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Associations between keystroke and stylus metadata and depressive symptoms in adolescents.

Moonyoung JangYoungeun ChoDo Hyung KimSunghyun ParkSeonghyeon ParkJi-Won HurMinah KimKwangsu ChoChang-Gun LeeJun Soo Kwon
Published in: Psychological medicine (2024)
This study demonstrates the potential of automatically collected data during school exams or classes for the early screening of clinical depressive symptoms in students. This study has the potential to serve as a cornerstone in the development of digital data frameworks for the early detection of depressive symptoms in adolescents.
Keyphrases
  • depressive symptoms
  • physical activity
  • young adults
  • social support
  • electronic health record
  • mental health
  • big data
  • sleep quality
  • machine learning
  • risk assessment
  • human health
  • deep learning
  • data analysis