Article
Lifestyle predictors of depression and anxiety during COVID-19: a machine learning approach
Registro en:
SIMJANOSKI, Mario et al. Lifestyle predictors of depression and anxiety during COVID-19: a machine learning approach. Trends in Psychiatry and Psychotherapy, Porto Alegre, v. 44, p. 1-10, 2022.
2237-6089
10.47626/2237-6089-2021-0365
Autor
Simjanoski, Mario
Ballester, Pedro L.
Mota, Jurema Corrêa da
Boni, Raquel B. De
Balanzá-Martínez, Vicent
Atienza-Carbonell, Beatriz
Bastos, Francisco Inácio
Frey, Benicio N.
Minuzzi, Luciano
Cardoso, Taiane de Azevedo
Kapczinski, Flavio
Resumen
O título abreviado do periódico é Trends Psychiatry Psychother Associação de Psiquiatria do Rio Grande do Sul (APRS). Introduction: Recent research has suggested an increase in the global prevalence of psychiatric
symptoms during the COVID-19 pandemic. This study aimed to assess whether lifestyle behaviors
can predict the presence of depression and anxiety in the Brazilian general population, using a model
developed in Spain.
Methods: A web survey was conducted during April-May 2020, which included the Short Multidimensional
Inventory Lifestyle Evaluation (SMILE) scale, assessing lifestyle behaviors during the COVID-19 pandemic.
Depression and anxiety were examined using the PHQ-2 and the GAD-7, respectively. Elastic net, random
forest, and gradient tree boosting were used to develop predictive models. Each technique used a subset
of the Spanish sample to train the models, which were then tested internally (vs. the remainder of the
Spanish sample) and externally (vs. the full Brazilian sample), evaluating their effectiveness.
Results: The study sample included 22,562 individuals (19,069 from Brazil, and 3,493 from Spain). The
models developed performed similarly and were equally effective in predicting depression and anxiety in
both tests, with internal test AUC-ROC values of 0.85 (depression) and 0.86 (anxiety), and external test
AUC-ROC values of 0.85 (depression) and 0.84 (anxiety). Meaning of life was the strongest predictor of
depression, while sleep quality was the strongest predictor of anxiety during the COVID-19 epidemic.
Conclusions: Specific lifestyle behaviors during the early COVID-19 epidemic successfully predicted the
presence of depression and anxiety in a large Brazilian sample using machine learning models developed
on a Spanish sample. Targeted interventions focused on promoting healthier lifestyles are encouraged.
Keywords: Mental health, SARS-CoV-2, lifestyle, machine learning, pandemic.