Use of mind genomics for public health and wellbeing: Lessons from COVID 19 pandemic

Abstract

ABSTRACT Background: Machine learning (ML) tools can be used to analyze human mindsets and forecast behavioral patterns. ML can be used to understand the psychological processes and behavioral principles underlying public decision-making patterns. The aim of this study was to explore participants’ mindsets using ML and accordingly build messages for each mindset to enhance compliance with a public health policy, specifically physical distancing during the coronavirus disease 2019 (COVID-19) pandemic. Methods: An online questionnaire was administered using systematically varied combinations of elements and science of mind genomics. The questions focused on the perceived risk level of COVID-19, strategies to enhance physical distancing compliance, appropriate communicators of the policy, and different physical distancing practices. Snowball sampling was used to recruit participants until sample saturation was achieved among residents of the United Arab Emirates (UAE), aged 18– 80 years. Results: A total of 117 patients were included in this study. In the total panel, the strongest performing elements were those communicated by the government (P<0.01) and clergy (P < 0.05), with no differences between sex and age groups. Three mindset segments were identified: (1) followers of general strategies for physical distancing, (2) those interested in novel ways of practicing physical distancing, and (3) fascinating onlookers of the pandemic. Conclusion: Our results revealed that COVID-19 health-related messages are best communicated by the government and clergy in the UAE. These strategies may aid in the implementation and adoption of other public health policies.