Mobile Health in Home-Based Palliative Care: A Realist Review of Contextual Factors and Mechanisms Influencing Outcomes (Preprint)
Notice bibliographique
Résumé
Background: The increasing prevalence of chronic illness presents a significant global health challenge due to growing end-of-life suffering. Palliative care is now an essential health service under Universal Health Coverage. Its integration into primary health care and use of mobile health (mHealth) have been recommended to improve access. Objective: This study aimed to synthesize evidence on how mHealth interventions in home-based palliative care work, for whom, and in what context. Methods: This realist review identified global evidence relevant to mHealth interventions for home-based palliative care through searches of 9 electronic databases (ie, MEDLINE, Embase, PsycINFO, Global Health, CINAHL, Web of Science, Cochrane Database of Systematic Reviews, Scopus, and Global Index Medicus), and forward citation tracking of included articles conducted in March 2026. The review followed the 5 key stages of a realist review: scoping the literature to assess what is important about the context of mHealth intervention in home-based palliative care and what mechanisms might be important in how such interventions result in their intended outcomes; articulating the underlying program theory and refining the review scope through consultation with international palliative care experts; conducting iterative searches for and appraisal of relevant evidence; extracting data; and narratively synthesizing the data, prioritized by relevance and rigor to generate conclusions and recommendations. The Framework of Complexity in Palliative Care Context, adapted from Bronfenbrenner's Ecological Systems Theory, was applied to examine the contextual influences and interactions among factors within the multilayered system. Context-mechanism-outcome configurations were developed and iteratively tested to refine the program theory for mHealth in home-based palliative care. Results: A total of 4134 records were identified, of which 422 (10.21%) articles were retained for full-text screening, and 126 (3.05%) studies were included in the final synthesis. The contextual factors and mechanisms that positively influence the intended outcomes include (1) alleviating concerns and mitigating perceived threats about mHealth's suitability in palliative care through proper orientation for patients and carers, along with clear guidelines for health care professionals; (2) minimizing infrastructural and technological barriers through user-friendly designs and investment in sustainable models of mHealth for home-based palliative care; (3) engaging diverse stakeholders to ensure mHealth aligns with priority health care needs, user preferences, and the operations of implementing organizations and the wider health care system; (4) streamlining health management information systems through interoperable mHealth systems implemented at different levels of care; (5) enabling access to and regular communication with relevant health care teams; and (6) facilitating access to adequate information to empower users in palliative care services. Conclusions: mHealth interventions can enhance home-based palliative care but must align with local contexts. It is recommended that mHealth interventions ensure safety and comfort, technology competence, tailored communication, empowerment of end users, family involvement, health care worker motivation, and system integration.
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 | 0,013 | 0,038 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,002 | 0,002 |
| Bibliométrie | 0,003 | 0,005 |
| Études des sciences et des technologies | 0,001 | 0,002 |
| Communication savante | 0,005 | 0,003 |
| Science ouverte | 0,001 | 0,002 |
| 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 ».