Child and adolescent exposure to unhealthy food marketing across digital platforms in Canada
Notice bibliographique
Résumé
Abstract Background Children and adolescents are exposed to a high volume of unhealthy food marketing across digital media. No previous Canadian data has estimated child exposure to food marketing across digital media platforms. This study aimed to compare the frequency, healthfulness and power of food marketing viewed by children and adolescents across all digital platforms in Canada. Methods For this cross-sectional study, a quota sample of 100 youth aged 6–17 years old (50 children, 50 adolescents distributed equally by sex) were recruited online and in-person in Canada in 2022. Each participant completed the WHO screen capture protocol where they were recorded using their smartphone or tablet for 30-minutes in an online Zoom session. Research assistants identified all instances of food marketing in the captured video footage. A content analysis of each marketing instance was then completed to examine the use of marketing techniques. Nutritional data were collected on each product viewed and healthfulness was determined using Health Canada’s 2018 Nutrient Profile Model. Estimated daily and yearly exposure to food marketing was calculated using self-reported device usage data. Results On average, children viewed approximately 1.96 instances of food marketing in 30 minutes, while adolescents viewed an estimated 2.56 ads in the same timeframe. Both children and adolescents were most exposed on social media platforms (83%), followed by mobile games (13%). We estimated that children are exposed to 1.96 ads/child/30-minutes (4067 ads/child/year) and adolescents are exposed to 2.56 ads/adolescent/30-minutes (8301 ads/adolescent/year), on average. Both children and adolescents were most exposed to fast food promotions (22% of advertisements) compared to other food categories. Nearly 90% of all marketing instances were considered less healthy according to Health Canada’s proposed 2018 Nutrient Profile Model, and youth-appealing marketing techniques such as graphic effects and music were used frequently. Conclusions Using the WHO screen capture protocol, we were able to determine that child and adolescent exposure to the marketing of unhealthy foods across digital media platforms is high. Government regulation to protect these vulnerable populations from the negative effects of this marketing is warranted.
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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 | 0,003 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,001 | 0,003 |
| Intégrité de la recherche | 0,000 | 0,002 |
| 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 ».