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Personalized Depression Treatment Supported by Mobile Sensor Analytics

NCT06292221 · Status: COMPLETED · Phase: NA · Type: INTERVENTIONAL · Enrollment: 22

Last updated 2025-07-31

No results posted yet for this study

Summary

The current best practice guidelines for treating depression call for close monitoring of patients, and periodically adjusting treatment as needed. This present study seeks to develop and investigate an innovative digital system, DepWatch, that leverages mobile health technologies and machine learning tools to provide clinicians objective, accurate, and timely assessment of depression symptoms to assist with their clinical decision making process. Specifically, DepWatch collects sensory data passively from smartphones and wristbands, without any user interaction, and uses simple user-friendly interfaces to collect ecological momentary assessments (EMA), medication adherence and safety related data from patients. The collected data will be fed to machine learning models to be developed in the project to provide weekly assessment of patient symptom levels and predict the trajectory of treatment response over time. The assessment and prediction results are then presented using a graphic interface to clinicians to help them make critical treatment decisions. The main question the present clinical trial aims to answer are as follows:

1. Feasibility of the digital tool, DepWatch, to assist clinicians in depression treatment and inform their clinical decision process
2. Effectiveness of the digital tool, DepWatch, to improve depression treatment outcomes All study participants will carry the DepWatch app on their smartphones and wear a Fitbit provided by the study team during the study period. They will also complete brief questionnaires via the app at specific time intervals throughout the study period.

Conditions

Interventions

OTHER

A mobile Health (mHealth) tool called 'DepWatch'

The mobile Health (mHealth) tool 'DepWatch' developed by the study team consists of the DepWatch app that is uploaded on participant's smart phones with their consent and a Fitbit provided to the participants

Sponsors & Collaborators

  • National Institute of Mental Health (NIMH)

    collaborator NIH

  • UConn Health

    lead OTHER

Principal Investigators

  • Jayesh Kamath, MD PhD · UConn Health

Study Design

Allocation
RANDOMIZED
Purpose
DIAGNOSTIC
Masking
NONE
Model
PARALLEL

Eligibility

Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2024-04-04
Primary Completion
2025-06-08
Completion
2025-06-08

Countries

  • United States

Study Locations

More Related Trials

Entities

Diseases

Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT06292221 on ClinicalTrials.gov