Can eating pleasure be a lever for healthy eating? A systematic scoping review of eating pleasure and its links with dietary behaviors and health
Bibliographic record
Abstract
The aims of this review were to map and summarize data currently available about 1) key dimensions of eating pleasure; 2) associations of eating pleasure, and its key dimensions, with dietary and health outcomes and 3) the most promising intervention strategies using eating pleasure to promote healthy eating. Using the scoping review methodology, a comprehensive search of the peer-reviewed literature (Medline, PsycInfo, Embase, ERIC, Web of Science, CINAHL, ABI/Inform global and Sociology Abstract) and of the grey literature (ProQuest Dissertations & Theses and Google) was carried out by two independent reviewers. We included 119 of the 28,908 studies found. In total, 89 sub-dimensions of eating pleasure were grouped into 22 key dimensions. The most frequently found related to sensory experiences (in 50.9% of the documents), social experiences (42.7%), food characteristics besides sensory attributes (27.3%), food preparation process (19.1%), novelty (16.4%), variety (14.5%), mindful eating (13.6%), visceral eating (12.7%), place where food is consumed (11.8%) and memories associated with eating (10.9%). Forty-five studies, mostly cross-sectional (62.2%), have documented links between eating pleasure and dietary and/or health outcomes. Most studies (57.1%) reported favorable associations between eating pleasure and dietary outcomes. For health outcomes, results were less consistent. The links between eating pleasure and both dietary and health outcomes varied according to the dimensions of eating pleasure studied. Finally, results from 11 independent interventions suggested that strategies focusing on sensory experiences, cooking and/or sharing activities, mindful eating, and positive memories related to healthy food may be most promising. Thus, eating pleasure may be an ally in the promotion of healthy eating. However, systematically developed, evidence-based interventions are needed to better understand how eating pleasure may be a lever for healthy eating.
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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 | 0.020 | 0.084 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.014 | 0.015 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".