This study aimed to determine whether smartphone addiction and depression sequentially mediate the relationship between body dissatisfaction and disordered eating behaviors (e.g., restrained eating, emotional eating and external eating). A total of 5986 participants (54.1% females, average age = 19.8 years, age range = 17–32) completed the Satisfaction and Dissatisfaction with Body Parts Scale, the Three-Factor Eating Questionnaire, the Smartphone Addiction Scale and the Patient Health Questionnaire-9. Mediational analysis showed that, after controlling for age, sex and body mass index, body dissatisfaction was related to disordered eating behaviors through (a) the mediating effect of smartphone addiction, (b) the mediating effect of depression, and (c) the serial mediating effect of smartphone addiction and depression. In conclusion, our study showed for the first time that smartphone addiction and depression can be sequential mediator variables in the association between body dissatisfaction and disordered eating. However, this study is a cross-sectional study; future longitudinal studies could further test the causal associations between these study variables.
Severity prediction of COVID-19 remains one of the major clinical challenges for the ongoing pandemic. Here, we have recruited a 144 COVID-19 patient cohort consisting of training, validation, and internal test sets, longitudinally recorded 124 routine clinical and laboratory parameters, and built a machine learning model to predict the disease progression based on measurements from the first 12 days since the disease onset when no patient became severe. A panel of 11 routine clinical factors, including oxygenation index, basophil counts, aspartate aminotransferase, gender, magnesium, gamma glutamyl transpeptidase, platelet counts, activated partial thromboplastin time, oxygen saturation, body temperature and days after symptom onset, constructed a classifier for COVID-19 severity prediction, achieving accuracy of over 94%. Validation of the model in an independent cohort containing 25 patients achieved accuracy of 80%. The overall sensitivity, specificity, PPV and NPV were 0.70, 0.99, 0.93 and 0.93, respectively. Our model captured predictive dynamics of LDH and CK while their levels were in the normal range. This study presents a practical model for timely severity prediction and surveillance for COVID-19, which is freely available at webserver https://guomics.shinyapps.io/covidAI/.
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