Social Network Characteristics Are Correlated with Dietary Patterns Among Middle Aged and Older South Asians (SA) Living in the United States (US) (P04-124-19)
Notice bibliographique
Résumé
Social and cultural norms, operating through social networks, may influence an individual’s dietary choices. We examined the correlations among social network characteristics and dietary patterns among SA in the US. Data from the Mediators of Atherosclerosis in South Asians Living in America Social Network study were analyzed among 756 participants (mean age 59 y standard deviation [SD] = 9 y; 44% women). A culturally adapted, validated food frequency questionnaire was used for dietary assessment. Principal component analysis yielded, three dietary patterns named: “Animal protein”, “Fried snacks, sweets and high-fat dairy”, and “Fruits, vegetables, nuts and legumes”, based on contribution of food groups. Social network characteristics were assessed using a standard egocentric approach, where participants (egos) self-reported data on perceived dietary habits of their network members. Partial correlations between social network characteristics and egos’ dietary patterns were examined. The mean social network size of egos was 4.2 (SD = 1.1), with high proportion of network members being family (72%), SA ethnicity (89%), and half having daily contact. Higher scores for the “Animal protein” pattern among egos, were negatively correlated with daily fruits and cooked vegetables consumption of their network. This pattern was also positively correlated with the proportion of network who weekly consumed non-South Asian foods, diet drinks, non-vegetarian foods, processed meat and fried foods, and dined out. Scores for the “Fried snacks, sweets and high-fat dairy” pattern were positively correlated with proportion of network who weekly consumed sugar-sweetened beverages, South Asian sweets, fried foods, fast foods and ghee (clarified butter). Higher scores for the “Fruits, vegetables, nuts and legumes” pattern were positively associated with proportion of network who daily consumed both raw and cooked vegetables and fruits, and brown rice/quinoa weekly. Network member characteristics and perceived dietary behaviors of network members were correlated with dietary patterns of SA in the US. Dietary intervention studies among SA should consider social network characteristics as candidate components for dietary intervention. National Institutes of Health.
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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 | 0,000 |
| 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 | 0,000 | 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 ».