An observational study of social and emotional support in smoking cessation Twitter accounts: content analysis of tweets

Peer-Written Caring Letters for Veterans after a Suicidal Crisis

OBJECTIVE

In the evidence-based suicide prevention intervention, Caring Letters, healthcare providers send brief, caring messages to patients following psychiatric inpatient care, a time of elevated suicide risk. However, recent studies with military populations have found mixed results. An adaptation of Caring Letters employed a peer framework in which veterans from the community wrote brief caring messages to veterans discharging from psychiatric inpatient treatment after a suicidal crisis.

METHODS

The present study utilized content analysis to assess 90 caring messages generated by 15 peer veterans recruited from veteran service organizations (e.g., American Legion).

RESULTS

Three themes emerged: (1) Shared Military Service, (2) Care, and (3) Overcoming Adversity. Peer-generated content varied in how the coded themes were expressed in the messages.

CONCLUSION

These veteran-to-veteran caring messages may bolster belongingness, social support, and destigmatize mental health struggles, and have the potential to augment existing Caring Letters effects and interventions.

Smokers' Likelihood to Engage With Information and Misinformation on Twitter About the Relative Harms of e-Cigarette Use: Results From a Randomized Controlled Trial

Background

Information and misinformation on the internet about e-cigarette harms may increase smokers’ misperceptions of e-cigarettes. There is limited research on smokers’ engagement with information and misinformation about e-cigarettes on social media.

Objective

This study assessed smokers’ likelihood to engage with—defined as replying, retweeting, liking, and sharing—tweets that contain information and misinformation and uncertainty about the harms of e-cigarettes.

Methods

We conducted a web-based randomized controlled trial among 2400 UK and US adult smokers who did not vape in the past 30 days. Participants were randomly assigned to view four tweets in one of four conditions: (1) e-cigarettes are as harmful or more harmful than smoking, (2) e-cigarettes are completely harmless, (3) uncertainty about e-cigarette harms, or (4) control (physical activity). The outcome measure was participants’ likelihood of engaging with tweets, which comprised the sum of whether they would reply, retweet, like, and share each tweet. We fitted Poisson regression models to predict the likelihood of engagement with tweets among 974 Twitter users and 1287 non-Twitter social media users, adjusting for covariates and stratified by UK and US participants.

Results

Among Twitter users, participants were more likely to engage with tweets in condition 1 (e-cigarettes are as harmful or more harmful than smoking) than in condition 2 (e-cigarettes are completely harmless). Among other social media users, participants were more likely to likely to engage with tweets in condition 1 than in conditions 2 and 3 (e-cigarettes are completely harmless and uncertainty about e-cigarette harms).

Conclusions

Tweets stating information and misinformation that e-cigarettes were as harmful or more harmful than smoking regular cigarettes may receive higher engagement than tweets indicating e-cigarettes were completely harmless.

Trial Registration

International Standard Randomized Controlled Trial Number (ISRCTN) 16082420; https://doi.org/10.1186/ISRCTN16082420

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

BACKGROUND AND AIMS

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

DESIGN

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

SETTING

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

PARTICIPANTS

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

INTERVENTIONS

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

MEASUREMENTS

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

COMMENTS

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

Conversations and Misconceptions About Chemotherapy in Arabic Tweets: Content Analysis

Background

Although chemotherapy was first introduced for the treatment of cancer more than 60 years ago, the public understanding and acceptance of chemotherapy is still debatable. To the best of our knowledge, no study has assessed the conversations and misconceptions about chemotherapy as a treatment for cancer on social media platforms among the Arabic-speaking populations.

Objective

The aim of this study was to assess the types of conversations and misconceptions that were shared on Twitter regarding chemotherapy as a treatment for cancer among the Arabic-speaking populations.

Methods

All Arabic tweets containing any of the representative set of keywords related to chemotherapy and written between May 1, 2017 and October 31, 2017 were retrieved. A manual content analysis was performed to identify the categories of the users, general themes of the tweets, and the common misconceptions about chemotherapy. A chi-square test for independence with adjusted residuals was used to assess the significant associations between the categories of the users and the themes of the tweets.

Results

A total of 402,157 tweets were retrieved, of which, we excluded 309,602 retweets and 62,651 irrelevant tweets. Therefore, 29,904 tweets were included in the final analysis. The majority of the tweets were posted by general users (25,774/29,904, 86.2%), followed by the relatives and friends of patients with cancer (1913/29,904, 6.4%). The tweets were classified into 9 themes; prayers and wishes for the well-being of patients undergoing chemotherapy was the most common theme (20,288/29,904, 67.8%), followed by misconceptions about chemotherapy (2084/29,904, 7.0%). There was a highly significant association between the category of the users and the themes of the tweets (χ²₄₀= 16904.4, P<.001).

Conclusions

Our findings support those of the previous infodemiology studies that Twitter is a valuable social media platform for assessing public conversations, discussions, and misconceptions about various health-related topics. The most prevalent theme of the tweets in our sample population was supportive messages for the patients undergoing chemotherapy, thereby suggesting that Twitter could play a role as a support mechanism for such patients. The second most prevalent theme of the tweets in our study was the various misconceptions about chemotherapy. The findings of our exploratory analysis can help physicians and health care organizations tailor educational efforts in the future to address different misconceptions about chemotherapy, thereby leading to increased public acceptance of chemotherapy as a suitable mode of treatment for cancer.

Infodemiology and Infoveillance: Scoping Review

Background

Web-based sources are increasingly employed in the analysis, detection, and forecasting of diseases and epidemics, and in predicting human behavior toward several health topics. This use of the internet has come to be known as infodemiology, a concept introduced by Gunther Eysenbach. Infodemiology and infoveillance studies use web-based data and have become an integral part of health informatics research over the past decade.

Objective

The aim of this paper is to provide a scoping review of the state-of-the-art in infodemiology along with the background and history of the concept, to identify sources and health categories and topics, to elaborate on the validity of the employed methods, and to discuss the gaps identified in current research.

Methods

The PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines were followed to extract the publications that fall under the umbrella of infodemiology and infoveillance from the JMIR, PubMed, and Scopus databases. A total of 338 documents were extracted for assessment.

Results

Of the 338 studies, the vast majority (n=282, 83.4%) were published with JMIR Publications. The Journal of Medical Internet Research features almost half of the publications (n=168, 49.7%), and JMIR Public Health and Surveillance has more than one-fifth of the examined studies (n=74, 21.9%). The interest in the subject has been increasing every year, with 2018 featuring more than one-fourth of the total publications (n=89, 26.3%), and the publications in 2017 and 2018 combined accounted for more than half (n=171, 50.6%) of the total number of publications in the last decade. The most popular source was Twitter with 45.0% (n=152), followed by Google with 24.6% (n=83), websites and platforms with 13.9% (n=47), blogs and forums with 10.1% (n=34), Facebook with 8.9% (n=30), and other search engines with 5.6% (n=19). As for the subjects examined, conditions and diseases with 17.2% (n=58) and epidemics and outbreaks with 15.7% (n=53) were the most popular categories identified in this review, followed by health care (n=39, 11.5%), drugs (n=40, 10.4%), and smoking and alcohol (n=29, 8.6%).

Conclusions

The field of infodemiology is becoming increasingly popular, employing innovative methods and approaches for health assessment. The use of web-based sources, which provide us with information that would not be accessible otherwise and tackles the issues arising from the time-consuming traditional methods, shows that infodemiology plays an important role in health informatics research.

Awareness of Yoga for Supportive Care in Cancer: Implications for Dissemination

Objectives: Evidence indicates there are beneficial physical and psychosocial effects from practicing yoga in cancer patients and survivors. Despite yoga having been incorporated into National Comprehensive Cancer Network guidelines for symptoms ranging from fatigue to pain, patients' use of yoga for supportive care is low, ranging from 6% to 12%. This study aims to evaluate the awareness of yoga as therapy in an academic cancer center and the preferences for information delivery in this population. Design: We conducted a cross-sectional survey study at an urban academic cancer center. Responses regarding awareness and use of yoga were evaluated; those responding "not aware" were analyzed for preferences in information delivery. Univariate analysis was used to further characterize awareness of yoga for supportive care. Results: Of 303 respondents, 68% were female, 77% were white, and 75% were college educated. Despite access to yoga at the cancer center, 171 (56%) patients expressed they were not aware of the availability of yoga. Male patients were more likely to be unaware of yoga (72.4% vs. 48.8%, p = 0.045). Awareness did not vary by age, race, educational attainment, marital status, cancer type, or cancer stage. Of the 171 "not aware" patients, 87.6% expressed desire for information in the form of printed material, followed by 80.4% for e-mail, 37.6% for smartphone application, and 27.6% for social media. Non-white respondents were more likely to express interest in receiving information by smartphone. Conclusions: More than half of cancer patients were unaware of the yoga program despite advertising across the institution. Patients prefer varying methods for information receipt, with preferences differing by sociodemographic factors. Targeted education and outreach using appropriate engagement is needed to improve the awareness of yoga for symptom control in cancer patients.

Exploring online communication about cigarette smoking among Twitter users who self-identify as having schizophrenia

Novel approaches are needed to address elevated tobacco use among people with schizophrenia. This exploratory study examined the frequency, timing, and type of communication about tobacco-related content on Twitter among users who self-identify as having schizophrenia compared with users from the general population. Over a 200-day period from January to July 2016, Twitter users who self-identify as having a schizophrenia spectrum disorder (n = 203) and a randomly selected group of general population control users (n = 173) posted 1,544,122 tweets. Communication frequency did not differ between groups. Tweets containing tobacco-related keywords were extracted. Twitter users with schizophrenia posted significantly more tweets containing any tobacco-related terms (mean = 3.74; SD = 16.3) compared with control users (mean = 0.82; SD = 1.8). A significantly greater proportion of Twitter users with schizophrenia (45%; n = 92) posted tweets containing any tobacco terms compared with control users (30%; n = 52). Schizophrenia users showed significantly greater odds of tweeting about tobacco compared with control users (OR = 1.99; 95% CI 1.29-3.07). These findings suggest that online communication about tobacco may parallel real world trends of elevated tobacco use observed among people with schizophrenia. By showing that Twitter users who self-identify as having schizophrenia discuss tobacco-related content online, popular social media could inform smoking cessation efforts targeting this at-risk group.

Methods for Coding Tobacco-Related Twitter Data: A Systematic Review

Background

As Twitter has grown in popularity to 313 million monthly active users, researchers have increasingly been using it as a data source for tobacco-related research.

Objective

The objective of this systematic review was to assess the methodological approaches of categorically coded tobacco Twitter data and make recommendations for future studies.

Methods

Data sources included PsycINFO, Web of Science, PubMed, ABI/INFORM, Communication Source, and Tobacco Regulatory Science. Searches were limited to peer-reviewed journals and conference proceedings in English from January 2006 to July 2016. The initial search identified 274 articles using a Twitter keyword and a tobacco keyword. One coder reviewed all abstracts and identified 27 articles that met the following inclusion criteria: (1) original research, (2) focused on tobacco or a tobacco product, (3) analyzed Twitter data, and (4) coded Twitter data categorically. One coder extracted data collection and coding methods.

Results

E-cigarettes were the most common type of Twitter data analyzed, followed by specific tobacco campaigns. The most prevalent data sources were Gnip and Twitter’s Streaming application programming interface (API). The primary methods of coding were hand-coding and machine learning. The studies predominantly coded for relevance, sentiment, theme, user or account, and location of user.

Conclusions

Standards for data collection and coding should be developed to be able to more easily compare and replicate tobacco-related Twitter results. Additional recommendations include the following: sample Twitter’s databases multiple times, make a distinction between message attitude and emotional tone for sentiment, code images and URLs, and analyze user profiles. Being relatively novel and widely used among adolescents and black and Hispanic individuals, Twitter could provide a rich source of tobacco surveillance data among vulnerable populations.

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

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

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

BACKGROUND

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

INTRODUCTION

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

MATERIALS AND METHODS

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

RESULTS

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

DISCUSSION

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

CONCLUSION

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

Challenges in Diabetes Care: Can Digital Health Help Address Them?

In Brief There is great enthusiasm for the potential of digital health solutions in medicine and diabetes to address key care challenges: patient and provider burden, lack of data to inform therapeutic decision-making, poor access to care, and costs. However, the field is still in its nascent days; many patients and providers do not currently engage with digital health tools, and for those who do, the burden is still often high. Over time, digital health has excellent potential to collect data more seamlessly, make collected data more useful, and drive better outcomes at lower costs in less time. But there is still much to prove. This review offers key background information on the current state of digital health in diabetes, six of the most promising digital health technologies and services, and the challenges that remain.

A social way to experience a scientific event: Twitter use at the 7th European Public Health Conference

AIMS

Many studies have analysed Twitter's use by attendees of scientific meetings and the characteristics of conference-related messages and most active attendees. Despite these previous reports, to date no studies have described the use of Twitter during Public Health conferences. For this reason, we decided to perform an analysis of Twitter's use during the 7th European Public Health (EPH) Conference (Glasgow, November 2014).

METHODS

All the tweets published from 21 July to 2 December 2014 and including the hashtag #ephglasgow were retrieved and much information (author, date, retweets, favourites, mentions, presence of pictures and/or external links, content type and topics) was analysed.

RESULTS

A total of 1066 tweets with the hashtag #ephglasgow were retrieved; 86.3% of these were tweeted during the conference. A total of 209 single accounts tweeted, pictures were present in 29.7% tweets while external links were published in 13.8%. Conference speakers were mentioned in around 30% of tweets. Almost 60% of the tweets had a session-related content. Considering only the session-related tweets, one-third had as the main topic 'Health inequalities and migrant and ethnic minority health', while 20% were 'Health policy and health economics' oriented.

CONCLUSIONS

The results of this study have demonstrated a massive use of Twitter by conference attendees during the 7th EPH conference, and that conference attendees are willing to share quotes and impressions particularly about conference-related topics. It is mandatory for conference organisers to promote online discussion and knowledge dissemination during conferences, especially in the public health field.

Social Listening: A Content Analysis of E-Cigarette Discussions on Twitter

Background

Electronic cigarette (e-cigarette) use has increased in the United States, leading to active debate in the public health sphere regarding e-cigarette use and regulation. To better understand trends in e-cigarette attitudes and behaviors, public health and communication professionals can turn to the dialogue taking place on popular social media platforms such as Twitter.

Objective

The objective of this study was to conduct a content analysis to identify key conversation trends and patterns over time using historical Twitter data.

Methods

A 5-category content analysis was conducted on a random sample of tweets chosen from all publicly available tweets sent between May 1, 2013, and April 30, 2014, that matched strategic keywords related to e-cigarettes. Relevant tweets were isolated from the random sample of approximately 10,000 tweets and classified according to sentiment, user description, genre, and theme. Descriptive analyses including univariate and bivariate associations, as well as correlation analyses were performed on all categories in order to identify patterns and trends.

Results

The analysis revealed an increase in e-cigarette–related tweets from May 2013 through April 2014, with tweets generally being positive; 71% of the sample tweets were classified as having a positive sentiment. The top two user categories were everyday people (65%) and individuals who are part of the e-cigarette community movement (16%). These two user groups were responsible for a majority of informational (79%) and news tweets (75%), compared to reputable news sources and foundations or organizations, which combined provided 5% of informational tweets and 12% of news tweets. Personal opinion (28%), marketing (21%), and first person e-cigarette use or intent (20%) were the three most common genres of tweets, which tended to have a positive sentiment. Marketing was the most common theme (26%), and policy and government was the second most common theme (20%), with 86% of these tweets coming from everyday people and the e-cigarette community movement combined, compared to 5% of policy and government tweets coming from government, reputable news sources, and foundations or organizations combined.

Conclusions

Everyday people and the e-cigarette community are dominant forces across several genres and themes, warranting continued monitoring to understand trends and their implications regarding public opinion, e-cigarette use, and smoking cessation. Analyzing social media trends is a meaningful way to inform public health practitioners of current sentiments regarding e-cigarettes, and this study contributes a replicable methodology.

Assessing Electronic Cigarette-Related Tweets for Sentiment and Content Using Supervised Machine Learning

Background

Electronic cigarettes (e-cigarettes) continue to be a growing topic among social media users, especially on Twitter. The ability to analyze conversations about e-cigarettes in real-time can provide important insight into trends in the public’s knowledge, attitudes, and beliefs surrounding e-cigarettes, and subsequently guide public health interventions.

Objective

Our aim was to establish a supervised machine learning algorithm to build predictive classification models that assess Twitter data for a range of factors related to e-cigarettes.

Methods

Manual content analysis was conducted for 17,098 tweets. These tweets were coded for five categories: e-cigarette relevance, sentiment, user description, genre, and theme. Machine learning classification models were then built for each of these five categories, and word groupings (n-grams) were used to define the feature space for each classifier.

Results

Predictive performance scores for classification models indicated that the models correctly labeled the tweets with the appropriate variables between 68.40% and 99.34% of the time, and the percentage of maximum possible improvement over a random baseline that was achieved by the classification models ranged from 41.59% to 80.62%. Classifiers with the highest performance scores that also achieved the highest percentage of the maximum possible improvement over a random baseline were Policy/Government (performance: 0.94; % improvement: 80.62%), Relevance (performance: 0.94; % improvement: 75.26%), Ad or Promotion (performance: 0.89; % improvement: 72.69%), and Marketing (performance: 0.91; % improvement: 72.56%). The most appropriate word-grouping unit (n-gram) was 1 for the majority of classifiers. Performance continued to marginally increase with the size of the training dataset of manually annotated data, but eventually leveled off. Even at low dataset sizes of 4000 observations, performance characteristics were fairly sound.

Conclusions

Social media outlets like Twitter can uncover real-time snapshots of personal sentiment, knowledge, attitudes, and behavior that are not as accessible, at this scale, through any other offline platform. Using the vast data available through social media presents an opportunity for social science and public health methodologies to utilize computational methodologies to enhance and extend research and practice. This study was successful in automating a complex five-category manual content analysis of e-cigarette-related content on Twitter using machine learning techniques. The study details machine learning model specifications that provided the best accuracy for data related to e-cigarettes, as well as a replicable methodology to allow extension of these methods to additional topics.