Effect of supramaximal exercise training on metabolic outcomes in obese adults
Bibliographic record
Abstract
The purpose of this study was to evaluate the effects of 6 weeks of supramaximal exercise training (SET) on performance variables and metabolic changes in sedentary obese adults.Twenty-four obese adults were randomly allocated into a non-trained (NT) [n = 12; body mass index (BMI) = 33(3)] and SET group [n = 12; BMI = (33(2)]. After baseline metabolic and fitness measurements, the participants completed a 6-week SET intervention. Metabolic, anthropometric, and fitness assessments were repeated post-intervention.For SET, fasting glucose (4.64(0.15) vs. 4.32(0.22) mmol · l–1; P < 0.01), insulin (23.2(4.6) vs. 13.8(3.3) µmol · ml–1; P < 0.01), homoeostasis model assessment-insulin resistance index (4.78(1.2) vs. 2.65(1.5); P < 0.01) and systolic blood pressure (127(3) vs. 120(3) mmHg; P < 0.01) were significantly lower 24-h post-intervention than at baseline and for the NT group, and these changes remained significant at 72-h and 2-weeks post-intervention (P < 0.01, respectively). Interestingly, nonesterified fatty acids (0.62(0.09) vs. 0.71(0.11) mmol · l–1; P < 0.01) and resting fat oxidation rate (57(11) vs. 63(4)%; P < 0.01) increased significantly from baseline 24-h post-intervention in the SET group and from baseline at 72-h (P < 0.01, respectively) and 2-weeks post-intervention (P < 0.01, respectively). Six weeks of SET improved a number of metabolic and vascular risk factors in obese, sedentary adults, highlighting the potential of SET to provide an alternative exercise model for the improvement of metabolic health in this population.
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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) | 0.001 | 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".