Using machine learning to unveil relevant predictors of adherence to recommended health-protective behaviors during the COVID-19 pandemic in Denmark
Research output: Contribution to journal › Journal article › Research › peer-review
What were relevant predictors of individuals' proclivity to adhere to recommended health-protective behaviors during the COVID-19 pandemic in Denmark? Applying machine learning (namely, lasso regression) to a repeated cross-sectional survey spanning 10 months comprising 25 variables (Study 1; N = 15,062), we found empathy toward those most vulnerable to COVID-19, knowledge about how to protect oneself from getting infected, and perceived moral costs of nonadherence to be strong predictors of individuals' self-reported adherence to recommended health-protective behaviors. We further explored the relations between these three factors and individuals' self-reported proclivity for adherence to recommended health-protective behaviors as they unfold between and within individuals over time in a second study, a Danish panel study comprising eight measurement occasions spanning eight months (N = 441). Results of this study suggest that the relations largely occurred at the trait-like interindividual level, as opposed to at the state-like intraindividual level. Together, the findings provide insights into what were relevant predictors for individuals' overall level of adherence to recommended health-protective behaviors (in Denmark) as well as how these predictors might (not) be leveraged to promote public adherence in future epidemics or pandemics.
Original language | English |
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Journal | Applied Psychology: Health and Well-Being |
ISSN | 1758-0846 |
DOIs | |
Publication status | Accepted/In press - 2024 |
Bibliographical note
Publisher Copyright:
© 2024 The Author(s). Applied Psychology: Health and Well-Being published by John Wiley & Sons Ltd on behalf of International Association of Applied Psychology.
- adherence, COVID-19, health-protective behaviors, machine learning, pandemic
Research areas
ID: 394979523