Easy-to-learn dietary behavior change intervention does not significantly improve diet quality of college students: a randomized controlled trial
Bibliographic record
Abstract
College students consistently report poor dietary patterns, and face numerous barriers towards improving diet quality. The objective of this study was to determine the impact of an easy-to-learn, semi-personalized diet behavior change intervention on diet quality in college students. This parallel-group, randomized controlled trial was comprised of an ethnically diverse group of full-time college students (N = 114) from two large university campuses in Southern California. Students were screened and excluded if they reported medical conditions requiring a modified diet, were considered at risk for eating disorders, or had a dietary pattern already aligned with the Dietary Guidelines for Americans (DGAs). The experimental group selected 2 of 7 predefined diet change statements that aligned with DGAs and carried them daily as keychain tags for four weeks. The control group was asked to read and consider the DGAs, without any specific dietary directives. We hypothesized that the experimental group would report a significant increase in Healthy Eating Index (HEI)-2020 total and component scores from baseline to 4 weeks, compared to control group. The population ratio method was used to statistically compare HEI-2020 scores between groups. No significant differences were observed between groups for the HEI-2020 total score or its component scores at 4 weeks or at 8 weeks follow-up. This short-term, easy-to-learn dietary intervention for college students was unsuccessful in improving diet quality. Future studies should consider more frequent participant contact and tailoring behavior change options to aspects of students' diets most in need of improvement. This trial was registered under ISRCTN #53920728.
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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".