The triadic nexus of digital literacy, patient engagement and mHealth for sustainable health outcomes: A scoping review (Preprint)
Bibliographic record
Abstract
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.
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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 | 0.027 | 0.118 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.007 |
| Bibliometrics | 0.015 | 0.018 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".