Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Bipolar Disorder01:30

Bipolar Disorder

1.4K
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.
1.4K
Pulse rhythm01:30

Pulse rhythm

1.7K
Pulse rhythm refers to the pattern of pulsations within specific intervals, offering valuable insights into the regularity or irregularity of the heart's beats as observed through the pattern of pulsation within specific intervals. A regular pulse exhibits a consistent heart rate with uniform waveforms and pulsation force, variations of which can be classified as normal, weak, or bounding.
Conversely, an irregular pulse pattern is termed dysrhythmia, stemming from disruptions in cardiac...
1.7K
Holter Monitor: 24-Hour Monitoring01:23

Holter Monitor: 24-Hour Monitoring

4.6K
Holter monitoring is a continuous electrocardiography (ECG) recording that tracks the heart's electrical activity over an extended period, generally 24 to 48 hours. This noninvasive diagnostic tool detects irregular heart rhythms that may not be captured during a standard ECG performed in a clinical setting.DeviceThe Holter monitor is a portable, small device connected to several electrodes on the patient's chest. These electrodes detect the heart's electrical signals and transmit them to the...
4.6K
Mania and Antimanic Drugs: Overview01:24

Mania and Antimanic Drugs: Overview

781
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...
781

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Epigenetic Age Accelerations and the Clinical Presentation of Bipolar Disorders: A Latent Profile Analysis in the FACE-BD Sample.

Bipolar disorders·2026
Same author

Acceptability of DBS for psychiatric disorders by French psychiatrists: a network clustering analysis based on a mixed method study.

Comprehensive psychiatry·2026
Same author

Structural Brain Network Alterations in Relation to Treatment and Illness Severity in Bipolar Disorder.

Biological psychiatry·2026
Same author

Peripheral immune-vascular correlates of suicidal thoughts and behaviors in bipolar disorder and schizophrenia: cross-sectional and prospective analyses from the FACE-BD and FACE-SZ cohorts.

Brain, behavior, and immunity·2026
Same author

Examining the associations between manic symptoms and cognitive performance in bipolar disorders: evidence from a cross-sectional replication study in the FACE-BD cohort.

International journal of bipolar disorders·2026
Same author

Structural Brain Network Alterations in Relation to Treatment and Illness Severity in Bipolar Disorder.

bioRxiv : the preprint server for biology·2026

Related Experiment Video

Updated: Apr 7, 2026

Patient Directed Recording of a Bipolar Three-Lead Electrocardiogram using a Smartwatch with ECG Function
05:03

Patient Directed Recording of a Bipolar Three-Lead Electrocardiogram using a Smartwatch with ECG Function

Published on: December 11, 2019

9.2K

Bipolar disorder relapse detection and prediction using smartwatches. A pilot study for machine learning models using

Arnaud Pouchon1, Saifeddine Aloui2, Luca Mayer Dalverny2

  • 1Univ. Grenoble Alpes, Inserm, CHU Grenoble-Alpes, GIN, 38000, Grenoble, France; Department of Psychiatry, Grenoble Alpes University Hospital, 38000, Grenoble, France.

Journal of Affective Disorders
|April 5, 2026
PubMed
Summary

Smartwatch data can detect bipolar disorder relapses and prodromal symptoms. This technology shows promise for early intervention and improved clinical care for bipolar disorder patients.

Keywords:
Bipolar disorderDigital phenotypingPredictionRelapseSmartwatches

More Related Videos

An Application for Pairing with Wearable Devices to Monitor Personal Health Status
06:58

An Application for Pairing with Wearable Devices to Monitor Personal Health Status

Published on: February 3, 2022

3.5K
Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack
07:31

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack

Published on: May 15, 2020

8.3K

Related Experiment Videos

Last Updated: Apr 7, 2026

Patient Directed Recording of a Bipolar Three-Lead Electrocardiogram using a Smartwatch with ECG Function
05:03

Patient Directed Recording of a Bipolar Three-Lead Electrocardiogram using a Smartwatch with ECG Function

Published on: December 11, 2019

9.2K
An Application for Pairing with Wearable Devices to Monitor Personal Health Status
06:58

An Application for Pairing with Wearable Devices to Monitor Personal Health Status

Published on: February 3, 2022

3.5K
Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack
07:31

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack

Published on: May 15, 2020

8.3K

Area of Science:

  • Digital phenotyping and machine learning in mental health.
  • Wearable technology for physiological monitoring.

Background:

  • Bipolar disorder (BD) is a chronic illness with high relapse rates, necessitating early detection of prodromal symptoms.
  • Current methods for detecting mood episodes are challenging.
  • Wearable devices offer real-time monitoring of physiological and behavioral data.

Purpose of the Study:

  • To evaluate the feasibility of using machine learning on smartwatch data for detecting and predicting mood relapses and prodromal phases in BD.
  • To assess the potential of digital phenotyping in managing bipolar disorder.

Main Methods:

  • Ten BD patients were monitored for six months using smartwatches.
  • Data collected included heart rate, heart rate variability (HRstd), sleep stages, and physical activity.
  • Unsupervised anomaly detection models were trained on healthy data to identify deviations indicative of mood episodes.

Main Results:

  • Physiological changes correlated with prodromal and relapse phases in BD patients.
  • Heart rate variability and steps were effective in detecting depressive relapses (PR AUC = 0.67).
  • Light sleep and mean heart rate showed potential for identifying hypomanic relapses (PR AUC = 0.33) and predicting prodromal depression (PR AUC = 0.34).

Conclusions:

  • Smartwatch data and AI can detect mood relapses in BD.
  • Early detection of prodromal states is feasible, though performance is moderate.
  • Integration of wearable technology and AI holds potential for proactive clinical intervention in bipolar disorder management.