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Record W3154114083 · doi:10.2196/26673

Development of an mHealth App for Patients With Psoriasis Undergoing Biological Treatment: Participatory Design Study

2021· article· en· W3154114083 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueJMIR Dermatology · 2021
Typearticle
Languageen
FieldImmunology and Microbiology
TopicPsoriasis: Treatment and Pathogenesis
Canadian institutionsnot available
FundersSyddansk UniversitetOdense Universitetshospital
KeywordsmHealthPsoriasisParticipatory designCitizen journalismSmartphone appMobile appsMedicineComputer scienceInternet privacyWorld Wide WebEngineeringNursingDermatologyOperations managementPsychological intervention

Abstract

fetched live from OpenAlex

BACKGROUND: In Denmark, patients with psoriasis undergoing biological treatment have regular follow-ups, typically every 3 months. This may pose a challenge for patients who live far away from the hospital. Mobile health (mHealth) is a promising and reliable tool for the long-term management of patients with psoriasis undergoing biological treatment because the disease course can be properly monitored. Despite recent developments in mHealth, the full potential of teledermatology remains to be tapped by newer, more attractive forms of services focused on patients' needs. OBJECTIVE: This study aims to design and develop an mHealth app to support the self-management of patients with psoriasis using a participatory design. METHODS: Using participatory design, we conducted 1 future workshop, 4 mock-up workshops, and 1 prototype test with patients and health care professionals to co-design a prototype. The process was iterative to ensure that all stakeholders would provide input into the design and outcome; this approach enabled continuous revision of the prototype until an acceptable solution was agreed upon. Data were analyzed according to the steps-plan, act, observe, and reflect-in the methodology of participatory design. RESULTS: Health care professionals and patients emphasized the importance of a more patient-centered approach, focusing on the communication and maintenance of relationships. Patients perceived consultations to be impersonal and repetitive and wanted the opportunity to contribute to the agenda while attending a consultation. Patients also stated they would prefer not to attend visits in person every 3 months. On the basis of these findings, we designed an mHealth app that could replace in-person visits and support patients at in-person visits. Video consultations, self-monitoring, and registration of patient-reported outcome data were embedded in the app. CONCLUSIONS: Using participatory design facilitated mutual learning and democratic processes that gave end users a significant influence over the solution. Despite the advantages of using participatory design in developing mHealth solutions, organizational conditions may still represent a barrier to the optimization of solutions.

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.

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)0.000
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 score0.230
Threshold uncertainty score0.848

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.081
GPT teacher head0.313
Teacher spread0.232 · 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