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Using the multiphase optimization strategy (MOST) framework to optimize an intervention to increase COVID-19 testing
Marya Gwadz1,2, Charles M Cleland3,4, Maria Lizardo5
1Intervention Innovations Team Lab (IIT-Lab), NYU Silver School of Social Work, 1 Washington Square North, New York, NY, 10003, USA. mg2890@nyu.edu.
BMC Public Health
|June 21, 2022
Summary
This study tested behavioral interventions to increase COVID-19 testing among Black and Hispanic frontline workers. Findings will inform an optimized, scalable intervention to address testing disparities.
Area of Science:
- Public Health
- Health Behavior Interventions
- Health Equity
Background:
- Frontline essential workers, particularly Black and Hispanic individuals, face high COVID-19 exposure risks.
- Significant barriers impede COVID-19 testing among these populations, leading to insufficient testing rates.
Purpose of the Study:
- To identify effective behavioral intervention components for increasing COVID-19 testing in underrepresented frontline workers.
- To develop an optimized, scalable, and cost-effective intervention balancing effectiveness with practical considerations.
Main Methods:
- A community-engaged study using the multiphase optimization strategy (MOST) framework and factorial design.
- Tested four components: motivational interviewing, behavioral economics text messaging, peer education, and testing access.
- 448 unvaccinated Black and Hispanic frontline workers were randomized into 16 conditions; qualitative interviews with 50 participants.
Main Results:
- This section is not available in the provided abstract.
Conclusions:
- The trial aims to yield an effective, affordable, and scalable behavioral intervention for community settings.
- The study will contribute to understanding intervention approaches for social inequities in public health crises like COVID-19.
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