Lifestyle Behavior Changes and Associated Risk Factors During the COVID-19 Pandemic: Results from the Canadian COVIDiet Online Cohort Study
Notice bibliographique
Résumé
BACKGROUND: The COVID-19 pandemic and related lockdowns have impacted lifestyle behaviors, including eating habits and physical activity; yet, few studies have identified the emerging patterns of such changes and associated risk factors. OBJECTIVE: This study aims to identify the patterns of weight and lifestyle behavior changes, and the potential risk factors, resulting from the pandemic in Canadian adults. METHODS: Analyses were conducted on 1609 adults (18-89 years old; n=1450, 90.1%, women; n=1316, 81.8%, White) of the Canadian COVIDiet study baseline data (May-December 2020). Self-reported current and prepandemic weight, physical activity, smoking status, perceived eating habits, alcohol intake, and sleep quality were collected through online questionnaires. Based on these 6 indicator variables, latent class analysis (LCA) was used to identify lifestyle behavior change patterns. Associations with potential risk factors, including age, gender, ethnicity, education, income, chronic diseases, body image perception, and changes in the stress level, living situation, and work arrangement, were examined with logistic regressions. RESULTS: . Of the 1609 participants, 980 (60.9%) had a bachelor's degree or above. Since the pandemic, 563 (35%) had decreased income and 788 (49%) changed their work arrangement. Most participants reported unchanged weight, sleep quality, physical activity level, and smoking and alcohol consumption, yet 708 (44%) reported a perceived decrease in eating habit quality. From LCA, 2 classes of lifestyle behavior change emerged: healthy and less healthy (probability: 0.605 and 0.395, respectively; Bayesian information criterion [BIC]=15574, entropy=4.8). The healthy lifestyle behavior change group more frequently reported unchanged weight, sleep quality, smoking and alcohol intake, unchanged/improved eating habits, and increased physical activity. The less healthy lifestyle behavior change group reported significant weight gain, deteriorated eating habits and sleep quality, unchanged/increased alcohol intake and smoking, and decreased physical activity. Among risk factors, body image dissatisfaction (odds ratio [OR] 8.8, 95% CI 5.3-14.7), depression (OR 1.8, 95% CI 1.3-2.5), increased stress level (OR 3.4, 95% CI 2.0-5.8), and gender minority identity (OR 5.5, 95% CI 1.3-22.3) were associated with adopting less healthy behaviors in adjusted models. CONCLUSIONS: The COVID-19 pandemic has appeared to have influenced lifestyle behaviors unfavorably in some but favorably in others. Body image perception, change in stress level, and gender identity are factors associated with behavior change patterns; whether these will sustain over time remains to be studied. Findings provide insights into developing strategies for supporting adults with poorer mental well-being in the postpandemic context and promoting healthful behaviors during future disease outbreaks. TRIAL REGISTRATION: ClinicalTrials.gov NCT04407533; https://clinicaltrials.gov/ct2/show/NCT04407533.
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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 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,004 |
| Études des sciences et des technologies | 0,002 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 ».