Marketing of ultra-processed foods on popular radio channels in Kenya
Bibliographic record
Abstract
BACKGROUND: There is a dearth of information on the extent of marketing of ultra-processed foods on traditional media such as radio in low- and middle-income countries (LMICs). Consumption of ultra-processed foods is associated with nutrition-related noncommunicable diseases such as overweight/obesity, type 2 diabetes, and cardiovascular diseases. This study aimed to examine the marketing of ultra-processed foods on the most widely accessed radio stations across three counties in Kenya. METHODS: This was a cross-sectional quantitative study conducted in three counties: Nairobi, Mombasa, and Baringo counties in the period between December 2021 to February 2022. We purposively selected 5 radio stations based on their popularity in different counties according to the Communications Authority of Kenya ratings. Using stratified sampling, we selected 8 recording days: 4 weekdays and 4 weekend days for three months. The recorded data were coded using a structured questionnaire. The key variables included the food and non-food and beverage products advertised on the radio stations, categorization of the food, non-food beverage products, time slots of the advertisements, promotional strategies, and premium offers. RESULTS: Of the 1499 advertisements on the radio, 15.7% (n = 235) were food and beverage products. The most advertised food categories were sugar-sweetened beverages (44.7%) and alcoholic drinks (23.4%). Ultra-processed foods (UPFs) accounted for 58.3% of the non-alcoholic food and beverage product advertisements on the radio. There was a significantly higher level of UPFs during the weekdays (58.3%) compared to weekend days (39.0%) (p < 0.001), school holiday seasons (73.4%) compared to non-school holiday seasons (46.5%) (p < 0.001), and in urban areas (70.1%) compared to rural areas (34.5%) (p < 0.001). CONCLUSIONS: We observed a high level of UPF marketing across both rural and urban settings, with higher proportions recorded in urban areas and during school holiday periods. To support healthier food environments, there is a need for regulatory action targeting radio advertising of unhealthy foods. This may include restrictions during peak child listening hours, regulation of persuasive content, and clear identification of sponsored food promotions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".