ChatGPT-Based Chatbot for Help Quitting Smoking via Text Messaging: An Interventional Study
Bibliographic record
Abstract
BACKGROUND: Large language model chatbots such as ChatGPT may be able to provide support to people who smoke cigarettes and are trying to quit. OBJECTIVE: This 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. METHODS: Participants 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. RESULTS: Most 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%). CONCLUSIONS: A 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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".