A Multirelational Social Network Analysis of an Online Health Community for Smoking Cessation

Smoking cessation program preferences of individuals with chronic obstructive pulmonary disease: a qualitative study

AIM

To explore the views of tobacco-smoking chronic obstructive pulmonary disease (COPD) and asthma-COPD overlap (ACO) patients on telehealth-based cessation programs and the role of e-cigarettes as an aid to quit smoking.

BACKGROUND

Tobacco smoking accelerates the progression of COPD. Traditional smoking cessation programs often do not entirely address the unique needs of COPD patients, leading to suboptimal effectiveness for this population. This research is aimed at describing the attitudes and preferences of COPD and ACO patients toward innovative, telehealth-based smoking cessation strategies and the potential application of e-cigarettes as a quitting aid.

METHODS

A qualitative exploratory approach was adopted in this study, employing both focus groups and individual interviews with English-speaking adults with diagnosed COPD or ACO. Participants included both current smokers (≥ 5 cigarettes/day) and recent ex-smokers (who quit < 12 months ago). Data were systematically coded with iterative reliability checks and subjected to thematic analysis to extract key themes.

FINDINGS

A total of 24 individuals participated in this study. The emergent themes were the perceived structure and elements of a successful smoking cessation program, the possible integration of telehealth with digital technologies, and the strategic use of e-cigarettes for smoking reduction or cessation. The participants stressed the importance of both social and professional support in facilitating smoking cessation, expressing a high value for insights provided by ex-smokers serving as mentors. A preference was observed for group settings; however, the need for individualized plans was also highlighted, considering the diverse motivations individuals had to quit smoking. The participants perceived online program delivery as potentially beneficial as it could provide immediate access to support during cravings or withdrawals and was accessible to remote users. Opinions on e-cigarettes were mixed; some participants saw them as a less harmful alternative to conventional smoking, while others were skeptical of their efficacy and safety and called for further research.

Facilitators and barriers for implementing screening brief intervention and referral for health promotion in a rural hospital in Alberta: using consolidated framework for implementation research

BACKGROUND

Screening, brief intervention, and referral (SBIR) is an evidence-based, comprehensive health promotion approach commonly implemented to reduce alcohol and substance use. Implementation research on SBIR demonstrate that patients find it acceptable, reduces hospital costs, and it is effective. However, SBIR implementation in hospital settings for multiple risk factors (fruit and vegetable consumption, physical activity, alcohol and tobacco use) is still emergent. More evidence is needed to guide SBIR implementation for multiple risk factors in hospital settings.

OBJECTIVE

To explore the facilitators and barriers of SBIR implementation in a rural hospital using the Consolidated Framework for Implementation Research (CFIR).

METHODS

We conducted a descriptive qualitative investigation consisting of both inductive and deductive analyses. We conducted virtual, semi-structured interviews, guided by the CFIR framework. All interviews were audio-recorded, and transcribed verbatim. NVivo 12 Pro was used to organize and code the raw data.

RESULTS

A total of six key informant semi-structured interviews, ranging from 45 to 60 min, were carried out with members of the implementation support team and clinical implementers. Implementation support members reported that collaborating with health departments facilitated SBIR implementation by helping (a) align health promotion risk factors with existing guidelines; (b) develop training and educational resources for clinicians and patients; and (c) foster leadership buy-in. Conversely, clinical implementers reported several barriers to SBIR implementation including, increased and disrupted workflow due to SBIR-related documentation, a lack of knowledge on patients' readiness and motivation to change, as well as perceived patient stigma in relation to SBIR risk factors.

CONCLUSION

The CFIR provided a comprehensive framework to gauge facilitators and barriers relating to SBIR implementation. Our pilot investigation revealed that future SBIR implementation must address organizational, clinical implementer, and patient readiness to implement SBIR at all phases of the implementation process in a hospital.

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.

EasyGraph: A multifunctional, cross-platform, and effective library for interdisciplinary network analysis

Networks are powerful tools for representing the relationships and interactions between entities in various disciplines. However, existing network analysis tools and packages either lack powerful functionality or are not scalable for large networks. In this descriptor, we present EasyGraph, an open-source network analysis library that supports several network data formats and powerful network mining algorithms. EasyGraph provides excellent operating efficiency through a hybrid Python/C++ implementation and multiprocessing optimization. It is applicable to various disciplines and can handle large-scale networks. We demonstrate the effectiveness and efficiency of EasyGraph by applying crucial metrics and algorithms to random and real-world networks in domains such as physics, chemistry, and biology. The results demonstrate that EasyGraph improves the network analysis efficiency for users and reduces the difficulty of conducting large-scale network analysis. Overall, it is a comprehensive and efficient open-source tool for interdisciplinary network analysis.

Effects of maize resistance and leaf chemical substances on the structure of phyllosphere fungal communities

It is well known that plant genotype can regulate phyllosphere fungi at the species level. However, little is known about how plant varieties shape the fungal communities in the phyllosphere. In this study, four types of maize varieties with various levels of resistances to Exserohilum turcicum were subjected to high-throughput sequencing to reveal the properties that influences the composition of phyllosphere fungal communities. The dominant fungi genera for all four maize varieties were Alternaria at different relative abundances, followed by Nigrospora. Hierarchical clustering analysis, non-metric multidimensional scaling and similarity analysis confirmed that the fungal communities in the phyllosphere of the four varieties were significantly different and clustered into the respective maize variety they inhabited. The findings from Redundancy Analysis (RDA) indicated that both maize resistance and leaf chemical constituents, including nitrogen, phosphorus, tannins, and flavonoids, were the major drivers in determining the composition of phyllosphere fungal communities. Among these factors, maize resistance was found to be the most influential, followed by phosphorus. The co-occurrence network of the fungal communities in the phyllosphere of highly resistant variety had higher complexity, integrity and stability compared to others maize varieties. In a conclusion, maize variety resistance and leaf chemical constituents play a major role in shaping the phyllosphere fungal community. The work proposes a link between the assembled fungal communities within the phyllosphere with maize variety that is resistant to pathogenic fungi infection.

The Effects of Patient Health Information Seeking in Online Health Communities on Patient Compliance in China: Social Perspective

BACKGROUND

Online health communities (OHCs) can alleviate the uneven distribution and use of medical resources and severe hospital congestion. Patients may seek health information through OHCs before or after visiting physicians, which may affect their cognition, health literacy, decision-making preferences, and health-related behaviors such as compliance. Social factors (social support, social presence, and responsiveness) are closely related to patients' health information-seeking behavior and are significantly considered in OHCs.

OBJECTIVE

This study aimed to explore the effects of patients' health information-seeking behavior (way and effectiveness) on compliance with physicians from the perspectives of patients' perceived social support, social presence, and responsiveness.

METHODS

This study established a research model from the perspective of social information processing by using the social exchange theory. An anonymous questionnaire survey was conducted with several Chinese OHCs to collect data. Partial least squares and structural equation modeling were adopted to test the hypotheses and develop the model.

RESULTS

This study received 403 responses, of which 332 were valid, giving a validity rate of 82.4% (332/403). Among the sample, 78.6% (261/332) of the individuals were aged between 20 and 40 years, 59.3% (197/332) were woman, 69.9% (232/332) lived in urban areas, and 50% (166/332) had at least a bachelor's degree. The reliability, convergent validity, and discriminant validity were acceptable. Both the way and effectiveness of patients seeking health information through OHCs have a positive impact on their compliance through the mediation of their perceived social support, social presence, and responsiveness from OHCs and other users, and patient compliance can be improved by guiding patient health information-seeking behavior in OHCs from a social perspective.

CONCLUSIONS

This study proposes a research model to corroborate that patient health information-seeking behavior (way and effectiveness) in OHCs exerts positive effects on patient compliance with the treatment and physician's advice and provides suggestions for patients, physicians, and OHC service providers in China to help guide patients' health-related behaviors through OHCs to improve patient compliance, patient satisfaction, treatment efficiency, and health outcomes.

Smoking Cessation Smartphone App Use Over Time: Predicting 12-Month Cessation Outcomes in a 2-Arm Randomized Trial

BACKGROUND

Little is known about how individuals engage over time with smartphone app interventions and whether this engagement predicts health outcomes.

OBJECTIVE

In the context of a randomized trial comparing 2 smartphone apps for smoking cessation, this study aimed to determine distinct groups of smartphone app log-in trajectories over a 6-month period, their association with smoking cessation outcomes at 12 months, and baseline user characteristics that predict data-driven trajectory group membership.

METHODS

Functional clustering of 182 consecutive days of smoothed log-in data from both arms of a large (N=2415) randomized trial of 2 smartphone apps for smoking cessation (iCanQuit and QuitGuide) was used to identify distinct trajectory groups. Logistic regression was used to determine the association of group membership with the primary outcome of 30-day point prevalence of smoking abstinence at 12 months. Finally, the baseline characteristics associated with group membership were examined using logistic and multinomial logistic regression. The analyses were conducted separately for each app.

RESULTS

For iCanQuit, participants were clustered into 3 groups: "1-week users" (610/1069, 57.06%), "4-week users" (303/1069, 28.34%), and "26-week users" (156/1069, 14.59%). For smoking cessation rates at the 12-month follow-up, compared with 1-week users, 4-week users had 50% higher odds of cessation (30% vs 23%; odds ratio [OR] 1.50, 95% CI 1.05-2.14; P=.03), whereas 26-week users had 397% higher odds (56% vs 23%; OR 4.97, 95% CI 3.31-7.52; P<.001). For QuitGuide, participants were clustered into 2 groups: "1-week users" (695/1064, 65.32%) and "3-week users" (369/1064, 34.68%). The difference in the odds of being abstinent at 12 months for 3-week users versus 1-week users was minimal (23% vs 21%; OR 1.16, 95% CI 0.84-1.62; P=.37). Different baseline characteristics predicted the trajectory group membership for each app.

CONCLUSIONS

Patterns of 1-, 3-, and 4-week smartphone app use for smoking cessation may be common in how people engage in digital health interventions. There were significantly higher odds of quitting smoking among 4-week users and especially among 26-week users of the iCanQuit app. To improve study outcomes, strategies for detecting users who disengage early from these interventions (1-week users) and proactively offering them a more intensive intervention could be fruitful.

User Behaviors and User-Generated Content in Chinese Online Health Communities: Comparative Study

BACKGROUND

Online 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.

OBJECTIVE

This 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.

METHODS

We 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.

RESULTS

Our analysis showed that the number of reads per thread on each forum followed gamma distribution (H_L=0, H_B=0, and H_D=0); the number of replies on each forum followed exponential distribution (adjusted R_L²=0.946, adjusted R_B²=0.958, and adjusted R_D²=0.971); and the number of threads a user is involved with (adjusted R_L²=0.978, adjusted R_B²=0.964, and adjusted R_D²=0.970), the number of followers of a user (adjusted R_L²=0.989, adjusted R_B²=0.962, and adjusted R_D²=0.990), and a user's degrees (adjusted R_L²=0.997, adjusted R_B²=0.994, and adjusted R_D²=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 σ_L=517.15, mean σ_B=275.23, and mean σ_D=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.

CONCLUSIONS

UGC 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.

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.

Application of natural language processing techniques to identify off-label drug usage from various online health communities

Objective

Outcomes mentioned on online health communities (OHCs) by patients can serve as a source of evidence for off-label drug usage evaluation, but identifying these outcomes manually is tedious work. We have built a natural language processing model to identify off-label usage of drugs mentioned in these patient posts.

Materials and Methods

Single patient posts from 4 major OHCs were considered for this study. A text classification model was built to classify the posts as either relevant or not relevant based on patient experience. The relevant posts were passed through a spelling correction tool, CSpell, and then medications and indications from these posts were identified using cTAKES (clinical Text Analysis and Knowledge Extraction System), a named entity recognition tool. Drug and indication pairs were identified using a dependency parser. Finally, if the paired indication was not mentioned on the label of the drug approved by U.S. Food and Drug Administration, it was tagged as off-label use of that drug.

Results

Using this algorithm, we identified 289 off-label indications, achieving a recall of 76%.

Conclusions

The method designed in this study identifies and extracts the semantic relationship between drugs and indications from demotic posts in OHCs. The results demonstrate the feasibility of using natural language processing techniques in identifying off-label drug usage across online health forums for a variety of drugs. Understanding patients’ off-label use of drugs may be able to help manufacturers innovate to better address patients’ needs and assist doctors’ prescribing decisions.

Behavioural interventions delivered through interactive social media for health behaviour change, health outcomes, and health equity in the adult population

BACKGROUND

Social networking platforms offer a wide reach for public health interventions allowing communication with broad audiences using tools that are generally free and straightforward to use and may be combined with other components, such as public health policies. We define interactive social media as activities, practices, or behaviours among communities of people who have gathered online to interactively share information, knowledge, and opinions.

OBJECTIVES

We aimed to assess the effectiveness of interactive social media interventions, in which adults are able to communicate directly with each other, on changing health behaviours, body functions, psychological health, well-being, and adverse effects. Our secondary objective was to assess the effects of these interventions on the health of populations who experience health inequity as defined by PROGRESS-Plus. We assessed whether there is evidence about PROGRESS-Plus populations being included in studies and whether results are analysed across any of these characteristics.

SEARCH METHODS

We searched CENTRAL, CINAHL, Embase, MEDLINE (including trial registries) and PsycINFO. We used Google, Web of Science, and relevant web sites to identify additional studies and searched reference lists of included studies. We searched for published and unpublished studies from 2001 until June 1, 2020. We did not limit results by language.

SELECTION CRITERIA

We included randomised controlled trials (RCTs), controlled before-and-after (CBAs) and interrupted time series studies (ITSs). We included studies in which the intervention website, app, or social media platform described a goal of changing a health behaviour, or included a behaviour change technique. The social media intervention had to be delivered to adults via a commonly-used social media platform or one that mimicked a commonly-used platform. We included studies comparing an interactive social media intervention alone or as a component of a multi-component intervention with either a non-interactive social media control or an active but less-interactive social media comparator (e.g. a moderated versus an unmoderated discussion group). Our main outcomes were health behaviours (e.g. physical activity), body function outcomes (e.g. blood glucose), psychological health outcomes (e.g. depression), well-being, and adverse events. Our secondary outcomes were process outcomes important for behaviour change and included knowledge, attitudes, intention and motivation, perceived susceptibility, self-efficacy, and social support.

DATA COLLECTION AND ANALYSIS

We used a pre-tested data extraction form and collected data independently, in duplicate. Because we aimed to assess broad outcomes, we extracted only one outcome per main and secondary outcome categories prioritised by those that were the primary outcome as reported by the study authors, used in a sample size calculation, and patient-important.

MAIN RESULTS

We included 88 studies (871,378 participants), of which 84 were RCTs, three were CBAs and one was an ITS. The majority of the studies were conducted in the USA (54%). In total, 86% were conducted in high-income countries and the remaining 14% in upper middle-income countries. The most commonly used social media platform was Facebook (39%) with few studies utilising other platforms such as WeChat, Twitter, WhatsApp, and Google Hangouts. Many studies (48%) used web-based communities or apps that mimic functions of these well-known social media platforms. We compared studies assessing interactive social media interventions with non-interactive social media interventions, which included paper-based or in-person interventions or no intervention. We only reported the RCT results in our 'Summary of findings' table. We found a range of effects on health behaviours, such as breastfeeding, condom use, diet quality, medication adherence, medical screening and testing, physical activity, tobacco use, and vaccination. For example, these interventions may increase physical activity and medical screening tests but there was little to no effect for other health behaviours, such as improved diet or reduced tobacco use (20,139 participants in 54 RCTs). For body function outcomes, interactive social media interventions may result in small but important positive effects, such as a small but important positive effect on weight loss and a small but important reduction in resting heart rate (4521 participants in 30 RCTs). Interactive social media may improve overall well-being (standardised mean difference (SMD) 0.46, 95% confidence interval (CI) 0.14 to 0.79, moderate effect, low-certainty evidence) demonstrated by an increase of 3.77 points on a general well-being scale (from 1.15 to 6.48 points higher) where scores range from 14 to 70 (3792 participants in 16 studies). We found no difference in effect on psychological outcomes (depression and distress) representing a difference of 0.1 points on a standard scale in which scores range from 0 to 63 points (SMD -0.01, 95% CI -0.14 to 0.12, low-certainty evidence, 2070 participants in 12 RCTs). We also compared studies assessing interactive social media interventions with those with an active but less interactive social media control (11 studies). Four RCTs (1523 participants) that reported on physical activity found an improvement demonstrated by an increase of 28 minutes of moderate-to-vigorous physical activity per week (from 10 to 47 minutes more, SMD 0.35, 95% CI 0.12 to 0.59, small effect, very low-certainty evidence). Two studies found little to no difference in well-being for those in the intervention and control groups (SMD 0.02, 95% CI -0.08 to 0.13, small effect, low-certainty evidence), demonstrated by a mean change of 0.4 points on a scale with a range of 0 to 100. Adverse events related to the social media component of the interventions, such as privacy issues, were not reported in any of our included studies. We were unable to conduct planned subgroup analyses related to health equity as only four studies reported relevant data.

AUTHORS' CONCLUSIONS

This review combined data for a variety of outcomes and found that social media interventions that aim to increase physical activity may be effective and social media interventions may improve well-being. While we assessed many other outcomes, there were too few studies to compare or, where there were studies, the evidence was uncertain. None of our included studies reported adverse effects related to the social media component of the intervention. Future studies should assess adverse events related to the interactive social media component and should report on population characteristics to increase our understanding of the potential effect of these interventions on reducing health inequities.

Social Network Analysis of an Online Smoking Cessation Community to Identify Users' Smoking Status

Objectives

Users share valuable information through online smoking cessation communities (OSCCs), which help people maintain and improve smoking cessation behavior. Although OSCC utilization is common among smokers, limitations exist in identifying the smoking status of OSCC users (“quit” vs. “not quit”). Thus, the current study implicitly analyzed user-generated content (UGC) to identify individual users’ smoking status through advanced computational methods and real data from an OSCC.

Methods

Secondary data analysis was conducted using data from 3,833 users of BcomeAnEX.org. Domain experts reviewed posts and comments to determine the authors’ smoking status when they wrote them. Seven types of feature sets were extracted from UGC (textual, Doc2Vec, social influence, domain-specific, author-based, and thread-based features, as well as adjacent posts).

Results

Introducing novel features boosted smoking status recognition (quit vs. not quit) by 9.3% relative to the use of text-only post features. Furthermore, advanced computational methods outperformed baseline algorithms across all models and increased the smoking status prediction performance by up to 12%.

Conclusions

The results of this study suggest that the current research method provides a valuable platform for researchers involved in online cessation interventions and furnishes a framework for on-going machine learning applications. The results may help practitioners design a sustainable real-time intervention via personalized post recommendations in OSCCs. A major limitation is that only users’ smoking status was detected. Future research might involve programming machine learning classification methods to identify abstinence duration using larger datasets.

Potential of Bradyrhizobia inoculation to promote peanut growth and beneficial Rhizobacteria abundance

AIMS

To investigate the effects of three symbiotic Bradyrhizobium strains on peanut growth and on rhizobacterial communities in flowering and harvest stages in an organic farm, also to evaluate the role of plant development in influencing peanut rhizobacterial microbiota and correlations among the inoculants, rhizobacterial communities and plant growth.

METHODS AND RESULTS

Peanut seeds were inoculated with three individual Bradyrhizobium strains, plant growth performance was measured in two developmental stages and rhizobacterial communities were analysed by Illumina sequencing of rpoB gene amplicons from peanut rhizosphere. The three bradyrhizobial inoculants significantly increased the nodule numbers and aboveground fresh weight of peanut plants regardless of the different growth stages, and the pod yields were increased to some extent and significantly positively correlated with Bradyrhizobium abundances in rhizosphere. Principal coordinate analysis indicated that the rhizobacterial communities were strongly influenced by the inoculation and peanut developmental stages. The bradyrhizobia inoculation increased relative abundances of potentially beneficial bacteria in peanut rhizosphere, and also altered rhizobacterial co-occurrence association networks and important network hub taxa. Similarly, plant development also significantly influenced the structure, composition and co-occurrence association networks of rhizobacterial communities.

CONCLUSIONS

Bradyrhizobial inoculants increased peanut growth and yields, they and plant development affected the assembly of peanut rhizobacterial communities.

SIGNIFICANCE AND IMPACT OF THE STUDY

Rhizobial inoculants improved the host plant performance that might also be associated with the dynamic changes in rhizobacterial community except enhancing the biological nitrogen fixation and helps to profoundly understand the mechanism how rhizobia inoculants improve plant growth and yields.

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.

Engaging People in Tobacco Prevention and Cessation: Reflecting Back Over 20 Years Since the Master Settlement Agreement

Over the past 20 years, tobacco prevention and cessation efforts have evolved to keep pace with the changing tobacco product landscape and the widespread adoption of digital technologies. In 2019, Truth Initiative was awarded the Society of Behavioral Medicine’s Jessie Gruman Award for Health Engagement in recognition of the major role it has played on both fronts since its inception in 1999. This manuscript reviews the challenges and opportunities that have emerged over the past two decades, the evolving tactics deployed by Truth Initiative to engage people in tobacco prevention and cessation efforts, the approaches used to evaluate those efforts, and key achievements. It concludes with a summary of lessons learned and considerations for tobacco control researchers and practitioners to accelerate their impact on public health.

Predicting User Posting Activities in Online Health Communities with Deep Learning

Online health communities (OHCs) represent a great source of social support for patients and their caregivers. Better predictions of user activities in OHCs can help improve user engagement and retention, which are important to manage and sustain a successful OHC. This article proposes a general framework to predict OHC user posting activities. Deep learning methods are adopted to learn from users’ temporal trajectories in both the volumes and content of posts published over time. Experiments based on data from a popular OHC for cancer survivors demonstrate that the proposed approach can improve the performance of user activity predictions. In addition, several topics of users’ posts are found to have strong impact on predicting users’ activities in the OHC.

Communication that changes lives: an exploratory research on a Chinese online hypertension community

Purpose

Online health communities (OHCs) are attracting more and more healthy consumers, including patients, their families, caregivers and the general public. This paper aims to explore the themes and characteristics of patient-generated content (PGC) in Chinese OHCs.

Design/methodology/approach

Baidu Tieba for hypertension was selected as the research site. Online ethnography (netnography) approach was utilized to explore the PGC and health communication in the online hypertension community. The final database included 300 randomly sampled threads and their 3,187 reply posts and was further analyzed from three perspectives: health information needs, attitudes and psychological reactions to hypertension and social support exchange.

Findings

The members' health information needs were mainly concentrated on five aspects: causes, symptoms, measuring instrument, tests and diagnosis and treatment. Their attitudes and psychological reactions to hypertension varied with the context, for example, disease stage, health condition. Within the health communication, three types of social support – information support, emotional support and network support – were generated, transmitted and exchanged among members.

Practical implications

OHCs are able to serve as important source of health information and tool for health education. The implications and suggestions for health promotion of individuals, health information services optimization of OHCs and national health strategy plans were also discussed.

Originality/value

This is the first netnography study in information field on Chinese online hypertension community. This study provides a new perspective to explore the needs, attitudes and social support behaviors of Chinese hypertension population and also enables the Chinese experience of using OHCs to reduce health disparities to come to the world.

Big Data, Computational Social Science, and Health Communication: A Review and Agenda for Advancing Theory

Contemporary research on health communication has been marked by the presence of big data and computational social science (CSS) techniques. The relative novelty of these approaches makes it worthwhile to consider their status and potential for advancing health communication scholarship. This essay offers an introduction focusing on how big data and CSS techniques are being employed to study health communication and their utility for theory development. Key trends in this body of research are summarized, including the use of big data and CSS for examining public perceptions of health conditions or events, investigating network-related dimensions of health phenomena, and illness monitoring. The implications of big data and CSS for health communication theory are also evaluated. Opportunities presented by big data and CSS to help extend existing theories and build new communication theories are discussed.

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.

Mining User-Generated Content in an Online Smoking Cessation Community to Identify Smoking Status: A Machine Learning Approach

Online smoking cessation communities help hundreds of thousands of smokers quit smoking and stay abstinent each year. Content shared by users of such communities may contain important information that could enable more effective and personally tailored cessation treatment recommendations. This study demonstrates a novel approach to determine individuals' smoking status by applying machine learning techniques to classify user-generated content in an online cessation community. Study data were from BecomeAnEX.org, a large, online smoking cessation community. We extracted three types of novel features from a post: domain-specific features, author-based features, and thread-based features. These features helped to improve the smoking status identification (quit vs. not) performance by 9.7% compared to using only text features of a post's content. In other words, knowledge from domain experts, data regarding the post author's patterns of online engagement, and other community member reactions to the post can help to determine the focal post author's smoking status, over and above the actual content of a focal post. We demonstrated that machine learning methods can be applied to user-generated data from online cessation communities to validly and reliably discern important user characteristics, which could aid decision support on intervention tailoring.

Exposure to positive peer sentiment about nicotine replacement therapy in an online smoking cessation community is associated with NRT use

BACKGROUND

Little is known about the influence of online peer interactions on health behavior change. This study examined the relationship between exposure to peer sentiment about nicotine replacement therapy (NRT) in an online social network for smoking cessation and NRT use.

METHODS

Participants were 3297 current smokers who enrolled in an Internet smoking cessation program, participated in a randomized trial, and completed a 3-month follow-up. Half received free NRT as part of the trial. Automated text classification identified 27,038 posts about NRT that one or more participants were exposed to in the social network. Sentiment towards NRT was rated on Amazon Mechanical Turk. Participants' exposure to peer sentiment about NRT was determined by analysis of clickstream data. Modified Poisson regression examined self-reported use of NRT at 3-months as a function of exposure to NRT sentiment, controlling for study arm and post exposure.

RESULTS

One in five participants (19.3%, n = 639) were exposed to any NRT-related posts (mean exposure = 6.5 ± 14.7, mean sentiment = 5.4 ± 0.8). The association between sentiment exposure and NRT use varied by receipt of free NRT. Greater exposure to positive NRT sentiment was associated with an increased likelihood of NRT use among participants who did not receive free NRT (adjusted rate ratio 1.22, 95% CI 1.01, 1.47; p = .043), whereas no such relationship was observed among participants who did receive free NRT (p = .48).

CONCLUSIONS

Exposure to positive sentiment about NRT was associated with increased NRT use when smokers obtained it on their own. Highlighting user-generated content containing positive NRT sentiment may increase NRT use among treatment-seeking smokers.

Discussions of Alcohol Use in an Online Social Network for Smoking Cessation: Analysis of Topics, Sentiment, and Social Network Centrality

BACKGROUND

Few Internet smoking cessation programs specifically address the impact of alcohol use during a quit attempt, despite its common role in relapse. This study used topic modeling to describe the most prevalent topics about alcohol in an online smoking cessation community, the prevalence of negative sentiment expressed about alcohol use in the context of a quit attempt (i.e., alcohol should be limited or avoided during a quit attempt) within topics, and the degree to which topics differed by user social connectivity within the network.

METHODS

Data were analyzed from posts from the online community of a larger Internet cessation program, spanning January 1, 2012 to May 31, 2015 and included records of 814,258 online posts. Posts containing alcohol-related content (n = 7,199) were coded via supervised machine learning text classification to determine whether the post expressed negative sentiment about drinking in the context of a quit attempt. Correlated topic modeling (CTM) was used to identify a set of 10 topics of at least 1% prevalence based on the frequency of word occurrences among alcohol-related posts; the distribution of negative sentiment and user social network connectivity was examined across the most salient topics.

RESULTS

Three salient topics (with prevalence ≥10%) emerged from the CTM, with distinct themes of (i) cravings and temptations; (ii) parallel between nicotine addiction and alcoholism; and (iii) celebratory discussions of quit milestones including "virtual" alcohol use and toasts. Most topics skewed toward nonnegative sentiment about alcohol. The prevalence of each topic differed by users' social connectivity in the network.

CONCLUSIONS

Future work should examine whether outcomes in Internet interventions are improved by tailoring social network content to match user characteristics, topics, and network behavior.

Use of social network analysis in the development, dissemination, implementation, and sustainability of health behavior interventions for adults: A systematic review

Interest in conceptualizing, measuring, and applying social network analysis (SNA) in public health has grown tremendously in recent years. While these studies have broadened our understanding of the role that social networks play in health, there has been less research that has investigated the application of SNA to inform health-related interventions. This systematic review aimed to capture the current applied use of SNA in the development, dissemination, implementation, and sustainability of health behavior interventions for adults. We identified 52 articles published between 2004 and 2016. A wide variety of study settings were identified, most commonly in the US context and most often related to sexual health and HIV prevention. We found that 38% of articles explicitly applied SNA to inform some aspect of interventions. Use of SNA to inform intervention development (as opposed to dissemination, implementation, or sustainability) was most common. The majority of articles represented in this review (n = 39) were quantitative studies, and 13 articles included a qualitative component. Partial networks were most represented across articles, and over 100 different networks measures were assessed. The most commonly described measures were network density, size, and degree centrality. Finally, very few articles defined SNA and not all articles using SNA were theoretically-informed. Given the nascent and heterogeneous state of the literature in this area, this is an important time for the field to coalesce on terminology, measures, and theoretical frameworks. We highlight areas for researchers to advance work on the application of SNA in the design, dissemination, implementation and sustainability of behavioral interventions.

Applying Gini coefficient to evaluate the author research domains associated with the ordering of author names: A bibliometric study

BACKGROUND

Team science research includes the number of coauthors in publications. Many papers have discussed the ordering of author names and the contributions of authors to a paper. However, no paper addresses the relation between authors' research domains and personal impact factors (PIF) with the ordering of author names. We aimed to apply Gini coefficient (GC) to evaluate the author research domains associated with the PIF and the ordering of author names on academic papers.

METHODS

By searching the PubMed database (Pubmed.com), we used the keyword "medicine" [journal] and downloaded 10,854 articles published from 1969 to 2018. A total number of 7502 articles labeled with complete author's countries/areas were included in data analysis. We also proposed a PIF index and jointly applied social network analysis (SNA), the GC, and Google Maps to report the following data with visual representations: the trend of author collaboration in Medicine; the dominant nations and keywords in Medicine; and the author research domains in Medicine associated with the PIF and the ordering of author names on academic papers.

RESULTS

The trend of author collaboration in Medicine is slightly declining (= -0.06) based on the number of authors per article. The mean number of individuals listed as authors in articles is 7.5. Most first authors are from China (3649, 48.64%) and Taiwan (847, 11.29%). The median of GC (0.32) and PIF (0.74) for the middle authors are obviously less than those for the first (0.53, 2.19) and the last authors (0.42, 2.61). A perfect positive linear relation with a large effect exists between GC and PIF because the correlation coefficient is 0.68 (>0.50, t = 2.48, n = 9).

CONCLUSION

Results suggest that the corresponding author is submitting the manuscript to the target journal with a core author's academic background and the personal impact factor related to the research domain and the journal scope in the future. As such, peer reviewers can quickly determine whether the manuscript is a potentially citable research paper.

How Online Communities of People With Long-Term Conditions Function and Evolve: Network Analysis of the Structure and Dynamics of the Asthma UK and British Lung Foundation Online Communities

Background

Self-management support can improve health and reduce health care utilization by people with long-term conditions. Online communities for people with long-term conditions have the potential to influence health, usage of health care resources, and facilitate illness self-management. Only recently, however, has evidence been reported on how such communities function and evolve, and how they support self-management of long-term conditions in practice.

Objective

The aim of this study is to gain a better understanding of the mechanisms underlying online self-management support systems by analyzing the structure and dynamics of the networks connecting users who write posts over time.

Methods

We conducted a longitudinal network analysis of anonymized data from 2 patients’ online communities from the United Kingdom: the Asthma UK and the British Lung Foundation (BLF) communities in 2006-2016 and 2012-2016, respectively.

Results

The number of users and activity grew steadily over time, reaching 3345 users and 32,780 posts in the Asthma UK community, and 19,837 users and 875,151 posts in the BLF community. People who wrote posts in the Asthma UK forum tended to write at an interval of 1-20 days and six months, while those in the BLF community wrote at an interval of two days. In both communities, most pairs of users could reach one another either directly or indirectly through other users. Those who wrote a disproportionally large number of posts (the superusers) represented 1% of the overall population of both Asthma UK and BLF communities and accounted for 32% and 49% of the posts, respectively. Sensitivity analysis showed that the removal of superusers would cause the communities to collapse. Thus, interactions were held together by very few superusers, who posted frequently and regularly, 65% of them at least every 1.7 days in the BLF community and 70% every 3.1 days in the Asthma UK community. Their posting activity indirectly facilitated tie formation between other users. Superusers were a constantly available resource, with a mean of 80 and 20 superusers active at any one time in the BLF and Asthma UK communities, respectively. Over time, the more active users became, the more likely they were to reply to other users’ posts rather than to write new ones, shifting from a help-seeking to a help-giving role. This might suggest that superusers were more likely to provide than to seek advice.

Conclusions

In this study, we uncover key structural properties related to the way users interact and sustain online health communities. Superusers’ engagement plays a fundamental sustaining role and deserves research attention. Further studies are needed to explore network determinants of the effectiveness of online engagement concerning health-related outcomes. In resource-constrained health care systems, scaling up online communities may offer a potentially accessible, wide-reaching and cost-effective intervention facilitating greater levels of self-management.

Analyzing big data in social media: Text and network analyses of an eating disorder forum

OBJECTIVE

Social media plays an important role in everyday life of young people. Numerous studies claim negative effects of social media and media in general on eating disorder risk factors. Despite the availability of big data, only few studies have exploited the possibilities so far in the field of eating disorders.

METHOD

Methods for data extraction, computerized content analysis, and network analysis will be introduced. Strategies and methods will be exemplified for an ad-hoc dataset of 4,247 posts and 34,118 comments by 3,029 users of the proed forum on Reddit.

RESULTS

Text analysis with latent Dirichlet allocation identified nine topics related to social support and eating disorder specific content. Social network analysis describes the overall communication patterns, and could identify community structures and most influential users. A linear network autocorrelation model was applied to estimate associations in language among network neighbors. The supplement contains R code for data extraction and analyses.

DISCUSSION

This paper provides an introduction to investigating social media data, and will hopefully stimulate big data social media research in eating disorders. When applied in real-time, the methods presented in this manuscript could contribute to improving the safety of ED-related online communication.

Understanding the productive author who published papers in medicine using National Health Insurance Database: A systematic review and meta-analysis

Many researchers used National Health Insurance database to publish medical papers which are often retrospective, population-based, and cohort studies. However, the author's research domain and academic characteristics are still unclear.By searching the PubMed database (Pubmed.com), we used the keyword of [Taiwan] and [National Health Insurance Research Database], then downloaded 2913 articles published from 1995 to 2017. Social network analysis (SNA), Gini coefficient, and Google Maps were applied to gather these data for visualizing: the most productive author; the pattern of coauthor collaboration teams; and the author's research domain denoted by abstract keywords and Pubmed MESH (medical subject heading) terms.Utilizing the 2913 papers from Taiwan's National Health Insurance database, we chose the top 10 research teams shown on Google Maps and analyzed one author (Dr. Kao) who published 149 papers in the database in 2015. In the past 15 years, we found Dr. Kao had 2987 connections with other coauthors from 13 research teams. The cooccurrence abstract keywords with the highest frequency are cohort study and National Health Insurance Research Database. The most coexistent MESH terms are tomography, X-ray computed, and positron-emission tomography. The strength of the author research distinct domain is very low (Gini < 0.40).SNA incorporated with Google Maps and Gini coefficient provides insight into the relationships between entities. The results obtained in this study can be applied for a comprehensive understanding of other productive authors in the field of academics.

The failure to increase social support: it just might be time to stop intervening (and start rigorously observing)

In 1986, Lichtenstein et al. (Behav Ther. 17(5):607-19, 1986) presented the results of five studies focused on enhancing social support for smoking cessation in community-based clinic and worksite interventions. The manuscript was titled Social Support in Smoking Cessation: In Search of Effective Interventions and its main conclusion was that "attempts to both increase social support and to enhance treatment effectiveness have not been successful." Thirty years later, the paper by Cutrona et al. (Transl Behav Med. 6(4):546-57, 2016) draws a similar conclusion from a study focused on providing social support through an online social network for smoking cessation. In reviewing these findings - and based on our knowledge of the extensive literature on social support interventions that has been published over the past 30+ years - we believe there is a need for a fundamental shift in research on social support. Our focus here is largely on smoking cessation, but our comments are applicable to other areas of behavior change.

A Descriptive Study of the Prevalence and Typology of Alcohol-Related Posts in an Online Social Network for Smoking Cessation

OBJECTIVE

Alcohol use and problem drinking are associated with smoking relapse and poor smoking-cessation success. User-generated content in online social networks for smoking cessation provides an opportunity to understand the challenges and treatment needs of smokers. This study used machine-learning text classification to identify the prevalence, sentiment, and social network correlates of alcohol-related content in the social network of a large online smoking-cessation program, BecomeAnEX.org.

METHOD

Data were analyzed from 814,258 posts (January 2012 to May 2015). Posts containing alcohol keywords were coded via supervised machine-learning text classification for information about the user's personal experience with drinking, whether the user self-identified as a problem drinker or indicated problem drinking, and negative sentiment about drinking in the context of a quit attempt (i.e., alcohol should be avoided during a quit attempt).

RESULTS

Less than 1% of posts were related to alcohol, contributed by 13% of users. Roughly a third of alcohol posts described a personal experience with drinking; very few (3%) indicated "problem drinking." The majority (70%) of alcohol posts did not express negative sentiment about drinking alcohol during a quit attempt. Users who did express negative sentiment about drinking were more centrally located within the network compared with those who did not.

CONCLUSIONS

Discussion of alcohol was rare, and most posts did not signal the need to quit or abstain from drinking during a quit attempt. Featuring expert information or highlighting discussions that are consistent with treatment guidelines may be important steps to ensure smokers are educated about drinking risks.

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.