Personalized Depression Treatment Supported by Mobile Sensor Analytics
Purpose
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.
Condition
- Depression
Eligibility
- Eligible Ages
- All ages
- Eligible Genders
- All
- Accepts Healthy Volunteers
- No
Inclusion Criteria
- 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.
Exclusion Criteria
- 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
Study Design
- Phase
- N/A
- Study Type
- Interventional
- Allocation
- Randomized
- Intervention Model
- Parallel Assignment
- Intervention Model Description
- 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
- Primary Purpose
- Diagnostic
- Masking
- None (Open Label)
- Masking Description
- There is no masking
Arm Groups
| Arm | Description | Assigned Intervention |
|---|---|---|
|
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 Control |
For this group of participants: The study clinicians will NOT receive the weekly depression and behavioral assessment reports generated by the mHealth tool 'DepWatch' |
|
Recruiting Locations
More Details
- Status
- Recruiting
- Sponsor
- UConn Health
