The purpose of this research is to conduct a cross-sectional survey to investigate how people's lifestyle decisions and social distancing choices are affected by the medical information they receive. The hypothesis is that a positive COVID-19 test result will lead to study participants having the greatest self-isolation intentions compared to those who are only clinically diagnosed for COVID-19 without a confirmatory diagnostic test result or those who receive a negative COVID-19 test result.
When people make choices about how to socially distance, they may make different choices when
they have information from a doctor or other medical provider versus when they have
information from a test result. Are people's lifestyle decisions and choices to socially
distance or isolate affected by how information about their health status is communicated?
Does the presence of testing change the lifestyle decisions people make? We study and attempt
to answer these questions through a cross-sectional, online survey.
Two pilot studies will be run prior to the launching the main survey. The preliminary results
from these two surveys will be analyzed through EFA (exploratory factor analysis) and power
analysis in order to determine subscales, effect size, and appropriate sample size for the
main study. Additionally, a focus group consisting of approximately 15 college-educated
individuals will be asked to take the main survey in order to evaluate a reasonable time of
completion for the survey. The lower bound of this study completion duration will then be
utilized to establish reasonable survey completion time and thus create an exclusion
criterion based on time to survey completion.
Study participants will be first invited to complete a 9 question, pre-test survey on
Amazon's Mechanical Turk. Respondents who pass the 4 attention check questions within the
pre-test will then be invited to complete the main survey.
An estimated 1400 participants will be recruited using Amazon's Mechanical Turk to complete
the main survey. Of those, we anticipate 1194 participants to meet all inclusion criteria who
will then be included in the main study analysis. Participants will be included in the main
survey analysis if they read and agree to the English language consent form, are U.S.
residents (based on zip code data), correctly answer the attention check questions in both
the pre-test and main survey, and complete the main survey within a reasonable amount of time
(deemed to be 120 seconds or more).
Participants will be first invited to complete a 9 question, pre-test survey on Amazon's
Mechanical Turk. Respondents who pass the 4 attention check questions within the pre-test
will then be invited to complete the main survey. After consenting to participate,
participants will be randomized to take one of three surveys each describing a different
scenario: one where they likely have COVID-19 but testing is not available, one where they
likely have COVID-19 and testing results show a positive result, and one where they likely
have COVID-19 and testing results show a negative result. Then, participants will be asked
questions about their activity and behavior intentions (e.g., stay in a specific room in my
home and stay away from all other people and pets, visit a friend or family member in
person). Participants will also be asked construct questions based off of Theory of Planned
Behavior/Reason Action Approach (located in the pre-test survey), along with a set of
demographic questions.
Survey responses will be summarized for the full sample, as well as stratified by testing
scenario. Quantitative responses will be summarized using means, standard deviations and
quartiles, and categorical and ordinal responses will be summarized using frequency
distributions. Comparisons between scenarios will be performed using one-way analysis of
variance (ANOVA) for quantitative variables, Kruskal-Wallis tests for ordinal variables, and
chi-squared or Fisher's exact tests as appropriate for categorical variables.
The primary outcome is a difference in the behavioral sum score constructed using 11 items,
composed of two subscales. Secondary outcomes include the 'personal decisions' and 'social
expectations' subscales respectively. The 'personal decisions' subscale will consist of the
items pertaining to masking, self-isolation, visiting friends, purchasing supplies,
undertaking physical activity, eating at a restaurant and having dinner at home with friends.
The 'social expectations' subscale will consist of the items pertaining to getting a haircut,
attending weddings, funerals, and birthday parties. Other secondary outcomes including
likelihood of voting, protesting/political gathering, and public transportation 1-item
questions will also be analyzed in a similar fashion across the three different arms.
The primary hypothesis is that there will be a statistically significant difference in
willingness to engage in risky behavior based on COVID test results. This will be evaluated
using a linear regression model of the total 11-item score. The primary model term will be
scenario, and covariates will include age, sex, race/ethnicity, political affiliation,
education level, location, and type of residence. We will perform pairwise comparisons of the
3 scenarios, and use an 0.017 significance level (3-fold Bonferroni correction for an overall
alpha of 0.05). Secondary analyses will evaluate the subscales separately using a similar
approach. We will also perform exploratory analyses evaluating individual item responses
using ordinal logistic regression models with similar specifications. A 5% significance level
will be used for all secondary and exploratory hypothesis tests. All analyses will be
performed using R v. 3.6.2 (http://www.r-project.org).
Behavioral: Positive COVID Test Result - Hypothetical Scenario
Participants will be asked to imagine that they have tested positive (PCR) for an active COVID-19 infection and that their physician has clinically diagnosed them with COVID-19.
Behavioral: Negative COVID Test Result - Hypothetical Scenario
Participants will be asked to imagine that they have tested negative (PCR) for an active COVID-19 infection.
Behavioral: Unavailable COVID Test Result - Hypothetical Scenario
Participants will be asked to imagine that testing is not available for active COVID-19 infections but that their physician has clinically diagnosed them with COVID-19.
Inclusion Criteria:
- 18 years of age or older
- U.S.-based (based off self-reported zip code)
- Able to read and agree to English-based consent form
- Able to pass the attention check questions in the pre-test survey
- Able to pass the attention check questions in the main survey
- Complete the main survey in 120 seconds or more
Exclusion Criteria
- <18 years of age
- Unable to complete English-based consent form
- Fail any of the attention check questions from the pre-test and main survey
- Complete the main survey in 119 seconds or under
UCLA Health Department of Medicine, Quality Office
Los Angeles, California, United States