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)
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,008 | 0,014 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,003 | 0,002 |
| Communication savante | 0,003 | 0,002 |
| Science ouverte | 0,001 | 0,003 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».