Abstract 4363460: Depressive Symptoms Predict Sleep-Related Functional Outcomes in Rural Patients with Cardiovascular Disease
Bibliographic record
Abstract
Background: Sleep disturbances are prevalent in individuals with cardiovascular disease (CVD) and can have a substantially negative impact on daily functioning (e.g., reading, working, driving, and decision-making), also known as sleep-related functional outcomes. However, the relative contribution of demographic and clinical factors, cognitive function, health literacy, and depressive symptoms on sleep-related functional outcomes remains poorly understood. Objective: To examine the influence of demographic and clinical factors, cognitive function, health literacy, and level of depressive symptoms on sleep-related functional outcomes in depressed rural patients with CVD. Methods: We conducted secondary analyses using data from a large longitudinal randomized controlled trial and developed a hierarchical multiple linear regression model in depressed rural patients with CVD (N = 257; mean age 57±13; 47% women). Predictors of sleep-related functional outcomes were entered in five blocks, 1) demographic (age, sex, marital status, financial status), 2) New York Heart Association (NYHA) functional class, 3) cognitive function (Montreal Cognitive Assessment), 4) health literacy (Newest Vital Sign), and 5) depressive symptoms, (Patient Health Questionnaire-9). Sleep-related functional outcomes were measured using the Functional Outcome of Sleep Questionnaire-10 (FOSQ-10). Results: The final model significantly predicted sleep-related functional outcome (R 2 = 0.135, adjusted R 2 = 0.108, p < .001). Among all predictors, only depressive symptoms were independently associated with sleep-related functional outcomes (B = -0.363, p < .001), indicating that greater depressive symptom burden was independently associated with worse sleep-related functioning. Demographic characteristics, NYHA class, cognitive scores, and health literacy were not significant predictors in the final model. Conclusion: Depressive symptoms were the strongest and only significant predictors of sleep-related functional outcomes in rural depressed patients with CVD. These findings underscore the importance of integrating routine mental health screening and intervention, particularly for depressive symptoms, into cardiovascular care to address sleep-related functional outcomes.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| 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".