Comparison of energy-matched high-intensity interval and moderate-intensity continuous exercise sessions on latency to eat, energy intake, and appetite
Bibliographic record
Abstract
High-intensity interval exercises (HIIex) have gained popularity but their effects on eating behavior are poorly known. The aim of this study was to evaluate whether the effects of HIIex on the 3 main components of eating behavior (appetite, intake, and latency to eat) differ from those of moderate-intensity continuous exercises (MICex) for the same energy expenditure. Fifteen young normal-weight males completed 3 sessions in a counterbalanced order: HIIex (30-s bouts at 90% of maximal oxygen uptake interceded with 60-s bouts at 35% of maximal oxygen uptake for 20 min), MICex (42% of maximal oxygen uptake for 40 min), and a resting session (REST). Trials were scheduled 80 and 100 min after a standard breakfast for MICex and HIIex, respectively. At 120 min, participants were isolated until they asked for lunch. Appetite was rated on 4 visual analog scales (hunger, desire to eat, fullness, and prospective consumption) every 15 min until meal request. Results showed that the mean latency of requesting lunch was significantly longer after HIIex than after REST (+17.3 ± 4.3 min, P = 0.004), but not after MICex (P = 0.686). Energy intake was not different between conditions, leading to a negative energy balance in the 2 exercise sessions. Thus, the effects of HIIex on eating behavior are likely primarily mediated through the latency of meal initiation. However, inter-individual variability was large and further studies are needed to identify the predictive factors of this response.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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".