Determinants and Health Outcomes of Digital Health Literacy in Patients With Cardiovascular Disease: Systematic Review and Meta-Analysis (Preprint)
Notice bibliographique
Résumé
BACKGROUND With expansion of technology-enabled care, digital health literacy (DHL) has become integral to effective cardiovascular disease (CVD) management. However, quantitative evidence regarding determinants and health outcomes of DHL in CVD remains limited and heterogeneous, necessitating comprehensive evidence synthesis. OBJECTIVE This study aimed to (1) estimate DHL levels, (2) synthesize DHL-associated factors, and (3) examine DHL-related health outcomes in CVD. METHODS A systematic review and meta-analysis of DHL in adults with CVD was conducted per PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) 2020 guidelines. PubMed, Embase, Cochrane CENTRAL, CINAHL, Scopus, Web of Science, and Google Scholar were searched for peer-reviewed studies published between 2006 and January 31, 2026. Quantitative studies enrolling adults with CVD, which reported a measure of DHL were included. Studies focusing exclusively on primary cerebrovascular disease and non–peer-reviewed articles were excluded. Risk of bias (ROB) was assessed using the Appraisal Tool for Cross-Sectional Studies tool, the Newcastle-Ottawa Scale, the Revised Cochrane Risk-of-Bias Tool for Randomized Trials, and the Risk of Bias in Nonrandomized Studies of Interventions. Certainty of evidence was evaluated using the Grading of Recommendations Assessment, Development, and Evaluation approach. Pooled mean eHealth Literacy Scale (eHEALS) scores were synthesized using a random-effects meta-analysis. Heterogeneity was quantified using the I2 statistic and 95% prediction intervals. RESULTS Twenty studies involving 8581 adults with CVD were included. The overall pooled mean eHEALS score was 24.26 (95% CI 21.19-27.32), with substantial heterogeneity (I2=98.4%; τ2=15.55; τ=3.94) and a wide 95% prediction interval (14.66-33.85). Lower DHL was consistently associated with older age, lower educational attainment, female sex, limited social support, and less experience with digital technologies. Higher DHL was associated with more favorable health-related outcomes, including health behaviors, better quality of life, and greater use and acceptance of digital health technologies. Subgroup analyses showed no statistically significant differences in DHL by region, disease type, or age group. The certainty of evidence was rated as low to very low, and substantial heterogeneity persisted across analyses. CONCLUSIONS Our findings underscore DHL as a foundational capability for digitally supported self-management in CVD care and reveal disparities associated with age and socioeconomic factors. By integrating evidence on DHL levels, associated factors, and DHL-related health outcomes in CVD populations, this review provides a more comprehensive, clinically relevant understanding of DHL beyond studies relying on a single instrument (eg, eHEALS) or examining isolated domains. DHL appears to be a context-dependent competency shaped by broader structural and social determinants. From a clinical and health system perspective, digital health interventions should be accompanied by structured digital inclusion strategies, including routine assessment of DHL and care delivery to patients’ digital capacities. Further longitudinal and interventional studies are warranted to clarify the causal pathways linking DHL to health outcomes in adults with CVD and to incorporate provider- and system-level perspectives beyond individual-level assessments. CLINICALTRIAL PROSPERO International Prospective Register of Systematic Reviews CRD420251068000; https://www.crd.york.ac.uk/PROSPERO/view/CRD420251068000
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,028 | 0,079 |
| Méta-épidémiologie (sens strict) | 0,003 | 0,002 |
| Méta-épidémiologie (sens large) | 0,018 | 0,048 |
| Bibliométrie | 0,008 | 0,009 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,005 | 0,003 |
| Science ouverte | 0,002 | 0,003 |
| Intégrité de la recherche | 0,003 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,006 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».