Prediction on the Recurrence of Manic and Depressive Episodes in Bipolar Disorder

Condition(s) studied

Investigational drug(s) / intervention(s)

Wearable activity tracker: Garmin smartwatch will record features, such as activities, heart rate, sleep, through smartphone App

Study summary

Mood disorders (including bipolar disorder and major depressive disorder) are chronic mental disorders with high recurrent rate. The more the number of recurrence is, the worse long-term prognosis is. This study aims to establish a prediction model of recurrence of manic and depressive episodes in mood disorders, with a hope to detect recurrence relapse as early as possible for timely clinical intervention. We will adopt wearable smart watch to collect heart rate, sleep pattern, activity level, as well as emotional status for one year long in 100 patients with bipolar disorder, and annotated their mood status (i.e., manic episode, depressive episode, and euthymic state). We expect to establish prediction models to predict the recurrence of mood episodes.

Eligibility

Primary outcome measure(s)

• Development and verification of mood episode prediction algorithm — 1 year
  Collected data will apply to learning algorithm, random forest, which constructs a multitude of decision trees at training time and outputting a class that is the mode of the classes of the individual trees. Performance of the trained prediction model was evaluated by assessing the model's accuracy, sensitivity, specificity, and the area under the curve. In a machine learning evaluation process, a part of data is used for model training, and the other portion is used for model testing.

Trial sites (1)

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