COVID-19 Distress, Parenting Stress, Maladaptive Parenting and Children’s Problematic Media Use in China: Evidence from 2022 Spring in Shanghai (preprint)

The COVID-19 lockdowns have forced young children to spend more time in front of the media and significantly impacted their mothers' mental health. This study explored how mothers' individual distress influences children's problematic media use during the Shanghai citywide lockdown caused by COVID-19. Data were collected from 1889 Chinese mothers (Mage = 34.69 years, SD = 3.94 years) with preschoolers aged 3–6 years (Mage = 4.38 years, SD = 1.06 years; 49.0% boys) via an online survey. The statistical analyses relied on SPSS Statistics version 26.0 and macro-program PROCESS 3.3. to investigate the associations and mediation analysis among all the study variables. The results indicated that: (1) significant associations between individual maternal distress with children's problematic media use; (2) maternal parenting stress and maladaptive parenting serial mediated the relationship between mothers' individual distress and children's problematic media use. The findings imply that parents need to enhance their ability to manage risk and promote mental health during periods of significant stress and routine disruption to reduce children's problematic media use.

Using Machine Learning to Explore the Risk Factors of Problematic Smartphone Use among Canadian Adolescents during COVID-19: The Important Role of Fear of Missing Out (FoMO)

The goal of the present study was to use machine learning to identify how gender, age, ethnicity, screen time, internalizing problems, self-regulation, and FoMO were related to problematic smartphone use in a sample of Canadian adolescents during the COVID-19 pandemic. Participants were N = 2527 (1269 boys;Mage = 15.17 years, SD = 1.48 years) high school students from the Lower Mainland of British Columbia, Canada. Data on problematic smartphone use, screen time, internalizing problems (e.g., depression, anxiety, and stress), self-regulation, and FoMO were collected via an online questionnaire. Several different machine learning algorithms were used to train the statistical model of predictive variables in predicting problematic smartphone use. The results indicated that Shrinkage algorithms (lasso, ridge, and elastic net regression) performed better than other algorithms. Moreover, FoMO, emotional, and cognitive self-regulation made the largest relative contribution to predicting problematic smartphone use. These findings highlight the importance of FoMO and self-regulation in understanding problematic smartphone use.