An Immersive Virtual Reality Intervention for Preoperative Anxiety and Distress Among Adults Undergoing Oncological Surgery: Protocol for a 3-Phase Development and Feasibility Trial
Bibliographic record
Abstract
BACKGROUND: Preoperative state anxiety (PSA) is distress and anxiety directly associated with perioperative events. PSA is associated with negative postoperative outcomes such as longer hospital length of stay, increased pain and opioid use, and higher rates of rehospitalization. Psychological prehabilitation, such as education, exposure to hospital environments, and relaxation strategies, has been shown to mitigate PSA; however, there are limited skilled personnel to deliver such interventions in clinical practice. Immersive virtual reality (VR) has the potential for greater accessibility and enhanced integration into an immersive and interactive experience. VR is rarely used in the preoperative setting, but similar forms of stress inoculation training involving exposure to stressful events have improved psychological preparation in contexts such as military deployment. OBJECTIVE: This study seeks to develop and investigate a targeted PSA intervention in patients undergoing oncological surgery using a single preoperative VR exposure. The primary objectives are to (1) develop a novel VR program for patients undergoing oncological surgery with general anesthesia; (2) assess the feasibility, including acceptability, of a single exposure to this intervention; (3) assess the feasibility, including acceptability, of outcome measures of PSA; and (4) use these results to refine the VR content and outcome measures for a larger trial. A secondary objective is to preliminarily assess the clinical utility of the intervention for PSA. METHODS: This study comprises 3 phases. Phase 1 (completed) involved the development of a VR prototype targeting PSA, using multidisciplinary iterative input. Phase 2 (data collection completed) involves examining the feasibility aspects of the VR intervention. This randomized feasibility trial involves assessing the novel VR preoperative intervention compared to a VR control (ie, nature trek) condition and a treatment-as-usual group among patients undergoing breast cancer surgery. Phase 3 will involve refining the prototype based on feasibility findings and input from people with lived experience for a future clinical trial, using focus groups with participants from phase 2. RESULTS: This study was funded in March 2019. Phase 1 was completed in April 2020. Phase 2 data collection was completed in January 2024 and data analysis is ongoing. Focus groups were completed in February 2024. Both the feasibility study and focus groups will contribute to further refinement of the initial VR prototype (phase 3), with the final simulation to be completed by mid-2024. CONCLUSIONS: The findings from this work will contribute to the limited body of research examining feasible and broadly accessible interventions for PSA. Knowledge gained from this research will contribute to the final development of a novel VR intervention to be tested in a large population of patients with cancer before surgery in a randomized clinical trial. TRIAL REGISTRATION: ClinicalTrials.gov NCT04544618; https://www.clinicaltrials.gov/study/NCT04544618. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/55692.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.017 | 0.016 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.006 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.043 | 0.007 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".