Sense2Quit APP

Sense2Quit uses wearable motion sensing and machine learning to identify smoking and pre-smoking gestures in real time. By delivering just-in-time support through a mobile app, it helps users quit through proactive, data-driven intervention rather than self-report alone.

Project Description

Tobacco dependence remains a significant global public health challenge, with over 1.3 billion tobacco users worldwide, and China alone accounts for nearly 300 million smokers, making it the largest tobacco-consuming population in the world. Unlike traditional cessation tools—which often rely on self-reports, periodic counseling, or pharmacological aids—Sense2Quit provides continuous, objective monitoring and delivers tailored, just-in-time support. This innovative system creates personalized cessation plans tailored to each individual’s goals by monitoring smoking behaviors, progress, relapses, and transitions between different stages of cessation. By using smartwatch and smartphone-based sensing, Sense2Quit identifies characteristic hand gestures associated with reaching for, preparing, and smoking a cigarette, allowing interventions to occur before smoking actually begins. This novel approach makes smoking cessation assistance more proactive, responsive, and personalized for users.

Given the rapid rise in smoking rates, a comprehensive, multifaceted approach is essential—combining stricter regulations, public awareness campaigns, community involvement, and targeted interventions. Beyond being an intriguing scientific challenge, this effort seeks to formulate evidence-based smoking cessation policies while addressing cultural norms, such as the gifting of cigarettes, which perpetuate smoking habits. Our study will bridge critical gaps in current smoking cessation strategies in China by employing a rigorous, theory-driven framework for context-aware intervention strategy development. The proposed measures could also integrate accessible smoking cessation support, including counseling and nicotine replacement therapies, while training healthcare professionals to provide effective technology assistance to smokers. Special attention must be given to preventing youth smoking through school-based education and fostering smoke-free environments on campus to promote a healthier, smoke-free lifestyle.

We also believe that China must urgently address the growing use of e-cigarettes by implementing similar strict regulations and focusing on rural areas, where smoking rates remain disproportionately high. Community-based initiatives, workplace smoke-free policies, and leveraging technology platforms can play a pivotal role in disseminating information and providing necessary quitting resources. To ensure long-term success, regular monitoring and evaluation of anti-smoking programs must be conducted, alongside measures to hold the tobacco industry accountable. Our goal is to generate meaningful and lasting impacts that extend beyond academia and economics, improving public health outcomes and benefiting society as a whole.

System Overview
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Publications
Development and Evaluation of Visualizations of Smoking Data for Integration Into the Sense2Quit app for Tobacco Cessation
Maeve Brin, Paul Trujillo, Ming-Chun Huang, Patricia Cioe, Huan Chen, Wenyao Xu, Rebecca Schnall

Journal of the American Medical Informatics Association 31 (2), 354-362

PDF | DOI

Theoretically Guided Iterative Design of the Sense2Quit App for Tobacco Cessation in Persons Living with HIV
Rebecca Schnall, Paul Trujillo, Gabriella Alvarez, Claudia L Michaels, Maeve Brin, Ming-Chun Huang, Huan Chen, Wenyao Xu, Patricia A Cioe

International Journal of Environmental Research and Public Health 20 (5), 4219

PDF | DOI

A Robust Cross-Platform Solution With the Sense2Quit System to Enhance Smoking Gesture Recog- nition: Model Development and Validation Study
Anarghya Das, Juntao Feng, Maeve Brin, Patricia Cioe, Rebecca Schnall, Ming-Chun Huang, Wenyao Xu

Journal of Medical Internet Research (JMIR), Volume 27, e67186, May 2025

PDF | DOI

Smoking Cessation System for Preemptive Smoking Detection
Gabriel Maguire, Huan Chen, Rebecca Schnall, Wenyao Xu, Ming-Chun Huang

IEEE internet of things journal 9 (5), 3204-3214

PDF | DOI

PI Leads
MCH

Prof. Ming-Chun Huang

Associate Professor, Duke Kunshan University

Xu

Prof. Wenyao Xu

Professor, University at Buffalo 

003

Prof. Rebecca Schnall

Professor of Nursing, Columbia University

004

Prof. Patricia Cioe

Associate Professor of Behavioral and Social Sciences, Brown University

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