Automatic detection of social rhythms in bipolar disorder. (14th March 2016)

• Record Type: Journal Article
• Authors: Abdullah, Saeed; Matthews, Mark; Frank, Ellen; Doherty, Gavin; Gay, Geri; Choudhury, Tanzeem
• Is Part Of: Journal of the American Medical Informatics Association. Volume 23:Number 3(2016:May)
• Page Start: 538
• Page End: 543
• Publication Date: 2016-03-14
• Subjects: mHealth -- bipolar disorder -- ubiquitous computing -- mobile sensing; Medical informatics -- Periodicals; Information Services -- Periodicals; Medical Informatics -- Periodicals; Médecine -- Informatique -- Périodiques; Informatica; Geneeskunde; Informatique médicale; Computer network resources; Electronic journals; 610.285
• DOI: 10.1093/jamia/ocv200
• Languages: English
• ISSNs: 1067-5027

Abstract

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 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). Conclusions Automatic smartphone sensing is a feasible approach for inferring rhythmicity, a key marker of wellbeing for individuals with BD.