Food marketing, eating and health outcomes in children and adults: a systematic review and meta-analysis
Bibliographic record
Abstract
Abstract The marketing of unhealthy foods has been implicated in poor diet and rising levels of obesity. Rapid developments in the digital food marketing ecosystem and associated research mean that contemporary review of the evidence is warranted. This preregistered (CRD420212337091) 1 systematic review and meta-analysis aimed to provide an updated synthesis of the evidence for behavioural and health impacts of food marketing on both children and adults, using the 4Ps framework (Promotion, Product, Price, Place). Ten databases were searched from 2014 to 2021 for primary data articles of quantitative or mixed design, reporting on one or more outcome of interest following food marketing exposure compared with a relevant control. Reviews, abstracts, letters/editorials and qualitative studies were excluded. Eighty-two studies were included in the narrative review and twenty-three in the meta-analyses. Study quality (RoB2/Newcastle–Ottawa scale) was mixed. Studies examined ‘promotion’ ( n 55), ‘product’ ( n 17), ‘price’ ( n 15) and ‘place’ ( n 2) (some > 1 category). There is evidence of impacts of food marketing in multiple media and settings on outcomes, including increased purchase intention, purchase requests, purchase, preference, choice, and consumption in children and adults. Meta-analysis demonstrated a significant impact of food marketing on increased choice of unhealthy foods (OR = 2·45 (95 % CI 1·41, 4·27), Z = 3·18, P = 0·002, I 2 = 93·1 %) and increased food consumption (standardised mean difference = 0·311 (95 % CI 0·185, 0·437), Z = 4·83, P < 0·001, I 2 = 53·0 %). Evidence gaps were identified for the impact of brand-only and outdoor streetscape food marketing, and for data on the extent to which food marketing may contribute to health inequalities which, if available, would support UK and international public health policy development.
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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 | 0.019 | 0.040 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.017 | 0.038 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".