Integrating Virtual Mindfulness-Based Stress Reduction Into Inflammatory Bowel Disease Care: Mixed Methods Feasibility Trial
Bibliographic record
Abstract
BACKGROUND: Individuals with inflammatory bowel disease (IBD) experience cycles of aggressive physical symptoms including abdominal pain, diarrhea, and fatigue. These acute symptoms regress and return, and chronic symptoms and complications often linger. The nature of the disease can also cause individuals to experience psychological distress including symptoms of anxiety and depression; however, unlike the physical symptoms of IBD, these psychological symptoms often remain untreated. OBJECTIVE: This study aims to evaluate the feasibility, acceptability, and effectiveness of virtual mindfulness-based stress reduction (v-MBSR) for adults with IBD. METHODS: IBD patients with self-reported anxiety or depression were recruited from clinics in Alberta, Canada to participate in an 8-week v-MSBR intervention. Eligible patients participated in v-MBSR delivered by psychiatrists using a videoconferencing platform. Primary feasibility outcomes included trial uptake, adherence, attendance, and attrition rates. Secondary effectiveness outcomes included measures of anxiety, depression, quality of life (QoL), and mindfulness. Effectiveness data were collected at 3 time points: baseline, at intervention completion, and 6 months after completion. To further assess feasibility and acceptability, participants were invited to participate in a semistructured interview after completing v-MBSR. RESULTS: A total of 16 of the 64 (25%) referred patients agreed to participate in v-MBSR with the most common reason for decline being a lack of time while 7 of the 16 (43.8%) participants completed the program and experienced encouraging effects including decreased anxiety and depression symptoms and increased health-related QoL with both improvements persisting at 6-month follow-up. Participants described improved coping strategies and disease management techniques as benefits of v-MBSR. CONCLUSIONS: Patients with IBD were interested in a psychiatrist-led virtual anxiety management intervention, but results demonstrate v-MBSR may be too time intensive for some patients with IBD patients. v-MBSR was acceptable to those who completed the intervention, and improvements to anxiety, depression, and QoL were promising and sustainable. Future studies should attempt to characterize the patients with IBD who may benefit most from interventions like v-MBSR.
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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 | 0.007 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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.
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".