Influenza Vaccination Digital Interventions for People Living With Diabetes
Phase: NA
Conditions: Diabetes
Sponsor: Evidation Health

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Influenza Vaccination Digital Interventions for People living 
with Diabetes 
 
NCT03870997 
August 3rd, 2018 
 

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Study Protocol: Influenza Vaccination Digital Interventions for 
People living with Diabetes   
  
Principal Investigator:   
Luca Foschini, PhD Co-
Principal Investigator: Jessie L. 
Juusola, PhD  
  
Sub-Investigators:  
Shefali Kumar, MPH  
Caroline Tai, PhD  
  
  
Sponsor: Evidation Health  
Protocol approval date: August 3rd, 2018  
  
  
  
 
    
  
Introduction  
Overview of Therapy Area   
  
It is estimated that each year 5-20% of the U.S. population have an influenza 
infection (also known as flu) (Molinari, 2007). Influenza may lead to adverse outcomes 
such as primary influenza pneumonia, secondary bacterial infection, and an increased risk 
of acute coronary syndromes  (Nicholson, 2003). The most effective way to reduce the risk 
of developing influenza is to get vaccinated. Influenza vaccination is also one of the most 
effective chronic care interventions; for example, it has shown to be an effective secondary 
prevention against myocardial infarction (MacIntyre, 2016). However in recent years, 
influenza vaccination rates for adults in the U.S. have remained suboptimal at 
approximately 40-50% (CDC, 2017).  
Compared to the general population, individuals with diabetes who get influenza 
have a greater risk of developing adverse outcomes such as pneumonia, bronchitis, sinus 
infection and ear infections, and can develop diabetes-specific adverse outcomes such as 
diabetic ketoacidosis (CDC, 2017). The Centers for Disease Control and Prevention 
recommends annual influenza vaccination for all people living with diabetes, however, 

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similar to overall vaccinations rates for adults in the U.S., vaccination rates for people 
living with diabetes also has remained suboptimal.  
Overview of Digital Intervention   
In this study, we aim to examine two different digital interventions: the Diabetes 
Digital Intervention and the General Digital Intervention. Both interventions will be driven 
via a digital campaign, which will provide interactive ways (e.g., videos, infographics, 
quizzes) for individuals to learn about influenza and related complications, and the 
importance of influenza vaccination. The Diabetes Digital Intervention will be tailored to 
people living with diabetes and will include an intervention that highlights the importance 
of influenza vaccination for this specific patient population. The General Digital 
Intervention will provide generic influenza vaccination interventions that can be applicable 
to any adult who should receive influenza vaccination.    
Objectives and Overall Study Design  
The overall objective of this 3-arm, 6-month long prospective randomized 
controlled study is to assess and quantify the impact of digital interventions to increase 
vaccination rates and vaccination-related outcomes in a connected population with 
selfreported diabetes. The digital intervention will be delivered via a campaign sent to 
participants, with interventions varying with each study arm. Participants will be blinded 
to study participation status.   
  
This study will consist of the following populations:   
● Population 1 (P1): Individuals with self-reported diabetes  
● Population 2 (P2): Individuals without self-reported diabetes who are 
similar to P1 based on demographics and activity tracker data   
● Population 3 (P3): Individuals without self-reported diabetes and are not in 
P2 based on demographics and activity tracker data, but are similar to P1 
with regards to age and gender distribution   
  
  
The three study arms are described below:  
● Arm 1: Individuals in P1 who receive the Diabetes Digital Intervention  
● Arm 2: Individuals in P2 and P3 who receive the Generic Digital 
Intervention   
● Arm 3: Individuals in P1, P2, and P3 who do not receive any digital 
intervention  
  
  
Based on these three populations and three arms, we will examine the following 
cohorts in this study:   
  

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Population  
 
P1: Individuals 
with 
selfreported 
diabetes  
P2: Individuals 
without 
selfreported 
diabetes who are 
similar to P1 
based on 
demographics 
and activity 
tracker data  
Population 3 
(P3): 
Individuals 
without 
selfreported 
diabetes and are 
not in P2, but 
are similar to 
P1 with regards 
to age and 
gender 
distribution  
Arm   Arm 1: Diabetes Digital 
Intervention  
Cohort 1  
NA  
NA  
Arm 2: Generic Digital 
Intervention   
NA  
Cohort 3  
Cohort 5  
Arm 3: No Influenza 
vaccination intervention  
Cohort 2  
Cohort 4  
Cohort 6  
  
Patient Eligibility  
Inclusion Criteria  
Exclusion Criteria   
●  Ages 18 and older
 
   ●  
Lives in the 
U.S.   
 
    
● < 18 years old   
● Does not live in the U.S.   
  
Achievement, a proprietary asset of Evidation Health, is an online program where people 
can connect their digital health tools, including wearable activity trackers and fitness 
apps. Existing Achievement members are typically ages 18 and older and live in the U.S. 
We will limit inclusion in the study sample to those who meet that criteria based on their 
Achievement registration information, and we will only include in analysis those who 
confirm these characteristics in study questionnaires.  
  

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Subject Recruitment, Enrollment and Study Procedures   
Achievement members agree to being contacted with study opportunities when they 
create an Achievement account. Evidation Health will leverage Achievement for this study.   
A set of existing Achievement members who meet the inclusion criteria will be 
tagged for study inclusion, termed “participants” from here on. Since participants will be 
blinded to their study participation status, participants will not be asked to take any action 
to enroll in the study. Participants will be randomized to one of the three study arms 
relevant to them, based on which population they are in. Based on which arm the participant 
is randomized to, they may receive the digital intervention throughout the study period.   
All participants will be invited to complete a baseline assessment 
(SeptemberOctober). The baseline assessment will include questions on demographics, 
health status, diabetes history, complications and treatment (if applicable), influenza and 
influenza vaccination history, perception of influenza vaccination, role of their health care 
provider in influenza vaccination decisions, and overall healthcare utilization.   
Mid-way through the influenza season (early- to mid-December), participants will 
be asked to complete an online mid-study assessment to assess whether they received an 
influenza vaccination or had influenza yet this season, and influenza experience for those 
who did have influenza. Information about demographics, health status, perception of 
influenza vaccinations, role of their health care provider in influenza vaccination decisions, 
and overall healthcare utilization will also be collected.  
At the end of influenza season (mid-March), participants will be asked to complete 
an online study-end assessment to assess whether they received influenza vaccination or 
had influenza this season, influenza experience for those who did have influenza, 
perception of influenza vaccination, and role of their health care provider in influenza 
vaccination decisions. Demographics, health status, and overall healthcare utilization will 
also be collected. Cohorts 1, 3 and 5 (individuals who received either the Diabetes Digital 
Intervention or the Generic Digital Intervention) will also be asked about their reactions to 
the intervention campaign.  
Participants will earn Achievement points, which can be redeemed for monetary 
rewards (in USD), for completing the questionnaires. Participants who do not complete 
questionnaires may be contacted by email, text message, push notification, and/or phone 
call with reminders to complete questionnaires. All submitted data will be included in the 
study analysis set, regardless of completion of previous or subsequent questionnaires.   
  
Informed Consent  
We request that informed consent is exempt for this study for the following reasons:  
● Participation in this study involves no more than minimal risk to the privacy of 
individuals.   
● Given the nature of the study, which aims to measure influenza vaccination rates in 
a real-world setting, priming the participants with a consent form will likely alter 
the perception of the interventions and may impact how individuals respond to the 

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study assessments, potentially biasing the results and limiting the generalizability 
of the findings.   
At the beginning of each questionnaire, participants will be notified on how their survey 
responses and behavioral data will be used for research purposes.   
  
Data Security and Confidentiality   
All identifiable information about participants, their medical conditions, and other 
study data will be secured by Evidation Health in accordance with all local and state laws, 
regulations, and IRB policies regarding collection and distribution of patient information.  
 Data will be transmitted using Transport Layer Security (TLS) and stored on encrypted 
disks on secure and hardened servers. Administrative access to these servers is limited to 
only the necessary IT staff at Evidation Health.  
Personally Identifiable Information (PII) is only accessible to a restricted set of 
individuals, and is only used to distribute invention and study material and for participant 
support purposes. Researchers at Evidation Health only have access to Coded Study Data 
where all PII is replaced with random unique identifiers. Coded Study Data may also be 
transferred to external research partners in accordance with IRB approved uses.  
  
Primary, Secondary and Supplemental Outcomes   
Outcome  
Description  
Primary  
Difference in self-reported influenza vaccination rates between Cohort 1 
and Cohort 2  
  
Secondary  
Secondary endpoints will include  
● Difference in differences in self-reported influenza vaccination 
rates between Cohort 1 and 2 as compared to Cohort 3 and 4  
● Impact of when intervention is sent (when participant is wearing 
an activity tracker vs. when participant is not wearing an activity 
tracker) on self-reported influenza vaccination rates   

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Supplemental  Potential supplemental endpoints may include  
● Difference in self-reported influenza vaccination rates from 
previous year to study year, by cohort  
● Difference in self-reported influenza vaccination rates between 
Cohort 3 and Cohort 4  
● Difference in self-reported influenza vaccination rates between 
Cohort 5 and Cohort 6  
● Agreement of influenza vaccination predictive score and 
selfreported influenza vaccination  
● Whether specific interventions changes participants’ mindset 
towards influenza vaccination  
● Self-reported impact of influenza (if participant develops 
influenza, how long influenza lasted, healthcare utilization, etc.)  
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Disease modulation (if participant develops influenza, 
influenza symptom severity)  
● Role of health care provider in influenza vaccination decisions  
● Difference in activity tracker-based behavioral characteristics (as 
measured by tracker use, steps, sleep and heart rate data) between 
various subgroups of the population   
  
  
Analytic Plan   
Descriptive analyses will report summary statistics such as mean, standard 
deviation, and percentage of all baseline characteristics by cohort. Baseline characteristics 
will be derived from data collected in the assessments such as demographics, health status, 
comorbidities, and overall healthcare utilization. Any differences between compared 
cohorts (Cohorts 1 vs. 2, Cohorts 3 vs. 4, and Cohorts 5 vs. 6) will be evaluated using the 
appropriate statistical test based on format and distribution of the variable (i.e., chi-square, 
t-test, Mann Whitney U test).   
Intent-to-treat analysis will be conducted for primary, secondary and supplemental 
outcomes. The primary analysis will test for a difference in proportion of self-reported 
influenza vaccination during the 2018-2019 influenza season between Cohort 1 and Cohort 
2 using chi-square tests. Secondary analyses will be evaluated as a difference in differences 
comparison where the difference in proportions of self-reported influenza vaccination 
between Cohort 1 and Cohort 2 will be compared to the difference in proportions of self-
reported influenza vaccination between Cohort 3 and Cohort 4 using t-tests and regression 

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models that test the statistical significance of an interaction between arm and population. 
Effect modification by activity tracker (intervention messages sent when wearing tracker 
vs. when not wearing tracker) will be evaluated as an additional interaction term in the 
regression model or by stratification, as appropriate. Any potentially confounding variables 
found to be unbalanced between compared cohorts will be adjusted for as covariates in 
regression models.   
Supplemental analyses will evaluate differences in the outcomes between compared 
cohorts and will utilize t-test, ANOVA, chi-square test, or McNemar’s (chisquare test for 
paired data) test based on format and distribution of outcome. Difference in difference 
comparisons for supplemental outcomes will use t-tests or regression models, similar to 
secondary analyses.   
Exploratory analyses will also be performed on activity data. We will reimplement 
all main analyses on sub-cohorts defined by behavioral features. e.g., high vs. low average 
physical activity (measured as daily step counts), high vs low sleep quality (measured as 
average sleep efficiency), high vs. low fitness level (measured as resting HR). Time-series 
analyses of activity data may be performed around specific index events (e.g., day when 
intervention is received).   
Sample Size  
We will aim to include 60,000-90,000 participants in this study. The study is 
powered to show effect in the primary outcome of difference in vaccination rate for 
individuals with diabetes who receive a diabetes-related influenza vaccination intervention 
(Cohort 1) and those who receive no influenza vaccination intervention (Cohort 2). We 
estimate a difference of 2.7 percentage points as a meaningful and reasonable effect size, 
based on 2 similar studies, described below.   
One unpublished study run by Evidation Health tested the impact of a messaging 
intervention encouraging vaccination delivered by push notification through the wellness 
platform of a large national health insurer. The observed increase in vaccination due to the 
messaging was 1.1 percentage points. We believe this constitutes a lower bound to the lift 
expected in the current study for two reasons: 1) vaccination increase was measured in 
December, thus not capturing the full duration of the influenza vaccination period and 2) 
vaccination rate was measured via claims data, which is expected to have a 45% unreported 
vaccination rate.  
A second recent study (Dale, 2018), tested the impact of a geographicallylocalized 
push notification encouraging vaccination to members of a wellness platform. This 
intervention was estimated to increase vaccination rate by 5.4 percentage points. The study 
featured a more powerful intervention than the one that will be tested here (e.g., 
intervention messages were sent when participant was near a participating pharmacy), 
therefore we believe it may be an upper bound to what we can expect in our study.   If we 
assume an effect size at least double study 1 but less than study 2, (i.e., 2.7 percentage 
points), then we need 4,043 individuals for each of the two arms of P1, for a total of 8,086 
study participants across the two arms.  

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We will label a larger population for study inclusion than determined by the 
powering calculations in order to account for realistic questionnaire participation rates. 
Assuming approximately 30-40% of individuals tagged as participants will complete the 
mid-study or study-end assessment, we would need to include approximately 20,00030,000 
individuals with diabetes (P1) as participants in the study. We will label similar numbers 
for P2 and P3, for a total of at least 60,000 participants in this study.   
  
Risks and Benefits   
Potential Risks Associated with the Study  
This is a minimal risk study that does not involve any risk beyond what a person 
would experience in their daily life. It is an exploratory study that will not provide any 
form of medical advice or medical intervention other than what is routine to standard of 
care (i.e., recommendation for annual influenza vaccination). While we strive to protect 
the privacy of personal information and implement appropriate safeguards, complete 
security cannot be guaranteed. There is the potential risk of loss of privacy associated 
with this study if another person sees the participant’s survey responses on their phone or 
computer screen. Participants may decline to answer certain survey questions.  
Adverse Events  
Given that there are no pharmaceutical products administered as part of this study, there 
is no potential to find any pharmacovigilance data thereof associated with the use of 
influenza vaccines.   
Any pharmacovigilance data potentially collected will be notified by Evidation Health to 
Sanofi Pasteur Pharmacovigilance, who is working with Evidation on this research 
project, through email (CL-CPV-Receipt@sanofi.com) within 1 (one) working day.   
 
    
    
A “serious adverse event (SAE)” is defined as -  
  
Any untoward medical occurrence that at any dose:   
• 
Results in death,  
• 
Is life threatening, (Note: the term “life-threatening” refers to an event/reaction in 
which the patient was at risk of death at the time of the event/reaction; it does not 
refer to an event/ reaction which hypothetically might have caused death if it were 
more severe),  
• 
Requires inpatient hospitalization or results in prolongation of existing 
hospitalization,  
• 
Results in persistent or significant disability/incapacity,  
• 
Is a congenital anomaly/birth defect, or  
• 
Is a medically important event or reaction. Medical and scientific judgment should 
be exercised in deciding whether other situations should be considered serious, such 
as important medical events that might not be immediately lifethreatening or result 

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in death or hospitalization, but might jeopardize the patient or might require 
intervention to prevent one of the other outcomes listed in the definition above.  
  
Potential Benefits Associated with the Study  
During this study, participants may develop a better understanding of their own 
health and may be encouraged to get their influenza shot, which has been shown to be the 
most effective method for influenza prevention. In addition, participants will be 
contributing to the understanding of how individuals can be motivated and encouraged to 
get their influenza vaccination.   
  
References  
1. Centers for Disease Control and Prevention,  Vaccination Coverage, United States, 
2016-17 
Influenza 
Season. 
https://www.cdc.gov/flu/fluvaxview/coverage1617estimates.htm  
2. Centers for Disease Control and Prevention, Flu and People with Diabetes. 
https://www.cdc.gov/flu/diabetes/index.htm  
3. Dale LP, White L, Mitchell M, et al. Smartphone app uses loyalty point incentives 
and push notifications to encourage influenza vaccine uptake. Vaccine 2018.   
4. MacIntyre CR, Mahimbo A, Moa AM, Barnes M. Influenza vaccine as a coronary 
intervention for prevention of myocardial infarction. Heart. 2016 Dec 
15;102(24):1953-1956.  
5. Molinari NA, Ortega-Sanchez IR, Messonnier ML, et al. The annual impact of 
seasonal influenza in the US: measuring disease burden and costs. Vaccine. 2007 
Jun 28;25(27):5086-96.   
6. Nicholson KG, Wood JM, Zambon M. Influenza. Lancet 2003;362:1733–45.