ICOLSH
Description
Based on the hypothesis: Has COVID-19 affected the lifestyle of Hispanics in Southern California? Data collected from the Hispanic population in Southern California to study the impact caused by COVID-19 on the lifestyle of Hispanics. The study focused on changes in sleep patterns, physical activity, smoking, alcohol consumption. The data shows a consistent effect of COVID-19 preventive measures on the lifestyle factors of Hispanics. Important findings in the study "The Impact of COVID-19 on the Lifestyle of Hispanics in Southern California" include social isolation was inversely associated with smoking and self-isolation was positively associated with improved sleep habits and exercise, while other preventive measures did not show significant associations. Besides demographics, the dataset contains most prevalent chronic diseases in this population, lifestyle factors such as sleep, exercise, smoking, and alcohol consumption, and covid preventive measures. But it also includes most common alternative remedies to prevent COVID-19: herbs, garlic, ginger, lemon, onion, eucalyptus, sacred leaf, other.
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Data was collected online and manually using a physical copy of a cross-sectional survey. Research can be reproduced with: Variables of interest: social isolation and self-isolation Question formulated. “Do you feel COVID-19 social isolation caused you to change your sleep, exercise, smoking, or alcohol habits?” Social Isolation (Soisos): 0=Social isolation 1=No social isolation. Self-isolation (SLFI): 0=Self-isolation 1= No self-isolation. Dependent variables of interest included: sleep, exercise, smoking, and alcohol drink. Sleep (Sleep_L): 0=increased sleep 1=decreased or no change in sleep; exercise (XCER2): 0=increased exercise 1=decreased or no change in exercise; smoking (SMK_L): 0=I do not smoke or decreased smoking 1=increased smoke; alcohol consumption (ALCO_L): 0=I do not drink or decreased drinking 1=no change or increased drinking. Logistic regression (binary): To assess the potential association between social isolation and lifestyle factors. We applied this method to model the probability of having COVID-19 preventive measures (social isolation, handwashing, hand sanitizer use, avoiding handshaking, mask-wearing, social distancing, self-isolation, and vaccination) impact a lifestyle factor such as sleep, exercise, smoking, and alcohol consumption and other covariates such as age, gender, ethnicity, marital status, household size, children, income, education, employment, and body mass index (BMI). Crude models for social isolation and other COVID-19 preventive measures mentioned lines above were created with each lifestyle factor: sleep, exercise, smoking, and alcohol consumption; and adjusted models with age, sex, and income as covariates.