Development of the ‘Sigue la Huella’ physical activity intervention for adolescents in Huesca, Spain
Bibliographic record
Abstract
Engaging in physical activity (PA) on a regular and adequate basis generates considerable benefits for health. In developed countries, the time spent doing PA is decreasing, whilst sedentary time (ST) is increasing. A multicomponent school-based intervention programme, called 'Sigue la Huella' (Follow the Footprint), was developed to reduce sedentary lifestyles and increase PA levels. This programme has proven to be effective in increasing the daily levels of moderate to vigorous PA, in decreasing ST and in improving motivational outcomes in secondary education students, in the city of Huesca (Spain). The study design was quasi-experimental, longitudinal and by cohorts, and it was carried out in four schools, two as an experimental group (n = 368) and two as a control group (n = 314). During the 25 months' intervention, this programme adopted a holistic approach aiming to create favourable environments to engage in PA, and the empowerment of students to get actively involved in the design and execution of the activities, assuming responsibility for managing and optimizing their own PA. The programme is theoretically based on the social-ecological model and self-determination theory, and it provided evidence for four actions or components that can be used in school-based PA promotion: tutorial action, Physical Education at school, dissemination of information and participation in institutional programmes and events. The aim of this article is to describe the main characteristics of the intervention programme that have proved to be effective with respect to the objectives proposed.
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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 | 0.001 | 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".