The Exchange of Informational Support in Online Health Communities at the Onset of the COVID-19 Pandemic: Content Analysis

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Abstract

Background

Online health communities (OHCs) provide social support for ongoing health-related problems. COVID-19, the disease caused by SARS-CoV-2, has been an acute and substantial stressor worldwide. The disease and its impact, especially in the beginning phases, left many people with questions about the nature, treatment, and prevention of COVID-19. Unlike typical chronic ailments discussed on OHCs, which are more established, COVID-19, at least at the onset of the pandemic, is distinct in that it lacks a consensus of clinical diagnosis and an existing community foundation.

Objective

The study aims to investigate a newly formed OHC for COVID-19 to determine the topics and types of information exchange as well as the sources of information this community referenced during the early phases of the COVID-19 pandemic in the United States.

Methods

A total of 357 posts from a COVID-19 OHC on the MedHelp platform were annotated according to an open-coding process. Participants’ engagement patterns, topics of posts, and sources of information were quantified.

Results

Participants who offered informational support had a significantly higher percentage of responding more than once than those seeking information ( P<.001). Among the information-seeking topics, symptoms and public health practice and psychological impacts were the most frequently discussed, with 26% (17/65) and 15% (10/65) of posts, respectively. Most informational support was expressed through feedback/opinion (181/220, 82.3%). Additionally, the most frequently referenced source of information was news outlets/websites, at 55% (11/20). Governmental websites were referenced less frequently.

Conclusions

The trends of this community could be useful in prioritizing public health responses to address the most common questions asked by the public during crisis communication and in identifying which venue of communication is most effective in reaching a public audience during such times.

Related collections

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Highlights • The vicarious traumatization scores for front-line nurses were significantly lower than those of non-front-line nurses; • The vicarious traumatization scores for the general public were significantly higher than those of front-line nurses. • Strategies that aim to prevent and treat vicarious traumatization in medical staff and general public are necessary.

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Is Open Access

Infodemiology and Infoveillance: Framework for an Emerging Set of Public Health Informatics Methods to Analyze Search, Communication and Publication Behavior on the Internet

(2009)

Infodemiology can be defined as the science of distribution and determinants of information in an electronic medium, specifically the Internet, or in a population, with the ultimate aim to inform public health and public policy. Infodemiology data can be collected and analyzed in near real time. Examples for infodemiology applications include: the analysis of queries from Internet search engines to predict disease outbreaks (eg. influenza); monitoring peoples' status updates on microblogs such as Twitter for syndromic surveillance; detecting and quantifying disparities in health information availability; identifying and monitoring of public health relevant publications on the Internet (eg. anti-vaccination sites, but also news articles or expert-curated outbreak reports); automated tools to measure information diffusion and knowledge translation, and tracking the effectiveness of health marketing campaigns. Moreover, analyzing how people search and navigate the Internet for health-related information, as well as how they communicate and share this information, can provide valuable insights into health-related behavior of populations. Seven years after the infodemiology concept was first introduced, this paper revisits the emerging fields of infodemiology and infoveillance and proposes an expanded framework, introducing some basic metrics such as information prevalence, concept occurrence ratios, and information incidence. The framework distinguishes supply-based applications (analyzing what is being published on the Internet, eg. on Web sites, newsgroups, blogs, microblogs and social media) from demand-based methods (search and navigation behavior), and further distinguishes passive from active infoveillance methods. Infodemiology metrics follow population health relevant events or predict them. Thus, these metrics and methods are potentially useful for public health practice and research, and should be further developed and standardized.

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Author and article information

Contributors

Christopher C Yang:

ORCID: https://orcid.org/0000-0001-5463-6926

College of Computing and InformaticsDrexel University3675 Market StOffice 1186Philadelphia, PA, 10094United States1 267 648 4077chris.yang@drexel.edu

Journal

Journal ID (nlm-ta): JMIRx Med

Journal ID (iso-abbrev): JMIRx Med

Journal ID (publisher-id): JMIRxMed

Title: Jmirx Med

Publisher: JMIR Publications (Toronto, Canada )

ISSN (Electronic): 2563-6316

Publication date Collection: Jul-Sep 2021

Publication date (Electronic): 22 July 2021

Volume: 2

Issue: 3

Electronic Location Identifier: e27485

Affiliations

[1 ] College of Medicine Drexel University Philadelphia, PA United States

[2 ] College of Computing and Informatics Drexel University Philadelphia, PA United States

Author notes

Corresponding Author: Christopher C Yang chris.yang@ 123456drexel.edu

Author information

Wesley Jong https://orcid.org/0000-0002-6159-6157

Ou Stella Liang https://orcid.org/0000-0002-9131-1183

Christopher C Yang https://orcid.org/0000-0001-5463-6926

Article

Publisher ID: v2i3e27485

DOI: 10.2196/27485

PMC ID: 8323823

PubMed ID: 34398165

SO-VID: 8f4b5619-6f88-43e6-99cc-78a2ebdce029

License:

This is an open-access article distributed under the terms of the Creative Commons Attribution License ( https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIRx Med, is properly cited. The complete bibliographic information, a link to the original publication on https://med.jmirx.org/, as well as this copyright and license information must be included.

History

Date received : 26 January 2021

Date revision requested : 19 March 2021

Date revision received : 23 April 2021

Date accepted : 14 May 2021

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