Life Course Impact of School-Based Promotion of Healthy Eating and Active Living to Prevent Childhood Obesity
Bibliographic record
Abstract
BACKGROUND: The Alberta Project Promoting active Living and healthy Eating in Schools (APPLE Schools) is a comprehensive school health program that is proven feasible and effective in preventing obesity among school aged children. To support decision making on expanding this program, evidence on its long-term health and economic impacts is particularly critical. In the present study we estimate the life course impact of the APPLE Schools programs in terms of future body weights and avoided health care costs. METHOD: We modeled growth rates of body mass index (BMI) using longitudinal data from the National Population Health Survey collected between 1996-2008. These growth rate characteristics were used to project BMI trajectories for students that attended APPLE Schools and for students who attended control schools (141 randomly selected schools) in the Canadian province of Alberta. RESULTS: Throughout the life course, the prevalence of overweight (including obesity) was 1.2% to 2.8% (1.7 on average) less among students attending APPLE Schools relative to their peers attending control schools. The life course prevalence of obesity was 0.4% to 1.4% (0.8% on average) less among APPLE Schools students. If the APPLE Schools program were to be scaled up, the potential cost savings would be $33 to 82 million per year for the province of Alberta, or $150 to 330 million per year for Canada. CONCLUSIONS: These projected health and economic benefits seem to support broader implementation of school-based health promotion programs.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".