# SCH: Personalized Depression Treatment Support by Mobile Sensor Analytics

> **NIH NIH R01** · UNIVERSITY OF CONNECTICUT STORRS · 2023 · $233,742

## Abstract

The current best practice guidelines for treating depression call for close monitoring of patients, and
periodically adjusting treatment as needed. This project will advance personalized depression treatment by
developing an innovative 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. Our project comprises two studies. Phase I collects sensory
data and other data (e.g., clinical data, EMA, tolerability and safety data) from 250 adult participants with
unstable depression symptomatology. The data thus collected will be used to develop and validate
assessment and prediction models, which will be incorporated into DepWatch system. In Phase II, three
clinicians will use DepWatch to support their clinical decision making process; a total of 50 participants
under treatment by the three participating clinicians will be recruited for the study. A number of innovative
machine learning techniques will be developed. These include a set of new learning formulations to
construct matrix-based longitudinal predictive models, and determine the temporal contingency and the
most influential features, and deep learning based data imputation methods that can handle both problems
of sporadic missing data as well as missing data in an entire view. In addition, multi-task feature learning
models and feature selection techniques will be expanded and refined for this challenging setting of large-scale
heterogeneous data.

## Key facts

- **NIH application ID:** 10418671
- **Project number:** 5R01MH119678-04
- **Recipient organization:** UNIVERSITY OF CONNECTICUT STORRS
- **Principal Investigator:** Jinbo Bi
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2023
- **Award amount:** $233,742
- **Award type:** 5
- **Project period:** 2019-07-18 → 2025-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10418671

## Citation

> US National Institutes of Health, RePORTER application 10418671, SCH: Personalized Depression Treatment Support by Mobile Sensor Analytics (5R01MH119678-04). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10418671. Licensed CC0.

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