The Longitudinal Evidence on Social Ties and Fruit and Vegetable Intake among Aging Adults: A Systematic Review
Notice bibliographique
Résumé
CONTEXT: Social ties are associated with the mortality and morbidity of aging populations; however, the role of social ties in healthy eating practices or gender differences in this link is less understood. OBJECTIVE: The objective of this study was to examine the longitudinal evidence for the impact of changes in social ties on fruit and vegetable (FV) intakes among aging adults, with attention to gender differences. DATA SOURCES: Medline, Embase, Scopus, CINAHL, and ProQuest databases were searched until December 2022. DATA EXTRACTION: Longitudinal studies evaluating changes in living arrangement, marital status, social network, or social participation and changes in FV intake among middle- and older-age adults were included. Data from the included studies were extracted using a standardized template and analyzed using a narrative approach. DATA ANALYSIS: A total of 4956 titles were eligible after deduplication, and 75 full texts were screened. Seven studies met the inclusion criteria, and all examined marital transitions only. Five marital transitions were assessed: staying married, becoming widowed, becoming divorced, remaining unmarried, and becoming married. Both the quantity and variety of fruit and/or vegetables eaten were studied. Three of the included studies had only male or only female populations. The studies found that marital dissolution (divorce or widowhood), and remaining unmarried, were associated with reduced FV intakes in older women or men, compared with staying married. The associations were stronger in men than in women. Two studies showed that becoming married was associated with increased vegetable intakes, but 3 reported null results. The included studies were of medium quality. CONCLUSIONS: There is a paucity of longitudinal research on whether changes in social ties are associated with changes in FV intakes among aging adults. This review showed that specific marital transitions may influence healthy eating habits, especially in older men. No evidence exists on whether changes in other social ties might alter healthy eating. SYSTEMATIC REVIEW REGISTRATION: PROSPERO registration No. CRD42022365795.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
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 | 0,006 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,003 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| 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 ».