Navigation, Adoption, and Use of Digital Health Technologies for Irritable Bowel Syndrome Self-Management: Focus Group Study of Patient Experience and Decision-Making (Preprint)
Bibliographic record
Abstract
BACKGROUND Irritable bowel syndrome (IBS) is a common chronic gastrointestinal disorder that impairs bowel functions and patients’ overall quality of life. IBS-focused digital health technologies (DHTs), including online health resources and mobile health (mHealth) apps, have recently proliferated for patient use. However, research exploring patients’ experiences with navigating, adopting, or using commercial or publicly available DHTs for IBS self-management is limited. OBJECTIVE This study aims to explore the user experiences and decision-making of patients with IBS as they navigate, adopt, and use diverse DHTs for disease self-management. METHODS We conducted virtual semistructured focus group interviews to explore the experiences of patients with IBS using DHTs, including their perspectives on design and features, their decision-making process in using DHTs, and recommendations for improving user experience and uptake, given the heterogeneous nature of these tools. Canada-based patients with IBS who were using or had used mHealth apps to manage symptoms were recruited through purposive sampling from previous IBS-related studies. Discussions were transcribed verbatim, and inductive thematic analysis was performed using NVivo (version 14; Lumivero). A modified version of the Expanded Unified Theory of Acceptance and Use of Technology (UTAUT2) model was applied to guide the interpretation of the dynamic relationship between the influences on participants’ decisions regarding DHT use. RESULTS Among the 8 participants (all female; mean age 55.3, SD 13.5 years), two themes were identified: (1) uncertainty impacts the trustworthiness of DHTs, and (2) influences that drive the decision-making process to adopt and use DHTs. The observed influences aligned with the constructs of the UTAUT2 model (performance expectancy, effort expectancy, social influence, facilitating conditions, hedonic motivation, price value, and habit), with the addition of trust and risk in participants’ decision-making. Digital health literacy and patient engagement were also raised as crucial components of participants’ experiences and perspectives on DHTs. CONCLUSIONS Findings of this study highlight the current landscape of digital health in IBS and existing gaps and challenges for patients in navigating, adopting, and using DHTs for IBS self-management. While DHTs were generally viewed positively for their value and potential, patients with IBS consider several coexisting factors and trade-offs in their decision-making. Further investigations on the influences on and perspectives toward DHTs could enhance future development and iterations of these tools and improve patient confidence and uptake.
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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 | 0.011 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".