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Bipolar Disorder

Bipolar Disorder

Bipolar disorder is a chronic mental health condition marked by significant mood fluctuations, including episodes of mania and depression. Elevated energy levels, heightened mood or irritability, impulsive behavior, reduced sleep needs, rapid speech, racing thoughts, inflated self-esteem, and distractibility characterize mania. Individuals with bipolar disorder often alternate between depressive and manic states, with periods of emotional stability lasting an average of six months to a year.

Mania and Antimanic Drugs: Overview

Mania and Antimanic Drugs: Overview

Mania, a psychological condition characterized by elevated mood, increased energy, and reduced sleep need, is part of the bipolar disorder cycle. The exact cause of mania isn't entirely known, but it is thought to be a combination of genetic, environmental, and neurological factors. Bipolar disorder involves alternating manic and depressive episodes. Mood stabilizers like lithium, antipsychotics, and anticonvulsants help manage these episodes. Lithium carbonate is particularly effective as...

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Using Wearable Device and Machine Learning to Predict Mood Symptoms in Bipolar Disorder: Development and Usability

Chia-Tung Wu¹, Ming H Hsieh², I-Ming Chen²

¹Master Program in Transdisciplinary Long-term Care and Management, National Yang Ming Chiao Tung University, Taipei, Taiwan.

JMIR Medical Informatics

|September 16, 2025

Summary

This summary is machine-generated.

This study shows that digital biomarkers from wearable devices can predict bipolar disorder (BD) mood symptoms. Early detection through these biomarkers can help prevent symptom recurrence.

Keywords:

bipolar disorder machine learning mood symptoms relapse prediction wearable device

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Published on: January 22, 2018

Area of Science:

Digital health
Machine learning in psychiatry
Biomarker discovery

Background:

Bipolar disorder (BD) is characterized by recurrent mood episodes.
Early detection and intervention are crucial for improving patient prognosis.
Preventing mood symptom recurrence is a key clinical goal.

Purpose of the Study:

To develop machine learning models for predicting bipolar disorder symptoms.
To utilize digital biomarkers from wearable devices for symptom prediction.

Main Methods:

Recruited 24 participants with BD.
Collected digital biomarker data from wearable devices.
Employed six machine learning algorithms to build predictive models.

Main Results:

Depressive symptom prediction model: 83% accuracy, 0.89 AUROC, 0.65 F1-score.
Manic symptom prediction model: 91% accuracy, 0.88 AUROC, 0.25 F1-score.
Interpretable AI identified high resting heart rate, low activity, and poor sleep as potential predictors of depressive symptoms.

Conclusions:

Digital biomarkers show promise in predicting manic and depressive symptoms in BD.
This predictive capability can aid in early symptom detection and timely treatment.
The models may help prevent mood symptom recurrence in bipolar disorder.