A digital self-care intervention for Ugandan patients with heart failure and their clinicians: User-centred design and usability study
Bibliographic record
Abstract
Background The prevalence of heart failure (HF) is increasing in Uganda. Ugandan patients with HF report receiving limited information about their illness and associated self-care behaviours. Interventions targeted at improving HF self-care have been shown to improve patient quality of life and reduce hospitalizations in high-income countries. However, such interventions remain underutilized in resource-limited settings like Uganda. This study aimed to develop a digital health intervention that enables improved self-care amongst HF patients in Uganda. Methods We implemented a user-centred design (UCD) process to develop a self-care intervention entitled Medly Uganda. The ideation phase comprised a scoping review and preliminary data collection amongst HF patients and clinicians in Uganda. An iterative design process was then used to advance an initial prototype into a functional digital health intervention. The evaluation phase involved usability testing of the intervention amongst Ugandan patients with HF and their clinicians. Results Medly Uganda is a digital health intervention that allows patients to report daily HF symptoms, receive tailored treatment advice and connect with a clinician when showing signs of decompensation. The system harnesses Unstructured Supplementary Service Data (USSD) technology that is already widely used in Uganda for mobile phone-based financial transactions. Usability testing showed Medly Uganda to be both acceptable and feasible amongst clinicians, patients and caregivers. Conclusions Medly Uganda is a functional digital health intervention with demonstrated acceptability and feasibility in enabling Ugandan HF patients to better care for themselves. We are hopeful that the system will improve self-care efficacy amongst HF patients in Uganda.
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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 | 0.031 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".