[PP.38.04] NET BLOOD PRESSURE REDUCTION FOLLOWING 9-MONTH OF LIFESTYLE AND HIGH-INTENSITY INTERVAL TRAINING INTERVENTION IN INDIVIDUALS WITH ABDOMINAL OBESITY
Bibliographic record
Abstract
Objective: We aimed to study the impact of a combined long-term lifestyle and high-intensity interval training (HIIT) intervention on blood pressure (BP) in individuals with abdominal obesity, taking into account the regression-to-the-mean (RTM) phenomena (net-change). Design and method: One hundred fifteen participants (age: 53 ± 9 years; 84 women; waist circumference (WC): 111 ± 13 cm; systolic/diastolic BP (SBP/DBP): 133 ± 13 / 82 ± 8 mmHg; 13% diabetics, 12% smokers, 30% under antihypertensive therapy) were retrospectively analyzed before and after a combined lifestyle (Mediterranean diet nutritional counselling) and HIIT program twice a week. Anthropometric measurements and resting BP were assessed at baseline and after 9 months of program. Results: After 9 months, we observed an improvement in weight (−5.2 ± 5.6 kg), BMI (−1.87 ± 1.98 kg/m2), WC (−6.3 ± 6.0 cm) and an average SBP/DBP net-decrease of −5.1 ± 13.7 / −2.8 ± 8.7 mmHg (Table 1). These changes were not uniform: 67 participants (58%) decreased their SBP by 2 mmHg or more, reaching −8.7 ± 12.3 / −4.1 ± 8.0 mmHg (SBP/DBP). The characteristics of responders were a higher baseline BP than non-responders (SBP/DBP: 137.2 ± 13.7 / 83.1 ± 7.3 vs. 127.0 ± 10.3 / 80.0 ± 7.3 mmHg, P < 0.05) and a higher proportion of participants with a baseline BP > = 130/85 mmHg (81% vs. 52%, P = 0.001) or with a metabolic syndrome (75% vs. 54%, P = 0.02).Conclusions: A combined long-term lifestyle and HIIT intervention has a beneficial effect on net-BP-change in patients with abdominal obesity and a baseline BP > = 130/85 mmHg.
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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) | 0.130 | 0.030 |
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".