title: Mobile Phone Sensor Correlates of Depressive Symptom Severity in Daily-Life Behavior: An Exploratory Study.
abstract: BACKGROUND
  Depression is a common, burdensome, often recurring mental health disorder that frequently goes undetected and untreated. Mobile phones are ubiquitous and have an increasingly large complement of sensors that can potentially be useful in monitoring behavioral patterns that might be indicative of depressive symptoms.
  
  
  OBJECTIVE
  The objective of this study was to explore the detection of daily-life behavioral markers using mobile phone global positioning systems (GPS) and usage sensors, and their use in identifying depressive symptom severity.
  
  
  METHODS
  A total of 40 adult participants were recruited from the general community to carry a mobile phone with a sensor data acquisition app (Purple Robot) for 2 weeks. Of these participants, 28 had sufficient sensor data received to conduct analysis. At the beginning of the 2-week period, participants completed a self-reported depression survey (PHQ-9). Behavioral features were developed and extracted from GPS location and phone usage data.
  
  
  RESULTS
  A number of features from GPS data were related to depressive symptom severity, including circadian movement (regularity in 24-hour rhythm; r=-.63, P=.005), normalized entropy (mobility between favorite locations; r=-.58, P=.012), and location variance (GPS mobility independent of location; r=-.58, P=.012). Phone usage features, usage duration, and usage frequency were also correlated (r=.54, P=.011, and r=.52, P=.015, respectively). Using the normalized entropy feature and a classifier that distinguished participants with depressive symptoms (PHQ-9 score ≥5) from those without (PHQ-9 score <5), we achieved an accuracy of 86.5%. Furthermore, a regression model that used the same feature to estimate the participants' PHQ-9 scores obtained an average error of 23.5%.
  
  
  CONCLUSIONS
  Features extracted from mobile phone sensor data, including GPS and phone usage, provided behavioral markers that were strongly related to depressive symptom severity. While these findings must be replicated in a larger study among participants with confirmed clinical symptoms, they suggest that phone sensors offer numerous clinical opportunities, including continuous monitoring of at-risk populations with little patient burden and interventions that can provide just-in-time outreach.
authors.affiliation: Center for Behavioral Intervention Technologies, Department of Preventive Medicine, Northwestern University, Chicago, IL, United States.
authors.forename: Sohrab
authors.identifier: http://orcid.org/0000-0002-4625-452X
authors.initials: S
authors.lastname: Saeb
authors.forename: Mi
authors.identifier: http://orcid.org/0000-0001-7002-6757
authors.initials: M
authors.lastname: Zhang
authors.forename: Christopher J
authors.identifier: http://orcid.org/0000-0003-4542-6809
authors.initials: CJ
authors.lastname: Karr
authors.forename: Stephen M
authors.identifier: http://orcid.org/0000-0002-1003-0399
authors.initials: SM
authors.lastname: Schueller
authors.forename: Marya E
authors.identifier: http://orcid.org/0000-0001-8199-004X
authors.initials: ME
authors.lastname: Corden
authors.forename: Konrad P
authors.identifier: http://orcid.org/0000-0001-8408-4499
authors.initials: KP
authors.lastname: Kording
authors.forename: David C
authors.identifier: http://orcid.org/0000-0002-5443-7596
authors.initials: DC
authors.lastname: Mohr
mesh_terms: D000328:Adult; D040421:Cell Phone / Q000706:statistics & numerical data*; D016000:Cluster Analysis; D003863:Depression / Q000175:diagnosis*; D005106:Exploratory Behavior / Q000145:classification*; D005260:Female; D040362:Geographic Information Systems / Q000706:statistics & numerical data*; D006801:Humans; D008297:Male; D008875:Middle Aged; D011788:Quality of Life; D057566:Self Report; D011795:Surveys and Questionnaires; D017216:Telemedicine / Q000379:methods*
keywords: activities of daily living; classification; cluster analysis; depression; mobile health (mHealth)