Tailoring for Health Literacy in the Design and Development of eHealth Interventions: Systematic Review
Bibliographic record
Abstract
Background: Tailoring is an important strategy to improve uptake and efficacy of medical information and guidance provided through eHealth interventions. Given the rapid expansion of eHealth, understanding the design rationale of such tailored interventions is vital for further development of and research into eHealth interventions aimed at improving health and healthy behavior. Objective: This systematic review examines the use of health literacy concepts through tailoring strategies in digital health interventions (eHealth) aimed at improving health and how these elements inform the overall design rationale. Methods: A systematic search of PubMed, PsycINFO, Web of Science, and ACM databases yielded 31 eligible randomized trials that focused on adult health improvement through eHealth interventions. Eligible studies compared tailored versus nontailored eHealth interventions for adults, excluding non-English papers and those addressing solely readability or targeting populations with accessibility barriers. Data extraction focused on study characteristics, health literacy components, tailoring methods, and design rationales, with study quality evaluated using Quality Assessment for Diverse Studies (QuADS) by independent reviewers. Results: Most interventions applied both cognitive and social health literacy concepts and predominantly used content matching as a tailoring strategy. Of all studies using content matching, most used one or more supporting theories as well as end-user data to inform the content matching. While choices for individual intervention components were mostly explicated, detailed descriptions of the design process were scarce, with only a few studies articulating an underlying narrative that integrated the most important chosen components. Conclusions: While tailored eHealth interventions demonstrate promise in enhancing health literacy and the trial design of the interventions overall was of good quality, inconsistent documentation of design rationales impedes replicability and broader application of the used eHealth concepts. This calls for more detailed reporting on the design choices of the intervention in efficacy studies, so that reported outcomes can be easier connected to choices made in the design of the eHealth intervention.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.065 | 0.217 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.010 | 0.012 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".