Social network structure of a large online community for smoking cessation.
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ABSTRACT: Objectives
We evaluated the social network structure of QuitNet, one of the largest online communities for behavior change, and compared its characteristics to other known social networks.Methods
Using modern network analysis methods, we identified QuitNet members who were active during a 60-day period, along with their ties. We then derived multiple subgroups, such as key players and integrators, from connections and communication patterns.Results
Among 7569 participants, we identified 103,592 connections to other members. Metrics of social network integration were associated with increased likelihood of being female, being older, having been in the system longer, and not smoking.Conclusions
The QuitNet community is a large-scale social network with the characteristics required for sustainability of social support and social influence to promote smoking cessation and abstinence. These characteristics include persistence of members over time, heterogeneity of smoking status, and evidence of rich, bidirectional communications. Some of the influential subgroups we identified may provide targets for future network-level interventions.
SUBMITTER: Cobb NK
PROVIDER: S-EPMC2882421 | biostudies-literature | 2010 Jul
REPOSITORIES: biostudies-literature
Publications
Social network structure of a large online community for smoking cessation.
Cobb Nathan K NK Graham Amanda L AL Abrams David B DB
American journal of public health 20100513 7
<h4>Objectives</h4>We evaluated the social network structure of QuitNet, one of the largest online communities for behavior change, and compared its characteristics to other known social networks.<h4>Methods</h4>Using modern network analysis methods, we identified QuitNet members who were active during a 60-day period, along with their ties. We then derived multiple subgroups, such as key players and integrators, from connections and communication patterns.<h4>Results</h4>Among 7569 participants ...[more]
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Project description:Online health communities (OHCs) provide a convenient and commonly used way for people to connect around shared health experiences, exchange information, and receive social support. Users often interact with peers via multiple communication methods, forming a multirelational social network. Use of OHCs is common among smokers, but to date, there have been no studies on users' online interactions via different means of online communications and how such interactions are related to smoking cessation. Such information can be retrieved in multirelational social networks and could be useful in the design and management of OHCs.To examine the social network structure of an OHC for smoking cessation using a multirelational approach, and to explore links between subnetwork position (ie, centrality) and smoking abstinence.We used NetworkX to construct 4 subnetworks based on users' interactions via blogs, group discussions, message boards, and private messages. We illustrated topological properties of each subnetwork, including its degree distribution, density, and connectedness, and compared similarities among these subnetworks by correlating node centrality and measuring edge overlap. We also investigated coevolution dynamics of this multirelational network by analyzing tie formation sequences across subnetworks. In a subset of users who participated in a randomized, smoking cessation treatment trial, we conducted user profiling based on users' centralities in the 4 subnetworks and identified user groups using clustering techniques. We further examined 30-day smoking abstinence at 3 months postenrollment in relation to users' centralities in the 4 subnetworks.The 4 subnetworks have different topological characteristics, with message board having the most nodes (36,536) and group discussion having the highest network density (4.35×10(-3)). Blog and message board subnetworks had the most similar structures with an in-degree correlation of .45, out-degree correlation of .55, and Jaccard coefficient of .23 for edge overlap. A new tie in the group discussion subnetwork had the lowest probability of triggering subsequent ties among the same two users in other subnetworks: 6.33% (54,142/855,893) for 2-tie sequences and 2.13% (18,207/855,893) for 3-tie sequences. Users' centralities varied across the 4 subnetworks. Among a subset of users enrolled in a randomized trial, those with higher centralities across subnetworks generally had higher abstinence rates, although high centrality in the group discussion subnetwork was not associated with higher abstinence rates.A multirelational approach revealed insights that could not be obtained by analyzing the aggregated network alone, such as the ineffectiveness of group discussions in triggering social ties of other types, the advantage of blogs, message boards, and private messages in leading to subsequent social ties of other types, and the weak connection between one's centrality in the group discussion subnetwork and smoking abstinence. These insights have implications for the design and management of online social networks for smoking cessation.
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Project description:IntroductionUse 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.MethodParticipants 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.ResultsAbstinence 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).ConclusionsThis 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.
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Project description:IntroductionAlthough technology-assisted tobacco interventions (TATIs) are effective, they are underused due to recruitment challenges. We tested whether we could successfully recruit smokers to a TATI using peer marketing through a social network (Facebook).MethodsWe recruited smokers on Facebook using online advertisements. These recruited smokers (seeds) and subsequent waves of smokers (peer recruits) were provided the Share2Quit peer recruitment Facebook app and other tools. Smokers were incentivized for up to seven successful peer recruitments and had 30 days to recruit from date of registration. Successful peer recruitment was defined as a peer recruited smoker completing the registration on the TATI following a referral. Our primary questions were (1) whether smokers would recruit other smokers and (2) whether peer recruitment would extend the reach of the intervention to harder-to-reach groups, including those not ready to quit and minority smokers.ResultsOverall, 759 smokers were recruited (seeds: 190; peer recruits: 569). Fifteen percent (n = 117) of smokers successfully recruited their peers (seeds: 24.7%; peer recruits: 7.7%) leading to four recruitment waves. Compared to seeds, peer recruits were less likely to be ready to quit (peer recruits 74.2% vs. seeds 95.1%), more likely to be male (67.1% vs. 32.9%), and more likely to be African American (23.8% vs. 10.8%) (p < .01 for all comparisons).ConclusionsPeer marketing quadrupled our engaged smokers and enriched the sample with not-ready-to-quit and African American smokers. Peer recruitment is promising, and our study uncovered several important challenges for future research.ImplicationsThis study demonstrates the successful recruitment of smokers to a TATI using a Facebook-based peer marketing strategy. Smokers on Facebook were willing and able to recruit other smokers to a TATI, yielding a large and diverse population of smokers.
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Project description: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.
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Project description:BackgroundThe prevalence of smoking has decreased substantially in the United States over the past 30 years. We examined the extent of the person-to-person spread of smoking behavior and the extent to which groups of widely connected people quit together.MethodsWe studied a densely interconnected social network of 12,067 people assessed repeatedly from 1971 to 2003 as part of the Framingham Heart Study. We used network analytic methods and longitudinal statistical models.ResultsDiscernible clusters of smokers and nonsmokers were present in the network, and the clusters extended to three degrees of separation. Despite the decrease in smoking in the overall population, the size of the clusters of smokers remained the same across time, suggesting that whole groups of people were quitting in concert. Smokers were also progressively found in the periphery of the social network. Smoking cessation by a spouse decreased a person's chances of smoking by 67% (95% confidence interval [CI], 59 to 73). Smoking cessation by a sibling decreased the chances by 25% (95% CI, 14 to 35). Smoking cessation by a friend decreased the chances by 36% (95% CI, 12 to 55 ). Among persons working in small firms, smoking cessation by a coworker decreased the chances by 34% (95% CI, 5 to 56). Friends with more education influenced one another more than those with less education. These effects were not seen among neighbors in the immediate geographic area.ConclusionsNetwork phenomena appear to be relevant to smoking cessation. Smoking behavior spreads through close and distant social ties, groups of interconnected people stop smoking in concert, and smokers are increasingly marginalized socially. These findings have implications for clinical and public health interventions to reduce and prevent smoking.
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Project description:Social and community service organizations (SCSOs) may be a promising new environment to more successfully reach people with a lower socioeconomic position (SEP) for smoking cessation support. However, studies that investigate clients' perspectives of the suitability of SCSOs as a setting to discuss smoking are scarce. This study aimed to (i) investigate the suitability of smoking cessation support provided by SCSOs, according to people with a low SEP, and (ii) explore their reasons for considering it suitable or unsuitable. Semi-structured interviews were conducted with 19 individuals with a low SEP who smoked regularly (N = 14) or had smoked regularly (N = 5). They have been in contact with SCSOs in a specific neighborhood in Amsterdam. Data were analyzed using a thematic approach. Participants generally considered SCSOs as suitable for providing smoking cessation support, as professionals are involved, build a relationship of trust, and offer personalized and holistic support. SCSOs are located nearby and familiar, they provide support in both group and individual settings and might offer additional supportive (group)activities. A number of participants expressed doubts about the waiting time for support, the fact that the needed support might exceed professionals' expertise, and the lack of aftercare. SCSOs can be an additional opportunity for providing smoking cessation support that aligns with the circumstances of lower SEP people. To harness the potential, smoking cessation could be integrated into education programs and training among professionals could be promoted. Policy changes within and outside SCSOs would be needed.
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Project description:BackgroundUse of e-cigarettes (inhalable vapour producing battery powered devices that aim to simulate tobacco cigarettes), is rising in a number of countries, but as yet none of these products are regulated as medicinal devices or available as smoking cessation treatments. Smokers seeking support from health professionals to stop smoking are interested in e-cigarettes and may be buying them to aid a quit attempt. Determining what smokers are asking, and what health professionals think about these products may have implications for smoking treatment services in a number of countries.MethodsStop smoking service advisors, managers and commissioners in the United Kingdom were asked to take part in two surveys on e-cigarettes. Data was analysed from 587 practitioners who completed a survey in 2011 and 705 practitioners who completed a repeat survey in 2013. Responses to multiple choice questions and free text comments were analysed.ResultsResponding practitioners reported that interest in, and use of, e-cigarettes is growing among adults seeking help to stop smoking in the UK. In 2013 91% of respondents reported that interest in e-cigarettes had grown in the past year and whilst in 2011, 2% of respondents reported a 'quarter to a half' of their clients saying that they were regularly using e-cigarettes, by 2013 this had increased to 23.5% (p < .001). Responding practitioners' views towards e-cigarettes became more positive between the first and second surveys (15% strongly agreed/agreed in 2011 that 'e-cigarettes are a good thing' rising to 26% in 2013). However, they continued to have concerns about the products. In particular, analysis of free text responses suggested practitioners were unsure about safety or efficacy for smoking cessation, and were worried that smokers may become dependent on the products. Practitioners were also aware of the potential of e-cigarettes to undermine smokers' willingness to use evidence-based methods to stop, and to challenge policies aiming to denormalise tobacco smoking.ConclusionsHealth professionals are asking for reliable and accurate information on e-cigarettes to convey to smokers who want to quit. Randomized controlled trials and ongoing surveillance of e-cigarette use and its consequences for smoking cessation rates and smoking treatment services are required.
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