Increasing Influenza Vaccination in Individuals With Cardiovascular Conditions: Assessing the Effectiveness of a Digital Intervention in a Decentralized Randomized Controlled Trial
Phase: NA
Conditions: Cardiovascular Diseases, Atrial Fibrillation, Abnormal Heart Rhythms, Cardiac Arrest, Heart Attack, Myocardial Infarction, Coronary Heart Disease, Heart Failure, Stroke, Cerebrovascular Accident
Sponsor: Evidation Health

Study Protocol: Increasing Influenza Vaccination in Individuals
with Cardiovascular Conditions: Assessing the Effectiveness of a
Digital Intervention in a Decentralized Randomized Controlled
Trial
Principal Investigator:
Nell Marshall, DrPH
Sub-Investigators:
Jennifer Lee, PhD
Wei-Nchih Lee, MD, PhD, MPH
Jessica Schroeder, PhD
Sponsor:
Evidation Health
Research Partner:
Sanofi Pasteur
Protocol approval date (Research Partner-Sanofi Pasteur): 11 August 2020
Protocol approval date (Evidation Health): 11 August 2020
NCT Number: NCT04584645
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Table of Contents
List of Abbreviations
4
Statement of Compliance
5
Protocol Summary
5
1.  Introduction
8
1.1. Overview of Therapy Area
8
1.2. Study Rationale
8
1.3. Intervention Overview
8
2.  Study Objectives
9
2.1. Primary Objective
9
2.2. Secondary Objectives
9
2.3 Exploratory Objectives
9
3.  Study Design
10
3.1. Overall Design
10
4.  Study Endpoints
10
4.1. Primary Endpoint
10
5. Study Population
11
5.1. Inclusion and Exclusion Criteria
11
5.2. Recruitment Methods
11
6.1. Study Enrollment
12
6.2. Informed Consent
12
6.3. Study Steps
12
6.4. Study Compliance
13
6.5. Compensation
13
6.6. Study Flow Diagram
13
8. Statistical Considerations
16
8.1. Sample Size Justification
16
8.2. Description of Analytical Plan
17
8.2.1. Baseline Descriptive Statistics
17
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8.2.2. Analysis of Primary Endpoint
17
8.2.3. Analysis of Secondary Endpoint
18
8.2.4. Exploratory Analyses
18
9.  Data Security and Confidentiality
19
10.  Risks and Benefits
19
10.1. Potential Risks Associated with the Study
20
10.2. Potential Benefits Associated with the Study
20
10.3. Adverse Events
20
Appendix
21
References
21
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List of Abbreviations
IRB
Institutional Review Board
LAR
Legally Authorized Representative
PII
Personally Identifiable Information
TLS
Transport Layer Security
CDC
Centers for Disease Control and Prevention
ePRO
Electronic Patient Reported Outcomes
RCT
Randomized Controlled Trial
CVD-I
Cardiovascular disease-intervention
CVD-C
Cardiovascular disease-control
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Statement of Compliance
The protocol, Data Usage Agreement & Permissions form, recruitment materials, and all
participant materials will be submitted to the Solutions Institutional Review Board for review
and approval. Approval of both the protocol and the Data Usage Agreement & Permissions form
must be obtained before any participant is enrolled. Any amendment to the protocol or Data
Usage Agreement & Permissions form will require review and approval by Solutions IRB before
the changes are implemented to the study. A determination will be made regarding whether a
new Data Usage Agreement & Permissions form needs to be obtained from participants who
provided agreement to participate using a previously approved Data Usage Agreement &
Permissions form.
Protocol Summary
Title
Increasing Influenza Vaccination in Individuals with
Cardiovascular Conditions: Assessing the Effectiveness of a
Digital Intervention in a Decentralized Randomized
Controlled Trial
Study Design
A 6-month prospective, digital randomized controlled trial
targeting approximately 35,000 individuals to evaluate the
effectiveness of an influenza vaccination intervention during
influenza season for people with cardiovascular conditions
Objectives
Primary:
To determine the effectiveness of a digital intervention
designed to increase influenza vaccination rates in individuals
with cardiovascular disease (CVD) by examining:
●
Differences in self-reported influenza vaccination rates
between individuals with cardiovascular disease who
receive a targeted digital intervention (CVD-I) aimed
at increasing influenza vaccination and those with
cardiovascular disease who received no intervention
(CVD-C) by the end of the study period
Secondary:
1) To examine differences in vaccination rates between
individuals in the intervention group who engaged
with different numbers of intervention messages, and
changes in self-reported vaccination rates following
intervention messages
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2) To describe levels of engagement with the intervention
messages and perceptions of the intervention messages
within the CVD-I group
3) To examine predictors of vaccination status, including
cardiovascular condition type, vaccine drivers/barriers,
and vaccine knowledge, across diagnoses and within
CVD-I and CVD-C groups
Exploratory:
1) To examine the potential impact of COVID-19
pandemic on influenza vaccination rates
2) To describe self-reported complications from
influenza experienced by individuals with different
cardiovascular conditions during the 6-month study
period, as well as differences in complications by
vaccination status
3) To examine differences in wearable data (e.g., sleep,
steps, heart rate) between individuals who experience
self-reported influenza and those who do not, as well
as differences in wearable data between cardiovascular
conditions
Endpoints
Primary endpoint
●
Self-reported influenza vaccination rates
Secondary endpoints
●
Engagement with interventions (i.e., clicking, opening,
completing)
●
Self-reported vaccine drivers/barriers, and vaccine
knowledge
Exploratory endpoints
●
Self-reported impact of COVID-19 on influenza
vaccination behavior
●
Self-reported complications from influenza
●
Passively collected wearable data (e.g., sleep, steps,
heart rate)
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Patient Population
Inclusion Criteria:
●
Age 18 years or older
●
Resides in the U.S.
●
Speaks, reads, and understands English
●
Has self-reported being diagnosed with any of the
following cardiac conditions below in their
Achievement profile:
○Atrial Fibrillation or Afib
○Abnormal or irregular heart rhythm, other
arrhythmic heart disease
○Cardiac arrest, or heart attack (myocardial
infarction)
○Coronary heart disease like a heart blockage,
treated with medications, a stent in the heart, or
sometimes bypass surgery
○Heart failure, like congestive heart failure
○Stroke or cerebrovascular accident (CVA)
Exclusion Criteria:
●
Participated in Step 1, Part 2 semi-structured
interviews used to obtain feedback on the intervention
messages
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1.
Introduction
1.1. Overview of Therapy Area
Influenza impacts approximately 5-20% of the population of the United States annually, resulting
in significant economic impact and public health burden (Molinari et al., 2007). For people with
cardiovascular conditions, influenza can be particularly burdensome, thus they are considered a
high-risk group (CDC, 2019). Vaccination for influenza remains the most effective primary
prevention method against influenza, with effectiveness ranging from 38-48% in the past few
years (Centers for Disease Control and Prevention (CDC), 2018; Dos Santos, Tahrat, &
Bekkat-Berkani, 2018; Doyle, 2019; Jackson et al., 2017; Rolfes et al., 2019). The CDC reports
that the vaccination rate in the 2018-2019 season was 47.9%  for individuals 18-64 years of age
who have high risk conditions such as cardiovascular conditions (CDC, 2019). While this is
higher than the rate of 36.4% in same age individuals without high risk conditions, it is far below
the 70% national vaccination rate goal (Healthy People 2020, 2019). Given the increased burden
that influenza represents for people with high risk conditions, even small improvements in
vaccination rates could result in significant impact and provide benefit to patients, healthcare
providers, and insurance payors (Molinari et al., 2007).
1.2. Study Rationale
To date, there are no large-scale RCTs on digital interventions for increasing vaccination rates in
people with cardiovascular conditions. One observational study has demonstrated the potential
effectiveness of general messaging and incentives via a health-related app to increase vaccine
uptake in the general Canadian population (Dale, White, Mitchell, & Faulkner, 2019), suggesting
this kind of intervention could be effective with people with cardiovascular conditions. In a
separate study, a large randomized controlled trial (RCT) using digital messaging with incentives
to vaccinate was found to be effective in increasing vaccination rates in a general population of
adults in the United States (Lee et al., 2020).  Of particular interest is a digital RCT in a sample
of individuals with diabetes, a population with increased risk of influenza-related complications,
that used digital messaging and was found to be effective in increasing vaccination rates
(Samson, et al., 2020). An additional study of individuals with diabetes that was a collaboration
between Sanofi Pasteur and Evidation also demonstrated the impact of influenza and related
complications on data retrieved from wearables; showing that people who developed influenza
engaged in less activity around the time of diagnosis and recorded an increased heart rate, a
unique passive marker of infection (Samson et al., 2019). These studies all suggest that the
development of a digital intervention targeted to individuals with cardiovascular conditions could
be beneficial in increasing vaccination rates.
1.3. Intervention Overview
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The digital intervention messages were developed using a 3-part approach: 1) a quantitative
assessment of more than 800 patient perspectives for people with cardiovascular disorders,
assessing their behaviors, thoughts, and beliefs surrounding vaccination; 2) semi-structured
interviews with approximately 25 patients who had cardiovascular diseases to gather their
perspectives and feedback on proposed intervention messages; and 3) an expert panel of
cardiologists, behavioral scientists, and vaccine policy experts for expert input on content and
accuracy of scientific information provided. This approach to intervention development was
consistent with the Patient Centered Outcomes Research Institute (PCORI) recommendation for
developing interventions that increase participant recruitment and retention, improve patient
outcomes, and increase research validity and relevance to the real world (Frank, Basch, & Selby,
2014). The intervention messages provide informational content on the influenza vaccine
(sourced from the Centers for Disease Control and Prevention and the American Heart
Association), specific information about influenza and cardiovascular disorders, and behavioral
prompts (e.g., reminders) surrounding influenza vaccination behaviors.
2.
Study Objectives
2.1. Primary Objective
●
To determine the effectiveness of a digital intervention designed to increase influenza
vaccination rates in individuals with cardiovascular diagnoses (CV diagnoses) by
examining differences in self-reported influenza vaccination rates between individuals
with cardiovascular disease who receive a targeted digital intervention (CVD-I) aimed at
increasing influenza vaccination and those with CV diagnoses who received no
intervention (CVD-C) by the end of the study period.
2.2. Secondary Objectives
●
To examine differences in vaccination rates between individuals in CVD-I who engaged
with different numbers of intervention messages, and changes in self-reported vaccination
rates following intervention messages
●
To describe levels of engagement with the intervention messages and perceptions of the
intervention messages within the CVD-I group
●
To examine predictors of vaccination status, including cardiovascular condition type,
vaccine drivers/barriers, and vaccine knowledge, across diagnoses and within CVD-I and
CVD-C groups
2.3 Exploratory Objectives
●
To examine the potential impact of COVID-19 pandemic on vaccination rates
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●
To describe self-reported complications from influenza experienced by individuals with
different cardiovascular conditions during the 6-month study period, as well as
differences in complications by vaccination status
●
To examine differences in wearable data (e.g., sleep, steps, heart rate) between
individuals who experience self-reported influenza and those who do not, as well as
differences in wearable data between cardiovascular conditions
3.
Study Design
3.1. Overall Design
●
A 6-month prospective, digital randomized controlled trial targeting approximately
35,000 individuals to evaluate the effectiveness of an influenza vaccination intervention
during influenza season for people with cardiovascular conditions
●
Two study arms: cardiovascular disorders digital intervention arm (CVD-I) &
cardiovascular disorders without digital intervention arm (control: CVD-C)
4.
Study Endpoints
4.1. Primary Endpoint
The primary endpoint in this study is to examine differences in self-reported influenza
vaccination rates between individuals with cardiovascular disease who receive a targeted digital
intervention (CVD-I) aimed at increasing influenza vaccination and those with cardiovascular
disease who received no intervention (CVD-C).
4.2. Secondary Endpoint
The secondary endpoints in this study are:
●
To examine predictors of vaccination status, including cardiovascular condition type,
vaccine drivers/barriers, and vaccine knowledge, across and between CVD-I and CVD-C.
●
Engagement with interventions, including clicking, opening, or completing the
intervention messages
4.3. Exploratory Endpoints
The exploratory endpoints in this study include:
●
To describe the self-reported impact of the COVID-19 pandemic on influenza vaccination
behavior
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●
To describe self-reported complications from influenza experienced by individuals with
different cardiovascular conditions during the 6-month study period, as well as
differences in complications by vaccination status.
●
To examine differences in wearable data (i.e., sleep, steps, heart rate) between individuals
who experience influenza and those who do not, as well as differences in wearable data
between cardiovascular conditions.
5.
Study Population
5.1. Inclusion and Exclusion Criteria
Inclusion Criteria
Exclusion Criteria
●
Age 18 years or older
●
Resides in the U.S.
●
Speaks, reads, and understands
English
●
Has self-reported being diagnosed
with any of the following cardiac
conditions below in their
Achievement profile:
o
Atrial Fibrillation or Afib
o
Abnormal or irregular heart
rhythm, other arrhythmic
heart disease
o
Cardiac arrest, or heart attack
(myocardial infarction)
o
Coronary heart disease like a
heart blockage, treated with
medications, a stent in the
heart, or sometimes bypass
surgery
o
Heart failure, like congestive
heart failure
o
Stroke or cerebrovascular
accident (CVA)
●
Participated in Step 1, Part 2
semi-structured interviews used to
obtain feedback on the intervention
messages.
5.2. Recruitment Methods
Achievement, a product of Evidation Health, is an online platform where people can connect
their digital health tools, including wearable activity trackers and fitness apps. Achievement
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members agree to being contacted with study opportunities when they create an Achievement
account. Evidation Health will leverage Achievement for recruitment, and will use an online
strategy and study platform to develop a target list of participants, verify eligibility, and enroll
participants into this study.
A set of existing Achievement members who have previously self-reported to meeting 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. In order to identify a target list of participants, Evidation
Health will leverage already permissioned information from Achievement members, including
data on their cardiovascular diagnoses, age, sex/gender, race/ethnicity, and country of residence.
These participants will be randomized using block randomization into CVD-Intervention or
CVD-Control prior to being offered the opportunity to complete any study activities.
Randomization will ensure that the groups are representative of the general Achievement
population with cardiovascular disease. Efforts will be made to target individuals for enrollment
who identify as being of a race/ethnicity that is something other than non-Hispanic White, as
well as representative in sex/gender. Participants will be sent offers via the Achievement
platform to complete study activities. Participants can complete any number of survey activities
(demographics, baseline, 3-month survey, and/or 6-month survey, intervention messages);
completion of intervention activities will not be required for enrollment for the CVD-I group.
6.
Study Procedures
6.1. Study Enrollment
Participants will be sent offers on the Achievement Studies Platform to complete study activities.
Participants can complete any number of survey activities (baseline, 3-month survey, and/or
6-month survey, intervention messages); completion of intervention activities will not be
required for enrollment for the CVD-I group.
6.2. Informed Consent
We will 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 study
assessments, potentially biasing the results and limiting the generalizability of the
findings. For this reason, this study could not be practically carried out without the
waiver of informed consent.
●
At the beginning of each questionnaire, participants will be notified on how their survey
responses and behavioral data will be used for research purposes via a Data Usage &
Permissions Agreement.
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6.3. Study Steps
Participants will be sent an offer to complete a survey at baseline, 3 months, and 6 months. A
separate demographic and medical history survey will remain available for participants to
complete throughout the study. Additional information on study experiences and intervention
feedback will be gathered in the 6-month survey for those in the CVD-I group. Data from an
activity tracker will be collected if one is already connected to the participant’s Achievement
account, consistent with their completion of the Data Usage & Permissions Agreement. Activity
tracker data will be collected retrospectively and prospectively. Participants in the CVD-I arm
only will be sent intervention messages on predetermined days for the duration of the 6-month
study intervention period. At the end of the intervention period, individuals who completed a
data usage and permissions agreement as a part of the surveys will be informed via email that the
survey information they contributed was for a research study investigating influenza vaccination
behavior in people with cardiovascular conditions.
6.4. Study Compliance
Evidation study staff will track whether participants are completing study-related tasks
throughout the study. Participants who do not complete study activities may be contacted by
email, text message, or push notifications with reminders to complete activities, within a time
window that is appropriate to the specific task, but will remain in the study sample regardless of
whether they submit data in a timely manner.
6.5. Compensation
Participants in both arms will be compensated for completing surveys. Participants in the CVD-I
will be compensated minimally for the completion of intervention tasks (e.g., completing short
quizzes, scheduling a date for their influenza vaccine, reading an article to learn more about the
flu, the flu shot, and their cardiovascular conditions), to avoid incentivizing vaccination status
unequally between the two arms. The compensation amounts will be detailed in the Data Usage
& Permissions Agreements at the beginning of each survey for CVD-I and CVD-C arms, and in
the offer to complete the intervention messages for the CVD-I arm. All compensation will be
awarded in Achievement Points, of which 100 points is equivalent to $0.10. Points can be
redeemed for monetary compensation or donated to charitable organizations. The following
compensation will be awarded: 50 points per intervention message completed, 3 points per
optional informational email requested, and 300 points per survey completed. The maximum
number of points possible is 1,518 points, which could be redeemed for approximately $1.52.
6.6. Study Flow Diagram
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Note. Dates are approximate.
7.
Data Types and Outcomes
●
Data from self-reported survey:
○
Demographics
○
Medical history
○
Health behaviors
○
Healthcare utilization
○
Influenza vaccination status, experience, and complications
○
COVID-19 experiences
○
Vaccine attitudes and barriers
○
Influenza risks and healthcare provider role
○
Experience and perceptions of intervention materials  (CVD-I only)
●
Data from engagement with interventions (CVD-I only)
○
For example, clicks, dismissals, or completions of interventions
●
Data from Achievement Profile
○
Activity data generated by wearables (e.g., heart rate, sleep, step data), from 12
months before participation and 12 months after participation (if available)
Source
Retrospective Demographics
Baseline
Month 3 Month 6
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12 months before to
12 months after
Survey
Self-reported data
Demographics
Survey
X
Medical history
Survey
X
Health behavior
history & influenza
vaccination
Survey
X
Healthcare
utilization
Survey
X
X
X
Influenza
vaccination status
Survey
X
X
X
Influenza experience
& complications
Survey
X
X
X
COVID-19
experiences &
perceptions
Survey
X
X
X
Vaccine drivers &
barriers
Survey
X
X
X
Influenza risks &
healthcare provider
role
Survey
X
X
X
Experience and
perceptions of
intervention
materials
Survey
X
(CVD-I
only)
Engagement with
interventions
Achievement
website/app
and Survey
Monkey
[------------continuous---------------]
(CVD-I only)
Activity tracker data
Steps
[e.g. Fitbit,
Garmin]
X
[------------continuous---------------]
Sleep
[e.g. Fitbit,
Beddit]
X
[-------------continuous--------------]
Heart rate
[e.g. Fitbit,
Apple]
X
[--------------continuous-------------]
8.
Statistical Considerations
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8.1. Sample Size Justification
We determined sample size for a two-arm interventional statistical superiority study design with
vaccination rates as the primary outcome of interest (Zhong 2009). Studies on the impact of
messaging and reminders to improve influenza vaccination rates show a range of effect sizes
from 2.5 - 3.5% (Nehme et al., 2019, Hurley et al., 2018). As noted earlier, CDC reported
vaccination rates are 47.9% for adults between the ages of 18 - 65.
Given these a-priori assumptions, we assumed different baseline vaccination rates of 35%, 50%
and 60%, and then computed the sample size needed to detect an increase in vaccination rate of
2%, 3% and 4% respectively. Sample size for the difference in proportion is determined by
(Zhong, 2009):
Where:
Zα/2and Zβ refer to the z critical values for a type I and II error of 0.05 and 0.2 respectively,
p0 and p are the baseline proportion and the new hypothesized proportion respectively, and
N is the total sample size, assuming a 1:1 ratio of intervention to control
Baseline
vaccination
rate
Hypothesized
vaccination
rate
Hypothesized
effect size
Hypothesized
Cohen's h
Sample size
35.0%
37.0%
2.0%
4.2%
18080
50.0%
52.0%
2.0%
4.0%
19610
60.0%
62.0%
2.0%
4.1%
18668
35.0%
38.0%
3.0%
6.2%
8080
50.0%
53.0%
3.0%
6.0%
8710
60.0%
63.0%
3.0%
6.2%
8256
35.0%
39.0%
4.0%
8.3%
4570
50.0%
54.0%
4.0%
8.0%
4894
60.0%
64.0%
4.0%
8.2%
4618
In the above table, for each baseline rate and hypothesized rate increase (and associated Cohen’s
h), we show the estimated total sample size needed for each assumption of baseline vaccination
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rate and effect size. We note that within each hypothesized effect size, changing assumptions
about baseline vaccination rates do not substantially alter the sample size.
Based on this, we believe that a 3% effect size is a reasonable assumption and that a sample size
of approximately 8000 participants will be needed to detect this difference with a type I error rate
of 0.05 and a presumed power of 0.80. A participation dropoff of 67% has been observed for
digital interventions for increasing influenza vaccination (i.e., Samson et al., 2020). Assuming
between 67% to 75% dropoff for this study, we estimate that between 24,000 - 35,000
participants will need to be recruited and randomized for this study. Based on these calculations,
we plan to recruit approximately 35,000 participants.
8.2. Description of Analytical Plan
8.2.1. Baseline Descriptive Statistics
Descriptive statistics (e.g., mean, standard deviation, frequency and percentage) of study
participants will be reported for demographic variables (e.g., age, gender, education, income) and
patient reported outcomes (e.g., vaccination rates). To confirm that block randomization is
successful, we will compare the intervention to control groups in terms of baseline
demographics, vaccine drivers/barriers, and vaccine knowledge for each of the block groups
described earlier.
8.2.2. Analysis of Primary Endpoint
An intention to treat analysis will be conducted of the primary outcomes for the study. Assuming
that a descriptive comparison of the intervention to control group does not demonstrate
statistically significant differences in terms of demographics, vaccine drivers/barriers, or vaccine
knowledge, we will report on the relative rate of influenza vaccination for the intervention group
compared to the control group. We will also conduct logistic regression modeling of influenza
vaccination as follows:
𝑙𝑜𝑔𝑖𝑡(𝑌) = β0 + β1 𝑀 +  ∑𝑗β𝑗α𝑗 +  ε
Where:
logit(Y) = ln(Y/1-Y) for the binary variable designation of influenza vaccination status
M = the binary designation for Intervention (messaging) vs Control (no messaging)
αj = the baseline covariates measured in the study (e.g. demographics, vaccine drivers/barriers or
knowledge)
ε = the error term of the regression.
For the logistic regression model the p-values associated with each of the β parameter estimates
will be reported.
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8.2.3. Analysis of Secondary Endpoint
To examine differences in vaccination rates between individuals in the CVD-I group and its
relationship to engagement with intervention messages, we will model:
𝑙𝑜𝑔𝑖𝑡(𝑌) = β0 + β1 𝑀 +  ∑𝑗β𝑗α𝑗+  ε
Where:
logit(Y) = ln(Y/1-Y) for the binary variable designation of influenza status
M = the number of engaged messages (ordinal variable 0, 1, 2, 3, 4, 5, 6)
αj = the baseline covariates measured in the study (e.g. demographics, vaccine drivers/barriers, or
knowledge)
To examine predictors of vaccination status, including cardiovascular condition type, vaccine
drivers/barriers, and vaccine knowledge, across CVD-I and CVD-C we will construct the model:
𝑙𝑜𝑔𝑖𝑡(𝑌) = β0 + β1 𝑃 +  ∑𝑗β𝑗α𝑗 +  ε
Where:
logit(Y) = ln(Y/1-Y) for the binary variable designation of influenza status
Ci = the binary designation Cardiovascular Condition as described earlier
P = the predictor of interest (e.g. cardiovascular condition, vaccine drivers/barriers, vaccine
knowledge index)
αj = the baseline covariates measured in the study (e.g. demographics, vaccine drivers/barriers, or
knowledge)
ε = the error term of the regression.
To examine predictors of vaccination status, including cardiovascular condition type, vaccine
drivers/barriers, and vaccine knowledge, within CVD-I or CVD-C we will construct the model:
𝑙𝑜𝑔𝑖𝑡(𝑌) = β0 + β1 𝑀 + β2 𝑃 +  ∑𝑗β𝑗α𝑗 +  ε
Where:
logit(Y) = ln(Y/1-Y) for the binary variable designation of influenza status
M = the messaging group being examined (either Intervention or Control)
P = the predictor of interest (e.g. cardiovascular condition, vaccine drivers/barriers, vaccine
knowledge index)
αj = the baseline covariates measured in the study (e.g. demographics, vaccine drivers/barriers, or
knowledge)
ε = the error term of the regression.
8.2.4. Exploratory Analyses
To examine the potential impact of COVID-19 on vaccination rates, we will add  a term to the
model used in the primary analysis model:
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𝑙𝑜𝑔𝑖𝑡(𝑌) = β0 + β1 𝑀 +  ∑𝑗β𝑗α𝑗 + β𝑐𝑐 + β𝑖𝑀* 𝑐+  ε
Where the additional term c is the Covid-19 status (1 if diagnosed with COVID-19, 0 if not) and
the parameter estimate for the interaction term,
will be reported. Similarly, we can also
β𝑖
evaluate the effect of COVID-19 attitudes towards influenza vaccination rates. In this case, the
term c would represent COVID-19 attitudes (1 if the participant reports more likely to vaccinate,
0 if the person does not).
To describe self-reported complications from influenza experienced by individuals with different
cardiovascular conditions during the 6-month study period, as well as differences in
complications by vaccination status, we will aggregate rates of self-reported complications by
cardiovascular conditions, as well as by vaccination status.
To examine differences in wearable data (i.e., sleep, steps, heart rate) between individuals who
experience influenza and those who do not, as well as differences in wearable data between
cardiovascular conditions, we will aggregate day-level wearable data over the entire study period
based on influenza status, as well as by cardiovascular condition.
Between-group comparisons of the outcomes described above (e.g. self reported complications
or mean daily steps) will be conducted with Student’s t-tests or the non-parametric
Mann-Whitney U test when appropriate, with correction for multiple testing using the
Benjamini-Hochberg approach for False Discovery Rate correction.
9.
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 applicable local and state laws,
regulations, and IRB policies regarding collection and distribution of participant information.
Data will be transmitted using secure encrypted protocols 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. Survey data may be stored for a minimum of 3 years,
consistent with contract and applicable law.
Personally Identifiable Information (PII) is only accessible to a restricted set of individuals, and
is only used to distribute study material and for participant support purposes. PII will not be
disclosed unless required by regulatory agencies or the IRB in instances consistent with
applicable law. Limited Achievement clinical, research and technical team members 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, including
Sanofi Pasteur, in accordance with IRB approved uses.
10.
Risks and Benefits
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10.1. 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. 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.
10.2. Potential Benefits Associated with the Study
Participants of this study will likely not receive any direct benefit from the proposed research;
however, society and investigators will benefit from the knowledge gained. This information
may contribute to public health and scientific efforts related to vaccination and the prevention of
disease for people with cardiovascular disorders. This information may help researchers develop
further interventions to increase influenza vaccination.
10.3. Adverse Events
Adverse events will not be actively collected because no pharmaceutical product or medical
treatment will be provided or evaluated in this study. Spontaneously reported adverse events
received by Evidation Health will be recorded per Evidation Health's Standard Operating
Procedures.
Any pharmacovigilance data pertaining to a Sanofi product 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 for
serious adverse events.
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 life-threatening or result 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.
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Appendix
Please see attached appendix for examples of the intervention messages.
References
Centers for Disease Control and Prevention (CDC). (2018). “Seasonal Influenza Vaccine
Effectiveness, 2016-2017.” Retrieved from
https://www.cdc.gov/flu/vaccines-work/2016-2017.html
Centers for Disease Control and Prevention (CDC) (2019). "Flu & Heart Disease and Stroke."
from https://www.cdc.gov/flu/highrisk/heartdisease.htm.
Centers for Disease Control and Prevention (CDC) (2019). FluVaxView Interactive, National
Center for Immunization and Respiratory Diseases (NCIRD), from
https://www.cdc.gov/flu/fluvaxview/interactive-help.htm
Dale, L. P., et al. (2019). "Smartphone app uses loyalty point incentives and push notifications to
encourage influenza vaccine uptake." Vaccine 37(32): 4594-4600.
Dos Santos, G., Tahrat, H., & Bekkat-Berkani, R. (2018). Immunogenicity, safety, and
effectiveness of seasonal influenza vaccination in patients with diabetes mellitus: A systematic
review. Human vaccines & immunotherapeutics, 14(8), 1853-1866.
doi:10.1080/21645515.2018.1446719
Doyle, JD, Chung, JR, Kim, SS, et al (2019). Interim Estimates of 2018–19 Seasonal Influenza
Vaccine Effectiveness — United States, February 2019. MMWR Morb Mortal Wkly Rep 2019;
68:135–139. doi: 10.15585/mmwr.mm6806a2
Frank L, Basch E, Selby JV, For the Patient-Centered Outcomes Research Institute. (2014). The
PCORI Perspective on Patient-Centered Outcomes Research. JAMA, 312(15):1513–1514. Doi:
10.1001/jama.2014.11100
Healthy People 2020. (2019). Retrieved from https://www.healthypeople.gov/. from Healthy
People 2020, United States Department of Health and Human Services, Office of Disease
Prevention and Health Promotion, https://www.healthypeople.gov/
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Hurley LP, Beaty B, Lockhart S, Gurfinkel D, Breslin K, Dickinson M, Whittington MD, Roth
H, Kempe A. RCT of Centralized Vaccine Reminder/Recall for Adults. Am J Prev Med. 2018
Aug;55(2):231-239.
Jackson, M. L., Chung, J. R., Jackson, L. A., Phillips, C. H., Benoit, J., Monto, A. S., . . .
Flannery, B. (2017). Influenza Vaccine Effectiveness in the United States during the 2015–2016
Season. New England Journal of Medicine, 377(6), 534-543. doi:10.1056/NEJMoa1700153
Lee, W.-N., et al. (2020). "Large-scale influenza vaccination promotion on a mobile app
platform: A randomized controlled trial." Vaccine 38(18): 3508-3514.
Molinari, N.-A. M., Ortega-Sanchez, I. R., Messonnier, M. L., Thompson, W. W., Wortley, P. M.,
Weintraub, E., & Bridges, C. B. (2007). The annual impact of seasonal influenza in the US:
Measuring disease burden and costs. Vaccine, 25(27), 5086-5096. doi:
10.1016/j.vaccine.2007.03.046
Nehme EK, Delphia M, Cha EM, Thomas M, Lakey D. Promoting Influenza Vaccination Among
an ACA Health Plan Subscriber Population: A Randomized Trial. Am J Health Promot. 2019
Jul;33(6):916-920.
Rolfes, M. A., Flannery, B., Chung, J. R., O’Halloran, A., Garg, S., Belongia, E. A., . . .
Prevention. (2019). Effects of Influenza Vaccination in the United States During the 2017–2018
Influenza Season. Clinical Infectious Diseases, 69(11), 1845-1853. doi:10.1093/cid/ciz075
Samson, S., Lee, J., Tai, C.,… & Foschini, L. (2020, September). Digital intervention is effective
at increasing influenza vaccination in people living with diabetes. Poster presented at the
European Association for the Science of Diabetes in Vienna, Austria – Conference to occur
digitally 22-25 September, 2020 due to COVID-19.
Samson, S. I., et al. (2019). "Quantifying the Impact of Influenza Among Persons With Type 2
Diabetes Mellitus: A New Approach to Determine Medical and Physical Activity Impact."
Journal of Diabetes Science and Technology: 1932296819883340.
Zhong B. How to calculate sample size in randomized controlled trial?. J Thorac Dis.
2009;1(1):51–54.
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