The influence of vigorous running and cycling exercise on hunger perceptions and plasma acylated ghrelin concentrations in lean young men
Bibliographic record
Abstract
Vigorous running suppresses plasma acylated ghrelin concentrations but the limited literature on cycling suggests that acylated ghrelin is unchanged, perhaps because body mass is supported during cycling. It is important from a research and applied perspective to determine whether acylated ghrelin and hunger responses are exercise-mode specific. This study sought to examine this. Eleven recreationally active males fasted overnight and completed three 4-h trials: control, running, and cycling, in a random order. Participants rested throughout the control trial and ran or cycled at 70% of mode-specific maximal oxygen uptake for the first hour during exercise trials, resting thereafter. Hunger was measured every 0.5 h using visual analogue scales. Eight venous blood samples were collected to determine acylated ghrelin concentrations and a standardised meal was consumed at 3 h. Compared with the control trial, acylated ghrelin concentrations were suppressed to a similar extent at 0.5 and 1 h during the running (p < 0.005) and cycling (p < 0.001) trials. Area under the curve values for ghrelin concentration over time were lower during exercise trials versus control (Control: 606 ± 379; Running: 455 ± 356; Cycling: 448 ± 315 pg·mL(-1)·4 h(-1); mean ± SD, p < 0.05). Hunger values did not differ significantly between trials but an interaction effect (p < 0.05) indicated a tendency for hunger to be suppressed during exercise. Thus, at similar relative exercise intensities, plasma acylated ghrelin concentrations are suppressed to a similar extent during running and cycling.
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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) | 0.001 | 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".