TY - JOUR

T1 - Using machine learning to unveil relevant predictors of adherence to recommended health-protective behaviors during the COVID-19 pandemic in Denmark

AU - Lilleholt, Lau

AU - Chapman, Gretchen B.

AU - Böhm, Robert

AU - Zettler, Ingo

N1 - 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.

PY - 2024

Y1 - 2024

N2 - 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.

AB - 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.

KW - adherence

KW - COVID-19

KW - health-protective behaviors

KW - machine learning

KW - pandemic

UR - http://www.scopus.com/inward/record.url?scp=85195507627&partnerID=8YFLogxK

U2 - 10.1111/aphw.12563

DO - 10.1111/aphw.12563

M3 - Journal article

C2 - 38850198

AN - SCOPUS:85195507627

JO - Applied Psychology: Health and Well-Being

JF - Applied Psychology: Health and Well-Being

SN - 1758-0846

ER -