Factors Associated with Eating in the Absence of Hunger among Children and Adolescents: A Systematic Review
Bibliographic record
Abstract
Eating in the absence of hunger (EAH) has been extensively studied over the past two decades and has been associated with excess body weight and the development of obesity. However, determinants of EAH remain uncertain. This systematic review aims to identify individual, familial, and environmental factors associated with EAH among children and adolescents. We included studies with a measure of EAH in participants aged 3-17 years old and including ≥1 factor associated with EAH. Our search identified 1494 articles. Of these, we included 81 studies: 53 cross-sectional, 19 longitudinal and nine intervention studies. In childhood (≤12 years old), EAH increases with age, it is greater in boys compared to girls, and it is positively associated with adiposity. Moreover, EAH development seems to be influenced by genetics. In adolescence, the number of studies is limited; yet, studies show that EAH slightly increases or remains stable with age, is not clearly different between sexes, and findings for overweight or obesity are less consistent across studies in adolescence. For familial factors, parental restrictive feeding practices are positively associated with EAH during childhood, mostly for girls. Studies assessing environmental factors are lacking and robust longitudinal studies spanning from early childhood to adolescence are needed.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".