The SmokeMon necklace is designed to protect the smoker's privacy by only tracking heat, not visual information, making it more comfortable for people to wear. According to Nabil Alshurafa, an associate professor of preventive medicine at Northwestern University Feinberg School of Medicine, the device can detect various smoking-related activities, such as lighting a cigarette, holding it to the mouth, taking a puff, inhaling, the time between puffs, and how long the cigarette is in the smoker's mouth. The device goes beyond simply counting the number of cigarettes smoked per day.
Smoking topography, which includes various smoking-related details, is significant for two reasons. Firstly, it enables scientists to gauge the extent of harmful carbon monoxide exposure among smokers and examine the correlation between chemical exposure and tobacco-related illnesses, such as cancer, heart disease, stroke, lung disease, diabetes, COPD, emphysema, and chronic bronchitis. Secondly, it helps individuals quit smoking by comprehending how smoking topography relates to relapse, which often happens to people who quit.
Detecting Smoking Topography
By analyzing smoking topography, scientists can determine if a former smoker has taken a few puffs or smoked five entire cigarettes, which can be used to predict if the person is at risk of a full relapse. This information can be used to intervene with timely support, such as a phone call from a health coach or a message to encourage the person to avoid a relapse. The device's effectiveness in detecting e-cigarette smoking puffs and topography will also be studied. The goal is to intervene before a person falls off the wagon, as it becomes more challenging for them to quit once they have relapsed. According to Alshurafa, catching them early is crucial to preventing a full relapse.
When attempting to quit smoking, a slip is typically classified as one or two cigarettes or even just a single puff, and it is not the same as a full relapse. Slips can serve as an opportunity for individuals to gain awareness that they have not failed and have only experienced a temporary setback. To prevent a full relapse, the focus can then shift to handling triggers and dealing with cravings, which can help individuals stay on track toward their goal of quitting smoking, according to Science Blog.
The study, which evaluates the accuracy of the device and people's willingness to use it, will be published on February 13 in Proceedings of the ACM on Interactive, Mobile, Wearable, and Ubiquitous Technologies. With the device's reliability established, researchers can now investigate its effectiveness in smoking cessation programs. Specifically, they plan to examine if real-time feedback and interventions are more effective than traditional care in preventing relapse for individuals who plan to quit smoking. Alshurafa believes the device has the potential to increase the success rate of smoking cessation programs.
Smoking Cessation Effectivity
Smoking is responsible for over 8 million deaths worldwide annually and remains a leading cause of preventable diseases, disabilities, and deaths in the United States. In the U.S., it is responsible for over 480,000 deaths each year, which accounts for one in five deaths. In 2018, the economic cost of smoking in the U.S. amounted to over $600 billion, including healthcare expenditures and lost productivity. About 12.5% of adults in the U.S. smoke, as per NewAtlas.
Current devices used to track smoking topography need to be attached to the cigarette, which can alter how someone smokes and make the data less trustworthy. Researchers have explored non-intrusive ways to measure smoking behavior, such as using sensors in smartwatches. However, these methods can be complicated by non-smoking gestures and produce many false positives. Wearable video cameras can also be used, but they raise concerns about privacy and stigma, making them unsuitable for natural settings.
The study involved 19 participants who participated in 115 smoking sessions under controlled and free-living experiments. During these sessions, the scientists trained a machine model to detect smoking events and their details using the device. This included details like the timing and duration of puffs, the number of puffs, and the time between puffs. They also conducted three focus groups with 18 tobacco-treatment specialists to get their opinion on the device.
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