Ambulatory blood pressure reduction following 2 weeks of high-intensity interval training on an immersed ergocycle
Bibliographic record
Abstract
BACKGROUND: Hypertension guidelines recommend moderate-intensity continuous training (MICT) for the primary or secondary prevention of hypertension. However, alternative modalities, such as high-intensity interval training (HIIT) on dry land or in water, have been studied less widely. AIM: To assess chronic blood pressure (BP) response to a 2-week training programme involving six sessions of either MICT or HIIT performed on dry land or HIIT performed in an immersed condition, in participants with baseline office systolic/diastolic BP (SBP/DBP)≥130/85mmHg. METHODS: We randomly assigned 42 individuals (mean age 65±7 years; 52% men) with baseline office SBP/DBP≥130/85mmHg to perform six 24-minute sessions on an ergocycle (three times a week for 2 weeks) of either MICT on dry land, HIIT on dry land or HIIT in a swimming pool, and assessed BP responses using 24-hour ambulatory BP monitoring. RESULTS: While 2-week MICT and HIIT on dry land modified none of the 24-hour average haemodynamic variables significantly, immersed HIIT induced a significant decrease in 24-hour BP (SBP -5.1±7.3 [P=0.02]; DBP -2.9±4.1mmHg [P=0.02]) and daytime BP (SBP -6.2±8.3 [P=0.015]; DBP -3.4±4.0mmHg [P=0.008]), and slightly improved 24-hour and daytime pulse wave velocity (PWV) (24-hour PWV -0.17±0.23m/s [P=0.015]; daytime PWV -0.18±0.24m/s [P=0.02]). CONCLUSION: HIIT on an immersed stationary ergocycle is an innovative method that should be considered as an efficient non-pharmacological treatment of hypertension. As such, it should now be implemented in a larger cohort to study its long-term effects on the cardiovascular system.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".