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Depression screening using mobile phone usage metadata: a machine learning approach.

Rouzbeh Razavi1, Amin Gharipour2, Mojgan Gharipour3

  • 1Department of Management and Information Systems, Kent State University, Kent, OH, USA.

Journal of the American Medical Informatics Association : JAMIA
|January 25, 2020
PubMed
Summary
This summary is machine-generated.

Mobile phone usage patterns can help screen for depression. This study found distinct usage differences in individuals with depression, enabling continuous monitoring via mental health apps.

Keywords:
depressionmachine learningmobile healthmobile usage

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Area of Science:

  • Digital Health
  • Computational Psychiatry
  • Machine Learning in Healthcare

Background:

  • Depression is a leading global cause of non-fatal disease burden, often remaining undiagnosed despite being treatable.
  • The ubiquity of mobile phones presents an opportunity for novel, continuous health monitoring solutions.

Purpose of the Study:

  • To investigate the potential of using mobile phone usage patterns for continuous screening of depressive symptoms.
  • To develop and evaluate machine learning models for detecting depression based on mobile usage data.

Main Methods:

  • Collected mobile usage statistics from 412 participants.
  • Administered the Beck Depression Inventory-2nd edition (BDI-II) to assess depression severity.
  • Trained various machine learning classifiers to identify participants with depressive symptoms (BDI-II score ≥ 14).

Main Results:

  • Individuals with depression exhibited fewer saved contacts, more screen time, fewer and shorter calls, and more text messages.
  • A random forest classifier achieved a balanced accuracy of 0.768.
  • Including age and gender improved the balanced accuracy to 0.811.

Conclusions:

  • Mobile usage metadata can effectively screen for depressive symptoms, aiding in initial diagnosis and treatment monitoring.
  • The use of aggregated, low-privacy-sensitivity metadata increases the likelihood of user consent for mental health applications.