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J Am Med Inform Assoc. 2016 May;23(3):538-43. doi: 10.1093/jamia/ocv200. Epub 2016 Mar 14.

Automatic detection of social rhythms in bipolar disorder.

Author information

1
Information Science, Gates Hall, Cornell University, Ithaca, NY 14853, USA sma249@cornell.edu.
2
Information Science, Gates Hall, Cornell University, Ithaca, NY 14853, USA.
3
Department of Psychiatry, University of Pittsburgh. Pittsburgh, USA.
4
Computer Science and Statistics, Trinity College Dublin, Dublin, Ireland.

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.

KEYWORDS:

bipolar disorder; mHealth; mobile sensing; ubiquitous computing

PMID:
26977102
DOI:
10.1093/jamia/ocv200
[Indexed for MEDLINE]

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