Mobile Phone Text Messaging for Tobacco Risk Communication Among Young Adult Community College Students: Protocol and Baseline Overview for a Randomized Controlled Trial

Alexander V Prokhorov, Georges Elias Khalil, Karen Sue Calabro, Tamara Costello Machado, Sophia Russell, Katarzyna W Czerniak, Gabrielle C Botello, Minxing Chen, Adriana Perez, Damon J Vidrine, Cheryl L Perry, Alexander V Prokhorov, Georges Elias Khalil, Karen Sue Calabro, Tamara Costello Machado, Sophia Russell, Katarzyna W Czerniak, Gabrielle C Botello, Minxing Chen, Adriana Perez, Damon J Vidrine, Cheryl L Perry

Abstract

Background: Community-college students are at high risk for tobacco use. Because the use of mobile phone text messaging is nearly ubiquitous today, short message service (SMS) may be an effective strategy for tobacco risk communication in this population. Little is known, however, concerning the message structure significantly influencing perceived tobacco risk.

Objective: We aim to outline the rationale and design of Project Debunk, a randomized trial comparing the effects of different SMS text message structures.

Methods: We conducted a 6-month randomized trial comparing 8 arms, based on the combination of the 3 message structures delivered to young adults in a 2×2×2 study design: framing (gain-framed or loss-framed), depth (simple or complex), and appeal (emotional or rational). Participants were invited to participate from 3 community colleges in Houston from September 2016 to July 2017. Participants were randomized to 1 arm and received text messages in 2 separate campaigns. Each campaign consisted of 2 text messages per day for 30 days. Perceived tobacco risk was assessed at baseline, 2 months after the first campaign, and 2 months after the second campaign. We assessed the perceived risk of using conventional products (eg, combustible cigarettes) and new and emerging products (eg, electronic cigarettes). The validity of message structures was assessed weekly for each campaign. A 1-week follow-up assessment was also conducted to understand immediate reactions from participants.

Results: We completed data collection for the baseline survey on a rolling basis during this time and assessed the validity of the message structure after 1 week of SMS text messages. For the entire sample (N=636), the average age was 20.92 years (SD 2.52), about two-thirds were male (430/636, 67.6%), and most were black or African American (259/636, 40.7%) or white (236/636, 37.1%). After 1 week of receiving text messages, the following was noted: (a) loss-framed messages were more likely to be perceived as presenting a loss than gain-framed messages (F7,522=13.13, P<.001), (b) complex messages were perceived to be more complex than simple messages (F7,520=2.04, P=.05), and (c) emotional messages were perceived to be more emotionally involving than rational messages (F7,520=6.46, P<.001).

Conclusions: This study confirms that the recruitment, randomization, and message composition have been successfully implemented. Further analyses will identify specific types of messages that are more effective than others in increasing the perceived risk of tobacco use. If our results suggest that any of the 8 specific message structures are more effective for helping young adults understand tobacco risk, this would provide evidence to include such messages as part of a larger technology-based campaign such as mobile phone apps, entertainment-based campaigns, and social media.

Trial registration: ClinicalTrials.gov NCT03457480; https://ichgcp.net/clinical-trials-registry/NCT03457480 (Archived by WebCite at http://www.webcitation.org/6ykd4IIap).

Registered report identifier: RR1-10.2196/10977.

Keywords: perception; risk; text messaging; tobacco use; young adult.

Conflict of interest statement

Conflicts of Interest: None declared.

©Alexander V Prokhorov, Georges Elias Khalil, Karen Sue Calabro, Tamara Costello Machado, Sophia Russell, Katarzyna W Czerniak, Gabrielle C Botello, Minxing Chen, Adriana Perez, Damon J Vidrine, Cheryl L Perry. Originally published in JMIR Research Protocols (http://www.researchprotocols.org), 15.10.2018.

Figures

Figure 1
Figure 1
Study randomization flowchart. Conventional indicates conventional tobacco products including cigarettes, cigars, smokeless; NETP indicates new and emerging tobacco products, including snus, hookah, and e-cigarettes. In this study design, there is a break of one week between post-campaign 1 survey and campaign 2. GSE: gain-framed, simple emotional; GCE: gain-framed, complex, emotional; GSR: gain-framed, simple, rational; GCR: gain-framed, complex, rational; LSE: loss-framed, simple, emotional; LCE: loss-framed, complex, emotional; LSR, loss-framed, simple, rational; LCR: loss-framed, complex, rational; PC1: post-campaign 1; PC2: post-campaign 2.
Figure 2
Figure 2
Data collection procedure for the study. SMS: short message service.

References

    1. Schulenberg JE, Johnston LD, O'Malley PM, Bachman JG, Miech RA, Patrick ME. Monitoringthefuture. 2016. [2018-07-10]. College students and adults ages 19-55 .
    1. Pokhrel P, Lam TH, Pagano I, Kawamoto CT, Herzog TA. Young adult e-cigarette use outcome expectancies: validity of a revised scale and a short scale. Addict Behav. 2018 Mar;78:193–9. doi: 10.1016/j.addbeh.2017.11.019.
    1. Hair E, Rath JM, Pitzer L, Emelle B, Ganz O, Halenar MJ, Cantrell J, Vallone D. Trajectories of hookah use: harm perceptions from youth to young adulthood. Am J Health Behav. 2017 May 1;41(3):240–7. doi: 10.5993/AJHB.41.3.3.
    1. Berg CJ, Stratton E, Schauer GL, Lewis M, Wang Y, Windle M, Kegler M. Perceived harm, addictiveness, and social acceptability of tobacco products and marijuana among young adults: marijuana, hookah, and electronic cigarettes win. Subst Use Misuse. 2015 Jan;50(1):79–89. doi: 10.3109/10826084.2014.958857.
    1. Kozlowski LT, Sweanor DT. Young or adult users of multiple tobacco/nicotine products urgently need to be informed of meaningful differences in product risks. Addict Behav. 2018 Jan;76:376–81. doi: 10.1016/j.addbeh.2017.01.026.
    1. Wong EC, Haardörfer R, Windle M, Berg CJ. Distinct motives for use among polytobacco versus cigarette only users and among single tobacco product users. Nicotine Tob Res. 2017 Dec 13;20(1):117–23. doi: 10.1093/ntr/ntw284.
    1. Redonnet B, Chollet A, Fombonne E, Bowes L, Melchior M. Tobacco, alcohol, cannabis and other illegal drug use among young adults: the socioeconomic context. Drug Alcohol Depend. 2012 Mar 1;121(3):231–9. doi: 10.1016/j.drugalcdep.2011.09.002.
    1. Mays D, Arrazola RA, Tworek C, Rolle IV, Neff LJ, Portnoy DB. Openness to using non-cigarette tobacco products among U.S. young adults. Am J Prev Med. 2016 Apr;50(4):528–34. doi: 10.1016/j.amepre.2015.08.015.
    1. Kasza KA, Ambrose BK, Conway KP, Borek N, Taylor K, Goniewicz ML, Cummings KM, Sharma E, Pearson JL, Green VR, Kaufman AR, Bansal-Travers M, Travers MJ, Kwan J, Tworek C, Cheng Y, Yang L, Pharris-Ciurej N, van Bemmel DM, Backinger CL, Compton WM, Hyland AJ. Tobacco-product use by adults and youths in the United States in 2013 and 2014. N Engl J Med. 2017 Dec 26;376(4):342–53. doi: 10.1056/NEJMsa1607538.
    1. Loukas A, Murphy JL, Gottlieb NH. Cigarette smoking and cessation among trade or technical school students in Texas. J Am Coll Health. 2008;56(4):401–7. doi: 10.3200/JACH.56.44.401-408.
    1. O'Brien F, Simons-Morton B, Chaurasia A, Luk J, Haynie D, Liu D. Post-high school changes in tobacco and cannabis use in the United States. Subst Use Misuse. 2018 Jan 2;53(1):26–35. doi: 10.1080/10826084.2017.1322983.
    1. American Association of Community Colleges. 2017. Mar, Financially challenged: each year almost half of community college students must reply on outside financial sources
    1. Carpenter CM, Wayne GF, Pauly JL, Koh HK, Connolly GN. New cigarette brands with flavors that appeal to youth: tobacco marketing strategies. Health Aff (Millwood) 2005;24(6):1601–10. doi: 10.1377/hlthaff.24.6.1601.
    1. Ling PM, Glantz SA. Why and how the tobacco industry sells cigarettes to young adults: evidence from industry documents. Am J Public Health. 2002 Jun;92(6):908–16.
    1. Bahreinifar S, Sheon NM, Ling PM. Is snus the same as dip? Smokers' perceptions of new smokeless tobacco advertising. Tob Control. 2013 Mar;22(2):84–90. doi: 10.1136/tobaccocontrol-2011-050022.
    1. Carter OB, Donovan R, Jalleh G. Using viral e-mails to distribute tobacco control advertisements: an experimental investigation. J Health Commun. 2011 Aug;16(7):698–707. doi: 10.1080/10810730.2011.551998.
    1. Perez DA, Grunseit AC, Rissel C, Kite J, Cotter T, Dunlop S, Bauman A. Tobacco promotion 'below-the-line': exposure among adolescents and young adults in NSW, Australia. BMC Public Health. 2012 Jun 12;12:429. doi: 10.1186/1471-2458-12-429.
    1. Jane Lewis M, Bover Manderski MT, Delnevo CD. Tobacco industry direct mail receipt and coupon use among young adult smokers. Prev Med. 2015 Feb;71:37–9. doi: 10.1016/j.ypmed.2014.11.030.
    1. Richardson A, Ganz O, Pearson J, Celcis N, Vallone D, Villanti AC. How the industry is marketing menthol cigarettes: the audience, the message and the medium. Tob Control. 2015 Nov;24(6):594–600. doi: 10.1136/tobaccocontrol-2014-051657.
    1. Richardson A, Ganz O, Vallone D. Tobacco on the web: surveillance and characterisation of online tobacco and e-cigarette advertising. Tob Control. 2015 Jul;24(4):341–7. doi: 10.1136/tobaccocontrol-2013-051246.
    1. Rainie L, Perrin A. 10 facts about smartphones as the iPhone turns 10. 2017. Jun 28,
    1. Reynolds RJ. Camel Snus: We had a feeling you would be stopping 2012.
    1. Phillip Morris USA. 2012. [2018-04-23]. Corporate Responsibility .
    1. Sears CG, Walker KL, Hart JL, Lee AS, Siu A, Smith C. Clean, cheap, convenient: promotion of electronic cigarettes on YouTube. Tob Prev Cessat. 2017 Apr;3 doi: 10.18332/tpc/69393.
    1. Huang J, Kornfield R, Emery SL. 100 million views of electronic cigarette YouTube videos and counting: quantification, content evaluation, and engagement levels of videos. J Med Internet Res. 2016 Mar 18;18(3):e67. doi: 10.2196/jmir.4265.
    1. BinDhim NF, Freeman B, Trevena L. Pro-smoking apps: where, how and who are most at risk. Tob Control. 2015 Mar;24(2):159–61. doi: 10.1136/tobaccocontrol-2013-051189.
    1. US Food and Drug Administration. 2017. Research Priorities .
    1. Ashley DL, Backinger CL, van Bemmel DM, Neveleff DJ. Tobacco regulatory science: research to inform regulatory action at the Food and Drug Administration's Center for Tobacco Products. Nicotine Tob Res. 2014 Aug;16(8):1045–9. doi: 10.1093/ntr/ntu038.
    1. Muench F, Weiss RA, Kuerbis A, Morgenstern J. Developing a theory driven text messaging intervention for addiction care with user driven content. Psychol Addict Behav. 2013 Mar;27(1):315–21. doi: 10.1037/a0029963.
    1. Obermayer JL, Riley WT, Asif O, Jean-Mary J. College smoking-cessation using cell phone text messaging. J Am Coll Health. 2004;53(2):71–8. doi: 10.3200/JACH.53.2.71-78.
    1. Riley W, Obermayer J, Jean-Mary J. Internet and mobile phone text messaging intervention for college smokers. J Am Coll Health. 2008;57(2):245–8. doi: 10.3200/JACH.57.2.245-248.
    1. Sandrick J, Tracy D, Eliasson A, Roth A, Bartel J, Simko M, Bowman T, Harouse-Bell K, Kashani M, Vernalis M. Effect of a counseling session bolstered by text messaging on self-selected health behaviors in college students: a preliminary randomized controlled trial. JMIR Mhealth Uhealth. 2017 May 17;5(5):e67. doi: 10.2196/mhealth.6638.
    1. Head KJ, Noar SM, Iannarino NT, Grant Harrington N. Efficacy of text messaging-based interventions for health promotion: a meta-analysis. Soc Sci Med. 2013 Nov;97:41–8. doi: 10.1016/j.socscimed.2013.08.003.
    1. Armanasco AA, Miller YD, Fjeldsoe BS, Marshall AL. Preventive health behavior change text message interventions: a meta-analysis. Am J Prev Med. 2017 Mar;52(3):391–402. doi: 10.1016/j.amepre.2016.10.042.
    1. Latimer AE, Krishnan-Sarin S, Cavallo DA, Duhig A, Salovey P, O'Malley SA. Targeted smoking cessation messages for adolescents. J Adolesc Health. 2012 Jan;50(1):47–53. doi: 10.1016/j.jadohealth.2011.04.013.
    1. Toll BA, Salovey P, O'Malley SS, Mazure CM, Latimer A, McKee SA. Message framing for smoking cessation: the interaction of risk perceptions and gender. Nicotine Tob Res. 2008 Jan;10(1):195–200. doi: 10.1080/14622200701767803.
    1. Biener L, McCallum-Keeler G, Nyman AL. Adults' response to Massachusetts anti-tobacco television advertisements: impact of viewer and advertisement characteristics. Tob Control. 2000 Dec;9(4):401–7.
    1. Steward WT, Schneider TR, Pizarro J, Salovey P. Need for cognition moderates responses to framed smoking-cessation messages 1. J Appl Soc Pyschol. 2003 Dec;33(12):2439–64. doi: 10.1111/j.1559-1816.2003.tb02775.x.
    1. Wilson DK, Wallston KA, King JE. Effects of contract framing, motivation to quit, and self-efficacy on smoking reduction 1. J Appl Soc Pyschol. 1990 Apr;20(7):531–47. doi: 10.1111/j.1559-1816.1990.tb00426.x.
    1. Wilson DK, Purdon SE, Wallston KA. Compliance to health recommendations: a theoretical overview of message framing. Health Educ Res. 1988 Jun 1;3(2):161–71. doi: 10.1093/her/3.2.161.
    1. O'Keefe DJ, Jensen JD. The relative persuasiveness of gain-framed and loss-framed messages for encouraging disease prevention behaviors: a meta-analytic review. J Health Commun. 2007;12(7):623–44. doi: 10.1080/10810730701615198.
    1. Burgoon J, Blair JP, Qin T, Nunamaker JF. Detecting deception through linguistic analysis. 2003 Proceedings: Intelligence and Security Informatics Intelligence and Security Informatics: First NSF/NIJ Symposium; Intelligence and Security Informatics Intelligence and Security Informatics: First NSF/NIJ Symposium; June 2-3, 2013; Tuscon, AZ, USA. 2003. Jun, pp. 91–101.
    1. Brochet F, Naranjo P, Yu G. Causes and consequences of linguistic complexity in non-US firm conference calls. 2012. Oct, .
    1. Barnett T, Hoang H, Furlan A. An analysis of the readability characteristics of oral health information literature available to the public in Tasmania, Australia. BMC Oral Health. 2016 Mar 17;16:35. doi: 10.1186/s12903-016-0196-x.
    1. Lee S, Lang A. Redefining media content and structure in terms of available resources: toward a dynamic human-centric theory of communication. Commun Res. 2013 May 30;42(5):599–625. doi: 10.1177/0093650213488416.
    1. Reilly J, Seibert L. Language and emotion. In: Davidson RJ, Sherer KR, Goldsmith HH, editors. Handbook of Affective Sciences. Oxford: Oxford University Press; 2003. p. 535.
    1. Shimanoff SB. Expressing emotions in words: verbal patterns of interaction. J Commun. 1985 Sep 1;35(3):16–31. doi: 10.1111/j.1460-2466.1985.tb02445.x.
    1. Shimanoff SB. Types of emotional disclosures and request compliance between spouses. Commun Monogr. 2009 Jun 2;54(1):85–100. doi: 10.1080/03637758709390217.
    1. Carey J. Paralanguage in computer mediated communication. ACL '80 Proceedings of the 18th annual meeting on Association for Computational Linguistics; the 18th annual meeting on Association for Computational Linguistics; June 19 - 22, 1980; Philadelphia. Stroudsburg, PA, USA: Association for Computational Linguistics; 1980. pp. 67–9.
    1. Vidrine JI, Simmons VN, Brandon TH. Construction of smoking-relevant risk perceptions among college students: The influence of need for cognition and message content. J Appl Soc Pyschol. 2007 Jan 10;37(1):91–114. doi: 10.1111/j.0021-9029.2007.00149.x.
    1. Petty RE, Cacioppo JT. Advances in Experimental Social Psychology. New York: Springer; 1986. The elaboration likelihood model of persuasion; pp. 123–205.
    1. O'Keefe DJ. The International Encyclopedia of Communication. New York: John Wiley & Sons; 2008. Mar 28, Elaboration likelihood model; pp. 1–7.
    1. Chen SJ, Lee KP. The role of personality traits and perceived values in persuasion: an elaboration likelihood model perspective on online shopping. Soc Behav Personal. 2008 Nov 1;36(10):1379–1400. doi: 10.2224/sbp.2008.36.10.1379.
    1. Lang A, Yegiyan NS. Understanding the interactive effects of emotional appeal and claim strength in health messages. J Broadcast Electron Media. 2008 Aug 8;52(3):432–47. doi: 10.1080/08838150802205629.
    1. Kahneman D, Tversky A. Prospect theory: An analysis of decision under risk. In: MacLean LC, Ziemba WT, editors. Handbook of The Fundamentals of Financial Decision Making: Part I. Singapore: World Scientific Publishing Co Pte Ltd; 2013. pp. 99–127.
    1. Kühberger A. The influence of framing on risky decisions: A meta-analysis. Organ Behav Hum Decis Process. 1998 Jul;75(1):23–55.
    1. O'Keefe DJ, Jensen JD. The relative persuasiveness of gain-framed and loss-framed messages for encouraging disease prevention behaviors: a meta-analytic review. J Health Commun. 2007;12(7):623–44. doi: 10.1080/10810730701615198.
    1. Gallagher KM, Updegraff JA. Health message framing effects on attitudes, intentions, and behavior: a meta-analytic review. Ann Behav Med. 2012 Feb;43(1):101–16. doi: 10.1007/s12160-011-9308-7.
    1. Committee on Improving the Health, Safety, and Well-Being of Young Adults. Board on Children, Youth, and Families; Institute of Medicine. National Research Council . Young adults in the 21st century. In: Bonnie RJ, Stroud C, Breiner H, editors. Investing in the Health and Well-Being of Young Adults. Washington (DC), USA: National Academies Press (US); 2015. Jan 27,
    1. Houston Community College. [2018-04-24]. Houston Community College Fact Book 2017 .
    1. Prokhorov AV, Yost T, Mullin-Jones M, de Moor C, Ford KH, Marani S, Kilfoy BA, Hein JP, Hudmon KS, Emmons KM. “Look at your health”: outcomes associated with a computer-assisted smoking cessation counseling intervention for community college students. Addict Behav. 2008 Jun;33(6):757–71. doi: 10.1016/j.addbeh.2007.12.005.
    1. Prokhorov AV, Machado TC, Calabro KS, Vanderwater EA, Vidrine DJ, Pasch KP, Marani SK, Buchberg M, Wagh A, Russell SC, Czerniak KW, Botello GC, Dobbins MH, Khalil GE, Perry CL. Developing mobile phone text messages for tobacco risk communication among college students: a mixed methods study. BMC Public Health. 2017 Dec 31;17(1):137. doi: 10.1186/s12889-017-4027-z.
    1. Khalil GE, Calabro KS, Crook B, Machado TC, Perry CL, Prokhorov AV. Validation of mobile phone text messages for nicotine and tobacco risk communication among college students: a content analysis. Tob Prev Cessation. 2018 Feb;4 doi: 10.18332/tpc/84866.
    1. Johnston LD, O'Malley PM, Bachman JG, Schulenberg JE, Miech RA. Monitoring the future national survey results on drug use, 1975-2015: Overview, key findings on adolescent drug use. Bethesda, MD: National Institute on Drug Abuse; 2016. [2018-09-14]. .
    1. Rumpf HJ, Meyer C, Hapke U, John U. Screening for mental health: validity of the MHI-5 using DSM-IV Axis I psychiatric disorders as gold standard. Psychiatry Res. 2001 Dec 31;105(3):243–53.
    1. Kebede M, Zeleke A, Asemahagn M, Fritz F. Willingness to receive text message medication reminders among patients on antiretroviral treatment in North West Ethiopia: a cross-sectional study. BMC Med Inform Decis Mak. 2015 Aug 13;15:65. doi: 10.1186/s12911-015-0193-z.
    1. Kahlor L, Dunwoody S, Griffin RJ, Neuwirth K. Seeking and processing information about impersonal risk. Sci Commun. 2016 Aug 18;28(2):163–94. doi: 10.1177/1075547006293916.
    1. Mason M, Cheung I, Walker L. Substance use, social networks, and the geography of urban adolescents. Subst Use Misuse. 2004;39(10-12):1751–77.
    1. Berwick DM, Murphy JM, Goldman PA, Ware JE, Barsky AJ, Weinstein MC. Performance of a five-item mental health screening test. Med Care. 1991 Feb;29(2):169–76.
    1. Fellner B, Holler M, Kirchler E, Schabmann A. Regulatory focus scale (RFS): development of a scale to record dispositional regulatory focus. Swiss J Psychol. 2007 Jun;66(2):109–16. doi: 10.1024/1421-0185.66.2.109.
    1. Stephenson MT, Hoyle RH, Palmgreen P, Slater MD. Brief measures of sensation seeking for screening and large-scale surveys. Drug Alcohol Depend. 2003 Dec 11;72(3):279–86.
    1. Nelson W, Reyna VF, Fagerlin A, Lipkus I, Peters E. Clinical implications of numeracy: theory and practice. Ann Behav Med. 2008 Jun;35(3):261–74. doi: 10.1007/s12160-008-9037-8.
    1. Toll BA, O'Malley SS, Katulak NA, Wu R, Dubin JA, Latimer A, Meandzija B, George TP, Jatlow P, Cooney JL, Salovey P. Comparing gain- and loss-framed messages for smoking cessation with sustained-release bupropion: a randomized controlled trial. Psychol Addict Behav. 2007 Dec;21(4):534–44. doi: 10.1037/0893-164X.21.4.534.
    1. See YH, Petty RE, Evans LM. The impact of perceived message complexity and need for cognition on information processing and attitudes. J Res Pers. 2009 Oct;43(5):880–9. doi: 10.1016/j.jrp.2009.04.006.
    1. Eastin MS. Credibility assessments of online health information: the effects of source expertise and knowledge of content. J Comput-Mediat Comm. 2001;6(4) doi: 10.1111/j.1083-6101.2001.tb00126.x.
    1. Cyr D, Head M, Ivanov A. Perceived interactivity leading to e-loyalty: Development of a model for cognitive-affective user responses. Int J Hum Comput Stud. 2009 Oct;67(10):850–69. doi: 10.1016/j.ijhcs.2009.07.004.
    1. Nysveen H, Pedersen P, Thorbjørnsen H. Intentions to use mobile services: antecedents and cross-service comparisons. JAMS. 2005 Jun;33(3) doi: 10.1177/0092070305.
    1. Coursaris CK, Sung J. Antecedents and consequents of a mobile website's interactivity. New Media Soc. 2012 Apr 26;14(7):1128–46. doi: 10.1177/1461444812439552.
    1. Green MC, Brock TC. The role of transportation in the persuasiveness of public narratives. J Pers Soc Psychol. 2000;79(5):701–21. doi: 10.1037/0022-3514.79.5.701.
    1. Khalil G. Fear and happiness in Re-Mission: teasing out emotional gaming events responsible for cancer risk perception. In: Schouten B, Fedtke S, Bekker T, Schijven M, Gekker A, editors. Games for Health. The Netherlands: Springer Vieweg, Wiesbaden; 2013. pp. 27–44.
    1. Khalil GE, Rintamaki LS. A televised entertainment-education drama to promote positive discussion about organ donation. Health Educ Res. 2014 Apr;29(2):284–96. doi: 10.1093/her/cyt106.
    1. Kelder SH, Prokhorov AV, Murray N, Shegog R, Conroy JL, Agurcia C. ASPIRE, project design of a CD-ROM-based smoking prevention and cessation curriculum for urban youth. Proceedings of the 131st Annual Meeting of the American Public Health Association; the 131st Annual Meeting of the American Public Health Association; 2003; San Francisco, CA. 2003.
    1. Johnston L, O'Malley PM, Bachman J, Schulenberg J. Monitoring the Future National Results on Adolescent Drug Use: Overview of Key Findings. Bethesda, MD: National Institute on Drug Abuse; 2009.
    1. Rath JM, Villanti AC, Abrams DB, Vallone DM. Patterns of tobacco use and dual use in US young adults: the missing link between youth prevention and adult cessation. J Environ Public Health. 2012;2012:679134. doi: 10.1155/2012/679134. doi: 10.1155/2012/679134.
    1. Pierce JP, Choi WS, Gilpin EA, Farkas AJ, Merritt RK. Validation of susceptibility as a predictor of which adolescents take up smoking in the United States. Health Psychol. 1996 Sep;15(5):355–61.
    1. Velicer WF, DiClemente CC, Prochaska JO, Brandenburg N. Decisional balance measure for assessing and predicting smoking status. J Pers Soc Psychol. 1985 May;48(5):1279–89.
    1. Pepper JK, Emery SL, Ribisl KM, Rini CM, Brewer NT. How risky is it to use e-cigarettes? Smokers' beliefs about their health risks from using novel and traditional tobacco products. J Behav Med. 2015 Apr;38(2):318–26. doi: 10.1007/s10865-014-9605-2.
    1. Haug S, Meyer C, Dymalski A, Lippke S, John U. Efficacy of a text messaging (SMS) based smoking cessation intervention for adolescents and young adults: study protocol of a cluster randomised controlled trial. BMC Public Health. 2012;12:51. doi: 10.1186/1471-2458-12-51.
    1. Bock BC, Barnett NP, Thind H, Rosen R, Walaska K, Traficante R, Foster R, Deutsch C, Fava JL, Scott-Sheldon LA. A text message intervention for alcohol risk reduction among community college students: TMAP. Addict Behav. 2016 Dec;63:107–13. doi: 10.1016/j.addbeh.2016.07.012.
    1. Scull TM, Kupersmidt JB, Malik CV, Keefe EM. Examining the efficacy of an mHealth media literacy education program for sexual health promotion in older adolescents attending community college. J Am Coll Health. 2018 Apr;66(3):165–77. doi: 10.1080/07448481.2017.1393822.
    1. Bandiera FC, Loukas A, Li X, Wilkinson AV, Perry CL. Depressive symptoms predict current e-cigarette use among college students in texas. Nicotine Tob Res. 2017 Sep 1;19(9):1102–6. doi: 10.1093/ntr/ntx014.

Source: PubMed

3
Subscribe