An Immersive Virtual Reality Intervention for Preoperative Anxiety and Distress Among Adults Undergoing Oncological Surgery: Protocol for a 3-Phase Development and Feasibility Trial.

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.

Preoperative Virtual Reality to Expose Patients With Breast Cancer to the Operating Room Environment: Feasibility and Pilot Case Series Study.

BACKGROUND: Clinically elevated preoperative distress and anxiety are common among patients undergoing cancer surgery. Preoperative interventions have been developed to mitigate this distress and anxiety but are inconsistent in efficacy and feasibility for broad implementation. OBJECTIVE: This preliminary pilot study aims to assess the feasibility and utility of a newly developed virtual reality (VR) intervention to expose patients awaiting breast cancer surgery to the operating room environment and a simulation of anesthetic induction. METHODS: Patients undergoing breast cancer surgery (N=7) were assigned to the VR intervention or control (treatment as usual) group and completed self-report measures of distress and anxiety before surgery, on the day of surgery, and after surgery (5 and 30 d postoperatively). Those in the intervention group trialed the VR simulation 1 to 2 weeks preoperatively and provided qualitative and quantitative feedback. We assessed the feasibility of recruitment capability and study design and evaluated participants' impressions of the intervention using self-report rating scales and open-ended questions. We also descriptively examined distress and anxiety levels throughout the duration of the study. RESULTS: Recruitment occurred between December 2021 and December 2022 and progressed slowly (rate: 1 participant/7 wk on average; some hesitancy because of stress and being overwhelmed). All participants who consented to participate completed the entire study. All participants were female and aged 56 (SD 10.56) years on average. In total, 57% (4/7) of the participants were assigned to the intervention group. On average, intervention participants spent 12 minutes engaged in the VR simulation. In general, the intervention was rated favorably (eg, clear information, enjoyable, and attractive presentation; mean% agreement 95.00-96.25, SD 4.79-10.00) and as helpful (mean% agreement 87.50, SD 25.00). Participants described the intervention as realistic (eg, "It was realistic to my past surgical experiences"), impacting their degree of preparedness and expectations for surgery (eg, "The sounds and sights and procedures give you a test run; they prepare you for the actual day"), and having a calming or relaxing effect (eg, "You feel more relaxed for the surgery"). CONCLUSIONS: This preoperative VR intervention demonstrated preliminary feasibility among a sample of patients undergoing breast cancer surgery. Results and participant feedback will inform modifications to the VR intervention and the study design of a large-scale randomized controlled trial to examine the efficacy of this intervention. TRIAL REGISTRATION: ClinicalTrials.gov NCT04544618; https://clinicaltrials.gov/study/NCT04544618.

An Immersive and Interactive Platform for Cognitive Assessment and Rehabilitation (bWell): Design and Iterative Development Process.

BACKGROUND: Immersive technologies like virtual reality can enable clinical care that meaningfully aligns with real-world deficits in cognitive functioning. However, options in immersive 3D environments are limited, partly because of the unique challenges presented by the development of a clinical care platform. These challenges include selecting clinically relevant features, enabling tasks that capture the full breadth of deficits, ensuring longevity in a rapidly changing technology landscape, and performing the extensive technical and clinical validation required for digital interventions. Complicating development, is the need to integrate recommendations from domain experts at all stages. OBJECTIVE: The Cognitive Health Technologies team at the National Research Council Canada aims to overcome these challenges with an iterative process for the development of bWell, a cognitive care platform providing multisensory cognitive tasks for adoption by treatment providers. METHODS: The team harnessed the affordances of immersive technologies while taking an interdisciplinary research and developmental approach, obtaining active input from domain experts with iterative deliveries of the platform. The process made use of technology readiness levels, agile software development, and human-centered design to advance four main activities: identification of basic requirements and key differentiators, prototype design and foundational research to implement components, testing and validation in lab settings, and recruitment of external clinical partners. RESULTS: bWell was implemented according to the findings from the design process. The main features of bWell include multimodal (fully, semi, or nonimmersive) and multiplatform (extended reality, mobile, and PC) implementation, configurable exercises that pair standardized assessment with adaptive and gamified variants for therapy, a therapist-facing user interface for task administration and dosing, and automated activity data logging. bWell has been designed to serve as a broadly applicable toolkit, targeting general aspects of cognition that are commonly impacted across many disorders, rather than focusing on 1 disorder or a specific cognitive domain. It comprises 8 exercises targeting different domains: states of attention (Egg), visual working memory (Theater), relaxation (Tent), inhibition and cognitive control (Mole), multitasking (Lab), self-regulation (Butterfly), sustained attention (Stroll), and visual search (Cloud). The prototype was tested and validated with healthy adults in a laboratory environment. In addition, a cognitive care network (5 sites across Canada and 1 in Japan) was established, enabling access to domain expertise and providing iterative input throughout the development process. CONCLUSIONS: Implementing an interdisciplinary and iterative approach considering technology maturity brought important considerations for the development of bWell. Altogether, this harnesses the affordances of immersive technology and design for a broad range of applications, and for use in both cognitive assessment and rehabilitation. The technology has attained a maturity level of prototype implementation with preliminary validation carried out in laboratory settings, with next steps to perform the validation required for its eventual adoption as a clinical tool.

Fractal dimension and directional analysis of elastic and collagen fiber arrangement in unsectioned arterial tissues affected by atherosclerosis and aging.

Structural proteins like collagen and elastin are major constituents of the extracellular matrix (ECM). ECM degradation and remodeling in diseases significantly impact the microorganization of these structural proteins. Therefore, tracking the changes of collagen and elastin fiber morphological features within ECM impacted by disease progression could provide valuable insight into pathological processes such as tissue fibrosis and atherosclerosis. Benefiting from its intrinsic high-resolution imaging power and superior biochemical specificity, nonlinear optical microscopy (NLOM) is capable of providing information critical to the understanding of ECM remodeling. In this study, alterations of structural fibrillar proteins such as collagen and elastin in arteries excised from atherosclerotic rabbits were assessed by the combination of NLOM images and textural analysis methods such as fractal dimension (FD) and directional analysis (DA). FD and DA were tested for their performance in tracking the changes of extracellular elastin and fibrillar collagen remodeling resulting from atherosclerosis progression/aging. Although other methods of image analysis to study the organization of elastin and collagen structures have been reported, the simplified calculations of FD and DA presented in this work prove that they are viable strategies for extracting and analyzing fiber-related morphology from disease-impacted tissues. Furthermore, this study also demonstrates the potential utility of FD and DA in studying ECM remodeling caused by other pathological processes such as respiratory diseases, several skin conditions, or even cancer. NEW & NOTEWORTHY Textural analyses such as fractal dimension (FD) and directional analysis (DA) are straightforward and computationally viable strategies to extract fiber-related morphological data from optical images. Therefore, objective, quantitative, and automated characterization of protein fiber morphology in extracellular matrix can be realized by using these methods in combination with digital imaging techniques such as nonlinear optical microscopy (NLOM), a highly effective visualization tool for fibrillar collagen and elastic network. Combining FD and DA with NLOM is an innovative approach to track alterations of structural fibrillar proteins. The results illustrated in this study not only prove the effectiveness of FD and DA methods in extracellular protein characterization but also demonstrate their potential value in clinical and basic biomedical research where protein microstructure characterization is critical.

Evaluation of texture parameters for the quantitative description of multimodal nonlinear optical images from atherosclerotic rabbit arteries.

The composition and structure of atherosclerotic lesions can be directly related to the risk they pose to the patient. Multimodal nonlinear optical (NLO) microscopy provides a powerful means to visualize the major extracellular components of the plaque that critically determine its structure. Textural features extracted from NLO images were investigated for their utility in providing quantitative descriptors of structural and compositional changes associated with plaque development. Ten texture parameters derived from the image histogram and gray level co-occurrence matrix were examined that highlight specific structural and compositional motifs that distinguish early and late stage plaques. Tonal-texture parameters could be linked to key histological features that characterize vulnerable plaque: the thickness and density of the fibrous cap, size of the atheroma, and the level of inflammation indicated through lipid deposition. Tonal and texture parameters from NLO images provide objective metrics that correspond to structural and biochemical changes that occur within the vessel wall in early and late stage atherosclerosis.

Multimodal nonlinear optical imaging of atherosclerotic plaque development in myocardial infarction-prone rabbits.

Label-free imaging of bulk arterial tissue is demonstrated using a multimodal nonlinear optical microscope based on a photonic crystal fiber and a single femtosecond oscillator operating at 800 nm. Colocalized imaging of extracellular elastin fibers, fibrillar collagen, and lipid-rich structures within aortic tissue obtained from atherosclerosis-prone myocardial infarction-prone Watanabe heritable hyperlipidemic (WHHLMI) rabbits is demonstrated through two-photon excited fluorescence, second harmonic generation, and coherent anti-Stokes Raman scattering, respectively. These images are shown to differentiate healthy arterial wall, early atherosclerotic lesions, and advanced plaques. Clear pathological changes are observed in the extracellular matrix of the arterial wall and correlated with progression of atherosclerotic disease as represented by the age of the WHHLMI rabbits.

Differentiating atherosclerotic plaque burden in arterial tissues using femtosecond CARS-based multimodal nonlinear optical imaging.

A femtosecond CARS-based nonlinear optical microscope was used to simultaneously image extracellular structural proteins and lipid-rich structures within intact aortic tissue obtained from myocardial infarction-prone Watanabe heritable hyperlipidemic rabbits (WHHLMI). Clear differences in the NLO microscopic images were observed between healthy arterial tissue and regions dominated by atherosclerotic lesions. In the current ex-vivo study, we present a single parameter based on intensity changes derived from multi-channel NLO image to classify plaque burden within the vessel. Using this parameter we were able to differentiate between healthy regions of the vessel and regions with plaque, as well as distinguish plaques relative to the age of the WHHLMI rabbit.

Semi-parametric estimation in the compositional modeling of multicomponent systems from Raman spectroscopic data.

Identification and quantification of molecular species are central applications of molecular spectroscopy. In complex multicomponent systems like tissue samples, linear parametric models are often used to estimate the relative concentrations of the biochemical components of the sample. In situations where not all of the components of the sample are known or modeled, such parametric models can suffer from omitted variable bias and result in skewed estimates of component concentrations. We propose a semi-parametric approach that tries to avoid this omitted variable bias by effectively including unknown covariates as a non-parametric term in the regression equation. Constituent concentrations estimated with such partial linear models should outperform strict parametric linear models when the user has limited information on the composition of a multi-constituent system.