Using smartphone and wearable data to track changes in depression symptoms
Official title: Personalized Deep Learning Models of Rapid Changes in Major Depressive Disorder Symptoms using Passive Sensor Data from Smartphones and Wearable Devices
Funded at Dartmouth College · NIH-10854971 · Data updated 2026-11-12
This study is looking at how smartphone and wearable device data can help track changes in symptoms for people with major depressive disorder, so if you're an adult seeking treatment, you could help us learn more about your mood and behaviors over 90 days to improve personalized care.
Quick facts
| Grant type | R01 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | Dartmouth College NIH-funded |
| Lab location | 1 site (Hanover, United States) |
| Project start | August 6, 2020 |
| Project end | May 31, 2026 |
| Funding year | 2024 |
| Award (this year) | $455,945 |
| NIH project number | 5R01MH123482-05 |
| Project ID | NIH-10854971 on NIH RePORTER |
Who could benefit from this research
✓Could help
Ideal candidates for this research are adults aged 21 and older who are seeking treatment for major depressive disorder.
✕Less likely to help
Patients who do not have major depressive disorder or are not seeking treatment for their symptoms may not benefit from this research.
What this research studies
This research investigates how personalized deep learning models can analyze data collected from smartphones and wearable devices to monitor rapid changes in symptoms of major depressive disorder (MDD). By utilizing passive sensor data, the study aims to create digital biomarkers that reflect behavioral and physiological signals associated with MDD, such as sleep patterns and social interactions. Over a period of 90 days, 120 adults seeking treatment for MDD will participate, providing a comprehensive dataset for analysis. The goal is to enhance the understanding of symptom fluctuations and improve personalized treatment approaches.
Why it matters
Potential benefit: If successful, this research could lead to more effective and tailored interventions for individuals with major depressive disorder.
How similar studies have gone: Preliminary data suggests that similar approaches using personalized artificial intelligence have shown promise in predicting changes in depression symptoms.
Where this research is happening
-
Dartmouth College
Hanover, United States Active
Researchers
- Principal investigator: Jacobson, Nicholas Charles — Dartmouth College
- Study coordinator: Jacobson, Nicholas Charles
Related research
Other NIH-funded projects on the same topics.
-
Psilocybin-Assisted ACT for Co-Occurring Cocaine Use Disorder and Major Depressive Disorder
A new treatment combines psilocybin with Acceptance and Commitment Therapy to help people who have both cocaine use disorder and major depressive disorder.
-
Single-cell study of mood-regulation brain circuits in depression
A large study will map which genes and cell types in mood-related brain circuits are linked to major depressive disorder, including differences by ancestry and sex.
-
Single-cell map of mood circuits in major depressive disorder
Researchers will map the tiny brain cells in mood-control areas to understand how genes and cell types relate to depression, with attention to ancestry and sex.
-
Making genetic research clearer to find genes linked to major depression
Researchers will use health records and genetic data to pinpoint genes that drive major depression, aiming to help people with depression get better-targeted treatments.
-
How iTBS and a brain drug could boost brain wiring to help depression
A study tests whether a brain-stimulating treatment (iTBS) works by improving brain plasticity and whether adding a drug (DCS) enhances this effect for people with major depressive disorder.
-
Portable, low-cost genetic test to guide antidepressant choices
This project is creating a small, affordable device that rapidly reads genes that affect how people with major depressive disorder process antidepressants to help choose safer, more effective medicines.
About this research
- This is an active NIH-funded research project — typically early-stage science, not a clinical trial accepting patient enrollment.
- Some NIH-funded labs run parallel clinical studies or seek volunteers for related work. To check, contact the principal investigator or institution listed above.
- For full project details, budget, and progress reports, visit the official NIH RePORTER page.