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Record W4312649725 · doi:10.2196/39945

Reducing Health Anxiety in Patients With Inflammatory Bowel Disease Using Video Testimonials: Pilot Assessment of a Video Intervention

2022· article· en· W4312649725 on OpenAlexvenueno aff
Joel Shor, Yusuke Miyatani, Etsuko Arita, Pohung Chen, Yusuke Ito, Hiroki Kayamah, Jacob Reiter, Kaho Kobayashi, Taku Kobayashi

Bibliographic record

VenueJMIR Formative Research · 2022
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicInflammatory Bowel Disease
Canadian institutionsnot available
FundersVerily Life Sciences
KeywordsAnxietyPsychological interventionMedicineVisual analogue scaleHospital Anxiety and Depression ScaleIntervention (counseling)PsychiatryPhysical therapy

Abstract

fetched live from OpenAlex

BACKGROUND: Health anxiety has many damaging effects on patients with chronic illness. Physicians are often unable to alleviate concerns related to living with a disease that has an impact on daily life, and unregulated websites can overrepresent extreme anxiety-inducing outcomes. Educational clinician video interventions have shown some success as an acute anxiolytic in health settings. However, little research has evaluated if peer-based video interventions would be a feasible alternative or improvement. OBJECTIVE: This pilot study assesses the efficacy of anxiety reduction for patients with Crohn disease (CD) and those with ulcerative colitis (UC) by showing patient testimonial videos during hospital visits. It investigates the degree to which patient testimonials can affect state anxiety, and whether patients are comfortable enough with the technology to share their stories. METHODS: Patients with CD (n=51) and those with UC (n=49) were shown testimonial videos of patients with CD during their physician consultations at Kitasato University Kitasato Institute Hospital in Japan. The video testimonials were collected from Dipex Japan, the Japan branch of an international organization specializing in understanding patient experiences. Patients completed a Visual Analogue Scale for Anxiety before and after viewing the videos, a Hospital Anxiety and Depression Scale (HADS) survey before the videos, and satisfaction surveys. Patients receiving infusion therapy participated in the study while receiving treatment to minimize hospital workflow disruption. RESULTS: =-2.54, 90% CI -3.47 to -0.72). Eighty percent (n=15) of patients with high HADS Anxiety (HADS-A) scores and 71% (n=24) of patients with high starting state anxiety experienced reduced anxiety after watching testimonials. Patients with high state anxiety but low HADS-A scores experienced anxiety reduction (69%, n=16). Forty-two percent (n=100) of patients responded that they would share their stories for future users. When patients with UC received testimonials from patients with CD, 71% (n=49) of patients reported that they were relevant despite differences in condition. CONCLUSIONS: Our pilot results suggest that patient testimonial videos can reduce illness-related state anxiety for patients with CD and those with UC, especially in those with higher baseline state anxiety. The success of this study in reducing anxiety and achieving patient involvement suggests that video interventions for reducing anxiety might be a low-cost intervention that could scale to any number of hospitals, suggesting that technology can help scale up efforts to record and share patient testimonials. Future work can establish whether patient testimonials can be helpful in other contexts, such as before major surgeries or when a family member receives a difficult diagnosis.

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)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.028
GPT teacher head0.376
Teacher spread0.348 · 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 designObservational
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".

Quick stats

Citations5
Published2022
Admission routes1
Has abstractyes

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