Measuring the Healthiness of Ready-to-Eat Child-Targeted Cereals: Evaluation of the FoodSwitch Platform in Sweden
Bibliographic record
Abstract
BACKGROUND: Childhood obesity is a major public health issue. The increase in the consumption of foods with poor nutritional value, such as processed foods, contributes to this. Breakfast cereals are often advertised as a healthy way to start the day, but the healthiness of these products varies greatly. OBJECTIVE: Our main objective was to gather information about the nutritional characteristics of ready-to-eat breakfast cereals in Sweden and to investigate the healthiness of products targeted at children compared to other cereals by use of the FoodSwitch platform. A secondary objective was to evaluate the alignment between the Keyhole symbol and the Health Star Rating. METHODS: The FoodSwitch app is a mobile health (mHealth) tool used to present nutrition data and healthier alternative products to consumers. Ready-to-eat breakfast cereals from the largest Swedish grocery retailers were collected using the FoodSwitch platform. Products were defined as targeting children if they presented features addressing children on the package. RESULTS: Overall, information on 261 ready-to-eat cereals was examined. Of this total, 8% (n=21) were targeted at children. Child-targeted cereals were higher in sugar (22.3 g/100 g vs 12.8 g/100 g, P<.001) and lower in fiber (6.2 g/100 g vs 9.8 g/100 g, P<.001) and protein (8.1 g/100 g vs 10.5 g/100 g, P<.001). Total fat (3 g/100 g vs 10.5 g/100 g, P<.001) and saturated fat (0.8 g/100 g vs 2.6 g/100 g, P<.001) were also lower. No difference was found in salt content (P=.61). Fewer child-targeted breakfast cereals displayed an on-pack Keyhole label (n=1, 5% vs n=53, 22%; P=.06), and the mean Health Star Rating value was 3.5 for child-targeted cereals compared to others (mean 3.8, P=.07). A correlation was found between the Keyhole symbol and the Health Star Rating. CONCLUSIONS: Ready-to-eat breakfast cereals targeted at children were less healthy in terms of sugar and fiber content compared to products not targeted at children. There is a need to improve the nutritional quality of child-targeted cereals.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".