Published on in Vol 15, No 5 (2013): May

Who Participates in Web-Assisted Tobacco Interventions? The Quit-Primo and National Dental Practice-Based Research Network Hi-Quit Studies

Who Participates in Web-Assisted Tobacco Interventions? The Quit-Primo and National Dental Practice-Based Research Network Hi-Quit Studies

Who Participates in Web-Assisted Tobacco Interventions? The Quit-Primo and National Dental Practice-Based Research Network Hi-Quit Studies

Original Paper

1Division of Health Informatics & Implementation Science, University of Massachusetts Medical School, Worcester, MA, United States

2VA eHealth Quality Enhancement Research Initiative, Bedford VAMC, Bedford, MA, United States

3Center for Health Quality, Outcomes & Economic Research (CHQOER), Bedford, MA, United States

4Department of Health Services Administration, School of Health Professions, University of Alabama at Birmingham, Birmingham, AL, United States

5Division of General Internal Medicine, University of Alabama at Birmingham, Birmingham, AL, United States

6Department of Clinical & Community Sciences, School of Dentistry, University of Alabama at Birmingham, Birmingham, AL, United States

7Division of General Internal Medicine, Johns Hopkins School of Medicine, Baltimore, MD, United States

8University of Alabama at Birmingham, Birmingham, AL, United States

Corresponding Author:

Rajani Shankar Sadasivam, PhD

Division of Health Informatics & Implementation Science

University of Massachusetts Medical School

55 Lake Ave North

Worcester, MA,

United States

Phone: 1 5089254846

Fax:1 5088568993


Introduction: Smoking is the most preventable cause of death. Although effective, Web-assisted tobacco interventions are underutilized and recruitment is challenging. Understanding who participates in Web-assisted tobacco interventions may help in improving recruitment.

Objectives: To understand characteristics of smokers participating in a Web-assisted tobacco intervention (

Methods: In addition to the typical Google advertisements, we expanded recruitment to include referrals from medical and dental providers. We assessed how the expanded recruitment of smokers changed the users’ characteristics, including comparison with a population-based sample of smokers from the national Behavioral Risk Factors Surveillance Survey (BRFSS). Using a negative binomial regression, we compared demographic and smoking characteristics by recruitment source, in particular readiness to quit and association with subsequent use.

Results: The cohort included 605 smokers; the 2010 BRFSS dataset included 69,992. Compared to BRFSS smokers, a higher proportion of smokers were female (65.2% vs 45.7%, P=.001), over age 35 (80.8% vs 67.0%, P=.001), and had some college or were college graduates (65.7% vs 45.9%, P=.001). Demographic and smoking characteristics varied by recruitment; for example, a lower proportion of medical- (22.1%) and dental-referred (18.9%) smokers had set a quit date or had already quit than Google smokers (40.1%, P<.001). Medical- and dental-referred smokers were less likely to use functions; in adjusted analysis, Google smokers (predicted count 17.04, 95% CI 14.97-19.11) had higher predicted counts of Web page visits than medical-referred (predicted count 12.73, 95% CI 11.42-14.04) and dental-referred (predicted count 11.97, 95% CI 10.13-13.82) smokers, and were more likely to contact tobacco treatment specialists.

Conclusions: Recruitment from clinical practices complimented Google recruitment attracting smokers less motivated to quit and less experienced with Web-assisted tobacco interventions.

Trial Registration: NCT00797628; (Archived by WebCite at

J Med Internet Res 2013;15(5):e77



Smoking continues to be the number one preventable cause of death [1-5]. To meet the 2020 Healthy People objectives of ending the tobacco epidemic and reducing illness, disability, and death related to tobacco use and secondhand smoke exposure [6], innovative approaches are needed to reach and engage current smokers. Web-assisted tobacco interventions have improved quit rates [7-18] and can potentially reach a large number of smokers [19-21]. However, current methods to recruit smokers to these interventions have some limitations [22-26].

Most Web-assisted tobacco interventions recruit smokers by using search engine or media advertisements [27-28]. These methods require the smokers to actively initiate the first contact with the intervention; thus, the recruited smokers may not be representative of the majority of smokers [22-23], particularly in their readiness to quit. A Cochrane review of 20 Web-assisted tobacco interventions reported that most of these interventions that used search engine recruitment were able to recruit only those smokers ready to quit [29]. These smokers tended to be female and white [29-31]. Recruiting smokers by using just search engine or media advertisements also misses an important opportunity to recruit the majority of smokers, the 70% who see a health care provider at least once per year [3,5].

In our study, we expanded the recruitment of smokers to our evidence-based, Web-assisted tobacco intervention ( [32]) to also include provider referrals from medical and dental practices in addition to the usual Google advertisements. Our goal in this paper was to assess whether our expanded recruitment improved variability of our cohort. In particular, we were interested to see if directly recruiting smokers from provider’s e-referrals would increase participation of smokers less ready to quit. Finally, we looked longitudinally at these smokers’ participation in; specifically, we looked at the association between recruitment source and subsequent use of

Study Design

We compared smokers who registered on from May 2010 through July 2011 with smokers who responded to the 2010 BRFSS survey.—a Web-assisted tobacco intervention containing information about quitting smoking, secure asynchronous messaging with a certified tobacco treatment specialist (TTS), an online support group, and a motivational, pushed-email, tailored messaging system—is the core patient intervention in 2 randomized trials. is both a cessation induction and a relapse prevention system. All smokers can benefit from the system, whether they are in the precontemplation, contemplation, or preparation stage, or in the action or maintenance stages. Thus, we recruited smokers at all motivational levels. We used multiple routes to recruit smokers. In the Quality Improvement in Tobacco Provider Referrals & Internet-delivered Microsystem Optimization (Quit-Primo) trial, smokers were referred to from medical practices [33] and in the Hygienists to Internet Quality Improvement in Tobacco (Hi-Quit) trial, smokers were referred from dental practices participating in a practice-based research network. In parallel, we also used Google advertisements to recruit smokers to These 2 trials were approved by the Institutional Review Boards of the University of Alabama at Birmingham and the University of Massachusetts Medical School.

The Behavioral Risk Factors Surveillance Survey (BRFSS) is a yearly, cross-sectional telephone survey conducted by state health departments with technical and methodological assistance provided by the Centers for Disease Control and Prevention to collect prevalence data on risk behaviors and preventive health practices that affect health status [34]. The health characteristics estimated from the BRFSS pertain to the adult population, aged 18 years or older. For our analysis, we used the 2010 BRFSS dataset because it was in the same time frame as the registrations.

Setting and Sample

The cohort included smokers recruited from 81 medical primary care practices and 51 dental practices across the United States, and through Google advertisements. Primary care medical practices were recruited from a registered database of internal medicine and family/general practitioners. Dental practices were recruited from state lists of registered dentists and through the Dental Practice-Based Research Network [35]. At these practices, we implemented an e-referral program that allowed providers to recruit smokers to at the point-of-care by entering their email addresses into an online form [33,36]. When e-referred, smokers were sent up to 10 email messages encouraging registration over an 8 week period or until the patient registered. To recruit smokers through Google advertisements, 3 ads were posted on Google AdWords [37]. Advertisements were linked to searches for keywords related to smoking (eg, smoking, quit smoking, stop smoking, quit, quit smoking tips, and quit smoking programs) and included a link that took participants directly to the home page where they could choose to register as new participants.

The BRFSS is conducted monthly to collect data about risk behaviors from people in all 50 states, the District of Columbia, Puerto Rico, Guam, and the Virgin Islands. Respondents to the BRFSS were identified by using telephone-based methods; although 95% of US households have telephones, the coverage varies across states and subgroups. No direct methods were used to compensate for nontelephone coverage; however, post-stratification weights were used to partially correct for any bias caused by lack of telephone coverage. These weights also adjusted for differences in probability of selection and nonresponse. A more complete description of the sampling methodology may be found on the BRFSS website [34].

Data Collection

All smokers registering on completed an online survey during registration. We collected demographics (sex, age, ethnicity, education, marital status, and Internet usage) and smoking characteristics (readiness to quit, quit history, number of cigarettes per day, and smoking at home). Readiness to quit was assessed using a question based on the Transtheoretical Model of Change [38]. The readiness question consisted of 5 options: I am not thinking of quitting (precontemplation), I am thinking of quitting (contemplation), I have set a quit date (preparation), I quit today (action), and I have already quit (maintenance). Online activity (number of visits, page visits, asynchronous interactions with TTS, and use of the online support group) was tracked through Web page scripts.

The BRFSS questionnaire includes a standard set of questions asked by all states about current health-related perceptions, conditions, and behaviors, including smoking, as well as demographic questions. We used the question “Do you now smoke cigarettes every day, some days, or not at all?” to identify smokers in the 2010 BRFSS dataset.

Statistical Analyses

All analyses were conducted using Stata version 11 (StataCorp LP, College Station, TX, USA). We first compared demographic characteristics of smokers and BRFSS smokers. Next, we compared demographic and smoking characteristics by recruitment source (Google advertisements vs clinical practice referrals). We also compared the readiness to quit of these smokers. We used the Pearson chi-square test to assess the significant differences between and BRFSS smokers (survey weighted proportions). For each recruitment source, we assessed the number of referrals per successful smoker registration. For medical and dental practices, we divided the total referral count by the number of smokers subsequently registered in that group. For Google, we used the number of click throughs on our advertisement as the referral number and divided this number by the subsequent number of smokers registered in that group. This is an estimate of the number of referred smokers needed per successful registration. Finally, we assessed whether recruitment source was associated with subsequent use of the system. In this analysis, our dependent variable was use of the system measured by number of pages visited, our independent variable was recruitment source, and our covariates were demographics and smoking characteristics.

We used a count regression because our dependent variable was the number of pages visited. Because of over dispersion of the variance of the distribution of the dependent variable, we used negative binomial regression instead of simple Poisson. From these models, incidence rate ratios (IRR) and adjusted counts of the dependent variable were calculated. Although we primarily used number of pages visited as our system use variable, we also assessed use by using number of visits to the system.


The sample included 605 registered smokers: 32.6% (n=197) were from Google advertisements, 46.4% (n=280) were from medical practice e-referrals, and 21.0% (n=127) were from dental practice e-referrals. The 2010 BRFSS dataset included 69,992 smokers aged 18 and older. Versus Behavioral Risk Factors Surveillance Survey

Compared with the national sample of smokers participating in the BRFSS, a higher proportion of smokers were female (65.2% vs 45.7%, P=.001), over the age of 35 years (80.8% vs 67.0%, P=.001), and had attended some college or were a college graduate (65.7% vs 45.9%, P=.001) (Table 1). A small but significant difference was seen in the proportion of smokers who attempted to quit in the past 12 months (5%, P=.01).

Number of Referrals Per Smoker Registration by Recruitment Source

The number of medical practice referrals was 1588 resulting in 280 successful registrations; thus, the number of referrals per registration was 5.7. The number of dental practice referrals was 739 with 127 registrations; the number of referrals per registration was 5.8. There were 6992 click throughs on our Google advertisements and a resulting 197 registrations; the number of referrals per registration was 35.5.

We compared’s smokers by recruitment method (Google advertisement, medical practice referral, or dental practice referral, see Table 2). Because Google is frequently the recruitment source for Web-assisted tobacco interventions, we consistently use it as the reference or comparison group.

Table 1. Characteristics of smokers participating in the Behavioral Risk Factor Surveillance Survey (BRFSS) and all smokers (Google, medical-referred, and dental-referred combined) who engaged in the Web-assisted tobacco intervention
Demographic combined, n (%)
(%) a

Male210 (34.7)(54.3)

Female394 (65.2)(45.7)

19-34116 (19.2)(33.0)

35-54307 (50.8)(44.1)

> 55181 (30.0)(22.9)

White529 (87.6)(80.7)

Nonwhite75 (12.4)(19.3)
Highest grade of schoolb

< High school36 (6.0)(15.7)

High school171(28.3)(38.5)

Some college or college graduate397 (65.7)(45.9)
During the past 12 months, have you stopped smoking for 1 day or longer because you were trying to quit smoking?c

No277 (45.9)(40.9)

Yes324 (54.1)(58.8)
Smoking status

Not thinking about quitting26 (4.3)

Thinking of quitting413 (68.4)

Set a quit date72 (11.9)

Already quit93 (15.4)

aWeighted for complex survey design



Table 2. Demographic characteristics and readiness to quit of smokers by recruitment source.
Demographic characteristicRecruitment source, n (%)

Google advertisement n=197Dental practice referrals n=127Medical practice referrals n=280

Male61 (31.0)43 (33.9)106 (37.9)

Female136 (69.0)84 (66.1)174 (62.1)
Age (years)

19-3429 (14.7)40 (31.5)47 (16.8)a

35-54105 (53.3)59 (46.5)143 (51.1)

< 5563 (32.0)28 (22.1)90 (32.1)

White171 (86.8)112 (88.2)246 (87.9)

Nonwhite26 (13.2)15 (11.8)34 (12.1)
Highest grade of school

< College graduate134 (68.0)91 (71.7)236 (84.3)b

College graduate63 (32.0)36 (28.4)44 (15.7)
Smoking status

Not thinking about quitting6 (3.1)11 (8.7)b9 (3.2)b

Thinking of quitting112 (56.9)92 (72.4)209 (74.6)

Set a quit date29 (14.7)13 (10.2)30 (10.7)

Already quit50 (25.4)11 (8.7)32 (11.4)

aP=.001 comparing Google and the applicable column

bP<.001 comparing Google and the applicable column

Demographics by Recruitment Source

Demographic and smoking characteristics varied by recruitment source (Table 2). Compared with Google and medical-referred smokers, the dental-referred smokers (21.0%) were younger (P=.001 and P=.002, respectively). Compared with Google (32.0%) and dental-referred (28.4%) smokers, a lower proportion of medical-referred smokers (15.7%) were college graduates (P<.001 and P=.003, respectively).

Readiness to Quit and Other Smoking Characteristics by Recruitment Source

A lower proportion of medical-referred (22.1%) and dental-referred (18.9%) smokers had set a quit date or had already quit than Google-referred smokers (40.1 %, P<.001 for both comparisons). The mean number of cigarettes smoked per day was similar between Google (mean 17.8, SD 10.5) and medical-referred smokers (mean 17.4, SD 9.2), but lower for dental-referred smokers (mean 14.5, SD 8.9; P=.002). Fewer dental-referred smokers allowed smoking at home compared with the other 2 groups (dental-referred 34% vs Google 48%, P=.36; dental-referred 34% vs medical-referred 45%, P=.005). Medical-referred (8.6%) and dental-referred (15.8%) smokers were less likely to have visited smoking cessation websites as compared to Google smokers (40.1%, P<.001 for both comparisons).

Smokers’ Participation in by Recruitment Source

Medical-referred (mean 2.4, SD 3.4) and dental-referred (mean 2.1, SD 2.6) smokers visited less frequently than Google smokers (mean 2.7, SD 4.0), but this was not statistically significant (P=.14 and P=.06, respectively). On average, they also visited fewer pages on the website per visit (medical-referred: mean 12.9, SD 13.6; dental-referred: mean 12.3, SD 12.4; Google: mean 17.4, SD 15.2; Google vs medical-referred P<.001; Google vs dental-referred P=.002). Compared with Google smokers (42.6%), a lower proportion of medical-referred (29.6%, P=.01) and dental-referred (22.8%, P=.01) smokers messaged the TTS at least once. Although not statistically significant, among those who messaged at least once, medical- and dental-referred smokers also interacted with the TTS less frequently than their Google counterparts (medical-referred: mean 2.0, SD 2.4; dental-referred: mean 2.0, SD 1.7; Google: mean 3.3, SD 6.1). However, this was not statistically significant (Google vs medical P=.05, Google vs dental P=.13). Medical-referred (23.6%) and dental-referred (12.6%) smokers also used the online support group less frequently than Google smokers (39.1%, P=.01 for both comparisons).

Multivariable Comparisons Among Google, Medical-Referred, and Dental-Referred Smokers

After adjustment for demographic characteristics and readiness to quit, Google smokers had higher predicted counts of Web page visits (IRR 17.0, 95% CI 15.0-19.1) compared with the medical-referred (IRR 12.7, 95% CI 11.4-14.0) and dental-referred smokers (IRR 12.0, 95% CI 10.1-13.8) (Table 3). Google smokers were also more likely to use the TTS and an online support group. When we assessed system use using number of visits to the website, the direction and magnitude of the point estimates remained the same.

Table 3. Associations between recruitment source and use of
Demographic characteristicUnadjustedAdjusted

IRR (95% CI)Countsa (95% CI)IRR (95% CI)Countsa (95% CI)
Patient origin

From Google advertisementReference17.4 (15.3-19.6)Reference17.0 (15.0-19.1)

From medical provider0.7 (0.6-0.9)12.9 (11.5-14.2)0.8 (0.7-0.9)12.7 (11.4-14.0)

From dental provider0.7 (0.6-0.9)12.3 (10.4-14.2)0.7 (0.6-0.9)12.0 (10.1-13.8)

FemaleReference15.3 (13.9-16.6)Reference14.8 (13.6-16.1)

Male0.8 (0.7-0.9)12.3 (10.8-13.8)0.8 (0.7-1.0)12.1 (10.7-13.5)

19-34Reference14.5 (12.7-16.9)Reference14.3 (12.0-16.6)

35-541.0 (0.8-1.2)13.8 (12.4-15.2)0.9 (0.8-1.1)13.4 (12.1-14.7)

> 551.0 (0.82-1.25)14.7 (12.8-16.7)1.0 (0.8-1.2)14.3 (12.5-16.1)

WhiteReference11.7 (9.3-14.1)Reference11.5 (9.2-13.9)

Nonwhite1.3 (1.0-1.6)14.6(13.5-15.7)1.2 (1.0-0.5)14.2 (13.1-15.2)

College graduateReference18.4 (15.8-21.1)Reference17.8 (15.3-20.4)

< College graduate0.7 (0.6-0.8)13.0 (11.9-14.0)0.7 (0.6-0.9)12.8 (11.8-13.8)
Smoking status

Already quitReference16.2(13.3-19.1)Reference15.9 (13.1-18.7)

Set a quit date0.9 (0.7-1.2)15.1 (12.0-18.3)1.0 (0.8-1.3)14.9 (11.9-17.9)

Thinking of quitting0.9 (0.7-1.1)13.9 (12.7-15.1)0.9 (0.8-1.1)13.5 (12.4-14.6)

Not thinking about quitting0.6 (0.4-0.9)9.9 (6.4-13.4)0.7 (0.5-1.0)10.1 (6.6-13.5)

aCounts are marginal predicted counts products postregression using the X command in STATA.

Findings and Conclusions

Approximately 70% of the 44.5 million adult smokers in the United States want to quit, but fewer than 5% of those who do try to quit in a given year succeed [6]. Thus, expanding the reach of effective treatments, such as Web-assisted tobacco interventions, is crucial in increasing quit rates. However, recruitment to Web-assisted tobacco interventions poses unique challenges [22,24-26]. In this study, we expanded recruitment by adding an e-referral approach to the traditional search engine method. In this paper, we assessed how this combination increased the variability of our cohort.

Compared with the population of smokers responding to the 2010 BRFSS, a higher proportion of smokers registering with were female (nearly 20% more). They also were more likely to identify themselves as white in race/ethnicity and be highly educated. However, among those recruited from medical practices, 15.7% reported college education, which was the same proportion reported by BRFSS-participating smokers. Inconsistent with expectations, smokers were older. The rate of prior quit attempts in the past 12 months was similar among those registering with and the national BRFSS.

Our comparisons (as shown in Table 1) highlight the sharp difference in the proportion of women participating in our Web-assisted tobacco intervention as compared with the national sample of smokers. The higher proportion of women also was consistent across our recruitment sources (Google advertisement 69.0% vs medical referral 62.1% vs dental referral 66.1%). Women, in general, may be more likely to participate in a Web-assisted tobacco intervention; therefore, our participation rates may just be reflecting general trends. Across 6 other Web-assisted tobacco interventions, the mean proportion of women participating was 60% (range 52-72) [39-44]. The lower participation rates of men in these interventions suggest that different recruitment approaches or different types of interventions might be needed to engage them in cessation activities.

Although we identified differences in race/ethnicity and education for our Web-engaged smokers compared to the BRFSS, our age distribution was older than the national sample. The older age may be because the decision to make a serious and successful attempt to quit smoking is typically made when a smoker has reached a greater level of maturity than the average smoker (mid- to upper-30s). Other Web-assisted tobacco interventions also report a mean age of 39 years (range 34-49, [39-44]). However, close to one-third of our smokers were over the age of 55. This number may be reflective of the decrease in age barriers to Internet adoption in recent years. [45].

Recruitment from medical and dental practices was more efficient than Google advertising with respect to the number of referrals needed to register a smoker (5.7, 5.8, and 35.5, respectively). Within the population of smokers registering at, the types of smokers who were recruited from clinical practices were different from those who found the intervention site via Google. The general trend in educational status among our Web-engaged smokers varied. Those recruited from medical practices were less educated and less likely to have previously used a Web-assisted tobacco intervention; thus, they more closely resembled the national sample. Dental practice smokers were younger than counterparts from the other recruitment groups. Many younger smokers are seen in dental practices, but may not be seen in medical practices; thus, recruitment from dental practices allowed us to target smokers who were not engaged through Google or medical practices. These dental participants also were lighter smokers, a group that may be especially difficult to engage in interventions to quit smoking.

In addition to engaging smokers with different demographic characteristics, clinical practice-based recruitments also resulted in participation by smokers with a wider range of motivational levels. Our results support previous concerns that recruiting using search engine-based recruitment methods alone may limit the reach of Web-assisted tobacco interventions mainly to smokers highly motivated to quit [46-50]. Currently, most smokers recruited to these interventions are through search engine advertisements or other mass media campaigns, which require the smoker to be motivated to register on the system. For example, out of 2523 smokers recruited to a Web-assisted tobacco intervention [27], most (71%) were recruited through Google Ads or direct mailing. Only 95 smokers (3.8%) were recruited through provider referrals or other proactive recruitment methods. The recruitment to the National Colorectal Cancer Research Alliance (NCCRA) and OncoLink Web-assisted tobacco intervention [28] also were primarily through search engine and mass media campaigns. In the OncoLink study, only 7.3% of 2162 smokers were registered from proactive provider referrals.

As noted, our results indicate that using only search engine or mass media recruitment methods limits the range of smokers engaged. In our study, a higher proportion of Google smokers were ready to quit or had already quit compared with other smokers; medical practice referrals brought in smokers who were often at a lower readiness to quit and less likely to have sought help from online resources. They are an important group of smokers to engage, and Web-assisted tobacco interventions can be designed for smokers not ready to quit (as cessation induction interventions) as well as for smokers ready to quit (as an aid to cessation).

Although we were successful in broadening our sample, we were unable to maintain the engagement of clinical practice-recruited smokers at the same level as Google-recruited smokers. Google participants had higher participation rates at Previous Web-assisted tobacco intervention studies have shown a relationship between smoking cessation and number of website visits [51], number of website sections viewed [52], and amount of time spent on the website [53]. Thus, Google participants may have disproportionally benefited from the intervention. Google smokers had a higher number of page hits, even after adjusting for demographics and readiness to quit. Google smokers were more educated, had prior experience participating in a Web-assisted tobacco intervention, and were more likely to have set a quit date or quit. Other important predictors of greater use included TTS use and access to an external social network. Future Web-assisted tobacco interventions may need to be flexible in their strategy to maintain engagement for smokers not quite ready to quit [54], perhaps by continually monitoring participation rates and programming their interventions to be more proactive with the groups that are less engaged.


Our study has limitations. First, we collected a limited number of characteristics of these smokers; thus, the samples may vary on important unmeasured characteristics. Some of the information was self-reported through an online survey and cannot be validated. We evaluated the impact of only 1 Web-assisted tobacco intervention (, which prevents strict generalizability to all Web-assisted tobacco interventions or other online behavior support. One major difference in participation in the intervention was by readiness to quit. Although, we adjusted for readiness to quit in our model (Table 3), it is certainly possible that residual confounding by readiness is mediating differences in participation by recruitment source.

In conclusion, to maximize the potential of Web-assisted tobacco interventions, expanding methods to attract more smokers is critical. In recruiting users who typically do not participate in these interventions, we demonstrated that clinical practice recruitment does complement Internet search engine recruitment. However, our results also suggest that once recruited, those smokers recruited from clinical practices may not be as active as the Google smokers, suggesting that Web-assisted tobacco interventions may need to tailor their engagement strategies.


Funding for these studies was received from the National Cancer Institute, grant R01 CA129091, and the National Institute of Dental and Craniofacial Research, grants U01 DE16746, U01 DE16747, and U19 DE22516. Research reported in this publication was also supported by the National Center for Advancing Translational Sciences of the National Institutes of Health under award number UL1TR000161. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH.

Conflicts of Interest

None declared.

  1. Murray CJ, Lopez AD. Alternative projections of mortality and disability by cause 1990-2020: Global Burden of Disease Study. Lancet 1997 May 24;349(9064):1498-1504. [CrossRef] [Medline]
  2. Critchley JA, Capewell S. Mortality risk reduction associated with smoking cessation in patients with coronary heart disease: a systematic review. JAMA 2003 Jul 2;290(1):86-97. [CrossRef] [Medline]
  3. Fiore MC, Croyle RT, Curry SJ, Cutler CM, Davis RM, Gordon C, et al. Preventing 3 million premature deaths and helping 5 million smokers quit: a national action plan for tobacco cessation. Am J Public Health 2004 Feb;94(2):205-210. [Medline]
  4. Centers for Disease Control and Prevention (CDC). MMWR Morb Mortal Wkly Rep. 2002 Jul 26. Cigarette smoking among adults--United States, 2000   URL: [accessed 2013-03-25] [WebCite Cache]
  5. Fiore C, Jaen CR, Baker TB, Tobacco Use and Dependence Guideline Panel. Treating Tobacco Use and Dependence: 2008 Update. Rockville, MD: US Department of Health and Human Services, Public Health Service; 2008 May 1.   URL: [accessed 2013-03-25] [WebCite Cache]
  6. National Institutes of Health, Office of the Director. NIH State-of-the-Science Conference Statement on Tobacco Use: Prevention, Cessation, and Control. NIH Consens State Sci Statements 2006;23(3):1-26 [FREE Full text] [Medline]
  7. Myung SK, McDonnell DD, Kazinets G, Seo HG, Moskowitz JM. Effects of Web- and computer-based smoking cessation programs: meta-analysis of randomized controlled trials. Arch Intern Med 2009 May 25;169(10):929-937. [CrossRef] [Medline]
  8. Lancaster T, Stead LF. Self-help interventions for smoking cessation. Cochrane Database Syst Rev 2002(3):CD001118. [CrossRef] [Medline]
  9. Feil EG, Noell J, Lichtenstein E, Boles SM, McKay HG. Evaluation of an Internet-based smoking cessation program: lessons learned from a pilot study. Nicotine Tob Res 2003 Apr;5(2):189-194. [Medline]
  10. 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):235-240 [FREE Full text] [CrossRef] [Medline]
  11. Escoffery C, McCormick L, Bateman K. Development and process evaluation of a web-based smoking cessation program for college smokers: innovative tool for education. Patient Educ Couns 2004 May;53(2):217-225. [CrossRef] [Medline]
  12. Norman C. CATCH-IT report: evaluation of an Internet-based smoking cessation program: lessons learned from a pilot study. J Med Internet Res 2004 Dec 31;6(4):e47; discussion e48 [FREE Full text] [CrossRef] [Medline]
  13. Strecher VJ, Shiffman S, West R. Randomized controlled trial of a web-based computer-tailored smoking cessation program as a supplement to nicotine patch therapy. Addiction 2005 May;100(5):682-688. [CrossRef] [Medline]
  14. Lenert L, Muñoz RF, Stoddard J, Delucchi K, Bansod A, Skoczen S, et al. Design and pilot evaluation of an internet smoking cessation program. J Am Med Inform Assoc 2003;10(1):16-20 [FREE Full text] [Medline]
  15. Etter JF. Comparing the efficacy of two Internet-based, computer-tailored smoking cessation programs: a randomized trial. J Med Internet Res 2005;7(1):e2 [FREE Full text] [CrossRef] [Medline]
  16. Swartz LH, Noell JW, Schroeder SW, Ary DV. A randomised control study of a fully automated internet based smoking cessation programme. Tob Control 2006 Feb;15(1):7-12 [FREE Full text] [CrossRef] [Medline]
  17. Muñoz RF, Lenert LL, Delucchi K, Stoddard J, Perez JE, Penilla C, et al. Toward evidence-based Internet interventions: A Spanish/English Web site for international smoking cessation trials. Nicotine Tob Res 2006 Feb;8(1):77-87. [CrossRef] [Medline]
  18. Norman CD, McIntosh S, Selby P, Eysenbach G. Web-assisted tobacco interventions: empowering change in the global fight for the public's (e)Health. J Med Internet Res 2008;10(5):e48 [FREE Full text] [CrossRef] [Medline]
  19. Cunningham JA. Access and interest: two important issues in considering the feasibility of web-assisted tobacco interventions. J Med Internet Res 2008;10(5):e37 [FREE Full text] [CrossRef] [Medline]
  20. Cunningham JA, Selby PL, Kypri K, Humphreys KN. Access to the Internet among drinkers, smokers and illicit drug users: is it a barrier to the provision of interventions on the World Wide Web? Med Inform Internet Med 2006 Mar;31(1):53-58. [CrossRef] [Medline]
  21. Velicer WF, Prochaska JO. An expert system intervention for smoking cessation. Patient Educ Couns 1999 Feb;36(2):119-129. [Medline]
  22. Graham AL, Milner P, Saul JE, Pfaff L. Online advertising as a public health and recruitment tool: comparison of different media campaigns to increase demand for smoking cessation interventions. J Med Internet Res 2008;10(5):e50 [FREE Full text] [CrossRef] [Medline]
  23. Murray E, Khadjesari Z, White IR, Kalaitzaki E, Godfrey C, McCambridge J, et al. Methodological challenges in online trials. J Med Internet Res 2009;11(2):e9 [FREE Full text] [CrossRef] [Medline]
  24. Koo M, Skinner H. Challenges of internet recruitment: a case study with disappointing results. J Med Internet Res 2005;7(1):e6 [FREE Full text] [CrossRef] [Medline]
  25. Stoddard J, Delucchi K, Muñoz R, Collins N, Stable EP, Augustson E, et al. Smoking cessation research via the internet: a feasibility study. J Health Commun 2005;10(1):27-41. [CrossRef] [Medline]
  26. Cobb NK, Graham AL. Characterizing Internet searchers of smoking cessation information. J Med Internet Res 2006;8(3):e17 [FREE Full text] [CrossRef] [Medline]
  27. Gordon JS, Akers L, Severson HH, Danaher BG, Boles SM. Successful participant recruitment strategies for an online smokeless tobacco cessation program. Nicotine Tob Res 2006 Dec;8 Suppl 1:S35-S41. [Medline]
  28. Wei SJ, Metz JM, Coyle C, Hampshire M, Jones HA, Markowitz S, et al. Recruitment of patients into an internet-based clinical trials database: the experience of OncoLink and the National Colorectal Cancer Research Alliance. J Clin Oncol 2004 Dec 1;22(23):4730-4736. [CrossRef] [Medline]
  29. Civljak M, Sheikh A, Stead LF, Car J. Internet-based interventions for smoking cessation. Cochrane Database Syst Rev 2010(9):CD007078. [CrossRef] [Medline]
  30. Hutton HE, Wilson LM, Apelberg BJ, Tang EA, Odelola O, Bass EB, et al. A systematic review of randomized controlled trials: Web-based interventions for smoking cessation among adolescents, college students, and adults. Nicotine Tob Res 2011 Apr;13(4):227-238. [CrossRef] [Medline]
  31. Shahab L, McEwen A. Online support for smoking cessation: a systematic review of the literature. Addiction 2009 Nov;104(11):1792-1804. [CrossRef] [Medline]
  32. Houston TK, Ford DE. A tailored Internet-delivered intervention for smoking cessation designed to encourage social support and treatment seeking: usability testing and user tracing. Inform Health Soc Care 2008 Mar;33(1):5-19. [CrossRef] [Medline]
  33. Houston TK, Sadasivam RS, Ford DE, Richman J, Ray MN, Allison JJ. The QUIT-PRIMO provider-patient Internet-delivered smoking cessation referral intervention: a cluster-randomized comparative effectiveness trial: study protocol. Implement Sci 2010;5:87 [FREE Full text] [CrossRef] [Medline]
  34. Centers for Disease Control and Prevention (CDC) Office of Surveillance, Epidemiology, and Laboratory Services. CDC. Behavioral Risk Factor Surveillance System: 2010 Survey Data   URL: [accessed 2012-10-08] [WebCite Cache]
  35. Gilbert GH, Williams OD, Rindal DB, Pihlstrom DJ, Benjamin PL, Wallace MC, DPBRN Collaborative Group. The creation and development of the dental practice-based research network. J Am Dent Assoc 2008 Jan;139(1):74-81. [Medline]
  36. Sadasivam RS, Delaughter K, Crenshaw K, Sobko HJ, Williams JH, Coley HL, et al. Development of an interactive, Web-delivered system to increase provider-patient engagement in smoking cessation. J Med Internet Res 2011;13(4):e87 [FREE Full text] [CrossRef] [Medline]
  37. Google. AdWords   URL: https:/​/www.​​accounts/​ServiceLogin?service=adwords&hl=en_US&ltmpl=regionalc&passive=false&ifr=false&alwf=true&continue=https:/​/adwords.​​um/​gaiaauth?apt%3DNone&error=newacct [WebCite Cache]
  38. DiClemente CC, Prochaska JO, Fairhurst SK, Velicer WF, Velasquez MM, Rossi JS. The process of smoking cessation: an analysis of precontemplation, contemplation, and preparation stages of change. J Consult Clin Psychol 1991 Apr;59(2):295-304. [Medline]
  39. Smit ES, de Vries H, Hoving C. Effectiveness of a Web-based multiple tailored smoking cessation program: a randomized controlled trial among Dutch adult smokers. J Med Internet Res 2012;14(3):e82 [FREE Full text] [CrossRef] [Medline]
  40. Wangberg SC, Nilsen O, Antypas K, Gram IT. Effect of tailoring in an internet-based intervention for smoking cessation: randomized controlled trial. J Med Internet Res 2011;13(4):e121 [FREE Full text] [CrossRef] [Medline]
  41. Danaher BG, Lichtenstein E, McKay HG, Seeley JR. Use of non-assigned smoking cessation programs among participants of a Web-based randomized controlled trial. J Med Internet Res 2009;11(2):e26 [FREE Full text] [CrossRef] [Medline]
  42. Brendryen H, Drozd F, Kraft P. A digital smoking cessation program delivered through internet and cell phone without nicotine replacement (happy ending): randomized controlled trial. J Med Internet Res 2008;10(5):e51 [FREE Full text] [CrossRef] [Medline]
  43. McKay HG, Danaher BG, Seeley JR, Lichtenstein E, Gau JM. Comparing two web-based smoking cessation programs: randomized controlled trial. J Med Internet Res 2008;10(5):e40 [FREE Full text] [CrossRef] [Medline]
  44. Japuntich SJ, Zehner ME, Smith SS, Jorenby DE, Valdez JA, Fiore MC, et al. Smoking cessation via the internet: a randomized clinical trial of an internet intervention as adjuvant treatment in a smoking cessation intervention. Nicotine Tob Res 2006 Dec;8 Suppl 1:S59-S67. [Medline]
  45. Zickuhr K, Smith A. Internet adoption over time. Washington, DC: Pew Internet & American Life Project; 2012 Jan 18.   URL: [accessed 2013-01-23] [WebCite Cache]
  46. Verheijden MW, Jans MP, Hildebrandt VH, Hopman-Rock M. Rates and determinants of repeated participation in a web-based behavior change program for healthy body weight and healthy lifestyle. J Med Internet Res 2007;9(1):e1 [FREE Full text] [CrossRef] [Medline]
  47. Koelen MA, van den Ban AW. Health Education and Health Promotion. Wageningen, NL: Wageningen Academic Publishers; 2004.
  48. Etter JF, Perneger TV. A comparison of cigarette smokers recruited through the Internet or by mail. Int J Epidemiol 2001 Jun;30(3):521-525 [FREE Full text] [Medline]
  49. Schonlau M, Fricker RD, Elliott MN. Conducting Research Surveys via E-mail and the Web. Santa Monica, CA: Rand; 2002.
  50. Im EO, Chee W. Quota sampling in internet research: practical issues. Comput Inform Nurs 2011 Jul;29(7):381-385. [CrossRef] [Medline]
  51. Saul JE, Schillo BA, Evered S, Luxenberg MG, Kavanaugh A, Cobb N, et al. Impact of a statewide Internet-based tobacco cessation intervention. J Med Internet Res 2007;9(3):e28 [FREE Full text] [CrossRef] [Medline]
  52. Strecher VJ, McClure J, Alexander G, Chakraborty B, Nair V, Konkel J, et al. The role of engagement in a tailored web-based smoking cessation program: randomized controlled trial. J Med Internet Res 2008;10(5):e36 [FREE Full text] [CrossRef] [Medline]
  53. Cobb NK, Graham AL, Bock BC, Papandonatos G, Abrams DB. Initial evaluation of a real-world Internet smoking cessation system. Nicotine Tob Res 2005 Apr;7(2):207-216 [FREE Full text] [CrossRef] [Medline]
  54. Japuntich SJ, Leventhal AM, Piper ME, Bolt DM, Roberts LJ, Fiore MC, et al. Smoker characteristics and smoking-cessation milestones. Am J Prev Med 2011 Mar;40(3):286-294 [FREE Full text] [CrossRef] [Medline]

BRFSS: Behavioral Risk Factors Surveillance Survey
IRR: incidence rate ratio
TTS: tobacco treatment specialist

Edited by G Eysenbach; submitted 09.10.12; peer-reviewed by L Akers, S McIntosh, J Fellows; comments to author 11.01.13; revised version received 01.02.13; accepted 21.02.13; published 01.05.13


©Rajani Shankar Sadasivam, Rebecca L Kinney, Kathryn Delaughter, Sowmya R Rao, Jessica Hillman Williams, Heather L Coley, Midge N Ray, Gregg H Gilbert, Jeroan J Allison, Daniel E Ford, Thomas K Houston, The National Dental PBRN And QUIT-PRIMO Collaborative Groups. Originally published in the Journal of Medical Internet Research (, 01.05.2013.

This is an open-access article distributed under the terms of the Creative Commons Attribution License (, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on, as well as this copyright and license information must be included.