Personalized Depression Treatment Supported by Mobile Sensor Analytics

UConn Health22 enrolled

Overview

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.

Study Type

INTERVENTIONAL

Allocation

RANDOMIZED

Purpose

DIAGNOSTIC

Masking

NONE

Enrollment

Conditions

Eligibility

Sex: ALL

Medical Language ↔ Plain English

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

Outcomes

Primary Outcomes

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

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

Secondary Outcomes

Depression outcomes

Depression outcomes will be compared between the two groups using the 'Quick Inventory of Depression Symptomatology' questionnaire. Quick Inventory of Depression Symptomatology (self-report), minimum value 0, maximum value 27, Higher scores mean worse outcome.

Time frame: 3 months