Impact of Digital Health Interventions on Health Literacy: A Systematic Review with Quality Appraisal
Bibliographic record
Abstract
Abstract In the digital era, health literacy is crucial for informed health decisions and improved outcomes. This systematic review examines the effectiveness of digital health interventions (DHIs) in improving health literacy, as defined by the WHO. We included studies (cross-sectional, surveys, and case reports), focusing on interventions like mobile health apps, online platforms, and telehealth services. Our search, adhering to PRISMA guidelines, spanned databases PubMed, IEEE, and ACM, covering publications from 2013 to 2024. From 1.029 initial articles, 39 met our inclusion criteria. Our findings highlight that DHIs, including multimedia tools and remote sessions, have been reported to improve health literacy across diverse populations. However, the impact varies due to the digital divide, influenced by factors like age and socioeconomic status. This review categorizes the included studies by digital intervention type, including: 10.3 % on mobile apps (n = 4), 30.8 % on websites and online platforms (n = 12), 5.1 % on multimedia tools (n = 2), 15.4 % on telehealth and mHealth (n = 6), 2.6 % on electronic health records and patient portals (n = 1), 17.9 % conference proceedings (n = 7), and 17.9 % review studies (n = 7). This study highlights the complex nature of DHIs designed to enhance health literacy and engagement, aiming to reduce health disparities and ensure equitable access to healthcare benefits regardless of socioeconomic background or digital literacy. It underscores the importance of user-centered design, cultural sensitivity, and ongoing support for maximizing the effectiveness of DHIs. Incorporating theoretical frameworks is shown to boost engagement and promote behavioral change, particularly by addressing intrinsic motivations and cultural factors. The findings emphasize the necessity of sustained strategies, such as gamification, to maintain improvements in health literacy, and advocate for standardized evaluation methods to guide policy and advance the global transition to digital-first healthcare. Author Summary How effective are digital health interventions in improving health literacy? This review of 39 studies found that digital health interventions, including mobile apps, websites, and telehealth, are reported to improve health literacy. Effectiveness of these tools depended on age, digital skills, and socioeconomic factors, with user-centered design, cultural competence, and sustained engagement being key to success. In summary, digital health interventions can improve health literacy but require cultural relevance and sustained engagement for success. The three main takeaways from this collaborative work are: Well-designed digital health interventions can improve health literacy. The digital divide limits access, especially for vulnerable groups. Long-term success requires support, user focus, and cultural relevance.
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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 | 0.048 | 0.191 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.016 | 0.017 |
| Bibliometrics | 0.018 | 0.017 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".