A5032002594- Smoking Behavior and Cessation
- Text and Document Classification Technologies
- Mental Health via Writing
- Behavioral Health and Interventions
- Job Satisfaction and Organizational Behavior
- Substance Abuse Treatment and Outcomes
- Cannabis and Cannabinoid Research
- Sentiment Analysis and Opinion Mining
- Psychological and Temporal Perspectives Research
- Data-Driven Disease Surveillance
- Retirement, Disability, and Employment
- Advanced Text Analysis Techniques
- Korean Urban and Social Studies
- Media Influence and Health
University of Kentucky
2019-2021
Objective: Examine Juul use patterns, sociodemographic and personal factors associated with use, reasons for initiation current 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 risk use. Results: Over 80% participants recognized Juul; 36% reported ever 21% past 30-day Significant were: male, White/non-Hispanic,...
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 southeastern U.S.Methods: Cross-sectional survey administered August 2018. Bivariate relationships assessed using chi-square test of association. Multinomial logistic regression to determine associated with status.Results: 41% had ever used Juul, 24% within past month. Among...
(1) Describe intention to quit, (2) identify relationships between various factors and (3) explore if Theory of Planned Behavior-informed constructs are associated with (4) discover descriptive norms strengthen association quit among emerging adults currently using Juul. Participants: First-year students Juul at a large public university (N = 182). Methods: A November 2018 online survey assessed sociodemographic characteristics, social influences, patterns use, intention, attitudes, norms,...
Abstract Introduction Can we predict whether someone uses Juul based on their social media activities? This is the central premise of effort reported in this paper. Several recent media-related studies use tend to focus characterization Juul-related messages media. In study, assess potential using machine learning methods automatically identify an individual (past 30-day usage) Twitter data. Methods We obtained a collection 588 instances, for training and testing, patterns (along with...