Narcissism, national narcissism, COVID-19 conspiracy belief, and social media use as predictors of compliance with COVID-19 public health guidelines

The Role of Social Media in Knowledge, Perceptions, and Self-Reported Adherence Toward COVID-19 Prevention Guidelines: Cross-Sectional Study

BACKGROUND

Throughout the COVID-19 pandemic, social media has served as a channel of communication, a venue for entertainment, and a mechanism for information dissemination.

OBJECTIVE

This study aims to assess the associations between social media use patterns; demographics; and knowledge, perceptions, and self-reported adherence toward COVID-19 prevention guidelines, due to growing and evolving social media use.

METHODS

Quota-sampled data were collected through a web-based survey of US adults through the Qualtrics platform, from March 15, 2022, to March 23, 2022, to assess covariates (eg, demographics, vaccination, and political affiliation), frequency of social media use, social media sources of COVID-19 information, as well as knowledge, perceptions, and self-reported adherence toward COVID-19 prevention guidelines. Three linear regression models were used for data analysis.

RESULTS

A total of 1043 participants responded to the survey, with an average age of 45.3 years, among which 49.61% (n=515) of participants were men, 66.79% (n=696) were White, 11.61% (n=121) were Black or African American, 13.15% (n=137) were Hispanic or Latino, 37.71% (n=382) were Democrat, 30.21% (n=306) were Republican, and 25% (n=260) were not vaccinated. After controlling for covariates, users of TikTok (β=-.29, 95% CI -0.58 to -0.004; P=.047) were associated with lower knowledge of COVID-19 guidelines, users of Instagram (β=-.40, 95% CI -0.68 to -0.12; P=.005) and Twitter (β=-.33, 95% CI -0.58 to -0.08; P=.01) were associated with perceiving guidelines as strict, and users of Facebook (β=-.23, 95% CI -0.42 to -0.043; P=.02) and TikTok (β=-.25, 95% CI -0.5 to -0.009; P=.04) were associated with lower adherence to the guidelines (R2 0.06-0.23).

CONCLUSIONS

These results allude to the complex interactions between online and physical environments. Future interventions should be tailored to subpopulations based on their demographics and social media site use. Efforts to mitigate misinformation and implement digital public health policy must account for the impact of the digital landscape on knowledge, perceptions, and level of adherence toward prevention guidelines for effective pandemic control.

Structural equation modeling to estimate treatment adherence based on the light triad of personality and sense of coherence in patients with type-2 diabetes: examining the mediating role of psychological well-being

Background

The current study investigated the relationship between the light triad of personality and self-coherence with treatment adherence in patients with Type 2 Diabetes Mellitus (T2DM) using Structural Equation Modeling (SEM). Psychological well-being was considered a mediator variable in this SEM assessment.

Method

A cross-sectional study by convenience sampling was conducted, and 368 patients with T2DM were recruited from 10 private practice and general hospitals from May to November 2021 in Gilan province, Iran. The Reef's Psychological Well-being Questionnaire, the Light Triad Rating Scale, and Antonovsky's Sense of Coherence Scale were used for data collection. Structural equation modeling was used to examine the model fitness and identify direct/indirect relationships among variables. Data were analyzed by the SPSS (Version 26) and AMOS software (Version 24).

Results

The average score of light triads, sense of coherence, psychological well-being, and treatment adherence were 70.36 ± 25.55, 108.25 ± 37.68, 57.03 ± 23.84, and 106.81 ± 39.61, respectively. Model fit statistics suggest that the measurement model fits the data well: χ2 (146, N = 368) = 314.011 (p < 0.001). The SEM results showed that the light triad of personality (β = 0.12) and self-coherence (β = 0.14) were positively associated with treatment adherence. Anxiety, the light triad of personality (β = 0.12), and self-coherence (β = 0.14) affect treatment adherence indirectly through psychological well-being.

Conclusion

The analysis of the SEM revealed that the suggested model had a suitable goodness of fit. So, using self-coherence and psychological well-being structures in designing interventional programs is recommended to optimize treatment adherence in patients with T2DM.

Enhancing the Predictive Power of Google Trends Data Through Network Analysis: Infodemiology Study of COVID-19

BACKGROUND

The COVID-19 outbreak has revealed a high demand for timely surveillance of pandemic developments. Google Trends (GT), which provides freely available search volume data, has been proven to be a reliable forecast and nowcast measure for public health issues. Previous studies have tended to use relative search volumes from GT directly to analyze associations and predict the progression of pandemic. However, GT's normalization of the search volumes data and data retrieval restrictions affect the data resolution in reflecting the actual search behaviors, thus limiting the potential for using GT data to predict disease outbreaks.

OBJECTIVE

This study aimed to introduce a merged algorithm that helps recover the resolution and accuracy of the search volume data extracted from GT over long observation periods. In addition, this study also aimed to demonstrate the extended application of merged search volumes (MSVs) in combination of network analysis, via tracking the COVID-19 pandemic risk.

METHODS

We collected relative search volumes from GT and transformed them into MSVs using our proposed merged algorithm. The MSVs of the selected coronavirus-related keywords were compiled using the rolling window method. The correlations between the MSVs were calculated to form a dynamic network. The network statistics, including network density and the global clustering coefficients between the MSVs, were also calculated.

RESULTS

Our research findings suggested that although GT restricts the search data retrieval into weekly data points over a long period, our proposed approach could recover the daily search volume over the same investigation period to facilitate subsequent research analyses. In addition, the dynamic time warping diagrams show that the dynamic networks were capable of predicting the COVID-19 pandemic trends, in terms of the number of COVID-19 confirmed cases and severity risk scores.

CONCLUSIONS

The innovative method for handling GT search data and the application of MSVs and network analysis to broaden the potential for GT data are useful for predicting the pandemic risk. Further investigation of the GT dynamic network can focus on noncommunicable diseases, health-related behaviors, and misinformation on the internet.