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Feasibility of a virtual reality intervention targeting distress and anxiety symptoms in patients with primary brain tumors: Interim analysis of a phase 2 clinical trial.

ABSTRACT: Purpose: Cancer patients experience distress and anxiety when undergoing imaging studies to monitor disease status, yet these symptoms are not always appropriately identified or well-managed. This interim analysis of a phase 2 clinical trial explored feasibility and acceptability of a virtual reality relaxation (VR) intervention for primary brain tumor (PBT) patients at the time of clinical evaluation. Methods: English speaking, adult PBT patients with previous reports of distress and upcoming neuroimaging were recruited between March of 2021 and March 2022. A brief VR session was done within 2 weeks prior to neuroimaging with patient-reported outcomes (PROs) collected before and immediately post-intervention. Self-directed VR use over the next 1 month was encouraged with additional PROs assessments at 1 and 4 weeks. Feasibility metrics included enrollment, eligibility, attrition, and device-related adverse effects with satisfaction measured with qualitative phone interviews. Results: 55 patients were approached via email, 40 (73%) responded and 20 (50%) enrolled (9 declines, 11 screen fails). 65% of participants were ≤ 50 years, 50% were male, 90% were White/non-Hispanic, 85% had good KPS (≥ 90), and most were on active treatment. All patients completed the VR intervention, PROs questionnaires, weekly check-ins, and qualitative interview. Most (90%) reported frequent VR use and high satisfaction and only 7 mild AEs were recorded (headache, dizziness, nausea, neck pain). Conclusion: This interim analysis confirmed feasibility and acceptability of a novel VR intervention to target psychological symptoms for PBT patients. Trial enrollment will continue to assess for intervention efficacy. Trial Registration: NCT04301089 registered on 3/9/2020.

SUBMITTER: King AL

PROVIDER: S-EPMC9928043 | biostudies-literature | 2023 Jan

REPOSITORIES: biostudies-literature

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Feasibility of a virtual reality intervention targeting distress and anxiety symptoms in patients with primary brain tumors: Interim analysis of a phase 2 clinical trial.

King Amanda L AL Roche Kayla N KN Leeper Heather E HE Vera Elizabeth E Mendoza Tito T Mentges Kelly K Acquaye Alvina A AA Adegbesan Kendra K Boris Lisa L Burton Eric E Choi Anna A Grajkowska Ewa E Kunst Tricia T Levine Jason J Lollo Nicole N Miller Hope H Panzer Marissa M Penas-Prado Marta M Pillai Valentina V Polskin Lily L Reyes Jennifer J Sahebjam Solmaz S Stockdill Macy M Theeler Brett J BJ Wu Jing J Gilbert Mark R MR Armstrong Terri S TS

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<b>Purpose:</b> Cancer patients experience distress and anxiety when undergoing imaging studies to monitor disease status, yet these symptoms are not always appropriately identified or well-managed. This interim analysis of a phase 2 clinical trial explored feasibility and acceptability of a virtual reality relaxation (VR) intervention for primary brain tumor (PBT) patients at the time of clinical evaluation. <b>Methods:</b> English speaking, adult PBT patients with previous reports of distress ...[more]

PMID: 36789426

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Project description:IntroductionMachine learning (ML) is an effective tool for predicting mental states and is a key technology in digital psychiatry. This study aimed to develop ML algorithms to predict the upper tertile group of various anxiety symptoms based on multimodal data from virtual reality (VR) therapy sessions for social anxiety disorder (SAD) patients and to evaluate their predictive performance across each data type.MethodsThis study included 32 SAD-diagnosed individuals, and finalized a dataset of 132 samples from 25 participants. It utilized multimodal (physiological and acoustic) data from VR sessions to simulate social anxiety scenarios. This study employed extended Geneva minimalistic acoustic parameter set for acoustic feature extraction and extracted statistical attributes from time series-based physiological responses. We developed ML models that predict the upper tertile group for various anxiety symptoms in SAD using Random Forest, extreme gradient boosting (XGBoost), light gradient boosting machine (LightGBM), and categorical boosting (CatBoost) models. The best parameters were explored through grid search or random search, and the models were validated using stratified cross-validation and leave-one-out cross-validation.ResultsThe CatBoost, using multimodal features, exhibited high performance, particularly for the Social Phobia Scale with an area under the receiver operating characteristics curve (AUROC) of 0.852. It also showed strong performance in predicting cognitive symptoms, with the highest AUROC of 0.866 for the Post-Event Rumination Scale. For generalized anxiety, the LightGBM's prediction for the State-Trait Anxiety Inventory-trait led to an AUROC of 0.819. In the same analysis, models using only physiological features had AUROCs of 0.626, 0.744, and 0.671, whereas models using only acoustic features had AUROCs of 0.788, 0.823, and 0.754.ConclusionsThis study showed that a ML algorithm using integrated multimodal data can predict upper tertile anxiety symptoms in patients with SAD with higher performance than acoustic or physiological data obtained during a VR session. The results of this study can be used as evidence for personalized VR sessions and to demonstrate the strength of the clinical use of multimodal data.

Project description:BackgroundSocial anxiety disorder (SAD) is the fear of social situations where a person anticipates being evaluated negatively. Changes in autonomic response patterns are related to the expression of anxiety symptoms. Virtual reality (VR) sickness can inhibit VR experiences.ObjectiveThis study aimed to predict the severity of specific anxiety symptoms and VR sickness in patients with SAD, using machine learning based on in situ autonomic physiological signals (heart rate and galvanic skin response) during VR treatment sessions.MethodsThis study included 32 participants with SAD taking part in 6 VR sessions. During each VR session, the heart rate and galvanic skin response of all participants were measured in real time. We assessed specific anxiety symptoms using the Internalized Shame Scale (ISS) and the Post-Event Rumination Scale (PERS), and VR sickness using the Simulator Sickness Questionnaire (SSQ) during 4 VR sessions (#1, #2, #4, and #6). Logistic regression, random forest, and naïve Bayes classification classified and predicted the severity groups in the ISS, PERS, and SSQ subdomains based on in situ autonomic physiological signal data.ResultsThe severity of SAD was predicted with 3 machine learning models. According to the F1 score, the highest prediction performance among each domain for severity was determined. The F1 score of the ISS mistake anxiety subdomain was 0.8421 using the logistic regression model, that of the PERS positive subdomain was 0.7619 using the naïve Bayes classifier, and that of total VR sickness was 0.7059 using the random forest model.ConclusionsThis study could predict specific anxiety symptoms and VR sickness during VR intervention by autonomic physiological signals alone in real time. Machine learning models can predict the severe and nonsevere psychological states of individuals based on in situ physiological signal data during VR interventions for real-time interactive services. These models can support the diagnosis of specific anxiety symptoms and VR sickness with minimal participant bias.Trial registrationClinical Research Information Service KCT0003854; https://cris.nih.go.kr/cris/search/detailSearch.do/13508.

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Dataset Information

Feasibility of a virtual reality intervention targeting distress and anxiety symptoms in patients with primary brain tumors: Interim analysis of a phase 2 clinical trial.

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Feasibility of a virtual reality intervention targeting distress and anxiety symptoms in patients with primary brain tumors: Interim analysis of a phase 2 clinical trial.

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