The nature and frequency of food and beverage marketing on Kenyan national television: a mixed-method analysis of food advertisements, parent and children’s perspectives
Bibliographic record
Abstract
BACKGROUND: Exposure of children and adolescents to unhealthy food through marketing and advertising on television (TV) is associated with increased consumption of unhealthy foods and subsequently, overweight/obesity and diet-related non-communicable diseases (NCDs). This study assessed the nature, frequency, and exposure of children to food and beverage advertisements on TV and the perspectives of children/adolescents and parents on food marketing on TV. METHODS: Mixed methods, nationally representative study, guided by the International Network for Food and Obesity/NCDs Research, Monitoring and Action Support (INFORMAS) protocol for monitoring food promotion. This entailed simultaneous recording of the three most popular national TV channels in Kenya for eight randomly selected days, 18 h/day, over a 3-month period in 2021-2022. The NOVA classification was used to categorize food based on level of processing. Differences in advertisements by food groups, recording days (weekday/weekend) and seasons (holiday/non-holiday) were assessed. Focus group discussions (n = 14) and in-depth interviews (n = 29) were conducted with school children/adolescents (~ 9-18 years) and parents respectively in three counties, to explore their experiences and perspectives of food advertisements. Data were coded in NVIVO and analyzed thematically. RESULTS: Of the 3700 advertisements recorded, about a third (36%, n = 1316) comprised of food and beverages; 94.7% (n = 1213) were ultra-processed foods (UPFs), with a mean rate of 2.8 ads/channel per hour, the majority (95.9%, n = 557) of which were broadcast during peak hours. Some of the children interviewed vividly remembered some of the advertised food brands, mainly those related to UPFs. Most parents acknowledged that their children paid attention to food advertisements and sometimes requested and expressed preference for the advertised foods or brands. Parents generally considered food advertising to be safe, with no concerns about unhealthy food exposure to children. CONCLUSIONS: Exposure of Kenyan children and adolescents to unhealthy foods through advertisements on national TV is higher than healthier food options. These advertisements enhance recognition and recall of the food brands, while parental concern about unhealthy food advertising is limited. Policies restricting advertising of unhealthy foods accompanied by nutrition education are urgently needed to limit unhealthy food exposure and consumption by children and adolescents, to address the increasing burden of overweight/obesity and NCDs.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".