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BackgroundAn extensive amount of information related to the novel coronavirus (COVID-19) pandemic was disseminated by mass and social media in China. To date, there is limited evidence on how this infodemic may influence psychobehavioral responses to the crisis. ObjectiveThe aim of this study is to assess the psychobehavioral responses to the COVID-19 outbreak and examine their associations with mass and social media exposure. MethodsA cross-sectional study among medical and health sciences students from the Fujian Medical University in Fuzhou, China, was conducted between April 6-22, 2020. ResultsA total of 2086 completed responses were received. Multivariable analyses demonstrated that four constructs of the Health Belief Model (HBM)—higher perception of susceptibility (odds ratio [OR] 1.44; 95% CI 1.07-1.94), severity (OR 1.32; 95% CI 1.10-1.59), self-efficacy (OR 1.61; 95% CI 1.21-2.15), and perceived control or intention to carry out prevention measures (OR 1.32; 95% CI 1.09-1.59)—were significantly associated with a higher mass media exposure score, whereas only three constructs—higher perception of severity (OR 1.43; 95% CI 1.19-1.72), self-efficacy (OR 1.85; 95% CI 1.38-2.48), and perceived control or intention to carry out prevention measures (OR 1.32; 95% CI 1.08-1.58)—were significantly associated with a higher social media exposure score. Lower emotional consequences and barriers to carry out prevention measures were also significantly associated with greater mass and social media exposure. Our findings on anxiety levels revealed that 38.1% (n=795; 95% CI 36.0-40.2) of respondents reported moderate-to-severe anxiety. A lower anxiety level was significantly associated with higher mass and social media exposure in the univariable analyses; however, the associations were not significant in the multivariable analyses. ConclusionsIn essence, both mass and social media are useful means of disseminating health messages and contribute to the betterment of psychobehavioral responses to COVID-19. Our findings stress the importance of the credibility of information shared through mass and social media outlets and viable strategies to counter misinformation during a pandemic.

BackgroundThe incidence of cancer pain increases in discharged patients because of discontinued standard treatments and reductions in medication adherence. Motivated by the need for better pain management in discharged patients, we developed a mobile phone app (Pain Guard) to provide continuous treatment information and feedback to discharged cancer patients suffering from pain. ObjectiveThe aim was to design, construct, and test the Pain Guard app in patients managing cancer pain, evaluate the total remission rate of pain and the improvement in quality of life (QoL) to improve pain management for cancer pain patients, and assess patient acceptance of the app. MethodsThis randomized controlled double-arm study involved 58 patients with cancer pain symptoms. Participants were randomly assigned to a group receiving care through the Pain Guard app (n=31) or to a control group (n=27) who received only traditional pharmaceutical care. In a pretest, participants were rated using a baseline cancer pain assessment and QoL evaluation. During treatment, the consumption levels of analgesic drugs were recorded every week. After a 4-week study period, another round of cancer pain assessment and QoL evaluation was conducted. The system’s usability, feasibility, app compliance, and satisfaction were also assessed. Our primary outcome was remission rate of pain, and secondary outcomes were medication adherence, improvements in QoL, frequency of breakthrough cancer pain (BTcP), incidence of adverse reactions, and satisfaction of patients. ResultsAll participants (N=58) successfully completed the study. There were no significant differences in baseline pain scores or baseline QoL scores between groups. At the end of the study, the rate of pain remission in the trial group was significantly higher than that in the control group (P

Information about the NMR metabolomics landscape of overall, and common cancers is still limited. Based on a cohort of 83,290 participants from the UK Biobank, we used multivariate Cox regression to assess the associations between each of the 168 metabolites with the risks of overall cancer and 20 specific types of cancer. Then, we applied LASSO to identify important metabolites for overall cancer risk and obtained their associations using multivariate cox regression. We further conducted mediation analysis to evaluate the mediated role of metabolites in the effects of traditional factors on overall cancer risk. Finally, we included the 13 identified metabolites as predictors in prediction models, and compared the accuracies of our traditional models. We found that there were commonalities among the metabolic profiles of overall and specific types of cancer: the top 20 frequently identified metabolites for 20 specific types of cancer were all associated with overall cancer; most of the specific types of cancer had common identified metabolites. Meanwhile, the associations between the same metabolite with different types of cancer can vary based on the site of origin. We identified 13 metabolic biomarkers associated with overall cancer, and found that they mediated the effects of traditional factors. The accuracies of prediction models improved when we added 13 identified metabolites in models. This study is helpful to understand the metabolic mechanisms of overall and a wide range of cancers, and our results also indicate that NMR metabolites are potential biomarkers in cancer diagnosis and prevention. [ABSTRACT FROM AUTHOR]