Evaluating the Safety and Efficacy of a Non–Weight-Centric Approach to Obesity Prevention in Rural and Urban Female Adolescents: Quasi-Experimental Study
Bibliographic record
Abstract
BACKGROUND: Obesity is increasing among Saudi adolescents, with rural females disproportionately underserved due to limited health education, sociocultural barriers, and scarce resources. While most school programs emphasize weight, global recommendations call for non-weight-centric approaches to avoid stigma. The Green Apple program is a school-based, weight-neutral intervention focusing on energy metabolism, nutrient balance, and dietary sources. Its enhanced version adds a unit on metabolic non-communicable disease (MNCD) prevention, grounded in the health belief model. Although previously tested in urban settings, it has not yet been evaluated among rural female students. OBJECTIVE: This study aimed to examine the effectiveness of the Green Apple educational intervention in improving knowledge about MNCDs, while also assessing its safety, defined as the absence of adverse psychological (body image discrepancy, disordered eating symptoms) and behavioral (increased sedentary behavior) outcomes. METHODS: This quasi-experimental study included 105 participants from urban and rural female schools in Saudi Arabia. Two classes within each school were assigned to one of two program versions: (1) enhanced intervention (Green Apple + MNCD): a three-unit, non-weight-centric educational program that included content on the prevention of MNCDs. (2) intervention (Green Apple only): a two-unit, non-weight-centric educational program without MNCD content. The intervention was delivered once per week over two consecutive weeks (two sessions), while the enhanced intervention included an additional third session. Linear mixed-effects models assessed intervention effects across three time points: baseline, post-intervention, and one-month follow-up. RESULTS: The mean age across participants was 16.42 years (SD = 0.66), with a significant difference between groups: 15.97 years (SD = 0.41) in the enhanced intervention group and 17.00 years (SD = 0.42) in the intervention group (P < .001). Both interventions significantly improved knowledge across schools. The enhanced intervention (Green Apple + MNCD) group demonstrated an increase of 1.65 (95% CI [0.61, 2.70], P < .001) from baseline to follow-up, while the intervention (Green Apple) group showed an increase of 1.26 (95% CI [0.10, 2.43], P = .02). However, no significant between-group differences were observed at baseline (mean difference = 0.20, P = .65), post-intervention (mean difference = 0.79, P = .08), or follow-up (mean difference = 0.73, P = .13). Although sedentary behavior did not significantly decrease across all schools, a significant reduction was observed in rural schools receiving the Green Apple intervention (-3.12, 95% CI [-5.67, -0.56], P = .02). CONCLUSIONS: The Green Apple program shows promise as a culturally tailored, weight-neutral intervention to enhance metabolic health literacy and reduce sedentary behavior among Saudi female adolescents without adverse effects on body image or disordered eating. By targeting a uniquely underserved population, this study addresses a critical gap in health promotion research and responds to global recommendations for inclusive, stigma-free interventions. Further studies across diverse regions of Saudi Arabia are needed to evaluate the broader applicability and long-term impact of such interventions.
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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 | 0.007 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".