To combat youth vaping epidemic, AI can help determine best cessation strategies
Researchers at the University at Buffalo used AI and machine learning to analyze vaping habits among 119 young adults, finding that starting before age 15 strongly predicts continued use and that tailored interventions like digital nudges and peer support could improve cessation rates. Their study, published in *PLOS Digital Health*, highlights the need for early prevention and age-specific strategies to combat nicotine addiction in youth.
Researchers at the University at Buffalo (UB) have developed AI-driven strategies to help young people quit vaping, a growing epidemic among those aged 18-24, with 38.4% reporting habitual use. The study, published May 5 in *PLOS Digital Health*, surveyed 119 vapers, most aged 21-26, to identify patterns in successful cessation. Using five AI models, including machine learning and explainable AI tools like Accumulated Local Effects (ALE) and Local Interpretable Model-Agnostic Explanations (LIME), the team found that starting vaping before age 15 was a key predictor of continued use. The models revealed that early intervention—such as short digital nudges, peer support, and trigger-management tools—could significantly improve quitting success, particularly for those under 21. Lead researcher Satheeshkumar Poolakkad Sankaran emphasized that prevention must begin before nicotine rewires the brain’s reward system. The study suggests universities like UB can implement tailored programs to reduce nicotine addiction among students. The findings also apply broadly to public health, offering AI-driven insights for other behavioral interventions. UB’s Division of Hematology/Oncology plans to translate these results into real-world cessation programs, addressing both clinical and social determinants of health.
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