The triadic nexus of digital literacy, patient engagement and mHealth for sustainable health outcomes: A scoping review (Preprint)
Notice bibliographique
Résumé
BACKGROUND mHealth is vital for improving healthcare delivery, especially in underserved and remote areas, where a substantial gap remains in primary healthcare services owing to a lack of healthcare facilities and providers. Even countries in the Global North, such as the United States, Canada, Australia, and parts of Europe, with advanced healthcare facilities, have remote regions that lack adequate healthcare access. However, mHealth has not achieved its desired impact owing to low engagement by target users. Limited digital literacy, especially among older adults and low-income and marginalized groups, remains a significant barrier to mHealth adoption and engagement. OBJECTIVE This scoping review synthesizes previous studies to construct the dynamic and complex interplay between digital literacy, patient engagement, and mHealth for sustainable health outcomes, as well as the theoretical underpinnings that guide this field. A triple discourse framework of digital literacy, patient engagement, and mHealth for sustainable health outcomes is also proposed. METHODS This scoping review was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) reporting guidelines developed by Arksey and Malley (2005). RESULTS Database searches identified 330 records, and based on the screening criteria, only 22 studies were included in the review. Our findings revealed a strong connection between digital literacy, patient engagement, and mHealth, with digital literacy serving as a significant predictor of mHealth adoption and patient involvement. We showed that mHealth interventions can improve long-term health outcomes when digital-literacy gaps are addressed. Our scoping review also demonstrates that Technology Acceptance Model (TAM), Unified Theory of Acceptance and use of Technology (UTAUT), and Diffusion of Innovation Theory (DOI) remain foundational for understanding mHealth adoption in different environments; however, their explanatory power is limited when it comes to advancing digital literacy and patient engagement, particularly in complex healthcare environments. They do not fully encompass the complexity and role of digital literacy in mHealth interventions. CONCLUSIONS Digital literacy, patient engagement, and mHealth are deeply interconnected, with digital literacy serving as the foundation for mHealth engagement and its usability. Strengthening digital literacy and patient engagement is essential for realizing the full potential of mHealth, and targeted equity-focused research is required to close persistent gaps. Therefore, as healthcare services move into cyberspace, patients’ competencies must evolve from traditional literacy to digital literacy, forming a synergistic triad with mHealth engagement that underpins sustainable health outcomes in the future. This will ensure that patient engagement is sustained in diverse populations, not just digitally literate cohorts.
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 | 0,027 | 0,118 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,004 | 0,007 |
| Bibliométrie | 0,015 | 0,018 |
| Études des sciences et des technologies | 0,002 | 0,002 |
| Communication savante | 0,008 | 0,007 |
| Science ouverte | 0,002 | 0,004 |
| Intégrité de la recherche | 0,003 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,008 | 0,001 |
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 ».