Food cravings are associated with increased self‐regulation, even in the face of strong instigation habits: A longitudinal study of the transition to plant‐based eating
Bibliographic record
Abstract
Frequently engaging in a positive health behaviour, like following a vegetarian or vegan (veg*n) diet, can bring benefits to both the individual and society. We investigated the roles of two psychological determinants of behaviour-instigation habits and self-regulation strategy use-in a cohort of individuals who were newly transitioning to a veg*n diet. In a longitudinal study over 6 months (7 waves including baseline), 222 individuals transitioning to a veg*n diet reported their monthly habit strength, craving frequency, self-regulation strategies and animal product consumption. Our results supported the benefits of having a healthy habit, as stronger habits predicted fewer cravings and lower consumption of animal products, in line with the person's target diet. However, in contrast to some theoretical accounts, having a strong instigation habit did not reduce the use of self-regulation strategies; people with strong habits used multiple strategies to maintain their diet, especially when they experienced frequent diet-inconsistent cravings. These findings challenge the idea that habits eliminate the need for self-regulation, and suggest that habits do not fully circumvent motivational challenges in the pursuit of complex health behaviours. Our results are consistent with recent suggestions that automatic and intentional processes act simultaneously during the enactment of complex health behaviours.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".