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Since the early studies exploring the use of SMS text messaging for health intervention, text messaging has played a pivotal role in the advancement of mobile health. As an intervention modality, text messaging has provided vital learnings for the design and delivery of interventions, particularly in low-resource settings. Despite the advances in technology over the last 25 years, text messaging is still being used in largely the same way to deliver health information, behavior change interventions, and support. The strong, consistent evidence for the benefits of this type of intervention has made text messaging a routine part of health interventions around the world. Key to its success is its simplicity, alongside the benefit of being arguably the most accessible form of consumer digital health intervention. Text message interventions are well suited for public health interventions due to their low cost, vast reach, frequent use, high read rates, and ability to be tailored and personalized. Furthermore, the nature of text messaging interventions makes them ideal for the delivery of multilingual, culturally tailored interventions, which is important in the context of increasing cultural diversity in many countries internationally. Indeed, studies assessing text message–based health interventions have shown them to be effective across sociodemographic and ethnic groups and have led to their adoption into national-level health promotion programs. With a growing focus on artificial intelligence, robotics, sensors, and other advances in digital health, there is an opportunity to integrate these technologies into text messaging programs. Simultaneously, it is essential that equity remains at the forefront for digital health researchers, developers, and implementers. Ensuring digital health solutions address inequities in health experienced across the world while taking action to maximize digital inclusion will ensure the true potential of digital health is realized. Text messaging has the potential to continue to play a pivotal role in the delivery of equitable digital health tools to communities around the world for many years to come. Further new technologies can build on the humble text message, leveraging its success to advance the field of digital health. This Viewpoint presents a retrospective of text messaging in health, drawing on the example of text message–based interventions for smoking cessation, and presents evidence for the continued relevance of this mobile health modality in 2025 and beyond.

Social media platforms routinely face pressure to restrict harmful content while protecting free speech; however, prior theory suggests that platform design might undermine the efficacy of content moderation. During the COVID-19 pandemic, major social media platforms removed content that violated their medical misinformation policies. Although controversial, it is widely assumed that these interventions, such as deplatforming and content removal, are efficacious; however, this claim has not been evaluated based on evidence. We therefore evaluated the efficacy of Twitter’s attempts to curtail vaccine misinformation during the COVID-19 pandemic. We found that several clusters of vaccine skeptical accounts generated a larger share of tweets about vaccines, increased in virality, and continued to spread low-quality and likely misinformative content despite Twitter’s sustained removal of content and accounts — both when compared to prior trends and to corresponding clusters of pro-vaccine accounts. Several of these accounts were subject to a contemporaneous moderation action, in which Twitter removed 70,000 accounts on January 8, 2021. Although this action reduced activity among targeted accounts, virality increased and information quality decreased among both these accounts and those sharing similar political affiliations, calling into question the efficacy of these removals. Novel platforms that share Twitter’s architecture may therefore face similar moderation challenges.

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. 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. 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. 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. 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.

Social media platforms have become part of the fabric of American life. Most Americans use social media and check their accounts at least daily.1 As public health professionals, we need to better understand the positive and negative health implications of social media use. These concerns go beyond protecting the public's privacy, which is the focus of current regulatory efforts. Just as we have shaped the built environment by legislating the inclusion of sidewalks to increase the walkability of new neighborhoods, we also must consider the structure of our social media environments and find ways to alter them to be health promoting.

While an increasing body of the literature has documented the exposure to emerging tobacco products including heated tobacco products (HTPs) on social media, few studies have investigated the various stakeholders involved in the generation of promotional tobacco content. This study constructed a social network of Instagram users who posted IQOS content, a leading HTP brand, between 1 January and 5 April 2021 and identified users who positioned near the center of the network. We identified 4526 unique Instagram users who had created 19,951 IQOS-related posts during the study period. Nearly half of the users (42.1%) were business accounts authorized by Instagram, among which 59.0% belonged to Personal Goods and General Merchandise Stores and 18.1% belonged to Creators and Celebrities. For users with higher in-degree, out-degree, betweenness, and closeness centrality in the network, the majority of them were accounts directly associated with IQOS (e.g., containing “iqos” in username) or related to tobacco business as self-identified in the bio. Our findings further refine the social media marketing presence of tobacco products and suggest that the current self-regulatory efforts led by social media platforms are far from enough.

Background Smoking during pregnancy has adverse health consequences for the mother and fetus. E-cigarettes could aid with smoking cessation but there is limited research on the prevalence and patterns of e-cigarette use, and their association with smoking cessation among pregnant smokers. Methods We conducted a secondary analysis of a randomized controlled trial of a text-messaging program for smoking cessation among a U.S. national cohort of pregnant smokers ( n  = 428). Outcomes assessed were trajectories of e-cigarettes use from baseline to one-month follow-up, and longitudinal association between e-cigarette use at baseline and smoking cessation at one-month follow-up. Results At baseline, 74 (17.29%) pregnant smokers used e-cigarettes in the past 30 days and 36 (8.41%) used e-cigarettes in the past 7 days. The primary reason stated for using e-cigarettes during pregnancy was for quitting. E-cigarette use between baseline and 1-month was inconsistent. Of 36 dual-users at baseline, 20 (55.56%) stopped using e-cigarettes by the 1-month follow-up and 14 initiated e-cigarette use. There was no evidence of an association between e-cigarette use at baseline and the primary smoking cessation outcome, 7-day point prevalence abstinence [adjusted odds ratio = 0.79, 95% confidence intervals = 0.33–1.92]. Conclusions A secondary analysis of a national sample of pregnant smokers indicates that use of e-cigarettes is inconsistent and is not associated with improved smoking cessation outcomes. There is an urgent need to further examine the risk and benefits of e-cigarette use, especially during pregnancy.

Smartphone apps are a convenient, low-cost approach to delivering smoking cessation support to large numbers of individuals. Yet, the apps are susceptible to low rates of user engagement and retention. This study aims to test the effects of a new game module (called Inner Dragon) integrated into Smoke Free (23 Limited), a leading smoking cessation app with established efficacy. The primary outcomes measured user engagement with the app. A 2-arm, parallel-group, randomized controlled trial was conducted in the United States with an 8-week follow-up. Adult individuals who smoked ≥1 cigarettes daily and planned to quit smoking within 7 days were recruited and randomized (N=500), with equal allocation. Both groups received free access to the original Smoke Free app with \"core\" features of its smoking cessation program (eg, a diary and craving log). The treated group received additional access to the integrated Inner Dragon game that incorporated several game mechanics designed to increase user engagement. User engagement outcomes were the number of unique app sessions, average minutes per session, days with a session, and program adherence. Self-reported and verified smoking abstinence and app satisfaction were also assessed. The main analysis estimated the intention-to-treat effect of access to Inner Dragon on each outcome. Further analyses assessed effect modification by participant characteristics and the association of intensity of game use with program adherence and abstinence. Overall, user engagement was greater for treated versus control participants: they had 5.3 more sessions of Smoke Free (mean 29.6, SD 36.5 sessions vs mean 24.3, SD 37.9 sessions; P=.06), 0.8 more minutes per session (mean 6.9, SD 5.4 min vs mean 6.1, SD 5.2 min; P=.047), and 3.4 more days with a session (mean 14.3, SD 15.3 days vs mean 11.9, SD 14.3 days; P=.03). Program adherence, based on the number of times core features of the original Smoke Free app were used, was higher for treated versus control participants (mean 29.4, SD 41.3 times vs mean 22.6, SD 35.6 times; P=.03). Self-reported 7-day and 30-day point-prevalence abstinence and verified 7-day point-prevalence abstinence at 8 weeks did not significantly differ by study group. The mean repeated 1-day prevalence of quitting was higher among the treated group versus the control group (mean 17.3%, SD 25.6 vs mean 12.4%, SD 21.3; P=.01). App satisfaction and the motivation to (stay) quit did not differ by study group. Higher intensity of game use was associated with increased program adherence and self-reported abstinence. Findings suggest that the Inner Dragon game increased user engagement and program adherence. Additional refinements to the game design may clarify whether the game increases abstinence rates. Overall, it is feasible to deploy games and gamification to enhance user engagement in existing smoking cessation interventions. ClinicalTrials.gov NCT05227027; https://clinicaltrials.gov/study/NCT05227027.

Search engines provide health information boxes as part of search results to address information gaps and misinformation for commonly searched symptoms. Few prior studies have sought to understand how individuals who are seeking information about health symptoms navigate different types of page elements on search engine results pages, including health information boxes. Using real-world search engine data, this study sought to investigate how users searching for common health-related symptoms with Bing interacted with health information boxes (info boxes) and other page elements. A sample of searches (N=28,552 unique searches) was compiled for the 17 most common medical symptoms queried on Microsoft Bing by users in the United States between September and November 2019. The association between the page elements that users saw, their characteristics, and the time spent on elements or clicks was investigated using linear and logistic regression. The number of searches ranged by symptom type from 55 searches for cramps to 7459 searches for anxiety. Users searching for common health-related symptoms saw pages with standard web results (n=24,034, 84%), itemized web results (n=23,354, 82%), ads (n=13,171, 46%), and info boxes (n=18,215, 64%). Users spent on average 22 (SD 26) seconds on the search engine results page. Users who saw all page elements spent 25% (7.1 s) of their time on the info box, 23% (6.1 s) on standard web results, 20% (5.7 s) on ads, and 10% (10 s) on itemized web results, with significantly more time on the info box compared to other elements and the least amount of time on itemized web results. Info box characteristics such as reading ease and appearance of related conditions were associated with longer time on the info box. Although none of the info box characteristics were associated with clicks on standard web results, info box characteristics such as reading ease and related searches were negatively correlated with clicks on ads. Info boxes were attended most by users compared with other page elements, and their characteristics may influence future web searching. Future studies are needed that further explore the utility of info boxes and their influence on real-world health-seeking behaviors.

Background In the US and Israel, non-medical (‘recreational’) cannabis use is illegal at the national level; however, use rates are high and decriminalization and legalization is spreading. Thus, theory-based intervention efforts, especially for youth prevention, are crucial. Methods This mixed-methods study of adults in the US ( n  = 1,128) and Israel ( n  = 1,094) analyzed: 1) cross-sectional survey data (Fall 2021) to identify theory-based correlates (risk perceptions, social norms) of past-month cannabis use, next-year use intentions, and intentions to use in the home or among children if non-medical cannabis was legal, using multivariable regression; and 2) qualitative interviews regarding perceptions of cannabis policies and use (US n  = 40, Israel n  = 44). Results 16.7% reported past-month use; 70.5%, 56.3%, and 82.6% indicated “not at all likely” regarding next-year use and use in the home and among children if legal. Lower perceived risk and greater social norms were associated with past-month use, greater use intentions, and greater intentions to use in the home or among children. Past-month use was more prevalent among US (vs. Israeli) participants (22.0% vs. 11.2%); however, in multivariable regression controlling for past-month use, being from Israel was associated with greater use intentions (next-year; in the home/among children). Qualitative themes indicated: concerns about use (e.g., increasing use, health risks, driving-related risks) and legalization (e.g., impact on society/economy, marketing), and perceived benefits of use (e.g., medical) and legalization (e.g., access/safety, economic, individual rights). Conclusions Despite differences in cannabis perceptions and use across countries, perceived risk and social norms are relevant intervention targets regardless of sociopolitical context.

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