Associations between use of self-regulatory strategies and daily eating patterns: An experience sampling study in college-aged women
Bibliographic record
Abstract
Abstract Previous theorizing suggests there are multiple means by which people regulate their emotions and impulses, but that these strategies vary in the degree to which they support goal attainment. Some have proposed that proactive strategies (e.g. situation selection, distraction) may be particularly effective, while interventive strategies (e.g. suppression) are less effective. Despite these diverging predictions, researchers have yet to examine spontaneous use of these strategies and their respective and combined efficacy when applied to momentary food desires experienced in daily life. In the present study, we assessed eating patterns for one week via ecological momentary assessment in college-aged women (N = 106). Results from pre-registered analyses indicated that using a variety of strategies, including preventative strategies such as situation selection and distraction, was associated with greater self-control success, as indexed by weaker desires, higher resistance, lower likelihood of enacting desires, and less food consumed. A similar pattern was observed when participants implemented additional strategies during desire episodes, which they were more likely to do when their desires conflicted with other self-regulatory goals. All associations were observed while controlling for momentary hunger levels, dieting status, age, and body mass index. These findings are consistent with a growing body of work assessing people’s spontaneous use of emotion regulation strategies in everyday contexts, suggesting potential meta-motivational tendencies marked by flexible and adaptive use of self-regulatory strategies.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".