Evidence map›Paper›PMID 41072009›Full record

ArticleJMIR formative research2025

ChatGPT-Based Chatbot for Help Quitting Smoking via Text Messaging: An Interventional Study.

Lorien C Abroms, Christina N Wysota, Artin Yousefi, Tien-Chin Wu, David A Broniatowski

Abstract read
In one paragraph

Article in JMIR formative research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Review
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Lorien C AbromsMilken Institute School of Public Health, George Washington University, Washington, DC, United States.ORCID 0000-0001-6859-283X
Christina N WysotaGrossman School of Medicine, New York University, New York, NY, United States.ORCID 0000-0002-4588-911X
Artin YousefiSchool of Engineering and Applied Science, George Washington University, Washington, DC, United States.ORCID 0009-0006-5408-9049
Tien-Chin WuMilken Institute School of Public Health, George Washington University, Washington, DC, United States.ORCID 0000-0003-2245-8170
David A BroniatowskiSchool of Engineering and Applied Science, George Washington University, Washington, DC, United States.ORCID 0000-0002-3302-9497

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundLarge language model chatbots such as ChatGPT may be able to provide support to people who smoke cigarettes and are trying to quit.

objectiveThis pilot study examined the feasibility and acceptability of integrating a specialized ChatGPT-based chatbot, BeFreeBot, into a smoking cessation text messaging intervention, BeFree. Chatbot fidelity was also examined.

methodsParticipants who smoked cigarettes in the previous 7 days were recruited from Amazon Mechanical Turk (N=23), enrolled in BeFree, and provided access to BeFreeBot. Surveys were administered at baseline and 4 weeks after enrollment to assess perceptions of BeFreeBot. Computer records of interactions between BeFreeBot and participants were also analyzed to assess participant engagement and adherence of BeFreeBot to its instructions. For the adherence analysis, transcripts were dual coded, and discrepancies were resolved by a third coder.

resultsMost participants (16/23, 70%) texted BeFreeBot with questions or concerns at least once. Participants sent 14.5 (SD 23.6) texts to BeFreeBot on average. Most participants were highly satisfied with BeFreeBot (13/18, 72%) and agreed that it was helpful for quitting (11/19, 58%). They also reported that the BeFreeBot responses were clear and easy to understand (16/17, 94%) and that they trusted responses from BeFreeBot (12/17, 71%). Most participants (17/19, 90%) reported trying to quit smoking for 1 day or longer, and 30% (7/23) self-reported no smoking in the previous 7 days. An analysis of transcripts of BeFreeBot responses (n=328) revealed that BeFreeBot functioned as instructed on most measures, with clear language (328/328, 100%), follow-up questions asked of participants (13/16, 81%), and recommendations to seek out professional counseling (13/16, 81%) or consider the use of Food and Drug Administration-approved medications (eg, nicotine replacement therapy; 14/16, 88%). Responses stayed on the topic of smoking cessation counseling (324/328, 98.8%) and did not include information that contradicted the US Preventive Services Task Force guidelines (328/328, 100%).

conclusionsA specialized large language model chatbot integrated into an SMS text messaging program and accessed through SMS text message was found to be feasible and acceptable to smokers.

Indexed as

Smoking CessationText MessagingAdultFeasibility StudiesFemaleGenerative Artificial IntelligenceHumansMaleMiddle AgedPilot ProjectsSurveys and Questionnairesartificial intelligencechatbotsChatGPTcigaretteslarge language modelsmoking cessationSMS text messagingtobacco

Identifiers

PMID41072009
PMCPMC12552811

What OpenQuestion holds

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LicenceCC BY
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.