Do eHealth literacy and socio-demographics predict patients' preferences for use of eHealth programmes after percutaneous coronary intervention?
Bibliographic record
Abstract
Abstract Funding Acknowledgements Type of funding sources: Public hospital(s). Main funding source(s): The Western Norway Health Authority Background Evidence supports the use of electronic health (eHealth) programmes for patients with coronary artery disease. To date, there has been little attention toward patients’ preferences for the use of eHealth programmes, and their associations with eHealth literacy and socio-demographic factors post percutaneous coronary intervention (PCI). Purpose To determine how eHealth literacy and socio-demographics are associated with patients’ preferences for use of eHealth programmes assessed 12-month post PCI. Methods An observational cohort study recruited 3417 adult patients treated by PCI at three Norwegian and four Danish university hospitals, June 2017-May 2019. Socio-demographic data and self-reported outcomes on eHealth literacy (eHealth literacy scale) were assessed at baseline. De novo questions on preferences for use of eHealth programmes were collected 12-month post PCI. Hierarchical logistic regression models were performed. Results The majority of patients were men (78%), and the mean age was 66 years. Almost 40% were interested in participating in eHealth programmes. The odds of being interested in accessing a webpage with quality ensured information, health applications and online chat function with healthcare providers increased with 2-3% for each point higher eHealth literacy score, which indicates better eHealth literacy. After controlling for age, education and gender (Step 2), eHealth literacy no longer remained a significant predictor for patients’ preferences. Males had 49% higher odds for interest in a webpage with quality ensured information than females. Females had 33-34% higher odds for interest in an online chat function with healthcare providers and an individually tailored text message. The odds for interest in a webpage with quality ensured information, health applications, online chat function with healthcare providers and individually tailored text messages, decreased with 2-5% per year higher age. For individual tailored feedback on email, the odds for interes was 1% higher per year higher age. Educational level above primary school was a robust predictor for the interest in a webpage with quality ensured information (63-240%). Compared to those with primary school education level, those with college/university education had 67% higher odds for interest in short online information videos, 123% higher odds for interest in online chat function with healthcare providers and 61% higher odds for interest in individually tailored feedback on email compared to patients’ whit primary school. Patients with high school and college/university education had 34-57% lower odds for interest in individually tailored text messages than those with primary school. Conclusions Age, educational level and gender were important predictors of patients’ preferences for using eHealth programmes post PCI. These results are important for the further development of personalized eHealth programmes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".