The corona blues according to daily life changes by COVID-19: A partial least squares regression model

Abstract

This study identifies determinants of the variation in depression resulting from COVID-19, specifies in detail the changes to daily life, and then compares the determinants' magnitude. The determinants were combined into three groups: first, the unpredictability of the disease and side effects by its response measures (specifically, restrictions on the freedom of movement and strain on social relationships); second, (mis)information through social media, public authorities, and mass media; and third, income reductions and other sociodemographic factors. Daily life changes were divided into four categories: travel/mobility, time at home (alone and with family), domestic activities (remote work, online shopping, food deliveries, reading, and online networking), and conflicts (with family and neighbors). We measured the total 29 predictors using data from the 2020 Seoul Survey, which is based on face-to-face interviews with a probability sample of adult residents. We made our estimations using partial least squares regression, which can analyze all original variables regardless of collinearity. The regression model found that major stressors include declines in out-of-home offline networking and the rise of domestic activities—and subsequent conflicts with family—restrictions on mobility (specifically, those of leisure travel), and income reductions. In contrast, changes to working and shopping (to remote work and online shopping) rather than leisure increased uses of private transportation modes. Moreover, we found influences of all forms of communications and media to be insignificant. We shall also provide a discussion on policy and academic implications of the findings.