Personalized Depression Treatment Supported by Mobile Sensor Analytics

NCT06292221 | Completed DMC

• 2 other identifiers: H19-078R01MH119678
• Study Type: interventional
• Target: 22
• Locations: 1 country
• Sites: 1

Brief 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. 1.Feasibility of the digital tool, DepWatch, to assist clinicians in depression treatment and inform their clinical decision process
2. 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

Depression

Keywords

Depression; smartphone

Outcome Measures

Primary Outcomes (1)

• Feasibility and Usability

  Study clinicians will complete surveys about feasibility and usability of the weekly. assessments provided to them on their patients in informing their clinical decision making process

  3 surveys conducted 4 months apart (over the 12 month study period)

Secondary Outcomes (1)

• Depression outcomes

  3 months

Study Arms (2)

Experimental

EXPERIMENTAL

For this group of participants: The study clinicians will receive the weekly depression and behavioral assessment reports generated by the mHealth tool 'DepWatch' via a secure clinician portal

Other: A mobile Health (mHealth) tool called 'DepWatch'

Control

OTHER

For this group of participants: The study clinicians will NOT receive the weekly depression and behavioral assessment reports generated by the mHealth tool 'DepWatch'

Other: A mobile Health (mHealth) tool called 'DepWatch'

Interventions

A mobile Health (mHealth) tool called 'DepWatch' OTHER

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

Control; Experimental

Eligibility Criteria

• Sex: all
• Healthy Volunteers: No
• Age Groups: Child (0-17), Adult (18-64), Older Adult (65+)

You may qualify if:

• Age 18 year or older
• Moderate level of depression as defined by a score of ≥ 11 on the 16 item Quick Inventory of Depressive Symptomatology (QIDS) self-report questionnaire
• Initiating a pharmacological treatment for depression as monotherapy or adjunctive treatment or reporting a dose increase with their existing depression treatment.

You may not qualify if:

• Diagnosis of a primary psychotic disorder such as schizophrenia or schizoaffective disorder
• Currently active substance use disorder (within 1 month of enrollment) dominating clinical scenario
• Other clinically significant medical of psychiatric conditions that may adversely affect participants' study participation and/or affect their adherence to study protocol (as determined by study clinician) e.g., significant cognitive deficits

Sponsors & Collaborators

• UConn Health: lead
• National Institute of Mental Health (NIMH): collaborator

Study Sites (1)

University of Connecticut Health Center

Farmington, Connecticut, 06030, United States

Location

Related Publications (1)

• Kamath J, Bi J, Russell A, Wang B. Grant Report on SCH: Personalized Depression Treatment Supported by Mobile Sensor Analytics. J Psychiatr Brain Sci. 2020;5:e200010. doi: 10.20900/jpbs.20200010. Epub 2020 Apr 29.

  PMID: 32529036 BACKGROUND

MeSH Terms

Conditions

Depression

Interventions

Telemedicine

Condition Hierarchy (Ancestors)

Behavioral Symptoms; Behavior

Intervention Hierarchy (Ancestors)

Delivery of Health Care; Patient Care Management; Health Services Administration

Study Officials

• Jayesh Kamath, MD PhD

  UConn Health

  PRINCIPAL INVESTIGATOR

Study Design

• Study Type: interventional
• Phase: not applicable
• Allocation: RANDOMIZED
• Masking: NONE
• Masking Details: There is no masking
• Purpose: DIAGNOSTIC
• Intervention Model: PARALLEL
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Professor

Model Details: Two groups of participants (64 per group) will be enrolled and will participate in the study over a 3 month period . Both will receive standard of care depression treatment with their respective providers in the clinic. Both groups will undergo standard depression assessment using depression questionnaires as well as behavioral assessments using a mobile health (mHealth) tool 'DepWatch' developed by the study team in the phase I of the study. Study clinicians will receive weekly behavioral assessment reports for participants enrolled in the first 'experimental' group and will not receive such reports for the second 'control' group

Study Record Dates

• First Submitted: February 27, 2024
• First Posted: March 5, 2024
• Study Start: April 4, 2024
• Primary Completion: June 8, 2025
• Study Completion: June 8, 2025
• Last Updated: July 31, 2025

Record last verified: 2025-07

Data Sharing

• IPD Sharing: Will not share

Locations

US (1)