Applying a theory like TTM to an entire at-risk population, like smokers, requires a systematic approach that begins with recruiting and retaining a high percentage of the eligible population. The program should help participants progress through the stages of change by applying a process that matches or tailors interventions to the needs of each individual at each stage of change. Such systematic applications can then be evaluated in terms of outcomes by assessing impacts on reducing the preva- lence of smoking in the target population.
Smoking is costly to individual smokers and to society. In the United States, ap- proximately 47,000,000 Americans continue to smoke. Over 500,000 preventable deaths per year are attributable to smoking (U.S. Department of Health and Human Services, 2000). Globally, the consequences are likely to be catastrophic. Of the peo- ple alive in the world today, 500,000,000 are expected to die from this single behav- ior, losing approximately 5 billion years of life to tobacco use. Providing smoking cessation to entire populations could prevent millions of premature deaths and help preserve billions of years of life.
Recruitment
Population cessation requires interventions that reach or recruit high percentages of smokers. In two home-based programs with about 5,000 smokers in each study, we reached out either by telephone alone or by personal letters, followed by telephone calls if needed, and recruited smokers to stage-matched interventions. For each of five stages, interventions included self-help manuals; individualized computer feed- back reports based on assessments of pros and cons, processes, self-efficacy and temp- tations; and/or counselor protocols based on computer reports. Using proactive recruitment methods and stage-matched interventions resulted in rates of 80 percent to 85 percent, respectively (Prochaska and others, 2001a, 2001b). Such high recruit- ment rates provide the potential to generate unprecedented impacts with entire pop- ulations of smokers.
Population impact has been defined as participation rate ×the rate of efficacy or action (Velicer and DiClemente, 1993). If a program produced 30 percent efficacy (for example, long-term abstinence), historically it was judged to be better than a program that produced 25 percent abstinence. A program that generates 30 percent efficacy but only 5 percent participation has an impact of only 1.5 percent (30 percent ×5 percent).
A program that produces only 25 percent efficacy but 60 percent participation has an impact of 15 percent. With health promotion programs, the impact on a high-risk pop- ulation would be ten times greater.
TTM programs shift outcomes from efficacy alone to impact. To achieve such high impact, we need to shift from reactive recruitment, where we advertise or an- nounce programs and react when people reach us, to proactive recruitments, where we reach out to interact with all potential participants, including those not yet ready to change behaviors.
Proactive recruitment alone will not work. In the most intensive recruitment pro- tocol to date (Lichtenstein and Hollis, 1992), physicians spent up to five minutes with smokers to get them to sign up for an action-oriented cessation clinic. If that didn’t work, a nurse spent ten minutes to persuade each smoker to sign up, followed by twelve minutes with a videotape and health educator and even a proactive counselor call, if necessary. This resulted in 1 percent initial participation. The proactive pro- tocol resulted in 35 percent of smokers in precontemplation enrolling; only 3 percent showed up, and 2 percent completed. With a combination of smokers in contempla- tion and preparation, 65 percent signed up, 15 percent showed up, and 11 percent completed the program.
To optimize impacts, proactive protocols are used to recruit participants to pro- grams that match their stages. Once high recruitment rates are achieved, the next prac- tical concern is to generate high retention rates, lest many of the initial participants drop out.
Retention
One of the skeletons in the closet of psychotherapy and behavior change interven- tions is their relatively poor retention rates. Across 125 studies, the average retention rate was only about 50 percent (Wierzbicki and Pekarik, 1993). Furthermore, this meta-analysis found few consistent predictors of who would drop out prematurely and who would continue in therapy. In studies on smoking, weight control, substance abuse, and a mixture of mental health problems, stage of change measures proved to be the best predictors of premature termination. In a study of psychotherapy for men- tal health problems, the pre-treatment stage profile of the entire 40 percent who dropped out prematurely, as judged by their therapists, was that of patients in precontempla- tion. The 20 percent who terminated quickly but appropriately had a profile of pa- tients in action. Pre-treatment stage-related measures correctly classified 93 percent of the three groups (Brogan, Prochaska, and Prochaska, 1999).
Therapists cannot treat people in precontemplation as if they were ready for ac- tion interventions and expect them to stay in treatment or to succeed as a consequence of treatment. Relapse prevention strategies would be indicated for smokers who are taking action. But those in precontemplation are more likely to need drop-out pre- vention strategies.
The best strategy to promote retention is to match interventions to stage of change.
In three smoking cessation studies using such matching strategies, smokers in pre- contemplation were retained at the same high levels as those who started in the prepa- ration stage (Prochaska, DiClemente, Velicer, and Rossi, 1993; Prochaska and others, 2001a, 2001b).
Progress
The amount of progress participants make following health promotion programs is directly related to their stage at the start of interventions. Across sixty-six different predictions of progress, smokers starting in contemplation were about two-thirds more successful than those in precontemplation at six-, twelve-, and eighteen-month fol- low-ups. Similarly, those in preparation were about two-thirds more successful than those in contemplation at the same follow-ups (Prochaska and others, 2001a).
These results can be used in practice. A reasonable goal for each therapeutic in- tervention with smokers is to help them progress one stage. If over the course of brief therapy they progress two stages, they will be about 2.66 times more successful at longer-term follow-ups (Prochaska and others, 2001a).
This strategy was taught to more than 6,000 primary care physicians, nurses, and physicians’ assistants in Britain’s National Health System. With stage-matched coun-
seling, the strategic goal is to help each patient progress one stage following one brief intervention. One of the first reports was a marked improvement in the morale of such health promoters intervening with all patients who smoke, abuse substances, and have unhealthy diets. These professionals now have strategies that match the needs of all of their patients, not just the minority prepared to take action. Furthermore, practi- tioners can assess progress across stages in the majority of these patients, where pre- viously they experienced mostly failure when taking action was their only measure of movement (Steptoe, Kerry, Rink, and Hilton, 2001).
Processes
Different processes of change need to be applied at different stages of change. Clas- sic conditioning processes like counterconditioning, stimulus control, and contin- gency control can be highly successful strategies for participants taking action, but using them can produce resistance with individuals in precontemplation. With these individuals, more experiential processes, like consciousness raising and dramatic re- lief, can move people cognitively and affectively and help them shift to contempla- tion (Prochaska, Norcross, and DiClemente, 1994).
After fifteen years of research, fourteen variables have been identified on which to intervene to accelerate progress across the first five stages of change (Prochaska, Norcross, and DiClemente, 1994). At any particular stage, a maximum of six vari- ables should be intervened upon. To help guide people at each stage of change, computer-based expert systems have been developed to deliver individualized, inter- active, TTM-tailored interventions to entire populations (Redding and others, 1999).
These computer programs can be used alone or in conjunction with counselors.
Outcomes
In our first large-scale clinical trial, we compared four treatments: (1) one of the best home-based action-oriented cessation programs (standardized); (2) stage-matched manuals (individualized); (3) expert system computer reports plus manuals (interac- tive); and (4) counselors plus computers and manuals (personalized). We randomly assigned by stage 739 smokers to one of the four treatments (Prochaska, DiClemente, Velicer, and Rossi, 1993).
In the computer condition, participants completed by mail or telephone forty ques- tions that were entered in our central computers and used as the basis for computer- generated feedback reports. These reports informed participants about their stage of change, their pros and cons of changing, and their use of change processes appropri- ate to their stages. At baseline, participants were given positive feedback on what they were doing correctly and guidance on which principles and processes they should apply to progress. In two progress reports delivered over the next six months, they also re- ceived positive feedback on any improvement they made on any of the variables rel- evant to progressing. Therefore, demoralized and defensive smokers could begin progressing without having to quit and without having to work too hard. Smokers in
the contemplation stage could begin taking small steps (like delaying their first ciga- rette in the morning for an extra thirty minutes) that would increase their self-efficacy and help them become better prepared for quitting. In the personalized condition, smok- ers received four proactive counselor calls over the six-month intervention period.
The two self-help manual conditions paralleled each other for twelve months. At eighteen months, the stage-matched manuals moved ahead. This is an example of a delayed action effect, which is often observed with stage-matched programs specif- ically and others have observed with self-help programs generally (Glynn, Anderson, and Schwarz, 1992). It takes time for participants in early stages to progress to ac- tion. Therefore, some treatment effects, measured by action, will be observed only after considerable delay. It is encouraging to find treatments producing therapeutic effects months and even years after treatment ended.
Computer alone and computer plus counselor conditions paralleled each other for twelve months. Then, effects of the counselor condition flattened out while the computer condition effects continued to increase. Participants in the personalized condition may have become somewhat dependent on the social support and social control of the counselor calling. The last call was after the six-months assessment, and benefits would be observed at twelve months. Termination of counselors could result in no further progress because of the loss of social support and control. The classic pattern in smoking cessation clinics is rapid relapse beginning as soon as the treatment is terminated. Some of this rapid relapse could well be due to the sudden loss of social support or social control provided by the counselors and other partici- pants in the clinic.
In this clinical trial, smokers were recruited reactively. They called us in response to advertisements, announcements, and articles. How would their results compare to the smokers proactively recruited to our programs? Most people would predict that smokers who call us for help would succeed more than smokers we called to help.
Figure 5.1 shows remarkable results comparing smokers in a study whom we called (reactive) (Prochaska, DiClemente, Velicer, and Rossi, 1993) to those in a study whom we called (proactive) (Prochaska and others, 2001a). Both groups received the same home-based expert system computer reports delivered over a six-month period.
Although the reactively recruited subjects were slightly more successful at each fol- low-up, what is striking is the similarity of results.
Outcomes with Diverse Groups
Outcomes with diverse groups were part of an analysis that combined data from five effectiveness trials where 2,972 smokers were proactively recruited; all received the same TTM-tailored intervention plus stage-matched manuals. The intervention pro- duced a consistent 22 percent to 26 percent long-term cessation rate across the five studies, with a mean of about 24 percent (Velicer, Redding, Sun, and Prochaska, 2007).
There were no significant differences in abstinence rates between females (24.6 per- cent) and males (23.6 percent). There were no significant differences between African Americans (30.2 percent) and Caucasians (23.9 percent) or between Hispanics and
non-Hispanics. Older smokers (65 and older) had abstinence rates (35.2 percent) that were 45 percent higher than the mean. College graduates had abstinence rates (30.1 percent) that were significantly higher than average.
The Surgeon General’s Report on Adolescent Smoking (U.S. Department of Health and Human Services, 1994) concluded that teenage smokers would not participate in treatments, and if they did, they would not quit. Hollis and colleagues (2005) proac- tively recruited 65 percent of teens in primary care to a smoking cessation program based on TTM-tailored communications. At long-term follow-up, regular smokers re- ceiving treatment had significantly higher cessation rates (23.9 percent) than the ran- domized control group (11.4 percent). Furthermore, their quit rate of 23.9 percent was essentially the same as the average quit rate in our adult treatment groups (Hollis and others, 2005).
Working with smokers who have co-morbidities (other diseases or conditions, such as diabetes or hypertension) presents important challenges. The Clinical Guide- lines for the Treatment of Tobacco (Fiore and others, 2000) identified no evidence- based treatments for smokers with mental illnesses, even though such smokers consume nearly 50 percent of all cigarettes in the United States. Hall and colleagues (2006) reached out to smokers who were being treated for depression in clinics at the Uni- versity of California at San Francisco. Those randomized to treatment received our TTM-tailored program plus counseling and nicotine replacement therapy. At twenty- four-month follow-up, the treatment group had significantly higher abstinence rates (24.6 percent) than the controls (19.1 percent), and their quit rates were remarkably similar to those of treated adults in other studies (Hall and others, 2006).
These outcomes with diverse groups receiving similar TTM-tailored treatments challenge stereotypes that assume some groups do not have the ability to change. Mi- norities, older adults, adolescents, and mentally ill groups may be assumed to have
FIGURE 5.1.
Point Prevalence Abstinence Rates Over Time for Smokers Recruited by Reactive Versus Proactive Strategies and Treated with TTM-Tailored Home-Based Expert System Interventions.30
Percentage
0 Baseline 6 months 12 months 18 months Assessment Periods
25 20 15 10 5
Reactive Recruitment
Proactive Recruitment
less ability to change. These results suggest that the issue may not be their ability to change. The issue may be their access to quality change programs.
If these results are replicated, health promotion programs could produce unprece- dented impacts on major risk behaviors like smoking among entire populations. We believe that such unprecedented impacts require scientific and professional shifts from
䊏 An action paradigm to a stage paradigm
䊏 Reactive to proactive recruitment
䊏 Expecting participants to match the needs of our programs to having our programs match their needs
䊏 Clinic-based to community-based behavioral health programs that still apply the field’s most powerful individualized and interactive intervention strategies
䊏 Assuming some groups do not have the ability to change to making sure that all groups have easy accessibility to evidence-based programs