Systematic Review of Easy-to-Learn Behavioral Interventions for Dietary Changes Among Young Adults
Bibliographic record
Abstract
INTRODUCTION: Improving the diet quality of young adults may support chronic disease prevention. The approaches used and efficacy of promoting small dietary behavior changes through easy-to-learn (ETL) interventions (requiring no more than 1 hour to teach the behavior) among young adults have not yet been systematically reviewed. METHODS: Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, 2 independent electronic searches across 6 databases were conducted to identify any articles describing ETL interventions among young adults (aged 18-35 years) and reporting dietary intake outcomes. RESULTS: Among 9,538 articles identified, 9 studies met eligibility criteria. Five studies reported significant improvement in the selected dietary outcome. Of these, 3 studies used an implementation intentions approach, in which participants were given or asked to write out a simple dietary behavior directive and carry it on their person. Less than half of included studies were rated as positive for overall quality. DISCUSSION: The available evidence suggests that ETL interventions targeting the dietary behaviors of young adults may be effective in improving dietary intake. Limitations of included studies were lack of follow-up after the intervention period and low generalizability. IMPLICATIONS FOR RESEARCH AND PRACTICE: Further dietary intervention studies targeting young adults should systematically evaluate the efficacy of ETL intervention approaches among diverse samples.
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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 | 0.009 | 0.037 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.008 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".