Influence of unhealthy food and beverage marketing on children's dietary intake and preference: a systematic review and meta‐analysis of randomized trials
Bibliographic record
Abstract
Marketing of foods and beverages high in fat, sugar and salt are suggested to contribute to poor dietary behaviours in children and diet-related diseases later in life. This systematic review and meta-analysis of randomized trials aimed to assess the effects of unhealthy food and beverage marketing on dietary intake (grams or kilocalories) and dietary preference (preference score or percentage of participants who selected specific foods/beverages) among children 2 to 18 years of age. We searched MEDLINE, EMBASE and PsycINFO up to January 2015 for terms related to advertising, unhealthy foods or beverages among children. Randomized trials that assessed the effects of unhealthy food and beverage marketing compared with non-dietary advertisement or no advertisement in children were considered eligible. Two authors independently extracted information on study characteristics and outcomes of interest and assessed risk of bias and the overall quality of evidence using grade methodology. Meta-analysis was conducted separately for dietary intake and preference using a random-effects model. We identified 29 eligible studies, of which 17 studies were included for meta-analysis of dietary preference and nine for meta-analysis of dietary intake. Almost half of the studies were at high risk of bias. Our meta-analysis showed that in children exposed to unhealthy dietary marketing, dietary intake significantly increased (mean difference [MD] = 30.4 kcal, 95% confidence interval [CI] 2.9 to 57.9, and MD = 4.8 g, 95%CI 0.8 to 8.8) during or shortly after exposure to advertisements. Similarly, children exposed to the unhealthy dietary marketing had a higher risk of selecting the advertised foods or beverages (relative risk = 1.1, 95%CI 1.0 to 1.2; P = 0.052). The evidence indicates that unhealthy food and beverage marketing increases dietary intake (moderate quality evidence) and preference (moderate to low quality evidence) for energy-dense, low-nutrition food and beverage. Unhealthy food and beverage marketing increased dietary intake and influenced dietary preference in children during or shortly after exposure to advertisements. © 2016 World Obesity.
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 | 0.029 | 0.064 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.043 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| 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".