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
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».