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

Examining Quitting Experiences on Quit Vaping Subreddits From 2015 to 2021: Content Analysis

BACKGROUND

Despite the prevalence of vaping nicotine, most nicotine cessation research remains focused on smoking cigarettes. However, the lived experience of quitting smoking is different from quitting vaping. As a result, research examining the unique experiences of those quitting vaping can better inform quitting resources and cessation programs specific to e-cigarette use. Examining Reddit forums (ie, subreddits) dedicated to the topics of quitting vaping nicotine can provide insight into the discussion around experiences on quitting vaping. Prior literature examining limited discussions around quitting vaping on Reddit has identified the sharing of barriers and facilitators for quitting, but more research is needed to investigate the content comprehensively across all subreddits.

OBJECTIVE

The objective of this study is to examine content across quit vaping subreddits since their inception to better understand quitting vaping within the context of the expanding nicotine market.

METHODS

All posts from January 2015 to October 2021 were scraped from all quit vaping subreddits: r/QuittingJuul, r/QuitVaping, r/quit_vaping, and r/stopvaping (N=7110). Rolling weekly average post volume was calculated. A codebook informed by a latent Dirichlet allocation topic model was developed to characterize themes in a subsample of 695 randomly selected posts. Frequencies and percentages of posts containing each coded theme were assessed along with the number of upvotes and comments.

RESULTS

Post volume increased across all subreddits over time, spiking from August - September of 2019 when vaping lung injury emerged. Just over 52% of posts discussed seeking social support and 16.83% discussed providing social support. Posts providing support received the most positive engagements (i.e. upvotes) of all coded categories. Posts also discussed physical and psychological symptoms of withdrawal (30.65% and 18.85%, respectively), strategies for quitting including: quitting cold turkey (38.33%), using alternative nicotine products (17%), and tapering down nicotine content (10.50%). Most posts shared a personal narrative (92.37%) and some discussed quit motivation (28.20%) and relapse (14.99%).

CONCLUSIONS

This work identifies a desire for peer-to-peer support for quitting vaping, which reinforces existing literature and highlights characteristics of quitting vaping specific to a changing nicotine product environment. Given that posts providing social support were the most upvoted, this suggests that subreddit contributors are seeking support from their peers when discussing quitting vaping. Additionally, this analysis shows the sharing of barriers and facilitators for quitting, supporting findings from prior exploration of quit vaping subreddits. Finally, quitting vaping in an ever-growing nicotine market has led to the evolution of vaping-specific quit methods such as tapering down nicotine content. These findings have direct implications for quit vaping product implementation and development.

The Role of Seed Users in Nurturing an Online Health Community for Smoking Cessation Among People With HIV/AIDS

BACKGROUND

To nurture a new online community for health behavior change, a fruitful strategy is to recruit "seed users" to create content and encourage participation.

PURPOSE

This study evaluated the impact of support from seed users in an online community for smoking cessation among people living with HIV/AIDS and explored the linguistic characteristics of their interactions.

METHODS

These secondary analyses examined data from a randomized trial of a smoking cessation intervention for HIV+ smokers delivered via an online health community (OHC). The analytic sample comprised n = 188 participants randomized to the intervention arm who participated in the community. Independent variables were OHC interactions categorized by participant interlocutor type (study participant, seed user) and interaction type (active, passive). The primary outcome was biochemically verified 7-day abstinence from cigarettes measured 3 months post-randomization; 30-day abstinence was examined for robustness.

RESULTS

Logistic regression models showed that participants' interactions with seed users were a positive predictor of abstinence but interactions with other study participants were not. Specifically, the odds of abstinence increased as the number of posts received from seed users increased. Exploratory linguistic analyses revealed that seed users wrote longer comments which included more frequent use of "we" and "you" pronouns and that study participants users used more first-person singular pronouns ("I").

CONCLUSIONS

Seeding a community at its inception and nurturing its growth through seed users may be a scalable way to foster behavior change among OHC members. These findings have implications for the design and management of an OHC capable of promoting smoking cessation.

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.

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.

Linking Network Characteristics of Online Social Networks to Individual Health: A Systematic Review of Literature

Social networks have long been viewed as a structural determinant of health. With the proliferation of digital technologies, numerous studies have examined the health implications of online social networks (OSNs). However, the mechanisms through which OSNs may influence individual health are poorly understood. Employing a social network approach, this paper presents a systematic review of the literature examining how network characteristics of OSNs are linked to individuals' health behavior and/or status. Drawing on keyword searches in nine databases, we identified and analyzed 22 relevant articles from 1,705 articles published prior to 2017. The findings show that individual health is associated with a number of network characteristics, including both individual-level attributes (e.g., centrality) and network-level attributes (e.g., density, clustering). All of the included studies (n = 22) have focused on egocentric networks, and nine studies also collected whole network data of online health communities. Based on our review, we highlight three fruitful areas in the application of OSNs in public health: (1) disease and risk detection, (2) disease prevention and intervention, and (3) health behavior change. However, the precise mechanisms and causal pathways through which OSNs affect health remain unclear. More theoretically grounded, longitudinal, and mixed methods research is needed to advance this line of research.

Exploring features of social support in a Chinese online smoking cessation community: A multidimensional content analysis of user interaction data

Due to the rapid development of information technology, an increasing number of smokers choose online smoking cessation communities to interact with other individuals to help themselves quit smoking. Though it is well known that social support plays a key role in the process of smoking cessation, the features of social support that one can get from online smoking cessation communities remain unclear. We collected user interaction data from the largest Chinese online smoking cessation community, the quit smoking forum of Baidu Tieba. We selected 2758 replies from 29 active repliers and 408 correlated posts as our data set. Multidimensional content analysis is carried out from three aspects: posting scenarios, user quitting behavior stages, and types of social support. This article also explores the co-occurrence relationships of different types of social support by social network analysis. Results showed that users receive different compositions of social support in various posting scenarios and behavior stages. In most cases, emotional support is the most typical support the community provides. The community will provide more informational support when needed. Besides, informational support, especially personal experience and perceptual knowledge, has more diverse combination patterns with other types of social support. "Gratitude-Mutual assistance" and "Encouragement-Mutual assistance" are the most frequent co-occurrence relationships. The online smoking cessation community brings people who quit smoking together, and users provide rich types of social support for each other. Users can effectively obtain expected social support in different posting scenarios and smoking cessation stages. Smoking cessation projects should be designed to promote user communication and interaction, which positively affects achieving users' smoking cessation goals.

Use of social media data for disease based social network analysis and network modeling: A Systematic Review

Burden due to infectious and noncommunicable disease is increasing at an alarming rate. Social media usage is growing rapidly and has become the new norm of communication. It is imperative to examine what is being discussed in the social media about diseases or conditions and the characteristics of the network of people involved in discussion. The objective is to assess the tools and techniques used to study social media disease networks using network analysis and network modeling. PubMed and IEEEXplore were searched from 2009 to 2020 and included 30 studies after screening and analysis. Twitter, QuitNet, and disease-specific online forums were widely used to study communications on various health conditions. Most of the studies have performed content analysis and network analysis, whereas network modeling has been done in six studies. Posts on cancer, COVID-19, and smoking have been widely studied. Tools and techniques used for network analysis are listed. Health-related social media data can be leveraged for network analysis. Network modeling technique would help to identify the structural factors associated with the affiliation of the disease networks, which is scarcely utilized. This will help public health professionals to tailor targeted 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.

Effectiveness of WhatsApp online group discussion for smoking relapse prevention: protocol for a pragmatic randomized controlled trial

BACKGROUND AND AIMS

Sustained psychosocial support via online social groups may help former tobacco users maintain abstinence. This study aims to examine the effectiveness of participating in a WhatsApp social group for long-term smoking cessation.

DESIGN

Two-arm, open-labelled, pragmatic, individually randomized controlled trial.

SETTING

All participants are service users of smoking cessation clinics, and all interventions are delivered via mobile phones.

PARTICIPANTS

Participants included 1008 adult quitters who self-report no tobacco use in the past 3-30 days.

INTERVENTIONS

The intervention group (n = 504) will join a WhatsApp social group to receive standardized and theory-based reminders of smoking relapse prevention and participate in discussion with other WhatsApp group members using their own mobile phones. All social groups will be led by counselors or specialist nurse practitioners. The control group (n = 504) will receive similar reminders via short messages to their own mobile phones but will not interact with other participants. The intervention duration for both groups is 8 weeks. Both groups will receive a booklet at baseline about how to prevent smoking relapse.

MEASUREMENTS

The primary outcome is biochemically validated tobacco abstinence at 12 months after consent.

COMMENTS

The findings will provide evidence concerning the utility of operating online social group discussion for prevention of smoking relapse and sustaining long-term abstinence.

Diabetes Self-Management in the Age of Social Media: Large-Scale Analysis of Peer Interactions Using Semiautomated Methods

BACKGROUND

Online communities have been gaining popularity as support venues for chronic disease management. User engagement, information exposure, and social influence mechanisms can play a significant role in the utility of these platforms.

OBJECTIVE

In this paper, we characterize peer interactions in an online community for chronic disease management. Our objective is to identify key communications and study their prevalence in online social interactions.

METHODS

The American Diabetes Association Online community is an online social network for diabetes self-management. We analyzed 80,481 randomly selected deidentified peer-to-peer messages from 1212 members, posted between June 1, 2012, and May 30, 2019. Our mixed methods approach comprised qualitative coding and automated text analysis to identify, visualize, and analyze content-specific communication patterns underlying diabetes self-management.

RESULTS

Qualitative analysis revealed that "social support" was the most prevalent theme (84.9%), followed by "readiness to change" (18.8%), "teachable moments" (14.7%), "pharmacotherapy" (13.7%), and "progress" (13.3%). The support vector machine classifier resulted in reasonable accuracy with a recall of 0.76 and precision 0.78 and allowed us to extend our thematic codes to the entire data set.

CONCLUSIONS

Modeling health-related communication through high throughput methods can enable the identification of specific content related to sustainable chronic disease management, which facilitates targeted health promotion.

Birth Control Connect: A randomized trial of an online group to disseminate contraceptive information

OBJECTIVE

We sought to test whether participation in an online group including IUD users influenced IUD-related knowledge, attitudes, and behavior among IUD non-users, as a proof-of-concept evaluation of information dissemination for less commonly used or novel contraceptives.

STUDY DESIGN

We conducted a blinded, randomized controlled trial on the effect of online communication with IUD users within an online program called Birth Control Connect. Participants were women age 18-45 living in the United States who had never used an IUD. We invited participants randomized to the intervention to join two-week, nine-member discussion groups including four satisfied IUD users and five IUD non-users; we invited control participants to groups including nine IUD non-users. We performed chi-squared tests on IUD knowledge, information-seeking, informational support and use in immediate post-surveys, and t-tests comparing change in IUD attitudes and frequency of logins to discussion groups.

RESULTS

We invited 488 IUD non-users and enrolled them into 70 groups between October 2015 and April 2016. We found increased positive attitudes towards the IUD in the intervention arm (0.65-point increase between pre- and post-surveys, versus 0.05 mean change for control arm, p = 0.03 for hormonal IUD, with a trend in the same direction for the non-hormonal IUD). Informational support also increased, with 70.3% of intervention arm participants self-reporting that they gained a better idea of what the IUD would be like, compared to 51.3% in control arm (p < 0.01). Of intervention participants, 63.3% versus 51.3% of control participants reported gaining new information from their group (p = 0.03). There were no differences in correct responses to knowledge items or information-seeking between groups.

CONCLUSIONS

Online exposure to IUD users increased positive attitudes toward the IUD and informational support for decision-making about the IUD among non-users.

IMPLICATIONS STATEMENT

Online spaces provide a promising environment for the exchange of accurate, useful contraceptive information based on real user experiences. Interventions aiming to harness social communication through structured online conversations (e.g., on existing social media platforms) about user experiences with lesser-known contraceptive methods such as the IUD may be worthwhile.

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.

Exploring the Social Structure of a Health-Related Online Community for Tobacco Cessation: A Two-Mode Network Approach

Unhealthy behaviors, such as tobacco use, increase individual health risk while also creating a global economic burden on the healthcare system. Social ties have been seen as an important, yet complex factor, to sustain abstinence from these modifiable risk behaviors. However, the underlying social mechanisms are still opaque and poorly understood. Digital health communities provide opportunities to understand social dependencies of behavior change because peer interactions in these platforms are digitized. In this paper, we present a novel approach that integrates theories of behavior change and Exponential Random Graph Models (ERGMs) to understand structural dependencies between users of an online community and the behavior change techniques that are manifested in their communication using an affiliation network. Results indicate population specific traits in terms of individuals' engagement in peer communication embed behavior change techniques in online social settings. Implications for personalized health promotion technologies are discussed.

Coevolution spreading in complex networks

The propagations of diseases, behaviors and information in real systems are rarely independent of each other, but they are coevolving with strong interactions. To uncover the dynamical mechanisms, the evolving spatiotemporal patterns and critical phenomena of networked coevolution spreading are extremely important, which provide theoretical foundations for us to control epidemic spreading, predict collective behaviors in social systems, and so on. The coevolution spreading dynamics in complex networks has thus attracted much attention in many disciplines. In this review, we introduce recent progress in the study of coevolution spreading dynamics, emphasizing the contributions from the perspectives of statistical mechanics and network science. The theoretical methods, critical phenomena, phase transitions, interacting mechanisms, and effects of network topology for four representative types of coevolution spreading mechanisms, including the coevolution of biological contagions, social contagions, epidemic-awareness, and epidemic-resources, are presented in detail, and the challenges in this field as well as open issues for future studies are also discussed.

NET-EXPO: A Gephi Plugin Towards Social Network Analysis of Network Exposure for Unipartite and Bipartite Graphs

Social network analysis (SNA) concerns itself in studying network structures in relation to individuals' behavior. Individuals may be influenced by their network members in their behavior, and thus past researchers have developed computational methods that allow us to measure the extent to which individuals are exposed to members with certain behavior within one's social network, and that be correlated with their own behavior. Some of these methods include network exposure model, affiliation exposure model, and decomposed network exposure models. We developed a Gephi plugin that computes and visualizes these various kinds of network exposure models called NET-EXPO. We experimented with NET-EXPO on some social network datasets to demonstrate its pragmatic use in social network research. This plugin has the potential to equip researchers with a tool to compute network exposures in a user friendly way and simplify the process to compute and visualize the network data.

Inferring Smoking Status from User Generated Content in an Online Cessation Community

Introduction

User generated content (UGC) is a valuable but underutilized source of information about individuals who participate in online cessation interventions. This study represents a first effort to passively detect smoking status among members of an online cessation program using UGC.

Methods

Secondary data analysis was performed on data from 826 participants in a web-based smoking cessation randomized trial that included an online community. Domain experts from the online community reviewed each post and comment written by participants and attempted to infer the author's smoking status at the time it was written. Inferences from UGC were validated by comparison with self-reported 30-day point prevalence abstinence (PPA). Following validation, the impact of this method was evaluated across all individuals and time points in the study period.

Results

Of the 826 participants in the analytic sample, 719 had written at least one post from which content inference was possible. Among participants for whom unambiguous smoking status was inferred during the 30 days preceding their 3-month follow-up survey, concordance with self-report was almost perfect (kappa = 0.94). Posts indicating abstinence tended to be written shortly after enrollment (median = 14 days).

Conclusions

Passive inference of smoking status from UGC in online cessation communities is possible and highly reliable for smokers who actively produce content. These results lay the groundwork for further development of observational research tools and intervention innovations.

Implications

A proof-of-concept methodology for inferring smoking status from user generated content in online cessation communities is presented and validated. Content inference of smoking status makes a key cessation variable available for use in observational designs. This method provides a powerful tool for researchers interested in online cessation interventions and establishes a foundation for larger scale application via machine learning.

Conversation Within a Facebook Smoking Cessation Intervention Trial For Young Adults (Tobacco Status Project): Qualitative Analysis

Background

Smoking cessation interventions delivered through social media have the potential to engage young people in behavior change.

Objective

The aim of this study was to describe participant-posted messages in a Facebook smoking cessation intervention for young adults to discern support for behavior change.

Methods

We qualitatively analyzed data from the treatment arm of a randomized trial testing the efficacy of the Tobacco Status Project Facebook intervention. Young adults (N=138) aged 18-25 years (female: 81/138, 58.7%; white: 101/138, 73.2%; mean age 21 years) were recruited using Facebook and placed into one of the 15 secret Facebook groups based on readiness-to-quit smoking. Messages posted to groups for 90 consecutive days were tailored to readiness-to-quit: Not Ready (46/138, 33.3%), Thinking (66/138, 47.8%), and Getting Ready (26/138, 18.8%). Groups were randomized to receive up to US $90 for posting or no incentive. Two independent coders conducted open coding of user posts. We considered content by readiness-to-quit group and incentive condition.

Results

There were 4 dominant themes across all groups: coping skills, friends and family, motivation to quit, and benefits of quitting. The dominant themes in Not Ready groups were friends and family (incentive) and motivation to quit (no incentive), whereas coping skills was the dominant theme in Thinking and Getting Ready groups. The expression of themes varied by readiness-to-quit group but not by incentive condition.

Conclusions

Intervention messages tailored to readiness-to-quit appear useful in eliciting the desired responses from young adult smokers, with limited influence by monetary incentive.

Trial Registration

ClinicalTrials.gov NCT02207036; https://clinicaltrials.gov/ct2/show/NCT02207036 (Archived by WebCite at http://www.webcitation.org/722XAEAAz)

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.

Public Perception Analysis of Tweets During the 2015 Measles Outbreak: Comparative Study Using Convolutional Neural Network Models

Background

Timely understanding of public perceptions allows public health agencies to provide up-to-date responses to health crises such as infectious diseases outbreaks. Social media such as Twitter provide an unprecedented way for the prompt assessment of the large-scale public response.

Objective

The aims of this study were to develop a scheme for a comprehensive public perception analysis of a measles outbreak based on Twitter data and demonstrate the superiority of the convolutional neural network (CNN) models (compared with conventional machine learning methods) on measles outbreak-related tweets classification tasks with a relatively small and highly unbalanced gold standard training set.

Methods

We first designed a comprehensive scheme for the analysis of public perception of measles based on tweets, including 3 dimensions: discussion themes, emotions expressed, and attitude toward vaccination. All 1,154,156 tweets containing the word “measles” posted between December 1, 2014, and April 30, 2015, were purchased and downloaded from DiscoverText.com. Two expert annotators curated a gold standard of 1151 tweets (approximately 0.1% of all tweets) based on the 3-dimensional scheme. Next, a tweet classification system based on the CNN framework was developed. We compared the performance of the CNN models to those of 4 conventional machine learning models and another neural network model. We also compared the impact of different word embeddings configurations for the CNN models: (1) Stanford GloVe embedding trained on billions of tweets in the general domain, (2) measles-specific embedding trained on our 1 million measles related tweets, and (3) a combination of the 2 embeddings.

Results

Cohen kappa intercoder reliability values for the annotation were: 0.78, 0.72, and 0.80 on the 3 dimensions, respectively. Class distributions within the gold standard were highly unbalanced for all dimensions. The CNN models performed better on all classification tasks than k-nearest neighbors, naïve Bayes, support vector machines, or random forest. Detailed comparison between support vector machines and the CNN models showed that the major contributor to the overall superiority of the CNN models is the improvement on recall, especially for classes with low occurrence. The CNN model with the 2 embedding combination led to better performance on discussion themes and emotions expressed (microaveraging F1 scores of 0.7811 and 0.8592, respectively), while the CNN model with Stanford embedding achieved best performance on attitude toward vaccination (microaveraging F1 score of 0.8642).

Conclusions

The proposed scheme can successfully classify the public’s opinions and emotions in multiple dimensions, which would facilitate the timely understanding of public perceptions during the outbreak of an infectious disease. Compared with conventional machine learning methods, our CNN models showed superiority on measles-related tweet classification tasks with a relatively small and highly unbalanced gold standard. With the success of these tasks, our proposed scheme and CNN-based tweets classification system is expected to be useful for the analysis of tweets about other infectious diseases such as influenza and Ebola.

Social Media and Obesity in Adults: a Review of Recent Research and Future Directions

PURPOSE OF REVIEW

Social media is widely used and has potential to connect adults with obesity with information and social support for weight loss and to deliver lifestyle interventions. The purpose of this review is to summarize recent observational and intervention research on social media and obesity.

RECENT FINDINGS

Online patient communities for weight loss abound but may include misinformation. Systematic reviews and meta-analyses suggest that social media-delivered lifestyle interventions modestly impact weight, yet how social media was used and participant engagement varies widely. The rapidly changing social media landscape poses challenges for patients, clinicians, and researchers. Research is needed on how patients can establish supportive communities for weight loss and the role of clinicians in these communities. Emerging research on meaningful engagement in, and the efficacy and cost-effectiveness of, social media-delivered lifestyle interventions should provide insights into how to leverage social media to address the obesity epidemic.

A prospective examination of online social network dynamics and smoking cessation

Introduction

Use of online social networks for smoking cessation has been associated with abstinence. Little is known about the mechanisms through which the formation of social ties in an online network may influence smoking behavior. Using dynamic social network analysis, we investigated how temporal changes of an individual’s number of social network ties are prospectively related to abstinence in an online social network for cessation. In a network where quitting is normative and is the focus of communications among members, we predicted that an increasing number of ties would be positively associated with abstinence.

Method

Participants were N = 2,657 adult smokers recruited to a randomized cessation treatment trial following enrollment on BecomeAnEX.org, a longstanding Internet cessation program with a large and mature online social network. At 3-months post-randomization, 30-day point prevalence abstinence was assessed and website engagement metrics were extracted. The social network was constructed with clickstream data to capture the flow of information among members. Two network centrality metrics were calculated at weekly intervals over 3 months: 1) in-degree, defined as the number of members whose posts a participant read; and 2) out-degree-aware, defined as the number of members who read a participant’s post and commented, which was subsequently viewed by the participant. Three groups of users were identified based on social network engagement patterns: non-users (N = 1,362), passive users (N = 812), and active users (N = 483). Logistic regression modeled 3-month abstinence by group as a function of baseline variables, website utilization, and network centrality metrics.

Results

Abstinence rates varied by group (non-users = 7.7%, passive users = 10.7%, active users = 20.7%). Significant baseline predictors of abstinence were age, nicotine dependence, confidence to quit, and smoking temptations in social situations among passive users (ps < .05); age and confidence to quit among active users. Among centrality metrics, positive associations with abstinence were observed for in-degree increases from Week 2 to Week 12 among passive and active users, and for out-degree-aware increases from Week 2 to Week 12 among active users (ps < .05).

Conclusions

This study is the first to demonstrate that increased tie formation among members of an online social network for smoking cessation is prospectively associated with abstinence. It also highlights the value of using individuals’ activities in online social networks to predict their offline health behaviors.

Longitudinal analysis of discussion topics in an online breast cancer community using convolutional neural networks

Identifying topics of discussions in online health communities (OHC) is critical to various information extraction applications, but can be difficult because topics of OHC content are usually heterogeneous and domain-dependent. In this paper, we provide a multi-class schema, an annotated dataset, and supervised classifiers based on convolutional neural network (CNN) and other models for the task of classifying discussion topics. We apply the CNN classifier to the most popular breast cancer online community, and carry out cross-sectional and longitudinal analyses to show topic distributions and topic dynamics throughout members' participation. Our experimental results suggest that CNN outperforms other classifiers in the task of topic classification and identify several patterns and trajectories. For example, although members discuss mainly disease-related topics, their interest may change through time and vary with their disease severities.

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.

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.

Online Social Support for the Prevention of Smoking Relapse: A Content Analysis of the WhatsApp and Facebook Social Groups

BACKGROUND

Online social groups have been increasingly used for smoking cessation intervention.

INTRODUCTION

This study aimed to explore the social support components of the online discussion through WhatsApp and Facebook, how these components addressed the need of relapse prevention, and how the participants evaluated this intervention.

MATERIALS AND METHODS

We coded and analyzed the posts (N = 467) by the 82 recent quitters in WhatsApp and Facebook social groups, who were recruited from the eight smoking cessation clinics in Hong Kong to participate in a pragmatic randomized trial of relapse prevention. Participants' postintervention feedback was collected from the 13 qualitative interviews after the intervention.

RESULTS

The WhatsApp social groups had more participants' posts than the Facebook counterparts. The participants' posts in the online social groups could be classified as sharing views and experiences (55.5%), encouragement (28.7%), and knowledge and information (15.8%). About half of the participants' posts (52.9%) addressed the themes listed in the U.S. Clinical Practice Guideline for preventing smoking relapse. The participants perceived the posts as useful reminders for smoking cessation, but avoidance of reporting relapse, inactive discussions, and uninteresting content were barriers to the success of the intervention.

DISCUSSION

Online social groups provided a useful platform for the delivery of cessation support and encouragement of reporting abstinence, which support relapse prevention. The effectiveness of such intervention can be improved by encouraging more self-report of relapse, active discussions, sharing of interesting content, and using an appropriate discussion platform.

CONCLUSION

Quitters who participate in the online social groups can benefit from peer support and information sharing, and hence prevent smoking relapse.

Prioritizing the mHealth Design Space: A Mixed-Methods Analysis of Smokers' Perspectives

Background

Smoking remains the leading cause of preventable disease and death in the United States. Therefore, researchers are constantly exploring new ways to promote smoking cessation. Mobile health (mHealth) technologies could be effective cessation tools. Despite the availability of commercial quit-smoking apps, little research to date has examined smokers’ preferred treatment intervention components (ie, design features). Honoring these preferences is important for designing programs that are appealing to smokers and may be more likely to be adopted and used.

Objective

The aim of this study was to understand smokers’ preferred design features of mHealth quit-smoking tools.

Methods

We used a mixed-methods approach consisting of focus groups and written surveys to understand the design preferences of adult smokers who were interested in quitting smoking (N=40). Focus groups were stratified by age to allow differing perspectives to emerge between older (>40 years) and younger (<40 years) participants. Focus group discussion included a “blue-sky” brainstorming exercise followed by participant reactions to contrasting design options for communicating with smokers, providing social support, and incentivizing program use. Participants rated the importance of preselected design features on an exit survey. Qualitative analyses examined emergent discussion themes and quantitative analyses compared feature ratings to determine which were perceived as most important.

Results

Participants preferred a highly personalized and adaptive mHealth experience. Their ideal mHealth quit-smoking tool would allow personalized tracking of their progress, adaptively tailored feedback, and real-time peer support to help manage smoking cravings. Based on qualitative analysis of focus group discussion, participants preferred pull messages (ie, delivered upon request) over push messages (ie, sent automatically) and preferred interaction with other smokers through closed social networks. Preferences for entertaining games or other rewarding incentives to encourage program use differed by age group. Based on quantitative analysis of surveys, participants rated the importance of select design features significantly differently (P<.001). Design features rated as most important included personalized content, the ability to track one’s progress, and features designed to help manage nicotine withdrawal and medication side effects. Design features rated least important were quit-smoking videos and posting on social media. Communicating with stop-smoking experts was rated more important than communicating with family and friends about quitting (P=.03). Perceived importance of various design features varied by age, experience with technology, and frequency of smoking.

Conclusions

Future mHealth cessation aids should be designed with an understanding of smokers’ needs and preferences for these tools. Doing so does not guarantee treatment effectiveness, but balancing user preferences with best-practice treatment considerations could enhance program adoption and improve treatment outcomes. Grounded in the perspectives of smokers, we identify several design considerations, which should be prioritized when designing future mHealth cessation tools and which warrant additional empirical validation.

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.

From Help-Seekers to Influential Users: A Systematic Review of Participation Styles in Online Health Communities

BACKGROUND

Understanding how people participate in and contribute to online health communities (OHCs) is useful knowledge in multiple domains. It is helpful for community managers in developing strategies for building community, for organizations in disseminating information about health interventions, and for researchers in understanding the social dynamics of peer support.

OBJECTIVE

We sought to determine if any patterns were apparent in the nature of user participation across online health communities.

METHODS

The current study involved a systematic review of all studies that have investigated the nature of participation in an online health community and have provided a quantifiable method for categorizing a person based on their participation style. A systematic search yielded 20 papers.

RESULTS

Participatory styles were classified as either multidimensional (based on multiple metrics) or unidimensional (based on one metric). With respect to the multidimensional category, a total of 41 different participation styles were identified ranging from Influential Users who were leaders on the board to Topic-Focused Responders who focused on a specific topic and tended to respond to rather than initiate posts. However, there was little overlap in participation styles identified both across OHCs for different health conditions and within OHCs for specific health conditions. Five of the 41 styles emerged in more than one study (Hubs, Authorities, Facilitators, Prime Givers, and Discussants), but the remainder were reported in only one study. The focus of the unidimensional studies was on level of engagement and particularly on high-engaged users. Eight different metrics were used to evaluate level of engagement with the greatest focus on frequency of posts.

CONCLUSIONS

With the exception of high-engaged users based on high post frequency, the current review found little evidence for consistent participatory styles across different health communities. However, this area of research is in its infancy, with most of the studies included in the review being published in the last 2 years. Nevertheless, the review delivers a nomenclature for OHC participation styles and metrics and discusses important methodological issues that will provide a basis for future comparative research in the area. Further studies are required to systematically investigate a range of participatory styles, to investigate their association with different types of online health communities and to determine the contribution of different participatory styles within and across online health communities.

Patient Engagement in Cancer Survivorship Care through mHealth: A Consumer-centered Review of Existing Mobile Applications

With improvements in early detection and treatment, the number of cancer survivors has been on the rise. Studies suggest that cancer survivors do not often receive proper follow-up care despite existing guidelines. Patient engagement is key to healthy survivorship, and mHealth provides a viable platform to empower survivors with just- in-time personalized support. However, our understanding of existing mHealth solutions in cancer survivorship is limited. In this paper, we use Patient Engagement Framework to investigate existing apps to bridge this knowledge gap. App features are mapped to the framework components to determine the level of engagement facilitated. Ability to record treatment summaries has been found in five out of seven apps examined. While collaborative care and social engagement are found minimally, the majority of features (95%) are limited to information and way finding, e-tools, and interactive forms. Limitations of the existing apps and possible improvements to the framework are discussed.