“Simpleness”: a qualitative description study exploring patient perspectives on the barriers and facilitators of using digital health tools to self-manage inflammatory bowel disease
Bibliographic record
Abstract
Background: Inflammatory bowel disease (IBD) is a chronic condition requiring lifelong management and frequent interactions with healthcare providers. Digital health tools have the potential to enhance disease management by providing real-time data and improving care coordination. Despite their potential, there is limited evidence on patient perspectives regarding barriers and facilitators to the adoption of these tools. Objectives: To explore patient perspectives on the barriers and facilitators associated with using digital health tools for IBD self-management, focusing on the adoption of a tool called MyIBDToolkit. Design: This study employed a qualitative description approach to gather detailed insights into patient experiences. Methods: Participants with a confirmed IBD diagnosis were recruited from clinics in Alberta, Canada. Data were collected via virtual semi-structured interviews conducted between June and July 2024. Thematic analysis was used to identify key themes, and member checking ensured the credibility of the findings. Results: Eighteen interviews were conducted, reaching thematic saturation. Participants viewed MyIBDToolkit as beneficial for enhancing disease monitoring and care coordination. However, concerns about data entry burden, privacy, and engagement emerged as significant barriers. Variability in healthcare provider use of the tool was another critical concern. Conclusion: While digital health tools such as MyIBDToolkit have the potential to improve IBD self-management, addressing barriers such as usability, privacy, and sustainability is crucial. Incorporating patient feedback during the design process can enhance the effectiveness and acceptability of these tools in chronic disease management.
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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 | 0.022 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.011 | 0.010 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".