Using Smartwatches to Monitor Smoking in Real-life Situations

Background: It is important to quickly identify when people are at risk of smoking. Most current methods rely on people reporting when they smoke or what makes them want to smoke. But this can be hard for people to do and may not always be accurate. Other methods use special gadgets to identify smoking movements, but these may not always work well in real life. We want to see if we can use devices available on the market such as smartwatches to identify signs in the body and hand movements that might indicate when someone is about to smoke, is currently smoking, or has just smoked. Objective: To record body signs and hand movements before, during, and after smoking in real-life and in a lab to see how they change when someone is craving cigarettes, while a person is smoking, and after a person has smoked. Eligibility: People who are 21 years or older and smoke, do not have more than a high school education, and are low-income earners. To participate in the study, participants have to pass a breath test that shows they smoke cigarettes and, for women, a urine test to show that they are not currently pregnant. Design: Participants will complete an eligibility survey to see if they qualify to be in the study. If they qualify, they will answer a brief baseline survey that includes questions about themselves, their health, and their smoking behavior. Participants will get a smartwatch to wear for 3 days at home, log each time they are about to smoke and have finished smoking, and answer a 5-question health survey via the app. They will get instructions on how to set up and wear the smartwatch. They will download a mobile application on their phone. The app will collect data from the smartwatch. Participants will then come to the lab but will be asked not to smoke or drink alcohol for at least 12 hours. They will have to take a breath test to show they have not smoked or had alcohol. They will also give a blood sample. In the lab, they will sit in a room where they will be hooked to devices that monitor their vitals such as heart rate and blood pressure for one hour. They will also wear a smartwatch on each hand. While they are in the smoking room, they will go through 3 different phases: (a) pre-smoking where they will be asked to stay seated for about 25 minutes, (b) smoking where they will be asked to smoke as many cigarettes of their choice as they want for about 10 to 15 minutes, (c) post-smoking where they will be asked to stay seated, not smoking, for about 25 minutes. They will answer a brief 10-minute health survey before and after the session. Participation will last for 3 days of home monitoring and 2 visits to the research clinic that last about 2 hours.

Background: The study is part of a systematic effort to develop and evaluate a smoking cessation intervention, Quit Journey, a smoking cessation intervention targeting individuals with low socioeconomic status. Specifically, the study will inform the development of just-in-time momentary support to preempt a lapse or prevent it from progressing to a relapse to users of Quit Journey. Additionally, the study fills a gap in wearable-based studies for smoking detection that have thus far relied exclusively on socially and economically advantaged populations by increasing the representation of underserved populations and more accurately developing algorithms for smoking detection that include input from minority populations and pave the way for its replication with a larger sample in real-world settings. Finally, the data collected from this laboratory-based study will serve as baseline parameters for detecting smoking events in free-living conditions. In a planned real-world study, we aim to finetune our smoking-detection algorithms with data collected in natural environments, potentially improving their robustness and generalizability. Insights from this study have real-world applications of smoking-detection algorithms in wearables and mobile applications, ultimately advancing technology-assisted smoking cessation and contributing to reduced smoking rates. Objectives: * The objective of this study is to identify wearables-based digital biomarkers that are associated with nicotine deprivation (i.e., pre-smoking) and satiation (i.e., post-smoking) and with smoking episodes (i.e., during smoking). Identifying signature digital biomarkers of smoking can help us to unobtrusively detect with great probability the proclivity to smoke or smoking behavior, which can inform the delivery of momentary support to preempt lapsing or progressing toward a relapse among people attempting to quit smoking. * We hypothesize that, relative to pre-smoking, (a) blood oxygen saturation will be lower, (b) heart rate will be higher, (c) heart rate variability will be higher, and (d) respiratory rate will be lower during a smoking episode and post-smoking. Endpoints: * Primary endpoint: Changes in wearables-measured biomarker measures including heart rate, heart rate variability, respiratory rate, and blood oxygenation, in addition to hand/arm movement, across three smoking stages. * Secondary endpoint: Not applicable.