Tobacco use is a chronic relapsing condition. That is, even with state-of-the-art treatment, >70% of smoking cessation attempts end in a return to regular smoking. Research demonstrates that everyday environments associated with smoking trigger craving for cigarettes, provoke smoking, and lead to relapse. However, despite this knowledge, understanding of environmental correlates of smoking has been limited by a reliance on self-report, leading to imprecise information about the physical environments in which people live. To overcome this challenge, the research team has pioneered the development of digital envirotyping, which uses digital tools (e.g., sensors, cameras, artificial intelligence) to efficiently and accurately characterize and categorize environments with the goal of identifying environmental markers of behavior and health. Foundational to the digital envirotyping research is computer vision (CV), a type of artificial intelligence (AI) that enables computer systems to recognize objects and scenes in digital images, mimicking how humans perceive and understand visual information. With CV researchers can extract detailed and accurate information (i.e., objects and location types) about the everyday environments of people who smoke (PWS) and relate that information to smoking behavior. After validating the use of CV, the researchers used CV to develop enviromarkers of relapse risk. Importantly, they identified a novel enviromarker in which people at greater risk for relapse when they quit are exposed to a more consistent level of environment-related smoking risk as they move between their smoking and nonsmoking environments.

Research is now needed to advance digital envirotyping and enviromarker development in the field of tobacco addiction. The study will recruit a diverse, national sample of n=500 adults who are interested in quitting smoking. For two weeks prior to quitting, they will undergo photoEMA in which they will take two pictures of their current environment when they smoke, and randomly 10 times per day resulting in >300,000 images total. Cessation will be supported by nicotine replacement therapy (i.e., nicotine patch). The primary clinical outcome will be days to relapse. Specific aims are to (1) further develop, refine, and validate methods for efficient digital envirotyping at scale, (2) leverage CV and AI approaches to develop enviromarkers of smoking relapse, and (3) conduct analyses to increase understanding of environmental smoking risk in women and individuals with low socioeconomic status.

Inclusion Criteria:

1. 21 years of age or older;
2. Smoke ≥ 10 cigarettes/day of a brand delivering 0.5 mg nicotine (FTC method);
3. Smoking daily for ≥ 2 years, with a stable smoking pattern for the past 6 months;
4. Intention to quit smoking and set a target quit date in the next 1 month;
5. Have an iPhone or Android smartphone capable of running the photoEMA and iCO software

Exclusion Criteria:

1. Pregnant, breastfeeding, or planning to become pregnant during the course of the study;
2. Currently in smoking cessation treatment or current use of smoking cessation products;
3. Regular use (i.e. >9 days/month) of non-cigarette nicotine-containing products (e.g. cigarillos, e-cigarettes);
4. Anticipating major life changes (e.g. new job, birth of a child) during the course of the study;
5. Unstable medical conditions;
6. Contraindication for nicotine replacement therapy.

We anticipate depositing the research data with the digital repository of the Inter-university Consortium for Political and Social Research (ICPSR), which supports NIDA-funded data sharing and restricted data access. Data will include photos, ecological momentary assessment data, breath CO readings, and survey data. Each data set will be made available along with the corresponding study protocol and codebook. The codebook will include a basic description of the variable, how and when it was collected, and relevant references or scoring information.

Data will be made available one year after study completion, and will continue as long as the study is open for data analysis.