The Role of Exercise Blood Pressure in Hypertension: Measurement, Mechanisms, and Management
Bibliographic record
Abstract
ABSTRACT: Hypertension affects one in three adults globally and is the leading modifiable risk factor for cardiovascular disease. Although blood pressure measurements at rest are fundamental to the detection and management of hypertension, abnormal blood pressure responses to exercise, namely, an exaggerated exercise blood pressure (EEBP), can provide additional independent information about current and future hypertension risk. This paper summarizes a symposium entitled, "The Role of Exercise Blood Pressure in Hypertension: Measurement, Mechanisms and Management" included at the 2023 American College of Sports Medicine annual meeting, which presented a timely discussion about the clinical utility of EEBP. Here we will summarize the evidence presented by the speakers including considerations for blood pressure measurement during exercise, an overview of EEBP thresholds and discussion about the value of EEBP during submaximal exercise for the identification and management of hypertension, a summary of the potential physiological mechanisms underpinning an EEBP, and a review of exercise prescription guidelines based on new and emerging evidence as they relate to the American College of Sports Medicine's exercise recommendations for hypertension. We conclude by highlighting areas for future research with the overarching goal of improving the measurement and management of hypertension.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".