# Feasibility of Using Wearable Technology for Just-in-Time Prediction of Smoking Lapses

> **NIH NIH R21** · UNIVERSITY OF MINNESOTA · 2021 · $232,500

## Abstract

The long-term goal of the proposed line of research is to develop technology-based interventions that increase
smoking cessation rates by delivering just-in-time support to minimize the likelihood that smoking triggers lead
to smoking lapses. Towards this objective we propose to use continuous physiological and environmental time-
series data obtained from sensors embedded in widely used consumer wearable technology to construct a
predictive model for detecting antecedents of smoking events (e.g., stressors, smoking cues, etc). In this
project, we will conduct an observational study of smokers during an ad libitum smoking period and during a
period in which they attempt to quit smoking. During the observation period, smokers will wear three devices
capable of collecting physiological and motion data. We hypothesize that consumer wearable technology can
reliably capture physiological response to events that precede smoking during the ad libitum smoking
period and precede lapse during the cessation period. Demonstrating that imminent smoking can be
predicted would lead to the development and testing of just-in-time interventions that can be delivered via
customized messaging on devices such as smartphones or smartwatches.

## Key facts

- **NIH application ID:** 10226020
- **Project number:** 5R21DA049446-02
- **Recipient organization:** UNIVERSITY OF MINNESOTA
- **Principal Investigator:** MICHAEL KOTLYAR
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $232,500
- **Award type:** 5
- **Project period:** 2020-08-01 → 2023-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10226020

## Citation

> US National Institutes of Health, RePORTER application 10226020, Feasibility of Using Wearable Technology for Just-in-Time Prediction of Smoking Lapses (5R21DA049446-02). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10226020. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*