The trajectory of self-care over a 12-month period in patients with heart failure, comparing those with consistently good and poor self-care
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».