Effects of isometric resistance training on resting blood pressure
Bibliographic record
Abstract
BACKGROUND: Previous meta-analyses based on aggregate group-level data report antihypertensive effects of isometric resistance training (IRT). However, individual participant data meta-analyses provide more robust effect size estimates and permit examination of demographic and clinical variables on IRT effectiveness. METHODS: We conducted a systematic search and individual participant data (IPD) analysis, using both a one-step and two-step approach, of controlled trials investigating at least 3 weeks of IRT on resting systolic, diastolic and mean arterial blood pressure. RESULTS: Anonymized individual participant data were provided from 12 studies (14 intervention group comparisons) involving 326 participants (52.7% medicated for hypertension); 191 assigned to IRT and 135 controls, 25.2% of participants had diagnosed coronary artery disease. IRT intensity varied (8-30% MVC) and training duration ranged from 3 to 12 weeks. The IPD (one-step) meta-analysis showed a significant treatment effect for the exercise group participants experiencing a reduction in resting SBP of -6.22 mmHg (95% CI -7.75 to -4.68; P < 0.00001); DBP of -2.78 mmHg (95% CI -3.92 to -1.65; P = 0.002); and mean arterial blood pressure (MAP) of -4.12 mmHg (95% CI -5.39 to -2.85; P < 0.00001). The two-step approach yielded similar results for change in SBP -7.35 mmHg (-8.95 to -5.75; P < 0.00001), DBP MD -3.29 mmHg (95% CI -5.12 to -1.46; P = 0.0004) and MAP MD -4.63 mmHg (95% CI -6.18 to -3.09: P < 0.00001). Sub-analysis revealed that neither clinical, medication, nor demographic participant characteristics, or exercise program features, modified the IRT treatment effect. CONCLUSION: This individual patient analysis confirms a clinically meaningful and statistically significant effect of IRT on resting SBP, DBP and mean arterial blood pressure.
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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 | 0.010 | 0.022 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.021 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| 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".