Bridging the Gap: Barriers and Facilitators to Mobile Health Use in Cardiovascular Prevention
Bibliographic record
Abstract
Abstract Background The persistent burden of cardiovascular diseases (CVDs) underscores the need to improve prevention strategies. Mobile health (mHealth) technologies offer promising solutions to tailor preventive interventions. However, several factors may hinder or accelerate their real-world implementation. This scoping review aimed to map barriers and facilitators to the implementation of mHealth in CVD prevention in the context of a larger project, titled INNOvative personalized cardiovascular disease PREVention in high-risk adults (INNOPREV) (PNRR-MAD-2022-12375795), which aims to evaluate the use of Polygenic Risk Score and digital technologies in personalized cardiovascular prevention in Italy. Methods PubMed and Scopus were searched without geographical or temporal restrictions up to April 2024. Eligible studies reporting evidence on barriers and/or facilitators were analyzed using the Consolidated Framework for Implementation Research (CFIR), enabling their systematic classification across five major domains (intervention characteristics, outer setting, inner setting, characteristics of individuals, and process). Results Out of 2,923 records, 27 articles were considered. Most studies were conducted in the United States (44%) and Canada (15%). Most frequently reported barriers and facilitators were related to intervention and individuals’ characteristics domains. In respect to the intervention characteristics key barriers were technical difficulties and lack of user-friendly design, which might contribute to poor users’ adherence and engagement. For the individuals’ characteristics barriers were linked to gender and racial disparities, socioeconomic status, low digital literacy, and level of physical and psychological distress coming from mHealth use. Conclusions Effective implementation of mHealth in CVD prevention requires careful consideration of the role played by individual factors, as well as by mHealth design and characteristics. Key messages • Successful mHealth implementation in CVD prevention depends on aligning technology with user needs, clinical workflows, and organizational support. • Targeting key barriers and enablers may enhance adoption and impact of mHealth interventions in cardiovascular care.
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 | 0.033 | 0.108 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".