Factors associated with the levels of eHealth literacy skills among patients in cardiovascular care: secondary analysis of data from users of a hypertension management app
Bibliographic record
Abstract
Abstract Background Digital health technologies (DHT) are increasingly integrated into cardiovascular care, yet patients’ perceived eHealth literacy skills (eHEALS) vary. Understanding the factors influencing these skills is crucial for to enhance self-management in cardiovascular patients and to optimize the effective use of DHT. Objective This study investigates the factors associated with perceived eHEALS among users of the Hypertension.App, a digital health application specifically designed for cardiovascular care patients. Methods A secondary analysis was conducted reusing data from Hypertension.App users in Germany. This cross-sectional study examined factors influencing eHeals total scores using Bayesian model averaging (BMA). Collinear covariates were excluded (variance inflation factor <2.5). Variables associated with eHEALS total scores in Bayesian univariate linear regression analysis (region of practical equivalence ≤5%) were considered in the BMA. All analyses were stratified by sex and age (<54 years and ≥ 54 years). Results A total of 250 hypertension patients (109 females, 139 males, and 2 non-binaries) who were on average 53.6 years (SD: 13.8 years) were included. On average, patients had an eHEALS total score of 31 (SD: 4.91). Twenty-three determinant factors were identified (Fig. 1), with notable differences between sex and age groups. Lower eHEALS scores (Fig. 2.) were associated with using the app for 1−6 months or less often than once a month, being a male, having difficulty performing physical activity for at least 30 min 5 to 7 days per week, having experienced a heart attack or stroke. In contrast, higher eHEALS scores were associated with owning a smartwatch or fitness tracker, having the app integrated into medical treatment for medication adjustment by a physician based on patient entries, strongly agreeing that most people can quickly learn how to use the app, wanting a voucher, living in the Berlin federal state, and using the app’s diary function. Conclusions This study identifies key factors influencing perceived eHEALS among Hypertension.App users in cardiovascular care. These findings highlight/emphasize that eHealth literacy is shaped not only by demographic and health-related factors, but also by the context of app usage, user motivation, engagement, and perceived usability. Taken together, these insights can inform the design and implementation of digital health interventions to better support cardiovascular patients.Figure 1 Figure 2
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".