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

Recruiting

• Conditions: Depressive Disorder; Bipolar Disorder
• Interventions: Device: Wearable activity tracker

• Registration Number: NCT05828056
• Lead Sponsor: National Taiwan University Hospital

Brief 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.

Detailed Description

Not available

Study Timeline

• Study Start: 2020-03-02
• Status Verified: 2023-03
• Study First Submitted: 2023-04-13
• Study First Submitted QC: 2023-04-13
• Study First Posted: 2023-04-25
• Last Update Submitted: 2023-05-04
• Last Update Posted: 2023-05-06
• Primary Completion: 2024-12-31
• Study Completion: 2024-12-31

Recruitment & Eligibility

• Status: RECRUITING
• Sex: All
• Target Recruitment: 100

Inclusion Criteria

• DSM-5 Bipolar disorder or depressive disorder
• 20~60 years old
• Willing to carry smartwatch and smartphone most of the time

Exclusion Criteria

• Comorbid with substance use disorder
• Unable to use smartwatch and smartphone

Study & Design

• Study Type: OBSERVATIONAL
• Study Design: Not specified

Arm && Interventions

Group	Intervention	Description
BP	Wearable activity tracker	100 patients with mood disorders from the psychiatric ward and outpatient services of the Department of Psychiatry, National Taiwan University Hospital

Primary Outcome Measures

Name	Time	Method
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 Locations

Locations (1)

National Taiwan University Hospital; 🇨🇳 Taipei, Taiwan