Twitter Sentiment About the US Federal Tobacco 21 Law: Mixed Methods Analysis.

BACKGROUND: On December 20, 2019, the US "Tobacco 21" law raised the minimum legal sales age of tobacco products to 21 years. Initial research suggests that misinformation about Tobacco 21 circulated via news sources on Twitter and that sentiment about the law was associated with particular types of tobacco products and included discussions about other age-related behaviors. However, underlying themes about this sentiment as well as temporal trends leading up to enactment of the law have not been explored. OBJECTIVE: This study sought to examine (1) sentiment (pro-, anti-, and neutral policy) about Tobacco 21 on Twitter and (2) volume patterns (number of tweets) of Twitter discussions leading up to the enactment of the federal law. METHODS: We collected tweets related to Tobacco 21 posted between September 4, 2019, and December 31, 2019. A 2% subsample of tweets (4628/231,447) was annotated by 2 experienced, trained coders for policy-related information and sentiment. To do this, a codebook was developed using an inductive procedure that outlined the operational definitions and examples for the human coders to annotate sentiment (pro-, anti-, and neutral policy). Following the annotation of the data, the researchers used a thematic analysis to determine emergent themes per sentiment category. The data were then annotated again to capture frequencies of emergent themes. Concurrently, we examined trends in the volume of Tobacco 21-related tweets (weekly rhythms and total number of tweets over the time data were collected) and analyzed the qualitative discussions occurring at those peak times. RESULTS: The most prevalent category of tweets related to Tobacco 21 was neutral policy (514/1113, 46.2%), followed by antipolicy (432/1113, 38.8%); 167 of 1113 (15%) were propolicy or supportive of the law. Key themes identified among neutral tweets were news reports and discussion of political figures, parties, or government involvement in general. Most discussions were generated from news sources and surfaced in the final days before enactment. Tweets opposing Tobacco 21 mentioned that the law was unfair to young audiences who were addicted to nicotine and were skeptical of the law's efficacy and importance. Methods used to evade the law were found to be represented in both neutral and antipolicy tweets. Propolicy tweets focused on the protection of youth and described the law as a sensible regulatory approach rather than a complete ban of all products or flavored products. Four spikes in daily volume were noted, 2 of which corresponded with political speeches and 2 with the preparation and passage of the legislation. CONCLUSIONS: Understanding themes of public sentiment-as well as when Twitter activity is most active-will help public health professionals to optimize health promotion activities to increase community readiness and respond to enforcement needs including education for retailers and the general public.

Viewer Reactions to EVALI Storylines on Popular Medical Dramas: A Thematic Analysis of Twitter Messages.

Previous research has found an association between awareness of e-cigarette, or vaping, product-use associated lung injury (EVALI) and lower intention to use e-cigarettes among young people. This study utilized Twitter data to evaluate if the January 2020 depiction of EVALI on New Amsterdam, Chicago Med, and Grey's Anatomy-three popular primetime medical dramas-could be a potential innovative avenue to raise awareness of EVALI. We obtained tweets containing e-cigarette-related search strings from 1/21/2020 to 02/18/2020 and filtered these with storyline-specific keywords, resulting in 1,493 tweets for qualitative coding by two trained human coders. Content codes were informed by prior research, theories of narrative influence, and e-cigarette related outcomes. Of 641 (42.9%) relevant tweets, the most frequent content codes were perceived realism (n = 292, 45.6%) and negative response (n = 264, 41.2%). A common theme among these tweets was that storylines were unrealistic because none of the characters with EVALI used THC-containing products. Approximately 12% of tweets (n = 78) mentioned e-cigarette knowledge and 28 (4.4%) mentioned behavior, including quitting e-cigarettes because of viewing the storylines. Implications for health communication research utilizing social media data and maximizing the achievement of positive health-related outcomes for storylines depicting current health topics are discussed.

Miscommunication about the US federal Tobacco 21 law: a content analysis of Twitter discussions.

OBJECTIVE: Tobacco 21 is a law that sets the minimum legal sales age of tobacco products to 21. On 20 December 2019, the USA passed a federal Tobacco 21 law. The objective of this study is to explore Twitter discussions about the federal Tobacco 21 law in the USA leading up to enacted. METHODS: Twitter messages about Tobacco 21 posted between September and December 2019 were collected via RITHM software. A 2% sample of all collected tweets were double coded by independent coders using a content analysis approach. RESULTS: Findings included three content categories of tweets (news, youth and young adults and methods of avoiding the law) with eight subcodes. Most news tweets incorrectly described the law as a purchase law (54.7%). However, Tobacco 21 is in fact a sales law-it only includes penalties for tobacco retailers who sell to under-age purchasers. About one-fourth (27%) of the tweets involved youth and young adults, with some claiming the law would reduce youth smoking and others doubting its ability to limit youth access to tobacco products. Few tweets (2.5%) mentioned methods of circumventing the policy, such as having an older peer purchase tobacco. CONCLUSIONS: As several countries explore raising their age of sale of tobacco laws to 21, they should couple policy enactment with clear and accurate communication about the law. Compliance agencies at all levels (eg, local, regional, national) can use social media to identify policy loopholes and support vulnerable populations throughout the policy implementation process.

Classification of Twitter Vaping Discourse Using BERTweet: Comparative Deep Learning Study.

BACKGROUND: Twitter provides a valuable platform for the surveillance and monitoring of public health topics; however, manually categorizing large quantities of Twitter data is labor intensive and presents barriers to identify major trends and sentiments. Additionally, while machine and deep learning approaches have been proposed with high accuracy, they require large, annotated data sets. Public pretrained deep learning classification models, such as BERTweet, produce higher-quality models while using smaller annotated training sets. OBJECTIVE: This study aims to derive and evaluate a pretrained deep learning model based on BERTweet that can identify tweets relevant to vaping, tweets (related to vaping) of commercial nature, and tweets with provape sentiment. Additionally, the performance of the BERTweet classifier will be compared against a long short-term memory (LSTM) model to show the improvements a pretrained model has over traditional deep learning approaches. METHODS: Twitter data were collected from August to October 2019 using vaping-related search terms. From this set, a random subsample of 2401 English tweets was manually annotated for relevance (vaping related or not), commercial nature (commercial or not), and sentiment (positive, negative, or neutral). Using the annotated data, 3 separate classifiers were built using BERTweet with the default parameters defined by the Simple Transformer application programming interface (API). Each model was trained for 20 iterations and evaluated with a random split of the annotated tweets, reserving 10% (n=165) of tweets for evaluations. RESULTS: The relevance, commercial, and sentiment classifiers achieved an area under the receiver operating characteristic curve (AUROC) of 94.5%, 99.3%, and 81.7%, respectively. Additionally, the weighted F1 scores of each were 97.6%, 99.0%, and 86.1%, respectively. We found that BERTweet outperformed the LSTM model in the classification of all categories. CONCLUSIONS: Large, open-source deep learning classifiers, such as BERTweet, can provide researchers the ability to reliably determine if tweets are relevant to vaping; include commercial content; and include positive, negative, or neutral content about vaping with a higher accuracy than traditional natural language processing deep learning models. Such enhancement to the utilization of Twitter data can allow for faster exploration and dissemination of time-sensitive data than traditional methodologies (eg, surveys, polling research).

A Genome-Wide Association Study of Prediabetes Status Change.

We conducted the first genome-wide association study of prediabetes status change (to diabetes or normal glycaemia) among 900 White participants of the Atherosclerosis Risk in Communities (ARIC) study. Single nucleotide polymorphism (SNP)-based analysis was performed by logistic regression models, controlling for age, gender, body mass index, and the first 3 genetic principal components. Gene-based analysis was conducted by combining SNP-based p values using effective Chi-square test method. Promising SNPs (p < 1×10-5) and genes (p < 1×10-4) were further evaluated for replication among 514 White participants of the Framingham Heart Study (FHS). To accommodate familial correlations, generalized estimation equation models were applied for SNP-based analyses in the FHS. Analysis results across ARIC and FHS were combined using inverse-variance-weighted meta-analysis method for SNPs and Fisher's method for genes. We robustly identified 5 novel genes that are associated with prediabetes status change using gene-based analyses, including SGCZ (ARIC p = 9.93×10-6, FHS p = 2.00×10-3, Meta p = 3.72×10-7) at 8p22, HPSE2 (ARIC p = 8.26×10-19, FHS p = 5.85×10-3, Meta p < 8.26×10-19) at 10q24.2, ADGRA1 (ARIC p = 1.34×10-5, FHS p = 1.13×10-3, Meta p = 2.88×10-7) at 10q26.3, GLB1L3 (ARIC p = 3.71×10-6, FHS p = 4.51×10-3, Meta p = 3.16×10-7) at 11q25, and PCSK6 (ARIC p = 6.51×10-6, FHS p = 1.10×10-2, Meta p = 1.25×10-6) at 15q26.3. eQTL analysis indicated that these genes were highly expressed in tissues related to diabetes development. However, we were not able to identify any novel locus in single SNP-based analysis. Future large scale genomic studies of prediabetes status change are warranted.

Puff Bars, Tobacco Policy Evasion, and Nicotine Dependence: Content Analysis of Tweets.

BACKGROUND: Puff Bars are e-cigarettes that continued marketing flavored products by exploiting the US Food and Drug Administration exemption for disposable devices. OBJECTIVE: This study aimed to examine discussions related to Puff Bar on Twitter to identify tobacco regulation and policy themes as well as unanticipated outcomes of regulatory loopholes. METHODS: Of 8519 original tweets related to Puff Bar collected from July 13, 2020, to August 13, 2020, a random 20% subsample (n=2661) was selected for qualitative coding of topics related to nicotine dependence and tobacco policy. RESULTS: Of the human-coded tweets, 2123 (80.2%) were coded as relevant to Puff Bar as the main topic. Of those tweets, 698 (32.9%) discussed tobacco policy, including flavors (n=320, 45.9%), regulations (n=124, 17.8%), purchases (n=117, 16.8%), and other products (n=110, 15.8%). Approximately 22% (n=480) of the tweets referenced dependence, including lack of access (n=273, 56.9%), appetite suppression (n=59, 12.3%), frequent use (n=47, 9.8%), and self-reported dependence (n=110, 22.9%). CONCLUSIONS: This study adds to the growing evidence base that the US Food and Drug Administration ban of e-cigarette flavors did not reduce interest, but rather shifted the discussion to brands utilizing a loophole that allowed flavored products to continue to be sold in disposable devices. Until comprehensive tobacco policy legislation is developed, new products or loopholes will continue to supply nicotine demand.

Policy and Behavior: Comparisons between Twitter Discussions about the US Tobacco 21 Law and Other Age-Related Behaviors.

To combat the e-cigarette epidemic among young audiences, a federal law was passed in the US that raised the minimum legal sales age of tobacco to 21 years (commonly known as Tobacco 21). Little is known about sentiment toward this law. Thus, the purpose of our study was to systematically explore trends about Tobacco 21 discussions and comparisons to other age-restriction behaviors on Twitter. Twitter data (n = 4628) were collected from September to December of 2019 that were related to Tobacco 21. A random subsample of identified tweets was used to develop a codebook. Two trained coders independently coded all data, with strong inter-rater reliability (κ = 0.71 to 0.93) found for all content categories. Associations between sentiment and content categories were calculated using χ2 analyses. Among relevant tweets (n = 955), the most common theme­the disjunction between ages for military enlistment and tobacco use­was found in 17.8% of all tweets. Anti-policy sentiment was strongly associated with the age of military enlistment, alcohol, voting, and adulthood (p < 0.001 for all). Opposition to Tobacco 21 propagates on social media because the US federal law does not exempt military members. However, the e-cigarette epidemic may have fueled some support for this law.

The Brain-Derived Neurotrophic Factor Functional Polymorphism and Hand Grip Strength Impact the Association between Brain-Derived Neurotrophic Factor Levels and Cognition in Older Adults in the United States.

INTRODUCTION: Aging is associated with subtle cognitive decline in attention, memory, executive function, processing speed, and reasoning. Although lower brain-derived neurotrophic factor (BDNF) has been linked to cognitive decline among older adults, it is not known if the association differs among individuals with various BDNF Val66Met (rs6265) genotypes. In addition, it is not clear whether these associations vary by hand grip strength or physical activity (PA). METHODS: A total of 2904 older adults were included in this study using data from the Health and Retirement Study. Associations between serum BDNF and measures of cognitive function were evaluated using multivariable linear regression models stratified by Met allele status. PA and hand grip strength were added to the model to evaluate whether including these variables altered associations between serum BDNF and cognition. RESULTS: Mean age was 71.4 years old, and mean body mass index was 28.3 kg/m2. Serum BDNF levels were positively associated with higher total cognitive score (beta = 0.34, p = .07), mental status (beta = 0.16, p = .07), and word recall (beta = 0.22, p =.04) among Met carriers, while serum BDNF levels were negatively associated with mental status (beta = -0.09, p = .07) among non-Met carriers. Furthermore, associations changed when hand grip strength was added to the model but not when PA was added to the model. CONCLUSIONS: The BDNF Val66Met variant may moderate the association between serum BDNF levels and cognitive function in older adults. Furthermore, such associations differ according to hand grip strength but not PA.

Social Media as It Interfaces with Psychosocial Development and Mental Illness in Transitional-Age Youth.

Social media (SM) can be defined as "a group of Internet-based applications that allow the creation and exchange of user-generated content." This includes formation of online communities and sharing of information, ideas, opinions, messages, images, and videos. Therefore, although all online video games would not necessarily count as SM, video games that allow for substantial sharing of information and development of online communities do fit this definition. SM has become an integral component of how people worldwide connect with friends and family, share personal content, and obtain news and entertainment. Use of SM is particularly prevalent among transitional-age youth, usually defined as individuals aged 16 to 24 years, who are at critical junctures around developmental tasks such as identity development and establishment of social norms.

Exploring Factors That Predict Marketing of e-Cigarette Products on Twitter: Infodemiology Approach Using Time Series.

Background: Electronic nicotine delivery systems (known as electronic cigarettes or e-cigarettes) increase risk for adverse health outcomes among naïve tobacco users, particularly youth and young adults. This vulnerable population is also at risk for exposed brand marketing and advertisement of e-cigarettes on social media. Understanding predictors of how e-cigarette manufacturers conduct social media advertising and marketing could benefit public health approaches to addressing e-cigarette use. Objective: This study documents factors that predict changes in daily frequency of commercial tweets about e-cigarettes using time series modeling techniques. Methods: We analyzed data on the daily frequency of commercial tweets about e-cigarettes collected between January 1, 2017, and December 31, 2020. We fit the data to an autoregressive integrated moving average (ARIMA) model and unobserved components model (UCM). Four measures assessed model prediction accuracy. Predictors in the UCM include days with events related to the US Food and Drug Administration (FDA), non-FDA-related events with significant importance such as academic or news announcements, weekday versus weekend, and the period when JUUL maintained an active Twitter account (ie, actively tweeting from their corporate Twitter account) versus when JUUL stopped tweeting. Results: When the 2 statistical models were fit to the data, the results indicate that the UCM was the best modeling technique for our data. All 4 predictors included in the UCM were significant predictors of the daily frequency of commercial tweets about e-cigarettes. On average, brand advertisement and marketing of e-cigarettes on Twitter was higher by more than 150 advertisements on days with FDA-related events compared to days without FDA events. Similarly, more than 40 commercial tweets about e-cigarettes were, on average, recorded on days with important non-FDA events compared to days without such events. We also found that there were more commercial tweets about e-cigarettes on weekdays than on weekends and more commercial tweets when JUUL maintained an active Twitter account. Conclusions: e-Cigarette companies promote their products on Twitter. Commercial tweets were significantly more likely to be posted on days with important FDA announcements, which may alter the narrative about information shared by the FDA. There remains a need for regulation of digital marketing of e-cigarette products in the United States.

Acceptability and Efficacy of the SMARxT Media Literacy Education Program to Counter Pharmaceutical Marketing Influences among Medical Trainees.

Background: Evidence-based prescribing (EBP) results in decreased morbidity and reduces medical costs. However, pharmaceutical marketing influences medication requests and prescribing habits, which can detract from EBP. Media literacy, which teaches critical thinking, is a promising approach for buffering marketing influences and encouraging EBP. The authors developed the "SMARxT" media literacy education program around marketing influences on EBP decision-making. The program consisted of six videos and knowledge assessments that were delivered as an online educational intervention through the Qualtrics platform. Methods: In 2017, we assessed program feasibility, acceptability, and efficacy of enhancing knowledge among resident physicians at the University of Pittsburgh. Resident physicians (n=73) responded to pre-test items assessing prior knowledge, viewed six SMARxT videos, and responded to post-test items. A 6-month follow-up test was completed to quantitatively assess sustained changes in knowledge and to qualitatively assess summative feedback about the program (n=54). Test scores were assessed from pre- to post-test and from pre-test to follow-up using paired-sample t-tests. Qualitative results were synthesized through content analysis. Results: Proportion of correct knowledge responses increased from pre-test to immediate post-test (31% to 64%, P<0.001) at baseline. Correct responses also increased from pre-test to 6-month follow-up (31% to 43%, P<0.001). Feasibility was demonstrated by 95% of enrolled participants completing all baseline procedures and 70% completing 6-month follow-up. Quantitative measures of acceptability yielded positive scores and qualitative responses indicated participants' increased confidence in understanding and countering marketing influences due to the intervention. However, participants stated they would prefer shorter videos, feedback about test scores, and additional resources to reinforce learning objectives. Conclusion: The SMARxT media literacy program was efficacious and acceptable to resident physicians. Participant suggestions could be incorporated into a subsequent version of SMARxT and inform similar clinical education programs. Future research should assess program impact on real-world prescribing practices.

Vape Shop Proliferation and Noncompliance in Pennsylvania: A Pre- and Post-tax Analysis.

BACKGROUND: The growing use of electronic nicotine delivery systems (ENDS) among adolescents is a public health concern. Taxation of these products is a viable approach to reduce ENDS use, particularly among adolescents. Opponents of taxation posit that it puts specialty retailers (ie, vape shops) out of business, thereby reducing availability of ENDS for adult smokers seeking harm reduction. Pennsylvania enacted substantial ENDS taxes in October 2016. This study sought to examine (1) the prevalence of Pennsylvania vape shops before and after ENDS taxes were enacted and (2) ENDS retail licensing compliance among vape shops. METHODS: We employed standardized searches for vape shops in Pennsylvania on the Yelp business-listing platform a month prior to and for 18 consecutive months following the imposition of ENDS taxes. We then compared listings to a public database of ENDS-related retail licenses to determine compliance status. RESULTS: The number of listed vape shops increased in a linear fashion by a magnitude of 23%. In addition, when we compared a final listing of retailers to data from the state tax authority, we found roughly a quarter (22%-29%) of vape shops to be noncompliant with maintaining a valid ENDS retail license. CONCLUSIONS: Overall, ENDS taxation in Pennsylvania has not appeared to reduce prevalence of vape shops as anticipated. However, stricter enforcement of the tax law is necessary to ensure compliance among retailers. These findings have implications for implementation and enforcement of ENDS tax policy nationwide, including states that currently lack such policies.

Temporal Associations Between Social Media Use and Depression.

INTRODUCTION: Previous studies have demonstrated cross-sectional associations between social media use and depression, but their temporal and directional associations have not been reported. METHODS: In 2018, participants aged 18-30 years were recruited in proportion to U.S. Census characteristics, including age, sex, race, education, household income, and geographic region. Participants self-reported social media use on the basis of a list of the top 10 social media networks, which represent >95% of social media use. Depression was assessed using the 9-Item Patient Health Questionnaire. A total of 9 relevant sociodemographic covariates were assessed. All measures were assessed at both baseline and 6-month follow-up. RESULTS: Among 990 participants who were not depressed at baseline, 95 (9.6%) developed depression by follow-up. In multivariable analyses conducted in 2020 that controlled for all covariates and included survey weights, there was a significant linear association (p<0.001) between baseline social media use and the development of depression for each level of social media use. Compared with those in the lowest quartile, participants in the highest quartile of baseline social media use had significantly increased odds of developing depression (AOR=2.77, 95% CI=1.38, 5.56). However, there was no association between the presence of baseline depression and increasing social media use at follow-up (OR=1.04, 95% CI=0.78, 1.38). Results were robust to all sensitivity analyses. CONCLUSIONS: In a national sample of young adults, baseline social media use was independently associated with the development of depression by follow-up, but baseline depression was not associated with an increase in social media use at follow-up. This pattern suggests temporal associations between social media use and depression, an important criterion for causality.

Machine Learning Classifiers for Twitter Surveillance of Vaping: Comparative Machine Learning Study.

BACKGROUND: Twitter presents a valuable and relevant social media platform to study the prevalence of information and sentiment on vaping that may be useful for public health surveillance. Machine learning classifiers that identify vaping-relevant tweets and characterize sentiments in them can underpin a Twitter-based vaping surveillance system. Compared with traditional machine learning classifiers that are reliant on annotations that are expensive to obtain, deep learning classifiers offer the advantage of requiring fewer annotated tweets by leveraging the large numbers of readily available unannotated tweets. OBJECTIVE: This study aims to derive and evaluate traditional and deep learning classifiers that can identify tweets relevant to vaping, tweets of a commercial nature, and tweets with provape sentiments. METHODS: We continuously collected tweets that matched vaping-related keywords over 2 months from August 2018 to October 2018. From this data set of tweets, a set of 4000 tweets was selected, and each tweet was manually annotated for relevance (vape relevant or not), commercial nature (commercial or not), and sentiment (provape or not). Using the annotated data, we derived traditional classifiers that included logistic regression, random forest, linear support vector machine, and multinomial naive Bayes. In addition, using the annotated data set and a larger unannotated data set of tweets, we derived deep learning classifiers that included a convolutional neural network (CNN), long short-term memory (LSTM) network, LSTM-CNN network, and bidirectional LSTM (BiLSTM) network. The unannotated tweet data were used to derive word vectors that deep learning classifiers can leverage to improve performance. RESULTS: LSTM-CNN performed the best with the highest area under the receiver operating characteristic curve (AUC) of 0.96 (95% CI 0.93-0.98) for relevance, all deep learning classifiers including LSTM-CNN performed better than the traditional classifiers with an AUC of 0.99 (95% CI 0.98-0.99) for distinguishing commercial from noncommercial tweets, and BiLSTM performed the best with an AUC of 0.83 (95% CI 0.78-0.89) for provape sentiment. Overall, LSTM-CNN performed the best across all 3 classification tasks. CONCLUSIONS: We derived and evaluated traditional machine learning and deep learning classifiers to identify vaping-related relevant, commercial, and provape tweets. Overall, deep learning classifiers such as LSTM-CNN had superior performance and had the added advantage of requiring no preprocessing. The performance of these classifiers supports the development of a vaping surveillance system.

Associations between positive and negative social media experiences and sleep disturbance among young adults.

OBJECTIVES: We sought to examine the association of positive and negative experiences using social media (SM) and sleep disturbance in a national survey of U.S. young adults. METHODS: Experiences using SM were assessed with 2 items asking participants about the percentage of time using SM that involved a negative/positive experience that they were personally involved in. Sleep disturbance was assessed using the validated PROMIS 4-item short form. Ordinal logistic regression was used to examine associations between positive and negative SM experiences and high sleep disturbance, while controlling for relevant covariates. RESULTS: Although reporting high levels of negative experiences was significantly associated with greater odds of high sleep disturbance (AOR = 1.49, 95%CI = 1.18-1.89), reporting high levels of positive experiences was not associated with sleep disturbance. CONCLUSIONS: These findings suggest that more robust examinations of negative SM experiences-especially as they relate to sleep disturbance-may be warranted.

Integrating Social Dynamics Into Modeling Cigarette and E-Cigarette Use.

Background. The use of electronic cigarettes (e-cigarette) offers potential to facilitate cigarette smoking cessation, yet potentially increases risk of cigarette smoking initiation. This relationship has been primarily modeled in mathematical ways that often do not represent real-world complexities, which could inform decisions regarding local prevention programs or policies. Aims. To develop a model of cigarette and e-cigarette use that combines current research on tobacco use and incorporates real-world geographic and demographic data. Method. We used a platform for developing agent-based models with demographic information representative of the population in Pennsylvania. We developed three models of cigarette and e-cigarette use. The primary outcome for each was the total number of users for cigarette, e-cigarette, and total nicotine. The first model applied current cigarette and e-cigarette data, the second tested the effect of implementing a program of e-cigarette education and policies, and the third considered a social contagion factor, where local schools functioned as a transmission vector. Results. The baseline and social contagion models found an overall decline in cigarette use but an increase in e-cigarette and total nicotine use. The education/policies model had declines in all categories. Sensitivity analysis suggested the importance of nuanced e-cigarette/cigarette interactions when modeling tobacco use. Discussion. Public health campaigns that focus on reducing youth e-cigarette usage can have a large effect. Social contagion should be strongly considered when studying e-cigarette spread. Conclusion. Targeted public health campaigns focused on reducing school prevalence of e-cigarette use may be particularly valuable.

Patient-centered Communication Behaviors on Primetime Television.

Previous research suggests that television programming may influence viewers' health-related knowledge, perceptions, and behaviors but has yet to examine patient-provider interactions on the most popular primetime television programs. We aimed to characterize the frequency and nature of patient-centered communication (PCC) behaviors on these programs, as cultivation and social cognitive theories suggest that these depictions may influence viewers' expectations of real-life health-care experiences. We examined 203 patient-provider interactions across 84 episodes of 22 primetime television programs-both medical and non-medical-that aired during the spring of 2016 and spring of 2017. Each interaction was analyzed for the presence of 21 optimal PCC behaviors. This study found that the most frequently observed PCC behaviors focused on the provider making good eye contact and displaying good manners. However, PCC behaviors related to managing patient uncertainty and self-management were rare. Overall, providers in medical programs were significantly more likely to exhibit certain PCC behaviors, such as asking a patient questions, having good manners, and self-disclosing personal information, compared to providers in non-medical programs. Implications of these findings include the potential for such depictions to influence patient expectations of real-life experiences and health outcomes. Future research is needed to examine these potential influences.

Emotional support from social media and face-to-face relationships: Associations with depression risk among young adults.

BACKGROUND: Emotional support is highly protective against poor mental health. Though several measures of emotional support exist, none specifically addresses social media (SM) as a source of emotional support. Therefore, the objectives of this study were to determine if SM-based emotional support is an extension of or distinct construct from face-to-face (FTF) emotional support and to assess the independent associations between each domain of emotional support and depression risk among U.S. young adults. METHODS: In March 2018, we surveyed 2408 18-30 year olds. We assessed perceived FTF emotional support with the brief PROMIS emotional support scale and perceived SM-based emotional support using a new four-item measure. Depression risk was assessed using the PHQ-9. We performed factor analysis (FA) to determine the underlying factor structure of all items and to develop composite scales. Multivariable logistic regression was used to examine the independent association between each resulting emotional support scale and depression risk. RESULTS: FA revealed two distinct constructs. FTF emotional support was associated with 43% lower odds of depression per 1-unit increase on the 5-point scale (AOR = 0.57, 95% CI = 0.52-0.63). However, SM-based emotional support was significantly associated with 20% greater odds of depression per 1-unit increase on the 5-point scale (AOR = 1.20, 95% CI = 1.09-1.32). LIMITATIONS: This study utilized a cross-sectional design and self-report data. CONCLUSIONS: While FTF emotional support was associated with slightly lower odds of depression, SM-based emotional support was associated with slightly greater odds of depression. It may be valuable for clinicians treating individuals with depression to ask about sources of emotional support.

JUUL on Twitter: Analyzing Tweets About Use of a New Nicotine Delivery System.

BACKGROUND: Initial reports suggest that JUUL, a popular e-cigarette, is being used in schools and other locations in which cigarette smoking is illegal or discouraged. However, there is little scholarly research documenting this. We aimed to make a systematic analysis of JUUL use themes and sentiment on Twitter. METHODS: Data were collected from Twitter's Filtered Streams Application Programming Interface from April 12, 2018 to May 10, 2018. This yielded 67,934 tweets, from which a random sample of 2% was selected for coding. The final dataset included 1209 tweets. Inter-rater reliability ranged κ = 0.64-0.85. RESULTS: The majority (71.5%) of tweets expressed positive sentiment toward JUUL. JUUL use in places where cigarette smoking is illegal or discouraged appeared in 111 tweets (9.2%); approximately one-third of these tweets referring to using the device in school. Nearly 20% of tweets mentioned using the device at home and/or directly in front of responsible adults. CONCLUSIONS: This study confirms anecdotal reports of JUUL use in places where cigarette smoking is illegal or discouraged. Positive sentiment about use of JUUL suggests that the product is being normalized among young people. It may be valuable for educators to discuss the addictive nature of nicotine delivered through JUUL with younger populations.

"I Don't Feel Like the Odd One": Utilizing Content Analysis to Compare the Effects of Social Media Use on Well-Being Among Sexual Minority and Nonminority US Young Adults.

PURPOSE: Although there is evidence of associations between social media (SM) use and mental well-being among the general population, these associations among lesbian, gay, and bisexual (LGB) persons are poorly understood. This study compared the influence of SM experiences on mental well-being between LGB and non-LGB persons. DESIGN AND SETTING: Online cross-sectional survey. PARTICIPANTS: National sample of 2408 US adults aged 18 to 30 years. METHOD: We asked participants to provide examples of when SM affected their well-being separately in good and bad ways. We coded, summed, and used rate ratios (RRs) to compare responses of LGB and non-LGB individuals. Thematically similar codes were described and grouped into categories. RESULTS: Most responses described positive SM effects. However, of 6 codes that were significantly more frequent among LGB respondents, only social capital (RR = 1.58, 95% confidence interval [CI], 1.17-2.12) described a positive effect. Five codes described negative effects of SM for LGB users: negative emotional contagion (RR = 1.28, 95% CI, 1.04-1.58), comparison with others (RR = 1.28, 95% CI, 1.01-1.62), real-life repercussions (RR = 1.86, 95% CI, 1.18-2.94), envy (RR = 2.49, 95% CI, 1.48-4.19), and need for profile management (RR = 2.32, 95% CI, 1.07-5.03). CONCLUSION: These findings suggest that, for LGB persons, gaining social capital from SM is valuable for establishing and maintaining connections. Increased negative SM experiences may pose a risk for the mental well-being of LGB individuals.