A prospective evaluation of telemonitoring use by seniors with chronic heart failure: Adoption, self-care, and empowerment
Bibliographic record
Abstract
Telemonitoring leverages technology for the follow-up of patients with heart failure. Limited evidence exists on how telemonitoring influences senior patients' attitudes and self-care practices. This study examines telemonitoring impacts on patient empowerment and self-care, and explores adoption factors among senior patients. A longitudinal study design was used, involving three surveys of elderly with chronic heart failure (n = 23) 1 week, 3 months, and 6 months after beginning telemonitoring use. Self-care, patient empowerment, and adoption factors were assessed using existing scales. The patients involved in this study perceived value of using telemonitoring, did not expect it to be difficult to use, and did not encounter adoption barriers. There was a significant improvement in patients' confidence in their ability to evaluate their symptoms, address them, and evaluate the effectiveness of the measures taken to address these symptoms. Yet, patients performed less self-care maintenance activities, and the capability of involvement in the decision-making related to their condition decreased. Telemonitoring can improve seniors' confidence in evaluating and addressing their symptoms in relation to heart failure. This patient management approach should be coupled with targeted education geared toward self-maintenance and self-management practices.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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".