Impact of Strategies for Preventing Obesity and Risk Factors for Eating Disorders Among Adolescents: A Systematic Review
Bibliographic record
Abstract
An effective behavior changes program is the first-line of prevention for youth obesity. However, effectiveness in prevention of adolescent obesity requires several approaches, with special attention paid to disordered eating behaviors and psychological support among other environmental factors. The aim of this systematic review was to compare the impact of two types of obesity prevention programs, inclusive of behavior change components on weight outcomes. Energy-balance studies were aimed at reducing calories from high-energy sources and increasing PA levels, while “shared risk factors for obesity and eating disorders” focused on reducing disordered eating behaviors to promote a positive relationship with food and eating. A systematic search of ProQuest, PubMed, PsycInfo, SciELO, and Web of Science identified 8825 articles. Twenty were considered “energy-balance” and fifteen “shared-risk factors for obesity and eating disorders”. Overall, energy-balance studies were unable to support a maintenance weight status, diet, and PA over time. Shared risk factors programs also did not result in significant differences in weight status over time. However, the majority of shared risk factors studies demonstrated reduced body dissatisfaction, dieting, and weight-control behaviors. More research is needed to examine how a shared risk factor approach can address both obesity and eating disorder.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.028 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.011 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".