# COMPASS: A comprehensive mobile precision approach for scalable solutions in mental health treatment

> **NIH NIH U01** · UNIVERSITY OF MICHIGAN AT ANN ARBOR · 2024 · $3,866,357

## Abstract

Matching patients to the treatment most effective for them can accelerate recovery and meaningfully
reduce the growing burden of mental health conditions. Key barriers to tailoring care are the lack of objective
data that can predict treatment response and effective approaches to translate data to improved clinical
outcomes. As a result, many patients experience multiple treatment trials before recovery and a substantial
proportion do not recover. The combination of mobile behavioral tracking and machine learning holds promise
to overcome this barrier. Smartphones and wearable sensors can collect passive, continuous and objective
measures and can be used to administer scalable, active behavioral tasks that capture constructs central to
mental health. These highly dense data can be combined with genomics and clinical records, and machine
learning holds promise to extract meaningful signals from these rich, multidimensional streams of information
and facilitate the development of accurate predictive models.
 Our long-term goal is to increase the effectiveness of mental health 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. We will recruit 4,400 patients initiating outpatient mental health care in a network
of primary and specialty clinics into the COMPASS Study (Comprehensive Mobile Precision Approach for
Scalable Solutions in Mental Health Treatment) as part of the IMPACT-MH program. Subjects will be tracked
through a wearable device and smartphone and complete active behavioral tasks. Because evidence-based
digital interventions are increasingly widespread, patients will first be followed as they are randomized to
receive one of two evidence-based digital interventions: cognitive behavioral therapy (CBT); or 2) mindfulness
training. Subsequently, patients will be followed as they receive the array of treatments selected by their
clinical teams. Our overarching hypothesis is that, through the use of mobile technology and machine learning
in a large cohort before and during mental health care, we can develop individualized prediction models that
will optimize mental health treatments. Our study is designed to test this hypothesis with the following specific
aims: (1) Develop predictive models for personalized digital intervention treatment; (2) Develop predictive
models for personalized, clinic-based mental health treatment; (3) Assess patient and clinician preferences for
and perceptions of, the use of predictive modeling and behavioral tracking in mental health care; and (4)
Actively participate in cross-IMPACT-MH project activities. Our approach is innovative because it applies
scalable 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 t...

## Key facts

- **NIH application ID:** 10866994
- **Project number:** 1U01MH136025-01
- **Recipient organization:** UNIVERSITY OF MICHIGAN AT ANN ARBOR
- **Principal Investigator:** Amy S B Bohnert
- **Activity code:** U01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $3,866,357
- **Award type:** 1
- **Project period:** 2024-07-01 → 2029-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10866994

## Citation

> US National Institutes of Health, RePORTER application 10866994, COMPASS: A comprehensive mobile precision approach for scalable solutions in mental health treatment (1U01MH136025-01). Retrieved via AI Analytics 2026-06-04 from https://api.ai-analytics.org/grant/nih/10866994. Licensed CC0.

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