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Predicting anxiety and depression over 12 months of the COVID‐19 pandemic: A machine learning study.

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Title: Predicting anxiety and depression over 12 months of the COVID‐19 pandemic: A machine learning study.
Authors: Bailey, Brooklynn (AUTHOR), Strunk, Daniel R. (AUTHOR)
Source: Journal of Clinical Psychology. Oct2023, Vol. 79 Issue 10, p2388-2403. 16p. 2 Charts, 2 Graphs.
Subjects: COVID-19 pandemic, Machine learning, Anxiety, COVID-19, Mental depression
Geographic Terms: United States
Abstract: Objectives: The coronavirus disease 2019 (COVID‐19) pandemic was associated with substantial increases in anxiety and depressive symptoms. To understand individual risk, we examined a large set of potential risk factors for anxiety and depression in the pandemic context. Methods: Adults in the United States (N = 1200) completed eight online self‐report assessments over 12 months of the COVID‐19 pandemic. Area under the curve scores summarized cumulative experiences of anxiety and depression over the assessment period. A machine learning approach to elastic net regularized regression was used to select predictors of cumulative anxiety and depression severity from a set of 68 sociodemographic, psychological, and pandemic‐related baseline variables. Results: Cumulative anxiety severity was most strongly explained by stress and depression‐related variables (such as perceived stress) and select sociodemographic characteristics. Cumulative depression severity was predicted by psychological variables, including generalized anxiety and depressive symptom reactivity. Being immunocompromised or having a medical condition were also important. Conclusions: By considering many predictors, findings provide a more complete view than previous studies focused on specific predictors. Important predictors included psychological variables suggested by prior research and variables more specific to the pandemic context. We discuss how such findings can be used in understanding risk and planning interventions. [ABSTRACT FROM AUTHOR]
Copyright of Journal of Clinical Psychology is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Psychology and Behavioral Sciences Collection
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  Data: Predicting anxiety and depression over 12 months of the COVID‐19 pandemic: A machine learning study.
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  Data: <searchLink fieldCode="AR" term="%22Bailey%2C+Brooklynn%22">Bailey, Brooklynn</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Strunk%2C+Daniel+R%2E%22">Strunk, Daniel R.</searchLink> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22Journal+of+Clinical+Psychology%22">Journal of Clinical Psychology</searchLink>. Oct2023, Vol. 79 Issue 10, p2388-2403. 16p. 2 Charts, 2 Graphs.
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  Data: <searchLink fieldCode="DE" term="%22COVID-19+pandemic%22">COVID-19 pandemic</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Anxiety%22">Anxiety</searchLink><br /><searchLink fieldCode="DE" term="%22COVID-19%22">COVID-19</searchLink><br /><searchLink fieldCode="DE" term="%22Mental+depression%22">Mental depression</searchLink>
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  Data: <searchLink fieldCode="DE" term="%22United+States%22">United States</searchLink>
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  Label: Abstract
  Group: Ab
  Data: Objectives: The coronavirus disease 2019 (COVID‐19) pandemic was associated with substantial increases in anxiety and depressive symptoms. To understand individual risk, we examined a large set of potential risk factors for anxiety and depression in the pandemic context. Methods: Adults in the United States (N = 1200) completed eight online self‐report assessments over 12 months of the COVID‐19 pandemic. Area under the curve scores summarized cumulative experiences of anxiety and depression over the assessment period. A machine learning approach to elastic net regularized regression was used to select predictors of cumulative anxiety and depression severity from a set of 68 sociodemographic, psychological, and pandemic‐related baseline variables. Results: Cumulative anxiety severity was most strongly explained by stress and depression‐related variables (such as perceived stress) and select sociodemographic characteristics. Cumulative depression severity was predicted by psychological variables, including generalized anxiety and depressive symptom reactivity. Being immunocompromised or having a medical condition were also important. Conclusions: By considering many predictors, findings provide a more complete view than previous studies focused on specific predictors. Important predictors included psychological variables suggested by prior research and variables more specific to the pandemic context. We discuss how such findings can be used in understanding risk and planning interventions. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Journal of Clinical Psychology is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1002/jclp.23555
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        Text: English
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      – SubjectFull: Anxiety
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      – SubjectFull: Mental depression
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              Text: Oct2023
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