The Effects of Food Labelling on Postexercise Energy Intake in Sedentary Women
Bibliographic record
Abstract
Food labelling has been previously reported to influence energy intake (EI). Whether food labels influence postexercise EI remains to be determined. We assessed how food labelling and exercise (Ex) interact to influence food perception and postexercise EI. In this randomized crossover design, 14 inactive women participated in 4 experimental conditions: Ex (300 kcal at 70% of<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M1"><mml:mrow><mml:msub><mml:mrow><mml:mi mathvariant="normal">V</mml:mi><mml:mi mathvariant="normal">O</mml:mi></mml:mrow><mml:mrow><mml:mn mathvariant="normal">2</mml:mn><mml:mi mathvariant="normal">p</mml:mi><mml:mi mathvariant="normal">e</mml:mi><mml:mi mathvariant="normal">a</mml:mi><mml:mi mathvariant="normal">k</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:math>) and lunch labelled as low in fat (LF), Ex and lunch labelled as high in fat (HF), Rest and LF, and Rest and HF. The lunch was composed of a plate of pasta, yogurt, and oatmeal cookies, which had the same nutritional composition across the 4 experimental conditions. EI at lunch and for the 48-hour period covering the testing day and the following day was assessed. Furthermore, perceived healthiness of the meal and appetite ratings were evaluated. There were no effects of exercise and food labelling on EI. However, meals labelled as LF were perceived as heathier, and this label was associated with higher prospective food consumption. Initial beliefs about food items had a stronger effect on healthiness perception than the different food labels and explain the positive correlation with the amount of food consumed (<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M2"><mml:mi>ρ</mml:mi><mml:mo>=</mml:mo><mml:mn fontstyle="italic">0.34</mml:mn></mml:math>,<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M3"><mml:mi>P</mml:mi><mml:mo><</mml:mo><mml:mn fontstyle="italic">0.001</mml:mn></mml:math>).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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) | 0.000 | 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 teacher head, 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".