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Exploring Sleep Duration and Insomnia Among Prospective University Students: A Study with Geographical Data and Machine Learning Techniques.

Firoj Al-MamunMohammed A MamunMd Emran HasanMoneerah Mohammad ALmerabDavid Gozal
Published in: Nature and science of sleep (2024)
Sleep disturbances are prevalent among prospective university students and are associated with various factors including gender, test-taking status, mock test satisfaction, and anxiety. Targeted interventions, including sleep education and psychological support, hold promise in ameliorating sleep health and overall well-being among students, potentially enhancing entrance test performance.
Keyphrases
  • sleep quality
  • physical activity
  • machine learning
  • healthcare
  • big data
  • depressive symptoms
  • mental health
  • public health
  • electronic health record
  • health information
  • risk assessment
  • deep learning
  • social media