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Combatting COVID-19: Measuring and Changing Beliefs, Knowledge and Behaviors
Last registered on May 26, 2020

Pre-Trial

Trial Information
General Information
Title
Combatting COVID-19: Measuring and Changing Beliefs, Knowledge and Behaviors
RCT ID
AEARCTR-0005862
Initial registration date
May 26, 2020
Last updated
May 26, 2020 4:53 PM EDT
Location(s)

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Primary Investigator
Affiliation
University of Michigan
Other Primary Investigator(s)
PI Affiliation
University of Michigan
PI Affiliation
University of Michigan
PI Affiliation
University of Michigan
PI Affiliation
University of Michigan
PI Affiliation
Beira Operational Research Center
Additional Trial Information
Status
In development
Start date
2020-06-01
End date
2020-12-31
Secondary IDs
Abstract
We seek to support the Mozambican COVID-19 response, in collaboration with the government’s health research center for the central region, by following up on a study sample of a randomized controlled trial in Mozambique. Sample households will be contacted by phone and administered several rounds of surveys regarding COVID-19 knowledge, beliefs, and behavior. We will randomize novel over-the-phone interventions to test if we can 1) encourage social distancing by accelerating changes in community norms, and 2) improve knowledge about COVID-19 via incentives and tailored feedback. Our findings will support the Mozambican response by informing policymakers of the public's COVID-19 knowledge and behaviors and on which public health messaging strategies are best to pursue given limited resources.
Registration Citation
Citation
Allen IV, James et al. 2020. "Combatting COVID-19: Measuring and Changing Beliefs, Knowledge and Behaviors." AEA RCT Registry. May 26. https://doi.org/10.1257/rct.5862-1.0.
Sponsors & Partners
Sponsor(s)
Partner(s)
Type
government
Url
Experimental Details
Interventions
Intervention(s)
We employ over-the-phone interventions designed to improve 1) social distancing, and 2) knowledge relating to COVID-19.

Social Distancing Treatments:
-SD1: Information on the value of social distancing. We will provide individuals with information on the value of social distancing, emphasizing the positive externality they confer on others by reducing the spread of COVID-19.
-SD2: Community Support for Social Distancing. We will ask individuals whether they themselves support social distancing, and use this information to calculation the fraction of households in the community who support social distancing. We will then ask them to guess the share of households in the community who support social distancing. In a later phone call, households who underestimate the true share of households in the community who support social distancing will be given information on the true (higher) share of support for social distancing. We will accompany this message with information on the value of social distancing, emphasizing the positive externality they confer (same information provided in Treatment SD1).
-SD3: Community leader support for social distancing. Community leaders have also participated in past rounds of our surveys. We will survey community leaders again, and ask them to endorse social distancing in their communities. In this treatment, we will inform households by phone call that these individuals support social distancing in their communities. We will accompany this message with information on the value of social distancing, emphasizing the positive externality they confer (the same information provided in Treatment SD1).

Knowledge Treatments:
-K1: Knowledge Incentives. We will randomly offer a subset of respondents an additional monetary reward for every correct knowledge response on a subsequent phone survey. We will examine the effect of the treatment on future knowledge and behavior.
-K2 & 3: Tailored Feedback. We will randomly give tailored feedback to a subset of respondents on their response to COVID-19 knowledge questions, by informing them of a subset of their correct responses and correcting a subset of their incorrect responses. We will examine if tailored feedback improves relevant knowledge and behavior in a subsequent telephone survey.
Intervention Start Date
2020-06-15
Intervention End Date
2020-08-15
Primary Outcomes
Primary Outcomes (end points)
For Social Distancing interventions:
-Others’ and self-report of social interactions
-Index of household social distancing behaviors

For Knowledge interventions:
-Index of COVID-19 overall knowledge
Primary Outcomes (explanation)
For Social Distancing interventions:
-Others’ and self-report of social interactions: Others’ reports of social interactions with respondent. Taken from the question ask for up to 10 other study participants in one’s social network and neighbors within 200m: Did you talk to / stand within 1.5 meters / shake hands or otherwise touch (insert name) in last 14 days?
-Index of household social distancing behaviors: Ask about whether or not the household attends social gatherings, leaves the household area, avoids crowded areas, keeps a distance of 1.5 meters from others, frequently wash hands, informs others if sick, and more…
◦ Is this something you think people should be doing?
◦ Is this something your household has been doing for the last 7 days?

For Knowledge interventions:
-Index of COVID-19 overall knowledge - number of correct responses to knowledge questions about coronavirus symptoms, prevention, how it spreads, and who is most at risk; household social distancing and self-prevention behaviors. Confidence with which responses are given.
Secondary Outcomes
Secondary Outcomes (end points)
Secondary Outcomes (explanation)
Experimental Design
Experimental Design
We randomize our over-the-phone interventions to test if we can 1) encourage social distancing by accelerating changes in community norms, and 2) improve knowledge about COVID-19 via incentives and tailored feedback. We consider the intent-to-treat (ITT) effect of the randomized interventions on a standardized version of our outcomes: indices of perceived social distancing norms and household social distancing behavior.
Experimental Design Details
Not available
Randomization Method
Randomization done in office by a computer
Randomization Unit
Household
Was the treatment clustered?
No
Experiment Characteristics
Sample size: planned number of clusters
2,000 Households
Sample size: planned number of observations
2,000 Households
Sample size (or number of clusters) by treatment arms
2,000 Households
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
The study has sufficient power to detect effects of reasonable size in the analyses for our primary hypotheses. For these power calculations, we consider the intent-to-treat (ITT) effect of the randomized interventions on a standardized version of our outcomes: indices of perceived social distancing norms and household social distancing behavior. The study sample will be drawn from the 3,135 individuals for whom we have phone numbers. We assume a conservative response rate of 54%—drawn from a follow-up telephone survey conducted in 2019—which we aim to improve through repeated calls at different times of the day. We calculate the minimum detectable effect (MDE) of comparing the control group to one of the three treatment groups (i.e., each group is one fourth of the responding sample). The MDE is 0.1929, meaning that our study is sufficiently powered to detect a difference of 0.2 standard deviations in our standardized outcome measures between the control group and each treatment group.
IRB
INSTITUTIONAL REVIEW BOARDS (IRBs)
IRB Name
University of Michigan Health Sciences and Behavioral Sciences Institutional Review Board
IRB Approval Date
2020-04-15
IRB Approval Number
HUM00113011