Pilot study: an acute bout of high intensity interval exercise increases 12.5 h GH secretion
Bibliographic record
Abstract
The purpose of this study was to test the hypothesis that high-intensity interval exercise (HIE) significantly increases growth hormone (GH) secretion to a greater extent than moderate-intensity continuous exercise (MOD) in young women. Five young, sedentary women (mean ± SD; age: 22.6±1.3 years; BMI: 27.4±3.1 kg/m2) were tested during the early follicular phase of their menstrual cycle on three occasions. For each visit, participants reported to the laboratory at 1700 h, exercised from 1730–1800 h, and remained in the laboratory until 0700 h the following morning. The exercise component consisted of either 30-min of moderate-intensity continuous cycling at 50% of measured peak power (MOD), four 30-s “all-out” sprints with 4.5 min of active recovery (HIE), or a time-matched sedentary control using a randomized, cross-over design. The overnight GH secretory profile of each trial was determined from 10-min sampling of venous blood from 1730–0600 h, using deconvolution analysis. Deconvolution GH parameters were log transformed prior to statistical analyses. Calculated GH AUC (0–120 min) was significantly greater in HIE than CON (P = 0.04), but HIE was not different from MOD. Total GH secretory rate (ng/mL/12.5 h) was significantly greater in the HIE than the CON (P = 0.05), but MOD was not different from CON or HIE. Nocturnal GH secretion (ng/mL/7.5 h) was not different between the three trials. For these women, in this pilot study, a single bout of HIE was sufficient to increase 12.5 h pulsatile GH secretion. It remains to be determined if regular HIE may contribute to increased daily GH secretion.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".