Using the intervention mapping protocol to promote school-based physical activity among children: A demonstration of the step-by-step process
Bibliographic record
Abstract
Objective: This study describes the step-by-step development of the I Mo ve30+ programme and outlines lessons derived from the authors’ experience using an intervention mapping protocol (IMP)-based programme design. The programme was designed to increase the moderate to vigorous physical activity (PA) level at school among Lebanese children, aged 10–12 years. Design: Participatory cross-sectional design including group and individual interviews. Setting: Sidon district of Lebanon. Method: The programme was co-designed with a planning committee and included a local leader in school health, school staff including nurses and members of the target population (schoolchildren). The programme was developed using the six steps of the IMP: elaborating a logic model of the problem, formulating programme objectives, choosing the theoretical methods (i.e. the theory-based techniques used to influence a change objective) and practical applications (i.e. the applied strategies based on those theoretical methods), designing the programme, planning programme implementation, and planning the evaluation. Participants’ involvement in the programme’s activities was entirely voluntary. Results: Implemented by teachers and school nurses, this 14-week school-based programme was designed to provide an additional 30 minutes of school-based PA per day through structural environmental change, educational activities, a PA-monitoring system and PA events at school and in the classroom as well as during recess. Conclusion: IMP enabled the rigorous and systematic development of the programme to improve children’s PA level. The programme description and the lessons learned can facilitate the replication and the scaling up of the programme in other settings.
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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 | 0.066 | 0.067 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".