Abstract:
This study aimed to identify lifestyle changes and associated sociodemographic factors in women and men participating in the Brazilian Longitudinal Study for Adult Health (ELSA-Brasil) cohort during the COVID-19 pandemic. Longitudinal study with 3,776 (aged 58.8 years; SD ± 8.5) employees of public higher education institutions in the second follow-up and the wave-COVID of ELSA-Brasil. Data collected using structured questionnaires. An exploratory analysis was performed using binary and multinomial logistic regression on the dependent variables with two and three categories, respectively, by obtaining crude and adjusted odds ratio estimates in SPSS 20.0, considering a p-value < 0.05. There was a reduction in physical activity of 195.5 (SD ± 1,146.4) metabolic equivalents per week in women and 240.5 (SD ± 1,474.2) in men, and in smoking by 15.2%. There was an increase in alcohol consumption in men and women (434.2 ± 5,144.0; and 366.1 ± 4,879.0, respectively), in the food quality score (0.8 ± 3.7, women; 0.5 ± 3.7, men), sleeping time (0.4 ± 1.2, women; 0.5 ± 1.1, men), screen time (1.7 ± 2.4, women; 1.4 ± 2.3, men), and sitting time (1.7 ± 2.6, women; 1.5 ± 2.4, men) (hours/day). In total, 18.6% increased the purchase of ultra-processed foods and 36% increased the purchase of natural foods. Age and work activity contributed to increase the chance of purchasing ultra-processed foods, and age and adherence to social distancing influenced the shift to a more sedentary behavior, while income and active work favored the increase in alcoholic beverage consumption. These factors should be considered when developing public policies to avoid individual behaviors that are harmful to health during pandemics.
Keywords:
COVID-19 Pandemic; Diet; Life Style; Risk Factors
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Note: multinomial logistic regression on the dependent variables with three categories − reference to the first (decreased), category not shown. Binary logistic regression on dichotomous dependent variables. Final model adjusted for age, skin color, per capita income, educational level, occupational status, marital status and adherence to social distancing when p < 0.2 in the crude analysis. Data are odds ratios (95% confidence intervals). * p-value < 0.05; ** p-value < 0.001.
Note: multinomial logistic regression on the dependent variables with three categories − reference to the first (decreased), category not shown. Binary logistic regression on dichotomous dependent variables. Final model adjusted for age, skin color, per capita income, education, occupational status, marital status and adherence to social distancing when p < 0.2 in the crude analysis. Data are odds ratios (95% confidence intervals). * p-value < 0.05; ** p-value < 0.001.