Hypertension Clinical Trial
Official title:
Twitter & Cardiovascular Health
Cardiovascular (CV) disease is associated with significant morbidity and mortality. In the current digital age, needed is a better understanding of how information on social media sites may inform our approaches to improving CV health through novel methodologies. Investigators propose to study the conversation on Twitter about several CV diseases and their associated sequelae.
Cardiovascular (CV) disease remains the leading cause of morbidity and mortality in the US
and is associated with significant economic burden. To advance against this major public
health problem, the American Heart Association (AHA) and others have identified the need for
a targeted focus on improving the CV health of populations, emphasizing an array of new tools
and competencies for implementing public health policy and population- and community-level
interventions to complement the traditional, predominantly medically oriented interventions
that have been promoted successfully in the past. Social media channels like Twitter offer a
new opportunity to explore health related communication generated by the public and for the
public.
Understanding and harnessing these new communication channels is of particular importance as
the digital divide narrows and individuals across age and demographic groups increasingly
share information online with known networks of friends (e.g., Facebook), and broader
networks of friends and others (e.g., Twitter). Person-to-person word-of-mouth communication
is one of the most enduring and persuasive ways in which people deliver and receive
information. Until recently, person-to-person communication was effectively impossible to
intercept, study, and alter. Central to this proposal is the recognition that these changes
make some person-to-person communication observable that was previously private. It is the
observability of these new communication channels that provides both innovation and promise
to this area of inquiry.
This proposal evaluates social media for both its efferent and afferent pathways as a source
not just to communicate to communities, but also to learn from them. There is considerable
evidence that letting people know what other people do is one of the most effective ways of
increasing that behavior. This social norming of behaviors is facilitated through online
sharing enabling others to model behavior against broader groups whose actions would have
been invisible and therefore uninfluential without these new media channels. For individuals
with chronic illnesses, automated self-management support (e.g. mHealth) and online
communities have been shown to improved clinical outcomes, patient satisfaction, and reduce
health care costs and utilization. This proposal seeks to improve CV health and reduce the
burden of CV disease by understanding how patients communicate about CV health online and
improving patients ability to manage their CV disease(s).
Twitter as a global social media platform: Twitter allows users to send and receive
140-character messages referred to as tweets. Tweets may include embedded web links to
information such as news articles, home pages, and pictures. Tweets originate from a single
person or organization (a tweeter) and are distributed broadly to individuals with an
interest in the topic of the tweet and to individuals who have voluntarily signed up to
follow that tweeter. Followers can then share messages with their own followers, a process of
message propagation known as re-tweeting. Tweeters can choose to share information about
themselves on their profile (e.g. age, race, gender, occupation, location, likes/dislikes,
picture, webpage link).
Both patients and researchers have used health-related Twitter data in novel ways. In natural
disasters (e.g. Hurricane Sandy, Haiti earthquake), Twitter was used in real time to link
people in need with resources. In pandemics (e.g. H1N1) Twitter was used as a surveillance
tool to target flu hot-spots more rapidly than traditional data collection tools. Twitters
impact in organizing individual and social attitudes was dramatically revealed in the 2011
political events in Northern Africa. In this setting Twitter, and similar social media such
as Facebook, allowed the propagation and concentration of ideas sufficient to threaten and in
some cases topple restrictive governments. In non-emergent settings, linguists have used
Twitter to pinpoint local dialects and sociologists have used tweets to forecast the mood and
emotion of specific geographic regions. Others have also used Twitter to characterize medical
misconceptions (e.g. sequelae of concussions) and propagation of poor medical compliance
(e.g. antibiotic use).
Studying person-to-person communication: An estimated 400 million tweets are posted daily by
more than 200 million active users. Twitter is representative of big data that are
increasingly being explored to better understand online information from large, broad
populations of patients. Twitter offers promise as a research tool due to its immense scale,
its immediacy (for example, emergency departments in Boston learned about the tragic marathon
bombings faster through Twitter than through news or established emergency service
communication channels), and the systematic searchability of its content. Also of interest is
that the site is not focused on health and so it draws people by their interest in
communicating more generally, and yet includes public discourse on a broad array of health
topics, from the perspective of patients, providers, policymakers, organizations and others.
Although the Twitter user base is not a nationally-representative sample it has a
surprisingly deep representation across age, geography, and social distributions.
African-Americans, Latinos, and those in urban populations are in fact overrepresented on
Twitter relative to the general population.
This proposal reflects early work, but work that is fundamental to developing a base for
understanding the scientific uses and limitations of Twitter and related social media. This
proposal aims to analyze CV health behaviors being discussed online and evaluate new
approaches for improving access to CV health information and implementing behavior
modification.
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