A Pilot Randomized Controlled Trial of a Web-Based Growth Mindset Intervention to Enhance the Effectiveness of a Smartphone App for Smoking Cessation

Vasundhara Sridharan, Yuichi Shoda, Jaimee Heffner, Jonathan Bricker, Vasundhara Sridharan, Yuichi Shoda, Jaimee Heffner, Jonathan Bricker

Abstract

Background: Although smartphone apps have shown promise for smoking cessation, there is a need to enhance their low engagement rates. This study evaluated the application of the growth mindset theory, which has demonstrated the potential to improve persistence in behavior change in other domains, as a means to improve engagement and cessation.

Objective: This study aimed to explore the feasibility, utility, and efficacy of a Web-based growth mindset intervention for addiction when used alongside a smoking cessation app.

Methods: Daily smokers (N=398) were all recruited on the Web and randomly assigned to receive either a cessation app alone or the app plus a Web-delivered growth mindset intervention. The primary outcome was engagement, that is, the number of log-ins to the smoking cessation app. The secondary outcome was 30-day point prevalence abstinence at 2-month follow-up collected through a Web-based survey.

Results: The 2-month outcome data retention rate was 91.5% (364/398). In addition, 77.9% (310/398) of the participants in the experimental arm viewed at least 1 page of their growth mindset intervention, and 21.1% (84/398) of the group viewed all the growth mindset intervention. The intention-to-treat analysis did not show statistically significant differences between the experimental and comparison arms on log-ins to the app (19.46 vs 21.61; P=.38). The experimental arm had cessation rates, which trended higher than the comparison arm (17% vs 13%; P=.10). The modified intent-to-treat analysis, including only participants who used their assigned intervention at least once (n=115 in experimental group and n=151 in the control group), showed that the experimental arm had a similar number of log-ins (32.31 vs 28.48; P=.55) but significantly higher cessation rates (21% vs 13%; P=.03) than the comparison arm.

Conclusions: A growth mindset intervention for addiction did not increase engagement rates, although it may increase cessation rates when used alongside a smartphone app for smoking cessation. Future research is required to refine the intervention and assess efficacy with long-term follow-up to evaluate the efficacy of the mindset intervention.

Trial registration: ClinicalTrials.gov NCT03174730; https://ichgcp.net/clinical-trials-registry/NCT03174730.

Keywords: addictive behavior; health technology; mobile apps; psychological theory; smoking behaviors; smoking cessation.

Conflict of interest statement

Conflicts of Interest: JB has served as a consultant for GlaxoSmithKline and serves on the advisory board of Chrono Therapeutics. JH has received research support from Pfizer. None of the other authors have financial conflicts to disclose.

©Vasundhara Sridharan, Yuichi Shoda, Jaimee Heffner, Jonathan Bricker. Originally published in JMIR Mhealth and Uhealth (http://mhealth.jmir.org), 09.07.2019.

Figures

Figure 1
Figure 1
Participant flow diagram. To increase the enrollment of racial and ethnic minorities, some nonminorities who were otherwise eligible for study enrollment were randomly selected to be excluded. IP: Internet Protocol; PIN: personal identification number.

References

    1. The Health Consequences of Smoking--50 Years of Progress: A Report of the Surgeon General. Atlanta, GA: US Department of Health and Human Services; 2014.
    1. Taylor GM, Dalili MN, Semwal M, Civljak M, Sheikh A, Car J. Internet-based interventions for smoking cessation. Cochrane Database Syst Rev. 2017 Dec 4;9:CD007078. doi: 10.1002/14651858.CD007078.pub5.
    1. Ubhi HK, Michie S, Kotz D, Wong WC, West R. A mobile app to aid smoking cessation: preliminary evaluation of SmokeFree28. J Med Internet Res. 2015 Jan 16;17(1):e17. doi: 10.2196/jmir.3479.
    1. Bricker JB, Mull KE, Kientz JA, Vilardaga R, Mercer LD, Akioka KJ, Heffner JL. Randomized, controlled pilot trial of a smartphone app for smoking cessation using acceptance and commitment therapy. Drug Alcohol Depend. 2014 Oct 1;143:87–94. doi: 10.1016/j.drugalcdep.2014.07.006.
    1. Bricker JB, Copeland W, Mull KE, Zeng EY, Watson NL, Akioka KJ, Heffner JL. Single-arm trial of the second version of an acceptance & commitment therapy smartphone application for smoking cessation. Drug Alcohol Depend. 2017 Jan 1;170:37–42. doi: 10.1016/j.drugalcdep.2016.10.029.
    1. Iacoviello BM, Steinerman JR, Klein DB, Silver TL, Berger AG, Luo SX, Schork NJ. Clickotine, a personalized smartphone app for smoking cessation: initial evaluation. JMIR Mhealth Uhealth. 2017 Apr 25;5(4):e56. doi: 10.2196/mhealth.7226.
    1. BinDhim NF, McGeechan K, Trevena L. Smartphone smoking cessation application (SSC app) trial: a multicountry double-blind automated randomised controlled trial of a smoking cessation decision-aid 'app'. BMJ Open. 2018 Dec 21;8(1):e017105. doi: 10.1136/bmjopen-2017-017105.
    1. Zeng EY, Heffner JL, Copeland WK, Mull KE, Bricker JB. Get with the program: adherence to a smartphone app for smoking cessation. Addict Behav. 2016 Dec;63:120–4. doi: 10.1016/j.addbeh.2016.07.007.
    1. Chiu C, Hong Y, Dweck CS. Lay dispositionism and implicit theories of personality. J Pers Soc Psychol. 1997 Jul;73(1):19–30. doi: 10.1037/0022-3514.73.1.19.
    1. Dweck CS, Chiu C, Hong Y. Implicit theories and their role in judgments and reactions: a word from two perspectives. Psychol Inq. 1995;6(4):267–85. doi: 10.1207/s15327965pli0604_1.
    1. Sridharan V, Shoda Y, Heffner JL, Bricker J. Addiction mindsets and psychological processes of quitting smoking. Subst Use Misuse. 2019;54(7):1086–95. doi: 10.1080/10826084.2018.1555259.
    1. Burnette JL, O'Boyle EH, VanEpps EM, Pollack JM, Finkel EJ. Mind-sets matter: a meta-analytic review of implicit theories and self-regulation. Psychol Bull. 2013 May;139(3):655–701. doi: 10.1037/a0029531.
    1. Blackwell LS, Trzesniewski KH, Dweck CS. Implicit theories of intelligence predict achievement across an adolescent transition: a longitudinal study and an intervention. Child Dev. 2007;78(1):246–63. doi: 10.1111/j.1467-8624.2007.00995.x.
    1. Burnette JL, Russell MV, Hoyt CL, Orvidas K, Widman L. An online growth mindset intervention in a sample of rural adolescent girls. Br J Educ Psychol. 2018 Sep;88(3):428–45. doi: 10.1111/bjep.12192.
    1. Paunesku D, Walton GM, Romero C, Smith EN, Yeager DS, Dweck CS. Mind-set interventions are a scalable treatment for academic underachievement. Psychol Sci. 2015 Jun;26(6):784–93. doi: 10.1177/0956797615571017.
    1. Yeager DS, Miu AS, Powers J, Dweck CS. Implicit theories of personality and attributions of hostile intent: a meta-analysis, an experiment, and a longitudinal intervention. Child Dev. 2013;84(5):1651–67. doi: 10.1111/cdev.12062.
    1. Yeager DS, Johnson R, Spitzer BJ, Trzesniewski KH, Powers J, Dweck CS. The far-reaching effects of believing people can change: implicit theories of personality shape stress, health, and achievement during adolescence. J Pers Soc Psychol. 2014 Jun;106(6):867–84. doi: 10.1037/a0036335.
    1. Burnette JL, Finkel EJ. Buffering against weight gain following dieting setbacks: an implicit theory intervention. J Exp Soc Psychol. 2012 May;48(3):721–5. doi: 10.1016/j.jesp.2011.12.020.
    1. Schleider JL, Abel MR, Weisz JR. Implicit theories and youth mental health problems: a random-effects meta-analysis. Clin Psychol Rev. 2015 Feb;35:1–9. doi: 10.1016/j.cpr.2014.11.001.
    1. Heffner JL, Vilardaga R, Mercer LD, Kientz JA, Bricker JB. Feature-level analysis of a novel smartphone application for smoking cessation. Am J Drug Alcohol Abuse. 2015 Jan;41(1):68–73. doi: 10.3109/00952990.2014.977486.
    1. Caponnetto P, Polosa R. Common predictors of smoking cessation in clinical practice. Respir Med. 2008 Aug;102(8):1182–92. doi: 10.1016/j.rmed.2008.02.017.
    1. Lee CW, Kahende J. Factors associated with successful smoking cessation in the United States, 2000. Am J Public Health. 2007 Aug;97(8):1503–9. doi: 10.2105/AJPH.2005.083527.
    1. Julious SA, Tan SB, Machin D. An Introduction to Statistics in Early Phase Trials. West Sussex, UK: John Wiley & Sons; 2010.
    1. Watson NL, Mull KE, Heffner JL, McClure JB, Bricker JB. Participant recruitment and retention in remote ehealth intervention trials: methods and lessons learned from a large randomized controlled trial of two web-based smoking interventions. J Med Internet Res. 2018 Dec 24;20(8):e10351. doi: 10.2196/10351.
    1. Tobacco and Genetics Consortium Genome-wide meta-analyses identify multiple loci associated with smoking behavior. Nat Genet. 2010 May;42(5):441–7. doi: 10.1038/ng.571.
    1. Liu JZ, Tozzi F, Waterworth DM, Pillai SG, Muglia P, Middleton L, Berrettini W, Knouff CW, Yuan X, Waeber G, Vollenweider P, Preisig M, Wareham NJ, Zhao JH, Loos RJ, Barroso I, Khaw KT, Grundy S, Barter P, Mahley R, Kesaniemi A, McPherson R, Vincent JB, Strauss J, Kennedy JL, Farmer A, McGuffin P, Day R, Matthews K, Bakke P, Gulsvik A, Lucae S, Ising M, Brueckl T, Horstmann S, Wichmann HE, Rawal R, Dahmen N, Lamina C, Polasek O, Zgaga L, Huffman J, Campbell S, Kooner J, Chambers JC, Burnett MS, Devaney JM, Pichard AD, Kent KM, Satler L, Lindsay JM, Waksman R, Epstein S, Wilson JF, Wild SH, Campbell H, Vitart V, Reilly MP, Li M, Qu L, Wilensky R, Matthai W, Hakonarson HH, Rader DJ, Franke A, Wittig M, Schäfer A, Uda M, Terracciano A, Xiao X, Busonero F, Scheet P, Schlessinger D, St Clair D, Rujescu D, Abecasis GR, Grabe HJ, Teumer A, Völzke H, Petersmann A, John U, Rudan I, Hayward C, Wright AF, Kolcic I, Wright BJ, Thompson JR, Balmforth AJ, Hall AS, Samani NJ, Anderson CA, Ahmad T, Mathew CG, Parkes M, Satsangi J, Caulfield M, Munroe PB, Farrall M, Dominiczak A, Worthington J, Thomson W, Eyre S, Barton A, Wellcome Trust Case Control Consortium. Mooser V, Francks C, Marchini J. Meta-analysis and imputation refines the association of 15q25 with smoking quantity. Nat Genet. 2010 May;42(5):436–40. doi: 10.1038/ng.572.
    1. Hinyard LJ, Kreuter MW. Using narrative communication as a tool for health behavior change: a conceptual, theoretical, and empirical overview. Health Educ Behav. 2007 Oct;34(5):777–92. doi: 10.1177/1090198106291963.
    1. Yeager DS, Romero C, Paunesku D, Hulleman CS, Schneider B, Hinojosa C, Lee HY, O'Brien J, Flint K, Roberts A, Trott J, Greene D, Walton GM, Dweck CS. Using design thinking to improve psychological interventions: the case of the growth mindset during the transition to high school. J Educ Psychol. 2016 Apr;108(3):374–91. doi: 10.1037/edu0000098.
    1. Hayes SC, Levin ME, Plumb-Vilardaga J, Villatte JL, Pistorello J. Acceptance and commitment therapy and contextual behavioral science: examining the progress of a distinctive model of behavioral and cognitive therapy. Behav Ther. 2013 Jun;44(2):180–98. doi: 10.1016/j.beth.2009.08.002.
    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. doi: 10.1111/j.1360-0443.1991.tb01879.x.
    1. Kahler CW, Lachance HR, Strong DR, Ramsey SE, Monti PM, Brown RA. The commitment to quitting smoking scale: initial validation in a smoking cessation trial for heavy social drinkers. Addict Behav. 2007 Oct;32(10):2420–4. doi: 10.1016/j.addbeh.2007.04.002.
    1. Gwaltney CJ, Metrik J, Kahler CW, Shiffman S. Self-efficacy and smoking cessation: a meta-analysis. Psychol Addict Behav. 2009 Mar;23(1):56–66. doi: 10.1037/a0013529.
    1. SRNT Subcommittee on Biochemical Verification Biochemical verification of tobacco use and cessation. Nicotine Tob Res. 2002 May;4(2):149–59. doi: 10.1080/14622200210123581.
    1. Cha S, Ganz O, Cohn AM, Ehlke SJ, Graham AL. Feasibility of biochemical verification in a web-based smoking cessation study. Addict Behav. 2017 Dec;73:204–8. doi: 10.1016/j.addbeh.2017.05.020.
    1. R Project. 2017. [2017-06-28]. The R Project for Statistical Computing
    1. Stang A, Baethge C. Imbalance p values for baseline covariates in randomized controlled trials: a last resort for the use of p values? A pro and contra debate. Clin Epidemiol. 2018;10:531–5. doi: 10.2147/CLEP.S161508. doi: 10.2147/CLEP.S161508.
    1. Corbett MS, Higgins JP, Woolacott NF. Assessing baseline imbalance in randomised trials: implications for the Cochrane risk of bias tool. Res Synth Methods. 2014 Mar;5(1):79–85. doi: 10.1002/jrsm.1090.
    1. Pocock SJ, McMurray JJ, Collier TJ. Statistical controversies in reporting of clinical trials: part 2 of a 4-part series on statistics for clinical trials. J Am Coll Cardiol. 2015 Dec 15;66(23):2648–62. doi: 10.1016/j.jacc.2015.10.023.
    1. West R, Hajek P, Stead L, Stapleton J. Outcome criteria in smoking cessation trials: proposal for a common standard. Addiction. 2005 Mar;100(3):299–303. doi: 10.1111/j.1360-0443.2004.00995.x.
    1. Hilbe JM. Negative Binomial Regression. Cambridge, UK: Cambridge University Press; 2011.
    1. Kahan BC, Jairath V, Doré CJ, Morris TP. The risks and rewards of covariate adjustment in randomized trials: an assessment of 12 outcomes from 8 studies. Trials. 2014 Apr 23;15:139. doi: 10.1186/1745-6215-15-139.
    1. Assmann SF, Pocock SJ, Enos LE, Kasten LE. Subgroup analysis and other (mis)uses of baseline data in clinical trials. Lancet. 2000 Mar 25;355(9209):1064–9. doi: 10.1016/S0140-6736(00)02039-0.
    1. Arean PA, Hallgren KA, Jordan JT, Gazzaley A, Atkins DC, Heagerty PJ, Anguera JA. The use and effectiveness of mobile apps for depression: results from a fully remote clinical trial. J Med Internet Res. 2016 Dec 20;18(12):e330. doi: 10.2196/jmir.6482.
    1. Roepke AM, Jaffee SR, Riffle OM, McGonigal J, Broome R, Maxwell B. Randomized controlled trial of superbetter, a smartphone-based/internet-based self-help tool to reduce depressive symptoms. Games Health J. 2015 Jun;4(3):235–46. doi: 10.1089/g4h.2014.0046.
    1. Ludden GD, van Rompay TJ, Kelders SM, van Gemert-Pijnen JE. How to increase reach and adherence of web-based interventions: a design research viewpoint. J Med Internet Res. 2015 Jul 10;17(7):e172. doi: 10.2196/jmir.4201.
    1. Sedgwick P. Intention to treat analysis versus per protocol analysis of trial data. Br Med J. 2015 Feb 6;350:h681. doi: 10.1136/bmj.h681.
    1. West R. The clinical significance of 'small' effects of smoking cessation treatments. Addiction. 2007 Apr;102(4):506–9. doi: 10.1111/j.1360-0443.2007.01750.x.
    1. Sedgwick P. Intention to treat analysis versus per protocol analysis of trial data. Br Med J. 2015 Feb 6;350:h681. doi: 10.1136/bmj.h681.

Source: PubMed

스폰서 및 공동 작업자

건강 상태

약물 개입

3
구독하다