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Bu sayfa, 26. Dünya Psikiyatri Kongresi'nde sunulan çalışmanın yapay zekâ desteğiyle hazırlanmış Türkçe özetidir. Çalışmanın sahibi sunan yazarlardır; atıf için orijinal kaynağa başvurun.

EV0735

Can AI-assisted vaping education reduce e-cigarette use and improve mental health outcomes among young people? Design of a randomized controlled trial

CAN AI-ASSISTED VAPING EDUCATION REDUCE E-CIGARETTE USE AND IMPROVE MENTAL HEALTH OUTCOMES IN YOUNG PEOPLE? THE DESIGN OF A RANDOMISED CONTROLLED TRIAL

The poster presents the design of a randomized controlled trial that will test whether AI-assisted vaping education materials co-designed with young people change e-cigarette use and mental health outcomes; no results have been reported yet.

The aim is to test whether AI-assisted vaping education reduces e-cigarette use and improves knowledge, attitudes, and mental health-related outcomes among young Australians aged 16–25; the poster reports that, among Australians aged 18–24 in 2022–23, lifetime use was 49%, current use was 21%, and daily use was 9.3%. The design is a funded, three-arm randomized controlled trial (RCT) that will recruit 900 young people aged 16–25: Arm 1, AI-assisted materials co-designed with young people (n=300); Arm 2, AI-assisted materials informed by synthetic participant data (n=300); and Arm 3, neutral control materials unrelated to e-cigarettes (n=300). All arms will receive one message per day for 7 days, after which participants will be allocated to either short exposure (2 messages per week until week 8) or long exposure (2 messages per week until week 16); messages will be delivered via MMS/SMS or WhatsApp, and receipt will be confirmed by prompting participants to reply. Assessments will take place at baseline, week 1, month 2, month 4, and month 12; primary outcomes are e-cigarette use, attitudes, and knowledge, while secondary outcomes are intention to use and normative perceptions, mental health measures (anxiety, depression, loneliness), gaming disorder, screen use, and other substance use. The hypotheses are that materials co-designed with young people will be more effective than materials based on synthetic participant data, and that longer exposure will have a stronger effect on vaping and psychosocial outcomes. Analysis will be conducted in R using generalized mixed-effects regression on an intention-to-treat basis, with multiple imputation for missing data. The poster states that this is the first study to test whether AI-assisted vaping education changes behavior over 12 months; recruitment is scheduled to begin in August 2026, with results expected in 2028.

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