Examination of daily-level associations between posttraumatic stress disorder symptoms and COVID-19 worries

Risk Factors and Protective Factors of Internet Addiction in University Students during the Pandemic: Implications for Prevention and Treatment

While the prevalence rates of Internet addiction (IA) amongst young people during the pandemic are disturbing, few studies have investigated the risk and protective factors of IA in Hong Kong university students under COVID-19. In this study, we examined the relationship between COVID-19-related stress and IA and the role of psychological morbidity and positive psychological attributes in the relationship. In summer 2022, 978 university students completed a survey assessing pandemic-related stress, psychological morbidity, and positive psychological attributes. While psychological morbidity was indexed by depression, post-traumatic stress disorder, and suicidal behavior, positive psychological attributes included life satisfaction, flourishing, adversity beliefs, emotional competence, resilience, and family functioning measures. Results showed that stress and psychological morbidity positively predicted IA, and psychological morbidity mediated the association between stress and IA. Positive psychological attributes negatively predicted stress and IA, and mediated the connection between stress and IA. Positive psychological attributes moderated the mediating effect of psychological morbidity on the relationship between stress and IA. In addition to theoretical contributions, this study contributes to IA prevention and treatment: reducing psychological morbidity and promoting positive psychological attributes are promising strategies to address IA issues in young people.

Time attitudes affecting psychological health during COVID-19 pandemic: a two-wave, six-month prospective study in Taiwan

The COVID-19 pandemic has induced traumatic and fear responses globally. Time attitudes, which refer to one's feelings toward the past, present and future, may have certain effects on psychological adaptations during this crisis period. This study employed a person-centered approach and a two-wave prospective design to investigate how people with different time attitude profiles change differently in their PTSD symptoms and COVID-19-related fears from a low-risk stage to the first big COVID-19 outbreak in Taiwan. Participants were 354 adults with a mean age of 27.79 years. The result provided support for the theoretical six-factor structure of the traditional Chinese Adolescent and Adult Time Inventory-Time Attitudes Scale (AATI-TA). Four clusters of time attitude profiles were identified (Positives, Negatives, Past Negatives and Pessimists). At both waves, Positives had lower levels of PTSD severity and COVID-19-related fears than most of the other groups, and the reverse was noted for Negatives. As for time effects, people across all profiles were significantly affected during the outbreak, but Negatives showed a greater increase in PTSD severity than other groups. In conclusion, mental health services should put efforts into early identification of those with highly negative time attitudes and implement interventions that nudge people toward a more balanced or positive attitude in each temporal frame, especially during adversity such as the COVID-19 pandemic.

A Machine Learning-Based Risk Prediction Model for Post-Traumatic Stress Disorder during the COVID-19 Pandemic

Background and Objectives: The COVID-19 pandemic has caused global public panic, leading to severe mental illnesses, such as post-traumatic stress disorder (PTSD). This study aimed to establish a risk prediction model of PTSD based on a machine learning algorithm to provide a basis for the extensive assessment and prediction of the PTSD risk status in adults during a pandemic. Materials and Methods: Model indexes were screened based on the cognitive-phenomenological-transactional (CPT) theoretical model. During the study period (1 March to 15 March 2020), 2067 Chinese residents were recruited using Research Electronic Data Capture (REDCap). Socio-demographic characteristics, PTSD, depression, anxiety, social support, general self-efficacy, coping style, and other indicators were collected in order to establish a neural network model to predict and evaluate the risk of PTSD. Results: The research findings showed that 368 of the 2067 participants (17.8%) developed PTSD. The model correctly predicted 90.0% (262) of the outcomes. Receiver operating characteristic (ROC) curves and their associated area under the ROC curve (AUC) values suggested that the prediction model possessed an accurate discrimination ability. In addition, depression, anxiety, age, coping style, whether the participants had seen a doctor during the COVID-19 quarantine period, and self-efficacy were important indexes. Conclusions: The high prediction accuracy of the model, constructed based on a machine learning algorithm, indicates its applicability in screening the public mental health status during the COVID-19 pandemic quickly and effectively. This model could also predict and identify high-risk groups early to prevent the worsening of PTSD symptoms.