Zhai D, van Stiphout R, Schiavone G, De Raedt W, Van Hoof C. (2022). Characterizing and modeling smoking behavior using automatic smoking event detection and mobile surveys in naturalistic environments: Observational study. JMIR Mhealth Uhealth 2022;10(2):e28159) doi: 10.2196/28159
A study was conducted to enable quantified monitoring of smoking behavior 24/7 using continuous automatic measurement techniques to identify and analyze smoking patterns. Researchers conducted a 4-week observational study among 46 current adult smokers. Participants tracked their smoking behavior by using an electronic lighter and smartphone app called ASSIST. The lighter was connected to the app and participants were informed to solely used the provided lighter when smoking. The app was used to prompt smoking-related ecological momentary assessment questionnaires and smoking rate was assessed by the timestamps of smoking. The study acquired data from a total of 8639 cigarettes smoked and 1839 ecological momentary assessments over 902 participant days. Among most participants, self-reported estimates of daily smoking were inaccurate and biased compared to the objectively measured smoking rate. Specifically, 74% of smokers made more than one wrong estimate and 70% overestimated smoking instances. Compared to light smokers, moderate and heavy smokers were significantly older in age and higher in nicotine dependence, craving, arousal, and difficulty resisting smoking. Results indicate that electronic lighters have potential for smoking behavior data in the real world. Technology-based methods for smoking behavior monitoring can be beneficial for smoking cessation applications. The study lends insights for future design and implementation of technology-based smoking cessation applications.