Sleep quality patterns in patients with heart failure: a person-centred latent class analysis from a secondary analysis of the MOTIVATE-HF trial
Bibliographic record
Abstract
OBJECTIVES: To identify distinct sleep quality patterns among patients with heart failure (HF) using a person-centred approach and explore demographic and clinical predictors of these patterns. DESIGN: Secondary analysis of baseline cross-sectional data from the MOTIVATE-HF (MOTIVATional intErviewing to improve self-care in Heart Failure patients) randomised controlled trial. Latent class analysis (LCA) was applied to Pittsburgh Sleep Quality Index (PSQI) component scores to identify distinct subgroups of patients. Demographic, clinical and psychological variables were examined as potential predictors of cluster membership. SETTING: Three healthcare settings in Italy: hospital, outpatient and community-based care. PARTICIPANTS: 510 adult patients diagnosed with HF (New York Heart Association (NYHA) class II-IV) with poor self-care were included. Patients with severe cognitive impairment or recent myocardial infarction were excluded. PRIMARY AND SECONDARY OUTCOME MEASURES: Primary outcome: Sleep quality, measured using the PSQI, analysed through LCA to identify sleep disturbance clusters. Secondary outcomes included demographic and clinical characteristics predicting cluster membership. RESULTS: The mean age was 72.4 years (SD=12.3), with most participants married or partnered (62%) and retired or unemployed (83.9%). Mild comorbidities were present in 53.3% of the sample (mean Charlson Comorbidity Index (CCI)=2.91, SD=1.98), and 61.4% were classified in NYHA class II. Three sleep quality clusters emerged: (1) adequate sleep duration but disturbed sleep and daytime dysfunction (46.1%); (2) severe sleep problems with low use of sleeping medications (25.3%); and (3) minor sleep problems with mild disturbances (28.6%). Patients in Cluster 1 were older (mean age=73.3 years), had lower physical and mental quality of life (Short-Form 12 Physical Component Summary=33.66; Mental Component Summary=42.65), and higher anxiety (Hospital Anxiety and Depression Scale-A=8.82). Patients in Cluster 2 had more severe comorbidities (CCI=3.55), poorer cognitive function (Montreal Cognitive Assessment (MoCA)=21.5) and lower ejection fraction (mean=40%). Patients in Cluster 3 were younger (mean age=68.2 years), had better cardiac function (ejection fraction=46.6%), better cognitive status (MoCA=24.5) and the highest quality of life (Kansas City Cardiomyopathy Questionnaire=63.1). CONCLUSIONS: Patients with HF exhibit heterogeneous sleep quality patterns with specific clinical and psychological profiles. These findings highlight the need for personalised interventions, systematic sleep assessments and the integration of cardiac rehabilitation strategies into standard HF care. TRIAL REGISTRATION NUMBER: NCT02894502.
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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 | 0.008 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".