Promoting Smoking Cessation in the Community Via "Quit to Win" Contest 2026: Engagement-Focused GenAI-based Chatbot for Real-Time Smoking Cessation Support

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

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:

1. Will an engagement-focused LLM-based chatbot smoking cessation intervention have a non-inferior validated abstinence rate than the control group?
2. 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:

1. AWARD advice
2. Personalised active referral
3. 12 weeks of chatbot-based instant messaging support (via WhatsApp)

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.

Inclusion Criteria:

1. Hong Kong residents aged 18 years or above
2. Smoke at least one cigarette (including heated tobacco products) per day or use an e-cigarette daily in the preceding 3 months
3. Able to communicate in Cantonese (including reading and writing Chinese)
4. Saliva cotinine level ≥30 ng/mL
5. Intention to quit or reduce smoking
6. Have WhatsApp installed
7. Able to use WhatsApp for communication

Exclusion Criteria:

1. Smokers who have communication barriers (either physical or cognitive)
2. Smokers who are currently participating in other smoking cessation programs or services