Processes and Recommendations for Creating mHealth Apps for Low-Income Populations
Bibliographic record
Abstract
BACKGROUND: Mobile health (mHealth) apps have shown to improve health indicators, but concerns remain about the inclusion of populations from low- and medium-income countries (LMIC) in these new technologies. Atrial fibrillation (AF) is a chronic condition with a challenging management. Previous studies have shown socioeconomic differences in the prescription of anticoagulant treatment and shared decision strategies are encouraged to achieve better outcomes. mHealth can aid both doctors and patients in this matter. OBJECTIVE: We describe the development of an mHealth app (aFib) idealized to aid shared decision between doctor and patient about anticoagulation prophylaxis in AF in a low-income and low-literacy population in Brazil. On the basis of our research, we suggest the processes to be followed when developing mHealth apps in this context. METHODS: A multidisciplinary team collected information about the target population and its needs and detected the best opportunity to insert the app in their current health care. Literature about the subject was reviewed and important data were selected to be delivered through good navigability, easy terminology, and friendly design. The app was evaluated in a multimethod setting. RESULTS: The steps suggested to develop an mHealth app target to LMIC are: (1) characterize the problem and the target user, (2) review the literature, (3) translate information to knowledge, (4) protect information, and (5) evaluate usability and efficacy. CONCLUSIONS: We expect that these recommendations can guide the development of new mHealth apps in LMIC, on a scientific basis.
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 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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.020 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".