To our knowledge, this article is the first to concurrently examine treatment utilization in phone, Web, and phone–Web behavioral treatment programs and is the first to concurrently examine factors associated with greater utilization in each of these types of treatment programs. Consistent with prior research, we found that treatment utilization was significantly associated with cessation outcomes. Completing more calls was associated with greater abstinence in those groups randomized to receive phone counseling (i.e., the phone and phone–Web groups), while the time spent on the phone with a Quit Coach (not the number of calls completed) and the number of logins were associated with increased cessation rates in those participants randomized to Web-based services only. Several studies on phone-based interventions have demonstrated a dose–response relation between use and cessation outcomes (Curry et al., 1998
; Hollis et al., 2007
; Stead et al., 2006
; Zhu et al., 1996
). Likewise, in the growing body of research on Web-based interventions, all but one study (McKay et al., 2008
) have found that the number of online logins is positively correlated with quit outcomes (An et al., 2008
; Graham et al., 2007
; Japuntich et al., 2006
; Saul et al., 2007
). Our study adds to this extant literature and extends it to include useful information on how utilization is associated with cessation outcomes in a combined phone–Web program.
Utilization of phone counseling was higher than Web utilization. Participants randomized to groups with phone counseling completed on average four of five calls, while the majority of participants randomized to groups with Web services used the Web less than two times and for a total of 30–50 min on average. The average number of calls completed in the COMPASS trial was higher than previously reported for the Quit For Life Program (approximately 4 calls vs. 2–2.5 calls; Zbikowski et al., 2008
). The cause of this difference is unclear but may simply reflect the fact that volunteer research participants had a higher level of commitment and engagement than smokers typically treated in the “real world.” Study-related access to a new medication not otherwise available at the study health plan (GH) may have enhanced this commitment. It also may reflect differences in demography and socioeconomic status among participants in the study health plan versus those treated in actual practice. Future research should seek to better understand how and why utilization outcomes differ between that observed in clinical trials and actual practice.
We observed a high percent of participants logging in at least once; however, in our previous work (Zbikowski et al., 2008
), we observed zero logins as most common. We believe that the study protocol impacted utilization rates in several ways. Participants in the Web group had a large percent of at least one logins due to the fact that they were required to set their quit date via the Web site in order to receive their medication. Additionally, participants in the Web group had a large number of ad-hoc calls (1/3 had at least one ad-hoc call). All Web group participants received a brief orientation call (to explain how to use the Web site) prior to starting the intervention, so it is possible that participants found it helpful to talk with a coach and as a result called back for additional support.
While many studies have explored the association between utilization of single treatment and outcomes, few have examined and compared predictors of multiple treatment programs. We identified several baseline participant variables as significant correlates of utilization, though only a few variables consistently predicted utilization: age, past use of cessation medications or other aids, and the belief that counseling/behavioral treatment programs improve the chances of quitting. We found that older smokers completed more calls, talked with a quit coach longer, logged in more, and spent more time online. Previous studies have varied with respect to findings regarding utilization of treatment and age. Japuntich et al. (2006)
found age to be predictive of utilization, while Strecher et al. (2006)
did not. We also found self-efficacy to be negatively correlated with Web utilization among participants in the phone–Web group. That is, individuals with lower confidence in quitting were more likely to use the Web-based services. Similarly, Danaher et al. (2008)
found that self-efficacy was an important mediator of outcomes in a Web-based intervention for smokeless tobacco users; however, utilization was no longer significantly related to outcomes after taking self-efficacy into account. Strecher did not identify self-efficacy to be a moderator of treatment outcomes (Strecher et al., 2006
). Similar to other studies (Japuntich et al., 2006
; Strecher et al., 2006
), we found that gender, ethnicity, education, motivation, baseline cigarette use, nicotine dependence, and stress were not significant moderators of treatment.
Limitations and Strengths
There are several limitations and strengths of this study to consider. First, the present study described utilization of treatment, but we cannot draw conclusions from these data about how engaged or compliant participants were in using the behavioral strategies and skills taught. Second, we defined utilization as the number and duration of counseling calls and Web logins: Our results generalize to other studies with similar outcomes. Some Web efficacy studies have observed and reported amount of content read or used. The tracking program used for the Web program allowed us to track visits to the Web site (logins) as well the time spent on each feature. While time spent gives provides an estimate of use, it may not fully represent the degree of actual engagement with Web content.
All study participants received varenicline, a powerful and new medication, which may have influenced utilization patterns. The phone version of the program was a mature offering, having been in use for over 20 years, whereas the Web version had just been created. Thus, perhaps a more seasoned Web program may have higher rates of utilization. Finally, we did not assess why participants stopped using treatment services. Future studies may benefit from assessing reasons why participants stop using a treatment. This information may be beneficial for modifying existing or developing new treatments.
Despite these limitations, this study has a number of strengths. Among them is our examination of utilization across three different behavioral treatment programs, the inclusion of a combined phone–Web program, and the fact that all study participants had access to the same pharmacotherapy, thus holding any influence of the study medication on behavioral treatment constant for all participants. The study also adds to the body of literature on predictors of cessation treatment utilization and provides more evidence for the importance of assessing utilization as a mediator of research findings.
Although this study used a specific tobacco cessation program (Quit For Life), the findings have real-world importance. The Quit For Life Program is currently offered in over half of the U.S. state tobacco quitlines (in 26 states, Washington DC, and the territory of Guam) and over 350 employer and health plans nationwide. Each year, over 250,000 tobacco users enroll in this treatment program. Additionally, the results likely generalize to other similar phone and Web-based programs that incorporate best-practice standards from the Clinical Practice Guidelines (Fiore et al., 2008
) and are designed to increase self-efficacy, problem solving and coping based on Social Cognitive Theory.
Implication for Future Treatments and Studies
It may be beneficial for behavioral programs to be tailored with consideration for participant characteristics (e.g., age, self-efficacy) and treatment experience and expectancies. Our study suggests that different approaches may be needed to engage younger smokers. It is possible that younger smokers think they can quit on their own or need little assistance. This population may particularly benefit from education (online or from a coach) about how the use of services can improve cessation outcomes. As reported above, we also found that attitudes toward treatment, in particular expected outcomes, affected utilization. Further research is needed to explore ways to capitalize on this information to improve participation. One possible idea is to have programs collect this information from participants when they enroll in programs and for coaches/specialists to address possible opinions and biases that enrollees may have that may affect utilization. Alternatively, if such beliefs drive people to use programs and ultimately achieve success, cessation programs can use this type of information to triage participants to programs and services they are inclined to use most and benefit from.