Login / Signup

Prevalence of sleep disturbances in children and adolescents during COVID-19 pandemic: a meta-analysis and systematic review of epidemiological surveys.

Hong CaiPan ChenYu JinQinge ZhangTeris CheungChee H NgYu-Tao XiangYuan Feng
Published in: Translational psychiatry (2024)
The COVID-19 pandemic and the ensuing widespread lockdown measures have had a negative impact on the mental health of children and adolescents. We thus conducted a meta-analysis of the worldwide prevalence of sleep disturbances in children and adolescents during the COVID-19 pandemic. We performed a systematic literature search of the major international (PubMed, PsycINFO, Web of Science) and Chinese (Chinese Nation Knowledge Infrastructure (CNKI) and WANFANG) databases from their commencement dates to 27 December 2022. Altogether, 57 articles covering 206,601 participants were included in the meta-analysis. The overall prevalence of sleep disturbances was 34.0% (95% confidence interval (CI): 28-41%). The prevalence of parent-reported sleep disturbances during the COVID-19 pandemic was significantly higher than that of self-reported (p = 0.005) sleep disturbances. Epidemiological studies jointly conducted across Asia and Europe had a higher prevalence of sleep disturbances compared to those conducted in Asia, Europe, America, Oceania, or South America alone (p < 0.001). Children had a significantly higher prevalence of sleep disturbances compared to adolescents alone or a mixed cohort of children and adolescents (p = 0.022). Meta-regression analyses revealed that mean age (p < 0.001), quality evaluation score (p < 0.001), and percentage of men (p < 0.001) showed negative associations, while time of survey (B = 1.82, z = 34.02, p < 0.001) showed a positive association with the prevalence of sleep disturbances. Sleep disturbances were common in children and adolescents during the COVID-19 pandemic.
Keyphrases
  • systematic review
  • physical activity
  • sleep quality
  • risk factors
  • mental health
  • young adults
  • meta analyses
  • healthcare
  • machine learning
  • artificial intelligence