# Characterizing initial recovery from alcohol use disorder and predicting heavy drinking using mobile biosensors

> **NIH NIH R01** · YALE UNIVERSITY · 2024 · $686,518

## Abstract

PROJECT SUMMARY/ABSTRACT
The overarching goal of this research project is to enhance the understanding and prediction of heavy drinking
episodes during initial recovery (IR) from alcohol use disorder (AUD). Leveraging the latest advancements in
wearable biosensor technology and artificial intelligence (AI), this project aims to address the significant gap in
current scientific knowledge regarding the fluctuating nature of AUD recovery. Our objective is to utilize cutting-
edge wearable biosensors, like the VitalConnect VitalPatch and SCRAM-CAM, combined with sophisticated AI
methodologies, including convolution neural networks and Hidden Markov Models, to elucidate the complex
interplay of physiological and neuroclinical factors in AUD recovery. Preliminary studies utilizing AI to analyze
wearable sensor data have shown promising results in identifying behavioral phenotypes indicative of alcohol
use risk, highlighting heart rate variability (HRV) as a particularly sensitive biosignal. The proposed research
encompasses two primary aims: 1) To develop real-time predictive models using wearable sensor data that
can accurately forecast heavy drinking episodes during IR. By integrating data from electrocardiograms and
transdermal alcohol sensors with traditional self-report and behavioral assessments, we aim to construct
comprehensive profiles that can anticipate periods of heightened drinking risk. 2) To identify and understand
the neuroclinical and physiological mechanisms that contribute to or mitigate against shifts toward heavy
drinking during IR. Through repeated biopsychosocial assessments and application of explainable AI
techniques, we intend to uncover critical factors influencing recovery trajectories. Crucially, this project is
supported by a dynamic and interdisciplinary team, bringing together experts in AUD treatment research,
wearable biosensor technology, and cutting-edge AI and computational methods. This collaborative approach
ensures a multifaceted perspective on the challenges of AUD recovery and enhances the project's capacity for
innovative solutions and advanced data analysis. This project represents a novel integration of mobile health
technologies and AI analytics in the study of AUD recovery. It has the potential to transform our understanding
of IR, leading to the development of personalized, just-in-time interventions for individuals battling AUD. The
collaboration with a state-funded community-based outpatient clinic specializing in SUD treatment provides a
unique opportunity to validate our models in a real-world setting, enhancing the potential for these findings to
be translated into practical, scalable monitoring and intervention tools.

## Key facts

- **NIH application ID:** 11024699
- **Project number:** 1R01AA031959-01
- **Recipient organization:** YALE UNIVERSITY
- **Principal Investigator:** Sherry Ann McKee
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $686,518
- **Award type:** 1
- **Project period:** 2024-09-17 → 2029-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/11024699

## Citation

> US National Institutes of Health, RePORTER application 11024699, Characterizing initial recovery from alcohol use disorder and predicting heavy drinking using mobile biosensors (1R01AA031959-01). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/11024699. Licensed CC0.

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