Evaluating a population-based recruitment approach and a stage-based expert system intervention for smoking cessation
Introduction
Of the people alive in the world today, 500 million are predicted to die from the use of tobacco (Petro & Lopez, 1990). They will lose an average of 10 years of life. Consequently, 5 billion years of human life will be lost to one behavior. Modest breakthroughs in developing interventions with greater impact on populations of smokers could prevent millions of premature deaths and billions of lost years of life. This study reports on a computer-based expert system intervention that has the potential to increase impacts on total populations of smokers.
Clinic-based interventions have been known to produce the greatest amount of abstinence at long-term follow-up. Smoking cessation clinics, for example, typically result in 20–30% abstinence at 12-month follow-up Fiore et al., 1996, Hunt et al., 1971, Schwartz, 1987. This is the case even when behavior change programs include nicotine replacements, such as nicotine gum or the patch Fiore et al., 1992, Hughes, 1991.
While such clinical interventions produce the highest abstinence rates, they also produce the lowest participation rates. State-of-the-science cessation clinics offered for free by HMOs typically result in about 1% participation (Lichtenstein & Hollis, 1992). Home-based programs, such as self-help manuals, can reach a larger percentage of an eligible population, typically 4–5% (Schmid, Jeffrey, & Hellerstedt, 1989), but these programs result in lower abstinence rates of 10–20% (Curry, 1993). Practice-based programs, such as physicians' advice, can reach much larger percentages of a population, typically 65–70% in a year, but these programs result in even lower abstinence rates of 5–12% Fiore et al., 1996, Kottke et al., 1988, Schwartz, 1987.
Community-based interventions, such as the Minnesota, Pawtucket, Stanford Heart Health, and COMMIT programs, have the potential to reach entire communities of smokers. Recent reports on these programs, however, indicate that they have produced no or little short- or long-term increases in abstinence Carleton et al., 1995, The COMMIT Research Group., 1995a, The COMMIT Research Group., 1995b, Farquhar et al., 1990, Fortmann et al., 1993, Lando et al., 1995, Luepker et al., 1994, Mittelmark et al., 1986. Public health policies, such as smoking bans at work or in public places, can reach entire populations of smokers. Such interventions have been shown to decrease the amount of cigarettes that people smoke but have not yet been shown to increase quit rates Biener et al., 1989, Fisher et al., 1990, Glasgow et al., 1995.
Interventions can be classified by their recruitment of subjects to the intervention as either reactive or proactive approaches. The most common approach has been a reactive approach, i.e., subjects are informed about the availability of an intervention program and must contact the program to participate. In contrast, a proactive recruitment approach contacts the subjects directly and offers the services to them. Reactive procedures typically result in low participation rates of 1–5% (Schmid et al., 1989). Reactively recruited samples will also be qualitatively different than the general population, with more smokers in the preparation stage (planning to quit in the next 30 days), more highly educated, and predominantly female (Velicer et al., 1995).
The dilemma the field faces, then, is a choice of programs that produces the highest abstinence rates but the lowest participation rates or the highest participation rates but the lowest abstinence rates. Both of these choices result in programs of low impact, where impact=abstinence rate×participation rate Velicer & DiClemente, 1993, Velicer & Prochaska, 1999. In the past, programs were usually evaluated by their abstinence rates. A program resulting in 30% abstinence was judged twice as effective as one producing 15% abstinence. But a program producing 30% abstinence and 3% participation has only a 0.9% impact. A program producing 15% abstinence with 60% participation has 9.0% impact, which is 1000% greater. What the field needs are interventions that can maximize participation rates without sacrificing abstinence rates.
From a stages of change perspective, a major problem with previous programs is they have been based on an action paradigm. This paradigm views behavior change like quitting smoking as dramatic and discrete movement from smoking to nonsmoking. Following this paradigm, programs are developed for smokers who are prepared to take such action. The problem is that less than 20% of smokers are prepared to take action, meaning they plan to quit in the next month and have tried to quit in the past year (Velicer et al., 1995). In a representative sample of 4144 smokers in Rhode Island, 9534 in California, and 4785 in a worksite sample representing four regions of the US, only 18%, 16%, and 20%, respectively were prepared to quit smoking. In contrast, 42%, 37%, and 41%, respectively were in the precontemplation stage and were not intending to quit in the next 6 months. The remainder was in the contemplation stage and was intending to take action in the next 6 months. From this perspective, there is a mismatch between the vast majority of smoking cessation interventions that are action oriented and the vast majority of intended recipients, smokers who are not prepared to quit.
Over the past 15 years, our research group has been developing intervention programs for the vast majority of smokers in the precontemplation and contemplation stages as well as the minority in the preparation stage (Prochaska, DiClemente, Velicer, & Rossi, 1993). We have identified processes and principles of change that individuals need to apply in order to progress through each stage of change DiClemente et al., 1991, Prochaska, 1994, Prochaska & DiClemente, 1983, Prochaska et al., 1991, Velicer et al., 1996. We use currently available technologies, such as computers, to apply these behavioral intervention principles in a manner that models clinical interventions. We employ an expert system to individualize the interventions to the participant's stage and interact with them around the processes and principles that are necessary for them to progress.
In an initial clinical trial, 756 smokers were reactively recruited via announcements and ads. After calling our center, they were randomly assigned by stage to one of four treatments. Our computer-based expert system produced 25% abstinence at 18-month follow-up compared to 11% in one of the best action-oriented home-based programs (Prochaska et al., 1993). Because the program was designed for an entire population of smokers and not just those in the preparation stage, the next step was to test its effectiveness on a representative sample of smokers who were proactively recruited by our center calling them.
Section snippets
Procedure
A variation of the two-stage Mitofsky–Waksberg random digit-dialing procedure Lepkowski, 1988, Waksberg, 1978 was used to identify a representative sample of smokers in three distinct geographic areas representing the state of Rhode Island. A total of 32,456 calls were made to identify 14,266 eligible subjects. Of these, 12,109 (84.8%) agreed to complete a brief phone survey. The survey participants included 4296 smokers (35.5%) and 7813 nonsmokers. (The procedure oversampled smokers in each
Overview
Table 2 presents the 24-h point prevalent abstinence rates, 7-day point prevalent abstinence rates, 30-day prolonged abstinence rates, and 6-month prolonged abstinence rates at 6, 12, 18, and 24 months for each of the two groups. The two groups are (1) Assessment Only and (2) Expert System intervention. All 15 possible comparisons between the Expert System condition and the Assessment Only condition for each of the four measures at each of the four different follow-up assessments are
Discussion
This research presents important and encouraging results. First, it shows that by using a proactive recruitment procedure, 80% of eligible smokers can be recruited into a clinical type treatment that provided individualized and interactive interventions and that the majority could be retained. Second, it demonstrates that even when starting with a population of smokers with less than 20% prepared to quit and more than 40% not intending to quit, the stage-matched expert system intervention was
Acknowledgements
This research was supported by Grants CA 50087 and CA 27821 from the National Cancer Institute. The authors would like to thank Robert Laforge and Brett Plummer for their contributions during the preparation of this manuscript.
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