A Quantitative Review of Prospective Evidence Linking Psychological Factors With Hypertension Development
Bibliographic record
Abstract
Objective To quantitatively review and critique evidence from prospective cohort studies (greater than 1 year follow-up) assessing associations between psychological factors (eg, anxiety, anger, depression) and hypertension development. Methods Keyword searches through the MEDLINE and Psychlit (1970 to present) databases produced in excess of 500 studies, of which only 10 met criteria as a prospective cohort design with a follow-up interval exceeding 1 year. Five additional longitudinal studies were found by tracing references from the above papers. Results The sample-weighted aggregate effect sizes for hypertension risk were small for continuously measured psychological factors (r = .08), and effect sizes were similar for separate categories of psychological variables (r values = .07–.09). Effect sizes were not associated with reported methodological or sample characteristics, including sample size, racial and sex composition, study duration, or age. Conclusions Overall, there is moderate support for psychological factors as predictors of hypertension development, with the strongest support for anger, anxiety, and depression variables. Pooled effects for these factors are of sufficient magnitude to suggest potential clinical as well as statistical relevance. Findings regarding potential mechanisms are scarce and the psychometric properties of the scales used to measure psychological variables are often unestablished. Indications for future research are discussed.
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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 | 0.011 | 0.046 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.003 |
| Bibliometrics | 0.021 | 0.020 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".