Training self-regulation to promote healthy eating: Evidence from a longitudinal intervention study
Bibliographic record
Abstract
Previous theorizing suggests that self-regulatory strategies in the appetitive domain may be variably effective, but few studies have directly examined the causal impact these strategies may have in altering daily eating behaviors. Participants (N = 360) were trained to use one of two classes of regulatory strategies-situation-based strategies or cognitive reappraisal-and were instructed to apply these strategies to either healthy or unhealthy foods. All participants' eating behaviors were assessed longitudinally: during the first two weeks post-training (short-term effects) and up to two months later (long-term effects). Results partly supported preregistered hypotheses, indicating that training in either strategy (vs. no training) was generally effective in promoting healthy eating-as indexed by greater craving for healthy foods, more resistance to unhealthy foods, and less consumption of unhealthy foods. There were also transfer effects, with training targeting healthy foods leading to greater resistance to unhealthy foods over time, while training targeting unhealthy foods resulted in stronger craving and consumption of healthy foods. These findings demonstrate the effectiveness of both situational strategies and cognitive reappraisal for influencing healthier eating, highlighting the potential for both immediate and lasting benefits, as well as unexpected transfer effects.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".