Trajectories of 12-Month Usage Patterns for Two Smoking Cessation Websites: Exploring How Users Engage Over Time

Jonathan B Bricker, Vasundhara Sridharan, Yifan Zhu, Kristin E Mull, Jaimee L Heffner, Noreen L Watson, Jennifer B McClure, Chongzhi Di, Jonathan B Bricker, Vasundhara Sridharan, Yifan Zhu, Kristin E Mull, Jaimee L Heffner, Noreen L Watson, Jennifer B McClure, Chongzhi Di

Abstract

Background: Little is known about how individuals engage with electronic health (eHealth) interventions over time and whether this engagement predicts health outcomes.

Objective: The objectives of this study, by using the example of a specific type of eHealth intervention (ie, websites for smoking cessation), were to determine (1) distinct groups of log-in trajectories over a 12-month period, (2) their association with smoking cessation, and (3) baseline user characteristics that predict trajectory group membership.

Methods: We conducted a functional clustering analysis of 365 consecutive days of log-in data from both arms of a large (N=2637) randomized trial of 2 website interventions for smoking cessation (WebQuit and Smokefree), with a primary outcome of 30-day point prevalence smoking abstinence at 12 months. We conducted analyses for each website separately.

Results: A total of 3 distinct trajectory groups emerged for each website. For WebQuit, participants were clustered into 3 groups: 1-week users (682/1240, 55.00% of the sample), 5-week users (399/1240, 32.18%), and 52-week users (159/1240, 12.82%). Compared with the 1-week users, the 5- and 52-week users had 57% higher odds (odds ratio [OR] 1.57, 95% CI 1.13-2.17; P=.007) and 124% higher odds (OR 2.24, 95% CI 1.45-3.43; P<.001), respectively, of being abstinent at 12 months. Smokefree users were clustered into 3 groups: 1-week users (645/1309, 49.27% of the sample), 4-week users (395/1309, 30.18%), and 5-week users (269/1309, 20.55%). Compared with the 1-week users, 5-week users (but not 4-week users; P=.99) had 48% higher odds (OR 1.48, 95% CI 1.05-2.07; P=.02) of being abstinent at 12 months. In general, the WebQuit intervention had a greater number of weekly log-ins within each of the 3 trajectory groups as compared with those of the Smokefree intervention. Baseline characteristics associated with trajectory group membership varied between websites.

Conclusions: Patterns of 1-, 4-, and 5-week usage of websites may be common for how people engage in eHealth interventions. The 5-week usage of either website, and 52-week usage only of WebQuit, predicted a higher odds of quitting smoking. Strategies to increase eHealth intervention engagement for 4 more weeks (ie, from 1 week to 5 weeks) could be highly cost effective.

Trial registration: ClinicalTrials.gov NCT01812278; https://www.clinicaltrials.gov/ct2/show/NCT01812278 (Archived by WebCite at http://www.webcitation.org/6yPO2OIKR).

Keywords: acceptance and commitment therapy; eHealth; engagement; patient participation; smokefree.gov; smoking; smoking cessation; telemedicine; tobacco; tobacco use cessation; trajectories; websites.

Conflict of interest statement

Conflicts of Interest: In July 2016, JBB was a consultant to GlaxoSmithKline, the makers of a nicotine replacement therapy. He now serves on the Scientific Advisory Board of Chrono Therapeutics, the makers of a nicotine replacement therapy device. JLH has received research support from Pfizer, the makers of a smoking cessation medication.

©Jonathan B Bricker, Vasundhara Sridharan, Yifan Zhu, Kristin E Mull, Jaimee L Heffner, Noreen L Watson, Jennifer B McClure, Chongzhi Di. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 20.04.2018.

Figures

Figure 1
Figure 1
Average weekly log-in trajectory for each cluster from the (left) WebQuit (n=1240) arm and (right) Smokefree (n=1309) arm for first 16 weeks of use.

References

    1. Wantland DJ, Portillo CJ, Holzemer WL, Slaughter R, McGhee EM. The effectiveness of Web-based vs. non-Web-based interventions: a meta-analysis of behavioral change outcomes. J Med Internet Res. 2004 Nov 10;6(4):e40. doi: 10.2196/jmir.6.4.e40.
    1. Donker T, Petrie K, Proudfoot J, Clarke J, Birch M, Christensen H. Smartphones for smarter delivery of mental health programs: a systematic review. J Med Internet Res. 2013;15(11):e247. doi: 10.2196/jmir.2791.
    1. Cole-Lewis H, Kershaw T. Text messaging as a tool for behavior change in disease prevention and management. Epidemiol Rev. 2010;32:56–69. doi: 10.1093/epirev/mxq004.
    1. Webb TL, Joseph J, Yardley L, Michie S. Using the internet to promote health behavior change: a systematic review and meta-analysis of the impact of theoretical basis, use of behavior change techniques, and mode of delivery on efficacy. J Med Internet Res. 2010;12(1):e4. doi: 10.2196/jmir.1376.
    1. Donkin L, Christensen H, Naismith SL, Neal B, Hickie IB, Glozier N. A systematic review of the impact of adherence on the effectiveness of e-therapies. J Med Internet Res. 2011;13(3):e52. doi: 10.2196/jmir.1772.
    1. Shaughnessy JJ. Long-term retention and the spacing effect in free-recall and frequency judgments. Am J Psychol. 1977 Dec;90(4):587. doi: 10.2307/1421733.
    1. Pyc MA, Rawson KA. Testing the retrieval effort hypothesis: does greater difficulty correctly recalling information lead to higher levels of memory? J Mem Lang. 2009 May;60(4):437–447. doi: 10.1016/j.jml.2009.01.004.
    1. Chassin L, Presson CC, Pitts SC, Sherman SJ. The natural history of cigarette smoking from adolescence to adulthood in a midwestern community sample: multiple trajectories and their psychosocial correlates. Health Psychol. 2000;19(3):223–231. doi: 10.1037/0278-6133.19.3.223.
    1. Donovan JE. Adolescent alcohol initiation: a review of psychosocial risk factors. J Adolesc Health. 2004 Dec;35(6):529.e7–18. doi: 10.1016/j.jadohealth.2004.02.003.
    1. Duncan SC, Duncan TE. A multivariate latent growth curve analysis of adolescent substance use. Struct Equ Model. 1996 Jan;3(4):323–347. doi: 10.1080/10705519609540050.
    1. Macleod J, Oakes R, Copello A, Crome I, Egger M, Hickman M, Oppenkowski T, Stokes-Lampard H, Davey SG. Psychological and social sequelae of cannabis and other illicit drug use by young people: a systematic review of longitudinal, general population studies. Lancet. 2004 May 15;363(9421):1579–88. doi: 10.1016/S0140-6736(04)16200-4.
    1. Goh G, Tan NC, Malhotra R, Padmanabhan U, Barbier S, Allen JC, Østbye T. Short-term trajectories of use of a caloric-monitoring mobile phone app among patients with type 2 diabetes mellitus in a primary care setting. J Med Internet Res. 2015;17(2):e33. doi: 10.2196/jmir.3938.
    1. Christofferson DE, Hertzberg JS, Beckham JC, Dennis PA, Hamlett-Berry K. Engagement and abstinence among users of a smoking cessation text message program for veterans. Addict Behav. 2016 Nov;62:47–53. doi: 10.1016/j.addbeh.2016.06.016.
    1. Wangberg SC, Bergmo TS, Johnsen JK. Adherence in Internet-based interventions. Patient Prefer Adherence. 2008;2:57–65.
    1. Strecher VJ, McClure J, Alexander G, Chakraborty B, Nair V, Konkel J, Greene S, Couper M, Carlier C, Wiese C, Little R, Pomerleau C, Pomerleau O. The role of engagement in a tailored web-based smoking cessation program: randomized controlled trial. J Med Internet Res. 2008;10(5):e36. doi: 10.2196/jmir.1002.
    1. Balmford J, Borland R, Benda P. Patterns of use of an automated interactive personalized coaching program for smoking cessation. J Med Internet Res. 2008;10(5):e54. doi: 10.2196/jmir.1016.
    1. Zbikowski SM, Hapgood J, Smucker BS, McAfee T. Phone and web-based tobacco cessation treatment: real-world utilization patterns and outcomes for 11,000 tobacco users. J Med Internet Res. 2008;10(5):e41. doi: 10.2196/jmir.999.
    1. Bricker JB, Mull KE, McClure JB, Watson NL, Heffner JL. Improving quit rates of web-delivered interventions for smoking cessation: full-scale randomized trial of versus . Addiction. 2017 Dec 13; doi: 10.1111/add.14127.
    1. US Department of Health and Human Services. National Instutues of Health. National Cancer Institute . . Tobacco Control Research Branch, National Cancer Institute; [2018-04-06].
    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. Fiore MC, Jaen CR, Baker T, Bailey WC, Benowitz NL, Curry SE, Dorfman SF, Froelicher ES, Goldstein MG, Healton CG, Henderson PN. Treating Tobacco Use and Dependence: 2008 Update. Rockville, MD: US Department of Health and Human Services; 2008.
    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. 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. Kahler CW, Metrik J, LaChance HR, Ramsey SE, Abrams DB, Monti PM, Brown RA. Addressing heavy drinking in smoking cessation treatment: a randomized clinical trial. J Consult Clin Psychol. 2008 Oct;76(5):852–62. doi: 10.1037/a0012717.
    1. Radloff LS. The CES-D Scale: a self-report depression scale for research in the general population. Appl Psychol Meas. 1977 Jun 01;1(3):385–401. doi: 10.1177/014662167700100306.
    1. Spitzer RL, Kroenke K, Williams JBW, Löwe B. A brief measure for assessing generalized anxiety disorder: the GAD-7. Arch Intern Med. 2006 May 22;166(10):1092–7. doi: 10.1001/archinte.166.10.1092.
    1. Stein MB, Roy-Byrne PP, McQuaid JR, Laffaye C, Russo J, McCahill ME, Katon W, Craske M, Bystritsky A, Sherbourne CD. Development of a brief diagnostic screen for panic disorder in primary care. Psychosom Med. 1999;61(3):359–64.
    1. Lang AJ, Stein MB. An abbreviated PTSD checklist for use as a screening instrument in primary care. Behav Res Ther. 2005 May;43(5):585–94. doi: 10.1016/j.brat.2004.04.005.
    1. Connor KM, Kobak KA, Churchill LE, Katzelnick D, Davidson JR. Mini-SPIN: a brief screening assessment for generalized social anxiety disorder. Depress Anxiety. 2001;14(2):137–40.
    1. Rotondi AJ, Eack SM, Hanusa BH, Spring MB, Haas GL. Critical design elements of e-health applications for users with severe mental illness: singular focus, simple architecture, prominent contents, explicit navigation, and inclusive hyperlinks. Schizophr Bull. 2015 Mar;41(2):440–8. doi: 10.1093/schbul/sbt194.
    1. Christensen H, Griffiths KM, Farrer L. Adherence in internet interventions for anxiety and depression. J Med Internet Res. 2009;11(2):e13. doi: 10.2196/jmir.1194.
    1. Civljak M, Stead LF, Hartmann-Boyce J, Sheikh A, Car J. Internet-based interventions for smoking cessation. Cochrane Database Syst Rev. 2013;7:CD007078. doi: 10.1002/14651858.CD007078.pub4.
    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 Oct;73:204–208. doi: 10.1016/j.addbeh.2017.05.020.
    1. Ramsay JO, Silverman BW. Applied Functional Data Analysis: Methods and Case Studies. New York, NY: Springer; 2007.
    1. Kaufman L, Rousseeuw PJ. Finding Groups in Data. Hoboken, NJ: Wiley Online Library; 1990.
    1. Tibshirani R, Walther G. Cluster validation by prediction strength. J Comput Graph Stat. 2005 Sep;14(3):511–528. doi: 10.1198/106186005X59243.

Source: PubMed

3
Předplatit