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

Traits, Mood, and Subjective Wellbeing01:22

Traits, Mood, and Subjective Wellbeing

358
Subjective well-being (SWB) refers to an individual's self-evaluation of their overall life satisfaction, happiness, and fulfillment. This multifaceted construct is typically assessed by analyzing the balance of positive and negative emotions alongside perceptions of life satisfaction. Personality traits such as neuroticism and extraversion are strongly associated with variations in SWB, offering critical insights into the underlying mechanisms of emotional well-being.
Neuroticism and...
358

You might also read

Related Articles

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

Sort by
Same author

Spatial distribution of the proteome in the human body and in cancers.

Nature·2026
Same author

Feasibility and Acceptability of a Mobile App and Wearable Device for Collecting Mental Health Survey and Passively Sensed Data Among Health Care Workers in Kenya: Mixed Methods Pilot Study.

JMIR mHealth and uHealth·2026
Same author

The Weight of Summer: Children's Fat and Body Mass Index Gain Accelerate during Summer.

Childhood obesity (Print)·2026
Same author

Understanding the impact of perceived app usability on the efficacy of mobile health intervention for traumatic brain injury caregivers.

Rehabilitation psychology·2026
Same author

US Trends in Long-Term Opioid Therapy.

JAMA·2026
Same author

Large-scale identification of protein biomarkers and therapeutic targets in heart and brain disease.

Nature cardiovascular research·2026

Related Experiment Video

Updated: Mar 14, 2026

Setup of Consumer Wearable Devices for Exposure and Health Monitoring in Population Studies
15:00

Setup of Consumer Wearable Devices for Exposure and Health Monitoring in Population Studies

Published on: February 3, 2023

3.2K

Personalized Insights Derived from Wearable Device Data and Large Language Models to Improve Well-Being.

Kai He1,2, Yu Fang3, Elena Frank3

  • 1Stanley and Judith Frankel Institute for Heart and Brain Health, University of Michigan Medical Center, Ann Arbor, MI, USA.

Medrxiv : the Preprint Server for Health Sciences
|March 13, 2026
PubMed
Summary

Individual health behaviors like sleep and exercise impact mental health uniquely. Personalized digital tools, like MoodDriver, offer tailored support by analyzing wearable data and large language models (LLMs).

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
Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

1.3K

Related Experiment Videos

Last Updated: Mar 14, 2026

Setup of Consumer Wearable Devices for Exposure and Health Monitoring in Population Studies
15:00

Setup of Consumer Wearable Devices for Exposure and Health Monitoring in Population Studies

Published on: February 3, 2023

3.2K
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
Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

1.3K

Area of Science:

  • Digital health
  • Mental health research
  • Behavioral science

Background:

  • Generic health recommendations often fail due to individual variability in how behaviors like physical activity and sleep affect mental health.
  • Understanding these individual differences is crucial for effective mental health interventions.

Purpose of the Study:

  • To investigate individual-level associations between wearable-derived health behaviors and mood.
  • To develop a personalized, data-driven system for mental health support using digital phenotyping and large language models (LLMs).

Main Methods:

  • Collected one year of continuous wearable and ecological momentary assessment data from 3,139 participants.
  • Examined individual-level correlations between wearable-derived features (e.g., wake-up time, step count) and mood.
  • Developed MoodDriver, an LLM-powered system for generating tailored feedback based on participant data.

Main Results:

  • Significant heterogeneity was observed in the health behaviors influencing mood across individuals.
  • Wake-up time was the strongest mood driver for 34.0% of participants, and step count for 10.6%.
  • 20.3% of participants showed no significant correlations between measured behaviors and mood.

Conclusions:

  • Population-level health recommendations are limited; personalized, data-driven approaches are essential for mental health.
  • Combining digital phenotyping with LLMs (MoodDriver) shows feasibility for advancing precision digital mental health.
  • This approach offers tailored support for high-risk populations by translating behavioral patterns into actionable insights.