Digital Health Interventions Are Effective for Irritable Bowel Syndrome Self-Management: A Systematic Review
Bibliographic record
Abstract
BACKGROUND: Digital health interventions (DHIs) could be a valuable self-management tool for patients with irritable bowel syndrome (IBS), but little research exists on IBS-focused DHIs and their effectiveness. This review aimed to identify DHIs for IBS and evaluate their characteristics, effectiveness, and feasibility. METHODS: Our study team, including patient partners, conducted a systematic review using Medline, PsycINFO, Embase, Web of Science, and CINAHL from database inception to May 2024. Experimental and observational studies evaluating DHIs designed for use by IBS patients were included. Data extraction and assessment included study and DHI characteristics, effectiveness outcomes (symptom severity, quality of life, psychological indices, patient empowerment), and feasibility measures (adherence, usability, user satisfaction). Study quality and bias were assessed using a modified checklist of Downs and Black. RESULTS: Of the 929 identified, 13 studies of DHIs were included and deemed good quality on average (21,510 total participants) with six primary areas of focus: education, diet, brain-gut behavior skills, physiological support, health monitoring, and community engagement. Most DHIs were self-directed and reported statistically significant improvements in most effectiveness outcomes. Evidence suggests that DHIs focusing on brain-gut behavior skills or health monitoring may be most effective compared to other types of DHIs. However, their feasibility remains unclear, and the generalization of their impacts is limited. CONCLUSION: This review underscores the potential of DHIs in supporting IBS patients and improving their outcomes. However, additional research is warranted for continued intervention use in this population, including assessments on feasibility, safety, cost-effectiveness, and patient empowerment and experiences.
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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 | 0.009 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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.
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".