Feasibility and acceptability of mPallCare, a digital health intervention for people living with advanced cancer in a refugee settlement in Uganda: a mixed-method study (Preprint)
Bibliographic record
Abstract
Background: Palliative care is a key component of comprehensive humanitarian health; yet, access and service capacity remain limited in displacement settings, where fragile health systems struggle to meet the complex needs of people living with advanced illness. Digital health technologies have the potential to enhance the reach and delivery of palliative care; yet, their feasibility and acceptability in humanitarian settings remain underexplored. Objective: We evaluated the feasibility and acceptability of mPallCare, a mobile health intervention integrating patient-reported symptom and outcome monitoring with a clinician dashboard, to support palliative care delivery in the Bidibidi Refugee Settlement, Uganda. Methods: A 6-week, uncontrolled, exploratory concurrent mixed methods feasibility study was conducted, involving 32 participants with advanced cancer. Community health workers (ie, village health teams) used the mobile app to document patient-reported symptoms and multidimensional outcomes, which were accessible to clinical teams via a dashboard. Following the use of mPallCare, patient and clinical team participants participated in face-to-face interviews. Data collected via mPallCare were analyzed using descriptive statistics to assess feasibility (ie, compliance with reporting, with a feasibility threshold of ≥65% of scheduled reports), and interview data from a subsample of patient and clinical team participants were analyzed using framework analysis to assess acceptability. Results: Participants completed 84.9% (163/192) of symptom reports and 59.4% (266/448) of outcome reports, with a combined 67% (429/640) of all scheduled reports completed. A modest decline in engagement with report submissions occurred across the 6-week study period. Commonly reported symptoms included headache (27/32, 84.4%), muscle pain (27/32, 84.4%), and dizziness (26/32, 81.3%). Interview findings indicated strong acceptability among patients and clinicians, who described improved communication, enhanced symptom management, and greater continuity of care. Reported challenges included initial navigation difficulties, limited translation accuracy, and technical synchronization issues. Participants and clinical leaders identified the potential for integrating mPallCare within Uganda's district health information system to strengthen data use and visibility of palliative care within health reporting structures. Conclusions: mPallCare is a feasible and acceptable digital health intervention for palliative care in a humanitarian setting. While initial uptake was high, sustaining engagement over time may require simplified reporting processes, enhanced language accessibility, and optimizing the mobile app's connectivity and usability. This feasibility phase highlights key priorities for scale-up, including integration with existing health information systems and adaptation for sustained, equitable use across low-resource and displaced populations.
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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 | 0.008 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".