Building Capacity and Promoting Smoking Cessation in the Community Via "Quit to Win" Contest 2026: Real-time Smoking Cessation Instant Messaging Support Using a Engagement-Focused Large Language Model (LLM)-Based Chatbot
试验速览
- 阶段
- 不适用
- 状态
- 尚未招募
- 入组人数
- 998
- 试验地点
- 1
- 主要终点
- Biochemically validated abstinence
研究概览
简要总结
The goal of this trial is to learn if chatbot-based instant messaging works to help smoking cessation in general adult smokers. It will also learn about the experience, attitude, and perception of using an LLM-based chatbot. The main questions it aims to answer are:
- Will an engagement-focused LLM-based chatbot smoking cessation intervention have a non-inferior validated abstinence rate than the control group?
- Will an LLM-based chatbot smoking cessation intervention have a non-inferior self-reported abstinence rate, smoking reduction rate, and smoking cessation services use rate than the control group?
Researchers will compare an LLM-based chatbot smoking-cessation intervention to a human-led instant messaging support group (brief advice based on AWARD and personalised active referral) to determine whether chatbot-based instant messaging support promotes smoking cessation.
Participants in the intervention group will receive:
- AWARD advice
- Personalised active referral
- 12 weeks of chatbot-based instant messaging support (via WhatsApp)
详细描述
Although smoking prevalence in Hong Kong has declined to 9.1% in 2023, achieving the government's target of 7.8% by 2025 remains a major public health challenge. Unassisted "cold turkey" quitting has a long-term success rate of less than 5%, whereas evidence-based behavioural and pharmacological interventions can raise success rates to approximately 20% or higher. However, existing cessation services in Hong Kong face a critical utilisation gap: only 17.5% of smokers have engaged with professional services, and merely 23% have used nicotine replacement therapy. This underutilisation suggests that traditional human-resource-intensive models may lack accessibility, scalability, and local appeal. Generative AI, particularly large language models, offers a transformative solution by delivering consistent, scalable, and personalised support. In the 2025 "Quit to Win" round, investigators integrated an LLM-based chatbot via WhatsApp and received positive qualitative feedback. Yet quantitative analysis revealed a sharp decline in engagement, with weekly participation dropping from 32% in week 1 to 14% by week 12, indicating that conversational ability alone does not guarantee sustained user commitment. To address this implementation gap, investigators have developed an engagement-focused GenAI companion that incorporates structured onboarding, context-aware personalisation, multimodal (text/audio) input, empathetic support, habit-aligned reminders, localised humour, and gamified features such as success stories and knowledge quizzes. Therefore, the current study aims to test, via a two-arm non-inferiority randomised controlled trial, the effectiveness of a comprehensive intervention combining brief cessation advice (AWARD), personalised active referral, and this engagement-enhanced GenAI chatbot support compared with human-led instant messaging counselling among current smokers who join the Quit to Win Contest across all 18 districts of Hong Kong.
研究设计
- 研究类型
- Interventional
- 分配方式
- Randomized
- 干预模型
- Parallel
- 主要目的
- Health Services Research
- 盲法
- Single (Outcomes Assessor)
入排标准
- 年龄范围
- 18 Years 至 —（Adult, Older Adult）
- 性别
- All
- 接受健康志愿者
- 否
入选标准
- •Hong Kong residents aged 18 years or above
- •Smoke at least one cigarette (including heated tobacco products) per day or use an e-cigarette daily in the preceding 3 months
- •Able to communicate in Cantonese (including reading and writing Chinese)
- •Saliva cotinine level ≥30 ng/mL
- •Intention to quit or reduce smoking
- •Have WhatsApp installed
- •Able to use WhatsApp for communication
排除标准
- •Smokers who have communication barriers (either physical or cognitive)
- •Smokers who are currently participating in other smoking cessation programs or services
结局指标
主要结局
Biochemically validated abstinence
时间窗: 6-month follow-up
Defined as exhaled CO level \<4ppm and saliva cotinine level ≤30 ng/ml
次要结局
- Biochemically validated abstinence(3-month follow-up)
- Self-reported 7-day point prevalence abstinence(3- and 6-month follow-ups)
- Self-reported reduction(1-, 2-, 3- and 6-month follow-ups)
- Self-reported use of smoking cessation service(1-, 2-, 3- and 6-month follow-ups)
- Prolonged abstinence(3-month and 6-month follow-ups)
- Quit attempt(1-, 2-, 3-, and 6-month follow-ups)
- Post-cessation weight change(6-month follow-up)
- Self-reported mental health conditions(Baseline and 6-month follow-up)
- Self-reported smoking-related health conditions(Baseline and 6-month follow-up)
- Chatbot user experience(3-month follow-up)
研究者
Prof. Wang Man-Ping
Professor
The University of Hong Kong