Two Weekly Sessions of High-Intensity Interval Training Improve Metabolic Syndrome and Hypertriglyceridemic Waist Phenotype in Older Adults: A Randomized Controlled Trial
Bibliographic record
Abstract
Background: Exercise training provides physiological benefits for maintaining good health. A common exercise strategy is high-intensity interval training (HIIT). HIIT may alleviate metabolic syndrome (MetS) and hypertriglyceridemic waist (HTGW) phenotype, but remains largely unstudied in ageing participants. The aim of this research was to investigate the impact of 2 weekly HIIT sessions on MetS markers and HTGW-related factors in older adults. Methods: In this randomized controlled trial, 140 older men and women were randomized into two groups, the experimental group (EG), and the control group (CG). The EG performed 2 weekly sessions of HIIT during 12 weeks. HIIT sessions consisted of 40 min treadmill running/walking: a 10 min warm-up at 50%–60% of maximum heart rate (HRmax), followed by 10 sets of 1 min bouts at 85%–90% of HRmax interspersed with 1 min walking at self-selected pace (totaling 20 min), and 10 min cool-down walking at self-selected pace. The CG did not perform any type of intense exercise during the intervention period. Results: Participants in the EG of both sexes decreased MetS, HTGW, blood pressure, cholesterol, and glycemia (P < 0.05). After training, the number of hypertensive men decreased by 100% and women by 70%. There was a 75% reduction in women with diabetes, a 100% reduction in MetS indicators and over 80% reduction in HTGW in participants of both sexes. Conclusion: Two weekly sessions of HIIT proved to be feasible and effective to induce clinically relevant improvements in MetS and HTGW indicators.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".