Effects of high-intensity interval exercise versus continuous moderate-intensity exercise on postprandial glycemic control assessed by continuous glucose monitoring in obese adults
Bibliographic record
Abstract
The purpose of this study was to examine the impact of acute high-intensity interval training (HIIT) compared with continuous moderate-intensity (CMI) exercise on postprandial hyperglycemia in overweight or obese adults. Ten inactive, overweight or obese adults (41 ± 11 yrs, BMI = 36 ± 7 kg/m(2)) performed an acute bout of HIIT (10 × 1 min at approximately 90% peak heart rate (HRpeak) with 1-min recovery periods) or matched work CMI (30 min at approximately 65% HRpeak) in a randomized, counterbalanced fashion. Exercise was performed 2 h after breakfast, and glucose control was assessed by continuous glucose monitoring under standardized dietary conditions over 24 h. Postprandial glucose (PPG) responses to lunch, dinner, and the following day's breakfast were analyzed and compared with a no-exercise control day. Exercise did not affect the PPG responses to lunch, but performing both HIIT and CMI in the morning significantly reduced the PPG incremental area under the curve (AUC) following dinner when compared with control (HIIT = 110 ± 35, CMI = 125 ± 34, control = 162 ± 46 mmol/L × 2 h, p < 0.05). The PPG AUC (HIIT = 125 ± 53, CMI = 186 ± 55, control = 194 ± 96 mmol/L × 2 h) and the PPG spike (HIIT = Δ2.1 ± 0.9, CMI = Δ3.0 ± 0.9, control = Δ3.0 ± 1.5 mmol/l) following breakfast on the following day were significantly lower following HIIT compared with both CMI and control (p < 0.05). Absolute AUC and absolute glucose spikes were not different between HIIT, CMI, or control for any meal (p > 0.05 for all). We conclude that a single session of HIIT has greater and more lasting effects on reducing incremental PPG when compared with CMI.
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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) | 0.000 | 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) | 0.001 | 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".