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

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.

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Key information

Conditions

Bipolar Disorder Bipolar and Related Disorders Depressive Disorder Mental Disorders Mood Disorders

Age range

20 year–60 year

Sex eligibility

All sexes

Study type

Observational

Primary location

National Taiwan University Hospital

Taipei, Taiwan

Location status: Recruiting

Location contact

Yi-Ling Chien, MD, PhD

CONTACT

[email protected]

+886223123456#66013

Who can participate

Healthy volunteers accepted: No

Only the study team can determine whether someone qualifies for participation.

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

Treatment and study plan

Wearable Activity Tracker

Device

Garmin smartwatch will record features, such as activities, heart rate, sleep, through smartphone App

Primary outcomes

Development and verification of mood episode prediction algorithm

Time frame: 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.

Study contacts

Contact information is provided by the study sponsor or research team.

Yi-Ling Chien

CONTACT

[email protected]

+886223123456#66013

Sponsors and collaborators

Lead sponsor

National Taiwan University Hospital

Other

Registry information

Important dates

Study start: 2020
Primary completion: 2027
Study completion: 2027
First posted: Apr 25, 2023
Registry last updated: Jul 31, 2026

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

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This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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