Title Determining the relative importance of risk and protective factors for adjustment disorder symptoms during the COVID-19 pandemic by mixed-effects random forests
Authors 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 ; Geležėlytė, 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
DOI 10.1017/S0033291726104048
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Is Part of Psychological Medicine.. Cambridge : Cambridge University Press (CUP). 2026, vol. 56, art. no. e181, p. [1-14].. ISSN 0033-2917. eISSN 1469-8978
Keywords [eng] adjustment disorder ; community stressors ; machine learning ; mixed-effects regression random forests ; pandemic ; random forests
Abstract [eng] 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.
Published Cambridge : Cambridge University Press (CUP)
Type Journal article
Language English
Publication date 2026
CC license CC license description