Background: Large language model (LLM) artificial intelligence chatbots using generative language can offer smoking cessation information and advice. However, little is known about the reliability of the information provided to users. Objective: This study aims to examine whether 3 ChatGPT chatbots—the World Health Organization’s Sarah, BeFreeGPT, and BasicGPT—provide reliable information on how to quit smoking. Methods: A list of quit smoking queries was generated from frequent quit smoking searches on Google related to “how to quit smoking” (n=12). Each query was given to each chatbot, and responses were analyzed for their adherence to an index developed from the US Preventive Services Task Force public health guidelines for quitting smoking and counseling principles. Responses were independently coded by 2 reviewers, and differences were resolved by a third coder. Results: Across chatbots and queries, on average, chatbot responses were rated as being adherent to 57.1% of the items on the adherence index. Sarah’s adherence (72.2%) was significantly higher than BeFreeGPT (50%) and BasicGPT (47.8%; P<.001). The majority of chatbot responses had clear language (97.3%) and included a recommendation to seek out professional counseling (80.3%). About half of the responses included the recommendation to consider using nicotine replacement therapy (52.7%), the recommendation to seek out social support from friends and family (55.6%), and information on how to deal with cravings when quitting smoking (44.4%). The least common was information about considering the use of non–nicotine replacement therapy prescription drugs (14.1%). Finally, some types of misinformation were present in 22% of responses. Specific queries that were most challenging for the chatbots included queries on “how to quit smoking cold turkey,” “...with vapes,” “...with gummies,” “...with a necklace,” and “...with hypnosis.” All chatbots showed resilience to adversarial attacks that were intended to derail the conversation. Conclusions: LLM chatbots varied in their adherence to quit-smoking guidelines and counseling principles. While chatbots reliably provided some types of information, they omitted other types, as well as occasionally provided misinformation, especially for queries about less evidence-based methods of quitting. LLM chatbot instructions can be revised to compensate for these weaknesses.

IQOS, the leading heated tobacco product globally, recently received ‘reduced exposure’ authorization from the US Food and Drug Administration. Independent research focusing on IQOS marketing and potential impact on consumers’ perceptions and behavior, and ultimately public health, is critical. The literature to date has underscored several concerns. First, Philip Morris’s (PM’s) marketing distribution requires scrutiny, particularly given its innovative promotional strategies. For example, IQOS is distributed via unique points-of-sale (POS; e.g., specialty and pop-up stores, “corners” in convenience stores) and uses various other opportunities (e.g., social media, sponsored events, direct-to-consumer). Second, although PM claims that IQOS’ target market is current combustible tobacco users and not young people, the literature indicates that in some populations, IQOS use is equally prominent among smokers and nonsmokers, and that specific subgroups (e.g., young adults, women) are targeted. Third, the impact of IQOS’ use of ad content promoting IQOS health benefits must be studied (e.g., how consumers interpret modified exposure messages). In conclusion, surveillance of IQOS marketing, particularly following reduced exposure authorization, is critical for obtaining valuable data to estimate population impact, particularly among population subgroups (e.g., young adults), and inform future tobacco regulation. These considerations have implications beyond IQOS—to other products and companies.

Background: Perceived harm, social influences, smoke-free policies, and media exposure have been understudied in relation to tobacco-related attitudes/behaviors in aggregate or in low and middle-income countries; thus, this study examined these factors collectively in relation to smoking-related outcomes among Armenian and Georgian adults. Methods: Using 2018 cross-sectional survey data (n = 1456), multivariable regression analyses examined these factors in relation to smoking status, perceived harm among nonsmokers, and readiness to quit and past-year quit attempts among smokers. Results: Significant predictors (p < 0.05) of current smoking (27.3%) included lower perceived harm, more smoking friends, and fewer home and vehicle restrictions. Among nonsmokers, more home and restaurant/bar restrictions, fewer vehicle restrictions, greater anti-tobacco media exposure, and less pro-tobacco media exposure predicted greater perceived harm. Among smokers, greater perceived social acceptability of smoking, less anti-tobacco media exposure, and greater pro-tobacco media exposure predicted readiness to quit (12.7% of smokers). More smoking friends, more home restrictions, less anti-tobacco media exposure, and greater pro-tobacco media exposure predicted past-year quit attempts (19.2%). Conclusions: Findings support the importance of smoke-free policies but were counterintuitive regarding the roles of social and media influences, underscoring the need to better understand how to address these influences, particularly in countries with high smoking rates.

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.

Low-income adults are significantly more likely to smoke, and face more difficulty in quitting, than people with high income. High rates of delay discounting (DD) may be an important factor contributing to the high rates of tobacco use among low-income adults. Future-oriented financial coaching may offer a novel approach in the treatment of smoking cessation among low-income adults. This secondary analysis (N = 251) of data from a randomized controlled trial examined the integration of future-oriented financial coaching into smoking cessation treatment for low-income smokers. Linear regression and finite mixture models (FMM) estimated the overall and the latent heterogeneity of the impact of the intervention versus usual care control on DD rates 6 months after randomization. Though standard linear regression found no overall difference in DD between intervention and control (β = −0.23, p = 0.338), the FMM identified two latent subgroups with different responses to the intervention. Subgroup 1 (79% of the sample) showed no difference in DD between intervention and control (β = 0.25, p = 0.08). Subgroup 2 (21% of the sample) showed significantly lower DD (β = −2.06, p = 0.003) among intervention group participants versus control at 6 months. Participants were more likely to be a member of subgroup 2 if they had lower baseline DD rates, were living at or below 100% of federal poverty, or were married/living with a partner. This study identified a group of low-income adults seeking to quit smoking who responded to financial coaching with decreased DD rates. These results can be used to inform future targeting of the intervention to individuals who may benefit most, as well as inform future treatment adaptations to support the subgroup of low-income smokers, who did not benefit.