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

211
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,...
211
Depressive Disorders: Etiology01:27

Depressive Disorders: Etiology

34
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...
34

You might also read

Related Articles

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

Sort by
Same author

Therapist-Guided Smartphone-Based Aftercare for Inpatients With Severe Anorexia Nervosa (SMART-AN): A Randomized Clinical Trial.

The International journal of eating disorders·2026
Same author

Shedding light on social dominance within the affective neuroscience personality scales.

Personality neuroscience·2026
Same author

Do we need animal models to better understand the molecular basis of internet use disorders?

Journal of behavioral addictions·2026
Same author

Fear of Missing Out and Problematic Social Media Use Among Chinese University Students: Latent Profiles and Two-Wave Network Comparisons.

Behavioral sciences (Basel, Switzerland)·2026
Same author

The role of videogame genre and platform in gaming disorder tendencies: A large-scale world-wide study of gamers' preferences.

Journal of behavioral addictions·2026
Same author

Approach-avoidance tendencies in problematic usage of the internet: Evidence from a multisite study.

Journal of behavioral addictions·2026
Same journal

Attitudes and Needs of Health Care Providers Toward Artificial Intelligence-Assisted Pediatric Palliative Care: Mixed Methods Study.

Journal of medical Internet research·2026
Same journal

Value and Credibility of Meta-Analysis: Tutorial on Enhancing Methodological Rigor and AI-Powered Efficiency.

Journal of medical Internet research·2026
Same journal

Extracting Medical Information From Unstructured Clinical Text Using Large Language Models to Enhance Health Care Interoperability: Proof-of-Concept Study.

Journal of medical Internet research·2026
Same journal

How Does That Large Language Model Make You Feel?

Journal of medical Internet research·2026
Same journal

Transformation Versus Innovation in Digital Health Care and the Future of Clinical AI.

Journal of medical Internet research·2026
Same journal

Building a Malaria Intelligence System for Real-Time Prediction and Data-Driven Intervention Planning.

Journal of medical Internet research·2026
See all related articles

Related Experiment Video

Updated: May 30, 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

2.2K

Investigating Smartphone-Based Sensing Features for Depression Severity Prediction: Observation Study.

Yannik Terhorst1,2,3, Eva-Maria Messner1, Kennedy Opoku Asare4

  • 1Department of Clinical Psychology and Psychotherapy, Institute of Psychology and Education, Ulm University, Ulm, Germany.

Journal of Medical Internet Research
|January 30, 2025
PubMed
Summary
This summary is machine-generated.

Smartphone sensing and ecological momentary assessment (EMA) can help predict depression severity. Combining both methods offers the most accurate prediction, suggesting their potential in future clinical decision support systems.

Keywords:
appassessmentsdepressiondigital phenotypingmHealthmental healthmobile healthobservation studysmart sensingsmartphonesymptoms

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

2.7K
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

599

Related Experiment Videos

Last Updated: May 30, 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

2.2K
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

2.7K
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

599

Area of Science:

  • Digital psychiatry
  • Computational mental health
  • Mobile health (mHealth)

Background:

  • Objective sensor data from smartphones (smart sensing) offers a novel way to infer mental health symptoms.
  • Depression is a key target for smart sensing due to its prevalence and impact.
  • Current research lacks clarity on which sensor features best predict depression severity and their added benefit over ecological momentary assessment (EMA).

Purpose of the Study:

  • To investigate smartphone screen, app usage, and call sensor features alongside EMA for depression severity inference.
  • To determine the incremental benefit of smart sensing features compared to each other and EMA.
  • To develop parsimonious regression models for depression severity prediction.

Main Methods:

  • An exploratory observational study involving 107 participants using Android smartphones.
  • Data collection via the INSIGHTS app, including sensor data and EMA.
  • Depression severity assessed using the 8-item Patient Health Questionnaire; stepwise linear regression and correlation analyses were performed.

Main Results:

  • Small to medium correlations were observed between depression severity and both EMA (e.g., valence: r=-0.55) and sensing features (e.g., screen duration: r=0.37).
  • EMA features explained 35.28% of variance, while sensing features explained 20.45%.
  • A combined model of EMA and sensing features yielded the highest predictive power (R²=45.15%).

Conclusions:

  • Smart sensing and EMA show significant potential for inferring depression severity, both individually and in combination.
  • These methods could form the basis of future clinical decision support systems for depression.
  • Further confirmatory studies are required, addressing privacy, ethical, and acceptance concerns before routine clinical application.