Effect of Availability of COVID-19 Testing on Choice to Isolate and Socially Distance
Condition: COVID-19
MeSH terms: COVID-19
Study type: Interventional

Summary: 
      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.
    

Detailed description: 
      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).
    

Intervention name(s): Positive COVID Test Result - Hypothetical Scenario; Negative COVID Test Result - Hypothetical Scenario; Unavailable COVID Test Result - Hypothetical Scenario
Intervention type(s): Behavioral; Behavioral; Behavioral
Intervention description(s): 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.; Participants will be asked to imagine that they have tested negative (PCR) for an active COVID-19 infection.; 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.
Allocation: Randomized
Intervention model: Parallel Assignment
Intervention model description: Parallel 3-arm, cross-sectional, behavioral intervention study
Masking: None (Open Label)
Primary purpose: Screening
Keywords: Behavior intentions; Testing; COVID-19; Theory of Planned Behavior; Reason Action Approch; SARS-CoV-2