This study captured daily and weekly mood ratings using a smartphone from bipolar disorder (BD) and unipolar major depression disorder (MDD) subjects at high (HRMDD) and low risk (LRMDD) for developing Bipolar Disorder (BD) and healthy controls (HC). Method: 40 subjects (18 – 30 yr) (6 BD, 13 HRMDD, 16 LRMDD and 5 HC) were studied and a total of 2401 daily and 744 weekly ratings were collected. HRMDD and LRMDD subjects were naturalistically treated with antidepressants. We investigate if latent-class analyses of ratings can detect mood instability among MDD and BD groups. Results: Our analyses revealed four underlying mood states correlating with clinical mood states. There was a trend for greater number of state changes in BD and HRMDD subjects compared to LRMDD and HC groups. Conclusion: Smartphone ratings may adequately capture mood instability in BD subjects and at risk HRMDD subjects and offers a prudent way for monitoring development of serious manic symptoms.

Learning Objective: After participating in this session, the learner should be better able to:
Understand the current issues in antidepressant treatment and mood instability in young patients, particularly at risk for Bipolar Disorder.
Learn challenges and possible solutions in monitoring treatment effect using smartphone technology
Learn how a data-driven approach of understanding monitoring states may be useful in providing a prudent solution.
Formulate an approach to adoption of such technology for wider use.


Vibha Anand, IBM
Bo Hu, Cleveland Clinic
Amit Anand (Presenter)
Cleveland Clinic

Presentation Materials: