Background: SARS-CoV-2 (severe acute respiratory coronavirus 2) was spreading rapidly in South Korea at the end of February 2020 following its initial outbreak in China, making Korea the new center of global attention. The role of social media amid the current coronavirus disease (COVID-19) pandemic has often been criticized, but little systematic research has been conducted on this issue. Social media functions as a convenient source of information in pandemic situations.
Objective: Few infodemiology studies have applied network analysis in conjunction with content analysis. This study investigates information transmission networks and news-sharing behaviors regarding COVID-19 on Twitter in Korea. The real time aggregation of social media data can serve as a starting point for designing strategic messages for health campaigns and establishing an effective communication system during this outbreak.
Methods: Korean COVID-19-related Twitter data were collected on February 29, 2020. Our final sample comprised of 43,832 users and 78,233 relationships on Twitter. We generated four networks in terms of key issues regarding COVID-19 in Korea. This study comparatively investigates how COVID-19-related issues have circulated on Twitter through network analysis. Next, we classified top news channels shared via tweets. Lastly, we conducted a content analysis of news frames used in the top-shared sources.
Results: The network analysis suggests that the spread of information was faster in the Coronavirus network than in the other networks (Corona19, Shincheon, and Daegu). People who used the word “Coronavirus” communicated more frequently with each other. The spread of information was faster, and the diameter value was lower than for those who used other terms. Many of the news items highlighted the positive roles being played by individuals and groups, directing readers’ attention to the crisis. Ethical issues such as deviant behavior among the population and an entertainment frame highlighting celebrity donations also emerged often. There was a significant difference in the use of nonportal (n=14) and portal news (n=26) sites between the four network types. The news frames used in the top sources were similar across the networks (P=.89, 95% CI 0.004-0.006). Tweets containing medically framed news articles (mean 7.571, SD 1.988) were found to be more popular than tweets that included news articles adopting nonmedical frames (mean 5.060, SD 2.904; N=40, P=.03, 95% CI 0.169-4.852).
Conclusions: Most of the popular news on Twitter had nonmedical frames. Nevertheless, the spillover effect of the news articles that delivered medical information about COVID-19 was greater than that of news with nonmedical frames. Social media network analytics cannot replace the work of public health officials; however, monitoring public conversations and media news that propagates rapidly can assist public health professionals in their complex and fast-paced decision-making processes.

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Conversations and Medical News Frames on Twitter: Infodemiological Study on COVID-19 in South Korea.

Background: Previous studies have suggested that patients’ online health information seeking affects their medical consultations and patient-doctor relationships. An up-to-date picture of patients’ online health information-seeking behaviors can inform and prepare frontline health care professionals to collaborate, facilitate, or empower their patients to access and manage health information found online. Objective: This study explores the prevalence, patterns, and predictors of online health information-seeking behaviors among primary care patients in Hong Kong, and the relations between online health information seeking and electronic health (eHealth) literacy. Methods: Patients attending a university primary care clinic in Hong Kong were asked to complete a questionnaire survey on their demographic backgrounds; health status; frequency and pattern of online health information seeking; contents, sources, and reasons for online health information seeking; and their eHealth literacy. eHealth literacy was measured by the validated eHealth Literacy Scale (eHEALS). Regression analyses explored various demographic and behavioral predictors to online health information seeking, and predictors to eHealth literacy. Results: In all, 97.32% (1162/1194) respondents used the internet, of which 87.44% (1016/1162) had used the internet to find health information. Most respondents (65.97%, 665/1008) searched once monthly or more. Few (26.88%, 271/1008) asked their doctor about health information found online, but most doctors (56.1%, 152/271) showed little or no interest at all. The most sought topic was symptom (81.59%, 829/1016), the top reason was noticing new symptoms or change in health (70.08%, 712/1016), the most popular source was online encyclopedia (69.98%, 711/1016), and the top reason for choosing a source was convenience (55.41%, 563/1016). Poisson regression analysis identified high eHEALS score, fair or poor self-rated health, having a chronic medical condition, and using the internet several times a day as significant predictors of online health information seeking. Multiple regression analysis identified lower age, better self-rated health, more frequent internet use, more frequent online health information seeking, and more types of health information sought as significant predictors to higher eHealth literacy. Conclusions: Online health information seeking is prevalent among primary care patients in Hong Kong, but only a minority shared the information with doctors. Websites were chosen more for convenience than for accuracy or authoritativeness. Doctors should recognize patients’ online health information-seeking behavior, and facilitate and empower them to search for high-quality online health information.

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Online Health Information Seeking and eHealth Literacy Among Patients Attending a Primary Care Clinic in Hong Kong: A Cross-Sectional Survey.

Background: Electronic health (eHealth) literacy is a growing area of research parallel to the ongoing development of eHealth interventions. There is, however, little and conflicting information regarding the factors that influence eHealth literacy, notably in chronic disease. We are similarly ill-informed about the relationship between eHealth and health literacy, 2 related yet distinct health-related literacies. Objective: The aim of our study was to investigate the demographic, socioeconomic, technology use, and health literacy predictors of eHealth literacy in a population with moderate-to-high cardiovascular risk. Methods: Demographic and socioeconomic data were collected from 453 participants of the CONNECT (Consumer Navigation of Electronic Cardiovascular Tools) study, which included age, gender, education, income, cardiovascular-related polypharmacy, private health care, main electronic device use, and time spent on the Internet. Participants also completed an eHealth Literacy Scale (eHEALS) and a Health Literacy Questionnaire (HLQ). Univariate analyses were performed to compare patient demographic and socioeconomic characteristics between the low (eHEALS<26) and high (eHEALS≥26) eHealth literacy groups. To then determine the predictors of low eHealth literacy, multiple-adjusted generalized estimating equation logistic regression model was used. This technique was also used to examine the correlation between eHealth literacy and health literacy for 4 predefined literacy themes: navigating resources, skills to use resources, usefulness for oneself, and critical evaluation. Results: The univariate analysis showed that patients with lower eHealth literacy were older (68 years vs 66 years, P=.01), had lower level of education (P=.007), and spent less time on the Internet (P<.001). However, multiple-adjusted generalized estimating equation logistic regression model demonstrated that only the time spent on the Internet (P=.01) was associated with the level of eHealth literacy. Regarding the comparison between the eHEALS items and HLQ scales, a positive linear relationship was found for the themes “usefulness for oneself” (P=.049) and “critical evaluation” (P=.01). Conclusions: This study shows the importance of evaluating patients’ familiarity with the Internet as reflected, in part, by the time spent on the Internet. It also shows the importance of specifically assessing eHealth literacy in conjunction with a health literacy assessment in order to assess patients’ navigational knowledge and skills using the Internet, specific to the use of eHealth applications. [JMIR Hum Factors 2017;4(1):e4]

eHealth Literacy: Predictors in a Population With Moderate-to-High Cardiovascular Risk.

Background: The eHealth Literacy Scale (eHEALS) is one of only a few available measurement scales to assess eHealth literacy. Perhaps due to the relative paucity of such measures and the rising importance of eHealth literacy, the eHEALS is increasingly a choice for inclusion in a range of studies across different groups, cultures, and nations. However, despite its growing popularity, questions have been raised over its theoretical foundations, and the factorial validity and multigroup measurement properties of the scale are yet to be investigated fully. Objective: The objective of our study was to examine the factorial validity and measurement invariance of the eHEALS among baby boomers (born between 1946 and 1964) in the United States, United Kingdom, and New Zealand who had used the Internet to search for health information in the last 6 months. Methods: Online questionnaires collected data from a random sample of baby boomers from the 3 countries of interest. The theoretical underpinning to eHEALS comprises social cognitive theory and self-efficacy theory. Close scrutiny of eHEALS with analysis of these theories suggests a 3-factor structure to be worth investigating, which has never before been explored. Structural equation modeling tested a 3-factor structure based on the theoretical underpinning to eHEALS and investigated multinational measurement invariance of the eHEALS. Results: We collected responses (N=996) to the questionnaires using random samples from the 3 countries. Results suggest that the eHEALS comprises a 3-factor structure with a measurement model that falls within all relevant fit indices (root mean square error of approximation, RMSEA=.041, comparative fit index, CFI=.986). Additionally, the scale demonstrates metric invariance (RMSEA=.040, CFI=.984, ΔCFI=.002) and even scalar invariance (RMSEA=.042, CFI=.978, ΔCFI=.008). Conclusions: To our knowledge, this is the first study to demonstrate multigroup factorial equivalence of the eHEALS, and did so based on data from 3 diverse nations and random samples drawn from an increasingly important cohort. The results give increased confidence to researchers using the scale in a range of eHealth assessment applications from primary care to health promotions. [J Med Internet Res 2017;19(2):e53]

Exploring the Measurement Properties of the eHealth Literacy Scale (eHEALS) Among Baby Boomers: A Multinational Test of Measurement Invariance.

Background: Electronic health (eHealth) information is ingrained in the healthcare experience to engage patients across the lifespan. Both eHealth accessibility and optimization are influenced by lifespan development, as older adults experience greater challenges accessing and using eHealth tools as compared to their younger counterparts. The eHealth Literacy Scale (eHEALS) is the most popular measure used to assess patient confidence locating, understanding, evaluating, and acting upon online health information. Currently, however, the factor structure of the eHEALS across discrete age groups is not well understood, which limits its usefulness as a measure of eHealth literacy across the lifespan. Objective: The purpose of this study was to examine the structure of eHEALS scores and the degree of measurement invariance among US adults representing the following generations: Millennials (18-35-year-olds), Generation X (36-51-year-olds), Baby Boomers (52-70-year-olds), and the Silent Generation (71-84-year-olds). Methods: Millennials (N=281, mean 26.64 years, SD 5.14), Generation X (N=164, mean 42.97 years, SD 5.01), and Baby Boomers/Silent Generation (N=384, mean 62.80 years, SD 6.66) members completed the eHEALS. The 3-factor (root mean square error of approximation, RMSEA=.06, comparative fit index, CFI=.99, Tucker-Lewis index, TLI=.98) and 4-factor (RMSEA=.06, CFI=.99, TLI=.98) models showed the best global fit, as compared to the 1- and 2-factor models. However, the 4-factor model did not have statistically significant factor loadings on the 4th factor, which led to the acceptance of the 3-factor eHEALS model. The 3-factor model included eHealth Information Awareness, Search, and Engagement. Pattern invariance for this 3-factor structure was supported with acceptable model fit (RMSEA=.07, Δχ2=P>.05, ΔCFI=0). Compared to Millennials and members of Generation X, those in the Baby Boomer and Silent Generations reported less confidence in their awareness of eHealth resources (P.05, ΔCFI=0). Compared with younger adults, older adults had less confidence in eHealth resource awareness (P<.001), information seeking skills (P<.01), and ability to evaluate and act upon online health information (P<.001). Conclusions: The eHEALS can be used to assess, monitor uniquely, and evaluate Internet users’ awareness of eHealth resources, information seeking skills, and engagement abilities. Configural and pattern invariance was observed across all generation groups in the 3-factor eHEALS model. To meet gold the standards for factor interpretation (ie, 3 items or indicators per factor), future research is needed to create and assess additional eHEALS items. Future research is also necessary to identify and test items for a fourth factor, one that captures the social nature of eHealth.

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Electronic Health Literacy Across the Lifespan: Measurement Invariance Study.

Background: The Internet has become a ubiquitous venue for information seeking, especially for health information. Public health practitioners have noticed the promise and potential of the Internet, however, little is known about individuals' skills of their eHealth literacy. The eHealth Literacy Scale, eHEALS, was designed to measure perceptions of individuals' eHealth literacy skills. Objective: The objective of the study was to examine the psychometric validity and reliability of the eHEALS with two adult populations using the Rasch Model. Methods: A college-aged sample and an Internet-based sample (Amazon's MTurk) were recruited to complete the eHEALS, demographic questions, and a health literacy scale. Using WINSTEPS and SPSS, unidimensionality, item fit, rating scale, item hierarchy, person ability-item match, and reliability were analyzed, compared, and contrasted against each sample and to other samples found in the literature. Results: An exploratory factor analysis supported unidimensionality in both samples. More than 90% of respondents from both samples fit the model. No items were outright misfitting. Both samples separated into three distinct groups. Conclusions: Based on the results, the eHEALS is a reliable and consistent measurement tool for a college sample and an Internet-based sample. As these individuals are most likely to use the Internet as a health resource, it is necessary to learn and know their skills versus perceiving that they can critically and successfully navigate the Internet. Further analyses are necessary to ensure that the eHEALS can serve as a standard eHealth literacy measure for public health. [JMIR Public Health Surveill 2016;2(1):e24]

Construct Validity of the eHealth Literacy Scale (eHEALS) Among Two Adult Populations: A Rasch Analysis.

Background: Twitter is the most popular form of microblogging that is being utilized in public health to engage audiences and to communicate health-related information. Although there is some research showing the various forms of Twitter use in public health, little is known about how individual public health professionals are using their personal Twitter accounts to disseminate health information. Objective: The purpose of this research was to categorize public health professionals’ tweets to evaluate how individual public health professionals are furthering the mission of public health. Methods: Twitter accounts held by public health professionals were identified, and researchers proceeded to record 6 months’ worth of each individual’s Twitter feed. During the 6-month period, a total of 15,236 tweets were collected and analyzed using the constant comparison method. Results: A total of 23 tweet categories among the 15,236 tweets were initially identified. Some of the most common topics among the 23 categories included the following: health nutrition (n=2008), conferences (n=815), Ebola (n=789), Affordable Care Act (ACA)/health care (n=627), and social justice (n=626). Each of these categories were then stratified into one of four themes: (1) informing and educating, (2) monitoring health statuses and trends, (3) social justice, and (4) professional development. Conclusions: Using Twitter, public health professionals are helping dispel misinformation through education and by translating technical research into lay terms, advocating for health inequalities, and using it as a means to promote professional development. [JMIR Public Health Surveill 2017;3(4):e54]

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Twitter and Public Health (Part 2): Qualitative Analysis of How Individual Health Professionals Outside Organizations Use Microblogging to Promote and Disseminate Health-Related Information

Background: The use of social networking sites is increasingly being adopted in public health, in part, because of the barriers to funding and reduced resources. Public health professionals are using social media platforms, specifically Twitter, as a way to facilitate professional development. Objective: The objective of this study was to identify public health professionals using Twitter and to analyze how they use this platform to enhance their formal and informal professional development within the context of public health. Methods: Keyword searches were conducted to identify and invite potential participants to complete a survey related to their use of Twitter for public health and professional experiences. Data regarding demographic attributes, Twitter usage, and qualitative information were obtained through an anonymous Web-based survey. Open-response survey questions were analyzed using the constant comparison method. Results: “Using Twitter makes it easier to expand my networking opportunities” and “I find Twitter useful for professional development” scored highest, with a mean score of 4.57 (standard deviation [SD] 0.74) and 4.43 (SD 0.76) on a 5-point Likert scale. Analysis of the qualitative data shows the emergence of the following themes for why public health professionals mostly use Twitter: (1) geography, (2) continuing education, (3) professional gain, and (4) communication. Conclusions: For public health professionals in this study, Twitter is a platform best used for their networking and professional development. Furthermore, the use of Twitter allows public health professionals to overcome a series of barriers and enhances opportunities for growth.

Twitter and Public Health (Part 1): How Individual Public Health Professionals Use Twitter for Professional Development.

Purpose Interprofessional education (IPE) is a promising endeavor for students to engage in team-oriented activities with other disciplines. Service-learning has emerged as a dynamic way in which students derive practical skills to address the needs of their community. Research has probed student perspectives but has seldom explored community feedback. This study assessed Interdisciplinary Family Health (IFH) Program participants perceived benefits associated with program participation at a southeastern university. Method Data from nineteen semi-structured telephone interviews, conducted between February 2015 and April 2015, were used to assess program participants perceived benefits. Semi-structured telephone interviews were analyzed using a grounded theory approach. Each interview was audio-recorded and transcribed before the coders established major themes. The emergence of themes was conceptualized through selective coding. Given an emergent SES-related response pattern, differences in perceived benefits were examined based on SES. Results All program participants reported positive wellness outcomes within a social support construct. Responses fell into four social support domains within a greater framework of bridging student-participant generations: informational support, emotional support, companionship support, and tangible support. Trends in social support domains observed were associated with participant SES. Participants with lower SES levels reported greater needs for health information and access, whereas participants' with higher SES levels desired building social relationships with students. Conclusions Tailoring IPE training to address specific social support domains and SES associations is an opportunity for enhanced participant experiences and perceived benefits. Educational planning can utilize social support domain-SES association findings as a guide for students to attune their efforts at improving the overall outcomes of their target population.

https://jdc.jefferson.edu/cgi/viewcontent.cgi?article=1085&context=jcipeconference

Sabrina Islam

Papers

Construct Validity of the eHealth Literacy Scale (eHEALS) Among Two Adult Populations: A Rasch Analysis.

Twitter and Public Health (Part 2): Qualitative Analysis of How Individual Health Professionals Outside Organizations Use Microblogging to Promote and Disseminate Health-Related Information

Twitter and Public Health (Part 1): How Individual Public Health Professionals Use Twitter for Professional Development.

What's in it for me? Perspectives from community participants in an interprofessional service learning program