Multi-Theory Model-Based AI Agent Intervention for Smoking Cessation in Early-Stage Cancer Patients
Construction and Effectiveness of a Multi-Theory Model-Based AI Agent Intervention for Smoking Cessation Among Early-Stage Cancer Patients
The goal of this clinical trial is to evaluate the effectiveness of a Multi-Theory Model (MTM)-based AI agent intervention for smoking cessation in early-stage cancer patients (clinical stage cTNM 0~II) who currently smoke. The main questions it aims to answer are:
Does the AI agent intervention improve the biochemically verified 7-day point prevalence abstinence rate at the 6-month follow-up compared to control groups?
Is the AI agent intervention feasible and acceptable for early-stage cancer patients?
Researchers will compare the AI agent intervention group to an professional counseling group and a routine health education groupto see if the AI agent yields higher smoking cessation rates and better maintenance of abstinence.
Participants will:
Be randomly assigned to one of three groups to receive either AI agent support via WeChat, professional counseling via Phone, or routine health education.
Interact with the AI agent (if in the intervention group) which provides personalized guidance, emotional support, and resource matching based on the Multi-Theory Model constructs (e.g., participatory dialogue, emotional transformation).
Complete questionnaires regarding smoking behavior, nicotine dependence, self-efficacy, and quality of life at baseline and follow-ups (1 week, 1 month, 3 months, and 6 months).
Provide exhaled carbon monoxide (CO) and saliva cotinine samples for biochemical verification if they report successful smoking cessation.
Visão geral do estudo
Status
Condições
Intervenção / Tratamento
Tipo de estudo
Inscrição (Estimado)
Estágio
- Não aplicável
Contactos e Locais
Contato de estudo
- Nome: Wei Xia, PhD
- Número de telefone: 8618823359471
- E-mail: xiaw23@mail.sysu.edu.cn
Estude backup de contato
- Nome: Jiebing Luo
- Número de telefone: 8618885639072
- E-mail: luojiebing2002@163.com
Critérios de participação
Critérios de elegibilidade
Idades elegíveis para estudo
- Filho
- Adulto
- Adulto mais velho
Aceita Voluntários Saudáveis
Descrição
Inclusion Criteria:
- Age ≥18 years, diagnosed with early-stage cancer (AJCC 8th edition clinical stage cTNM 0-II);
- Smoked in the past 30 days, with an average daily consumption of > 1 cigarette, and an exhaled Carbon Monoxide (CO) level ≥ 4 ppm;
- Able to communicate using WeChat;
- Able to understand and read Chinese, and possess conversational Mandarin skills;
- Willing to participate in this study and sign the informed consent form.
Exclusion Criteria:
- Individuals who are unable to communicate due to severe mental or physical illness;
- Individuals currently participating in other tobacco control research projects;
- Individuals whose cancer has metastasized.
Plano de estudo
Como o estudo é projetado?
Detalhes do projeto
- Finalidade Principal: Tratamento
- Alocação: Randomizado
- Modelo Intervencional: Atribuição Paralela
- Mascaramento: Solteiro
Armas e Intervenções
Grupo de Participantes / Braço |
Intervenção / Tratamento |
|---|---|
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Experimental: AI Agent Intervention Group
Participants in this group will access a customized "Smoking Cessation AI Agent" via the WeChat platform. The agent utilizes a Large Language Model with Retrieval-Augmented Generation (RAG) to provide professional, evidence-based support grounded in the Multi-Theory Model (MTM). Key features include: 24/7 Personalized Interaction: Offers open-ended dialogue, personalized advice, and proactive pushes (e.g., health education cards, check-in incentives). Stage-Matched Guidance: Initiation Phase: Focuses on participatory dialogue to weigh pros/cons and behavioral confidence building through goal setting. Maintenance Phase: Focuses on emotional transformation (managing withdrawal/emotions), practice for change, and modifying the social/physical environment (e.g., matching cessation resources, peer support). Dynamic Adaptation: The agent dynamically adjusts its strategies based on the user's interaction frequency and quitting progress. |
An AI agent powered by a Large Language Model with Retrieval-Augmented Generation (RAG). It provides 24/7 personalized smoking cessation support based on the Multi-Theory Model (MTM). Key Functions: Initiation Phase: Participatory dialogue to weigh pros/cons and goal setting to build behavioral confidence. Maintenance Phase: Emotional transformation support, habit tracking (practice for change), and social/physical environment resource matching (e.g., peer support). Dynamic Adaptation: Adjusts content and push frequency based on user interaction and quitting stage. |
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Comparador Ativo: Counseling Group
Participants in this group receive smoking cessation counseling by WeChat from specialists. Proactive Intervention: Specialists contact participants twice a month. Content: Brief counseling (approx. 30 seconds to 5 minutes) based on WHO guidelines, including assessing smoking status, difficulties, and progress, and providing customized advice (e.g., motivation boosting, coping strategies for withdrawal). Reactive Support: Participants can proactively contact the specialists during working hours (Mon-Fri, 8:00-17:00) for inquiries. |
WeChat-based counseling provided by smoking cessation specialists twice a month, following WHO guidelines.
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Sem intervenção: Health Education Group
Participants receive standard care only, which consists of routine brief smoking cessation advice from physicians during their regular hospital visits. This group does not receive any additional active intervention, education, or follow-up counseling from the research team, except for data collection at scheduled follow-up points (baseline, 1 week, 1 month, 3 months, and 6 months). |
O que o estudo está medindo?
Medidas de resultados primários
Medida de resultado |
Descrição da medida |
Prazo |
|---|---|---|
|
Biochemically validated 7-Day Point Prevalence Abstinence Rate
Prazo: 6 month follow-up after randomization
|
Participants are considered abstinent if they self-report having smoked 0 cigarettes (not even a puff) in the past 7 days, confirmed by a biochemical validation of exhaled Carbon Monoxide (CO) concentration < 4 ppm and saliva cotinine concentration < 115 ng/ml
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6 month follow-up after randomization
|
Medidas de resultados secundários
Medida de resultado |
Descrição da medida |
Prazo |
|---|---|---|
|
Self-Reported 7-Day Point Prevalence Abstinence Rate
Prazo: 1 week, 1 month, 3 months, and 6-month follow-up
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The proportion of participants who self-report having smoked no cigarettes in the past 7 days, without biochemical verification at interim time points.
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1 week, 1 month, 3 months, and 6-month follow-up
|
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Smoking Reduction Rate
Prazo: 1 week, 1 month, 3 months, and 6-month follow-up
|
Defined as a reduction in daily cigarette consumption by ≥50% compared to baseline levels.
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1 week, 1 month, 3 months, and 6-month follow-up
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Change in Smoking Self-Efficacy
Prazo: Baseline, 1 week, 1 month, 3 months, and 6-months follow-up
|
Measured using the Smoking Self-Efficacy Questionnaire (SEQ-12).
The scale contains 12 items rated on a 5-point Likert scale.
Total scores range from 12 to 60, with higher scores indicating greater confidence in the ability to refrain from smoking in various situations.
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Baseline, 1 week, 1 month, 3 months, and 6-months follow-up
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Change in Quality of Life
Prazo: Baseline,1 week, 1 month, 3 months, and 6-months follow-up
|
Health-related quality of life will be assessed using the EuroQol 5-Dimension 5-Level (EQ-5D-5L) questionnaire.
The EQ-5D-5L assesses five dimensions of health: mobility, self-care, usual activities, pain/discomfort, and anxiety/depression.
Responses will be converted to an EQ-5D-5L index score using the prespecified value set, with higher scores indicating better health-related quality of life.
The outcome will be reported as the change in EQ-5D-5L index score from baseline.
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Baseline,1 week, 1 month, 3 months, and 6-months follow-up
|
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Total Duration of AI Agent Use
Prazo: From randomization through 6 months
|
AI agent use will be assessed using automatically recorded backend system logs.
The cumulative duration of AI agent use for each participant during the follow-up period will be calculated.
The unit of measure is minutes.
|
From randomization through 6 months
|
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Mean AI Agent Response Time
Prazo: From randomization through 6 months
|
Mean AI agent response time will be calculated using backend system timestamps as the average time between submission of a participant message and generation of the corresponding AI agent response.
The unit of measure is seconds.
|
From randomization through 6 months
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Mean AI Agent Session Duration
Prazo: From randomization through 6 months
|
Mean session duration will be calculated from backend system logs as the total duration of AI agent use divided by the number of usage sessions for each participant.
The unit of measure is minutes per session.
|
From randomization through 6 months
|
Colaboradores e Investigadores
Patrocinador
Datas de registro do estudo
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Início do estudo (Estimado)
Conclusão Primária (Estimado)
Conclusão do estudo (Estimado)
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Enviado pela primeira vez que atendeu aos critérios de CQ
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Atualizações de registro de estudo
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Última atualização enviada que atendeu aos critérios de controle de qualidade
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Mais Informações
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Outros números de identificação do estudo
- L2025SYSU-HL-048
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- SEIVA
- ANALYTIC_CODE
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