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

Strategies of Self-Presentation III: Self-Monitoring01:24

Strategies of Self-Presentation III: Self-Monitoring

385
Self-monitoring is a central construct in understanding individual differences in self-presentation strategies across social contexts. It refers to how individuals observe, regulate, and control their expressive behavior and self-presentation following situational cues. Self-monitoring reflects a person's sensitivity to social appropriateness and willingness to adapt behavior to fit varying interpersonal demands.High vs. Low Self-Monitoring IndividualsIndividuals high in self-monitoring are...
385
Depression: Overview01:18

Depression: Overview

1.2K
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,...
1.2K
Depressive Disorders: MDD and Dysthymia01:27

Depressive Disorders: MDD and Dysthymia

1.1K
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...
1.1K
Depressive Disorders: Etiology01:27

Depressive Disorders: Etiology

859
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...
859
Long-term Depression01:05

Long-term Depression

33.7K
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.
33.7K
Long-term Depression01:03

Long-term Depression

3.6K
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.
Calcium Ion Concentration Mechanism
If over...
3.6K

You might also read

Related Articles

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

Sort by
Same author

Comparison Between Browser- and App-Based Versions of a Program for Self-Management of Mild to Moderate Depression: Log Data Analysis of a Convenience Sample.

JMIR mHealth and uHealth·2026
Same author

Study Protocol: It is time to dig deeper: A cross-country implementation mapping study of the iFightDepression® (online self-management) tool.

PloS one·2026
Same author

The association between feelings of loneliness and the number of social relationships in depression: a cross-sectional study of German adults.

BMC psychiatry·2026
Same author

Utilization of psychotherapy, pharmacotherapy, and their combination by individuals with current or residual depression: results from five annual nationally representative German surveys.

Scientific reports·2025
Same author

Use of Mobile Sensing Data for Longitudinal Monitoring and Prediction of Depression Severity: Systematic Review.

Journal of medical Internet research·2025
Same author

Cross-Platform Availability of Smartphone Sensors for Depression Indication Systems: Mixed-Methods Umbrella Review.

Interactive journal of medical research·2025

Related Experiment Video

Updated: Mar 29, 2026

Author Spotlight: Unveiling the Connection Between Sleep Disorders and Cognitive Symptoms in Depression
04:33

Author Spotlight: Unveiling the Connection Between Sleep Disorders and Cognitive Symptoms in Depression

Published on: April 26, 2024

1.7K

Distinguishing Common Digital Phenotyping and Self-Report Parameters for Monitoring and Predicting Depression:

Lisa Busshart1, Milica Petrovic1, Rebeka Amin1

  • 1Department of Psychiatry, Psychosomatics and Psychotherapy, University Hospital Frankfurt, Research Center of German Foundation for Depression and Suicide Prevention, Heinrich-Hoffmann-Str. 10, Frankfurt am Main, 60528, Germany, 49 162 9464667.

JMIR Mhealth and Uhealth
|March 3, 2026
PubMed
Summary

This scoping review identifies key digital phenotyping parameters for monitoring depression. It synthesizes evidence on sensor-based and self-reported data, highlighting 11 frequently used metrics for better digital mental health tools.

Keywords:
depressiondigital phenotypingobjective dataself-monitoringsensor data

More Related Videos

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

3.7K
A Computer-Based Platform for Aiding Clinicians in Eating Disorder Analysis and Diagnosis
04:19

A Computer-Based Platform for Aiding Clinicians in Eating Disorder Analysis and Diagnosis

Published on: May 10, 2022

4.7K

Related Experiment Videos

Last Updated: Mar 29, 2026

Author Spotlight: Unveiling the Connection Between Sleep Disorders and Cognitive Symptoms in Depression
04:33

Author Spotlight: Unveiling the Connection Between Sleep Disorders and Cognitive Symptoms in Depression

Published on: April 26, 2024

1.7K
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

3.7K
A Computer-Based Platform for Aiding Clinicians in Eating Disorder Analysis and Diagnosis
04:19

A Computer-Based Platform for Aiding Clinicians in Eating Disorder Analysis and Diagnosis

Published on: May 10, 2022

4.7K

Area of Science:

  • Digital health
  • Mental health technology
  • Computational psychiatry

Background:

  • Digital health interventions are increasingly used for depression self-management.
  • These tools utilize self-monitoring and passive sensor data for personalized feedback.
  • A gap exists in understanding which digital parameters best monitor and predict depression outcomes.

Purpose of the Study:

  • To identify and synthesize common digital phenotyping and self-report parameters for depression monitoring and prediction.
  • To address the knowledge gap regarding frequently used and predictive sensor-based and self-reported data.
  • To map common parameters across digital platforms for tracking depressive symptom changes.

Main Methods:

  • A scoping review was conducted across four major databases (PubMed, Embase, Cochrane Library, Web of Science).
  • Articles published between January 2021 and November 2025, focusing on adults with depression using digital phenotyping, were included.
  • The PRISMA-ScR guidelines and a 5-stage framework were employed, with quality assessed using Downs and Black Instrument and MMAT.

Main Results:

  • Nineteen studies involving 85,193 participants were included, primarily using smartphone/wearable-based tools and passive sensing.
  • Five parameter categories were identified: physical activity/location, behavioral patterns, physiological signals, sleep, and sociability/self-reports.
  • Eleven key metrics, including step count, heart rate variability, sleep duration, and mood self-ratings, were most frequently reported.

Conclusions:

  • This review offers a novel synthesis of digital parameters for depression monitoring and prediction, moving beyond modality-specific reviews.
  • It maps shared digital markers across observational, predictive, and interventional studies, aiding comparability and future model development.
  • Findings support the design of scalable digital mental health tools and the integration of digital phenotyping into clinical practice.