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Tracking Depression Dynamics in College Students Using Mobile Phone and Wearable Sensing.

Rui Wang1, Weichen Wang1, Alex Dasilva2

  • 1Dartmouth College, Computer Science, Hanover, NH, 03755, USA.

Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies
|October 25, 2024
PubMed
Summary

Researchers developed new ways to detect depression in college students using smartphone and wearable data. This approach, focusing on specific symptoms, can predict depression weekly with high accuracy.

Keywords:
Applied computing → Life and medical sciencesDepressionHuman-centered computing → Ubiquitous and mobile computingMental HealthMobile Sensing

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Area of Science:

  • Digital mental health
  • Computational psychiatry
  • Mobile sensing

Background:

  • Rising rates of depression among college students strain existing mental health services.
  • Current mobile sensing approaches for mental health lack specificity to major depressive disorder (MDD) symptoms.
  • A need exists for tailored digital phenotyping to accurately assess student mental well-being.

Purpose of the Study:

  • To introduce novel 'symptom features' from passive sensing data to proxy DSM-5 defined depression symptoms in college students.
  • To evaluate the efficacy of these symptom features in predicting depression among undergraduates.
  • To assess depression dynamics throughout academic terms using validated screening tools.

Main Methods:

  • Collected passive sensing data from smartphones and wearables of 83 undergraduate students over two 9-week terms.
  • Developed and utilized custom 'symptom features' designed to reflect specific DSM-5 depression criteria.
  • Administered weekly PHQ-4 and pre-post PHQ-8 surveys to track depression scores and dynamics.

Main Results:

  • Identified significant associations between novel symptom features and self-reported depression scores (PHQ-8, PHQ-4).
  • Demonstrated the capability of symptom features from mobile sensors to predict weekly depression status.
  • Achieved 81.5% recall and 69.1% precision in weekly depression prediction.

Conclusions:

  • Passive mobile sensing, using specifically designed symptom features, offers a promising method for continuous depression monitoring in college students.
  • This approach can supplement traditional mental health services by providing objective, real-time insights into student well-being.
  • The findings support the potential of digital phenotyping for early detection and intervention of depression in higher education settings.