Automatic detection of social rhythms in bipolar disorder

Saeed Abdullah, Mark Matthews, Ellen Frank, Gavin Doherty, Geri Gay, Tanzeem Choudhury

Research output: Contribution to journalArticle

59 Scopus citations

Abstract

Objective 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. Methods 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. Results We found that automated sensing can be used to infer the SRM score. Using location, distance traveled, conversation frequency, and nonstationary 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). Conclusions Automatic smartphone sensing is a feasible approach for inferring rhythmicity, a key marker of wellbeing for individuals with BD.

Original languageEnglish (US)
Pages (from-to)538-543
Number of pages6
JournalJournal of the American Medical Informatics Association
Volume23
Issue number3
DOIs
StatePublished - Jan 1 2016

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All Science Journal Classification (ASJC) codes

  • Health Informatics

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