Testing the reciprocal relationship between depressive symptoms and insomnia.
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| Title: | Testing the reciprocal relationship between depressive symptoms and insomnia. |
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| Authors: | Zhou, Robert J. (AUTHOR), Bailey, Brooklynn (AUTHOR), Strunk, Daniel R. (AUTHOR) |
| Source: | Journal of Clinical Psychology. Jun2024, Vol. 80 Issue 6, p1391-1404. 14p. |
| Subjects: | Mental depression, COVID-19, Insomnia, Sleep interruptions, Factor analysis, Sleep hygiene |
| Abstract: | Background: Although the importance of sleep difficulties in emotional disorders has long been acknowledged, the nature of the potential reciprocal relationship between sleep and depressive symptoms is not yet well understood. The coronavirus disease 19 pandemic provided a unique opportunity to study the interrelation of these symptoms over a period marked by increases in sleep and psychological difficulties. Methods: Using online data collection, we followed 1200 adults (59% male) through 1 year of the pandemic. Measures of sleep disturbance and depressive symptoms were assessed at eight time points. Factor analysis of the items from these two measures suggested separate insomnia and depressive symptom factors. A random intercept cross‐lagged panel model was used to assess within‐person relationships between factor analysis‐informed subscales of insomnia and depressive symptoms. Results: Depressive symptoms predicted subsequent changes in insomnia symptoms; however, insomnia did not predict changes in depressive symptoms. Conclusions: Our findings suggest the impact of depressive symptoms on insomnia is evident, but the reverse is not. Implications of this finding along with the need for research addressing depressive symptoms and insomnia as treatment targets are discussed. [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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