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S929 Assessment of Virtual Reality on Pain and Anxiety at an Infusion Clinic Setting in Patients With Inflammatory Bowel Disease: A Pilot Acceptability Study

2022· article· en· W4316086088 on OpenAlexaboutno aff
Edward Cay, Barbara Schmidtman, Thomas Birris

Bibliographic record

VenueThe American Journal of Gastroenterology · 2022
Typearticle
Languageen
FieldPsychology
TopicPerfectionism, Procrastination, Anxiety Studies
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineAnxietyAbdominal painMcGill Pain QuestionnairePhysical therapyDistressBeck Anxiety InventoryDiseaseInflammatory bowel diseaseInternal medicineBeck Depression InventoryPsychiatryVisual analogue scaleClinical psychology

Abstract

fetched live from OpenAlex

Introduction: Inflammatory Bowel Disease (IBD) is a chronic relapsing and remitting inflammatory condition of the bowel lumen. While abdominal pain is most severe when acute inflammation is present, many patients with IBD in endoscopic remission continue to have abdominal pain. Psychological conditions like anxiety may exacerbate symptoms and cause more frequent flares, leading to increased hospitalizations. Non-pharmacological methods such as virtual reality (VR) have been shown to decrease pain and anxiety in inpatient settings. While there has been support of VR in IBD patients in an clinic setting, no studies have assessed the use of VR in IBD patients at infusion centers, an important aspect of IBD management. If VR can improve both pain and anxiety, then it may lead to improved health outcomes. Methods: This is a prospective, single-center, paired-sample study of adult patients with IBD, where pain and anxiety were measured before and after their regular infusion clinic appointment, with the use of VR on their following infusion appointment. At the end of the study, there was an assessment of the feasibility of VR for future encounters. Anxiety was measured using the Beck Anxiety Inventory (BAI), and pain was measured using the Short-Form McGill Pain Questionnaire (SF-MPQ). The VR headset included immersive options such as guided meditations and deep sea diving, and prohibited content that would cause potential distress. Paired sample t-tests were utilized to compare any differences in pain or anxiety during their infusion therapy, with and without use of virtual reality. Results: In this pilot study, we report data of 14 adult patients with IBD (57% Crohn’s Disease, 43% Ulcerative Colitis). Mean age was 42.07 years. Demographic, BAI, SF-MPQ, and VR feasibility data are shown in Table (Table). Conclusion: While preliminary analyses show VR had no significant change in BAI (t = -0.244, p-value = 0.405) and SF-MPQ (t = -0.336, p-value = 0.371), participants reported positive experiences with VR. Patients rated their experience an average of 7.79 on a scale of 1 to 9. 71% of patients reported they would like to use VR during future appointments. These finding support the acceptability of VR in an infusion clinic setting, and provide a framework for further assessment of pain and anxiety in future larger randomized control trials. Table 1. - VR IBD Group Data Age Mean 42.07 years Sex • Male • Female 8 (57%)6 (43%) IBD Type • Crohn’s Disease • Ulcerative Colitis 8 (57%)6 (43%) Infusion Medication • Infliximab • Vedolizumab 7 (50%)7 (50%) Percent of Time with VR During Infusion • Total • Infliximab • Vedolizumab Mean: 74%Mean: 50%Mean: 98% Chronic Opioid Use 2 (14%) Pharmacologic Therapy for Anxiety 4 (29%) Nonprescription Therapy for Anxiety/Pain 5 (36%) Beck Anxiety Inventory (BAI) t = -0.244, p-value = 0.405 Short Form McGill Pain Questionnaire (SF-MPQ) t = -0.336, p-value = 0.371 Would Like to Use VR During Future Appointments 10 (71%) Rate Your Experience1 (Dislike) to 5 (Indifferent) to 9 (Enjoyed) Mean: 7.79 Infusion Experience Felt Faster with VR 12 (86%) Forgot was in Infusion Clinic with VR 3 (21%)

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 imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Non-randomized trial · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.001

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.

Opus teacher head0.012
GPT teacher head0.310
Teacher spread0.298 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNon-randomized trial
Domainnot available
GenreEmpirical

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".

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Citations1
Published2022
Admission routes1
Has abstractyes

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