Dietary changes among pregnant individuals compared to pre-pandemic: A cross-sectional analysis of the Pregnancy during the COVID-19 Pandemic (PdP) study
Notice bibliographique
Résumé
Introduction: Dietary changes are common in pregnancy and may affect pregnancy outcomes, yet these changes and the associated contributory factors during the COVID-19 pandemic have been understudied. We aimed to investigate the association between dietary change and socioeconomic variables, pre-pregnancy BMI, and mental health symptoms; the change in intake of seven food categories and their reasons; and the association between intake of these food categories and mental health symptoms. Materials and methods: = 9,870, gestational age ≤ 35 weeks) on socioeconomic factors, pandemic-related hardships, pre-pregnancy body mass index (BMI), dietary changes compared to pre-pandemic and the reasons for these changes. We assessed depressive and anxiety symptoms using the Edinburgh Postpartum Depression Scale (EPDS) and Patient-Reported Outcomes Measurement Information System (PROMIS)-Anxiety, respectively. Results: 54.3% of the participants reported a change in their diet. Non-white ethnicity (OR = 1.33), job loss (OR = 1.29), clinically elevated depressive and anxiety symptoms (OR = 1.26 and 1.14, respectively), self-isolation (OR = 1.20), pre-pregnancy BMI (OR = 1.19), fear of COVID-19 (OR = 1.15), and pandemic phase at enrolment (OR = 0.90) significantly predicted dietary change. Most participants ate about the same amounts of dairy, meats and canned foods/dried goods as pre-pandemic (61.5, 61.7, and 60.2%, respectively), increased their intake of fresh vegetables/fruits and sweets/snacks (43.2 and 54.5%, respectively), and decreased fast-food and take-out/home delivery (53.2 and 43.1%, respectively). Changes in consumption of the food categories had a curvilinear association with mental health symptoms (except resilience) indicating greater symptoms with either decreased or increased intakes. Changes in craving, having more time for cooking/preparing foods, and being unable to go grocery shopping frequently (but not reduced affordability) were the main reasons driving these dietary changes. Conclusion: Some factors increase the odds of dietary change among pregnant individuals during the pandemic, with some changes toward a healthy and others toward an unhealthy diet. Given the importance of a healthy diet during gestation, identifying the risk and protective factors might be the first essential step in reducing the detrimental effects of unfavorable dietary changes during the pandemic on this vulnerable population.
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 | 0,001 | 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 | 0,001 | 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 ».