Promoting Smoking Cessation in the Community Via "Quit to Win" Contest 2026: Engagement-Focused GenAI-based Chatbot for Real-Time Smoking Cessation Support (QTW2026)
2026年6月26日 更新者：Prof. Wang Man-Ping、The University of Hong Kong
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:
- 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)
調査の概要
状態
まだ募集していません
条件
詳細な説明
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.
研究の種類
介入
入学 (推定)
998
段階
- 適用できない
連絡先と場所
このセクションには、調査を実施する担当者の連絡先の詳細と、この調査が実施されている場所に関する情報が記載されています。
研究連絡先
- 名前：Man Ping Wang, PhD
- 電話番号：+852 3917 6636
- メール：mpwang@hku.hk
研究連絡先のバックアップ
- 名前：Yilan Wu
- 電話番号：+852 56497578
- メール：visspalan@connect.hku.hk
研究場所
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Hong Kong
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Hong Kong、Hong Kong、香港、999077
- Hong Kong Council on Smoking and Health (COSH)
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コンタクト：
- Man Ping Wang, PhD
- 電話番号：+852 3917 6636
- メール：mpwang@hku.hk
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副調査官：
- Shengzhi Zhao, PhD
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副調査官：
- Xiaoyun Xie, MPH
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副調査官：
- Mengyao Li, Mphil
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コンタクト：
- Yilan Wu
- 電話番号：+852 56497578
- メール：visspalan@connect.hku.hk
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主任研究者：
- Man Ping Wang
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副調査官：
- Ziqiu Guo, PhD
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副調査官：
- Yilan Wu, MGH
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副調査官：
- Patrick IP, MD, PhD
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副調査官：
- Ning Huang, PhD
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参加基準
研究者は、適格基準と呼ばれる特定の説明に適合する人を探します。これらの基準のいくつかの例は、人の一般的な健康状態または以前の治療です。
適格基準
就学可能な年齢
- 大人
- 高齢者
健康ボランティアの受け入れ
いいえ
説明
Inclusion Criteria:
- 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
Exclusion Criteria:
- Smokers who have communication barriers (either physical or cognitive)
- Smokers who are currently participating in other smoking cessation programs or services
研究計画
このセクションでは、研究がどのように設計され、研究が何を測定しているかなど、研究計画の詳細を提供します。
研究はどのように設計されていますか？
デザインの詳細
- 主な目的：ヘルスサービス研究
- 割り当て：ランダム化
- 介入モデル：並列代入
- マスキング：独身
武器と介入
参加者グループ / アーム |
介入・治療 |
|---|---|
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実験的：介入群
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フォローアップ調査リマインダーのWhatsAppメッセージ。
A brief (30-60 seconds) face-to-face or remote smoking cessation advice delivered using the validated AWARD model: Ask about smoking history; Warn about high health risks (accompanied by a health warning leaflet); Advise quitting as soon as possible and setting a quit date (to qualify for contest prizes); Refer to smoking cessation services using a referral card; Do it again - repeat the intervention at each follow-up, encouraging re-quitting after relapse or relapse prevention after success.
A two-sided, colour-printed A4 leaflet covering: (1) absolute risk of death from smoking; (2) full list of diseases caused by active and second-hand smoking; (3) ten pictorial warnings of health consequences on one page for maximum impact; (4) benefits of smoking cessation; and (5) simple encouraging messages to quit.
A three-folded card containing brief information and highlights of existing smoking cessation services in Hong Kong, contact methods, motivational messages, and strong supporting slogans.
A generic booklet provided covering: benefits of quitting, smoking-related diseases, methods to quit, how to handle withdrawal symptoms, a quitting declaration, and other practical tips.
Participants in the intervention group will receive 12 weeks of instant messaging support delivered by an LLM-based chatbot (GPT-4o or newer) on WhatsApp, supporting text and audio input.
Using prompt engineering, agent techniques, and Retrieval-Augmented Generation, the chatbot delivers theory-based 5As/5Rs-structured interventions alongside freeform, on-demand support, with engagement features including personalisation, proactive check-ins, and interactive Quick Commands.
Smokers will be introduced to various SC services in Hong Kong (via the referral card) and motivated to use them.
Well-trained SC ambassadors will assist smokers in choosing their favourite or most convenient type of service.
Research staff will assist participants in booking or re-booking the SC services at the 1- and 2-month follow-ups (after very brief questionnaire surveys).
Participants' contact information will be forwarded to SC service providers within 7 days, and providers are expected to contact participants within 1-2 weeks.
Research staff will also monitor participants' use of SC services at each follow-up (1-, 2-, 3-, and 6-month) and, at the 1- and 2-month follow-ups, assist participants in booking or rebooking appointments if necessary.
Investigators shall liaise with existing service providers and seek their assistance in promptly supporting our smokers.
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アクティブコンパレータ：対照群
|
フォローアップ調査リマインダーのWhatsAppメッセージ。
A brief (30-60 seconds) face-to-face or remote smoking cessation advice delivered using the validated AWARD model: Ask about smoking history; Warn about high health risks (accompanied by a health warning leaflet); Advise quitting as soon as possible and setting a quit date (to qualify for contest prizes); Refer to smoking cessation services using a referral card; Do it again - repeat the intervention at each follow-up, encouraging re-quitting after relapse or relapse prevention after success.
A two-sided, colour-printed A4 leaflet covering: (1) absolute risk of death from smoking; (2) full list of diseases caused by active and second-hand smoking; (3) ten pictorial warnings of health consequences on one page for maximum impact; (4) benefits of smoking cessation; and (5) simple encouraging messages to quit.
A three-folded card containing brief information and highlights of existing smoking cessation services in Hong Kong, contact methods, motivational messages, and strong supporting slogans.
A generic booklet provided covering: benefits of quitting, smoking-related diseases, methods to quit, how to handle withdrawal symptoms, a quitting declaration, and other practical tips.
Participants in the control group will receive 12 weeks of instant messaging support delivered by a trained human counsellor via WhatsApp.
Using the same theoretical frameworks as the chatbot intervention, the counsellor will provide real-time behavioural and psychosocial support grounded in the 5As/5Rs models, Motivational Interviewing (MI), and evidence-based Behaviour Change Techniques (BCTs).
The support will be personalised according to each participant's sociodemographic characteristics, smoking patterns, quit intentions, and plans.
Smokers will be introduced to various SC services in Hong Kong (via the referral card) and motivated to use them.
Well-trained SC ambassadors will assist smokers in choosing their favourite or most convenient type of service.
Research staff will assist participants in booking or re-booking the SC services at the 1- and 2-month follow-ups (after very brief questionnaire surveys).
Participants' contact information will be forwarded to SC service providers within 7 days, and providers are expected to contact participants within 1-2 weeks.
Research staff will also monitor participants' use of SC services at each follow-up (1-, 2-, 3-, and 6-month) and, at the 1- and 2-month follow-ups, assist participants in booking or rebooking appointments if necessary.
Investigators shall liaise with existing service providers and seek their assistance in promptly supporting our smokers.
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この研究は何を測定していますか？
主要な結果の測定
結果測定 |
メジャーの説明 |
時間枠 |
|---|---|---|
|
生化学的に検証された禁欲
時間枠：6ヶ月間のフォローアップ
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呼気COレベル<4ppmおよび唾液コチニンレベル≦30ng/mlとして定義されます。
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6ヶ月間のフォローアップ
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二次結果の測定
結果測定 |
メジャーの説明 |
時間枠 |
|---|---|---|
|
生化学的に検証された禁欲
時間枠：3ヶ月のフォローアップ
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呼気COレベル<4ppmおよび唾液コチニンレベル≦30ng/mlとして定義されます。
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3ヶ月のフォローアップ
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自己報告された7日間のポイントの有病率禁止
時間枠：3か月と6か月のフォローアップ
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フォローアップの前に7日間でパフを吸わなかった喫煙者
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3か月と6か月のフォローアップ
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自己報告された削減
時間枠：1、2、3、および6か月のフォローアップ
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ベースラインの少なくとも50％の減少によって定義されたタバコ数
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1、2、3、および6か月のフォローアップ
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禁煙サービスの自己報告された使用
時間枠：1、2、3、および6か月のフォローアップ
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1、2、3、および6か月のフォローアップでの禁煙サービスの使用。
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1、2、3、および6か月のフォローアップ
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Prolonged abstinence
時間枠：3-month and 6-month follow-ups
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Abstinence from smoking for 3 consecutive months at 3-month follow-up, or for 6 consecutive months at 6-month follow-up
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3-month and 6-month follow-ups
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Quit attempt
時間枠：1-, 2-, 3-, and 6-month follow-ups
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Abstinence for at least 24 hours
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1-, 2-, 3-, and 6-month follow-ups
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Post-cessation weight change
時間枠：6-month follow-up
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Self-reported change in body weight (in kilograms) from baseline to follow-up
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6-month follow-up
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Self-reported smoking-related health conditions
時間枠：Baseline and 6-month follow-up
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Answer "Yes" to experiencing any smoking-related health condition during smoking cessation or reduction
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Baseline and 6-month follow-up
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Self-reported mental health conditions
時間枠：Baseline and 6-month follow-up
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Patient Health Questionnaire-4 (PHQ-4): The PHQ-4 is a 4-item ultra-brief screening tool for anxiety and depression that combines the GAD-2 and PHQ-2 subscales.
Each item is scored 0-3, with a total score of 0-12; subscale scores of 3 or higher indicate positive screening and warrant further clinical assessment.
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Baseline and 6-month follow-up
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Chatbot user experience
時間枠：3-month follow-up
|
Chatbot Usability Scale, or the 11-item Bot Usability Scale (BUS), is a validated questionnaire that evaluates chatbot usability across five dimensions (accessibility, function quality, conversation/information quality, privacy/security, and response time) using a 5-point Likert scale.
The total score (11-55) is the sum of all items; a higher total score indicates better overall usability and greater user satisfaction.
Higher scores on individual dimensions similarly reflect superior performance in those areas.
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3-month follow-up
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協力者と研究者
ここでは、この調査に関係する人々や組織を見つけることができます。
捜査官
- 主任研究者：Man Ping Wang、The University of Hong Kong
研究記録日
これらの日付は、ClinicalTrials.gov への研究記録と要約結果の提出の進捗状況を追跡します。研究記録と報告された結果は、国立医学図書館 (NLM) によって審査され、公開 Web サイトに掲載される前に、特定の品質管理基準を満たしていることが確認されます。
主要日程の研究
研究開始 (推定)
2026年6月27日
一次修了 (推定)
2027年10月30日
研究の完了 (推定)
2028年6月30日
試験登録日
最初に提出
2026年6月9日
QC基準を満たした最初の提出物
2026年6月9日
最初の投稿 (実際)
2026年6月15日
学習記録の更新
投稿された最後の更新 (実際)
2026年6月30日
QC基準を満たした最後の更新が送信されました
2026年6月26日
最終確認日
2026年6月1日
詳しくは
この情報は、Web サイト clinicaltrials.gov から変更なしで直接取得したものです。研究の詳細を変更、削除、または更新するリクエストがある場合は、register@clinicaltrials.gov。 までご連絡ください。 clinicaltrials.gov に変更が加えられるとすぐに、ウェブサイトでも自動的に更新されます。