Personalized Depression Treatment Supported by Mobile Sensor Analytics

Feasibility and Usability

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.