Cardiovascular reactivity to psychophysiological stressors: association with hypotensive effects of isometric handgrip training
Bibliographic record
Abstract
BACKGROUND: Isometric handgrip (IHG) training has been found to have hypotensive effects in normotensive and hypertensive samples. Mechanisms responsible for the reductions in arterial blood pressure have been suggested, but remain equivocal. OBJECTIVE: To investigate whether cardiovascular reactivity to cold pressor and serial subtraction stressors are associated with changes in resting systolic blood pressure found with IHG training. METHODS: After completion of an 8 week IHG training program and a 6 month detraining washout period, 17 healthy older participants (66 +/- 2 years) completed cold pressor (2 min at 4 +/- 1 degrees C) and serial subtraction (2 min) stressor tasks to assess cardiovascular reactivity. RESULTS: Compared with baseline, cold pressor and serial subtraction stressor significantly increased systolic blood pressure, diastolic blood pressure, and heart rate (P < 0.001). Heart rate reactivity was significantly different between the cold pressor and serial subtraction tasks (P < 0.001). Residualized reductions in systolic blood pressure from IHG training were strongly correlated with serial subtraction task reactivity scores [Systolic blood pressure: r(16) = -0.58, diastolic blood pressure: r(16) = -0.66, heart rate: r(16) = -0.53, P < 0.05], but not with cold pressor reactivity [r(16) < 0.14, P > 0.50]. CONCLUSION: The association between serial subtraction task reactivity and hypotensive effects of IHG training may hint at myocardial mediating mechanisms behind IHG training attenuations and may provide a method for identifying patients who stand to benefit from IHG training.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 |
| 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".