Are vital exhaustion and depression independent risk factors for cardiovascular disease morbidity?
Bibliographic record
Abstract
OBJECTIVE: The aim of this study was to examine the concurrent effects of vital exhaustion and depression on the development of cardiovascular disease (CVD) morbidity. METHOD: The sample of this representative, 4-year longitudinal study comprised 2,725 participants (43.56% male, Mage = 58.39 years, SDage = 14.39 years). Individuals being treated for hypertension (n = 277) and cardio- and/or cerebrovascular incidents (n = 131) for the first time during the follow-up period were compared with participants never treated for CVD (n = 2,317). Joint principal component analysis was conducted on the items of the vital exhaustion (shortened Maastricht Questionnaire) and depression (shortened Beck Depression Inventory) measures simultaneously resulting in 3 components representing depression, vital exhaustion, and sleep difficulties. The role of these 3 components in predicting the incidence of CVD morbidity was examined using logistic regression-controlling for traditional risk factors such as sex, age, education, body mass index, smoking, alcohol use, and physical inactivity. RESULTS: In the multivariate analyses, vital exhaustion (OR = 1.20, CI = 1.03-1.39, p = .021) and sleep-related problem (OR = 1.16, CI = 1.00-1.33, p = .044) scores proved to be independent predictors of treatment initiation for hypertension, while sleep-related difficulties predicted CVD event incidence (OR = 1.27, CI = 1.06-1.52, p = .009). However, depressive symptomatology factor scores were not associated with either cardiovascular outcome in the regression analyses. CONCLUSIONS: Vital exhaustion and depressive symptomatology showed a different pattern in their relationship with CVD incidence, with vital exhaustion being the more robust predictor. These results suggest that the 2 constructs are not identical and that vital exhaustion deserves consideration when planning and implementing interventions to reduce CVD risk. (PsycINFO Database Record
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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".