dbGaP Study Accession: phs003866

NIH Institute/Center: NIMHD

RADx Data Program: RADx-UP

Release Date: 03/28/2025

Study Description: Pandemic fatigue—a phenomenon characterized by a demotivation to follow recommended protective behaviors that emerges over time and is affected by one’s emotions, experiences and perceptions—threatened the ability to end the COVID-19 pandemic. Waning vaccine-induced immunity, breakthrough infections, new variants, and uncertainty all contribute to pandemic fatigue. These challenges highlight the importance of sustaining COVID-19 mitigation strategies, including COVID-19 testing, over the long run to achieve pandemic control. While pandemic fatigue is an expected and natural response to a prolonged public health crisis, it compromises the ability to keep members of underserved and medically and/or socially vulnerable populations safe, including African Americans. Given that complete eradication or elimination are not feasible, scientists and public health officials focused on control measures to make COVID-19 endemic. To achieve endemic status, barriers to COVID-19 testing within vulnerable populations, including pandemic fatigue, must be identified and addressed. Moreover, communication science interventions must be advanced that enable the ability to determine how variations in the presentation of messages targeting perceived risk for COVID-19 can be leveraged to increase motivation for COVID-19 testing behaviors, and employ effective communication strategies to mitigate the impact of exposure to misinformation on testing acceptance and uptake. Guided by the Capability Opportunity Motivation—Behavior and Minority Health and Health Disparities Research Frameworks, this study leveraged participatory research methods, artificial intelligence, and infrastructure from ongoing community-engaged COVID-19 mitigation research to: 1) Host a design-a-thon to develop deep learning computer animations capable of conveying the importance of COVID-19 testing and promoting its uptake in community settings among African Americans in NC. 2) Determine whether a deep learning computer animation intervention (vs a control) improves COVID-19 testing uptake using a 1:1 randomized experiment. Study results will identify effective COVID-19 testing promotion messages for African Americans with the potential for generalization to other key populations.

Principal Investigator: Ritchwood, Tiarney

Has Data Files: No

Study Domain: Testing Rate or Uptake

Data Collection Method: Interview or Focus Group

Keywords: Pandemic Fatigue

Study Design: Interventional or Clinical Trial

Multi-Center Study: No

Data Types: Behavioral; Questionnaire or Survey; Social

Study Start Date: 11/01/2022

Study End Date: 10/31/2024

Species: Human

Estimated Cohort Size: 600

Study Population Focus: African Americans; Underserved or Vulnerable Populations

Acknowledgement Statement: This study was supported through funding, 7U01MD018306-02, for the National Institute on Minority Health and Health Disparities (NIMHD) as part of the RADx-UP program. We would like to thank study participants, project staff, and community partners for their contributions to this research. Approved users should acknowledge the provision of data access by dbGaP for accession phs0038866.v1.p1, and the NIH RADx Data Hub. Approved users should also acknowledge the specific version(s) of the dataset(s) obtained from the NIH RADx Data Hub.

Funding Opportunity Announcement (FOA) Number: PA-21-268

NIH Grant or Contract Number(s): 7U01MD018306-02