Energy Intake And Food Preferences Following Low And Moderate Intensities Of Cycling Desk
Bibliographic record
Abstract
With the increase of sedentary behavior in academia, solutions like active desks have gained attention to reduce prolonged sitting. However, their impact on eating behaviors remains unknown. PURPOSE: Evaluate the effects of a pedal desk at light (LPA) and moderate (MPA) exercise intensities, compared to a conventional sitting desk (SIT), on energy intake and food preferences, including visual fixation on food. METHOD: A randomized crossover study used LPA, MPA and SIT conditions during academic tasks. Energy intake and macronutrients were assessed using a standardized buffet, with preferences categorized into five food groups: Low-fat sweet, High-fat Sweet, Low-fat savoury, High-fat savoury, and Fat. Visual fixation during meals was tracked with Tobii Glasses 2 (100 Hz). Statistical analysis was done using ANOVA adopting p < 0.05 significance. RESULTS: No difference in energy or macronutrient intakes were observed between conditions. An increase in Low-fat sweet food consumption was seen in MPA compared to SIT (p = 0.004), mainly attributable to sugary drinks. A mean difference of 105 grams between MPA and SIT (p = 0.046) was also noted. Visual fixation data showed that participants fixated on food for an average of 13% of the thirty-minute mealtime, with similar total fixation times on different food categories across conditions. CONCLUSION: Cycling desks did not affect energy and macronutrient intakes, contributing to a negative energy balance. However, MPA impaired quality of the diet with increased Low-fat sweet food preference and sugary drink consumption compared to a conventional desk. Financing was obtained from Programme de soutien à la réussite de l'Université de Montréal #PA2020 and Canadian Institutes of Health Research and Fonds de recherche Québec -Santé #32983. ME Mathieu holds a Canada Research Chair on Physical Activity and Juvenile Obesity.
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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".