Research Project

Study Overview

This study addresses a critical gap in post-discharge care for patients with depression: high rates of recurrence. DH’s CoCM faces challenges in effectively monitoring for post-discharge depressive symptoms, which occur in approximately 1 in 3 patients. Digital surveillance for early depression recurrence is proposed by using passively collected data from smartphones. The overarching goal for this study is to evaluate the use of smartphone surveillance for depression in clinical practice within Dartmouth's learning health system to better support post-discharge care, quality improvement, and research amongst rural populations.

Aim 1: Data Collection and Algorithm Training to Predict Recurrence

We will develop and implement a passive detection system for predicting next-month recurrence of depression in CoCM patients post-discharge, utilizing smartphone sensor data to create a digital biomarker. 

Aim 2: Effectiveness Trial

We will evaluate the effectiveness of Aim 1’s digital biomarker “early warning system” in reducing the incidence of Major Depressive Disorder (MDD) recurrence.

Study Team