Smartphone Application With Health Coaching Facilitates Multi‐Symptom Improvement in <scp>IBS</scp> Patients: A Pilot Feasibility Trial
Bibliographic record
Abstract
BACKGROUND: Irritable bowel syndrome (IBS), a disorder of the gut-brain interaction, is associated with significant symptom burden and impaired psychosocial functioning. Evidence-based behavioral therapies are effective, but often underutilized due to accessibility barriers. Mobile health is an emerging field with the potential to bridge the gap between the needs of individuals with IBS and the limitations of the healthcare system. This study evaluated the feasibility and effectiveness of the LyfeMD app plus health coaching (HC) in improving IBS symptom severity and psychosocial wellbeing. METHODS: This 12-week interventional pilot study evaluated the effectiveness of a mobile application combined with HC in adults diagnosed with IBS. Participants were assessed at baseline, 6 weeks, and 12 weeks using validated surveys to assess symptom severity, psychosocial wellbeing, diet, physical activity, and sleep. A Fitbit was also used to track physical activity and sleep. RESULTS: Thirty-nine participants completed the 12-week intervention. IBS symptom severity improved significantly (p < 0.001) over the 12-week period, with 63.2% of the participants having a clinically meaningful improvement in their symptoms. In addition to symptom severity, participants improved in all measured psychosocial domains and their subjective sleep quality at 12 weeks. CONCLUSION: In summary, the LyfeMD platform, in combination with HC, shows potential in improving IBS symptom severity, psychosocial well-being, and sleep quality in individuals diagnosed with IBS. These findings highlight the potential of mobile health as a complement to traditional medical care. Further research, including randomized controlled trials with extended follow-up, is needed to confirm findings and the sustainability of these outcomes.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".