Relationship between eating behaviors and physical activity of preschoolers and their peers: a systematic review
Bibliographic record
Abstract
OBJECTIVES: Children learn by observing and imitating others, meaning that their eating behaviors and physical activity may be influenced by their peers. This paper systematically reviews how preschoolers' eating behaviors and physical activity relate to their peers' behaviors, and discusses avenues for future research. METHODS: Six databases were searched for quantitative, peer-reviewed studies published up to July 2015 reporting on the correlates, predictors or effectiveness of peers on eating behaviors and physical activity in preschoolers. Risk of bias was independently assessed by two evaluators using the Quality Assessment Tool for Quantitative Studies. RESULTS: Thirteen articles were included: six measured physical activity, and seven assessed eating behaviors. Four of the six physical activity studies reported that children were more active when peers were present, while large peer group size was negatively associated with physical activity in two cross-sectional studies. All nutrition interventions reported that children's eating behaviors may be influenced by their peers. CONCLUSIONS: Although supported by weak evidence, peers appear to influence children's eating behaviors and physical activity. However, this influence may be moderated by the number of peers, gender, age and the perceived status of the role models. Future obesity prevention interventions should consider involving peers as agents for positive eating behaviors and physical activity in preschoolers.
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 | 0.005 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".