Examining intention to quit using Juul among emerging adults.

OBJECTIVES: (1) Describe intention to quit, (2) identify relationships between various factors and intention to quit, (3) explore if Theory of Planned Behavior-informed constructs are associated with intention to quit, and (4) discover if descriptive norms strengthen association with intention to quit among emerging adults currently using Juul. Participants: First-year students currently using Juul at a large public university (N = 182). Methods: A November 2018 online survey assessed sociodemographic characteristics, social influences, patterns of use, quit intention, and attitudes, norms, and perceived behavioral control toward Juul. Results: A quarter of participants reported current use, with nearly half intending to quit within six months. Recent quit attempts was the only factor related to intention. Two models were created that showed association with intention to quit, but only perceived behavioral control was individually significant. Conclusions: Cessation-focused campaigns and interventions are needed on college campuses and could be less tailored than prevention.

Juul use among emerging adults transitioning from high school to college.

Objective: Assess the prevalence, perceptions, sociodemographic/personal factors that influence Juul use among incoming freshmen.Participants: Incoming undergraduate students (N = 1,706) attending a public university in the southeastern U.S.Methods: Cross-sectional survey administered August 2018. Bivariate relationships assessed using chi-square test of association. Multinomial logistic regression to determine factors associated with Juul use status.Results: 41% had ever used Juul, 24% had used within the past month. Among current users, one-third had used 20-30 days. Risk factors for current use: heterosexual orientation (relative to other sexual orientation) AOR = 2.16, 95% CI: 1.20-3.91), those who planned to join sorority/fraternity (relative to those who did not plan to; AOR = 2.15, 95% CI: 1.59-2.90), current smokers (relative to nonsmokers; AOR = 24.39, 95% CI: 7.52-76.92), current marijuana users (compared with nonusers of marijuana; AOR = 6.45, 95% CI: 3.92-10.64) and alcohol users (compared with nondrinkers; AOR = 7.81, 95% CI: 5.75-10.54).Conclusion: Prevention and treatment efforts are needed for emerging adults transitioning to college.

Identifying current Juul users among emerging adults through Twitter feeds.

INTRODUCTION: Juul is the most popular electronic cigarette on the market. Amid concerns around uptake of e-cigarettes by never smokers, can we detect whether someone uses Juul based on their social media activities? This is the central premise of the effort reported in this paper. Several recent social media-related studies on Juul use tend to focus on the characterization of Juul-related messages on social media. In this study, we assess the potential in using machine learning methods to automatically identify Juul users (past 30-day usage) based on their Twitter data. METHODS: We obtained a collection of 588 instances, for training and testing, of Juul use patterns (along with associated Twitter handles) via survey responses of college students. With this data, we built and tested supervised machine learning models based on linear and deep learning algorithms with textual, social network (friends and followers), and other hand-crafted features. RESULTS: The linear model with textual and follower network features performed best with a precision-recall trade-off such that precision (PPV) is 57 % at 24 % recall (sensitivity). Hence, at least every other college-attending Twitter user flagged by our model is expected to be a Juul user. Additionally, our results indicate that social network features tend to have a large impact (positive) on classification performance. CONCLUSION: There are enough latent signals from social feeds for supervised modeling of Juul use, even with limited training data, implying that such models are highly beneficial to very focused intervention campaigns. This initial success indicates potential for more involved automated surveillance of Juul use based on social media data, including Juul usage patterns, nicotine dependence, and risk awareness.

Prevalence and reasons for Juul use among college students.

Objective: Examine Juul use patterns, sociodemographic and personal factors associated with Juul use, and reasons for Juul initiation and current use, among college students. Participants: Convenience sample of 371 undergraduates at a large university in the southeast; recruited April 2018. Methods: Cross-sectional design using an online survey. Logistic regression identified the personal risk factors for current use. Results: Over 80% of participants recognized Juul; 36% reported ever use and 21% past 30-day use. Significant risk factors for current Juul use were: male, White/non-Hispanic, lower undergraduate, and current cigarette smoker. Current Juul users chose ease of use and lack of a bad smell as reasons for use. Ever Juul users most commonly endorsed curiosity and use by friends as reasons for trying Juul. Conclusions: Given the propensity for nicotine addiction among youth and young adults, rates of Juul use are alarming and warrant immediate intervention.