# Smartphone sensors to detect shifts toward healthy behavior during alcohol treatment

> **NIH NIH R21** · RUTGERS BIOMEDICAL AND HEALTH SCIENCES · 2022 · $264,594

## Abstract

ABSTRACT
Binge drinking (4+/5+ drinks/occasion for females/males) increases risk for preventable alcohol-related
consequences, particularly among young adults (ages 18-25). As part of our NIAAA-funded Mechanisms of
Alcohol Treatment Change (MATCH) text message intervention randomized controlled trial (R01 AA023650),
we were funded (CTSI pilot) to collect (but not analyze) smartphone sensor (e.g., GPS, communication logs)
and Ecological Momentary Assessment (EMA) data on drinking behavior in a MATCH subsample (N=108).
This secondary data analysis R21 will leverage the unique combination of phone sensor data collected in the
context of an alcohol clinical trial to gain new insight into processes underlying behavior change. Phone sensor
data collected during MATCH provides fine-grained objective measures of a person's daily routine in travel
pattern and places visited, and sociability (communication pattern). These fine-grained digital traces or digital
phenotypes provide objective markers of how a young adult's daily routine (e.g., travel, sociability) changes in
relation to response to TM intervention. Phone sensor data provide a means to objectively determine when and
how shifts in behavior occur in relation to treatment effects, which will inform phone-sensor-based personal-
ization of the next iteration of the digital intervention. Proposed secondary analyses focus on the MATCH
subsample (n=93; 71% female, range 18-25]) with EMA data and phone sensor data. Phone sensor (e.g.,
GPS, accelerometer, communication logs) and EMA data were collected over 14 weeks (2-week run-in + 12-
week intervention). The 2-week "run-in" provides a baseline daily "routine" inferred by phone sensors prior to
intervention. As in our prior work, we identify intervention "responder" and "non-responder" classes; or explore
defining response using reduction in World Health Organization risk drinking level. Aim 1 compares treatment
responders and non-responders on digital phenotypes (e.g., travel, communication) prior to TM intervention.
Aim 2 compares responders and non-responders on digital phenotypes during TM intervention. An exploratory
aim uses group iterative multiple model estimation (GIMME) to simultaneously estimate associations between
selected phone sensor features at person-specific (idiographic analysis) and group (responder/non-responder)
levels during TM intervention (to complement population-based analyses in Aims 1 and 2). Exploratory
analyses provide detailed individual-level information to guide personalization of intervention content, while
also indicating associations that are shared at the group-level. Analyses also will explore gender differences,
time effects (e.g., weekend/weekday), and treatment arm. These innovative secondary data analyses, which
are in line with NIAAA's strategic plan to advance personalized medicine, will (1) determine when and how
shifts in young adults' drinking behavior occur in relation to TM intervention, revealing altern...

## Key facts

- **NIH application ID:** 10455334
- **Project number:** 1R21AA030153-01
- **Recipient organization:** RUTGERS BIOMEDICAL AND HEALTH SCIENCES
- **Principal Investigator:** Tammy Chung
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $264,594
- **Award type:** 1
- **Project period:** 2022-09-07 → 2024-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10455334

## Citation

> US National Institutes of Health, RePORTER application 10455334, Smartphone sensors to detect shifts toward healthy behavior during alcohol treatment (1R21AA030153-01). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10455334. Licensed CC0.

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