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NCT Number: NCT07827066

Multi-Theory Model-Based AI Agent Intervention for Smoking Cessation in 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.

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Key information

Conditions

Smoking Cessation AI Agent Behavior Early-Stage Cancer Health Behavior Neoplasms Tobacco Use Cessation

Sex eligibility

All sexes

Study type

Interventional

Phase

Not applicable

Who can participate

Healthy volunteers accepted: No

Only the study team can determine whether someone qualifies for participation.

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.

Treatment and study plan

MTM-Based AI Agent for smoking cessation

Behavioral

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.

Counseling

Behavioral

WeChat-based counseling provided by smoking cessation specialists twice a month, following WHO guidelines.

Primary outcomes

  1. Biochemically validated 7-Day Point Prevalence Abstinence Rate

    Time frame: 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

Secondary outcomes

  1. Self-Reported 7-Day Point Prevalence Abstinence Rate

    Time frame: 1 week, 1 month, 3 months, and 6-month follow-up

    The proportion of participants who self-report having smoked no cigarettes in the past 7 days, without biochemical verification at interim time points.

  2. Smoking Reduction Rate

    Time frame: 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.

  3. Change in Smoking Self-Efficacy

    Time frame: 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.

  4. Change in Quality of Life

    Time frame: 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.

  5. Total Duration of AI Agent Use

    Time frame: 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.

  6. Mean AI Agent Response Time

    Time frame: 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.

  7. Mean AI Agent Session Duration

    Time frame: 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.

Study contacts

Contact information is provided by the study sponsor or research team.

Jiebing Luo

CONTACT

[email protected]

8618885639072

Wei Xia, PhD

CONTACT

[email protected]

8618823359471

Sponsors and collaborators

Lead sponsor

Sun Yat-sen University

Other

Registry information

Official study title

Construction and Effectiveness of a Multi-Theory Model-Based AI Agent Intervention for Smoking Cessation Among Early-Stage Cancer Patients

Important dates

Study start
2026
Primary completion
2027
Study completion
2027
First posted
Sep 18, 2026
Registry last updated
Sep 18, 2026

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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