Association of Technology-Related Skills and Self-Efficacy With Willingness to Participate in Heart Failure Telemonitoring: Cross-Sectional Observational Study
Bibliographic record
Abstract
Background: The adoption of telemonitoring in patients with heart failure (HF) is influenced by technology-related skills and self-efficacy, as well as psychological, clinical, and demographic factors. However, the relative importance of these factors with regard to willingness to use telemonitoring is insufficiently understood. objectives: This cross-sectional observational study examines the extent to which technology-related skills and self-efficacy are related to willingness to participate in telemonitoring in patients with HF. Methods: Patients completed questionnaires during hospitalization. Associations of technological skills and self-efficacy with willingness to participate in telemonitoring (dichotomous and continuous scale) were examined using regression models. Mediation-moderation analyses were used to investigate the role of self-efficacy in the association of technological skills with willingness to participate. Results: This study recruited 61 patients admitted for decompensated HF (mean age 79.9, SD 9.5 years; 24 women). Higher levels of technological skills were associated with higher willingness to participate in telemonitoring (odds ratio [OR] 1.073 per scale unit, 95% CI 1.031-1.117). Technological self-efficacy and learnability were also related to willingness to participate (OR 1.141, 95% CI 1.039-1.252; OR 1.029, 95% CI 1.006-1.052) but did not mediate the association of technological skills with willingness to participate in telemonitoring. Psychological factors (anxiety, depressive symptoms, and perceived social support), age, and cognitive and physical functioning did not moderate the association of technological skills with willingness to participate in telemonitoring. Conclusions: Technological skills, self-efficacy, and learnability are interrelated factors that need to be considered in patients with HF who are eligible for telemonitoring. Future intervention studies that target these factors could increase patients' willingness and competence in using telemonitoring after admission for HF.
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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 | 0.003 | 0.008 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".