The Benefits of Physical Activity and Positive Mental Health for Reducing the Burden of COVID-19: Validation from a Cross-sectional and Longitudinal Investigation in China and Germany.

Many people experience high burden by the outbreak of the coronavirus disease (COVID-19) and its consequences for health and everyday life. The present cross-national study investigated potential factors that can reduce the burden by COVID-19 in China and Germany. Cross-sectional and longitudinal (China: N = 474, baseline, BL: 2015, follow-up, FU: 2020; Germany: N = 359, BL: 2019, FU: 2020) data on physical activity (e.g., jogging) (BL/FU), positive mental health (PMH) (BL/FU), and burden by COVID-19 (FU) were collected via online surveys. In both countries, physical activity was positively associated with PMH, and both variables were negatively related to burden by COVID-19. Furthermore, PMH mediated the link between physical activity and burden. The mediation model was significant when physical activity and PMH were assessed at the BL, while burden was measured at the FU; and it was also significant when all variables were assessed at the FU. The present findings reveal that physical activity in combination with PMH can reduce the experience of burden by COVID-19. Conscious fostering of physical activity and PMH is supported as an effective strategy to reduce the negative impact of the pandemic outbreak on mental and physical health. Additional benefits such as increased adherence to governmental measures around COVID-19 are discussed.

Predicting Genotype Information Related to COVID-19 for Molecular Mechanism Based on Computational Methods

Novel coronavirus disease 2019 (COVID-19) is an ongoing health emergency. Several studies are related to COVID-19. However, its molecular mechanism remains unclear. The rapid publication of COVID-19 provides a new way to elucidate its mechanism through computational methods. This paper proposes a prediction method for mining genotype information related to COVID-19 from the perspective of molecular mechanisms based on machine learning. The method obtains seed genes based on prior knowledge. Candidate genes are mined from biomedical literature. The candidate genes are scored by machine learning based on the similarities measured between the seed and candidate genes. Furthermore, the results of the scores are used to perform functional enrichment analyses, including KEGG, interaction network, and Gene Ontology, for exploring the molecular mechanism of COVID-19. Experimental results show that the method is promising for mining genotype information to explore the molecular mechanism related to COVID-19.