Sleep quality as a mediator of the relationship between screen time and negative emotions among Chinese college freshmen.

College freshmen are special populations facing great challenges in adapting to the brand new environment, and their lifestyle and emotional states are worthy of attention. Especially during the COVID-19 pandemic, their screen time and prevalence of negative emotions were significantly increased, but few studies have focused on such situation of college freshmen and illustrated relevant mechanisms. Thus, based on a sample of Chinese college freshmen during the COVID-19 pandemic, the current study aimed to investigate the association between their screen time and negative emotions (depression, anxiety and stress), and further explore the mediating effects of sleep quality. Data from 2,014 college freshmen was analyzed. The screen time was self-reported by participants using predesigned questionnaires. The Pittsburgh Sleep Quality Index (PSQI) and Chinese Version of Depression Anxiety and Stress Scale-21 (DASS-21) were used to assess sleep quality and emotional states, respectively. The mediation analysis was conducted to examine the meditation effect. Results indicated that participants with negative emotions tended to have longer daily screen time and worse sleep quality, sleep quality partially mediated the association between screen time and negative emotions.The critical role of sleep quality and related intervention measures should be recognized and implemented.

Prevalence of depressive symptoms and associated factors during the COVID-19 pandemic: A national-based study.

BACKGROUND: Previous studies have reported that the prevalence of depression and depressive symptoms was significantly higher than that before the COVID-19 pandemic. This study aimed to explore the prevalence of depressive symptoms and evaluate the importance of influencing factors through Back Propagation Neural Network (BPNN). METHODS: Data were sourced from the psychology and behavior investigation of Chinese residents (PBICR). A total of 21,916 individuals in China were included in the current study. Multiple logistic regression was applied to preliminarily identify potential risk factors for depressive symptoms. BPNN was used to explore the order of contributing factors of depressive symptoms. RESULTS: The prevalence of depressive symptoms among the general population during the COVID-19 pandemic was 57.57 %. The top five important variables were determined based on the BPNN rank of importance: subjective sleep quality (100.00 %), loneliness (77.30 %), subjective well-being (67.90 %), stress (65.00 %), problematic internet use (51.20 %). CONCLUSIONS: The prevalence of depressive symptoms in the general population was high during the COVID-19 pandemic. The BPNN model established has significant preventive and clinical meaning to identify depressive symptoms lay theoretical foundation for individualized and targeted psychological intervention in the future.

Monitoring COVID-19 pandemic through the lens of social media using natural language processing and machine learning.

PURPOSE: It has been over a year since the first known case of coronavirus disease (COVID-19) emerged, yet the pandemic is far from over. To date, the coronavirus pandemic has infected over eighty million people and has killed more than 1.78 million worldwide. This study aims to explore "how useful is Reddit social media platform to surveil COVID-19 pandemic?" and "how do people's concerns/behaviors change over the course of COVID-19 pandemic in North Carolina?". The purpose of this study was to compare people's thoughts, behavior changes, discussion topics, and the number of confirmed cases and deaths by applying natural language processing (NLP) to COVID-19 related data. METHODS: In this study, we collected COVID-19 related data from 18 subreddits of North Carolina from March to August 2020. Next, we applied methods from natural language processing and machine learning to analyze collected Reddit posts using feature engineering, topic modeling, custom named-entity recognition (NER), and BERT-based (Bidirectional Encoder Representations from Transformers) sentence clustering. Using these methods, we were able to glean people's responses and their concerns about COVID-19 pandemic in North Carolina. RESULTS: We observed a positive change in attitudes towards masks for residents in North Carolina. The high-frequency words in all subreddit corpora for each of the COVID-19 mitigation strategy categories are: Distancing (DIST)-"social distance/distancing", "lockdown", and "work from home"; Disinfection (DIT)-"(hand) sanitizer/soap", "hygiene", and "wipe"; Personal Protective Equipment (PPE)-"mask/facemask(s)/face shield", "n95(s)/kn95", and "cloth/gown"; Symptoms (SYM)-"death", "flu/influenza", and "cough/coughed"; Testing (TEST)-"cases", "(antibody) test", and "test results (positive/negative)". CONCLUSION: The findings in our study show that the use of Reddit data to monitor COVID-19 pandemic in North Carolina (NC) was effective. The study shows the utility of NLP methods (e.g. cosine similarity, Latent Dirichlet Allocation (LDA) topic modeling, custom NER and BERT-based sentence clustering) in discovering the change of the public's concerns/behaviors over the course of COVID-19 pandemic in NC using Reddit data. Moreover, the results show that social media data can be utilized to surveil the epidemic situation in a specific community.