mHealth Use, Preferences, Barriers, and eHealth Literacy Among Patients With Inflammatory Bowel Disease: Survey Study
Bibliographic record
Abstract
BACKGROUND: Mobile health (mHealth), defined as health care facilitated by mobile devices, offers a promising strategy for enhancing disease management and treatment for patients with chronic conditions. However, there is limited information about how patients with inflammatory bowel disease (IBD) use mHealth and their digital preferences. OBJECTIVE: The aim of the study was to investigate the use of mHealth as well as the preferences, obstacles, and eHealth literacy reported by patients with IBD in Germany. METHODS: In April and May 2023, we sequentially enrolled patients diagnosed with IBD, including Crohn disease and ulcerative colitis, to participate in a paper-based survey. The survey included questions on sociodemographic details, health characteristics, mHealth use, internet use, eHealth literacy (measured with the eHealth Literacy Scale), and preferences regarding communication and information. RESULTS: Of the 200 surveyed participants, almost all (197/200, 98.5%) reported regular smartphone use, and more than two-thirds (139/200, 69.5%) indicated regular engagement with social media. Most of the respondents (168/200, 84%) expressed the belief that incorporating medical apps into their routine could positively impact their health. However, only 25 (12.5%) of the 200 patients acknowledged using medical apps, of which just 2 apps were IBD specific, used by only a few (n=3, 12%). Furthermore, awareness of useful websites or mobile apps tailored for IBD was limited (45/200, 22.5%). Nearly all participants (196/200, 98%) expressed willingness to share app data for research purposes, and most (171/200, 85.5%) consented to transmit app data to their treating physicians. A large majority (175/200, 87.5%) indicated readiness to regularly input data into an app, with a preferred duration of up to 5 minutes (109/200, 54.5%) and weekly input frequency (76/200, 38%). For an IBD-specific app, the most frequently requested functions were electronic prescriptions (110/200, 55%) and a newsletter about new scientific work and clinical studies (94/200, 47%). Usability and security were identified as key app attributes. The internet was the predominant source of health-related information (180/200, 90%). The average eHealth literacy score, measured with the eHealth Literacy Scale, was high (mean 28.9, SD 5.4; range 8-40), with a positive correlation observed between higher eHealth literacy and factors such as younger age and more frequent internet use for health information. CONCLUSIONS: Patients with IBD are well prepared and motivated to use mHealth technologies to better understand their chronic condition and optimize treatment. However, their enthusiasm is tempered by the currently low adoption of mHealth. To fully harness the potential of mHealth in IBD treatment, effective and tailored mHealth solutions, guidance for their implementation, and patient education are needed.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".