Design Research to Embed mHealth into a Community-Led Blood Pressure Management System in Uganda: Protocol for a Mixed Methods Study
Bibliographic record
Abstract
BACKGROUND: Uncontrolled hypertension is a leading risk factor for cardiovascular diseases. In Uganda, such diseases account for approximately 10% of all deaths, with 1 in 5 adults having hypertension (>90% of the hypertensive cases are uncontrolled). Although basic health care in the country is available free of cost at government facilities, regularly accessing medication to control hypertension is difficult because supply chain challenges impede availability. Clients therefore frequently suspend treatment or buy medication individually at private facilities or pharmacies (incurring significant costs). In recent years, mobile health (mHealth) interventions have shown increasing potential in addressing health system challenges in sub-Saharan Africa, but the acceptability, feasibility, and uptake conditions of mobile money approaches to chronic disease management remain understudied. OBJECTIVE: This study aims to design and pilot-test a mobile money-based intervention to increase the availability of antihypertensive medication and lower clients' out-of-pocket payments. We will build on existing local approaches and assess the acceptability, feasibility, and uptake of the designed intervention. Furthermore, rather than entering the study setting with a ready-made intervention, this research will place emphasis on gathering applied ethnographic insights early, which can then inform the parameters of the intervention prototype and concurrent trial. METHODS: We will conduct a mixed methods study following a human-centered design approach. We will begin by conducting extensive qualitative research with a range of stakeholders (clients; health care providers; religious, cultural, and community leaders; academics; and policy makers at district and national levels) on their perceptions of hypertension management, money-saving systems, and mobile money in the context of health care. Our results will inform the design, iterative adaptation, and implementation of an mHealth-facilitated pooled financing intervention prototype. At study conclusion, the finalized prototype will be evaluated quantitatively via a randomized controlled trial. RESULTS: As of August 2023, qualitative data collection, which started in November 2022, is ongoing, with data analysis of the first qualitative interviews underway to inform platform and implementation design. Recruitment for the quantitative part of this study began in August 2023. CONCLUSIONS: Our results aim to inform the ongoing discourse on novel and sustainable pathways to facilitate access to medication for the management of hypertension in resource-constrained settings. TRIAL REGISTRATION: German registry of clinical trials DRKS00030922; https://drks.de/search/en/trial/DRKS00030922. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/46614.
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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 | 0.115 | 0.069 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.061 | 0.014 |
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".