Can Psychological Factors Account for a Lack of Nocturnal Blood Pressure Dipping?
Bibliographic record
Abstract
BACKGROUND: In healthy individuals, blood pressure (BP) decreases, or "dips", during sleep. Ethnicity and high daytime blood pressure level are known markers of nondipping status. The literature on psychological markers of nondipping is scant but suggests that anger/hostility and chronic stress may be contributors to nondipping. PURPOSE: We have investigated this phenomenon in drug-free hypertensives who participated in a clinical trial and supplied extensive demographic, psychological, and biological risk factor data after medication washout prior to any treatment. METHOD: Sixty-two patients were available for analysis (n = 30 nondippers). While most studies focus only on systolic BP nondipping, we explicitly studied both systolic and diastolic BP dipping as outcomes given that both have prognostic value. RESULTS: Hierarchical multiple regression revealed that predictor variables in total accounted for 38% of variance in systolic blood pressure dipping and 44% of variance in diastolic blood pressure dipping. A significant positive predictor was alcohol consumption (beta = 0.37, t = 2.8, p = 0.007) for systolic BP and beta = 0.43, t = 3.7, p = 0.001 for diastolic BP), and an anger diffusion preference was also a positive predictor (beta = 0.42, t = 2.7, p = 0.01) for systolic BP dipping. No measure of trait negative affect reached significance as a predictor for systolic or diastolic BP dipping. CONCLUSION: These findings suggest that for a better understanding of the nondipping phenomenon, behavioral risk factors are important, and anger response styles may also be worthy of further study. Furthermore, anger coping preferences may be as important, or even more so, than levels of negative affect.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".