Development of a Twitter-based intervention for smoking cessation that encourages high-quality social media interactions via automessages

Cornelia Pechmann, Li Pan, Kevin Delucchi, Cynthia M Lakon, Judith J Prochaska, Cornelia Pechmann, Li Pan, Kevin Delucchi, Cynthia M Lakon, Judith J Prochaska

Abstract

Background: The medical field seeks to use social media to deliver health interventions, for example, to provide low-cost, self-directed, online self-help groups. However, engagement in online groups is often low and the informational content may be poor.

Objective: The specific study aims were to explore if sending automessages to online self-help groups encouraged engagement and to see if overall or specific types of engagement related to abstinence.

Methods: We conducted a Stage I Early Therapy Development Trial of a novel social media intervention for smoking cessation called Tweet2Quit that was delivered online over closed, 20-person quit-smoking groups on Twitter in 100 days. Social media such as Twitter traditionally involves non-directed peer-to-peer exchanges, but our hybrid social media intervention sought to increase and direct such exchanges by sending out two types of autocommunications daily: (1) an "automessage" that encouraged group discussion on an evidence-based cessation-related or community-building topic, and (2) individualized "autofeedback" to each participant on their past 24-hour tweeting. The intervention was purposefully designed without an expert group facilitator and with full automation to ensure low cost, easy implementation, and broad scalability. This purely Web-based trial examined two online quit-smoking groups with 20 members each. Participants were adult smokers who were interested in quitting and were recruited using Google AdWords. Participants' tweets were counted and content coded, distinguishing between responses to the intervention's automessages and spontaneous tweets. In addition, smoking abstinence was assessed at 7 days, 30 days, and 60 days post quit date. Statistical models assessed how tweeting related to abstinence.

Results: Combining the two groups, 78% (31/40) of the members sent at least one tweet; and on average, each member sent 72 tweets during the 100-day period. The automessage-suggested discussion topics and participants' responses to those daily automessages were related in terms of their content (r=.75, P=.012). Responses to automessages contributed 22.78% (653/2867) of the total tweets; 77.22% (2214/2867) were spontaneous. Overall tweeting related only marginally to abstinence (OR 1.03, P=.086). However, specific tweet content related to abstinence including tweets about setting of a quit date or use of nicotine patches (OR 1.52, P=.024), countering of roadblocks to quitting (OR 1.76, P=.008) and expressions of confidence about quitting (OR 1.71, SE 0.42, P=.032). Questionable, that is, non-evidence-based, information about quitting did not relate to abstinence (OR 1.12, P=.278).

Conclusions: A hybrid social media intervention that combines traditional online social support with daily automessages appears to hold promise for smoking cessation. This hybrid approach capitalizes on social media's spontaneous real-time peer-to-peer exchanges but supplements this with daily automessages that group members respond to, bolstering and sustaining the social network and directing the information content. Highly engaging, this approach should be studied further.

Trial registration: Clinicaltrials.gov NCT01602536; https://ichgcp.net/clinical-trials-registry/NCT01602536 (Archived by WebCite at http://www.webcitation.org/6WGbt0o1K).

Keywords: smoking cessation; social media; text messaging.

Conflict of interest statement

Conflicts of Interest: None declared.

Figures

Figure 1
Figure 1
Tweeting volume and duration in Group 1.
Figure 2
Figure 2
Tweeting volume and duration in Group 2.
Figure 3
Figure 3
Tweeting by time of day in Group 1.
Figure 4
Figure 4
Tweeting by time of day in Group 2.

References

    1. Baumann P. . 2009. [2015-02-13]. 140 health care uses for Twitter
    1. Duggan M, Smith A. 42% of online adults use multiple social networking sites, but Facebook remains the platform of choice December 30. 2013. Dec 30, [2015-02-13]. .
    1. Gruzd A, Wellman B, Takhteyev Y. Imagining Twitter as an imagined community. American Behavioral Scientist. 2011;55(10):1294–1318. doi: 10.1177/0002764211409378.
    1. Gruzd A, Haythornthwaite C. Enabling community through social media. J Med Internet Res. 2013;15(10):e248. doi: 10.2196/jmir.2796.
    1. Danaher BG, Boles SM, Akers L, Gordon JS, Severson HH. Defining participant exposure measures in Web-based health behavior change programs. J Med Internet Res. 2006;8(3):e15. doi: 10.2196/jmir.8.3.e15.
    1. Eysenbach G, Powell J, Englesakis M, Rizo C, Stern A. Health related virtual communities and electronic support groups: systematic review of the effects of online peer to peer interactions. BMJ. 2004 May 15;328(7449):1166. doi: 10.1136/bmj.328.7449.1166.
    1. Poirier J, Cobb NK. Social influence as a driver of engagement in a web-based health intervention. J Med Internet Res. 2012;14(1):e36. doi: 10.2196/jmir.1957.
    1. Eysenbach G. The law of attrition. J Med Internet Res. 2005;7(1):e11. doi: 10.2196/jmir.7.1.e11.
    1. Stoddard JL, Augustson EM, Moser RP. Effect of adding a virtual community (bulletin board) to : randomized controlled trial. J Med Internet Res. 2008;10(5):e53. doi: 10.2196/jmir.1124.
    1. Richardson A, Graham AL, Cobb N, Xiao H, Mushro A, Abrams D, Vallone D. Engagement promotes abstinence in a web-based cessation intervention: cohort study. J Med Internet Res. 2013;15(1):e14. doi: 10.2196/jmir.2277.
    1. An LC, Schillo BA, Saul JE, Wendling AH, Klatt CM, Berg CJ, Ahulwalia JS, Kavanaugh AM, Christenson M, Luxenberg MG. Utilization of smoking cessation informational, interactive, and online community resources as predictors of abstinence: cohort study. J Med Internet Res. 2008;10(5):e55. doi: 10.2196/jmir.1018.
    1. Anderson JE, Jorenby DE, Scott WJ, Fiore MC. Treating tobacco use and dependence: an evidence-based clinical practice guideline for tobacco cessation. Chest. 2002 Mar;121(3):932–41.
    1. Fiore MC. Public Health Service. US Department of Health and Human Services; 2008. [2015-02-13]. Treating tobacco use and dependence: 2008 update .
    1. Prochaska JJ, Pechmann C, Kim R, Leonhardt JM. Twitter=quitter? An analysis of Twitter quit smoking social networks. Tob Control. 2012 Jul;21(4):447–9. doi: 10.1136/tc.2010.042507.
    1. Myslín M, Zhu S-H, Chapman W, Conway M. Using twitter to examine smoking behavior and perceptions of emerging tobacco products. J Med Internet Res. 2013;15(8):e174. doi: 10.2196/jmir.2534.
    1. Free C, Whittaker R, Knight R, Abramsky T, Rodgers A, Roberts IG. Txt2stop: a pilot randomised controlled trial of mobile phone-based smoking cessation support. Tob Control. 2009 Apr;18(2):88–91. doi: 10.1136/tc.2008.026146.
    1. Brendryen H, Kraft P. Happy ending: A randomized controlled trial of a digital multi-media smoking cessation intervention. Addiction. 2008;103(3):478–484.
    1. Fjeldsoe BS, Marshall AL, Miller YD. Behavior change interventions delivered by mobile telephone short-message service. Am J Prev Med. 2009 Feb;36(2):165–73. doi: 10.1016/j.amepre.2008.09.040.
    1. Free C, Knight R, Robertson S, Whittaker R, Edwards P, Zhou W, Rodgers A, Cairns J, Kenward MG, Roberts I. Smoking cessation support delivered via mobile phone text messaging (txt2stop): a single-blind, randomised trial. Lancet. 2011 Jul 2;378(9785):49–55. doi: 10.1016/S0140-6736(11)60701-0.
    1. Lenert L, Muñoz RF, Perez JE, Bansod A. Automated e-mail messaging as a tool for improving quit rates in an internet smoking cessation intervention. J Am Med Inform Assoc. 2004;11(4):235–40. doi: 10.1197/jamia.M1464.
    1. Kong G, Ells D, Camenga DR, Krishnan-Sarin S. Text messaging-based smoking cessation intervention: A narrative review. Addictive Behaviors. 2014;39(5):907–917.
    1. Rounsaville BJ, Carroll KM, Onken LS. A stage model of behavioral therapies research: Getting started and moving on from stage I. Clinical Psychology: Science and Practice. 2001;8(2):133–142. doi: 10.1093/clipsy.8.2.133.
    1. Dallery J, Cassidy RN, Raiff BR. Single-case experimental designs to evaluate novel technology-based health interventions. J Med Internet Res. 2013;15(2):e22. doi: 10.2196/jmir.2227.
    1. Baker TB, Gustafson DH, Shah D. How can research keep up with eHealth? Ten strategies for increasing the timeliness and usefulness of eHealth research. J Med Internet Res. 2014;16(2):e36. doi: 10.2196/jmir.2925.
    1. Reid JL, Hammond D, Boudreau C, Fong GT, Siahpush M, Collaboration ITC. Socioeconomic disparities in quit intentions, quit attempts, and smoking abstinence among smokers in four western countries: findings from the International Tobacco Control Four Country Survey. Nicotine Tob Res. 2010 Oct;12 Suppl:S20–33. doi: 10.1093/ntr/ntq051.
    1. Piper ME, Cook JW, Schlam TR, Jorenby DE, Smith SS, Bolt DM, Loh WY. Gender, race, and education differences in abstinence rates among participants in two randomized smoking cessation trials. Nicotine Tob Res. 2010 Jun;12(6):647–57. doi: 10.1093/ntr/ntq067.
    1. Broms U, Silventoinen K, Lahelma E, Koskenvuo M, Kaprio J. Smoking cessation by socioeconomic status and marital status: The contribution of smoking behavior and family background. Nicotine & Tobacco Research. 2004;6(3):447–455.
    1. Wetter DW, Cofta-Gunn L, Fouladi RT, Irvin JE, Daza P, Mazas C, Wright K, Cinciripini PM, Gritz ER. Understanding the associations among education, employment characteristics, and smoking. Addict Behav. 2005 Jun;30(5):905–14. doi: 10.1016/j.addbeh.2004.09.006.
    1. Perkins KA, Scott J. Sex differences in long-term smoking cessation rates due to nicotine patch. Nicotine Tob Res. 2008 Jul;10(7):1245–50. doi: 10.1080/14622200802097506.
    1. Breslau N, Johnson EO. Predicting smoking cessation and major depression in nicotine-dependent smokers. American Journal of Public Health. 2000;90(7):1122–1127. doi: 10.2105/AJPH.90.7.1122PMID:.
    1. Chandola T, Head J, Bartley M. Socio-demographic predictors of quitting smoking: how important are household factors? Addiction. 2004 Jun;99(6):770–7. doi: 10.1111/j.1360-0443.2004.00756.x.
    1. Shi X, Adamic LA, Strauss MJ. Networks of strong ties. Physica A: Statistical Mechanics and its Applications. 2007 May;378(1):33–47. doi: 10.1016/j.physa.2006.11.072.
    1. Xiao Z, Guo L, Tracey J. Understanding instant messaging traffic characteristics. Distributed Computing Systems; 2007; Toronto, Canada. IEEE; 2007. Jun, pp. 1–8.
    1. Trusov M, Bodapati A, Bucklin RE. Determining Influential Users in Internet Social Networks. Journal of Marketing Research. 2010 Aug;47(4):643–658. doi: 10.1509/jmkr.47.4.643.
    1. Dhar R, Wertenbroch K. Consumer choice between hedonic and utilitarian goods. Journal of Marketing Research. 2000;37(1):60–71.
    1. Park CW, Eisingerich AB, Park JW. Attachment-aversion (AA) model of customer-brand relationships. Journal of Consumer Psychology. 2013;23(2):229–248.
    1. Heatherton TF, Kozlowski LT, Frecker RC, Fagerström Ko. The Fagerström Test for Nicotine Dependence: a revision of the Fagerström Tolerance Questionnaire. Br J Addict. 1991 Sep;86(9):1119–27.
    1. Fu SS, Kodl MM, Joseph AM, Hatsukami DK, Johnson EO, Breslau N, Wu B, Bierut L. Racial/Ethnic Disparities in the Use of Nicotine Replacement Therapy and Quit Ratios in Lifetime Smokers Ages 25 to 44 Years. Cancer Epidemiology Biomarkers & Prevention. 2008 Jul 01;17(7):1640–1647. doi: 10.1158/1055-9965.epi-07-2726.
    1. Fu SS, Sherman SE, Yano EM, van Ryn M, Lanto AB, Joseph AM. Ethnic disparities in the use of nicotine replacement therapy for smoking cessation in an equal access health care system. Am J Health Promot. 2005;20(2):108–16.

Source: PubMed

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