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Area of Science:

  • Digital Health
  • Machine Learning
  • Mental Health

Background:

  • Depression is a leading cause of global disability, affecting up to 30% of college students.
  • Early detection of depressive symptoms is crucial for timely intervention and mitigating negative consequences.
  • Passive sensing technology offers a low-burden method for continuous monitoring of mental health.

Purpose of the Study:

  • To detect depressive symptoms in college students using an ensemble machine learning model.
  • To leverage passive sensing data for mental health monitoring.

Main Methods:

  • An ensemble machine learning model (light gradient boosting machine) was employed.
  • Data was collected from 28 undergraduate students using an Oura ring, Samsung smartwatch, and the AWARE mobile application.
  • Passive sensing data, including sleep, physiology, movement, and screen time, was gathered over a 19- to 22-week period.

Main Results:

  • The light gradient boosting machine model achieved an F1-score of 0.744 and a Cohen kappa of 0.474.
  • The model demonstrated moderate agreement in detecting depressive symptoms.
  • Sleep quality and missed mobile interactions were the most significant predictors of depressive symptoms.

Conclusions:

  • Passive sensing data holds potential for real-time, low-cost detection of depressive symptoms in college students.
  • This approach may facilitate future prevention and intervention strategies for mental health.
  • Wearable devices and mobile sensing offer a promising avenue for monitoring student mental well-being.