Prediction on the Recurrence of Manic and Depressive Episodes in Bipolar Disorder
Trial statusRecruiting
Trial phaseNot listed
Trial typeObservational
Biological sexAll
Age20-60
SponsorNational Taiwan University Hospital
About this trial
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 criteria
This trial does not accept healthy volunteersQualifiers
DSM-5 Bipolar disorder or depressive disorder
20~60 years old
Willing to carry smartwatch and smartphone most of the time
Disqualifiers
Comorbid with substance use disorder
Unable to use smartwatch and smartphone
Trial population
We will recruit 100 patients with mood disorders from the psychiatric ward and outpatient services of the Department of Psychiatry, National Taiwan University Hospital. All the participants will be followed for one year to collect the daily activity level, sleep patterns, heart rate through actigraphy, as well as location, mood report, drug compliance and face photo through smartphone app that will be developed by Co-PI Lai.
Trial design
Design model
Case-only
Time perspective
Prospective
Treatments tested in this trial
Wearable activity tracker
DeviceGarmin smartwatch will record features, such as activities, heart rate, sleep, through smartphone App
Treatment groups
100 Participants
are divided into 1 treatment groupGroup A: BP1 intervention
Trial outcomes
Primary outcomes
1
Development and verification of mood episode prediction algorithm
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.
Time frame
1 yearSponsors and contacts
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Source ClinicalTrials.gov