Covid-19 Messaging to Underserved Communities - 2nd Experiment
Phase: NA
Conditions: Covid19
Sponsor: National Bureau of Economic Research, Inc.

(Round 2) Pre-Analysis Plan
COVID-19 Health Messaging to Underserved Communities
This Draft: September 4, 2020
1.
Introduction
The aim of this study is to build on results from our ﬁrst experiment and test how acknowledging
institutional racial injustice affects informational messaging. In particular, we will investigate how well
study participants retain knowledge and update beliefs and behavior with respect to COVID-19. We
will also test the effect of race concordance of providers with recipients, and whether highlighting the
unequal burden of the disease has additional effects on knowledge, beliefs and behavior regarding
COVID-19.
Compared to the ﬁrst study, there are four additional changes to the experimental design of note: 1)
the sample will include white respondents in addition to African American respondents;1 2) the group
that does not view videos about COVID-19 will watch a set of placebo videos about non-COVID health
topics; 3) we are collecting follow-up behavioral data from a subset of participants;2 4) this study does
not include a few of the treatment variants from the ﬁrst – speciﬁcally, there is no acknowledgment by
doctors in the videos discussing health behaviors, and there is no treatment arm to alter perceptions of
whether mask-wearing is socially-acceptable.
2.
Treatments, and Experimental Protocols
2.1
Treatments
Each subject receives one AMA statement and then watches three videos pertaining to health. The
AMA statement either addresses racial injustice (treatment [RI]) or drug pricing (placebo [DP]). The
videos pertaining to health behavior either discuss COVID-19 and prevention (treatment) or other non-
COVID-19 related issues (control).
• AMA Statement:
RI: Racial Injustice
DP: Drug Pricing
• Treatment Videos
1We continue to focus our sample on individuals with less than college education.
2We are also collecting baseline measures of these behaviors.
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1. Video T1:
– Introduction
– Discussion of symptoms
2. Video T2:
– Information about social distancing and hygiene
– Sub-treatment T2A:
* Acknowledgment of racial disparities in contagion and mortality.
3. Video T3:
– Information about masks
• Placebo Videos
1. Video C1:
– Information about ﬁtness routines
2. Video C2:
– Information about sleep hygiene
3. Video C3:
– Information about sugar intake
We are varying ﬁve aspects of the videos across treatments:
1. AMA statement regarding racial injustice or transparency in drug pricing.
2. Racial concordance with AMA spokespeople in the videos.
3. Racial concordance with partner doctors in the videos.
4. Informative videos about COVID-19 or non-COVID-19 issues.
5. Video T2 content:
• Standard message about hygiene and social distancing
• Previous message, plus acknowledgment of racial disparities in contagion and mortality.
All of the video scripts can be found in Appendix Section A. Figure 1 shows how these different treat-
ments are incorporated in the randomization design.
2.2
Recruitment and Sampling
We are using Lucid to recruit a sample and to compensate subjects for their participation. Our total
target sample size is 20,000 subjects, 10,000 African American respondents, and 10,000 White American
respondents. We require all participants to be age 18 or older, and we are targeting individuals who
have not completed a college degree.
2

Figure 1: Treatment Design
Note: There are 5 randomization instances summarized in this diagram. For the fully extended tree with all 24 ﬁnal
individual cells, see Figure 2.
2.3
Experimental Protocols and Treatments
Our experiment has the following structure:
1. Recruitment and Baseline survey
(a) Recruit target sample via Lucid
(b) Collect demographic information, political views, and mask ownership. Several demograph-
ics variables are being collected directly by Lucid.
2. Randomize subjects to treatment
• Treatment scripts are tailored to each racial group
• Within racial group, treatment is stratiﬁed on age, gender and geographic region.
• Randomization is at the individual level according to Figure 1.
• There will also be a control group who will not receive COVID-19 videos, but will receive
information at the very end of the survey.
3. Video delivery for all individuals.
(a) After each of the three videos, respondents evaluate the video’s usefulness, and trustwor-
thiness, along with the respondents intention to follow the advice in the video and share
information from the video.
4. Endline survey
5. Debrief script
3

6. Follow-up survey
(a) We will follow up with participants three days post-treatment.
(b) The chief purpose would be to measure health-seeking and health-preserving behaviors.
3.
General Hypotheses to be Tested
This section lists the general hypotheses we want to test. For those, we are planning to run broadly
pooled treatments (and speciﬁc interactions speciﬁed below). Main hypotheses:
1. Baseline treatment effect: Does any COVID-19 message affect knowledge/indended behavior/priorities
in donations/actual behavior?
(a) Compare the effectiveness of any doctor treatment video vs. any doctor placebo video.
• We will test this in the full sample and in the white and African American samples, sepa-
rately.
2. Concordance effect: Does the concordance of the doctor in the COVID-19 video affect the impacts
of the message?
(a) Within the treatment group receiving a COVID-19 doctor video, compare the effectiveness
of the video if the respondent is randomized to a doctor of concordant vs. discordant race.
• We will test this hypothesis separately in the white and African American samples. If the
effects go in the same direction for both populations, we will also test this hypothesis in the
full sample.
• An alternate way to deﬁne concorance is whether the subject views a racially concordant
messenger both (or either) in the COVID-19 doctor video and in an AMA statement of any
kind. We will also test the effectiveness of the messaging under this alternate measure.
3. AMA acknowledgement: Does the AMA message mediate the impacts of the COVID-19 messag-
ing or of the racial concordance of the messenger?
(a) Compare baseline treatment effect [1] for AMA acknowledgement vs AMA placebo.
(b) Compare concordance effect [2] for AMA acknowledgment vs AMA placebo. Is the concor-
dance of the doctor in the COVID-19 video more or less important when there is an AMA
message vs. the AMA placebo?
• We will test these hypotheses separately for white and African American respondents.
4. Does the racial concordance of the AMA messenger matter?
(a) Compare whether AMA acknowledgement [3a] is more effective if the AMA messenger is
racially concordant.
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(b) Compare AMA acknowledgement effect [3b] by racial concordance of AMA messenger. Is
the concordance of the doctor in the COVID-19 video more or less important when there is a
racially concordant AMA message vs. a non-racially concordant AMA message?
• We will test these hypotheses separately for white and African American respondents.
5. Content of message: does the mention of the racial disparities (video 2RD) in disease burden
affect knowledge/intended behavior/ priorities in donation/actual behavior?
(a) Within the COVID video treatment group, compare videos with RD information vs. no RD
information.
(b) Within the COVID video treatment group, how does information about RD interact with
the racial concordance of the doctor messenger? Does information about RD make racial
concodance more important, especially among African American respondents?
(c) Within the COVID video treatment group, how does information about RD interact with the
AMA racial injustice message? Does information about RD make the AMA acknowledge-
ment more effective, especially among African American respondents?
(d) We are also interested in how information about RD mediates Hypotheses 3b, 4a, 4b, and 4c.
• In addition to the standard outcomes, we are also particularly interested in impacts on dona-
tions to a COVID fund that is speciﬁcally for African Americans vs. a general COVID fund.
We also are interested in how this information changes perceptions of the policy responses
of state and federal governments.
• For 5a, we are interested in the white and African American groups, separately, and also
the full, pooled sample. For 5b and 5c, we are mainly interested in the effects separately for
white and African American respondents.
• RD should have the largest impacts for individuals who have incorrect beliefs. Moreover,
the impacts of the information should be different for individuals with priors about relative
impacts on African Americans that are too low versus too high. (See Section 5.4, below).
4.
Data Collection and Outcomes
We will run our experiment beginning on August 7, 2020. We are recruiting individuals that ﬁt our
screening criteria from an online survey ﬁrm. The data will be collected from respondent surveys.
There is a short baseline module before individuals are exposed to the videos. Following the ﬁnal
video, there is an endline outcome module that all participants complete. Respondents also evaluate
each video immediately after watching it. We also plan to follow up with individuals a few days after
the treatment delivery for an additional survey to measure whether the videos changed behaviors.
Prior to treatment, we ask all participants whether they would be willing to answer the follow-up
survey. We allow individuals to enroll in the study, independent of their follow-up availability. We
plan to control for willingness to answer a follow-up in all regressions. All of the survey responses will
be downloaded in a .csv ﬁle for cleaning and analysis in R.
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4.1
Baseline Survey Variables
The baseline information for all respondents includes the following demographic characteristics:
• Education
• Age
• Location
• Sex
• Political Views
We elicit additional health information at baseline, but participants may choose not to answer these
questions:
• Mask Ownership
• Public Excursions
• Health Behavior
See Section 5, below for a summary of our key endline survey variables.
4.2
Data quality checks
We have several questions in the survey that capture respondent attention. We use some of these
questions to screen out low-attention survey takers before assigning treatment. We further plan to
exclude those that take very little time on the survey overall and/or on the videos.
5.
Empirical Analysis
5.1
Balance Checks
We will conduct a series of balance tests across treatment arms to ensure that there are no chance differ-
ences between subjects in the various arms. We will regress characteristics measured pre-treatment on
indicators for the arms and test their individual and joint signiﬁcance. Balance tests will be conducted
using all of the variables measured in the baseline survey.
We will also test for balance in attrition rates (see Section 6.2, below).
5.2
Key Outcomes
We intend to measure the treatment effects on the following set of outcomes:
1. The number of participants with knowledge of COVID related symptoms and transmission as
assessed by a questionnaire we’ve developed speciﬁcally relevant to the intervention videos we
are using for the project. Speciﬁcally, we deﬁne the following three outcomes, measured both at
ﬁrst contact and followup:
6

• Knowledge of COVID symptoms: participants are asked to identify 4 symptoms of COVID
from a list of 9. We will deﬁne an indicator for whether participants select the 3 most common
symptoms (fever, cough, difﬁculty breathing).
• Knowledge of COVID prevention: participants are asked to identify 3 COVID prevention
behaviors from a list of 7. We will deﬁne an indicator for whether participants select the
3 behaviors emphasized in the videos (staying outside and 6ft from others; washing hands
when going and coming from home; mask wearing).
• Knowledge of asymptomatic infection: an indicator for whether participants correctly an-
swer that COVID can spread asymptomatically.
2. Behavioral outcome 1: Number of participants who report behavior change related to messages
provided in the intervention videos; measured via a speciﬁc questionnaire instrument we’ve de-
veloped to correspond to the intervention. Speciﬁc behaviors include physical distancing, mask
wearing, and hand hygiene. Since we ask about several safety behaviors, we will deﬁne a stan-
dardized index to combine them into one measure, calculated at both ﬁrst contact and endline.
The key outcome is the within-person change in this safety index from ﬁrst contact to endline.
3. Behavioral outcome 2: Revealed preference estimate of willingness to pay for masks. The subjects
will trade-off the willingness to get a pair of masks or an amazon gift card when participating in
a strategy-proof lottery.
4. Behavioral outcome 3: Number of links people click on for additional information on the COVID-
19 behaviors.
Links will include testing locations, state public health hotline, and symptom
tracker.
5. Behavioral outcome 4: Donations to a COVID-related charity. After providing information on the
number of weekly COVID cases, we are measuring the willingness to donate to a COVID-related
charity vs. a generic helath-related charity.
6. Behavioral outcome 5: Donations to African-American COVID-19 fund. After providing infor-
mation on the disproportionate burden on African-American communities, we are measuring the
willingness to donate to a COVID relief fund that focuses on African-American communities vs.
one that generically provides relief to disadvantaged individuals.
7. Evaluation of state and local COVID policy. We are measuring how how well participants think
their federal and state governments managed to balance opening the economy and limiting the
health impacts of Covid-19. We believe that information about racial disparities, in particular,
may change these perceptions, especially for people who believed that racial disparities did not
exist or were less severe at baseline.
5.3
Regression Analysis
We will perform different regression analyses to test the hypotheses listed above. Because our data
contains many possible control variables, we will use a double-lasso procedure to select regression
controls. We will also include a control for whether the participant’s availability to participate in a
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follow up survey (as indicted at baseline). These control variables are denoted as X in the regression
speciﬁcations below.
Unless otherwise noted, we will examine treatment effects on knowledge of COVID symptoms, knowl-
edge of COVID transmission, intended donation to a COVID-related charity, willingness to pay for
masks, changes in reported behavior. In what follows, we present the minimal regressions to test each
of the hypotheses, restricting to the smallest subset of treatments. However, we could also execute the
same tests in the full sample, but with more treatment interactions.3
Note that the “COVID” indicator covers cases when doctors mention COVID plus racism, or COVID
alone. In text, we refer to this group as the treatment group.
1. Baseline treatment effect: does any of the COVID-19 messaging affect knowledge, intended
behavior, priorities in donation, or actual behavior?
• Question: does COVID messaging from doctors have any effect?
Samples: separate analysis for all respondents, black respondents, and white respondents.
Regression:
Y = β · COVID Video + X′γ + ϵ
2. Does racial concordance of the doctor in the COVID-19 video change the effectiveness for
messaging?
• Question: Bolded, immediately above.
Samples: COVID video groups. Separate analysis for black and white respondents.
Regression:
Y = β · DocConcord + X′γ + ϵ
Alternate Speciﬁcation: To allow, for a level effect of doctor concordance, we can also run the
following modiﬁed regression, including both the COVID video and control groups.
Y = β · DocConcord · COVID Video + α · COVID Video + δ · DocConcord + X′γ + ϵ
• Question: Bolded above, but using an alternate deﬁnition of concordance. We’ll require a) all
messengers or b) any messenger to concord with the respondent’s race.
For any outcomes where β is of the same sign for white and black respondents, we will estimate
the above equation again in the full sample.
3. Does the AMA racism acknowledgment affect the impact of messaging on any outcomes? Does
it change the concordance effect?
3In some cases, especially when including controls, these full-sample tests may be preferrable to these stripped-down
regressions.
8

• Question: Does the AMA racism messaging heighten or dull COVID messaging from doc-
tors?
Samples: COVID video groups. Separate analysis for all respondents, black respondents,
and white respondents.
Regression:
Y = β · AMARacism + X′γ + ϵ
Alternate Speciﬁcation: To allow, for a level effect of the AMA racism message, we can also
run the following modiﬁed regression, including both the COVID video and control groups.4
Y = δ · AMARacism · COVID Video + α · COVID Video + β · AMARacism + X′γ + ϵ
• Question: Does the AMA racism acknowledgment heighten or dull any doctor concordance
effects?
Samples: COVID video groups. Separate analysis for black and white respondents.
Regression:
Y = δ · AMARacism · DocConcord + α · AMARacism + β · DocConcord + X′γ + ϵ
Alternate Speciﬁcation: Again, we can also run the following modiﬁed regression, including
both the COVID video and control groups.
Y = δ · DocConcord · AMARacism · COVID Video + λ · AMARacism · COVID Video
+φ · DocConcord · COVID Video + ρ · AMARacism · DocConcord + α · COVID Video
+β · AMARacism + ψ · DocConcord + X′γ + ϵ
4. Does concordance of the AMA messenger matter?
• Question: Does a race-concordant AMA messenger delivering a message about racial injus-
tice make COVID messaging from doctors more or less effective, relative to a race-discordant
AMA messenger?
Samples: Individuals receiving an AMA message about racial injustice and a treatment mes-
sage about COVID. Separate analysis for black and white respondents.
Regression:
Y = β · AMAConcord + X′γ + ϵ
Alternate Speciﬁcation: We can also run the following modiﬁed regression, the COVID video
and control groups, with both types of AMA messages.
Y = δ · COVID Video · AMAConcord · AMARacism + λ · AMAConcord · AMARacism
+ρ · AMAConcord · COVID Video + ψ · COVID Video · AMARacism
+α · AMAConcord + β · AMARacism + +φ · COVID Video + X′γ + ϵ
4Ex ante, we think that an AMA level effect in the control group is more likely than a doctor concordance level effect.
However, we include all alternates for completeness. These alternate speciﬁcations including the control group are relevant
in the presence of a level effect.
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• Question: Does a race-concordant AMA messenger make COVID messaging from a race-
concordant doctor more or less effective, when delivering the AMA message about racial
injustice?
Samples: COVID video groups receiving the AMA message about racism. Separate analysis
for black and white respondents.
Regression:
Y = δ · AMAConcord · DocConcord + α · AMAConcord + β · DocConcord + X′γ + ϵ
Alternate Speciﬁcation: We can also run the following modiﬁed regression, the COVID video
and control groups, with both types of AMA messages.
Y = δ · AMAConcord · AMARacism · DocConcord · Covid Video
+α1 · DocConcord · AMARacism · COVID + α2 · DocConcord · AMARacism · AMAConcord
+α3 · DocConcord · COVID · AMAConcord + α4 · AMARacism · COVID · AMAConcord
+β1 · DocConcord · AMARacism + β2 · DocConcord · COVID + β3 · DocConcord · AMAConcord
+β4 · AMARacism · COVID + β5 · AMARacism · AMAConcord + β6 · COVID · AMAConcord
+ρ1 · DocConcord + ρ2 · AMARacism + ρ3 · AMAConcord + ρ4 · COVID + X′γ + ϵ
5. What are the effects of acknowledging racial disparities in COVID incidence? In addition to
the typical set of outcomes, we will also analyze effects on allocated donations to black-speciﬁc
versus race-agnostic COVID-related charities.
• Question: what is the main effect of acknowledging racial disparities of COVID incidence?
Samples: treated respondents only. Separate analysis for black, white, and all respondents.
Regression:
Y = β · Vid2RacialDisp + X′γ + ϵ
• Question: are race-concordant doctors more effective messengers about racial disparity?
Samples: treated respondents only. Separate analysis for black, white, and all respondents.
Regression:
Y = δ · DocConcord · Vid2RacialDisp + α · Vid2RacialDisp + β · DocConcord + X′γ + ϵ
• Question: does acknowledging widespread racism alter the effectivness of later discussing
racial disparities in COVID?
Samples: treated respondents only. Separate analysis for black, white, and all respondents.
Regression:
Y = δ · AMARacism · Vid2RacialDisp + α · Vid2RacialDisp + β · AMARacism + X′γ + ϵ
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• Question: does a race-concordant AMA messenger’s prefacing statement have a different
amplifying effect than a race discordant AMA messenger? That is, if doctors are going to
discuss racial disparities in COVID and we’re going to preface this with the AMA racism
statement, do we expect different results if the statement comes from a concordant versus
discordant AMA messenger?
Samples: Must have seen both a COVID video and an AMA racism video. Separate analysis
for black and white respondents.
Regression:
Y = δ · AMAConcord · Vid2RacialDisp + α · Vid2RacialDisp + β · AMAConcord + X′γ + ϵ
Alternate speciﬁcation: restrict sample to those seeing a COVID video, include both types of
AMA messages. Regression:
Y = δ · AMARacism · AMAConcord · Vid2RacialDisp + λ · AMAConcord · Vid2RacialDisp
+ρ · AMAConcord · AMARacism + φ · AMARacism · Vid2RacialDisp
+α · Vid2RacialDisp + β · AMAConcord + ψ · AMARacism + X′γ + ϵ
5.4
Heterogeneous Effects
We plan to conduct several heterogeneity tests that we believe are of central importance: respondent
race, respondent political aﬁliation (within the white sample), prior beliefs about racial disparities and
COVID-19, and the timing of participation vis a vis the events of Kenosha, WI.
We are very interested in studying how the impacts of our treatments vary by the race of the respondent.
This is central to our research design. Speciﬁcally,
• Is the impact of racial concordance different by respondent race?
• Is the impact of a statement addressing racial injustice different by respondent race?
• Is the impact of information about racial disparities in the COVID-19 burden different by respon-
dent race?
Moreover, within the white respondent population, we predict that there may be substantial hetero-
geneity by the respondent’s political beliefs:
• Is the impact of racial concordance different for white republicans versus white democrats?
• Is the impact of a statement addressing racial injustice different for white republicans versus white
democrats?
• Is the impact of information on racial disparities in disease burden different for white republicans
versus white democrats?
We predict that the impacts of information on racial disparities in COVID burden should depend on
individuals’ prior beliefs:
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• The information about RD should cause individuals who initially believed at baseline that there
were small or non-existant racial disparities to update in the opposite direction of individuals
who believed that the racial disparities were larger than they actually are.
• Thus, for all of our tests involving RD, we will interact the regressions with indicators for whether
the priors were smaller or larger than the number we give in the videos.
• Moreover, there may be bigger impacts for individuals whose priors were less accurate, so we can
also interact by the size of the gap between the informaiton and the prior, separately for those with
priors that were too low versus too high. This can also help us measure whether the information
had an impact even for people with accurate priors, possibly through a salience effect.
We intend to compare the results of our hypothesis tests separately for the time period before the Jacob
Blake police shooting in Kenosha, WI on August 23, 2020, and the time period after. We propose to do
this for two reasons. First, we launched the study at a time when the large-scale protests from earlier
in the summer following the murder of George Floyd had somewhat ebbed. So the events in Kenosha
may bring issues of racial injustice to the fore. Second, and perhaps more importantly, polarization
surrounding the narrative of the protests has markedly increased following the events of Kenosha.
Several speakers at the Republican National Convention explicitly discussed the violent component
of the protests, for example, and both candidates for the US presidency are making trips to Kenosha.
This increased polarization is likely to have the most relevance for the white respondents in our study,
and may enhance any differential response we ﬁnd by political afﬁliation. We propose to split the
sample into the period before August 23, 2020 and the period following August 26, 2020. We will omit
surveys collected on days in the interim when Americans were only coming to learn about the events
of Kenosha, WI.
We are also interested in secondary analysis exploring heterogeneity on the following categories of
traits/characteristics:
• Age
• Level of baseline knowledge and health-preserving behaviors
• Place of residence (correlated with political afﬁliation, COVID-19 policies and phased reopenings,
and socio-economic characteristics)
Given the many ways to cut the data for this secondary analysis, we will follow the methodology of
Chernozhukov et al (2019) for this latter set of potential heterogeneous treatment effects.5
6.
Robustness
6.1
Threats to Interpretation
We would like to assume that differences across videos come from either differences in the racial iden-
tity of the doctors in the video or from differences in the content of the messages, rather than from other
chance differences across videos. Because we are including both white and black respondents who will
be watching the same exact videos, we will be able to include video ﬁxed effects in some speciﬁcations.
5Chernozhukov, Victor, Mert Demirer, Esther Duﬂo, and Ivan Fernandez-Val (2019). Generic machine learning inference
on heterogenous treatment effects in randomized experiments. No. w24678. National Bureau of Economic Research.
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6.2
Attrition
We have two separate endline surveys. The ﬁrst will take place immediately after treatment delivery.
The type of attrition that might arise here is through dropping out of the online session before complet-
ing all of the survey questions. To try to limit differential attrition, we are showing placebo videos to
the control group to ﬁll approximately the same amount of time.
Our second set of endline outcomes will take place a few days after the main survey. Lucid, the survey
ﬁrm, will try to recontact a speciﬁed list of initial participants. We are only expecting modest recontact
rates, and therefore high levels of attrition. Importantly, to try to limit differential attrition, all individ-
uals will be recontacted with the exact same message, and it will not be made salient that the survey is
a direct follow-up to the previous study.
We plan to test for differential attrition at both endlines across our key comparison groups.
7.
Funding and Human Subjects Review
Funding is provided by the National Science Foundation RAPID-2029880 for Covid-19 research, and
RAI Italian Broadcasting corporation (via an unrestricted gift to J-PAL that we attributed to this project).
The IRB at MIT is serving as the primary institution of record and has entered into a reliance agreement
with Harvard, Massachusetts General Hospital, and Yale. We have also received IRB approval from
Stanford.
13

Appendix
A.
Scripts
All respondents will receive either [Statement RI or Statement DP] + one set of [Treatment or Control]
videos.
A.1
Statements
Each respondent is assigned to one of the following statements. All respondents will see the statement
presented via video.
A.1.1
Treatment Statement RI (Racial Justice):
• The American Medical Association recognizes that racism in its systemic, structural, institutional,
and interpersonal forms is an urgent threat to public health, the advancement of health equity,
and a barrier to excellence in the delivery of medical care.
• The American Medical Association opposes all forms of racism.
• The American Medical Association denounces police brutality and all forms of racially-motivated
violence.
• The American Medical Association will actively work to dismantle racist and discriminatory poli-
cies and practices across all of health care.
A.1.2
Placebo Statement DP (Drug Pricing):
• The American Medical Association believes in transparency in prescription drug pricing, and we
are pleased the House Ways & Means Committee moved the issue forward.
• Patients and their physicians want to be armed with more information, yet the current situation
is opaque if not impenetrable.
• The committee is rightfully determined to expose factors that lead to high drug prices, and we
look forward to continuing our efforts in that regard.
A.2
Treatment Videos about COVID-19
A full set of treatment videos includes T1 + T2 + [T2A or nothing] + T3
Video T1:
Hello, I’m Dr [YOUR LAST NAME HERE] from [YOUR INSTITUTIONAL AFFILIATION HERE], and
I?d like to tell you a little about Coronavirus or COVID-19. COVID-19 is a new virus that can infect
the respiratory tract and lungs. Although many people who get sick from COVID will get better, some
people who get it become very ill and some even die.
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Although there’s no cure, there are ways medical professionals have found to protect you and your
community from COVID. I hope that this message can give you information that will help you protect
you or someone you love from COVID infection.
First, I would like to tell you about the symptoms of COVID-19. The most common symptoms of
COVID-19 are cough, fever, and trouble breathing. Another odd symptom some people have is loss of
taste or smell. A large number of people who have COVID-19 actually don’t show any symptoms at
all. Unfortunately, people can still spread the disease to others even with no symptoms. The next video
will provide you with more information on how you can protect yourself and others.
Video T2:
Hello, I’m Dr [YOUR LAST NAME HERE] from [YOUR INSTITUTIONAL AFFILIATION HERE],
You may be looking for ways to resume some activities as safely as possible.
However, COVID-19 remains contagious and shows no signs of disappearing. In fact, during the week
of July 6 there were 58,000 new COVID cases per day diagnosed in the United States.
[ONLY FOR ACKNOWLEDGMENT SUB-TREATMENT T2A] Black Americans and other minority groups
are three times as likely to get and, when you account for age, four times as likely to die from COVID as white
Americans.
Without a safe and effective vaccine or therapy, our only option is to continue taking precautionary
measures to protect ourselves, our communities, and the most vulnerable among us.
While there is no way to ensure zero risk of infection from COVID-19, observing these three practices
will help to protect you and others.
First, continue to practice social distancing whenever possible: Try to stay outdoors, and to the maxi-
mum extent possible, please stay 6 feet apart. If you must be indoors, use visual reminders—like signs,
chair arrangements, markings on the ﬂoor, or arrows—to help remind you to keep your distance from
others, and maintain physical barriers whenever possible.
Second, continue to wash your hands often for at least 20 seconds with soap and water, especially
before going out, and every time you return home.
Third, wear a mask when in public at all times, especially when indoors or when it is difﬁcult to stay 6
feet away. The next video will tell you a bit more about masks.
Video T3:
Hello, I am doctor [YOUR LAST NAME HERE] from [YOUR INSTITUTIONAL AFFILIATION HERE],
and I will tell you a bit more about masks. Wearing a mask is a key way to prevent the spread of
COVID-19. You are not just protecting yourself but also your grandma and your community, just in
case you have COVID-19 but don’t know it.
Even if wearing a mask may sometimes put you in a difﬁcult situation, it is important to protect you
and the community from COVID 19 disease. As medical professionals, I am committed to delivering
the best care I can to every patient. My goal is to make sure that you and everyone you love survives
this COVID-19 pandemic. Thank you for listening to these messages.
A.3
Control Videos about non-COVID-19 Health Behaviors
A full set of control videos includes C1 + C2 + C3
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Video C1:
Most adults need to sleep between 6 and 8 hours a night. Now, there are some people who get ﬁve
hours a night and they are ﬁne, so there is some variation across people. But for most adults, we need
6 to 8 hours in order to function well the next day. If you feel sleep deprived you might not be able to
function as well as you would normally like.
It’s important to have something called sleep hygiene which is a routine you follow at bedtime and
can help you fall asleep. Things that can disrupt sleep hygiene include caffeine or alcohol too close to
bedtime. Eating late at night can also cause indigestion. So keep a routine and trying to get 6-8 hours
is important.
Video C2:
Sugar is found in many different food items. Natural sugars are those that can be found in fruits,
vegetables and dairy products like milk. Sugars like these that are natural are not really problematic
because they are coming alongside lots of other vitamins and minerals.
There are other sugars, though, that are processed and added to a food item. These are called additive
sugars. A good rule of thumb is to eat foods with less than than 5g of sugar per serving. Avoid buying
products where one of the ﬁrst ﬁve products is a sugar.
And it can be better to buy an unsweetened product like an unsweetened cereal or oatmeal and then
add a teaspoon of sugar to it if you need the sweetness than to buy a heavily sweetened product, like a
sugar cereal which can have several teaspoons of sugar per serving.
Video C3:
New ﬁtness guidelines can be summed up as follows: just move and anything counts. Sneaking in a
few minutes of physical activity throughout the day adds up in the long run. The guidelines are trying
to make it easier for individuals to be ﬁt and drop the rule that activity must be in 10 minute blocks of
time. In a nutshell, activity has beneﬁts even if it’s for a short amount of time. Taking the stairs instead
of the elevator, parking your car far away from the entrance to a store or walking your dog around
the block can all help you be ﬁt. The guidelines still call for at least 150 minutes a week of moderately
intense aerobic exercise and two weekly sessions of muscle training activity, like lifting weights or yoga.
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Figure 2: Fully extended randomization tree
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