E-health Tools in Irritable Bowel Syndrome Management
Bibliographic record
Abstract
Background: Electronic health (e-health) technologies, including mobile apps, encourage patient engagement and empowerment in patients’ clinical journeys and facilitate self-management. THe commercialization of irritable bowel syndrome-focused (IBS) mobile apps proliferated in recent years, offering IBS patients a myriad of options in incorporating e-health technology and self-management strategies to alleviate symptoms. As IBS is complicated by the lack of known etiologies and their systemic and downstream effects, mobile apps could be an invaluable tool for patients in navigating varying strategies and developing a tailored and personalized management plan. However, there is limited research on understanding and evaluating e-health tools and their perceived utility and value by patients. Objective: To identify and evaluate digital interventions designed for self-management of IBS-related symptoms, and to explore IBS patients’ experiences using these tools to manage or reduce their symptoms. Methods: 1) A systematic review of the literature was conducted using Medline (Ovid), Embase, Web of Science, and CINAHL. Results from the search strategy were retrieved between database inception and May 2023. Data from the study designs, intervention, and associated effectiveness and feasibility outcomes were extracted. 2) A scoping review of commercially available IBS mobile e-health apps is currently underway. Eligible apps will be collated from the iOS App Store and the Android Google Play Store to evaluate their features and functions, areas of focus, and financial burden. 3) Qualitative focus groups will be conducted with adult IBS patients in Canada who are currently using or have previously used a mobile app to manage their IBS symptoms. Data will be coded and assessed using inductive thematic analysis, and descriptive statistics will be utilized to evaluate demographic variables. Focus groups are expected to commence in January 2024. Results: The implemented search strategy for the systematic review yielded 1164 records, of which 11 were eligible and included. Eight studies developed the intervention, and three assessed existing interventions accessible to the public; most were developed for the mobile platform. Intervention features were focused on education, dietary modification, psychological-based therapies and programs, and health tracking and were largely self-directed. The interventions effectively reduced symptom severity and improved quality of life and mental health, while demonstrating feasibility in the form of adherence, compliance, and usability. More in-depth analysis and results for the scoping review and focus groups will be presented. Conclusion: The evidence suggests e-health interventions may be beneficial tools for patients to manage their IBS. However, despite the influx and saturated market of commercialized apps, their effectiveness is yet to be determined. Thus, additional research is warranted for continued digital intervention assessments in this population to help inform researchers and developers in advancing the quality and accessibility of current and future e-health resources for the IBS community. Furthermore, the evaluation of the mobile apps from the systematic and scoping reviews will support the development of a web-based platform to compile detailed information and user feedback of IBS-focused mobile apps to enable patients to efficiently and effectively find the most suitable apps to support their unique needs and circumstances.
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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 | 0.008 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 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".