Effect of Acute Exercise and Cycling Desk on Energy Intake and Appetite Response to Mental Work: The CORTEX Study
Bibliographic record
Abstract
BACKGROUND: While mental work has been shown to favor overconsumption, the present study compared the effect of a cognitive task alone, followed by acute exercise, or performed on a cycling desk, on short-term food intake and appetite in adults. METHODS: A total of 19 normal-weight adults randomly completed: resting session (CON), 30-minute cognitive task (CT), 30-minute cognitive task followed by a 15-minute high-intensity interval exercise bout (CT-EX), and 30-minute cognitive task performed on a cycling desk (CT-CD). Energy expenditure was estimated (heart rate-workload relationship), and energy intake (EI; ad libitum) and appetite (visual analog scales) were assessed. RESULTS: Energy expenditure was higher in CT-EX (P < .001) compared with the other conditions and in CT-CD compared with CON and CT (P < .01). EI was higher in CON (P < .05) and CT-CD compared with CT (P < .01). Relative EI was higher in CON compared with CT (P < .05) and lower in CT-EX compared with CT, CT-CD, and CON (all Ps < .001). Area under the curve desire to eat was higher in CON compared with CT (P < .05) and CT-EX (P < .01). Area under the curve prospective food consumption was higher in CON compared with CT-EX (P < .01). Overall composite appetite score was not different between conditions. CONCLUSION: While cycling desks are recommended to break up sedentary time, the induced increase in energy expenditure might not be enough to significantly reduce overall short-term relative EI after mental work.
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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 | 0.000 |
| 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) | 0.002 | 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".