This is an open-access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0/), 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 http://www.jmir.org/, as well as this copyright and license information must be included.
The association between greater utilization of Web-assisted tobacco interventions and increased abstinence rates is well recognized. However, there is little information on how utilization of specific website features influences quitting.
To determine the association between utilization of informational, interactive, and online community resources (eg. bulletin boards) and abstinence rates, with the broader objective to identify potential strategies for improving outcomes for Web-assisted tobacco interventions.
In Spring 2004, a cohort of 607 quitplan.com users consented to participate in an evaluation of quitplan.com, a Minnesota branded version of QuitNet.com. We developed utilization measures for different site features: general information, interactive diagnostic tools and quit planning tools, online expert counseling, passive (ie, reading of bulletin boards) and active (ie, public posting) online community engagement, and one-to-one messaging with other virtual community members. Using bivariate, multivariate, and path analyses, we examined the relationship between utilization of specific site features and 30-day abstinence at 6 months.
The most commonly used resources were the interactive quit planning tools (used by 77% of site users). Other informational resources (ie, quitting guides) were used more commonly (60% of users) than passive (38%) or active (24%) community features. Online community engagement through one-to-one messaging was low (11%) as was use of online counseling (5%). The 30-day abstinence rate among study participants at 6 months was 9.7% (95% Confidence Interval [CI] 7.3% - 12.1%). In the logistic regression model, neither the demographic data (eg, age, gender, education level, employment, or insurance status) nor the smoking-related data (eg, cigarettes per day, time to first morning cigarette, baseline readiness to quit) nor use of smoking cessation medications entered the model as significant predictors of abstinence. Individuals who used the interactive quit planning tools once, two to three times, or four or more times had an odds of abstinence of 0.65 (95% Confidence Interval [CI] 0.22 - 1.94), 1.87 (95% CI 0.77 - 4.56), and 2.35 (95% CI 1.0 - 5.58), respectively. The use of one-to-one messages (reference = none vs 1 or more) entered the final model as potential predictor for abstinence, though the significance of this measure was marginal (OR = 1.91, 95% CI 0.92 - 3.97, P = .083). In the path analysis, an apparent association between active online community engagement and abstinence was accounted for in large part by increased use of interactive quitting tools and one-to-one messaging.
Use of interactive quitting tools, and perhaps one-to-one messaging with other members of the online community, was associated with increased abstinence rates among quitplan.com users. Designs that facilitate use of these features should be considered.
Improving delivery of tobacco treatment services is a national health priority [
There are several ways in which an individual who is considering stopping tobacco use might find assistance on the Internet. Website visitors may find useful information on how to quit smoking. A recent meta-analysis suggests that simply providing general self-help materials results in a modest increase in quit rates (OR = 1.24, 95% CI 1.07 - 1.45) [
A consistent finding in the evaluation of Web-assisted tobacco interventions is the positive association between website utilization and success in quitting. Lenert reported a positive association between the number of online cessation modules completed and short-term abstinence rates [
Typically, website utilization has been measured simply in terms of number of modules completed, number of visits, or time spent on the site [
In this paper, this critical gap in the literature is addressed by examining in detail the association between utilization of specific website features and cessation outcomes. The website under study is quitplan.com, a Minnesota branded version of QuitNet.com. The quitplan.com website offers general information, tailored feedback, and expert counselor services, as well as a large online community.
ClearWay Minnesota, a non-profit organization created as part of the state’s settlement with the tobacco industry, provides free access to quitplan.com for all Minnesota residents. Since its initial offering in 2003 through July 2008, over 300,000 individuals have visited the site with over 36,000 registering for services making quitplan.com the most widely used of ClearWay Minnesota’s cessation programs [
Content and programming for quitplan.com is provided by QuitNet.com. The QuitNet service has been described elsewhere in detail [
The QuitNet website has an “open” design that is intended to give users easy access to all site features. Information on quitting is presented to site users in the form of general information guides, interactive tools that provide tailored feedback, and online support from expert counselors. General information guides address different stages in the quitting process (eg, Making the decision to quit, Getting ready to quit, Quitting, and Staying quit), quit smoking medications, and frequently asked questions (eg, dealing with symptoms of quitting, including symptoms of withdrawal, weight concerns, etc). There are two categories of interactive tools available at quitplan.com. The first set may be considered diagnostic tools and provide smokers with information about their smoking behaviors. These include the Fagerstrom Tolerance Questionnaire [
The quitplan.com website also allows users to connect to the large QuitNet online community of current and former tobacco users. Site users may browse public discussion boards and forums (bulletin boards) to view posts by members of the online community. Site users may interact with other members by making posts to these public forums or by sending private internal email directly to other members. Approximately 2000 messages per day are posted in public forums with thousands more exchanged privately.
This study recruited new registrants to quitplan.com from February 2 to April 13, 2004. In order to be eligible, registrants had to (1) be Minnesota residents, (2) be aged 18 years of age or older, (3) be registered as a current tobacco users, and (4) have not already reported quitting at the time of registration. Of 1295 quitplan.com registrants during this period, 1006 were eligible for this study and received an invitation to enroll in the study and complete a follow-up survey in 6 months. An offer of US $10 was made for completion of this survey in 6 months. Of the 1006 eligible registrants, 607 (60.3%) consented to participate in this study.
Follow-up consisted of a mixed-mode follow-up survey using an initial online survey followed by a phone survey of online non-respondents. Participants were mailed a pre-notification letter 6 months after program registration and then sent an email inviting them to complete an online evaluation survey. Reminder emails were sent to non-respondents 3 and 7 days after this initial email. Online survey non-respondents were contacted by phone 12 days after the initial email. The response rate to the follow-up survey was 77.6% (n = 471/607) with 39.4% (n = 239) completing the online survey and 38.2% (n = 232) completing the phone survey.
Three data sources are used for this study: registration data, detailed site utilization data, and evaluation survey results. Demographic and clinical variables collected during online registration include age, gender, education, insurance status, readiness to quit, cigarettes smoked per day, and time to first morning cigarette.
To record website utilization information, the QuitNet application server uses a metadata-based tracking model to log all interactions between participants and the system into a relational database. The model is similar to the commonly used W3C Resource Description Framework (RDF) data description model [
For the purpose of this analysis we created seven unique utilization measures capturing use of quitplan.com’s informational resources and engagement with the online community. Measures 1 - 4 below assess utilization of different informational resources. Measures 5 - 7 assess engagement with the online community. These measures were defined as follows:
General Information: the number of times a user viewed any of the general information guides (ie, Quit Guide, Medication Guide, Frequently Asked Questions).
Interactive Diagnostic Tools: the number of times the individual used the Fagerstrom Tolerance Questionnaire, What makes you smoke?, or Readiness to Quit questionnaires.
Interactive Quit Planning Tools: the number of times the individual used interactive tools to (a) set their quit date, (b) select a quit smoking medication, or (c) track days and dollars saved since quitting.
Counselor Services: number of questions submitted to online expert counselors.
Passive Community Engagement: the number of times the user viewed or read discussion board, forum, or journal posts by other community members.
Active Community Engagement: the number of times the user made a post to a public discussion board, forum, or journal.
One-to-One Messaging: the number of messages sent privately to other community members using the website's internal email system.
The primary outcome is self-reported abstinence for the 30 days prior to the 6-month follow-up evaluation. In determining abstinence, all non-respondents are considered to be continuing to smoke. The follow-up survey also assessed use of smoking cessation medications (nicotine patch, nicotine gum, other nicotine replacement therapy, or bupropion/Zyban) since registration.
Consistent with other studies, raw counts for utilization of different site features were highly skewed. These measures were categorized and median values were reported for each utilization measure category. The correlation between different utilization measures was assessed using a Spearman rho correlation matrix to account for use of categorical measures. Bivariate association between utilization measures and abstinence rates was assessed using Pearson’s chi-square statistic. Logistic regression examined the independent effect of each utilization measure. The dependent variable was self-reported 30-day abstinence. The predictor variables were entered in five blocks using a forward step-wise approach. The first block included demographic characteristics; the second block included smoking variables; the third block included stage of change; the fourth block consisted of the use of any stop smoking medication since quitplan.com registration; and the fifth block consisted of the use of the seven categorical utilization measures.
Considerable interest has been focused on the role of the online community in encouraging smoking cessation. To examine the direct and indirect association between engagement with the online community and abstinence outcomes, path analysis was performed comparing results from two models. Model 1 examined the relationship between Active Community Engagement and Abstinence. Model 2 examined the direct and indirect effects of Active Community Engagement after consideration of potential mediators identified in the logistic regression model described above. Bivariate comparisons and logistic regression models were preformed using SPSS 16.0. Path analyses were performed using AMOS 16.0 software from SPSS Inc.
The demographic and smoking related characteristics of participants are shown in
Participant characteristics
N = 607 | % | ||
|
|||
18 - 24 | 82 | 13.5 | |
25 - 34 | 177 | 29.2 | |
34 - 44 | 171 | 28.2 | |
45 - 54 | 123 | 20.3 | |
55 or older | 54 | 8.9 | |
|
|||
Male | 216 | 35.6 | |
Female | 391 | 64.4 | |
|
|||
High School or less | 100 | 18.0 | |
Some college | 268 | 48.1 | |
College graduate | 189 | 33.9 | |
|
|||
Unemployed/other | 151 | 25.2 | |
Employed for wages | 449 | 74.8 | |
|
|||
Uninsured | 79 | 13.5 | |
Insured | 506 | 86.5 | |
|
|||
< 15 | 168 | 27.7 | |
15 - 24 | 296 | 48.8 | |
25+ | 143 | 23.6 | |
|
|||
Within 5 minutes | 180 | 29.7 | |
6 - 30 minutes | 259 | 42.7 | |
31 - 60 minutes | 100 | 16.5 | |
After 60 minutes | 68 | 11.2 | |
|
|||
Precontemplation or Contemplation | 302 | 49.8 | |
Preparation | 305 | 50.2 |
aSum less than 607 due to item non-response
Participants’ utilization of specific website features is shown in
Use of informational resources was more common than passive or active engagement with the online community. The most commonly used resources were the interactive quit planning tools. Nearly 80% of participants used these tools on at least one occasion, and nearly one-third of participants used these quit planning tools more than four times. The next most commonly used informational resources were the general information guides with over half of participants viewing one or more information guides. Use of the interactive diagnostic tools was less common with somewhat less than half of participants using this resource. Counselor services were used only rarely with less than 5% of participants posting one or more questions to the expert-moderated forums.
Passive engagement with the online community (ie, reading discussion board posts) was more common than active engagement (ie, posting messages). Approximately 40% of participants viewed any posts made by other members of the online community. Active engagement with the community was less common with only approximately one in four participants making any public post. One-to-one messaging between members of the online community was similarly rare with only one in ten participants taking advantage of this feature.
A matrix demonstrating the correlation between these seven utilization measures is shown in
Website utilization patterns in the 6 months after initial registration
Informational Resources | Median | Range | N | % | ||
|
# times guides viewed | |||||
None | 0 | 0 | 245 | 40.4 | ||
Low | 1 | 1 - 2 | 113 | 18.6 | ||
Med | 4 | 3 - 5 | 134 | 22.1 | ||
High | 10 | 6 - 46 | 115 | 18.9 | ||
|
# times used | |||||
None | 0 | 0 | 335 | 55.2 | ||
1 | 1 | 1 | 127 | 20.9 | ||
2 | 2 | 2 | 61 | 10.0 | ||
3+ | 3 | 3 - 7 | 84 | 13.8 | ||
|
# times used | |||||
None | 0 | 0 | 139 | 22.9 | ||
1 | 1 | 1 | 145 | 23.9 | ||
2 - 3 | 2 | 2 - 3 | 143 | 23.6 | ||
4+ | 6 | 4 - 63 | 180 | 29.7 | ||
|
# questions sent | |||||
None | 0 | 0 | 578 | 95.2 | ||
1 or more | 1 | 1 - 2 | 29 | 4.8 | ||
|
# of post viewed | |||||
None | 0 | 0 | 374 | 61.6 | ||
Low | 2 | 1 - 5 | 112 | 18.5 | ||
High | 20 | 6 - 56 | 121 | 19.9 | ||
|
# of public posts made | |||||
None | 0 | 0 | 463 | 76.3 | ||
Low | 1 | 1 - 2 | 72 | 11.9 | ||
High | 7 | 3 - 42 | 72 | 11.9 | ||
|
# of private messages sent | |||||
None | 0 | 0 | 543 | 89.5 | ||
1 or more | 3 | 1 - 643 | 64 | 10.5 |
Correlation matrix for utilization measures (N = 607)a
Interactive Diagnostic Tools | Interactive Quitting Tools | Counselor Services | Passive Online Community Engagement | Active Online Community Engagement | One-to-One Messaging | |
General Information | .513 | .522 | .205 | .479 | .387 | .327 |
Diagnostic Tools | .509 | .226 | .361 | .309 | .215 | |
Quitting Tools | .210 | .510 | .469 | .308 | ||
Counselor Services | .256 | .278 | .275 | |||
Passive Online Community Engagement | .617 | .470 | ||||
Active Online Community Engagement | .494 |
aSpearman rho coefficients all significant
Counting non-respondents as smokers, the self-reported 30-day abstinence rate among the 607 study participants was 9.7% (n = 59/607, 95% CI 7.3% - 12.1%). The relationship between utilization of specific website features and abstinence rates is shown in
In the logistic regression model, neither the demographic data (eg, age, gender, education level, employment, or insurance status) nor the smoking-related data (eg, cigarettes per day, time to first morning cigarette, baseline readiness to quit) entered the model as significant predictors of abstinence. Of the 471 survey respondents, 236 (50.1%) reported use of any smoking cessation medications. Use of smoking cessation medications was not associated with 30-day abstinence at follow-up (abstinence 14.0% for medication users vs 11.1% for non-users,
The results of path analyses are shown in
An examination of the path coefficients in Model 2 illustrates how the active community path coefficient (0.122) from Model 1 consists of a direct effect on abstinence (0.025) and indirect effects acting through increased use of interactive quit planning tools (.466 x .093 = .043) and one-to-one messaging (.544 x .099 = .054). In fact, these indirect effects for interactive quitting tools (0.043) and one-to-one messaging (0.054) account for a large part (.097/.122 = 79.5%) of the apparent association between active community engagement and abstinence. After accounting for these indirect effects, the direct effect between active community engagement and abstinence is no longer significant.
Comparison of website feature utilization and 30-day abstinence rates
30-day Abstinence |
|
|||||
No | Yes | |||||
N | % | N | % | |||
|
.004 | |||||
None 0 views | 229 | 93.5 | 16 | 6.5 | ||
Low 1 - 2 views | 101 | 89.4 | 12 | 10.6 | ||
Med 3 - 5 views | 124 | 92.5 | 10 | 7.5 | ||
High 6 - 46 views | 94 | 81.7 | 21 | 18.3 | ||
|
.266 | |||||
None | 309 | 92.2 | 26 | 7.8 | ||
1 use | 112 | 88.2 | 15 | 11.8 | ||
2 uses | 52 | 85.2 | 9 | 14.8 | ||
3+ uses | 75 | 89.3 | 9 | 10.7 | ||
|
.003 | |||||
None | 130 | 93.5 | 9 | 6.5 | ||
1 use | 139 | 95.9 | 6 | 4.1 | ||
2 - 3 uses | 127 | 88.8 | 16 | 11.2 | ||
4+ uses | 152 | 84.4 | 28 | 15.6 | ||
|
.041 | |||||
None | 525 | 90.8 | 53 | 9.2 | ||
1 or more use | 23 | 79.3 | 6 | 20.7 | ||
|
.198 | |||||
None 0 views | 342 | 91.4 | 32 | 8.6 | ||
Low 1 - 5 views | 102 | 91.1 | 10 | 8.9 | ||
High 6 - 56 views | 104 | 86.0 | 17 | 14.0 | ||
|
.003 | |||||
None 0 posts | 425 | 91.8 | 38 | 8.2 | ||
Low 1 - 2 posts | 66 | 91.7 | 6 | 8.3 | ||
High 3 - 42 posts | 57 | 79.2 | 15 | 20.8 | ||
|
.001 | |||||
None | 498 | 91.7 | 45 | 8.3 | ||
1 or more | 50 | 78.1 | 14 | 21.9 |
Path analysis of active community engagement and abstinence rates
In this observational study of a statewide smoking cessation website, we found an association between the use of interactive quitting tools providing tailored feedback and abstinence rates at 6-month follow-up. Given this finding, it is encouraging to note that nearly 80% of website users made use of one or more of the interactive quit planning tools available through quitplan.com. The finding of positive associations with abstinence related specifically to use of these quit planning tools is consistent with the focus of these tools on key aspects of evidence-based behavioral interventions (ie, setting a quit date, using pharmacological therapy, and follow-up assessment after the quit date) recommended in tobacco treatment guidelines [
The abstinence rate observed in this study is consistent with findings from other studies that offered online interactive or tailored feedback. Though timing of the evaluations differ, Etter found that access to a more versus less tailored online program had modest effect on abstinence (10.9% vs 9.8%,
Interesting findings regarding the potential contribution of different aspects of the online community merit further discussion. Only a minority of participants engaged with the online community in either a passive or active fashion. Interaction with “online experts” was even more rare, a finding that has been reported previously for an online smokeless tobacco intervention [
There was not an independent association between viewing of general information guides and abstinence rates after controlling for utilization of interactive quit planning tools. This is consistent with results of meta-analyses which demonstrate a greater benefit of tailored compared with untailored self-help materials [
There are several limitations to consider when interpreting the findings presented here. First, it is important to acknowledge this was an observational study and not a randomized controlled trial. We are therefore not able to make causal claims related to the use of different website features and abstinence rates. Selection bias (both in the initial study participation and in website utilization) or unmeasured factors (eg, use of telephone or other counseling services) could have influenced the observed associations. These findings therefore may not generalize to the larger population of smokers who use Web-based cessation services. Second, the findings here are based upon visitors to one (albeit high volume) stop smoking website. Differences in website design would be expected to influence utilization. For example, a site might require completion of certain features as part of registration or strongly promote use of website features in a specific order (ie, “tunnel” design). Besides this basic architecture, other design features such as level of interactivity and incorporation of audio and video might influence utilization. Danaher et al reported much higher utilization for an interactive, “media-rich” website compared to a static text-based comparison site [
Despite these limitations, the findings reported here contribute to the understanding of effective Web-based tobacco interventions. At present, tailored interventions appear to be a key—and perhaps the key—component to include when creating an effective cessation website. Designers seeking to create effective cessation websites should incorporate interactive assessment and tailored feedback and find ways to feature these resources prominently. Further study of the role that online communities may play in the cessation process is clearly warranted. Future studies could seek to identify and characterize members of online communities who are particularly helpful to others. Eventually interventions may be designed to enhance the quality of online interactions (perhaps through the provision of training in evidence-based practices) to maximize the direct and indirect benefits of online communities.
None declared.
Resource Description Framework