The Association Between Sleep Disturbance, Depressive Symptoms, and Health-Related Quality of Life Among Cardiac Rehabilitation Participants
Bibliographic record
Abstract
In Brief PURPOSE: Recent guidelines from the Canadian Association of Cardiac Rehabilitation highlight the importance of addressing sleep disturbance among participants of cardiac rehabilitation (CR) programs. The primary objective of this study was to examine the relationship between depressive symptoms, health-related quality of life, and sleep disturbance in CR participants. The secondary objective was to estimate the prevalence of sleep disturbance among CR participants with and without depressive symptoms and explore demographic, medical, and psychological predictors of poor sleep quality. METHODS: Cardiac rehabilitation participants (N = 259) were included in this study. Participants completed a standardized questionnaire package including demographic, health-related, and psychosocial measures. Physiologic and anthropometric measurements were taken at baseline. Descriptive statistics were calculated for all variables, and data were analyzed using multivariate logistic regression. RESULTS: Poor sleep quality was reported by 52% of participants in the sample, and 47% of participants in the sample reported experiencing at least mild depressive symptoms. Poor sleep occurred more often in individuals with depressive symptoms, and after adjustment for medical factors and health-related quality of life, participants with symptoms of depression were still more likely to experience sleep disturbance than those without depressive symptoms (OR = 2.80; 95% CI, 1.37–5.77). An important gender difference emerged in the relationship between symptoms of depression and sleep disturbance. CONCLUSION: Among participants of a CR program, disturbed sleep was strongly associated with depressive symptoms and decreased health-related quality of life. Results demonstrate the importance of sleep evaluation in CR programs. The primary objective of this study was to examine the relationship between depressive symptoms, health-related quality of life, and sleep disturbance among cardiac rehabilitation participants. Results demonstrated that depressive symptoms were the most significant determinant of poor sleep, even after controlling for medical factors and health-related quality of life.
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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 | 0.001 | 0.003 |
| 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) | 0.001 | 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".