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

Depression: Overview01:18

Depression: Overview

327
Depression is a prevalent mental illness marked by persistent sadness and lack of interest in previously enjoyable activities. It can take several forms, including major depression, persistent depressive disorder, and bipolar I and II disorders. Symptoms range from emotional changes like chronic worry to physical changes like sleep disturbances and suicidal thoughts. From a neurobiological perspective, depression is believed to be triggered by abnormalities in the brain's prefrontal cortex,...
327
Depressive Disorders: Etiology01:27

Depressive Disorders: Etiology

162
Depressive disorders result from a complex interplay of biological, psychological, and sociocultural factors, each contributing uniquely to the development and persistence of the condition. Understanding these factors provides critical insight into the multifaceted nature of depression.
Biological Factors in Depression
Biological predispositions significantly influence the risk of developing depressive disorders. Genetic studies highlight the role of variations in the serotonin transporter...
162
Depressive Disorders: MDD and Dysthymia01:27

Depressive Disorders: MDD and Dysthymia

202
Depressive disorders are a group of mental health conditions characterized by pervasive feelings of sadness, diminished pleasure in life, and a significant impact on daily functioning. These conditions are most prevalent in individuals during their 30s and affect women at twice the rate of men. Contrary to popular belief, younger individuals are generally more susceptible to these disorders than older adults. Two key types of depressive disorders include Major Depressive Disorder (MDD) and...
202
Long-term Depression01:05

Long-term Depression

31.3K
Long-term depression, or LTD, is one of the ways by which synaptic plasticity—changes in the strength of chemical synapses—can occur in the brain. LTD is the process of synaptic weakening that occurs over time between pre and postsynaptic neuronal connections. The synaptic weakening of LTD works in opposition to synaptic strengthening by long-term potentiation (LTP) and together are the main mechanisms that underlie learning and memory.
31.3K

You might also read

Related Articles

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

Sort by
Same author

Aerobic exercise capacity and incident chronic kidney disease in patients with heart failure with preserved ejection fraction.

Kidney research and clinical practice·2026
Same author

Vutrisiran in East Asian Patients With Transthyretin Amyloidosis With Cardiomyopathy: Post-Hoc Analysis of HELIOS-B.

JACC. Asia·2026
Same author

Re-Exploration for Postoperative Bleeding in Heart Transplantation: Outcomes and Predictors From the Korean Organ Transplantation Registry.

Korean circulation journal·2026
Same author

Korean Society of Heart Failure Guidelines for the Palliative Care and Hospice for Heart Failure Patients.

International journal of heart failure·2026
Same author

Effects of pacing sites on substrate mapping using decrement-evoked potential mapping for scar-related ventricular tachycardia.

Heart rhythm·2026
Same author

Preliminary Evidence for Changes in Functional Connectivity Associated with Emotional Awareness after Mobile-Based Mindfulness Meditation.

Yonsei medical journal·2026

Related Experiment Video

Updated: Sep 3, 2025

Author Spotlight: Therapeutic Benefit of Closed-Loop Deep Brain Stimulation in Depression Treatment
05:19

Author Spotlight: Therapeutic Benefit of Closed-Loop Deep Brain Stimulation in Depression Treatment

Published on: July 7, 2023

2.5K

Depressive Symptoms Feature-Based Machine Learning Approach to Predicting Depression Using Smartphone.

Juyoung Hong1, Jiwon Kim1, Sunmi Kim2

  • 1Department of Intelligent Mechatronics Engineering, Sejong University, Seoul 05006, Korea.

Healthcare (Basel, Switzerland)
|July 27, 2022
PubMed
Summary
This summary is machine-generated.

A new smartphone system automatically predicts depression using multimodal sensor data. This technology offers a practical, accessible approach to early mental health diagnosis and treatment recommendations.

Keywords:
depression predictiondepressive symptoms featuremachine learningsmartphone

More Related Videos

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

7.6K
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

7.2K

Related Experiment Videos

Last Updated: Sep 3, 2025

Author Spotlight: Therapeutic Benefit of Closed-Loop Deep Brain Stimulation in Depression Treatment
05:19

Author Spotlight: Therapeutic Benefit of Closed-Loop Deep Brain Stimulation in Depression Treatment

Published on: July 7, 2023

2.5K
A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

7.6K
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

7.2K

Area of Science:

  • Digital Health
  • Mental Health Technology
  • Computational Psychiatry

Background:

  • Rising global depression rates necessitate improved diagnostic tools.
  • Traditional self-report questionnaires are limited by patient engagement.
  • Early detection and treatment are crucial for managing depression.

Purpose of the Study:

  • To propose a novel smartphone-based system for automatic depression prediction.
  • To develop and validate multimodal depressive features derived from smartphone sensor data.
  • To offer a more accessible and adaptable alternative to conventional diagnostic methods.

Main Methods:

  • Development of a smartphone application ('Mental Health Protector') for data collection.
  • Utilizing multimodal sensor data and self-reported questionnaires from 106 mental patients.
  • Designing depressive features aligned with DSM-5 criteria for depression symptoms.
  • Employing a big data cloud platform for data processing and analysis.

Main Results:

  • The system achieved a 76.92% F1-score for depression prediction in a test set.
  • Successfully predicted depression in 93.75% of diagnosed patients (15 out of 16).
  • Demonstrated high adaptability by relying solely on smartphone data.

Conclusions:

  • The proposed smartphone-based system shows significant potential for accurate and accessible depression prediction.
  • This approach offers a practical solution for early mental health screening, especially post-pandemic.
  • Future research can further refine features and validate the system in diverse populations.