A Multimethod Evaluation of Tobacco Treatment Trial Recruitment Messages for Current Smokers Recently Diagnosed With Cancer: Pilot Factorial Randomized Controlled Trial

Jordan M Neil, Christian Senecal, Lauren Ballini, Yuchiao Chang, Brett Goshe, Efren Flores, Jamie S Ostroff, Elyse R Park, Jordan M Neil, Christian Senecal, Lauren Ballini, Yuchiao Chang, Brett Goshe, Efren Flores, Jamie S Ostroff, Elyse R Park

Abstract

Background: A cancer diagnosis can catalyze motivation to quit smoking. Tobacco treatment trials offer cessation resources but have low accrual rates. Digital outreach may improve accrual, but knowledge of how best to recruit smokers with recent diagnoses is limited.

Objective: This study aims to identify the message frames that were most effective in promoting intent to talk to a physician about participating in a tobacco treatment trial for smokers recently diagnosed with cancer.

Methods: From February to April 2019, current smokers diagnosed within the past 24 months were recruited from a national web-based panel for a multimethod pilot randomized trial (N=99). Participants were randomized to a 2×3 plus control factorial design that tested 3 unique message frames: proximal versus distal threats of smoking, costs of continued smoking versus benefits of quitting, and gains of participating versus losses of not participating in a tobacco treatment trial. The primary outcome was intent to talk to a physician about participating in a tobacco treatment trial. In phase 1, the main effect within each message factor level was examined using ANOVA and compared with the control condition. Other message evaluation and effectiveness measures were collected and explored in a multivariable model predicting intent to talk to a physician. In phase 2, open-text evaluations of the messages were analyzed using natural language processing software (Leximancer) to generate a thematic concept map and Linguistic Inquiry Word Count to identify and compare the prevalence of linguistic markers among message factors.

Results: Of the 99 participants, 76 (77%) completed the intervention. Participants who received the cost of continued smoking frame were significantly more likely to intend to talk to their physician about participating in a tobacco treatment trial than those who received the benefits of the quitting frame (mean costs 5.13, SD 1.70 vs mean benefits 4.23, SD 1.86; P=.04). Participants who received the proximal risks of continued smoking frame were significantly more likely to seek more information about participating (mean distal 4.83, SD 1.61 vs mean proximal 5.55, SD 1.15; P=.04), and those who received the losses of not participating frame reported significantly improved perceptions of smoking cessation research (mean gain 3.98, SD 0.83 vs mean loss 4.38, SD 0.78; P=.01). Male participants (P=.006) and those with greater message relevancy (P=.001) were significantly more likely to intend to talk to their physician. Participants' perceptions of their smoking habits, as well as their motivation to quit smoking, were prevalent themes in the open-text data. Differences in the percentages of affective words across message frames were identified.

Conclusions: Multimethod approaches are needed to develop evidence-based recruitment messages for patients recently diagnosed with cancer. Future tobacco treatment trials should evaluate the effectiveness of different message frames on smoker enrollment rates.

Trial registration: Clinicaltrials.gov NCT05471284; https://ichgcp.net/clinical-trials-registry/NCT05471284.

Keywords: cancer; message framing; recruitment; smoking; teachable moment; tobacco treatment trial.

Conflict of interest statement

Conflicts of Interest: EF has received speaker's honorarium from Medscape.

©Jordan M Neil, Christian Senecal, Lauren Ballini, Yuchiao Chang, Brett Goshe, Efren Flores, Jamie S Ostroff, Elyse R Park. Originally published in JMIR Cancer (https://cancer.jmir.org), 24.08.2022.

Figures

Figure 1
Figure 1
CONSORT (Consolidated Standards of Reporting Trials) flow diagram.
Figure 2
Figure 2
Leximancer-generated concept map detailing participant responses when asked to evaluate the video.

References

    1. Jamal A, King BA, Neff LJ, Whitmill J, Babb SD, Graffunder CM. Current cigarette smoking among adults - United States, 2005-2015. MMWR Morb Mortal Wkly Rep. 2016 Nov 11;65(44):1205–11. doi: 10.15585/mmwr.mm6544a2. doi: 10.15585/mmwr.mm6544a2.
    1. Westmaas JL, Newton CC, Stevens VL, Flanders WD, Gapstur SM, Jacobs EJ. Does a recent cancer diagnosis predict smoking cessation? An analysis from a large prospective US cohort. J Clin Oncol. 2015 May 20;33(15):1647–52. doi: 10.1200/JCO.2014.58.3088.JCO.2014.58.3088
    1. National Center for Chronic Disease Prevention and Health Promotion (US) Office on Smoking and Health . The Health Consequences of Smoking—50 Years of Progress: A Report of the Surgeon General. Atlanta, GA, USA: Centers for Disease Control and Prevention (US); 2014.
    1. Warren GW, Alberg AJ, Cummings KM, Dresler C. Smoking cessation after a cancer diagnosis is associated with improved survival. J Thorac Oncol. 2020 May;15(5):705–8. doi: 10.1016/j.jtho.2020.02.002. S1556-0864(20)30101-5
    1. Parsons A, Daley A, Begh R, Aveyard P. Influence of smoking cessation after diagnosis of early stage lung cancer on prognosis: systematic review of observational studies with meta-analysis. BMJ. 2010 Jan 21;340:b5569. doi: 10.1136/bmj.b5569.
    1. Tobacco Reports And Publications. Office of the Surgeon General. 2019. Apr 5, [2021-10-03]. .
    1. Sheikh M, Mukeriya A, Shangina O, Brennan P, Zaridze D. Postdiagnosis smoking cessation and reduced risk for lung cancer progression and mortality : a prospective cohort study. Ann Intern Med. 2021 Sep;174(9):1232–9. doi: 10.7326/M21-0252.
    1. Fiore MC, Baker TB, Nolan MB, Emamekhoo H. Providing cessation treatment to every oncology patient who smokes: an essential component of cancer care. Cancer. 2022 Mar 15;128(6):1162–4. doi: 10.1002/cncr.34052.
    1. Jassem J. Tobacco smoking after diagnosis of cancer: clinical aspects. Transl Lung Cancer Res. 2019 May;8(Suppl 1):S50–8. doi: 10.21037/tlcr.2019.04.01. doi: 10.21037/tlcr.2019.04.01.tlcr-08-S1-S50
    1. Unger JM, Cook E, Tai E, Bleyer A. The role of clinical trial participation in cancer research: barriers, evidence, and strategies. Am Soc Clin Oncol Educ Book. 2016;35:185–98. doi: 10.1200/EDBK_156686. 156686
    1. Hall MB, Vos P. Comparison of cancer fatalism among rural smokers and nonsmokers. J Community Health. 2019 Apr;44(2):215–21. doi: 10.1007/s10900-018-0576-z.10.1007/s10900-018-0576-z
    1. Streck JM, Luberto CM, Muzikansky A, Skurla S, Ponzani CJ, Perez GK, Hall DL, Gonzalez A, Mahaffey B, Rigotti NA, Ostroff JS, Park ER. Examining the effects of stress and psychological distress on smoking abstinence in cancer patients. Prev Med Rep. 2021 Sep;23:101402. doi: 10.1016/j.pmedr.2021.101402. S2211-3355(21)00092-9
    1. Hamann HA, Ostroff JS, Marks EG, Gerber DE, Schiller JH, Lee SJ. Stigma among patients with lung cancer: a patient-reported measurement model. Psychooncology. 2014 Jan;23(1):81–92. doi: 10.1002/pon.3371.
    1. Park ER, Perez GK, Regan S, Muzikansky A, Levy DE, Temel JS, Rigotti NA, Pirl WF, Irwin KE, Partridge AH, Cooley ME, Friedman ER, Rabin J, Ponzani C, Hyland KA, Holland S, Borderud S, Sprunck K, Kwon D, Peterson L, Miller-Sobel J, Gonzalez I, Whitlock CW, Malloy L, de León-Sanchez S, O'Brien M, Ostroff JS. Effect of sustained smoking cessation counseling and provision of medication vs shorter-term counseling and medication advice on smoking abstinence in patients recently diagnosed with cancer: a randomized clinical trial. JAMA. 2020 Oct 13;324(14):1406–18. doi: 10.1001/jama.2020.14581. 2771608
    1. Neil JM, Price SN, Friedman ER, Ponzani C, Ostroff JS, Muzikansky A, Park ER. Patient-level factors associated with oncology provider-delivered brief tobacco treatment among recently diagnosed cancer patients. Tob Use Insights. 2020 Aug 17;13:1179173X20949270. doi: 10.1177/1179173X20949270. 10.1177_1179173X20949270
    1. Warner ET, Park ER, Luberto CM, Rabin J, Perez GK, Ostroff JS. Internalized stigma among cancer patients enrolled in a smoking cessation trial: the role of cancer type and associations with psychological distress. Psychooncology. 2022 May;31(5):753–60. doi: 10.1002/pon.5859.
    1. Trope Y, Liberman N. Construal-level theory of psychological distance. Psychol Rev. 2010 Apr;117(2):440–63. doi: 10.1037/a0018963. 2010-06891-005
    1. Chandran S, Menon G. When a day means more than a year: effects of temporal framing on judgments of health risk. J Consum Res. 2004 Sep 01;31(2):375–89. doi: 10.1086/422116.
    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. 2007-18113-012
    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. 789473333
    1. Vlăsceanu S, Vasile M. Gain or loss: how to frame an antismoking message? Procedia Soc Behav Sci. 2015 Aug;203:141–6. doi: 10.1016/j.sbspro.2015.08.272.
    1. Tripp HL, Strickland JC, Mercincavage M, Audrain-McGovern J, Donny EC, Strasser AA. Tailored cigarette warning messages: how individualized loss aversion and delay discounting rates can influence perceived message effectiveness. Int J Environ Res Public Health. 2021 Oct 06;18(19):10492. doi: 10.3390/ijerph181910492. ijerph181910492
    1. Cornacchione J, Smith SW. The effects of message framing within the stages of change on smoking cessation intentions and behaviors. Health Commun. 2012;27(6):612–22. doi: 10.1080/10410236.2011.619252.
    1. Moorman M, van den Putte B. The influence of message framing, intention to quit smoking, and nicotine dependence on the persuasiveness of smoking cessation messages. Addict Behav. 2008 Oct;33(10):1267–75. doi: 10.1016/j.addbeh.2008.05.010.S0306-4603(08)00153-6
    1. Van 't Riet J, Cox AD, Cox D, Zimet GD, De Bruijn GJ, Van den Putte B, De Vries H, Werrij MQ, Ruiter RA. Does perceived risk influence the effects of message framing? Revisiting the link between prospect theory and message framing. Health Psychol Rev. 2016 Dec;10(4):447–59. doi: 10.1080/17437199.2016.1176865.
    1. Gray JB, Harrington NG. Narrative and framing: a test of an integrated message strategy in the exercise context. J Health Commun. 2011 Mar;16(3):264–81. doi: 10.1080/10810730.2010.529490.929168589
    1. Kozlowski LT, Porter CQ, Orleans CT, Pope MA, Heatherton T. Predicting smoking cessation with self-reported measures of nicotine dependence: FTQ, FTND, and HSI. Drug Alcohol Depend. 1994 Feb;34(3):211–6. doi: 10.1016/0376-8716(94)90158-9.0376-8716(94)90158-9
    1. Biener L, Abrams DB. The Contemplation Ladder: validation of a measure of readiness to consider smoking cessation. Health Psychol. 1991;10(5):360–5. doi: 10.1037//0278-6133.10.5.360.
    1. Jensen JD, King AJ, Carcioppolo N, Davis L. Why are tailored messages more effective? A multiple mediation analysis of a breast cancer screening intervention. J Commun. 2012 Oct;62(5):851–68. doi: 10.1111/j.1460-2466.2012.01668.x.
    1. Jensen JD, King AJ, Carcioppolo N, Krakow M, Samadder NJ, Morgan S. Comparing tailored and narrative worksite interventions at increasing colonoscopy adherence in adults 50-75: a randomized controlled trial. Soc Sci Med. 2014 Mar;104:31–40. doi: 10.1016/j.socscimed.2013.12.003.S0277-9536(13)00677-1
    1. Appelman A, Sundar SS. Measuring message credibility: construction and validation of an exclusive scale. J Mass Commun Q. 2015 Oct 05;93(1):59–79. doi: 10.1177/1077699015606057.
    1. Cacioppo JT, Petty RE, Morris KJ. Effects of need for cognition on message evaluation, recall, and persuasion. J Pers Soc Psychol. 1983 Oct;45(4):805–18. doi: 10.1037/0022-3514.45.4.805.
    1. Lemon LL, Hayes J. Enhancing trustworthiness of qualitative findings: using Leximancer for qualitative data analysis triangulation. Qual Rep. 2020 Mar 1;25(3):604–14. doi: 10.46743/2160-3715/2020.4222.
    1. Liu W, Lai CH, Xu W. Tweeting about emergency: a semantic network analysis of government organizations’ social media messaging during Hurricane Harvey. Public Relat Rev. 2018 Dec;44(5):807–19. doi: 10.1016/j.pubrev.2018.10.009.
    1. Neil JM, Parker ND, Levites Strekalova YA, Duke K, George T, Krieger JL. Communicating risk to promote colorectal cancer screening: a multi-method study to test tailored versus targeted message strategies. Health Educ Res. 2022 Mar 24;37(2):79–93. doi: 10.1093/her/cyac002. 6540717
    1. Falkenstein A, Tran B, Ludi D, Molkara A, Nguyen H, Tabuenca A, Sweeny K. Characteristics and correlates of word use in physician-patient communication. Ann Behav Med. 2016 Oct;50(5):664–77. doi: 10.1007/s12160-016-9792-x.10.1007/s12160-016-9792-x
    1. Alpers GW, Winzelberg AJ, Classen C, Roberts H, Dev P, Koopman C, Barr Taylor C. Evaluation of computerized text analysis in an Internet breast cancer support group. Comput Human Behav. 2005 Mar;21(2):361–76. doi: 10.1016/j.chb.2004.02.008.
    1. Bantum EO, Owen JE. Evaluating the validity of computerized content analysis programs for identification of emotional expression in cancer narratives. Psychol Assess. 2009 Mar;21(1):79–88. doi: 10.1037/a0014643.2009-03401-011
    1. Andy A, Andy U. Understanding communication in an online cancer forum: content analysis study. JMIR Cancer. 2021 Sep 07;7(3):e29555. doi: 10.2196/29555. v7i3e29555
    1. Holtzman NS, Tackman AM, Carey AL, Brucks MS, Küfner AC, Deters FG, Back MD, Donnellan MB, Pennebaker JW, Sherman RA, Mehl MR. Linguistic markers of grandiose narcissism: a LIWC analysis of 15 samples. J Lang Soc Psychol. 2019 Sep 11;38(5-6):773–86. doi: 10.1177/0261927x19871084.
    1. Zalake M, Tavassoli F, Duke K, George T, Modave F, Neil J, Krieger J, Lok B. Internet-based tailored virtual human health intervention to promote colorectal cancer screening: design guidelines from two user studies. J Multimodal User Interfaces. 2021 Jan 02;15(2):147–62. doi: 10.1007/s12193-020-00357-5.
    1. Petty RE, Cacioppo JT. The elaboration likelihood model of persuasion. In: Petty RE, Cacioppo JT, editors. Communication and Persuasion: Central and Peripheral Routes to Attitude Change. New York, NY, USA: Springer; 1986. pp. 1–24.
    1. Kahlor L. PRISM: a planned risk information seeking model. Health Commun. 2010 Jun;25(4):345–56. doi: 10.1080/10410231003775172.922575523
    1. Hovick SR, Kahlor L, Liang MC. Personal cancer knowledge and information seeking through PRISM: the planned risk information seeking model. J Health Commun. 2014 Apr;19(4):511–27. doi: 10.1080/10810730.2013.821556.
    1. Wong NC, Cappella JN. Antismoking threat and efficacy appeals: effects on smoking cessation intentions for smokers with low and high readiness to quit. J Appl Commun Res. 2009;37(1):1–20. doi: 10.1080/00909880802593928.
    1. Cortland CI, Shapiro JR, Guzman IY, Ray LA. The ironic effects of stigmatizing smoking: combining stereotype threat theory with behavioral pharmacology. Addiction. 2019 Oct;114(10):1842–8. doi: 10.1111/add.14696.
    1. Williamson LD, Bigman CA. A systematic review of medical mistrust measures. Patient Educ Couns. 2018 Oct;101(10):1786–94. doi: 10.1016/j.pec.2018.05.007.S0738-3991(18)30204-0
    1. Baum LV, Friedman D. The uncertain science of predicting death. JAMA Netw Open. 2020 Apr 01;3(4):e201736. doi: 10.1001/jamanetworkopen.2020.1736. 2763659
    1. Back AL, Arnold RM. Discussing prognosis: "how much do you want to know?" talking to patients who are prepared for explicit information. J Clin Oncol. 2006 Sep 01;24(25):4209–13. doi: 10.1200/JCO.2006.06.007.24/25/4209

Source: PubMed

3
구독하다