User-Centered Design Process of an mHealth App for Health Professionals: Case Study
Bibliographic record
Abstract
BACKGROUND: User-centered design processes are infrequently employed and not fully explored for building mobile health (mHealth) apps that are particularly targeted to health professionals as end users. The authors have used a user-centered design-based approach to build an mHealth app for health professionals, tasked to deliver medical laboratory-related information on a daily basis. OBJECTIVE: Our objective is to generate a simple and functional user-centered design process for mHealth apps for health professionals. This paper presents the key learnings from design activities. METHODS: A stratified random sample of doctors and nurses was recruited for the study. The design activities were planned in the following sequence: focus group discussion for situation analysis and information architecture, design activity 1 for wireframe designing, design activity 2 for wireframe testing, and user testing sessions 1 and 2. RESULTS: The final design and functions of the app, information architecture, and interactive elements were largely influenced by the participatory design-based user-centered design activities. As a result of the design process, we could identify the mental models of processing requests for information and personal preferences based on the experience. These findings were directly or indirectly incorporated into the app design. Furthermore, finding alternative ways of working within time constraints and cultural barriers and the methods employed to manage the challenges of interdisciplinary discourse stood out among the lessons learned. CONCLUSIONS: We recommend a user-centered design process based on a participatory design approach in mHealth app design, enriched with focus group discussions where possible.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.004 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".