number of patients in the EG and the CG will be arranged in a ratio of 1:1, and the primary outcome ‘incidence of anxiety’ will be used as the evaluation index. Through the literature and expert consultation, in the CG the incidence of anxiety was 38.3%, and the incidence of hypothesis test anxiety can be reduced to 20%.³¹ The power of the test considered was 90%, and the alpha was 0.05 (two-tailed). The sample size for this study was determined using a sample size calculation formula for superiority trials (figure 2).³² The number of samples in each group was calculated to be 49. To allow for a 10% loss to follow-up, 54 patients were required in each group, giving a total sample size of 108.

Figure 2. The sample size of the superiority experiment was calculated using the formula. CG, control group; EG, experimental group; Nc, number of samples in the CG; Nt, number of samples in the EG; Pc, incidence in the EG; Pt, incidence in the CG.

Intervention

Patients will be randomised to receive either conventional preoperative guidance (CG) or conventional guidance supplemented with experiential preoperative guidance using a VR-based ICU (EG). Throughout the study period, both groups will follow identical procedures for hospital admission, preoperative nursing instruction and standardised postoperative ICU care.

Conventional preoperative guidance (CG)

Participants in the CG will receive routine preoperative guidance. This primarily involves assisting them with completing essential preoperative examinations, undergoing neurosurgical specialist nursing assessments and making corresponding preoperative preparations. All preoperative education will be completed 1 day before surgery. Nurses will provide ICU-related education using oral explanations and written materials, covering the necessity and importance of postoperative ICU stay, the ICU environment, and potential challenges such as protective limb restraint, endotracheal intubation, continuous lighting and alarm sounds, electrocardiograph monitoring, drainage tube, urinary catheter, daily care routines and postoperative activities.

Experiential preoperative guidance using a VR-based ICU (EG)

In addition to routine preoperative guidance, the EG group will receive this VR-based experiential guidance. The intervention is scheduled for the day before surgery. This timing aligns with our centre’s nursing practice, where preoperative preparation and education typically occur within 24 hours prior to surgery, and when the final surgical plan and ICU bed reservation are confirmed. While current evidence indicates that preoperative VR exposure can effectively reduce anxiety, the optimal timing remains inconclusive.³³ Conducting the intervention close to surgery is intended to keep the simulated experience the simulated experience fresh, potentially maximising its effect on alleviating perioperative stress.

The ICU-VR scenario was developed based on a literature search and qualitative interviews identifying patients’ sources of fear in the early perioperative period, which helped screen for themes of adverse ICU stimuli. The digital neurosurgical ICU-VR environment was created by scripting and preparing materials, encompassing a visit to and exploration of the neurosurgery ICU, depictions of treatment and nursing measures on the day of surgery and the first postoperative day, as well as related patient experiences and coping strategies (partially illustrated in figure 3). The intervention will be delivered using specified VR equipment (Pico Neo 3 Enterprise Edition; resolution: 3664×1920, 773 PPI; refresh rate: 72 Hz/90 Hz; field of view: 98°). Trained research team members will administer the VR intervention. Specific intervention protocols are detailed in table 1.

Figure 3. The scene of virtual reality-based intensive care unit.

Table 1. Intervention content of experiment group.

Point-in-time	Content
Preintervention	Before the trial begins, the advanced practice nurse (APN) will explain the intervention procedure’s objectives, methods and considerations to the patients. When the patient is ready, the APN will assist the patient in selecting the appropriate position while checking the patient for therapeutic tubing or monitoring device connection. The patient will then put on the headset and earphones and try to use them to confirm the device’s suitability further. In order to avoid cross-infection, a 75% alcohol wipe will be used to wipe or ultraviolet light will be used to disinfect the surface of VR equipment before each intervention, and gauze or a sterile treatment towel will be used to wrap the area in contact with the patient’s skin.
Process of intervention	1 day before surgery, an immersive experience based on VR technology will be conducted in the neurosurgery ward for patients who will be going to be admitted to the ICU after surgery. Patients will be given a head-mounted VR display, a VR scene will be used to build the related environment of the ICU, and the environment, equipment and nursing process of the ICU will be played to help patients get familiar with the environment, treatment, nursing and coping methods of the ICU in advance, to provide patients with an immersive experience. The duration of each session is no more than 20 min, depending on the participant’s health status and individual response. To minimise contamination between groups during the preoperative VR intervention, patients in the experimental group will receive the VR experience in a separate room, while control group patients will undergo conventional preoperative guidance in their original wards. Importantly, postoperative ICU care will be standardised for both groups in the same ICU environment, with identical monitoring protocols and nursing staff. This design ensures that any observed differences in outcomes can be attributed to the VR intervention rather than variations in ICU conditions.

ICU, intensive care unit; VR, virtual reality.

Outcomes and measurements

Primary outcome

The primary outcome measure is the incidence of anxiety within 24 hours before discharge from the ICU (T2), which will be assessed using the anxiety subscale of the Chinese version of the Hospital Anxiety and Depression Scale (HADS-A).³⁴ Anxiety is defined as a HADS-A score ≥8. This scale has good psychometric properties, with a Cronbach’s α coefficient of 0.879 for the complete scale and 0.806 for the HADS-A subscale.³⁴ The preintervention HADS-A score (T0) will be used as a baseline indicator and a stratification factor for subgroup analysis. We will stratify the analysis based on preintervention anxiety status, dividing participants into a baseline anxiety group and a baseline non-anxiety group to assess the effects of the intervention on persistent anxiety and newly developed anxiety, respectively. The incidence of anxiety will be calculated as the number of participants with a HADS-A score ≥8 in the 24 hours before discharge from the ICU, divided by the total number of participants in each group, multiplied by 100%.

Secondary outcomes

Incidence of depression

The incidence of depression within 24 hours before ICU discharge will be assessed using the depression subscale of the Hospital Anxiety and Depression Scale (HADS-D).³⁴ The HADS-D score of ≥8 was defined as depression. The HADS-D has good reliability and validity, with a Cronbach’s α coefficient of 0.806.³⁴ We will stratify the analysis based on the HADS-D score before intervention (T0), dividing participants into a baseline depressed group and a baseline non-depressed group, to assess the effects of the intervention on persistent depression and new-onset depression, respectively. The incidence of depression will be calculated as the number of participants with a HADS-D score ≥8 in the 24 hours before discharge from the ICU, divided by the total number of participants in each group, multiplied by 100%.

Incidence and duration of delirium

Incidence of delirium will be assessed using the Confusion Assessment Method for the ICU (CAM-ICU).³⁵ The CAM-ICU is a validated diagnostic tool for delirium in critically ill patients, evaluating four key features: (1) acute onset or fluctuating course, (2) inattention, (3) disorganised thinking and (4) altered level of consciousness. A diagnosis of features 1 and 2, plus either feature 3 or 4. The sensitivity of the English version of CAM-ICU was 93%–100%, the specificity was 98%–100%.³⁶ After admission to the ICU, participants will be evaluated for delirium three times per day. The incidence of delirium in each group will be calculated as the number of patients who developed delirium at any point during their ICU stay, divided by the total number of patients in that group, multiplied by 100%. The time to first resolution of delirium will be recorded during the ICU stay. This is defined as the duration from the first positive CAM-ICU assessment to the first of two consecutive negative assessments, separated by at least 4 hours.³⁷

Incidence of cognitive impairment

Cognitive function will be assessed at two time points: within 24 hours before ICU discharge (T2) and 30 days after hospital discharge (T3) using the Chinese version of the MMSE, which provides a total score from 0 to 30.³⁸ Scores below 23 indicate cognitive impairment, scores from 24 to 27 indicate mild impairment, and scores between 28 and 30 indicate normal cognitive function. This Chinese version has demonstrated good reliability and validity, with a Cronbach’s α coefficient of 0.833 and a test-retest reliability of 0.924.³⁸ The preintervention MMSE score (T0) will serve as a baseline indicator and a stratification factor for subgroup analysis; we will stratify participants based on their preintervention cognitive status into a baseline cognitive impairment group and a baseline no cognitive impairment group to assess the effects of the intervention on persistent and new-onset cognitive impairment, respectively. The incidence of cognitive impairment at each time point will be calculated as the number of participants with an MMSE score below 23 at that time point divided by the total number of participants in each group, multiplied by 100%.

Incidence of unplanned extubation

Unplanned extubation, defined as any extubation occurring without the consent of the medical staff or resulting from improper operation.³⁹ The incidence of unplanned extubation will be calculated as the number of unplanned extubation events during the ICU stay divided by the total number of participants in each group, multiplied by 100%.

Safety outcomes

Safety monitoring will focus on adverse reactions during VR sessions, including motion sickness, headache, nausea, vomiting, sweating and fatigue. A predefined protocol requires immediate pausing of the VR experience on participant report or observer identification of severe discomfort (eg, significant nausea or anxiety). All incidents will be recorded in a registry detailing the symptoms, severity, timing and action taken and will be communicated to the principal investigator. Tolerability will be assessed by monitoring for agitation and recording the duration of VR engagement. Safety outcomes will be evaluated within 5 min after each VR session.

Length of hospital and ICU stay

The total length of hospital stay will be defined as the total number of calendar days from the date of surgery to the date of discharge from the hospital. The ICU length of stay will be defined as the total number of calendar days from ICU admission postsurgery to discharge from the ICU.

Total ICU costs

The total ICU cost will be calculated as the sum of all direct medical expenses incurred during the participant’s entire ICU stay. This includes costs related to monitoring, medications, procedures and bed occupancy, as recorded in the hospital’s financial administration system.

Mortality

Mortality will be specifically defined as in-ICU mortality, referring to death from any cause that occurs during the participant’s stay in the ICU postsurgery. The in-ICU mortality rate for each group will be calculated as the number of participants who die during the ICU stay divided by the total number of participants assigned to that group, multiplied by 100%.

The details of study enrolment, intervention and assessments are illustrated in table 2.

Table 2. Participant timeline: schedule of enrolment, interventions and assessments.

Time point	Trial period
	Enrolment		Postallocation		Close-out
	−T1	T0 (baseline)	T1 (24 hours before surgery)	T2 (duration in ICU)	T3 (30 days after discharge)
Enrolment
Eligibility screen	X
Informed consent	X
Allocation	X
Interventions
(Intervention group (experiential preoperative guidance from a virtual reality-based intensive care unit))
(control group (conventional preoperative guidance))
Assessments
Incidence of anxiety/depression		X		X (within 24 hours before ICU discharge)
Incidence of cognitive impairment		X		X (within 24 hours before ICU discharge)	X
Incidence and duration of delirium				X
Incidence of unplanned extubation				X
Safety outcomes			X

Statistical analysis

Statistical analysis will be performed using SPSS software (V.26.0). All analyses will adhere to the intention-to-treat principle, including all randomised participants in their originally assigned groups. Descriptive statistics will present continuous variables as mean±SD or median and IQR, and categorical variables as counts and percentages. Baseline characteristics will be compared between groups using independent sample t-tests, Mann-Whitney U tests or χ² tests to confirm successful randomisation.

For the prespecified primary and secondary outcomes, the analytic approach will be determined by data type. Dichotomous outcomes, such as the incidence of anxiety, depression, delirium or cognitive impairment, will be analysed using χ² tests, with results reported as risk ratios or ORs and their 95% CIs. Continuous outcomes, including scores on the HADS-A, HADS-D and MMSE, will be compared between groups using independent sample t-tests or non-parametric Mann-Whitney U tests, as warranted by their distribution. Variables such as the length of ICU stay, which may influence outcomes like delirium, will be recorded.¹⁰ Should a significant imbalance exist between groups, such variables can be incorporated as covariates in appropriate regression models (eg, logistic or Cox regression) to adjust for their potential confounding effects. For time-to-event outcomes, the competing risk of death will be accounted for by explicitly reporting mortality and considering its impact in the interpretation; sensitivity analyses may be conducted to assess the robustness of findings.

The handling of missing data will follow a pre-established plan to minimise bias. All study data will be entered in an anonymised, coded format, and the dataset will only contain study identification numbers, without any information identifying treatment group assignments. Any methods for imputing missing data will be determined by a statistician blinded to treatment allocation and outcomes, based on the recorded missing data patterns. Furthermore, this statistician will not be aware of the distribution of outcome measures across the groups. Exploratory analyses will include the calculation and comparison of physiological variability metrics between groups, and an exploratory structural equation model may be used to investigate relationships between the VR intervention, negative emotions and delirium. Statistical significance for all prespecified hypothesis tests is set at a two-tailed p<0.05.

Data collection and information source

Data for this study will be collected from two primary sources: electronic patient medical records and structured interviews with patients. To ensure data quality and minimise inter-rater variability, all assessments will be conducted by research personnel who have received standardised training. The collected information encompasses baseline characteristics, perioperative monitoring and treatment variables, and physiological metrics during the ICU stay. In addition, the raw scores from the HADS-A, HADS-D and MMSE will also be collected and reported to provide descriptive data on the severity of symptoms and cognitive function. All data will be codified and recorded in an encrypted database accessible only to the research team via unique identification numbers to ensure confidentiality. Regarding future data sharing, the informed consent form will explicitly seek participants’ permission for their de-identified data to be potentially shared with other researchers under a controlled data access agreement, ensuring secondary use aligns with the study’s ethical approvals; this mechanism eliminates the need to re-contact participants later, and data will not be disclosed to third parties without additional consent. The study will not have an independent data monitoring committee as main decisions will be agreed on by the research team. The implementor will record any intervention-derived adverse events in a registry, and these will be communicated to the principal investigator.

Ethics and dissemination

The study received ethical approval from the Ethics Committee of Xuanwu Hospital, Capital Medical University (ID (2024) NO.152-002). The initial approval was granted on 4 July 2024 and has been extended through 4 July 2026. All participants will sign a written informed consent form before enrolment. The investigators plan to disseminate the study results through publication in peer-reviewed scientific journals.

Discussion

This protocol describes a randomised controlled trial designed to evaluate the effectiveness of a preoperative VR-based tour of the neurosurgical ICU for patients undergoing neurosurgery. The primary objective is to determine whether this immersive experience intervention can reduce perioperative anxiety and improve clinical outcomes. This study is based on existing evidence demonstrating the role of VR in medical education and anxiety reduction, by simulating the clinical environment and familiarising patients with the upcoming surgery and environment, thereby reducing preoperative stress.^{40 41} The development of the VR content incorporated existing evidence and qualitative research on patient fears, ensuring it is tailored to the specific environment and sensory experiences during postoperative ICU stay. This tailored approach aligns with the principles of personalised patient education, potentially enhancing patient understanding and preparedness.⁴²

Research suggests that the effectiveness of digital health interventions may vary due to individual differences such as age and educational background.⁴³ Older patients or those with lower educational attainment may face challenges in technology acceptance and use, which could limit the effectiveness of VR-based interventions.⁴⁴ According to the Unified Theory of Acceptance and Use of Technology, factors such as age, education level and digital literacy significantly influence users’ behavioural intentions and actual engagement with new technologies.⁴⁵ In our study, older age or less familiarity with technology may affect initial comfort levels with the VR headset, while cognitive processing differences related to education level may influence the absorption of immersive information. These individual differences can moderate the effectiveness of the intervention by influencing how patients engage with the VR tour and the extent to which they benefit from it. To address these challenges, our protocol includes strategies such as providing additional guidance, simplified user interfaces and tailored instructional support to mitigate differences in technology acceptance and improve the effectiveness of the intervention across diverse patient populations.

The potential significance of this study is particularly relevant to clinical practice in regions like China, where neurosurgical ICUs often face limitations due to high patient-to-nurse ratios. If proven effective, a standardised VR preoperative module could provide a scalable solution for delivering consistent, high-quality education, potentially mitigating disparities caused by staff time constraints. Furthermore, considering prevalent cultural factors in China, including the high level of family member involvement in patient care, this intervention has broader practical applicability. By visually revealing the environment of the ICU, this approach can also be used to educate family caregivers, helping them manage expectations, reduce anxiety and enable them to provide more informed and calm support to the patient. This culturally sensitive approach aligns with research emphasising the critical role of family involvement in patient recovery and the need to support the family unit throughout the care process.⁴⁶ Therefore, integrating VR education in this context can enhance patient and family preparedness, potentially improving overall clinical outcomes.

This study protocol has several limitations that need to be considered. First, as a single-centre trial, the generalisability of our findings to other institutions with different ICU layouts, staffing patterns or patient populations may be limited. The unique characteristics of our centre and patient population may affect the applicability of this intervention elsewhere. Therefore, future research should prioritise multicentre trials to validate the effectiveness of this intervention in diverse clinical settings and improve the external validity of the results. Second, due to practical resource limitations, the planned follow-up period primarily focuses on perioperative outcomes. Further research is needed to investigate the long-term impact of VR preoperative education on recovery trajectories and quality of life. Finally, the practical integration and cost-effectiveness of implementing such VR programmes within routine preoperative workflows still need to be comprehensively evaluated. Future studies should incorporate health economic analyses to assess its value and sustainability in real-world clinical practice.