ENERGY expenditure of COmmuting to school (ENERGYCO): protocol for a cluster randomized controlled trial
Bibliographic record
Abstract
Introduction This article outlines the rationale and methodology of the ENERGY expenditure of COmmuting to school study (the ENERGYCO study), a cluster-randomized controlled trial. The ENERGYCO study is divided into two phases: Phase I will aim; to assess the physical activity energy expenditure (PAEE) of different modes of commuting to school (i.e., walking, cycling, and motorized-vehicle) using indirect calorimetry in Spanish adolescents; and Phase II will aim to assess the effect of a school-based cycling intervention on resting metabolic rate and PAEE, as well as on other physiological, physical, and psychosocial outcomes on Spanish adolescents. Method For Phase I, a convenience sample of ~50 adolescents will be recruited. These participants will have their PAEE assessed in three different modes of commuting. Regarding phase II, a total of 300 adolescents from different schools in three Spanish cities will participate in this cluster randomized controlled trial. As many schools as necessary to meet the target sample will be included. In addition, each school will be randomized as either an intervention or control group. Participants from intervention schools will be asked to complete a school-based cycling intervention, while participants from control schools will be asked to continue their same habits for 8 weeks. The school-based cycling intervention will last for 8 weeks and will include Bikeability sessions, along with encouragement strategies to increase adherence to cycling to and from school. Conclusion The ENERGYCO study will provide novel insights into the PAEE associated with different modes of commuting to school using indirect calorimetry, as well as a comprehensive overview of how an 8-week school-based cycling intervention impacts resting energy expenditure, daily energy expenditure, and the physical and psychosocial health of adolescents.
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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 | 0.043 | 0.035 |
| Meta-epidemiology (narrow) | 0.006 | 0.004 |
| Meta-epidemiology (broad) | 0.011 | 0.005 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.103 | 0.017 |
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".