Psychological medicine · 2026
Determining the relative importance of risk and protective factors for adjustment disorder symptoms during the COVID-19 pandemic by mixed-effects random forests.
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Lotzin Annett, Finne Emily, Schildbach Georg, Acquarini Elena, Ajdukovic Dean, Ajdukovic Marina, Anastassiou-Hadjicharalambous Xenia, Ardino Vittoria, Hensler Ida, Arnberg Filip K, Böttche Maria, Dragan Małgorzata, Figueiredo-Braga Margarida, Gelezelyte Odeta, Grajewski Piotr, Groen Simon, van Hoof Marie-José, Javakhishvili Jana Darejan, Kazlauskas Evaldas, Lioupi Chrysanthi, Lueger-Schuster Brigitte, Sales Luisa, Tsiskarishvili Lela, Zrnic Novakovic Irina, Schäfer Ingo, ADJUST Study Consortium
Аннотация
BACKGROUND: The COVID-19 pandemic exposed individuals to numerous psychosocial and health-related stressors associated with adjustment disorder (AjD) symptoms, yet it remains unclear which factors are most predictive.
METHODS: Using mixed-effects regression random forests (MERF), a machine learning approach that combines random forests with mixed-effects regressions, we analyzed longitudinal data from 15,155 adults across 11 European countries collected at three time points between June 2020 and January 2022. We evaluated 245 candidate predictors, including sociodemographic, pandemic-related, and health-related factors, for their relative importance in predicting AjD symptoms (ADNM-8).
RESULTS: The seven most influential predictors, ranked in descending order of importance, were uncertainty about the pandemic's duration and risks, poor health, social isolation, conflicts at home, loss of daily structure, fear of infection, and restricted personal contact with close others.
CONCLUSIONS: AjD symptoms were most strongly linked to factors related to lack of control (e.g., uncertainty, loss of daily structure, fear of infection), as well as current poor health and reduced social connectedness. Interventions that enhance a sense of control through clear communication, help individuals re-establish daily routines, and strengthen social connectedness may mitigate AjD symptoms during future public health crises. Our findings also highlight the potential of machine learning approaches for identifying complex patterns across high-dimensional predictors of clinical symptoms, which may improve prediction accuracy in mental health research.
METHODS: Using mixed-effects regression random forests (MERF), a machine learning approach that combines random forests with mixed-effects regressions, we analyzed longitudinal data from 15,155 adults across 11 European countries collected at three time points between June 2020 and January 2022. We evaluated 245 candidate predictors, including sociodemographic, pandemic-related, and health-related factors, for their relative importance in predicting AjD symptoms (ADNM-8).
RESULTS: The seven most influential predictors, ranked in descending order of importance, were uncertainty about the pandemic's duration and risks, poor health, social isolation, conflicts at home, loss of daily structure, fear of infection, and restricted personal contact with close others.
CONCLUSIONS: AjD symptoms were most strongly linked to factors related to lack of control (e.g., uncertainty, loss of daily structure, fear of infection), as well as current poor health and reduced social connectedness. Interventions that enhance a sense of control through clear communication, help individuals re-establish daily routines, and strengthen social connectedness may mitigate AjD symptoms during future public health crises. Our findings also highlight the potential of machine learning approaches for identifying complex patterns across high-dimensional predictors of clinical symptoms, which may improve prediction accuracy in mental health research.