Twitter and Diabetes
Phase: NA
Conditions: Diabetes, Cardiovascular Disease
Sponsor: University of Pennsylvania

 
1
Cover Page 
Study Title: Twitter and Cardiovascular Health 
NCT: NCT02806700 
PI: Raina Merchant MD 
University of Pennsylvania 
 
Re: Study Protocol 
Last update 4.10.2018 
 
 

 
2
Study Protocol (Twitter and Cardiovascular Health:Twitter and Diabetes) 
submitted prior to data analysis or un-blinding 
 
Background 
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.  
 
While effective therapies are available for diabetes mellitus (DM), disease management 
is often poor. Outcomes are often determined by patient activities outside of the clinical 
encounter such as diet, exercise, symptom monitoring, and medication adherence. 
Several approaches (e.g. self-care courses, home tele-monitoring) have been tested for 
aiding patients but are often resource intensive, and effectiveness trials have had varied 
results. Strategies with targeted text messaging, however, have shown promising results 
for improving clinical outcomes, patient satisfaction, improving health care utilization, 
and reducing healthcare costs.  
 
Objective 
For this study we seek to use Twitter, a public online platform to deliver health content 
over 6 months to participants with self-reported diabetes and evaluate the potential 
impact of this messaging on patient activation (a patients’ reported ability and willingness 
to take on the role of managing their health and healthcare). 
 
Methods 
We will identify participants with self-reported diabetes online. Participants will be 
identified through: self report on Twitter (e.g. “I have diabetes”), use of diabetes related 
hashtags and keywords on Twitter, ads posted online (e.g. Google, Reddit). Potential 
participants will then be “followed” on Twitter and privately direct messaged with a link to 
the study website. Ads would similarly include a link to the study website. 
 
Individuals clicking on the study website would be directed to complete eligibility 
assessment questions.  Eligibility questions include: age >21, self-report of diabetes, not 
pregnant (self-report), US residency.  
 
Eligible patients who consent to participate would then complete a baseline survey and 
then be randomized to the control or intervention group. 
 
Survey compensation of a $25 dollar gift card would be provided for completion of the 
final survey at 6 months. Baseline and follow-up surveys will include questions regarding 
patient activation (PAM-13), self-rated health (SF-1), ideal CV health (BRFSS), self-care 
practices for DM (Summary of Diabetes Self-Care Activities Measure).  To assess 
technology use, we will also ask questions from the Perceived usefulness, and 
Perceived ease of use surveys.  
 

 
3
Control: The control group study participation would include completing a baseline 
survey and survey 6 months later. 
 
Intervention: Participants randomized to the intervention group will be asked to “follow” 
the study team (e.g. @study handle). Once participants are “following” the study team, 
participants would receive 3x per week private messages via Twitter about heart health.  
 
Twitter heart health messages will be identified via an automated program, which locates 
highly shared (i.e. retweets) tweets which contain the keyword “diabetes.” The 50 most 
retweeted in a given day (with a minimum 6 hour lag time to accumulate retweets) would 
then be curated by our team and 3 would shared with participants 3x a week. We will 
use hand coding to describe themes associated with the curated tweets displayed to 
participants. 
 
Outcome measure: The primary outcome measure will be patient activation measured 
by the PAM (a measure of behavior change and has been shown to be changeable over 
time). Predictor variables will include demographic variables (gender, age, race/ethnicity, 
and geographic region of residence), SF1, HbA1c. The secondary outcome will be 
change in self-reported HbA1c. 
 
Sample size: Sample size estimates are based on the PAM measure. Assuming a 2-3 
point change in the index PAM score (average 58) we estimate requiring 75 patients per 
arm to allow detection of this difference with 90% power and a type I error rate of 5%. 
We assume potentially 30% over-enrollment for loss to follow-up for a Twitter 
intervention. 
 
Data Analysis: We will use summary statistics to compare demographics and survey 
responses (health status, ideal CV health status, self-practices) across groups. 
Summary statistics will be used to present data from the Perceived usefulness and 
Perceived ease of use measures for the intervention group.  
 
For the primary outcome measure, we will use paired t-test for pre-post differences of 
PAM scores. In addition, multivariable linear regression models will be used to model 
change in PAM score with adjustment for demographic and baseline health 
characteristics not evenly distributed by arm. In all models, a binary indicator for 
control/intervention group will be included. Baseline PAM score will also be adjusted for 
in the model. Covariates with missing data will be assessed for patterns of missingness 
and non-ignorability and will be multiply imputed if deemed necessary and reasonable.