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The predictive power of insomnia symptoms on other aspects of mental health during the COVID-19 pandemic: a longitudinal study.

Gabriela G WernerBarbara CludiusPhilipp SckopkeAngelika StefanFelix D SchönbrodtCaroline Zygar-Hoffmann
Published in: Journal of sleep research (2022)
Symptoms of insomnia are an important risk factor for the development of mental disorders, especially during stressful life periods such as the coronavirus disease 2019 (COVID-19) pandemic. However, up to now, most studies have used cross-sectional data, and the prolonged impact of insomnia symptoms during the pandemic on later mental health remains unclear. Therefore, we investigated insomnia symptoms as a predictor of other aspects of mental health across 6 months, with altogether seven assessments (every 30 days, t0-t6), in a community sample (N = 166-267). Results showed no mean-level increase of insomnia symptoms and/or deterioration of mental health between baseline assessment (t0) and the 6- month follow-up (t6). As preregistered, higher insomnia symptoms (between persons) across all time points predicted reduced mental health at the 6-month follow-up. Interestingly, contrary to our hypothesis, higher insomnia symptoms at 1 month, within each person (i.e., compared to that person's symptoms at other time points), predicted improved rather than reduced aspects of mental health 1 month later. Hence, we replicated the predictive effect of averagely increased insomnia symptoms on impaired later mental health during the COVID-19 pandemic. However, we were surprised that increased insomnia symptoms at 1 month predicted aspects of improved mental health 1 month later. This unexpected effect might be specific for our study population and a consequence of our study design. Overall, increased insomnia symptoms may have served as a signal to engage in, and successfully implement, targeted countermeasures, which led to better short-term mental health in this healthy sample.
Keyphrases
  • mental health
  • sleep quality
  • mental illness
  • coronavirus disease
  • depressive symptoms
  • cross sectional
  • healthcare
  • physical activity
  • deep learning
  • machine learning
  • high resolution
  • atomic force microscopy
  • high speed