# Mobile Technology to Optimize Depression Treatment

> **NIH NIH R01** · UNIVERSITY OF MICHIGAN AT ANN ARBOR · 2024 · $744,054

## Abstract

Abstract
Tailoring care to match patients to the treatment most effective for them has the potential to accelerate
recovery and meaningfully reduce the growing burden of depression. A key barrier to tailoring care is the
absence of objective, real-time methods to effectively predict and assess treatment response. Mobile
technology holds promise to overcome this barrier. Specifically, smartphones and wearable sensors collect
passive, continuous and objective measures of constructs central to depression, such as sleep, physical
activity, cardiovascular function, and social engagement. Studies have demonstrated associations of single
measures from these domains with depression. However, because most prior wearable studies have had
limited sample sizes, they have not been able to synthesize actionable information across multiple domains of
mobile technology data and effectively guide treatment. Our long-term goal is to substantially increase the
effectiveness of depression treatments and the capacity of our mental health care system. Our objective in this
application is to identify factors that can be used to effectively match patients to treatments and track their
recovery. Through the PROviding Mental health Precision Treatment (PROMPT) study, we will complete the
following specific aims: Aim 1) Identify factors that predict which treatment is most likely to reduce depression
symptoms for a specific patient; and Aim 2) Identify passive mobile technology-based measures that serve as
signals of treatment response. To achieve these aims, we will recruit 2,200 subjects from waitlist for outpatient
depression treatment. We will then track patients for six months through wearable sensors, smartphones, and
repeated surveys. For both aims, we will use machine learning approaches to develop comprehensive
prediction models. Our approach is innovative because it applies technology and analytic tools to a large and
diverse sample of subjects receiving treatment under real world conditions. Further, the project is designed to
lead directly to an organization-level intervention that matches patients to treatments and continuously
monitors their response to treatment. Finally, this project is significant because it has the potential to greatly
accelerate recovery by identifying the treatment from which each person is likely to derive the most benefit,
ultimately helping to address the high population burden of depression.

## Key facts

- **NIH application ID:** 10908307
- **Project number:** 5R01MH131617-03
- **Recipient organization:** UNIVERSITY OF MICHIGAN AT ANN ARBOR
- **Principal Investigator:** Amy S B Bohnert
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $744,054
- **Award type:** 5
- **Project period:** 2022-09-07 → 2027-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10908307

## Citation

> US National Institutes of Health, RePORTER application 10908307, Mobile Technology to Optimize Depression Treatment (5R01MH131617-03). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10908307. Licensed CC0.

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