# mDOT TR&D1 (Discovery) - Enabling the Discovery of Temporally-Precise Intervention Targets and Timing Triggers from mHealth Biomarkers via Uncertainty-Aware Modeling of Personalized Risk Dynamics

> **NIH NIH P41** · UNIVERSITY OF MEMPHIS · 2022 · $159,885

## Abstract

Project Lead: Rehg, Jim Primary Investigator: Kumar, Santosh
TR&D1: Enabling the Discovery of Temporally-Precise Intervention Targets and Timing Triggers from
 mHealth Biomarkers via Uncertainty-Aware Modeling of Personalized Risk Dynamics
 Lead: Dr. Jim Rehg, Georgia Tech; 10% effort (0.9 CM)
Abstract: The mHealth Center for Discovery, Optimization & Translation of Temporally-Precise Interventions
(the mDOT Center) will enable a new paradigm of temporally-precise medicine to maintain health and manage
the growing burden of chronic diseases. The mDOT Center will develop and disseminate the methods, tools,
and infrastructure necessary for researchers to pursue the discovery, optimization and translation of temporally-
precise mHealth interventions. Such interventions, when dynamically personalized to the moment-to-moment
biopsychosocial-environmental context of each individual, will precipitate a much-needed transformation in
healthcare by enabling patients to initiate and sustain the healthy lifestyle choices necessary for directly
managing, treating, and in some cases even preventing the development of medical conditions. Organized
around three Technology Research & Development (TR&D) projects, mDOT represents a unique national
resource that will develop multiple technological innovations and support their translation into research and
practice by the mHealth community in the form of easily deployable wearables, apps for wearables and
smartphones, and a companion mHealth cloud system, all open-source.
TR&D1 will develop, evaluate and disseminate methods to analyze population-scale multi-modal time series of
mHealth biomarkers to enable research on identifying the momentary risk factors and risk dynamics that drive
adverse health outcomes, while accounting for the uncertainty and missingness inherent in these data sources.
TR&D1 will do this under three aims. Aim 1 will address missing sensor data in mHealth field studies and develop
state-of-the art imputation models using novel deep probabilistic neural networks that leverage the hierarchical
nature of biomarker computation graphs. Aim 2 will address compressing a collection of biomarkers that serve
as risk factors for a particular adverse health event into a single risk score, to support the online adaptation of
decision rules in TR&D2, using longitudinal data that include multiple instances of adverse events and their
contexts. In addition to risk scoring, we will also develop models for receptivity to intervention and participant
engagement, which complement the assessment of risk in guiding intervention design. Aim 3 will begin to tackle
the critical issue of providing model-based tools for identifying which potential risk factors actually impact risk in
different contexts for different individuals, in order to support the intervention design process. TR&D1 will work
with its collaborative projects to ensure that it focuses on the most pressing problems facing the mobile health
research com...

## Key facts

- **NIH application ID:** 10215514
- **Project number:** 5P41EB028242-02
- **Recipient organization:** UNIVERSITY OF MEMPHIS
- **Principal Investigator:** James M. Rehg
- **Activity code:** P41 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $159,885
- **Award type:** 5
- **Project period:** 2020-07-15 → 2025-11-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10215514

## Citation

> US National Institutes of Health, RePORTER application 10215514, mDOT TR&D1 (Discovery) - Enabling the Discovery of Temporally-Precise Intervention Targets and Timing Triggers from mHealth Biomarkers via Uncertainty-Aware Modeling of Personalized Risk Dynamics (5P41EB028242-02). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10215514. Licensed CC0.

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