Blood pressure-lowering efficacy of a 6-week multi-modal isometric exercise intervention
Bibliographic record
Abstract
Isometric exercise training (IET) is an effective method for reducing resting blood pressure (BP). To date, no research studies have been conducted using multiple exercises within an IET intervention. Previous research has suggested that varied exercise programmes may have a positive effect on adherence. Therefore, this randomized controlled study aimed to investigate the BP-lowering efficacy of a multi-modal IET (MIET) intervention in healthy young adults. Twenty healthy participants were randomized to an MIET [n = 10; four women; SBP 117.9 ± 6.9 mmHg; DBP 66.3 ± 5.1 mmHg] or control (CON) group (n = 10; five women; SBP, 123.3 ± 10.4 mmHg; DBP, 77.3 ± 6.7 mmHg). The MIET group completed three sessions per week of 4, 2-min isometric contractions, with a 1-min rest between each contraction, for 6 weeks. Resting BP and heart rate (HR) were measured at baseline and post-intervention. Pre-to-post intervention within-group reductions in resting BP were observed (SBP: 5.3 ± 6.1 mmHg, DBP: 3.4 ± 3.7 mmHg, MAP: 4.0 ± 3.9 mmHg, HR: 4.8 ±6 .6 bpm), although clinically relevant (≥2 mmHg), these changes were not statistically significant. Significant (p < 0.05) between-group differences were found between the intervention and control groups, indicating that the MIET intervention has a greater BP-lowering effect compared to control. The clinically relevant post-training reductions in resting BP suggest that MIET may be a promising additional IET method for hypertension prevention. These findings; however, must be interpreted with caution due to the small sample size and the non-clinical cohort.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".