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.
Studienübersicht
Status
Bedingungen
Intervention / Behandlung
Studientyp
Einschreibung (Geschätzt)
Phase
- Unzutreffend
Kontakte und Standorte
Studienkontakt
- Name: Wei Xia, PhD
- Telefonnummer: 8618823359471
- E-Mail: xiaw23@mail.sysu.edu.cn
Studieren Sie die Kontaktsicherung
- Name: Jiebing Luo
- Telefonnummer: 8618885639072
- E-Mail: luojiebing2002@163.com
Teilnahmekriterien
Zulassungskriterien
Studienberechtigtes Alter
- Kind
- Erwachsene
- Älterer Erwachsener
Akzeptiert gesunde Freiwillige
Beschreibung
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.
Studienplan
Wie ist die Studie aufgebaut?
Designdetails
- Hauptzweck: Behandlung
- Zuteilung: Zufällig
- Interventionsmodell: Parallele Zuordnung
- Maskierung: Single
Waffen und Interventionen
Teilnehmergruppe / Arm |
Intervention / Behandlung |
|---|---|
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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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Aktiver Komparator: 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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Kein Eingriff: 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). |
Was misst die Studie?
Primäre Ergebnismessungen
Ergebnis Maßnahme |
Maßnahmenbeschreibung |
Zeitfenster |
|---|---|---|
|
Biochemically validated 7-Day Point Prevalence Abstinence Rate
Zeitfenster: 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
|
6 month follow-up after randomization
|
Sekundäre Ergebnismessungen
Ergebnis Maßnahme |
Maßnahmenbeschreibung |
Zeitfenster |
|---|---|---|
|
Self-Reported 7-Day Point Prevalence Abstinence Rate
Zeitfenster: 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.
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1 week, 1 month, 3 months, and 6-month follow-up
|
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Smoking Reduction Rate
Zeitfenster: 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
Zeitfenster: 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
Zeitfenster: 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.
|
Baseline,1 week, 1 month, 3 months, and 6-months follow-up
|
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Total Duration of AI Agent Use
Zeitfenster: 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.
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From randomization through 6 months
|
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Mean AI Agent Response Time
Zeitfenster: 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
Zeitfenster: 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
|
Mitarbeiter und Ermittler
Sponsor
Studienaufzeichnungsdaten
Haupttermine studieren
Studienbeginn (Geschätzt)
Primärer Abschluss (Geschätzt)
Studienabschluss (Geschätzt)
Studienanmeldedaten
Zuerst eingereicht
Zuerst eingereicht, das die QC-Kriterien erfüllt hat
Zuerst gepostet (Tatsächlich)
Studienaufzeichnungsaktualisierungen
Letztes Update gepostet (Tatsächlich)
Letztes eingereichtes Update, das die QC-Kriterien erfüllt
Zuletzt verifiziert
Mehr Informationen
Begriffe im Zusammenhang mit dieser Studie
Schlüsselwörter
Zusätzliche relevante MeSH-Bedingungen
Andere Studien-ID-Nummern
- L2025SYSU-HL-048
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- ANALYTIC_CODE
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