Discerning conversational context in online health communities for personalized digital behavior change solutions using Pragmatics to Reveal Intent in Social Media (PRISM) framework.

BACKGROUND: Online health communities (OHCs) have emerged as prominent platforms for behavior modification, and the digitization of online peer interactions has afforded researchers with unique opportunities to model multilevel mechanisms that drive behavior change. Existing studies, however, have been limited by a lack of methods that allow the capture of conversational context and socio-behavioral dynamics at scale, as manifested in these digital platforms. OBJECTIVE: We develop, evaluate, and apply a novel methodological framework, Pragmatics to Reveal Intent in Social Media (PRISM), to facilitate granular characterization of peer interactions by combining multidimensional facets of human communication. METHODS: We developed and applied PRISM to analyze peer interactions (N = 2.23 million) in QuitNet, an OHC for tobacco cessation. First, we generated a labeled set of peer interactions (n = 2,005) through manual annotation along three dimensions: communication themes (CTs), behavior change techniques (BCTs), and speech acts (SAs). Second, we used deep learning models to apply our qualitative codes at scale. Third, we applied our validated model to perform a retrospective analysis. Finally, using social network analysis (SNA), we portrayed large-scale patterns and relationships among the aforementioned communication dimensions embedded in peer interactions in QuitNet. RESULTS: Qualitative analysis showed that the themes of social support and behavioral progress were common. The most used BCTs were feedback and monitoring and comparison of behavior, and users most commonly expressed their intentions using SAs-expressive and emotion. With additional in-domain pre-training, bidirectional encoder representations from Transformers (BERT) outperformed other deep learning models on the classification tasks. Content-specific SNA revealed that users' engagement or abstinence status is associated with the prevalence of various categories of BCTs and SAs, which also was evident from the visualization of network structures. CONCLUSIONS: Our study describes the interplay of multilevel characteristics of online communication and their association with individual health behaviors.

Thrombotic and Clinical Outcomes in Patients with Hematologic Malignancy and COVID-19.

BACKGROUND: Venous thromboembolism (VTE) is a common complication in acute COVID-19 and those with hematologic malignancy (HM) may be at an even higher risk. We performed a retrospective analysis of patients with history of HM and acute COVID-19 to evaluate thrombotic and clinical outcomes. METHODS: Patients with COVID-19 were identified by positive SARS-CoV-2 PCR test. Our primary endpoints were rate of VTE and CVA in patients with HM compared to the general population (GP). Secondary outcomes included composite thrombotic events (CVA + VTE), COVID-19 fatality, respiratory support, ICU admission rates, and length of ICU stay RESULTS: A total of 833 patients were evaluated, 709 in the GP cohort, 124 patients in the HM cohort. CVA was more prevalent in the HM cohort (5.4% vs. 1.6%, P = .011). Rates of VTE were numerically higher for the HM cohort (8.0% vs. 3.6%, P = .069). The composite thrombotic rate was increased in the HM cohort (13.4% vs. 5.2%, P = .005). Patients with HM had a higher inpatient fatality rate (35.5% vs. 11.3%, P < .001), required more respiratory support (74.6% vs. 46.5%, P < .001) and had a higher rate of ICU admission (31.9% vs. 12.1%, P = .001). CONCLUSION: Our data demonstrated an increased rate of composite thrombotic (CVA + VTE) outcomes, indicating HM patients with acute COVID-19 are at increased risk of thrombosis. Irrespective of disease status, HM patients also have significantly increased need for intensive care, respiratory support, and have higher fatality rates.

Pragmatics to Reveal Intent in Social Media Peer Interactions: Mixed Methods Study.

BACKGROUND: Online health communities (OHCs) have emerged as the leading venues for behavior change and health-related information seeking. The soul and success of these digital platforms lie in their ability to foster social togetherness and a sense of community by providing personalized support. However, we have a minimal understanding of how conversational posts in these settings lead to collaborative societies and ultimately result in positive health changes through social influence. OBJECTIVE: Our objective is to develop a content-specific and intent-sensitive methodological framework for analyzing peer interactions in OHCs. METHODS: We developed and applied a mixed-methods approach to understand the manifestation of expressions in peer interactions in OHCs. We applied our approach to describe online social dialogue in the context of two online communities, QuitNet (QN) and the American Diabetes Association (ADA) support community. A total of 3011 randomly selected peer interactions (n=2005 from QN, n=1006 from ADA) were analyzed. Specifically, we conducted thematic analysis to characterize communication content and linguistic expressions (speech acts) embedded within the two data sets. We also developed an empirical user persona based on their engagement levels and behavior profiles. Further, we examined the association between speech acts and communication themes across observed tiers of user engagement and self-reported behavior profiles using the chi-square test or the Fisher test. RESULTS: Although social support, the most prevalent communication theme in both communities, was expressed in several subtle manners, the prevalence of emotions was higher in the tobacco cessation community and assertions were higher in the diabetes self-management (DSM) community. Specific communication theme-speech act relationships were revealed, such as the social support theme was significantly associated (P<.05) with 9 speech acts from a total of 10 speech acts (ie, assertion, commissive, declarative, desire, directive, expressive, question, stance, and statement) within the QN community. Only four speech acts (ie, commissive, emotion, expressive, and stance) were significantly associated (P<.05) with the social support theme in the ADA community. The speech acts were also significantly associated with the users' abstinence status within the QN community and with the users' lifestyle status within the ADA community (P<.05). CONCLUSIONS: Such an overlay of communication intent implicit in online peer interactions alongside content-specific theory-linked characterizations of social media discourse can inform the development of effective digital health technologies in the field of health promotion and behavior change. Our analysis revealed a rich gradient of expressions across a standardized thematic vocabulary, with a distinct variation in emotional and informational needs, depending on the behavioral and disease management profiles within and across the communities. This signifies the need and opportunities for coupling pragmatic messaging in digital therapeutics and care management pathways for personalized support.

Transitions Between Electronic and Combustible Cigarettes: A Mixed Methods Analysis of Peer Interactions in an Online Community for Tobacco Cessation.

The growing popularity of e-cigarettes is a public health concern. There is an emerging need to understand the pathways between electronic and combustible modes due to the specialized nature of risks associated with each transition. Online social media has become the most dominant knowledge space for these evolving behaviors, and as such, can provide unique opportunities for modeling switching patterns. In this paper, we describe the utility of online peer interactions using qualitative inquiry and network visualizations using 500 messages to characterize (a) transition pathways and (b) psychosocial attributes as individuals contemplate and act on such transitions. Our results indicate that the E2A pathway is the most prevalent in e-cigarette-related transitions, where most of the individuals are in the "active e-cig use" stage. Perceived benefits and barriers are the most commonly held health beliefs, while counterconditioning and stimulus control behavior change processes are frequently manifested. Such insights can help in the design of personalized pathway-specific behavior change interventions.

Social Media as a Research Tool (SMaaRT) for Risky Behavior Analytics: Methodological Review.

BACKGROUND: Modifiable risky health behaviors, such as tobacco use, excessive alcohol use, being overweight, lack of physical activity, and unhealthy eating habits, are some of the major factors for developing chronic health conditions. Social media platforms have become indispensable means of communication in the digital era. They provide an opportunity for individuals to express themselves, as well as share their health-related concerns with peers and health care providers, with respect to risky behaviors. Such peer interactions can be utilized as valuable data sources to better understand inter-and intrapersonal psychosocial mediators and the mechanisms of social influence that drive behavior change. OBJECTIVE: The objective of this review is to summarize computational and quantitative techniques facilitating the analysis of data generated through peer interactions pertaining to risky health behaviors on social media platforms. METHODS: We performed a systematic review of the literature in September 2020 by searching three databases-PubMed, Web of Science, and Scopus-using relevant keywords, such as "social media," "online health communities," "machine learning," "data mining," etc. The reporting of the studies was directed by the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. Two reviewers independently assessed the eligibility of studies based on the inclusion and exclusion criteria. We extracted the required information from the selected studies. RESULTS: The initial search returned a total of 1554 studies, and after careful analysis of titles, abstracts, and full texts, a total of 64 studies were included in this review. We extracted the following key characteristics from all of the studies: social media platform used for conducting the study, risky health behavior studied, the number of posts analyzed, study focus, key methodological functions and tools used for data analysis, evaluation metrics used, and summary of the key findings. The most commonly used social media platform was Twitter, followed by Facebook, QuitNet, and Reddit. The most commonly studied risky health behavior was nicotine use, followed by drug or substance abuse and alcohol use. Various supervised and unsupervised machine learning approaches were used for analyzing textual data generated from online peer interactions. Few studies utilized deep learning methods for analyzing textual data as well as image or video data. Social network analysis was also performed, as reported in some studies. CONCLUSIONS: Our review consolidates the methodological underpinnings for analyzing risky health behaviors and has enhanced our understanding of how social media can be leveraged for nuanced behavioral modeling and representation. The knowledge gained from our review can serve as a foundational component for the development of persuasive health communication and effective behavior modification technologies aimed at the individual and population levels.

E-Cigarettes, Vaping Devices, and Acute Lung Injury.

"E-cigarettes" are a class of consumer devices designed to deliver drugs, primarily nicotine or marijuana oils, to the lung by vaporization. Regulation of the devices in the United States is relatively minimal, and research on both epidemiology and potential toxicity has focused on nicotine devices. In 2019, an outbreak of an acute respiratory illness in the United States was traced back to the contamination of e-cigarette fluids with vitamin E acetate, which had been used to disguise the dilution of marijuana oils. The outbreak, termed "e-cigarette or vaping associated lung injury" by the U.S. Centers for Disease Control, was characterized by pulmonary infiltrates and hypoxia, which usually required hospitalization and, often, admission to ICUs. The syndrome sickened >2,600 people, mostly young men, and killed >50 people before it began to abate 6 months later. No current regulations exist to prevent a similar event with the same or different chemical contaminants. Absent such regulation, respiratory practitioners should be prepared to evaluate, identify, and treat future cases of acute lung toxicity from e-cigarettes.

Characterization of Behavioral Transitions Through Social Media Analysis: A Mixed-Methods Approach.

Unhealthy behaviors are a socioeconomic burden and lead to the development of chronic diseases. Relapse is a common issue that most individuals deal with as they adopt and sustain a positive healthy lifestyle. Proper identification of behavioral transitions can help design agile, adaptive, and just-in-time interventions. In this paper, we present a methodology that integrates qualitative coding, machine learning, and formal data analysis using stage transition probabilities and linguistics-based text analysis to track shifts in stages of behavior change as embedded in journal entries recorded by users in an online community for tobacco cessation. Results indicate that our semi-automated stage identification method has an accuracy of 90%. Further analysis revealed stage-specific language features and transition probabilities. Implications for targeted social interventions are discussed.

Content-Sensitive Characterization of Peer Interactions of Highly Engaged Users in an Online Community for Smoking Cessation: Mixed-Methods Approach for Modeling User Engagement in Health Promotion Interventions.

BACKGROUND: Online communities provide affordable venues for behavior change. However, active user engagement holds the key to the success of these platforms. In order to enhance user engagement and in turn, health outcomes, it is essential to offer targeted interventional and informational support. OBJECTIVE: In this paper, we describe a content plus frequency framework to enable the characterization of highly engaged users in online communities and study theoretical techniques employed by these users through analysis of exchanged communication. METHODS: We applied the proposed methodology for analysis of peer interactions within QuitNet, an online community for smoking cessation. Firstly, we identified 144 highly engaged users based on communication frequency within QuitNet over a period of 16 years. Secondly, we used the taxonomy of behavior change techniques, text analysis methods from distributional semantics, machine learning, and sentiment analysis to assign theory-driven labels to content. Finally, we extracted content-specific insights from peer interactions (n=159,483 messages) among highly engaged QuitNet users. RESULTS: Studying user engagement using our proposed framework led to the definition of 3 user categories-conversation initiators, conversation attractors, and frequent posters. Specific behavior change techniques employed by top tier users (threshold set at top 3) within these 3 user groups were found to be goal setting, social support, rewards and threat, and comparison of outcomes. Engagement-specific trends within sentiment manifestations were also identified. CONCLUSIONS: Use of content-inclusive analytics has offered deep insight into specific behavior change techniques employed by highly engaged users within QuitNet. Implications for personalization and active user engagement are discussed.

An exploration of the Facebook social networks of smokers and non-smokers.

BACKGROUND: Social networks influence health behavior, including tobacco use and cessation. To date, little is known about whether and how the networks of online smokers and non-smokers may differ, or the potential implications of such differences with regards to intervention efforts. Understanding how social networks vary by smoking status could inform public health efforts to accelerate cessation or slow the adoption of tobacco use. OBJECTIVES: These secondary analyses explore the structure of ego networks of both smokers and non-smokers collected as part of a randomized control trial conducted within Facebook. METHODS: During the trial, a total of 14,010 individuals installed a Facebook smoking cessation app: 9,042 smokers who were randomized in the trial, an additional 2,881 smokers who did not meet full eligibility criteria, and 2,087 non-smokers. The ego network for all individuals was constructed out to second-degree connections. Four kinds of networks were constructed: friendship, family, photo, and group networks. From these networks we measured edges, isolates, density, mean betweenness, transitivity, and mean closeness. We also measured diameter, clustering, and modularity without ego and isolates. Logistic regressions were performed with smoking status as the response and network metrics as the primary independent variables and demographics and Facebook utilization metrics as covariates. RESULTS: The four networks had different characteristics, indicated by different multicollinearity issues and by logistic regression output. Among Friendship networks, the odds of smoking were higher in networks with lower betweenness (p = 0.00), lower transitivity (p = 0.00), and larger diameter (p = 0.00). Among Family networks, the odds of smoking were higher in networks with more vertices (p = .01), less transitivity (p = .04), and fewer isolates (p = .01). Among Photo networks, none of the network metrics were predictive of smoking status. Among Group networks, the odds of smoking were higher when diameter was smaller (p = .04). Together, these findings suggested that compared to non-smokers, smokers in this sample had less connected, more dispersed Facebook Friendship networks; larger but more fractured Family networks with fewer isolates; more compact Group networks; and Photo networks that were similar in network structure to those of non-smokers. CONCLUSIONS: This study illustrates the importance of examining structural differences in online social networks as a critical component for network-based interventions and lays the foundation for future research that examines the ways that social networks differ based on individual health behavior. Interventions that seek to target the behavior of individuals in the context of their social environment would be well served to understand social network structures of participants.

Temporal Trends of Psychosociobehavioral Factors Underlying Tobacco Use: A Semi-Automated Exploratory Analysis of Peer-to-Peer Communication in a Health-Related Online Community.

Online communities have been an integral part of tobacco cessation programs. They are rich in content, and offer insights into factors affecting an individual's behavior change efforts. We used word representation techniques to infer implicit meaning embedded in messages exchanged in a health-related online community. Our analysis of peer interactions revealed that individuals factor in safety, glamour, expense, and media projection when choosing a form of nicotine intake. When choosing pharmacotherapy techniques, individuals focus on brands, dosage, and side effects associated with each form (e.g. gums, patches). Our analysis sheds light on factors embedded in peer interactions, which might lead to opinion formation based on peer influence and knowledge dissemination in these social platforms. Such understanding enables design of high-engagement behavior change technologies, through personalization of content delivery by factoring in individual-level beliefs, behavioral state, and community-level influences.

Improving Adherence to Smoking Cessation Treatment: Intervention Effects in a Web-Based Randomized Trial.

BACKGROUND: Web-based smoking cessation interventions can deliver evidence-based treatments to a wide swath of the population, but effectiveness is often limited by insufficient adherence to proven treatment components. This study evaluated the impact of a social network (SN) intervention and free nicotine replacement therapy (NRT) on adherence to evidence-based components of smoking cessation treatment in the context of a Web-based intervention. METHODS: A sample of adult U.S. smokers (N = 5290) was recruited via BecomeAnEX.org, a free smoking cessation Web site. Smokers were randomized to one of four arms: (1) an interactive, evidence-based smoking cessation Web site (WEB) alone; (2) WEB in conjunction with an SN intervention designed to integrate participants into the online community (WEB+SN); (3) WEB plus free NRT (WEB+NRT); and (4) the combination of all treatments (WEB+SN+NRT). Adherence outcomes assessed at 3-month follow-up were as follows: Web site utilization metrics, use of skills training components, intratreatment social support, and pharmacotherapy use. RESULTS: WEB+SN+NRT outperformed all others on Web site utilization metrics, use of practical counseling tools, intratreatment social support, and NRT use. It was the only intervention to promote the sending of private messages and the viewing of community pages over WEB alone. Both social network arms outperformed WEB on most metrics of online community engagement. Both NRT arms showed higher medication use compared to WEB alone. CONCLUSIONS: This study demonstrated the effectiveness of two approaches for improving adherence to evidence-based components of smoking cessation treatment. Integrated approaches to medication provision and social network engagement can enhance adherence to components known to improve cessation. IMPLICATIONS: This study demonstrated that an integrated approach to medication provision and social network integration, when delivered through an online program, can enhance adherence across all three recommended components of an evidence-based smoking cessation program (skills training, social support, and pharmacotherapy use). Nicotine replacement therapy-when provided as part of an integrated program-increases adherence to other program elements, which in turn augment its own therapeutic effects. An explicit focus on approaches to improve treatment adherence is an important first step to identifying leverage points for optimizing intervention effectiveness.

E-Cigarettes: The Science Behind the Smoke and Mirrors.

E-cigarettes are a diverse set of devices that are designed for pulmonary delivery of nicotine through an aerosol, usually consisting of propylene glycol, nicotine, and flavorings. The devices heat the nicotine solution using a battery-powered circuit and deliver the resulting vapor into the proximal airways and lung. Although the current devices on the market appear to be safer than smoking combusted tobacco, they have their own inherent risks, which remain poorly characterized due to widespread product variability. Despite rising use throughout the United States, predominantly by smokers, limited evidence exists for their efficacy in smoking cessation. Pending regulation by the FDA will enforce limited disclosures on the industry but will not directly impact safety or efficacy. Meanwhile, respiratory health practitioners will need to tailor their discussions with patients, taking into account the broad range of existing effective smoking cessation techniques, including pharmaceutical nicotine replacement therapy.

Online tobacco websites and online communities-who uses them and do users quit smoking? The quit-primo and national dental practice-based research network Hi-Quit studies.

Online tobacco cessation communities are beneficial but underused. Our study examined whether, among smokers participating in a web-assisted tobacco intervention (Decide2quit.org), specific characteristics were associated with navigating to BecomeAnEx.org, an online cessation community, and with subsequent quit rates. Among smokers (N = 759) registered with Decide2quit.org, we identified visitors to BecomeAnEx.org, examining associations between smoker characteristics and likelihood of visiting. We then tested for associations between visits and 6-month cessation (point prevalence). We also tested for an interaction between use of other online support-seeking (Decide2quit.org tobacco cessation coaches), visiting, and 6-month cessation. One quarter (26.0 %; n = 197) of the smokers visited BecomeAnEx.org; less than one tenth (7.5 %; n = 57) registered to participate in the online forum. Visitors were more likely to be female (73.0 vs. 62.6 % of non-visitors, P < 0.01) to have visited a cessation website before (33.0 vs. 17.4 %, P < 0.01) and to report quit attempts in the previous year (62.0 vs. 53.0 %, P = 0.03). In analyses of all participants, BecomeAnEx.org visiting was not associated with 6-month quit completion. Among participants who communicated with a coach, BecomeAnEx.org visiting also lacked a significant association with 6 month quit completion, although a non-significant trend toward quit completion in visitors was noted (OR 2.21, 95 % CI 0.81-3.1). Online cessation communities attract smokers with previous cessation website experience and recent quit attempts. Community visiting was not associated with quit rates in our study, but low use may have limited our power to detect differences. Further research should explore whether an additive effect can be achieved by offering community visitors support via online coaches.

Diffusion of an Evidence-Based Smoking Cessation Intervention Through Facebook: A Randomized Controlled Trial.

OBJECTIVES: To examine the diffusion of an evidence-based smoking cessation application ("app") through Facebook social networks and identify specific intervention components that accelerate diffusion. METHODS: Between December 2012 and October 2013, we recruited adult US smokers ("seeds") via Facebook advertising and randomized them to 1 of 12 app variants using a factorial design. App variants targeted components of diffusion: duration of use (t), "contagiousness" (ß), and number of contacts (Z). The primary outcome was the reproductive ratio (R), defined as the number of individuals installing the app ("descendants") divided by the number of a seed participant's Facebook friends. RESULTS: We randomized 9042 smokers. App utilization metrics demonstrated between-variant differences in expected directions. The highest level of diffusion (R = 0.087) occurred when we combined active contagion strategies with strategies to increase duration of use (incidence rate ratio = 9.99; 95% confidence interval = 5.58, 17.91; P < .001). Involving nonsmokers did not affect diffusion. CONCLUSIONS: The maximal R value (0.087) is sufficient to increase the numbers of individuals receiving treatment if applied on a large scale. Online interventions can be designed a priori to spread through social networks.

In Pursuit of Theoretical Ground in Behavior Change Support Systems: Analysis of Peer-to-Peer Communication in a Health-Related Online Community.

BACKGROUND: Research studies involving health-related online communities have focused on examining network structure to understand mechanisms underlying behavior change. Content analysis of the messages exchanged in these communities has been limited to the "social support" perspective. However, existing behavior change theories suggest that message content plays a prominent role reflecting several sociocognitive factors that affect an individual's efforts to make a lifestyle change. An understanding of these factors is imperative to identify and harness the mechanisms of behavior change in the Health 2.0 era. OBJECTIVE: The objective of this work is two-fold: (1) to harness digital communication data to capture essential meaning of communication and factors affecting a desired behavior change, and (2) to understand the applicability of existing behavior change theories to characterize peer-to-peer communication in online platforms. METHODS: In this paper, we describe grounded theory-based qualitative analysis of digital communication in QuitNet, an online community promoting smoking cessation. A database of 16,492 de-identified public messages from 1456 users from March 1-April 30, 2007, was used in our study. We analyzed 795 messages using grounded theory techniques to ensure thematic saturation. This analysis enabled identification of key concepts contained in the messages exchanged by QuitNet members, allowing us to understand the sociobehavioral intricacies underlying an individual's efforts to cease smoking in a group setting. We further ascertained the relevance of the identified themes to theoretical constructs in existing behavior change theories (eg, Health Belief Model) and theoretically linked techniques of behavior change taxonomy. RESULTS: We identified 43 different concepts, which were then grouped under 12 themes based on analysis of 795 messages. Examples of concepts include "sleepiness," "pledge," "patch," "spouse," and "slip." Examples of themes include "traditions," "social support," "obstacles," "relapse," and "cravings." Results indicate that themes consisting of member-generated strategies such as "virtual bonfires" and "pledges" were related to the highest number of theoretical constructs from the existing behavior change theories. In addition, results indicate that the member-generated communication content supports sociocognitive constructs from more than one behavior change model, unlike the majority of the existing theory-driven interventions. CONCLUSIONS: With the onset of mobile phones and ubiquitous Internet connectivity, online social network data reflect the intricacies of human health behavior as experienced by health consumers in real time. This study offers methodological insights for qualitative investigations that examine the various kinds of behavioral constructs prevalent in the messages exchanged among users of online communities. Theoretically, this study establishes the manifestation of existing behavior change theories in QuitNet-like online health communities. Pragmatically, it sets the stage for real-time, data-driven sociobehavioral interventions promoting healthy lifestyle modifications by allowing us to understand the emergent user needs to sustain a desired behavior change.

Content-specific network analysis of peer-to-peer communication in an online community for smoking cessation.

Analysis of user interactions in online communities could improve our understanding of health-related behaviors and inform the design of technological solutions that support behavior change. However, to achieve this we would need methods that provide granular perspective, yet are scalable. In this paper, we present a methodology for high-throughput semantic and network analysis of large social media datasets, combining semi-automated text categorization with social network analytics. We apply this method to derive content-specific network visualizations of 16,492 user interactions in an online community for smoking cessation. Performance of the categorization system was reasonable (average F-measure of 0.74, with system-rater reliability approaching rater-rater reliability). The resulting semantically specific network analysis of user interactions reveals content- and behavior-specific network topologies. Implications for socio-behavioral health and wellness platforms are also discussed.

Characterization of Temporal Semantic Shifts of Peer-to-Peer Communication in a Health-Related Online Community: Implications for Data-driven Health Promotion.

With online social platforms gaining popularity as venues of behavior change, it is important to understand the ways in which these platforms facilitate peer interactions. In this paper, we characterize temporal trends in user communication through mapping of theoretically-linked semantic content. We used qualitative coding and automated text analysis to assign theoretical techniques to peer interactions in an online community for smoking cessation, subsequently facilitating temporal visualization of the observed techniques. Results indicate manifestation of several behavior change techniques such as feedback and monitoring' and 'rewards'. Automated methods yielded reasonable results (F-measure=0.77). Temporal trends among relapsers revealed reduction in communication after a relapse event. This social withdrawal may be attributed to failure guilt after the relapse. Results indicate significant change in thematic categories such as 'social support', 'natural consequences', and 'comparison of outcomes' pre and post relapse. Implications for development of behavioral support technologies that promote long-term abstinence are discussed.

Content-driven analysis of an online community for smoking cessation: integration of qualitative techniques, automated text analysis, and affiliation networks.

OBJECTIVES: We identified content-specific patterns of network diffusion underlying smoking cessation in the context of online platforms, with the aim of generating targeted intervention strategies. METHODS: QuitNet is an online social network for smoking cessation. We analyzed 16 492 de-identified peer-to-peer messages from 1423 members, posted between March 1 and April 30, 2007. Our mixed-methods approach comprised qualitative coding, automated text analysis, and affiliation network analysis to identify, visualize, and analyze content-specific communication patterns underlying smoking behavior. RESULTS: Themes we identified in QuitNet messages included relapse, QuitNet-specific traditions, and cravings. QuitNet members who were exposed to other abstinent members by exchanging content related to interpersonal themes (e.g., social support, traditions, progress) tended to abstain. Themes found in other types of content did not show significant correlation with abstinence. CONCLUSIONS: Modeling health-related affiliation networks through content-driven methods can enable the identification of specific content related to higher abstinence rates, which facilitates targeted health promotion.

Internet and Telephone Treatment for smoking cessation: mediators and moderators of short-term abstinence.

INTRODUCTION: This study examined mediators and moderators of short-term treatment effectiveness from the iQUITT Study (Quit Using Internet and Telephone Treatment), a 3-arm randomized trial that compared an interactive smoking cessation Web site with an online social network (enhanced Internet) alone and in conjunction with proactive telephone counseling (enhanced Internet plus phone) to a static Internet comparison condition (basic Internet). METHODS: The analytic sample was N = 1,236 participants with complete 3-month data on all mediating variables. The primary outcome was 30-day point prevalence abstinence (ppa) at 3 months. Recognizing the importance of temporal precedence in mediation analyses, we also present findings for 6-month outcomes. Purported mediators were treatment utilization and changes in psychosocial constructs. Proposed moderators included baseline demographic, smoking, and psychosocial variables. Mediation analyses examined the extent to which between-arm differences in 30-day ppa could be attributed to differential Web site utilization, telephone counseling, and associated changes in smoking self-efficacy and social support for quitting. Effect modification analyses fitted interactions between treatment and prespecified moderators on abstinence. RESULTS: Significant mediators of 30-day ppa were changes in smoking temptations, quitting confidence, and positive and negative partner support, which were strongly associated with increased Web site utilization. The addition of telephone counseling to an enhanced Web site further improved abstinence rates, partly via an association with increased quitting confidence. Baseline smoking rate was the only significant moderator. CONCLUSIONS: Increased treatment utilization and associated changes in several psychosocial measures yielded higher abstinence rates. Findings validate the importance of treatment utilization, smoking self-efficacy, and social support to promote abstinence.

Forensic analysis of online marketing for electronic nicotine delivery systems.

BACKGROUND: Electronic nicotine delivery systems (ENDS) are growing in awareness and use in the USA. They are currently unregulated as the Food and Drug Administration has yet to assert jurisdiction under its tobacco authority over these products, and a US Court of Appeals held they cannot be regulated as drugs/delivery devices if they are not marketed for a therapeutic purpose. Observation of the current online marketplace suggests ENDS, like some nutraceutical products, are being promoted using affiliate marketing techniques using claims concerning purported health benefits. OBJECTIVE: This study performed a forensic analysis to characterise the relationships between online ENDS affiliate advertisements and ENDS sellers, and evaluated descriptive content on advertisements and websites to inform future policy and regulatory efforts. METHODS: A purposive sampling strategy was used to identify three forms of ENDS advertising. Web proxy software recorded identifiable objects and their ties to each other. Network analysis of these ties followed, as well as analysis of descriptive content on advertisements and websites identified. RESULTS: The forensic analysis included four ENDS advertisements, two linked affiliate websites, and two linked seller websites, and demonstrated a multilevel relationship between advertisements and sellers with multiple layers of redirection. Descriptive analysis indicated that advertisements and affiliates, but not linked sellers, included smoking cessation claims. Results suggest that ENDS sellers may be trying to distance marketing efforts containing unsubstantiated claims from sales. A separate descriptive analysis of 20 ENDS seller web pages indicated that the use of affiliate marketing by sellers may be widespread. CONCLUSIONS: These findings support increased monitoring and regulation of ENDS marketing to prevent deceptive marketing tactics and ensure consumer safety.