Global Changes and Factors of Increase in Caloric/Salty Food Intake, Screen Use, and Substance Use During the Early COVID-19 Containment Phase in the General Population in France: Survey Study
Notice bibliographique
Résumé
BACKGROUND: The international outbreak of coronavirus disease (COVID-19) has led many countries to enforce drastic containment measures. It has been suggested that this abrupt lockdown of populations will foster addiction-related habits such as caloric/salty food intake, screen use, and substance use. OBJECTIVE: Our aim was to assess the global changes and factors of increase in addiction-related habits during the early COVID-19 containment phase in France. METHODS: A web-based survey was provided from day 8 to day 13 of the containment and was completed by 11,391 participants. The questions explored sociodemographic features, psychiatric/addiction history, material conditions of lockdown, general stress, mental well-being, and reported changes in several addiction-related behaviors. Global changes were described and factors of increase were explored using population-weighted and adjusted logistic regression models, providing adjusted odds ratios (aORs) and their 95% confidence intervals. RESULTS: Overall, the respondents reported more increases in addiction-related habits than decreases, specifically 28.4% (caloric/salty food intake), 64.6% (screen use), 35.6% (tobacco use), 24.8% (alcohol use), and 31.2% (cannabis use). Reduced well-being scores and increased stress scores were general factors of increase in addiction-related habits (P<.001 for all habits). Factors of increase in caloric/salty food intake (n=10,771) were female gender (aOR 1.62, 95% CI 1.48-1.77), age less than 29 years (P<.001), having a partner (aOR 1.19, 95% CI 1.06-1.35), being locked down in a more confined space (per 1 square meter/person decrease: aOR 1.02, 95% CI 1.01-1.03), being locked down alone (aOR 1.29, 95% CI 1.11-1.49), and reporting current (aOR 1.94, 95% CI 1.62-2.31) or past (aOR 1.27, 95% CI 1.09-1.47) psychiatric treatment. Factors of increase in screen use (n=11,267) were female gender (aOR 1.31, 95% CI 1.21-1.43), age less than 29 years (P<.001), having no partner (aOR 1.18, 95% CI 1.06-1.32), being employed (P<.001), intermediate/high education level (P<.001), being locked down with no access to an outdoor space (aOR 1.16, 95% CI 1.05-1.29), being locked down alone (aOR 1.15, 95% CI 1.01-1.32), living in an urban environment (P<.01), and not working (P<.001). Factors of increase in tobacco use (n=2787) were female gender (aOR 1.31, 95% CI 1.11-1.55), having no partner (aOR 1.30, 95% CI 1.06-1.59), intermediate/low education level (P<.01), and still working in the workplace (aOR 1.47, 95% CI 1.17-1.86). Factors of increase in alcohol use (n=7108) were age 30-49 years (P<.05), a high level of education (P<.001), and current psychiatric treatment (aOR 1.44, 95% CI 1.10-1.88). The only significant factor of increase in cannabis use (n=620) was intermediate/low level of education (P<.001). CONCLUSIONS: The early phase of COVID-19 containment in France led to widespread increases in addiction-related habits in the general population. Reduced well-being and increased stress were universal factors of increase. More specific factors were associated with increases in each of the explored habits.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 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 source (Gemma direct ou Codex distillé), 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 ».