Dataset Information

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

ABSTRACT: 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.

SUBMITTER: Amato MS

PROVIDER: S-EPMC6329402 | biostudies-literature | 2019 Jan

REPOSITORIES: biostudies-literature

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

Amato Michael S MS Papandonatos George D GD Cha Sarah S Wang Xi X Zhao Kang K Cohn Amy M AM Pearson Jennifer L JL Graham Amanda L AL

Nicotine & tobacco research : official journal of the Society for Research on Nicotine and Tobacco 20190101 2

<h4>Introduction</h4>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.<h4>Methods</h4>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 ...[more]

PMID: 29365157

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Project description:BackgroundOnline health communities (OHCs) have increasingly gained traction with patients, caregivers, and supporters globally. Chinese OHCs are no exception. However, user-generated content (UGC) and the associated user behaviors in Chinese OHCs are largely underexplored and rarely analyzed systematically, forfeiting valuable opportunities for optimizing treatment design and care delivery with insights gained from OHCs.ObjectiveThis study aimed to reveal both the shared and distinct characteristics of 2 popular OHCs in China by systematically and comprehensively analyzing their UGC and the associated user behaviors.MethodsWe concentrated on studying the lung cancer forum (LCF) and breast cancer forum (BCF) on Mijian, and the diabetes consultation forum (DCF) on Sweet Home, because of the importance of the 3 diseases among Chinese patients and their prevalence on Chinese OHCs in general. Our analysis explored the key user activities, small-world effect, and scale-free characteristics of each social network. We examined the UGC of these forums comprehensively and adopted the weighted knowledge network technique to discover salient topics and latent relations among these topics on each forum. Finally, we discussed the public health implications of our analysis findings.ResultsOur analysis showed that the number of reads per thread on each forum followed gamma distribution (HL=0, HB=0, and HD=0); the number of replies on each forum followed exponential distribution (adjusted RL2=0.946, adjusted RB2=0.958, and adjusted RD2=0.971); and the number of threads a user is involved with (adjusted RL2=0.978, adjusted RB2=0.964, and adjusted RD2=0.970), the number of followers of a user (adjusted RL2=0.989, adjusted RB2=0.962, and adjusted RD2=0.990), and a user's degrees (adjusted RL2=0.997, adjusted RB2=0.994, and adjusted RD2=0.968) all followed power-law distribution. The study further revealed that users are generally more active during weekdays, as commonly witnessed in all 3 forums. In particular, the LCF and DCF exhibited high temporal similarity (ρ=0.927; P<.001) in terms of the relative thread posting frequencies during each hour of the day. Besides, the study showed that all 3 forums exhibited the small-world effect (mean σL=517.15, mean σB=275.23, and mean σD=525.18) and scale-free characteristics, while the global clustering coefficients were lower than those of counterpart international OHCs. The study also discovered several hot topics commonly shared among the 3 disease forums, such as disease treatment, disease examination, and diagnosis. In particular, the study found that after the outbreak of COVID-19, users on the LCF and BCF were much more likely to bring up COVID-19-related issues while discussing their medical issues.ConclusionsUGC and related online user behaviors in Chinese OHCs can be leveraged as important sources of information to gain insights regarding individual and population health conditions. Effective and timely mining and utilization of such content can continuously provide valuable firsthand clues for enhancing the situational awareness of health providers and policymakers.

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.

Project description:IntroductionUsing WebQuit as a case study, a smoking cessation website grounded in Acceptance and Commitment Therapy, we aimed to identify sequence clusters of content usage and examine their associations with baseline characteristics, change to a key mechanism of action, and smoking cessation.MethodsParticipants were adult smokers allocated to the WebQuit arm in a randomized controlled trial (n = 1,313). WebQuit contains theory-informed content including goal setting, self-monitoring and feedback, and values- and acceptance-based exercises. Sequence analysis was used to temporally order 30-s website usage segments for each participant. Similarities between sequences were assessed with the optimal matching distance algorithm and used as input in an agglomerative hierarchical clustering analysis. Associations between sequence clusters and baseline characteristics, acceptance of cravings at 3 months and self-reported 30-day point prevalence abstinence at 12 months were examined with linear and logistic regression.ResultsThree qualitatively different sequence clusters were identified. "Disengagers" (576/1,313) almost exclusively used the goal-setting feature. "Tryers" (375/1,313) used goal setting and two of the values- and acceptance-based components ("Be Aware," "Be Willing"). "Committers" (362/1,313) primarily used two of the values- and acceptance-based components ("Be Willing," "Be Inspired"), goal setting, and self-monitoring and feedback. Compared with Disengagers, Committers demonstrated greater increases in acceptance of cravings (p = .01) and 64% greater odds of quit success (ORadj = 1.64, 95% CI = 1.18, 2.29, p = .003).DiscussionWebQuit users were categorized into Disengagers, Tryers, and Committers based on their qualitatively different content usage patterns. Committers saw increases in a key mechanism of action and greater odds of quit success.ImplicationsThis case study demonstrates how employing sequence and cluster analysis of usage data can help researchers and practitioners gain a better understanding of how users engage with a given eHealth intervention over time and use findings to test theory and/or to improve future iterations to the intervention. Future WebQuit users may benefit from being directed to the values- and acceptance-based and the self-monitoring and feedback components via reminders over the course of the program.

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.

Project description:IntroductionPrevalence of tobacco smoking among adults in substance misuse treatment is much higher than the wider population, yet limited research is available, and residential treatment services have been overlooked as a potential setting for cessation interventions. Exploring the perceptions of service users about smoking cessation in residential rehabilitation is important to gain better understanding of this issue and identify ways to inform future intervention development.MethodsTen semi-structured interviews were conducted in the Northwest of England in 2017 with adults (7 male, 3 female) who were currently or had previously been in residential treatment for substance misuse. Five participants were current smokers, three had never smoked, and two were former smokers. Participants were asked about their smoking behaviours, factors relating to smoking and smoking cessation and the relationship between smoking and substance use. All interviews were transcribed and data was analysed thematically.ResultsStudy findings highlighted a general consensus amongst participants that residential treatment services offered an ideal opportunity for cessation but there were concerns that doing so might jeopardise recovery. Smoking in substance use treatment services is still the norm and factors such as perceived social and psychological benefits, normative behaviours and lack of perceived risk or prioritisation pose challenges for implementing smoking cessation within this setting, although facilitators such as motivation to change and appropriateness of the setting were also identified.ConclusionsThis study suggests that service users perceive residential treatment services as suitable environments to introduce smoking cessation. To address the needs of adults who smoke and are in recovery from substance use, further research and cooperation from treatment organisations is needed to integrate substance misuse and smoking cessation services. More conclusive evidence on the effectiveness of tackling both issues at the same time is also required.

Dataset Information

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

Publications

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

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