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Association of Digital Health Literacy with Future Anxiety as Mediated by Information Satisfaction and Fear of COVID-19: A Pathway Analysis among Taiwanese Students.

Sheng-Chih ChenLe Duc HuyCheng-Yu LinChih-Feng LaiNhi Thi Hong NguyenNhi Y HoangThao Phuong Thi NguyenLoan T DangNguyen L T TruongTan N PhanTuyen Van Duong
Published in: International journal of environmental research and public health (2022)
Digital Health Literacy (DHL) helps online users with navigating the infodemic and co-existing conspiracy beliefs to avoid mental distress and maintain well-being. We aimed to investigate the association between DHL and future anxiety (FA); and examine the potential mediation roles of information satisfaction and fear of COVID-19 (F-CoV). A web-based cross-sectional survey was carried out among 1631 Taiwanese university students aged 18 years and above from June 2021 to March 2022. Data collected were socio-demographic characteristics (sex, age, social status, university location), information satisfaction, F-CoV, DHL and FA (using Future Dark scale). The linear regression model was used to explore factors associated with FA. The pathway analysis was further used to evaluate the direct and indirect relationship between DHL and FA. A higher score of DHL (B = -0.21; 95% CI, -0.37, -0.06; p = 0.006), and information satisfaction (B = -0.16; 95% CI, -0.24, -0.08; p < 0.001) were associated with a lower FA score, whereas a higher F-CoV score was associated with a higher FA score (B = 0.43; 95% CI, 0.36, 0.50; p < 0.001). DHL showed the direct impact (B = -0.1; 95% CI, -0.17, -0.04; p = 0.002) and indirect impact on FA as mediated by information satisfaction (B = -0.04; 95% CI, -0.06, -0.01; p = 0.002) and F-CoV (B = -0.06, 95% CI, -0.08, -0.04; p < 0.001). Strategic approaches to promote DHL, information satisfaction, lower F-CoV are suggested to reduce FA among students.
Keyphrases
  • sars cov
  • health information
  • respiratory syndrome coronavirus
  • coronavirus disease
  • social media
  • current status
  • healthcare
  • machine learning
  • electronic health record
  • depressive symptoms