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Record W4412059844 · doi:10.2196/69093

Development of an mHealth Solution for Tailored Communication Between Patients With Inflammatory Bowel Disease and Health Care Professionals: Participatory Design Study

2025· article· en· W4412059844 on OpenAlexvenueno aff
Janni Petersen Fallesen, Marie Louise Krogh, Torben Knudsen, Lisbeth Rosenbek Minet, Jens Kjeldsen, Mette Maria Skjøth

Bibliographic record

VenueJournal of Participatory Medicine · 2025
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicInflammatory Bowel Disease
Canadian institutionsnot available
Fundersnot available
KeywordsPreprintmHealthHealth professionalsInflammatory bowel diseaseCitizen journalismMedicineHealth carePsychologyNursingDiseaseComputer scienceWorld Wide WebPolitical sciencePsychological interventionInternal medicine

Abstract

fetched live from OpenAlex

Background: Patients with inflammatory bowel disease (IBD) have periods with flare-ups, including abdominal pain, diarrhea, bloody stools, and systemic symptoms that may negatively influence the patients' quality of life. Hence, prompt and intensified treatment is often required, and patients need to pay attention to self-management, including easy access to health care professionals. Seeking support is essential in patients' self-management and beneficial for their quality of life. However, patients may experience difficulties in gaining access to health care professionals by phone or email when needed. Mobile health (mHealth) interventions have been shown to support patients with flexible, timely, and ongoing communication with health care professionals. However, the most prevalent functions of current apps for patients with IBD are tracking disease symptoms and accessing information. In addition, patient and clinician involvement in the design and development of eHealth apps for patients with IBD has been limited, although engaging patients is emphasized as essential for identifying tools and functionalities that they find relevant and effective. Objective: This study aimed to develop an mHealth solution for patients with IBD using participatory design to support tailored communication between patients and health care professionals. Methods: Through participatory design, we completed 3 focus groups, 4 mock-up workshops, and 2 prototype tests involving patients, health care professionals, and an IT designer to collaboratively develop a prototype. The iterative process allowed for feedback from all stakeholders to inform the design and development. This approach facilitated ongoing refinement of the prototype until a mutually satisfactory solution was achieved. Data analysis followed the structured phases inherent to participatory design: planning, acting, observing, and reflecting. Results: A total of 14 patients with IBD aged 18-65 years and 9 health care professionals from 2 outpatient clinics in Denmark contributed to the mHealth design. The analysis generated 6 themes of patients' suggestions for app content: easy-access messaging, agreement overviews, self-initiated patient-reported outcomes with free text, treatment and blood test notifications, an IBD knowledge base, and self-monitoring via diary and symptom registration. An intervention that reflected users' needs and requests to support patients' access to and communication with health care professionals in outpatient clinics was developed. The intervention included messaging, symptom registration, notifications, questionnaires with free-text space, a knowledge base, and an appointment overview. Conclusions: The participatory design served as a usable approach to designing and developing a tailored mHealth solution for patients with IBD and their health care professionals in an outpatient clinic. On the basis of the iterative design process with mutual learning and democratic voices, the participants had a significant impact on the solution, which reflected users' needs and resulted in the effective adaptation of the solution to the clinical setting.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.574

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.077
GPT teacher head0.394
Teacher spread0.317 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations0
Published2025
Admission routes1
Has abstractyes

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