Affective symptoms, cognitive function and self-care behaviours in adults with heart failure according to ejection fraction phenotype
Bibliographic record
Abstract
AIMS: The aim of this study was to compare affective symptoms, cognitive dysfunction, and self-care behaviours among different heart failure (HF) phenotypes and to explore their interrelationships, particularly examining how cognitive and affective factors influence self-care practices. METHODS AND RESULTS: This cross-sectional study involved 250 older adults hospitalized for acute decompensated HF, categorized into three groups based on left ventricular ejection fraction (EF): HF with reduced EF (HFrEF), mildly reduced EF (HFmrEF), and preserved EF (HFpEF). Affective symptoms were assessed using the Hospital Anxiety and Depression Scale and Patient Health Questionnaire-9 (PHQ-9), while cognitive function was evaluated with the Montreal Cognitive Assessment (MoCA) and Mini-Mental State Examination (MMSE). Self-care behaviours were measured using the European Heart Failure Self-Care Behavior Scale. Among participants, 42% had HFrEF, 18.4% had HFmrEF, and 39.6% had HFpEF. Cognitive dysfunction was more pronounced in HFpEF patients (MMSE median = 28.0, IQR = 26.0-29.0) compared with those with HFrEF (median = 28.0, IQR = 27.0-29.0) or HFmrEF (median = 29.0, IQR = 27.3-29.0, P = 0.008). Higher MMSE scores were significantly associated with better self-care behaviours in HFpEF patients (Spearman's r = -0.299, P = 0.003) but not in the other groups. Significant differences were found in specific self-care behaviours, including contacting healthcare providers and adherence to a low-sodium diet. CONCLUSION: Although variations in cognitive function and self-care behaviors were observed across HF phenotypes, these differences were not statistically significant after adjusting for demographic and clinical factors. Tailored interventions should be based on a comprehensive assessment of cognitive and emotional health, rather than HF phenotype alone.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".