# Identifying Personality-Related Behavioral Phenotypes for Binge Drinking Using Smartphone Sensors and Machine Learning

> **NIH NIH F31** · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · 2022 · $46,753

## Abstract

PROJECT SUMMARY/ABSTRACT
 Binge drinking in young adults is a significant public health problem. A major barrier to increasing the
efficacy of binge drinking interventions is the heterogeneity between people in predictors of alcohol
use/misuse. Treatment can be improved by matching people to interventions based on personality traits that
increase risk for binge drinking, but a better understanding of the everyday behaviors linking traits to drinking
episodes is needed for such interventions to be effective. Theories of alcohol use/misuse specify multiple
behavioral pathways through which personality traits influence problematic drinking, including tendencies to
engage broadly in high-risk behavior, self-select into high-risk social drinking contexts, and regulate emotions
with alcohol. Such contextualized behavior patterns are key risk factors that can be modified with more
personalized treatment. The proposed study will use machine learning methods to identify naturalistic,
personality-related behavioral phenotypes that predict binge drinking from smartphone sensor data (e.g., GPS,
text/call activity). Data for this project will be drawn from an ongoing NIAAA-funded study of young adults
that regularly binge drink (anticipated N = 300). Daily alcohol use and continuous, unobtrusive tracking of
smartphone sensor data are collected from participants in the parent study’s 120-day ambulatory assessment
protocol. Towards the long-term objective of developing more targeted interventions, this study has 3
specific aims: (1) clarify who is at risk for binge drinking and addressing the problem of recall bias that
affects prior research reliant on retrospective reports of alcohol use by establishing associations between
personality traits and drinking assessed at the daily level, (2) uncover passively sensed behavioral/contextual
risk factors related to personality traits that predict binge drinking with machine learning methods, (3)
quantify how much of the relationship between personality traits and binge drinking is explained by passively
sensed behavioral phenotypes. The proposed research and training activities will be conducted at the
University of Pittsburgh. This fellowship will provide specialized training necessary for the applicant to
become an impactful independent clinical scientist. Training will focus on three goals: (1) enhance knowledge
of alcohol use etiology/maintenance mechanisms with regular mentor meetings, guided readings, seminars,
and journal clubs, (2) gain expertise in applying ambulatory assessment for tracking alcohol use by assisting
with the parent study data collection, attending lab meetings, and guided applied practice, and (3) learn
machine learning techniques for analyzing passive sensing data with mentored application of methods,
relevant courses, workshops, and seminars. Results of the proposed study will advance precision
medicine by identifying behavioral markers that can inform development of interventions
based on...

## Key facts

- **NIH application ID:** 10537056
- **Project number:** 1F31AA030500-01
- **Recipient organization:** UNIVERSITY OF PITTSBURGH AT PITTSBURGH
- **Principal Investigator:** Whitney Ringwald
- **Activity code:** F31 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $46,753
- **Award type:** 1
- **Project period:** 2022-08-01 → 2023-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10537056

## Citation

> US National Institutes of Health, RePORTER application 10537056, Identifying Personality-Related Behavioral Phenotypes for Binge Drinking Using Smartphone Sensors and Machine Learning (1F31AA030500-01). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10537056. Licensed CC0.

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