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.

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.

Analyzing Twitter Chatter About Tobacco Use Within Intoxication-related Contexts of Alcohol Use: "Can Someone Tell Me Why Nicotine is So Fire When You're Drunk?"

INTRODUCTION: Alcohol and tobacco are commonly used together. Social influences within online social networking platforms contribute to youth and young adult substance use behaviors. This study used a sample of alcohol- and tobacco-related tweets to evaluate: (1) sentiment toward co-use of alcohol and tobacco, (2) increased susceptibility to tobacco use when consuming alcohol, and (3) the role of alcohol in contributing to a failed attempt to quit tobacco use. METHODS: Data were collected from the Twitter API from January 1, 2019 through December 31, 2019 using tobacco-related keywords (e.g., vape, ecig, smoking, juul*) and alcohol-related filters (e.g., drunk, blackout*). A total of 78,235 tweets were collected, from which a random subsample (n = 1,564) was drawn for coding. Cohen's Kappa values ranged from 0.66 to 0.99. RESULTS: Most tweets were pro co-use of alcohol and tobacco (75%). One of every ten tweets reported increased susceptibility to tobacco use when intoxicated. Non-regular tobacco users reported cravings for and tobacco use when consuming alcohol despite disliking tobacco use factors such as the taste, smell, and/or negative health effects. Regular tobacco users reported using markedly higher quantities of tobacco when intoxicated. Individuals discussed the role of alcohol undermining tobacco cessation attempts less often (2.0%), though some who had quit smoking for prolonged periods of time reported reinitiating tobacco use during acute intoxication episodes. CONCLUSIONS: Tobacco cessation interventions may benefit from including alcohol-focused components designed to educate participants about the association between increased susceptibility to tobacco use when consuming alcohol and the role of alcohol in undermining tobacco cessation attempts. IMPLICATIONS: Sentiment toward co-use of alcohol and tobacco on Twitter is largely positive. Individuals reported regret about using tobacco, or using more than intended, when intoxicated. Those who had quit smoking or vaping for prolonged periods of time reported reinitiating tobacco use when consuming alcohol. While social media-based tobacco cessation interventions like the Truth Initiative's "Ditch the Juul" campaign demonstrate potential to change tobacco use behaviors, these campaigns may benefit from including alcohol-focused components designed to educate participants about the association between increased susceptibility to tobacco use when consuming alcohol and the role of alcohol in undermining tobacco cessation attempts.

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.

E-Cigarette-Related Nicotine Misinformation on Social Media.

Background. Twitter provides an opportunity to examine misperceptions about nicotine and addiction as they pertain to electronic nicotine delivery systems (ENDS). The purpose of this study was to systematically examine a sample of ENDS-related tweets that presented information about nicotine or addiction for the presence of potential misinformation.Methods. A total of 10.1 million ENDS-related tweets were obtained from April 2018 through March 2019 and were filtered for unique tweets containing keywords for nicotine and addiction. A subsample (n = 3,116) were human coded for type of account (individual, group, commercial, or news) and presence of potential misinformation.Results. Of tweets that presented ENDS-related nicotine or addiction information (n = 904), 41.7% (n = 377) contained potential misinformation coded as anti-vaping exaggeration, pro-vaping exaggeration, nicotine is not addictive or is never harmful, or unproven health benefits.Conclusions. Anti-vaping exaggeration tweets distorted or embellished claims about ENDS nicotine and addiction; pro-vaping exaggeration tweets misinterpreted results from scientific studies. Misinformation that nicotine is not addictive or is never harmful or has unproven health benefits appeared less but are potentially problematic. ENDS-related messaging should be designed to be easily understood by the public and monitored to detect the spread of misinterpretation or misinformation on social media.

Re-evaluating standards of human subjects protection for sensitive health data in social media networks.

This study addresses ethical questions about conducting health science research using network data from social media platforms. We provide examples of ethically problematic areas related to participant consent, expectation of privacy, and social media networks. Further, to illustrate how researchers can maintain ethical integrity while leveraging social media networks, we describe a study that demonstrates the ability to use social media to identify individuals affected by cancer. We discuss best practices and ethical guidelines for studying social media network data, including data collection, analysis, and reporting.

Using Nicotine Gum to Assist Nondaily Smokers in Quitting: A Randomized Clinical Trial.

INTRODUCTION: Non-daily intermittent smokers (ITS) comprise 30% of US adult smokers. ITS smoke for nicotine and have trouble quitting, but tend to smoke in particular situations. This study tested the effect of nicotine gum, used to prevent or react to situational temptations, for helping ITS quit. METHODS: ITS (smoking 4-27 days/month) seeking help quitting were randomized to 2 mg nicotine gum (n = 181) or placebo (n = 188), to be used to anticipate or react to temptations to smoke, for 8 weeks. Participants received up to six sessions of behavioral counseling. The primary outcome was 6-month biochemically verified continuous abstinence; analyses also examined 14-day point-prevalence abstinence at multiple time points, and used event-history analyses to assess progression to abstinence, lapsing, and relapsing. Analyses adjusted for group differences in age and baseline smoking, and considered several potential moderators of treatment effects. RESULTS: Nicotine gum did not significantly improve outcomes on any measure. Biochemically verified 6-month continuous abstinence rates were 7.2% for active gum and 5.3% for placebo (AOR = 1.39, 0.58-3.29, p > .25). ITS with any degree of dependence (Fagerstrom Test of Nicotine Dependence scores >0) showed poorer outcomes on multiple endpoints, and did more poorly on active gum on some outcomes. Gum use was low, starting at 1 gum per day on average and declining over time. CONCLUSIONS: Nicotine gum (2 mg), used intermittently, did not improve cessation rates among ITS, including those demonstrating some degree of dependence. IMPLICATIONS: Nicotine replacement has been extensively tested with daily smokers, especially those who smoke relatively heavily. Nondaily smoking is now common, creating a need for treatment for ITS. Despite evidence that ITS' smoking is motivated by nicotine-seeking, a theoretically and empirically derived situational approach to using acute nicotine replacement was not successful at helping ITS quit. Gum use was low; whether higher or more frequent dosing is needed, or whether an entirely different approach is needed, is not clear. Effective treatment options are needed for ITS, especially those with some degree of dependence.

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.

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.

Waterpipe tobacco use in college and non-college young adults in the USA.

BACKGROUND: Waterpipe tobacco smoking (WTS or 'hookah') is common among adolescents and college students in the USA. However, there has not yet been a large-scale, nationally representative study independently examining WTS among young adults who are not in college. OBJECTIVE: This study sought to examine associations between attitudes, normative beliefs, certain socio-demographic factors and current WTS among young adults not in college and compare them to young adults in college. METHODS: A total of 3131 US adults ages 18-30 completed an online survey about WTS behaviour, attitudes, normative beliefs and relevant socio-demographic factors. Multivariable logistic regression was used to examine independent associations between these variables and current WTS stratified by student status. RESULTS: Ever WTS was reported by 29% of young adults not in college and by 35% of those in college, and current use rates were 3 and 7%, respectively. Multivariable models demonstrated that positive attitudes and perceived peer acceptability of WTS were significantly associated with increased current WTS for both young adults not in college [adjusted odds ratio (AOR) = 2.72; 95% CI: 2.00-3.71 and AOR = 2.02; 95% CI: 1.50-2.71, respectively] and young adults in college (AOR = 3.37; 95% CI: 2.48-4.58 and AOR = 2.05; 95% CI: 1.49-2.83, respectively). The magnitude of these associations was not significantly different when comparing individuals in college and not in college. CONCLUSIONS: Among young adults, WTS is common in non-college-based populations as well as in college-based populations. Therefore, prevention programming should extend to all young adults, not only to those in college.

Identifying Key Target Audiences for Public Health Campaigns: Leveraging Machine Learning in the Case of Hookah Tobacco Smoking.

BACKGROUND: Hookah tobacco smoking (HTS) is a particularly important issue for public health professionals to address owing to its prevalence and deleterious health effects. Social media sites can be a valuable tool for public health officials to conduct informational health campaigns. Current social media platforms provide researchers with opportunities to better identify and target specific audiences and even individuals. However, we are not aware of systematic research attempting to identify audiences with mixed or ambivalent views toward HTS. OBJECTIVE: The objective of this study was to (1) confirm previous research showing positively skewed HTS sentiment on Twitter using a larger dataset by leveraging machine learning techniques and (2) systematically identify individuals who exhibit mixed opinions about HTS via the Twitter platform and therefore represent key audiences for intervention. METHODS: We prospectively collected tweets related to HTS from January to June 2016. We double-coded sentiment for a subset of approximately 5000 randomly sampled tweets for sentiment toward HTS and used these data to train a machine learning classifier to assess the remaining approximately 556,000 HTS-related Twitter posts. Natural language processing software was used to extract linguistic features (ie, language-based covariates). The data were processed by machine learning tools and algorithms using R. Finally, we used the results to identify individuals who, because they had consistently posted both positive and negative content, might be ambivalent toward HTS and represent an ideal audience for intervention. RESULTS: There were 561,960 HTS-related tweets: 373,911 were classified as positive and 183,139 were classified as negative. A set of 12,861 users met a priori criteria indicating that they posted both positive and negative tweets about HTS. CONCLUSIONS: Sentiment analysis can allow researchers to identify audience segments on social media that demonstrate ambiguity toward key public health issues, such as HTS, and therefore represent ideal populations for intervention. Using large social media datasets can help public health officials to preemptively identify specific audience segments that would be most receptive to targeted campaigns.

Toward Real-Time Infoveillance of Twitter Health Messages.

There is growing interest in conducting public health research using data from social media. In particular, Twitter "infoveillance" has demonstrated utility across health contexts. However, rigorous and reproducible methodologies for using Twitter data in public health are not yet well articulated, particularly those related to content analysis, which is a highly popular approach. In 2014, we gathered an interdisciplinary team of health science researchers, computer scientists, and methodologists to begin implementing an open-source framework for real-time infoveillance of Twitter health messages (RITHM). Through this process, we documented common challenges and novel solutions to inform future work in real-time Twitter data collection and subsequent human coding. The RITHM framework allows researchers and practitioners to use well-planned and reproducible processes in retrieving, storing, filtering, subsampling, and formatting data for health topics of interest. Further considerations for human coding of Twitter data include coder selection and training, data representation, codebook development and refinement, and monitoring coding accuracy and productivity. We illustrate methodological considerations through practical examples from formative work related to hookah tobacco smoking, and we reference essential methods literature related to understanding and using Twitter data.

The association between valence of social media experiences and depressive symptoms.

BACKGROUND: Social media (SM) may confer emotional benefits via connection with others. However, epidemiologic studies suggest that overall SM is paradoxically associated with increased depressive symptoms. To better understand these findings, we examined the association between positive and negative experiences on SM and depressive symptoms. METHODS: We conducted a cross-sectional survey of 1,179 full-time students at the University of West Virginia, aged 18 to 30, in August 2016. Independent variables were self-reported positive and negative experiences on SM. The dependent variable was depressive symptoms as measured using the Patient-Reported Outcomes Measurement Information System. We used multivariable logistic regression to assess associations between SM experiences and depressive symptoms controlling for sociodemographic factors including age, sex, race/ethnicity, education, relationship status, and living situation. RESULTS: Of the 1,179 participants, 62% were female, 28% were non-White, and 51% were single. After controlling for covariates, each 10% increase in positive experiences on SM was associated with a 4% decrease in odds of depressive symptoms, but this was not statistically significant (adjusted odds ratio [AOR] = 0.96; 95% confidence interval [CI] = 0.91-1.002). However, each 10% increase in negative experiences was associated with a 20% increase in odds of depressive symptoms (AOR = 1.20; 95% CI = 1.11-1.31). When both independent variables were included in the same model, the association between negative experiences and depressive symptoms remained significant (AOR = 1.19, 95% CI = 1.10-1.30). CONCLUSIONS: Negative experiences online may have higher potency than positive ones because of negativity bias. Future research should examine temporality to determine if it is also possible that individuals with depressive symptomatology are inclined toward negative interactions.

Comparison of toxicant load from waterpipe and cigarette tobacco smoking among young adults in the USA.

OBJECTIVES: To form population-level comparisons of total smoke volume, tar, carbon monoxide and nicotine consumed from waterpipe tobacco smoking (WTS) and cigarette smoking using data from a nationally representative sample of smokers and non-smokers aged 18-30 years. METHODS: In March and April 2013, we surveyed a nationally representative sample of 3254 US young adults to assess the frequency and volume of WTS and cigarette smoking. We used Monte Carlo analyses with 5000 repetitions to estimate the proportions of toxicants originating from WTS and cigarette smoking. Analyses incorporated survey weights and used recent meta-analytic data to estimate toxicant exposures associated with WTS and cigarette smoking. RESULTS: Compared with the additive estimates of WTS and cigarette smoking combined, 54.9% (95% CI 37.5% to 72.2%) of smoke volume was attributed to WTS. The proportions of tar attributable to WTS was 20.8% (95% CI 6.5% to 35.2%), carbon monoxide 10.3% (95% CI 3.3% to 17.3%) and nicotine 2.4% (95% CI 0.9% to 3.8%). CONCLUSIONS: WTS accounted for over half of the tobacco smoke volume consumed among young US adult waterpipe and cigarette smokers. Toxicant exposures to tar, carbon monoxide and nicotine were lower, but still substantial, for WTS alone compared with WTS and cigarette smoking. Public health and policy interventions to reduce harm from tobacco smoking in young US adults should explicitly address WTS toxicant exposures.

Use of fictional medical television in health sciences education: a systematic review.

While medical television programs are popular among health profession trainees, it is not clear to what extent these programs affect their knowledge, perceptions, and/or behaviors. Therefore, we conducted a systematic review of research evaluating associations between program exposure and outcomes. We conducted systematic literature searches in Pubmed, CINAHL, and PsycINFO. Selected studies were required to be scholarly research, involve exposure to fictionalized medical television programming by health professional students, and assess associations between exposure and outcomes. Studies were classified according to quality and factors related to population, exposure, and outcomes. Of 3541 studies identified, 13 met selection criteria. Six studies involved undergraduate medical students, one involved nursing students, two involved both medical and nursing students, two involved medical residents, one involved medical students, residents and attending physicians, and one involved graduate epidemiology students. Mean study quality according to the MERSQI was 8.27. The most commonly assessed television programs were ER and Grey's Anatomy (six each). Five studies assessed regular viewing habits, and found that fictional medical programs are popular among students and that students recall health topics from episodes. The eight studies that assessed the association with outcomes when using clips as educational tools reported high satisfaction and increased knowledge of the presented health topics. While relatively few published studies have explored influences of fictional medical television on health professional students, those conducted suggest that students often view these television programs independently and that integration of this programming into medical education is feasible and acceptable.

Use of Twitter to Assess Viewer Reactions to the Medical Drama, Code Black.

Fictional medical television programs are popular with viewers and have been shown to influence health-related outcomes. We sought to systematically analyze real-time viewer discourse on Twitter related to the new medical drama, Code Black. We retrieved all Twitter posts (tweets) and metadata around the time of the airing of Code Black for four consecutive weeks. We developed a codebook using both content assessment of Twitter messages (tweets) and theory-based variables used in entertainment education analyses. We coded all tweets that occurred during the Eastern Standard Time (EST) airing of the program. Tweets that fell into at least one coding category were further analyzed by two independent researchers. We collected a total of 19,369 tweets, with 54% of total tweets originating during the EST airing of the program. There were 1,888 tweets that fit into one or more of six broad coding categories. Qualitative analysis revealed several key themes including real-life motivation to pursue health sciences careers based on the program, engagement regarding medical accuracy, and respect for the nursing profession. Examination of tweets related to Code Black provides insight into viewer discourse and suggests that Twitter may provide a vehicle for leveraging program engagement into real-life discussion and inquiry.

Consensus statement on assessment of waterpipe smoking in epidemiological studies.

Numerous epidemiological accounts suggest that waterpipe smoking (aka hookah, shisha, narghile) has become a global phenomenon, especially among youth. The alarming spread of waterpipe and accumulating evidence of its addictive and harmful effects represent a new threat in the global fight to limit tobacco-related morbidity and mortality. In response to waterpipe's alarming trends, major public health and tobacco control organisations have started or are considering systematic collection of data about waterpipe smoking to monitor its trends and assess its harmful effects in different societies. Such plans require coordination and agreement on epidemiological measurement tools that reflect the uniqueness of this tobacco use method, and at the same time allow comparison of waterpipe trends across time and place, and with other tobacco use methods. We started a decade ago our work to develop standardised measures and definitions for the assessment of waterpipe smoking in epidemiological studies. In this communication, we try to expand and update these assessment tools in light of our increased knowledge and understanding of waterpipe use patterns, its context and marketing, as well as the need for evidence-guided policies and regulations to curb its spread. We have assembled for this purpose a group of leading waterpipe researchers worldwide, and worked through an iterative process to develop the suggested instruments and definitions based on what we know currently about the waterpipe epidemic. While the suggested measures are by no means comprehensive, we hope that they can provide the building blocks for standard and comparable surveillance of waterpipe smoking globally.

Exposure to fictional medical television and health: a systematic review.

Fictional medical television programs have long been a staple of television programming, and they remain popular today. We aimed to examine published literature assessing the influence of medical television programs on health outcomes. We conducted systematic literature searches in PubMed, PsychINFO and CINAHL. Selected studies had to be scholarly research, to involve exposure to fictionalized medical television programming, and to assess associations between exposures and outcomes. Of 3541 unique studies identified, nineteen met selection criteria. The most commonly studied programs were ER (73%), Grey's Anatomy (58%) and House M.D. (37%). Outcomes included knowledge, perceptions and behaviors related to topics as diverse as organ donation, cancer screening, sexually transmitted infections, and heart disease. Viewing fictional medical television programs had a negative influence on viewers' health-related knowledge, perceptions and/or behavior in 11% of studies, a positive influence in 32% of studies, and mixed influence in 58%. While most studies (58%) were characterized as having fair quality in terms of rigor of study design, 21% were classified as good and 21% were classified as poor. As such, medical television can affect health education and outcomes. Future work should utilize randomization, more longitudinal assessments, and more direct assessments of health education and behavioral outcomes.

The association between social media use and sleep disturbance among young adults.

INTRODUCTION: Many factors contribute to sleep disturbance among young adults. Social media (SM) use is increasing rapidly, and little is known regarding its association with sleep disturbance. METHODS: In 2014 we assessed a nationally representative sample of 1788 U.S. young adults ages 19-32. SM volume and frequency were assessed by self-reported minutes per day spent on SM (volume) and visits per week (frequency) using items adapted from the Pew Internet Research Questionnaire. We assessed sleep disturbance using the brief Patient-Reported Outcomes Measurement Information System (PROMIS®) sleep disturbance measure. Analyses performed in Pittsburgh utilized chi-square tests and ordered logistic regression using sample weights in order to estimate effects for the total U.S. RESULTS: In models that adjusted for all sociodemographic covariates, participants with higher SM use volume and frequency had significantly greater odds of having sleep disturbance. For example, compared with those in the lowest quartile of SM use per day, those in the highest quartile had an AOR of 1.95 (95% CI=1.37-2.79) for sleep disturbance. Similarly, compared with those in the lowest quartile of SM use frequency per week, those in the highest quartile had an AOR of 2.92 (95% CI=1.97-4.32) for sleep disturbance. All associations demonstrated a significant linear trend. DISCUSSION: The strong association between SM use and sleep disturbance has important clinical implications for the health and well-being of young adults. Future work should aim to assess directionality and to better understand the influence of contextual factors associated with SM use.

ASSOCIATION BETWEEN SOCIAL MEDIA USE AND DEPRESSION AMONG U.S. YOUNG ADULTS.

BACKGROUND: Social media (SM) use is increasing among U.S. young adults, and its association with mental well-being remains unclear. This study assessed the association between SM use and depression in a nationally representative sample of young adults. METHODS: We surveyed 1,787 adults ages 19 to 32 about SM use and depression. Participants were recruited via random digit dialing and address-based sampling. SM use was assessed by self-reported total time per day spent on SM, visits per week, and a global frequency score based on the Pew Internet Research Questionnaire. Depression was assessed using the Patient-Reported Outcomes Measurement Information System (PROMIS) Depression Scale Short Form. Chi-squared tests and ordered logistic regressions were performed with sample weights. RESULTS: The weighted sample was 50.3% female and 57.5% White. Compared to those in the lowest quartile of total time per day spent on SM, participants in the highest quartile had significantly increased odds of depression (AOR = 1.66, 95% CI = 1.14-2.42) after controlling for all covariates. Compared with those in the lowest quartile, individuals in the highest quartile of SM site visits per week and those with a higher global frequency score had significantly increased odds of depression (AOR = 2.74, 95% CI = 1.86-4.04; AOR = 3.05, 95% CI = 2.03-4.59, respectively). All associations between independent variables and depression had strong, linear, dose-response trends. Results were robust to all sensitivity analyses. CONCLUSIONS: SM use was significantly associated with increased depression. Given the proliferation of SM, identifying the mechanisms and direction of this association is critical for informing interventions that address SM use and depression.