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Smoking continues to be the number one preventable cause of premature death in the United States. While evidence for the effectiveness of smoking cessation interventions has increased rapidly, questions remain on how to effectively disseminate these findings. Twitter, the second largest online social network, provides a natural way of disseminating information. Health communicators can use Twitter to inform smokers, provide social support, and attract them to other interventions. A key challenge for health researchers is how to frame their communications to maximize the engagement of smokers.
Our aim was to examine current Twitter activity for smoking cessation.
Active smoking cessation related Twitter accounts (N=18) were identified. Their 50 most recent tweets were content coded using a schema adapted from the Roter Interaction Analysis System (RIAS), a theory-based, validated coding method. Using negative binomial regression, the association of number of followers and frequency of individual tweet content at baseline was assessed. The difference in followership at 6 months (compared to baseline) to the frequency of tweet content was compared using linear regression. Both analyses were adjusted by account type (organizational or not organizational).
The 18 accounts had 60,609 followers at baseline and 68,167 at 6 months. A total of 24% of tweets were socioemotional support (mean 11.8, SD 9.8), 14% (mean 7, SD 8.4) were encouraging/engagement, and 62% (mean 31.2, SD 15.2) were informational. At baseline, higher frequency of socioemotional support and encouraging/engaging tweets was significantly associated with higher number of followers (socioemotional: incident rate ratio [IRR] 1.09, 95% CI 1.02-1.20; encouraging/engaging: IRR 1.06, 95% CI 1.00-1.12). Conversely, higher frequency of informational tweets was significantly associated with lower number of followers (IRR 0.95, 95% CI 0.92-0.98). At 6 months, for every increase by 1 in socioemotional tweets, the change in followership significantly increased by 43.94 (
Smoking cessation activity does exist on Twitter. Preliminary findings suggest that certain content strategies can be used to encourage followership, and this needs to be further investigated.
While effectiveness evidence for smoking cessation interventions has increased rapidly [
The potential of online social networks to disseminate health information has been recognized [
This study examined activities of Twitter accounts promoting smoking cessation. A content review was conducted of the tweets of these accounts and assessed the association between the tweet content and followership. We used a theoretically driven coding scheme—Roter Interaction Analysis System (RIAS)—which has been designed for biomedical and psychosocial content and is associated with important patient and provider outcomes [
A retrospective examination of a cohort of active Twitter accounts promoting smoking cessation was conducted. This study was reviewed and determined to be non-human subjects research by the University of Massachusetts Medical School Institutional Review Board.
A search for smoking cessation-related accounts was conducted on Twitter using the terms “quit smoking” and “smoking cessation”. Only accounts in English were considered for the sample. An inventory cohort of 130 smoking cessation Twitter accounts was identified. The date that the account was activated was determined by using the “how long have you been tweeting” Web service, which provides information about how long a Twitter account has been active [
There are several ways of coding communication, including using constructs from behavioral theory to guide the coding process. Behavior change theories frequently used in cancer prevention include Social Cognitive Theory, the Transtheoretical Model, and Theory of Reasoned Action [
We used a coding scale based on the RIAS motivational coding scheme. RIAS is a validated method of coding health communication and is associated with important patient and provider outcomes [
A subset of the RIAS codes was selected based on their applicability to the short message style of tweets. Seven mutually exclusive categories were used to code all tweets (see
Tweets were categorized into three groups: (1) socioemotional support tweets, which included any tweet that involved personal remarks and reassuring statements, (2) encouraging/engaging tweets, which included the tweets categorized as gives orientation or suggestions, and ask open-ended questions, and (3) informational tweets, which promoted a product or event, as well as unrelated tweets that were not relevant to smoking.
Account tweets, followers, and classification.
Organization | Twitter handle | Description | Active days | # tweets | Baseline followers | Followers at 6 months | Change in followers |
Yes | @NICORETTE | Tweets from Nicorette | 961 | 6336 | 15645 | 15568 | -77 |
Yes | @FDATOBACCO | News updates from FDA Center for Tobacco Products | 759 | 1285 | 10455 | 12779 | 2324 |
Yes | @SMOKEFREEWOMEN | Tweets from National Cancer Institute | 1050 | 2866 | 7807 | 9928 | 2121 |
Yes | @QUITTEA | Tweets from the Quit Tea company | 959 | 2324 | 6891 | 8509 | 1618 |
No | @how2quitsmoking | Tweets from successful quitter of 2 years | 1200 | 18596 | 3544 | 3912 | 368 |
Yes | @QUITSMOKING | Tweets from Everyday Health, an online health information company (http://www.everydayhealth.com) | 406 | 3851 | 3156 | 3021 | -135 |
No | @smokefreelife | Tweets from an individual with the explanation: exploring the possibilities at the intersection of digital media & public health. Motto: Don't give up! Tweets/thinking my own | 953 | 8926 | 3014 | 3213 | 199 |
Yes | @TRUTHORANGE | Tweets from truth.com, an organization against the tobacco industry | 1184 | 1602 | 1986 | 3348 | 1362 |
Yes | @SMOKEFREEINDY | Tweets from Smoke Free Indy, a coalition of state, local public health, and community organizations dedicated to reducing secondhand smoke, tobacco usage, and tobacco initiation through education, prevention, and advocacy | 1259 | 2127 | 1792 | 1936 | 144 |
No | @altersmoking | Smokers of 10 years trying to quit smoking for a year | 847 | 2738 | 1733 | 1844 | 111 |
Yes | @QUITFULLSTOP_UK | Tweets from quitfullstop.co.uk, a Web-based smoking cessation site | 217 | 507 | 1209 | 934 | -275 |
Yes | @QUITSMOKINGSOON | Tweets from http://quitsmokingonlineblog.blogspot.com/, a resource for quit smoking related articles | 883 | 1156 | 753 | 859 | 106 |
No | @quitsmoking6 | Tweets providing useful tips and advice to help users quit smoking | 424 | 3724 | 589 | 612 | 23 |
No | @quit_smokin_now | Tweets about best ways to quit smoking | 863 | 2392 | 586 | 586 | 0 |
Yes | @SMOKEFREEPCT | Tweets from the NHS West Kent SmokeFree Service, a specialist team helping local people to quit smoking for free http://www.smokefreewestkent.co.uk | 706 | 729 | 453 | 0 | -453 |
No | @quitsmokingform | Tweets providing information to help users to stop smoking | 621 | 2161 | 342 | 354 | 12 |
Yes | @QUIT_SMOKING_OW | Organization sharing smoking cessation resources shared by health experts, advocates, and organizations into wisdom cards | 1359 | 5174 | 340 | 403 | 63 |
Yes | @MNT_SMOKING | The latest smoking & quit smoking news published daily; articles from research centers, universities, and prestigious journals; http://www.medicalnewstoday.com/sections/smoking/ | 888 | 955 | 314 | 361 | 47 |
Coding scheme of tweet content.
Code grouping | Code and definition | Codes/Tweet (N=900), n (%) | Example |
|
Socioemotional support | Personal remarks, social conversation: Success stories, thanking other users for following | 125 (13.9) | @StopSmokingCIOS Thanks for the link! Yes, we've seen them. Very thought provoking. 10 Years - Full Circle: At 10 years smoke-free, Michelle has plenty to say about how she quit, and the benefits...http://bit.ly/z8gIkC |
|
Reassures, encourages, or shows optimism: Any tweet related to motivation, inspirational quotes | 87 (9.7) | Finally, it's Saturday! Wishing you all a healthy and happy weekend. Make sure to pack it full of motivational activities. |
|
|
Encouraging/ engaging | Gives orientation, instructions, suggestions: How to, any tips related to cravings, smoking cessation, and long-term success with quitting | 91 (10.1) | #Tip Know your triggers. Create a plan for each. Exmpl: Smoke after meals-->Wash dishes, brush teeth, take a short walk to break routine. |
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Asks open-ended questions: Any tweet that prompts a response or encourages interactions between users | 35 (3.9) | How many days into quitting are you? Tweet at us, and we'll share for inspiration! #ThisIsYOURYear |
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Informational | Promotion of a product or event: Any tweet that mentions or endorses a product, or encourages attendance of an event on a specific date | 122 (13.6) | It's a new day. A new week. A new you. Try to quit smoking today with @quittea http://ow.ly/cgqTt http://ow.ly/i/LZyy Retweeted by Quit Tea |
|
Unrelated comments: Does not explicitly mention smoking or smoking cessation methods | 52 (5.8) | Heidi Klum and Seal separate: when's the downward spiral of celeb divorce going to end? http://trib.al/jUQ9Sz |
|
|
Gives information on a medical condition: Specific mention of a disease or condition related to smoking (lung cancer, respiratory problems) | 48 (5.3) | NYT: Smoker presents w/ coughing fits & holes in bones: pulmonary Langerhans cell histiocytosis #PLCH. http://ow.ly/ciyFH |
|
|
Gives information on lifestyle: Day-to-day effects of quitting smoking including dietary changes, exercise suggestions, and smoking alternatives | 83 (9.2) | Once you quit: Your bad breath is gone. The stains on your teeth, fingers, and fingernails fade. You have more overall energy to enjoy life. |
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|
Gives information on psychosocial: Related to changing behavior as a result of social interactions, environment, and individual thoughts | 38 (4.2) | Can Facebook Make You Quit Smoking The Daily Beast http://bit.ly/NpqaWQ |
|
|
Gives information on news: New developments related to quit smoking technology, recently published journal articles | 174 (19.3) | ABC Nightline News: Is #BigTobacco profiting from kids? #Video. http://ow.ly/cj1lP |
|
|
Gives information on other: Contains content unrelated to a medical condition, lifestyle habit, psychosocial factor, or news | 45 (5.0) | Indonesia Zoo Helping Orangutan Quit Smoking After 10 Years (Video) http://bit.ly/NIBp8V |
|
We considered several methods for selecting tweets. We needed a sufficient number of tweets to achieve a stable within account estimates. RIAS has been found to be conservative resource making it possible to conduct research with smaller sample sizes [
Thus we chose 50 tweets. We considered a random sample, but because number of tweets varied by account and by time, we chose the 50 most recent tweets to reflect current account activity. An inventory of the 50 most recent tweets was manually collected for each of the selected 18 accounts on July 18, 2012. The median number of days for the 50 tweets was 27 (intraquartile range 10.75-48). From the account’s homepage, we collected the number of followers that each account had at baseline and at 6 months. The type of account was also identified: Organization or Not Organization. Accounts that specifically stated that they represent or are associated with an organization, product, or initiative were classified as Organization accounts. Accounts owned by an individual tweeting about their experience with smoking cessation, or accounts that did not specifically relate to an organization, product or initiative, were classified as Not Organization accounts.
A cross-sectional comparison of the association of number of followers (dependent variable) and frequency of individual tweet content (independent variable) at baseline was performed. We used a negative binomial regression model due to over-dispersion of the variance of the distribution of the dependent variable.
The change in followership was compared to the frequency of tweet content using linear regression. We calculated the change in followership as the difference in the number of followers of an account at follow-up (at 6 months) compared to baseline.
One challenge in using the absolute difference in followers is that this crude measure does not account for the size of followership at baseline. Thus, a new measure was developed—followership ratio. The followership ratio was calculated as the observed change in followership for a specific account divided by the mean change in followership for all accounts (ie, actual/expected).
Analyses were adjusted for by account type, and all analyses were performed using Stata version 11.
The 18 accounts had 60,609 followers at baseline; 68167 at 6 months. More than half (12/18, 67%) of the accounts were organizations. Six could not be clearly identified as organizations and may represent individual accounts. Mean number of days the accounts had been active was 863 (SD 306, range 217-1359). Over the duration of their existence, these 18 accounts sent a mean number of 3747.17 tweets (SD 4281, range 507-18596). At baseline, the accounts had a mean followership of 3367 (SD 4224, range 314-15645); at 6 months the followership changed to 3787 (SD 4692, range 0-15568). One organization account had closed at 6 months (
As noted, the total number of tweets was 900. We found that 13.9% (125/900, mean 6.9, SD 8.5) of tweets were personal remarks or social conversation, 9.7% (87/900, mean 4.8, SD 5.0) reassured, encouraged, or showed optimism, 10.1% (91/900, mean 5.1, SD 5.6) gave orientation, instructions, or suggestion, and 13.6% (122/900, mean 6.8, SD 10.0) promoted a product or event. Very few of the tweets (5.8%, 52/900, mean 2.9, SD 4.2) were unrelated to smoking cessation (
At baseline, after adjustment for account type, tweets with higher frequency of reassuring messages were significantly associated with higher number of followers (incident rate ratio [IRR] 1.14, 95% CI 1.03-1.26) (
Association of number of followers and frequency of tweets.
|
IRR (95% CI) | IRR (95% CI) after adjustment by account type |
Personal | 1.03 (0.97-1.09) | 1.02 (0.96-1.08) |
Reassure | 1.15 (1.04-1.26)b | 1.14 (1.03-1.26)a |
Suggest | 1.06 (0.96-1.18) | 1.06 (0.97-1.17) |
Question | 1.08 (0.99-1.18) | 1.08 (0.99-1.17) |
Info | 0.94 (0.92-0.97)b | 0.95 (0.92-0.97)b |
Product | 0.98 (0.93-1.04) | 0.99 (0.94-1.05) |
Unrelated | 0.97 (0.87-1.08) | 1.01 (0.88-1.16) |
Socioemotional | 1.08 (1.03-1.14)b | 1.09 (1.02-1.16)b |
Encourage/Engage | 1.06 (1.01-1.11)a | 1.06 (1.00-1.11)b |
Informational | 0.95 (0.92-0.97)b | 0.95 (0.92-0.98)a |
a
b
The longitudinal analysis was conducted first using change in followers—the difference in the number of followers at follow-up (compared to baseline). The median change in followers was 84.50 (interquartile range 19.25-616.50). For every increase by 1 in socioemotional tweets, the change in followers increased by (beta coefficient 43.94,
Additionally, we conducted a longitudinal analysis using the followership ratio calculated as the observed or the actual change in followership over the expected or the mean change in followership. The median followership ratio was 0.20 (interquartile range -0.04-1.50). For every increase by 1 in socioemotional tweets, the followership ratio increased by (beta coefficient 0.10,
Numerous accounts exist that promote smoking cessation on Twitter. The accounts identified in this study had 60,609 followers in total. The content of the accounts was informational and also included socioemotional and encouraging/engaging tweets. Interestingly, socioemotional content was associated with increased number of followers at baseline and over 6 months, while accounts that tweeted mostly informational tweets about the harmful effects of smoking had fewer followers. Identifying strategies that increase engagement is an important social networking and public health question [
Twitter has been used to recruit subjects for health behavioral studies [
Furthermore, this study also has a methodological contribution. A new estimate (followership ratio) was developed to account for the size of population at baseline. Similar ratios such as the Standardized Mortality Rates are used outside the social networking research to account for a change in a factor of a subgroup with respect to the general population [
One limitation of this study was the sample size. Only 18 relevant accounts were identified. Additionally, only 50 tweets were viewed in a snapshot of time per account, and these might not be representative of the account. The goal was to assess tweets at a particular instance in time and then to prospectively look at followership at 6 months. Thus, it may not represent everything that happened within the account. Additionally, to achieve a sufficient number of tweets to achieve a stable within-account estimate, we chose 50 of the most recent tweets, not a random sample of tweets. Furthermore, this study did not assess whether these accounts had any impact on cessation efforts. It is also unknown if followers of these accounts are primarily smokers.
Twitter has the potential to be a new channel for smoking cessation interventions. Although easily accessible, evidenced-based tools exist in smoking cessation, they are underused [
incident rate ratio
Roter Interaction Analysis System
Funding for these studies was received from the National Cancer Institute grants R01 CA129091 and R21 (R21CA158968), and the National Center for Advancing Translational Sciences of the National Institutes of Health under award number UL1TR000161. Dr Sadasivam is also funded by a National Cancer Institute Career Development Award (K07CA172677). Dr Houston is also supported by the VA eHealth Quality Enhancement Research Initiative (eHealth QUERI) that he directs. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health or the United States government.
Dr Pagoto is on the advisory board for Empower Fitness, has consulted for Apple, and receives funds to produce social media content for Sears FitStudio.