JOURNAL ARTICLE

Screening for Sleep Disturbances in Children and Adolescents with Tics, Headache Disorders or Type 1 Diabetes.

  • Published In: Journal of Child Neurology, 2025, v. 40, n. 10. P. 811 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Bengtsen, Charlotte P. A.; Paulsrud, Cecilie; Thorsen, Steffen U.; Jørgen Jennum, Paul; Svensson, Jannet; Mol Debes, Nanette M. 3 of 3

Abstract

This article focuses on the prevalence and characteristics of sleep disturbances in children and adolescents aged 6-17 years with medical conditions—specifically tics/Tourette syndrome, headache disorders, and type 1 diabetes—compared to healthy peers, using the validated Sleep Screening Questionnaire–Children and Adolescents (SSQ-CA). The study found that 81.5% of children with these medical conditions reported sleep disturbances, notably poorer sleep quality and more frequent awakenings, compared with 70.9% of healthy children, though differences in sleep duration and latency were not significant. Increased screen time before bedtime was more common among children with medical conditions and was associated with poor sleep quality, identifying it as a modifiable risk factor. The findings highlight the widespread nature of sleep disturbances in pediatric populations and suggest that routine screening and interventions targeting sleep hygiene, particularly reducing prebedtime screen exposure, may improve sleep health and overall well-being.

Additional Information

  • Source:Journal of Child Neurology. 2025/11, Vol. 40, Issue 10, p811
  • Document Type:Article
  • Subject Area:Health and Medicine
  • Publication Date:2025
  • ISSN:0883-0738
  • DOI:10.1177/08830738251331750
  • Accession Number:188582039
  • Copyright Statement:Copyright of Journal of Child Neurology is the property of Sage Publications Inc. 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.)

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