title: Automatic detection of social rhythms in bipolar disorder.
abstract: To evaluate the feasibility of automatically assessing the Social Rhythm Metric (SRM), a clinically-validated marker of stability and rhythmicity for individuals with bipolar disorder (BD), using passively-sensed data from smartphones.
  
  
  
  Seven patients with BD used smartphones for 4 weeks passively collecting sensor data including accelerometer, microphone, location, and communication information to infer behavioral and contextual patterns. Participants also completed SRM entries using a smartphone app.
  
  
  
  We found that automated sensing can be used to infer the SRM score. Using location, distance traveled, conversation frequency, and non-stationary duration as inputs, our generalized model achieves root-mean-square-error of 1.40, a reasonable performance given the range of SRM score (0-7). Personalized models further improve performance with mean root-mean-square-error of 0.92 across users. Classifiers using sensor streams can predict stable (SRM score ≥3.5) and unstable (SRM score <3.5) states with high accuracy (precision: 0.85 and recall: 0.86).
  
  
  
  Automatic smartphone sensing is a feasible approach for inferring rhythmicity, a key marker of wellbeing for individuals with BD.
authors.affiliation: Information Science, Gates Hall, Cornell University, Ithaca, NY 14853, USA sma249@cornell.edu.
authors.forename: Saeed
authors.initials: S
authors.lastname: Abdullah
authors.affiliation: Information Science, Gates Hall, Cornell University, Ithaca, NY 14853, USA.
authors.forename: Mark
authors.initials: M
authors.lastname: Matthews
authors.affiliation: Department of Psychiatry, University of Pittsburgh. Pittsburgh, USA.
authors.forename: Ellen
authors.initials: E
authors.lastname: Frank
authors.affiliation: Computer Science and Statistics, Trinity College Dublin, Dublin, Ireland.
authors.forename: Gavin
authors.initials: G
authors.lastname: Doherty
authors.affiliation: Information Science, Gates Hall, Cornell University, Ithaca, NY 14853, USA.
authors.forename: Geri
authors.initials: G
authors.lastname: Gay
authors.affiliation: Information Science, Gates Hall, Cornell University, Ithaca, NY 14853, USA.
authors.forename: Tanzeem
authors.initials: T
authors.lastname: Choudhury
mesh_terms: D000328:Adult; D001714:Bipolar Disorder / Q000503:physiopathology* / Q000523:psychology*; D005240:Feasibility Studies; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D008991:Monitoring, Physiologic / Q000379:methods*; D010507:Periodicity; D000068997:Smartphone; D012919:Social Behavior
keywords: bipolar disorder; mHealth; mobile sensing; ubiquitous computing