Impact of mHealth and eHealth on oral health literacy: A systematic review
Bibliographic record
Abstract
Background: Enhancing oral health-related quality of life requires oral health knowledge. Mobile healthcare increases assistance and healthcare through mobile devices and wireless technologies. Using information and technology, eHealth enhances healthcare services by enabling digital administration and improving data interchange and coordination among providers, but its influence on oral health knowledge and practice is unclear. Therefore, the present article will examine how mHealth and eHealth could improve oral health knowledge and practices. Methods: Original research on the function of mHealth and eHealth in enhancing oral health literacy was identified through searches of PubMed, Cumulative Index to Nursing and Allied Health Literature, ScienceDirect, IEEE Xplore, Dimensions, and the Cochrane Library. The potential articles were selected based on modified Patient/Problem, Intervention, Comparison, Outcome, Study criteria. The risk of bias in suitable studies was evaluated using the risk of bias visualization tool (Robvis 2.0) and the risk of bias in non-randomized studies with intervention. Results: The database search generated 2197 entries, of which 13 publications were used in this analysis. Narrative synthesis revealed that mHealth and eHealth interventions consistently improved oral health knowledge and practices across diverse populations, including caregivers, elderly adults, and dental students. Short message service (SMS)-based interventions enhanced mothers' knowledge and practices related to children's oral health, while virtual reality technologies improved learning outcomes for dental professionals and students compared to traditional methods. However, improvements in knowledge did not consistently translate to sustained behavior changes, with variations in practice outcomes across studies due to differences in measurement tools and intervention designs. These findings suggest that while digital interventions enhance knowledge, their impact on long-term behavior requires further exploration. Conclusion: Mobile apps, SMS-based therapies, and virtual reality applications greatly improved oral health knowledge, habits, and literacy scores across different age groups.
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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 | 0.012 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.009 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".