Proposal for the User-Centered Design Approach for Health Apps Based on Successful Experiences: Integrative Review
Bibliographic record
Abstract
BACKGROUND: Different strategies encompassed within mHealth have shown themselves to be effective for maintaining good health or controlling certain diseases. However, there is usually a very high rate of abandonment of health apps. Therefore, it would seem obvious that there is a need for involving the end users (whether they are health professionals, patients, or both) in the design process from the early stages in order to enable their needs and characteristics to be identified. In this sense, it is common knowledge that focusing on the user permits the consideration of valuable details aimed at making the correct adjustment between the patient, the technology, and the organization of attention. OBJECTIVE: The goal of the research was to propose a methodology based on the review of previous successful user experiences in setting up health apps by using qualitative techniques (focus groups and discussion groups) that includes the participation of information technology and health professionals and the patients themselves. METHODS: An integrative review was made of studies in which a qualitative methodology was employed mainly through focus and/or discussion groups for the design and development of health apps, consulting diverse databases (PubMed, Scopus, and Proquest) with the following search strategy: "mHealth AND apps AND focus group OR discussion group." A total of 69 papers were included in the review. RESULTS: A proposal structured in 4 sessions of variable duration was made in which information technology and health professionals and patients take part: composing, preparing, and organizing contents (session 1); testing structure and usability (session 2); does the app fit the needs of end users? (session 3); and last testing-keep on improving (session 4). Throughout the sessions, we propose studying aspects like previous user experiences in mHealth, barriers to the adoption of mHealth, interface contents, management and browsability, usability, perceived quality, security and privacy, capacity to self-manage disease with the app, ergonomics, and glanceability, etc. Specific tools that have proved useful in previous research for measuring these aspects are presented. CONCLUSIONS: These work sessions would be based on predominantly qualitative methodologies although, as they evolve, validated questionnaires permitting the assessment of the objectivity of certain technical aspects could be incorporated. With this proposal, a project centered on end users could be effected, responding to their needs. However, this requires validation that will be made via implementation in the development of health apps, with the subsequent measurement of results in terms of adherence and improvement in the clinical variables of the end users.
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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 | 0.148 | 0.119 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.008 | 0.009 |
| Bibliometrics | 0.036 | 0.021 |
| Science and technology studies | 0.005 | 0.011 |
| Scholarly communication | 0.014 | 0.023 |
| Open science | 0.010 | 0.015 |
| Research integrity | 0.009 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 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".