The trajectory of self-care over a 12-month period in patients with heart failure, comparing those with consistently good and poor self-care
Bibliographic record
Abstract
Abstract Background Self-care behaviour plays a pivotal role in enhancing the quality of life for patients with heart failure (HF) and reducing mortality and morbidity rates, but many patients struggle with suboptimal self-care behaviours. It is crucial to understand the factors influencing long-term self-care management. Purpose To assess the trajectory of self-care over a 12-month period and assess differences between patients who have constantly good self-care over a 12-month period compared to constantly poor self-care. Method This study use data from a randomised controlled intervention study, called the HF-Wii study. We included patients who had complete data on self-care during on baseline, 3, 6 and 12 months. Self-care was measured with the European Heart Failure Self-care Behaviour scale (EHFSBs). A threshold score of equal or higher than 70 was used to define self-care behaviour as good and a score lower than 70 as poor self-care. Based on literature, the following characteristics (collected from questionnaires and medical records at baseline) were considered in the analysis: age, sex, marital status, education, history of myocardial infarction, duration of HF, New York Heart Association (NYHA) functional class, LVEF, comorbidity. Other factors assessed were Health-related Quality of Life (HrQoL, Minnesota living with HF questionnaire), depressive symptoms (Hospital Anxiety and Depression scale), cognition (Montreal Cognitive Assessment), sleep (Minimal Insomnia Symptom Scale) and level of HF knowledge (Dutch HF knowledge scale). Logistic regression was performed with backwards elimination of the factors considered associated with the differences between patients who have constantly good self-care at baseline, 3, 6 and 12 months compared to constantly poor self-care. Results In total, 407 patients were included in this study. At baseline, the mean self-care was 65±21, with 41% of the patients (n=245) performing good self-care. In total, 126 patients had a constantly good self-care over 12 month (31%), 120 patients had a constantly bad self-care over 12 months (30%) and 161 (39%) fluctuated in their self-care (Figure). In the multivariate analysis, patients that had continuously good self-care over 12-months were of older age (68±10 vs. 64±12, odds ratio (OR) 1.061, P-value<0.001), had higher HF knowledge (score 13±2 vs. 12±2, OR 1.439, P-value<0.001) and had higher HrQoL (score 28±20 vs 36±22, OR 0.984, P-value=0.022). compared to patients with consistently poor self-care. The variance was explained by 20%. Conclusion Younger patients with HF tend to struggle with consistent self-care over a year, underscoring the necessity for tailored interventions for this group. Conversely, patients with greater HF knowledge and higher HrQoL maintained good self-care habits.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".