Ignite Creation Date:
2025-12-24 @ 3:44 PM
Ignite Modification Date:
2025-12-31 @ 5:11 PM
Study NCT ID:
NCT06914492
Status:
ACTIVE_NOT_RECRUITING
Last Update Posted:
2025-12-01
First Post:
2025-03-31
Is NOT Gene Therapy:
True
Has Adverse Events:
False
Brief Title:
Real-time Smoking Cessation Instant Messaging Support Using a Large Language Model (LLM)-Based Chatbot Via "Quit to Win" 2025 (QTW2025)
Sponsor:
Organization:
Raw JSON
{'hasResults': False, 'derivedSection': {'miscInfoModule': {'versionHolder': '2025-12-24'}, 'conditionBrowseModule': {'meshes': [{'id': 'D016540', 'term': 'Smoking Cessation'}], 'ancestors': [{'id': 'D015438', 'term': 'Health Behavior'}, {'id': 'D001519', 'term': 'Behavior'}]}}, 'protocolSection': {'designModule': {'phases': ['NA'], 'studyType': 'INTERVENTIONAL', 'designInfo': {'allocation': 'RANDOMIZED', 'maskingInfo': {'masking': 'SINGLE', 'whoMasked': ['OUTCOMES_ASSESSOR']}, 'primaryPurpose': 'HEALTH_SERVICES_RESEARCH', 'interventionModel': 'PARALLEL'}, 'enrollmentInfo': {'type': 'ESTIMATED', 'count': 1094}}, 'statusModule': {'overallStatus': 'ACTIVE_NOT_RECRUITING', 'startDateStruct': {'date': '2025-06-21', 'type': 'ACTUAL'}, 'expandedAccessInfo': {'hasExpandedAccess': False}, 'statusVerifiedDate': '2025-05', 'completionDateStruct': {'date': '2027-06-30', 'type': 'ESTIMATED'}, 'lastUpdateSubmitDate': '2025-11-22', 'studyFirstSubmitDate': '2025-03-31', 'studyFirstSubmitQcDate': '2025-03-31', 'lastUpdatePostDateStruct': {'date': '2025-12-01', 'type': 'ESTIMATED'}, 'studyFirstPostDateStruct': {'date': '2025-04-06', 'type': 'ACTUAL'}, 'primaryCompletionDateStruct': {'date': '2026-06-30', 'type': 'ESTIMATED'}}, 'outcomesModule': {'primaryOutcomes': [{'measure': 'Biochemically validated abstinence', 'timeFrame': '6-month follow-up', 'description': 'Defined as exhaled CO level \\<4ppm and saliva cotinine level ≤30 ng/ml'}], 'secondaryOutcomes': [{'measure': 'Biochemically validated abstinence', 'timeFrame': '3-month follow-up', 'description': 'Defined as exhaled CO level \\<4ppm and saliva cotinine level ≤30 ng/ml'}, {'measure': 'Self-reported 7-day point prevalence abstinence', 'timeFrame': '3- and 6-month follow-ups', 'description': 'Smokers who did not smoke even a puff in the 7 days preceding the follow-up'}, {'measure': 'Self-reported reduction', 'timeFrame': '1-, 2-, 3- and 6-month follow-ups', 'description': 'Defined by at least 50% reduction in baseline daily number of cigarettes'}, {'measure': 'Self-reported use of smoking cessation service', 'timeFrame': '1-, 2-, 3- and 6-month follow-ups', 'description': 'Use of smoking cessation service at 1-, 2-, 3- and 6-month follow-ups.'}]}, 'oversightModule': {'oversightHasDmc': True, 'isFdaRegulatedDrug': False, 'isFdaRegulatedDevice': False}, 'conditionsModule': {'keywords': ['Large-language model', 'Chatbot', 'Smoking cessation', 'Behavioral counselling', 'Digital health'], 'conditions': ['Smoking Cessation']}, 'descriptionModule': {'briefSummary': '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:\n\n1. Will LLM-based chatbot smoking cessation intervention have a higher validated abstinence rate than the control group?\n2. Will LLM-based chatbot smoking cessation intervention have a higher self-reported abstinence rate, smoking reduction rate, and smoking cessation services use rate than the control group?\n\nResearchers will compare LLM-based chatbot smoking cessation intervention to a usual care group (brief advice based on AWARD and personalized active referral) to see if chatbot-based instant messaging support works to promote smoking cessation.\n\nParticipants in the intervention group will receive:\n\n1. AWARD advice\n2. Personalized active referral\n3. 12 weeks of chatbot-based instant messaging support (via WhatsApp)', 'detailedDescription': 'Although smoking prevalence is decreasing in Hong Kong (1982: 23.3%; 2023: 9.1%), it accounts for over 7,000 deaths per year and a large amount of medical cost, long-term care and productivity loss of US$ 688 million (0.6% Hong Kong GDP). Quitting is difficult because nicotine is highly addictive. Long-term habitual tobacco smoking could foster a series of physical and psychological dependence on nicotine, and thus induce cravings and nicotine withdrawal symptoms when remaining abstinent. Tradition "one-intervention-for-all" approach cannot work optimally for overall smoking population because of the individual differences in the background characteristic and variations in response to the intervention. Intervention approaches that account for personalization and variation should be explored.\n\nPreliminary studies suggest that AI-based chatbots can deliver structured counseling to support tobacco cessation through personalised, empathetic, and authentic conversations, thus enhancing the effectiveness of smoking cessation interventions. When integrated into social media, AI chatbots can provide timely, targeted responses and connect users with a resources on widely used platforms. A 2023 meta-analysis involving 58,796 participants further highlighted the promise of chatbots for tobacco cessation (RR=1.29, 95%CI 1.13-1.46).\n\nIn late 2022, the release of ChatGPT revolutionized AI and large language models by offering unprecedented reasoning and conversational capabilities, which enables the development of more sophisticated, human-like chatbots. Although ChatGPT (and the GenAI in general) was trained as a general-purpose virtual assistant, its tasks-specific performance can be significantly enhanced through prompt engineering. Leveraging this approach, we developed an LLM-based chatbot to autonomously deliver smoking cessation support via WhatsApp according to established protocols. Our previous rule-based chatbot effectively prevented smoking relapse, and a pilot trial with an LLM-based chatbot for youth smokers (n=154) showed feasibility, achieving an 80.2% retention rate. These findings support the implementation of GenAI chatbots as promising tools for brief smoking cessation interventions in adult smokers.\n\nTherefore, our study aims to test the effectiveness of a comprehensive intervention using brief cessation advice, personalized active referral, and chatbot-based instant messaging support compared with the control group on current smokers who join the Quit to Win Contest.'}, 'eligibilityModule': {'sex': 'ALL', 'stdAges': ['ADULT', 'OLDER_ADULT'], 'minimumAge': '18 Years', 'healthyVolunteers': False, 'eligibilityCriteria': 'Inclusion Criteria:\n\n* Hong Kong residents aged 18 or above\n* Smoke at least 1 tobacco stick (includes HTP) per day or use e-cigarette daily in the past 3-month\n* Able to communicate in Chinese\n* Exhaled carbon monoxide level ≥4 part per million or a positive salivary cotinine test\n* Intention to quit/reducing smoking\n* Have instant messaging tool (WhatsApp) installed\n* Able to use the instant messaging tool (e.g., WhatsApp) for communication\n\nExclusion Criteria:\n\n* Smokers who have communication barriers (either physical or cognitive).\n* Smokers who are currently participating in other smoking cessation programs or services.'}, 'identificationModule': {'nctId': 'NCT06914492', 'briefTitle': 'Real-time Smoking Cessation Instant Messaging Support Using a Large Language Model (LLM)-Based Chatbot Via "Quit to Win" 2025 (QTW2025)', 'organization': {'class': 'OTHER', 'fullName': 'The University of Hong Kong'}, 'officialTitle': 'Building Capacity and Promoting Smoking Cessation in the Community Via "Quit to Win" Contest 2025: Real-time Smoking Cessation Instant Messaging Support Using a Large Language Model (LLM)-Based Chatbot', 'orgStudyIdInfo': {'id': 'QTW2025'}}, 'armsInterventionsModule': {'armGroups': [{'type': 'EXPERIMENTAL', 'label': 'Intervention group', 'interventionNames': ['Behavioral: AWARD advice', 'Behavioral: Brief leaflet on health warning and smoking cessation', 'Behavioral: Referral card', 'Behavioral: Self-help smoking cessation booklet', 'Behavioral: Personalized active referral', 'Behavioral: 12 weeks of chatbot-based instant messaging support', 'Behavioral: Reminder messages']}, {'type': 'ACTIVE_COMPARATOR', 'label': 'Control group', 'interventionNames': ['Behavioral: AWARD advice', 'Behavioral: Brief leaflet on health warning and smoking cessation', 'Behavioral: Referral card', 'Behavioral: Self-help smoking cessation booklet', 'Behavioral: Personalized active referral', 'Behavioral: Reminder messages']}], 'interventions': [{'name': 'AWARD advice', 'type': 'BEHAVIORAL', 'description': 'AWARD advice include Ask about smoking history, Warn about the high risk, Advise to quit, Refer smokers to smoking cessation services (with a referral card), and Do it again.', 'armGroupLabels': ['Control group', 'Intervention group']}, {'name': 'Brief leaflet on health warning and smoking cessation', 'type': 'BEHAVIORAL', 'description': 'The contents of the leaflet include (1) highlights of the absolute risk of death due to smoking; (2) the whole list of diseases caused by active and second-hand smoking; (3) ten horrible pictorial warnings of health consequences of smoking and second-hand smoking in one page to maximize the impacts; (4) benefits of SC and (5) simple messages to encourage participants to quit smoking.', 'armGroupLabels': ['Control group', 'Intervention group']}, {'name': 'Referral card', 'type': 'BEHAVIORAL', 'description': 'The contents consist of brief information and a highlight of existing smoking cessation services, contact methods, motivation information and strong supporting messages or slogans.', 'armGroupLabels': ['Control group', 'Intervention group']}, {'name': 'Self-help smoking cessation booklet', 'type': 'BEHAVIORAL', 'description': 'The contents include information about the benefits of quitting, smoking and diseases, methods to quit, how to handle withdrawal symptoms, declaration of quitting, etc.', 'armGroupLabels': ['Control group', 'Intervention group']}, {'name': 'Personalized active referral', 'type': 'BEHAVIORAL', 'description': "Smokers will be introduced to various SC services in Hong Kong (using the referral card) and be motivated to use these services. Well-trained SC ambassadors will assist smoker to choose favourite/most convenient or preferred type of services. 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 services providers within seven days, and providers are expected to contact the participants within 1-2 weeks. Research staff will also monitor SC services use of the participants at each follow-up (1-, 2-, 3- and 6- month) and assist participants to book or re-book the appointments if necessary at 1- and 2- month follow-up. We shall liaise with the existing service providers and seek their assistance in helping our smokers in helping our smokers in a timely manner.", 'armGroupLabels': ['Control group', 'Intervention group']}, {'name': '12 weeks of chatbot-based instant messaging support', 'type': 'BEHAVIORAL', 'description': 'Participants in the intervention group will receive 12 weeks of instant messaging support delivered by the LLM-based chatbot accessible via WhatsApp platform. The chatbot, powered by GPT-4o model (or more advanced model available) using prompt-engineering and agent-based techniques, will deliver brief theory-based, structured SC intervention alongside freeform, on-demand support. The structured intervention session deploys the 5As model (Ask, Advise, Assess, Assist, and Arrange follow-up) and 5Rs model (Relevance, Risks, Rewards, Roadblocks, Repetition), as used in our previous telephone-counselling trials and recommended by WHO for brief SC intervention.', 'armGroupLabels': ['Intervention group']}, {'name': 'Reminder messages', 'type': 'BEHAVIORAL', 'description': 'WhatsApp messages on follow-up survey reminders.', 'armGroupLabels': ['Control group', 'Intervention group']}]}, 'contactsLocationsModule': {'locations': [{'zip': '999077', 'city': 'Hong Kong', 'state': 'Hong Kong', 'country': 'Hong Kong', 'facility': 'Hong Kong Council on Smoking and Health (COSH)', 'geoPoint': {'lat': 22.27832, 'lon': 114.17469}}], 'overallOfficials': [{'name': 'Man Ping Wang, PhD', 'role': 'PRINCIPAL_INVESTIGATOR', 'affiliation': 'The University of Hong Kong'}]}, 'ipdSharingStatementModule': {'ipdSharing': 'NO'}, 'sponsorCollaboratorsModule': {'leadSponsor': {'name': 'The University of Hong Kong', 'class': 'OTHER'}, 'responsibleParty': {'type': 'PRINCIPAL_INVESTIGATOR', 'investigatorTitle': 'Professor', 'investigatorFullName': 'Prof. Wang Man-Ping', 'investigatorAffiliation': 'The University of Hong Kong'}}}}