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date: 25 June 2017

Transtheoretical Model and Stages of Change in Health and Risk Messaging

Summary and Keywords

The Transtheoretical Model (TTM) is an integrative health behavior change theory that describes the process of how people change their behavior. The central organizing construct in the theory is stages of change, which are five distinct stages of readiness to change behavior, ranging from not ready to change (precontemplation), thinking about change (contemplation), preparing to change (preparation), changing (action), and maintaining the change (maintenance). Movement through the stages may be nonlinear, and cycling and recycling through the stages is viewed as a natural part of the change process. Other model constructs explain what drives individuals forward through the stages of change. Decisional balance involves a weighing of pros and cons of changing behavior, while self-efficacy involves situation-specific confidence that one can change. Increases in pros, deceases in cons, and increases in self-efficacy propel people forward through the stages of change. The processes of change are experiential and behavioral strategies that people use to change their behavior. In early stages of change, people use experiential strategies while they use behaviorally oriented strategies in later stages of change. The TTM holds significant implications for message design. Most notably, messages should be targeted and tailored to stages of change, and where possible, to other model variables as well. Studies indicate that the TTM has been successfully applied to health communication campaigns, and to a larger extent, to computer-tailored interventions to change health behavior. Meta-analyses indicate that scores of computer-tailored interventions have been efficacious, including many based upon the TTM and stages of change. New applications of the model include a focus on novel health behaviors, multiple behavior change, and advancing an understanding of message design in the context of the TTM in combination with other theoretical approaches.

Keywords: transtheoretical model, stages of change, decisional balance, self-efficacy, processes of change, theory, message, intervention, tailor, health behavior

Introduction and Chapter Purpose

By their very definition, messages embedded in health campaigns and interventions are often at odds with individuals’ beliefs, attitudes, and norms regarding particular behaviors. For example, smoking cessation campaigns often aim to turn smokers’ attitudes toward smoking from positive to negative as well as to convince smokers that they can build the confidence necessary to quit smoking. While this is a challenge, further complicating this is the fact that individuals vary dramatically in their existing attitudes, beliefs, and intentions regarding smoking, including their readiness to consider changing this behavior. For example, some smokers may not be planning to quit smoking, while others are thinking about quitting and still others are actively planning to quit (Velicer et al., 1995). Thus, a question for message designers is the following: how can one develop messages that are appropriate for each of these different audiences?

The purpose of the current chapter is to describe and discuss the Transtheoretical Model (TTM) and stages of change. First, the TTM and stages of change are described. Second, applications to message design are discussed. Third, examples of the TTM and stages of change are provided in two health communication contexts: targeted and tailored interventions. Finally, criticisms and future directions for TTM research are offered.

Stages of Change: A Paradigm Shift for the Field

While early literature on health behavior change hinted at the idea that individuals may progress through various “phases” when making behavioral changes, this idea was not well conceptualized until Prochaska and DiClemente’s groundbreaking work on smoking cessation. Their seminal 1983 study classified N = 872 smokers or former smokers into various groups. Their research found not only that individuals were dispersed into distinct groups, but also that individuals in these groups had different cognitive and behavioral tendencies when it came to smoking. For example, one group of smokers processed little information about smoking, spent little time reevaluating themselves as smokers, experienced few emotional reactions to negative aspects of smoking, and did little to change their environment to keep from smoking. In contrast to this, another group of smokers were much more open to information about smoking and spent more time evaluating their smoking habit. Further, they found that former smokers engaged in a number of behavioral strategies (e.g., making changes to their environment, seeking social support) in efforts to “stay quit” (Prochaska & DiClemente, 1983).

The implications of this research were substantial, and represented a paradigm shift for the study of health behavior change. Most notably, this research demonstrated that behavioral change was better conceptualized as a process rather than an event. Thus, replacing the more simplistic view that change either happened or failed to happen, this research suggested a more complex view in which subtle (or at times explicit) cognitive and behavioral shifts were at play during the behavior change process. This research strongly suggested that assessments could be made of where individuals were in the change process, and, ultimately, that messages and interventions could be uniquely targeted and tailored to match where an individual was in this process. The concept of matching messages and interventions to individuals’ readiness to change was quite novel at the time and, as Prochaska and DiClemente observed, did not reflect current practice in the area. That is, “rather than assume that all smokers coming for treatment are ready for action, as is the case in most behaviorally based programs … clients would be grouped according to which stage of change they are in” (Prochaska & DiClemente, 1983, p. 394).

This research ultimately led to the development of the transtheoretical model of change (TTM) (Prochaska, DiClemente, & Norcross, 1992; Prochaska, Redding, & Evers, 2008). We now discuss the key concepts of the TTM.

Transtheoretical Model of Change: Core Constructs

Stages of Change

Stage of change refers to a person’s readiness to change a particular behavior, and is the central organizing construct of the TTM (Prochaska & Velicer, 1997). The TTM posits that there are five stages of change. These stages are precontemplation (not intending to change in the next 6 months), contemplation (intending to change in the next 6 months), preparation (planning to change in the next 30 days), action (having changed in the past 6 months), and maintenance (having changed and sustained the behavior change for 6 months or more). People do not progress through these stages in purely a linear fashion, however. Rather, the TTM posited the change process as dynamic, cyclical, and nonlinear, and research indicates that individuals may move forward through the stages, backslide, and then continue cycling and recycling through the stages as they attempt to change (Prochaska, Velicer, Guadagnoli, Rossi, & DiClemente, 1991). In fact, relapsing to unhealthy behaviors is not viewed as failure (as it previously often was) but rather as a natural part of the change process (Prochaska et al., 1992).

Earlier work left out the preparation stage and included a relapse stage (Prochaska & DiClemente, 1983). Later it was realized that preparation, a stage that was proposed in initial work, was an important and valid stage. In addition, relapse came to be viewed not as a separate stage, but rather as part of the change process (DiClemente et al., 1991; Prochaska et al., 1992). Furthermore, some TTM work has included a termination stage, in which individuals have zero temptation and 100% self-efficacy regarding the problem behavior (Prochaska & Velicer, 1997). This stage appears to be a more realistic goal with cessation behaviors such as smoking and drinking, and perhaps a less realistic goal with adoption behaviors such as consistent condom use or sunscreen use, which require continual effort to maintain (Prochaska & Velicer, 1997).

The stages of change themselves are descriptive stages that characterize an individual’s readiness to change behavior. But what motivates one to progress through the stages toward a healthy behavioral change? This is the point at which the other variables in the TTM come into play.

Decisional Balance

Decisional balance refers to the pros and cons of changing a behavior, and this construct was derived from Janis and Mann’s work on decision-making (Janis & Mann, 1977). However, TTM research demonstrated that Janis and Mann’s more complex, eight-quadrant, decisional balance sheet could be simplified to just two dimensions—the pros and cons of changing a behavior (Prochaska et al., 1994; Velicer, DiClemente, Prochaska, & Brandenburg, 1985). The pros are defined as the benefits of changing a behavior, while the cons are defined as the barriers or drawbacks of changing.

Prochaska and others have compared the relationship between stages of change and decisional balance across numerous health behaviors (Hall & Rossi, 2008; Prochaska, 1994; Prochaska et al., 1994). From this work, several principles have emerged. First, the cons of changing a behavior are typically higher than the pros in the precontemplation stage, and the reverse of this is true for those in the action stage. This suggests that as individuals’ positive beliefs about changing increase and their negative beliefs decrease, they progress through the stages of change. Second, a crossover of the pros and cons occurs, typically within the contemplation stage of change, suggesting that that is when individuals begin to view more pros and fewer cons to changing their behavior. Finally, from precontemplation through the action stage of change, the pros of changing increase more than the cons decrease. This suggests that to be successful, pros need to increase and cons need to decrease, but that it is the increase in pros that is most important for motivating movement through the stages of change.

Self-Efficacy and Temptation

Self-efficacy is divided into two dimensions in the TTM—confidence and temptation. Confidence is the “situation specific confidence people have that they can cope with high-risk situations without relapsing to their unhealthy or high-risk habits” (Prochaska, Redding, & Evers, 1997, p. 65). This concept was derived from Bandura’s Social Cognitive Theory (Bandura, 1986). Temptation is the “intensity of urges to engage in a specific habit when in the midst of difficult situations” (Prochaska et al., 1997, p. 65). Also, in particular behavioral areas, self-efficacy and temptations can be broken into additional dimensions. For instance, in the smoking area, temptations consist of three higher order factors: negative affective, positive social, and craving temptations.

Early work found that in a large sample of smokers, confidence was lowest among those in the precontemplation stage and highest among those in the maintenance stage. For temptations, the reverse was true (Prochaska & DiClemente, 1984). Thus, as an individual’s confidence regarding the behavior change increases, they progress through the stages of change; similarly, temptations decrease as one advances through the stages.

Processes of Change

Processes of change are conceptualized as strategies for change, and it is people’s use of the processes of change that may most advance movement through the stages of change (Prochaska et al., 1992). The processes of change help answer the question of how people change, and they were the first dimension of the TTM. They come from diverse systems of psychotherapy, and are defined as “covert and overt activities that people use to progress through the stages” (Prochaska & Velicer, 1997, p. 39).

There are 10 processes of change, which can be divided into experiential and behavioral processes. The experiential processes of change are the cognitive and affective strategies of consciousness raising, dramatic relief, environmental reevaluation, self-reevaluation, and social liberation. The behavioral processes of change are the behavior management strategies of reinforcement management, helping relationships, counterconditioning, stimulus control, and self-liberation (Prochaska, Velicer, DiClemente, & Fava, 1988).

Experiential processes are emphasized more in the earlier stages of change, such as precontemplation and contemplation. Behavioral processes, on the other hand, are emphasized in the later stages of change, such as action and maintenance (Prochaska et al., 1992). It is at these different stages that individuals use different strategies in their efforts to change behavior. For instance, consciousness raising is increasing awareness about information regarding the problem behavior, including its consequences (Prochaska et al., 2008). For someone in the precontemplation stage who is not even considering change, this process is very important. Starting to become more aware of information regarding the consequences of behavior is a helpful first step in getting someone to considering advancing to contemplation. For a smoker this may simply mean starting to listen and become more engaged with information regarding the multitude of negative effects of smoking on self and others, whether from media messages or friends and family.

For someone in action or maintenance, a behavioral process such as stimulus control is more important. Stimulus control is removing reminders and cues to engage in the unhealthy behavior or adding cues to engage in the healthy behavior (Prochaska et al., 2008). Removing or adding important stimuli is very important to a person in action or maintenance, especially in order to avoid relapse. For example, for a smoker in the action stage, getting all cigarettes out of the house and not going to bars where one used to smoke are important processes for maintaining the behavior change.

Impact of the Transtheoretical Model on the Field

The TTM has integrated concepts from a variety of theoretical perspectives—thus the name trans-theoretical. This model has also stimulated an enormous amount of research. Reviews of the literature indicate that the TTM and stages of change are one of the most widely used theories of behavioral change (Glanz, Rimer, & Lewis, 2002; Noar, Benac, & Harris, 2007; Noar & Zimmerman, 2005; Painter, Borba, Hynes, Mays, & Glanz, 2008). Although early TTM research centered on smoking cessation, the TTM has now been applied to a large number of different health behaviors (Hall & Rossi, 2008; Prochaska et al., 2008). Across multiple health behaviors, constructs from the TTM have been shown to predict behaviors both cross-sectionally (Hall & Rossi, 2008; Prochaska et al., 2008) and longitudinally (Blissmer et al., 2010; Velicer, Redding, Sun, & Prochaska, 2007). Moreover, the TTM has led to the creation of additional stage theories of behavioral change, including the precaution adoption process model (Weinstein, Sandman, & Blalock, 2008), attitude-social influence-efficacy model (de Vries & Mudde, 1998), the AIDS risk reduction model (Catania, Kegeles, & Coates, 1990), and a community readiness model (Edwards, Jumper-Thurman, Plested, Oetting, & Swanson, 2000).

Applying the TTM to Message Design

The TTM holds several clear implications for message design (Noar & Van Stee, 2012) (Table 1). Most notably, it suggests that audiences should be assessed on and subsequently segmented into stages of change before messages are designed for a campaign or intervention. For example, population-based surveys in smoking cessation find that 40% of smokers are in the precontemplation stage, 40% are in contemplation, and 20% are in the preparation stage (Velicer et al., 1995). In the absence of these data, a campaign designer might assume that all smokers are ready to quit smoking and might design all campaign messages for individuals in the preparation stage. Indeed, it is not uncommon for smoking cessation programs to be developed with such a mindset, as such an “action-oriented” approach has traditionally been the norm (Prochaska et al., 1992). With the benefit of these data, however, it is clear that the majority of individuals (80%) are in fact not ready to change, with 40% explicitly not planning to change and 40% only thinking about doing so. Thus, assessing one’s audience on the stage dimension, in and of itself, provides a wealth of useful information that has clear implications for audience segmentation and message design.

Table 1. Principles for Applying the TTM to Message Design.

Principle

Application

Segment audience into discrete stages of change for each behavior being studied.

Assess and categorize individuals into Precontemplation, Contemplation, Preparation, Action, and Maintenance stages of change using validated staging measures and algorithms.

Recommend qualitatively different activities and actions for individuals in different stages of change.

Recommend cognitive activities for those in early stages of change and behavioral activities for those in later stages of change

Construct messages differently for individuals in different stages of change.

Write messages for those in early stages with the goal of keeping them engaged in the topic and recommending small steps forward; for those in later stages, explicit changes can be emphasized.

Recognize heterogeneity among those in a single stage of change; use other model variables for messaging in addition to stage of change.

Emphasize variables that theory suggests will encourage forward stage movement. Where possible, individually tailor messages on these mediating variables within stages of change.

Second, the TTM suggests that while experiential processes of change are more likely to be applied by those in early stages, behavioral processes are more likely to be applied by those in later stages of change. An important goal for someone in the precontemplation stage (i.e., someone who is not thinking about changing) is learning new information about the effects of smoking on themselves (consciousness raising) and on others (environmental reevalation); thus, getting one simply thinking and learning about these topics is an important goal. On the other hand, someone in preparation is likely ready to make a firm commitment to quit by telling others (self-liberation), garner social support (helping relationships), and begin removing ashtrays from one’s house (stimulus control). Thus, message designers can use this theoretical knowledge in the development of appropriate messages for each stage.

Third, particularly in the area of addictions but also in other areas of health behavior change, reactance and defensiveness to health recommendations are common (Witte & Allen, 2000). This may be particularly true for those in the precontemplation stage (Cho & Salmon, 2006). Thus, those using a TTM approach can carefully design messages for those in the early stages (e.g., precontemplation, contemplation). For example, messages for those in the precontemplation stage can simply focus on keeping individuals engaged and thinking about the topic. In contrast to this, those in the preparation stage can be given much more explicit behavioral change messages recommending concrete actions.

Fourth and finally, the strength of the TTM rests not only on the stages of change but also on the other model constructs that are posited to drive forward movement through the stages (Table 2). Thus, messages should be designed (using the principles suggested above) using these mediating variables. For example, the TTM posits that the pros of changing must increase, especially in the early stages, and the cons of changing must come down as one progresses through the stages. Thus, messages emphasizing these theoretical determinants, targeted as appropriate to each stage of change, should be applied. Also, in the context of tailored interventions, individuals in the same stage of change can be given different messages if they score differently on levels of these mediating variables. This tailoring approach avoids an overreliance on stages of change alone, and is more sensitive to variability that exists among those within a single stage of change.

Table 2. TTM-Based Message Design Guidance for Encouraging Progress Through the Stages of Change.

Theoretical Construct(s)

Precontemplation to Contemplation

Contemplation to Preparation

Preparation to Action

Action to Maintenance

Decisional balance

Encourage thinking about the pros of behavior change and ways to overcome cons of behavior change; encourage reevaluation of pros and cons.

Emphasize pros of behavior change and ways to overcome cons of behavior change.

Reinforce pros of behavior change and ways to overcome cons of behavior change.

Reinforce pros of behavior change and ways to overcome cons of behavior change.

Self-efficacy/temp-tations

Begin building confidence that individual can carry out the behavior.

Continue building confidence that individual can carry out the behavior; encourage trying of the behavior.

Continue building confidence that individual can carry out the behavior; model skills necessary to perform the behavior.

Reinforce self-efficacy for behavior; model and reinforce skills, especially those relevant to relapse.

Processes of Change

Encourage information seeking; emphasize novel information about behavior; encourage expressing feelings about behavior; emphasize how one’s behavior affects others.

Stimulate reevaluation of one’s attitudes and feelings about self in relation to the behavior—that is, self-image.

Encourage commitment to and goal setting for the behavior change.

Provide rewards for healthy behavior; garner social support for change; substitute alternative behaviors; restructure environment to avoid cues for negative behavior or add cues for positive behavior.

Using the TTM in Targeted and Tailored Interventions

Targeted Interventions

Given that health communication campaigns are delivered at the community level, they typically target messages to groups. In fact, principles of effective campaign design include audience segmentation and message targeting, or the practices of dividing audiences into homogenous groups and subsequently designing messages to uniquely target those groups (Grunig, 1989). Audiences can be segmented on a host of different types of variables, including demographic, geographic, psychographic, attitudinal, cultural, and/or behavioral variables.

Promising segmentation and targeting variables are those that (1) are related to the behavior under study; (2) segment the audience into groups that are more alike than different, and (3) have clear implications for message design. The TTM approach clearly meets these three criteria. First, stages are useful in dividing an audience into groups that are different with regard to planned behavioral change, including not planning to change, thinking about changing, starting to change, changing, and maintaining change. Second, the groups that result are fairly homogeneous, as both early (Prochaska & DiClemente, 1983) and later (Prochaska et al., 1992) research applying the TTM has found stage groups to have meaningful similarities (within stage) and differences (across stages). Finally, the TTM has clear implications for message design. Indeed, it is difficult to see how one could design a message that would be appropriate for both those in precontemplation (who do not intend to change) and those in preparation (who are getting ready to make a change).

Previous writings in health communication have discussed how, at a theoretical level, the stages of change could be applied to campaign message design (Maibach & Cotton, 1995; Slater, 1999), and reviews of the campaign literature demonstrate that the stage approach has been applied in health communication campaigns (Noar, 2006; Noar, Palmgreen, Chabot, Dobransky, & Zimmerman, 2009; Randolph & Viswanath, 2004). In practice, however, the campaigns literature varies quite a bit in terms of how the approach has been applied. One early and high-profile example of a community-level campaign using this approach was the Center for Disease Control’s Community Demonstration Project (CDC AIDS Community Demonstration Projects Research Group, 1999). This HIV prevention project applied a community-level intervention that included (1) verbal messages spread by trained volunteers; (2) small media materials (including brochures, flyers, pamphlets, and newsletters) featuring role model stories; and (3) condom and bleach kits. The goal of the project was to persuade injection drug users in five cities to bleach their needles and use condoms consistently.

The print materials in this project included theory-based “role model stories” that were designed using an integration of behavioral theory with stages of change. Each community in the trial was surveyed to understand what stages the target population tended to be in for the focal behaviors—condom and bleach use. Then, role model stories were designed to match those stages. That is, if the majority of the population was found to be in the contemplation SOC for condom use with a casual partner, then the majority of role model stories were designed to facilitate the contemplation-to-preparation stage transition for that behavior. This targeting of intervention content was done for each behavior and within each community. In addition, to assess individual and community-wide progress toward health behaviors, stage was used as an outcome variable. Analyses indicated that the campaign resulted in forward stage progress on stages of change for both condom use and bleach use (CDC AIDS Community Demonstration Projects Research Group, 1999).

Other campaign studies have used the TTM approach to focus specifically on precontemplators and contemplators (Meyer, Roberto, & Atkin, 2003; Renger, Steinfelt, & Lazarus, 2002). In those cases, the approach provides guidance as to how one might design messages to encourage movement out of those early stages. For example, a community-level media campaign aiming to improve physical activity among sedentary adults in Yuma County, Arizona, focused on early stages of change (Renger et al., 2002). Considering what types of change processes might lead individuals forward through the stages, the Yuma County Campaign decided to focus mostly on consciousness raising, primarily focusing on informing people about new facts, ideas, and tips regarding physical activity. Messages delivered using public service announcements (PSAs), comic strips, and worksite posters addressed physical activity benefits, barriers, and self-efficacy in efforts to move people out of those early stages. Results indicated that the campaign was successful and also had a stronger behavioral effect than was expected.

Campaign studies have also used the TTM to provide theoretical guidance with regard to sequencing of campaign messages in televised campaigns. For example, when PSAs are developed targeting several different theoretical determinants of health behavior change, in what order or sequence should they be aired? The TTM approach implies that theoretical determinants such as perceived threat and attitudes may be useful to early stage (e.g., precontemplation to contemplation) movement, while determinants such as self-efficacy and skills acquisition may be more important for later stage (e.g., preparation to action) movement. Two campaign studies in the HIV prevention area used exactly such an approach (Noar, Palmgreen, Zimmerman, & Cupp, 2008). For example, in a 12-week campaign effort aimed at increasing condom use among young adults, PSAs aired in the first 3 weeks focused on perceived threat of HIV and STDs; in the second 3 weeks, on personalization of that risk (i.e., messages such as testimonials that focused on the fact that this could happen to you, not just someone else); in the third 3 weeks, on benefits of condom use and negative consequences of nonuse; and in the final 3 weeks, on self-efficacy and skills acquisition. This sequencing was an attempt to “lead people” through the stages from precontemplation to action with regard to consistent condom use (Palmgreen, Noar, & Zimmerman, 2008).

Finally, while the TTM has been successfully applied to message design, as indicated above, it is worth noting that the most common application of the TTM in campaigns appears to be with regard to evaluation. This may be the case because stage measures give evaluators a sensitive tool to measure progress toward change. While existing data suggest that campaigns can change behavior but that effects are typically modest (Noar, 2006), adding stage measures to campaign evaluation can allow “smaller steps” toward behavior change to be detected. Thus, a number of studies have used stages of change as an outcome measure to help in evaluating campaign effects (Maddock et al., 2007; Reger et al., 2002; Vaughan & Rogers, 2000; Wellman, Kamp, Kirk-Sanchez, & Johnson, 2007). The typical approach taken is to assess stage both before and after the campaign is launched, in order to examine whether there is stage progression through the stages of change as a result of the campaign effort.

Computer-Tailored Interventions

In contrast to campaigns that are designed and delivered at the community level, computer-tailored interventions are delivered at the level of the individual. Tailoring refers to the process of customizing information/messages for an individual based upon an assessment of characteristics of that individual (Kreuter, Strecher, & Glassman, 1999). Similar to targeting, tailoring can be applied to a wide range of variables. Indeed, virtually any variable that can be assessed and which varies at the individual level can be tailored upon. In practice, however, tailoring has mostly been applied using psychosocial variables derived from behavioral theory, notably stages of change and other TTM constructs (Noar et al., 2007; Noar, Harrington, & Aldrich, 2009).

While the early literature on tailoring was primarily focused on computer-generated print materials, the literature is increasingly focused on use of the Internet for delivery of tailored content (Noar, Harrington, et al., 2009). Reviews of both print and Internet tailoring literatures indicate that stages of change are used in a significant number of studies. For example, a meta-analysis of 57 tailored print intervention studies found that 62% used SOC (or the TTM) either alone or in combination with other theories (Noar et al., 2007). Similarly, a systematic review of web-tailored interventions found that 40% did so (Lustria, Cortese, Noar, & Glueckauf, 2009). A review of tailored interventions for lifestyle behaviors (conducted across delivery channels) found that stages of change or the full TTM were used in approximately 75% of physical activity studies, 50% of diet/nutrition studies, at least 50% of smoking studies (Noar, Harrington, Van Stee, & Aldrich, 2011).

When the TTM approach is used in tailored interventions, how is it applied? Typically, stages of change are used as a central organizing construct for the intervention. What this means is that individuals are assessed on stages of change early in intervention, and subsequently all intervention messaging is affected by what stage an individual is in. For example, tailored interventions that have applied the full TTM have often used this approach, such that once someone is staged, feedback on the other TTM variables is stage appropriate. This approach has been used in numerous studies, including smoking cessation (Velicer, Prochaska, & Redding, 2006), dietary change (Park et al., 2008), physical activity (Marcus et al., 2007), and multiple behavior change (Prochaska et al., 2005). For instance, in their study focused on dietary change among young adults, Park et al. (2008) explains this approach by stating:

each stage-specific submodule addressed key determinants of behavior change to maximize content relevance to the audience. For example, individuals in earlier (pre-action) stages of change tend to view barriers to change as outweighing benefits, have low self-efficacy, and are unlikely to use specific processes to make change. As a result, the interactive module for individuals in precontemplation focused on improving these traits.

(p. 290)

This approach has also been applied by integrating the stages of change with other theories. In these cases, stage is used as the central organizing construct and the other variables are used (as appropriate by stage) for messaging. For example, tailored intervention studies have combined stages of change with Ajzen’s (1991) theory of planned behavior (Smeets, Brug, & de Vries, 2008) or Bandura’s (1986) social cognitive theory (Dijkstra, De Vries, & Roijackers, 1999). Other behavioral theories, such as the theory of reasoned action (Fishbein & Ajzen, 1975) and the health belief model (Janz & Becker, 1984), have also been applied in tailored interventions in combination with the stages of change (Ezendam, Oenema, van de Looij-Jansen, & Brug, 2007).

Efficacy of Computer-Tailored Interventions

More than two decades of literature provide a strong empirical basis upon which to judge the efficacy of computer-tailored interventions (CTIs). A number of seminal studies in the early 1990s—driven heavily by stages of change and the TTM—demonstrated efficacy and thus provided reasons for optimism (Campbell et al., 1994; Prochaska, DiClemente, Velicer, & Rossi, 1993; Skinner, Strecher, & Hospers, 1994). These are now briefly described.

Seminal Studies

The first computer-tailored smoking cessation intervention study conducted by Prochaska and colleagues tested different types of tailored self-help materials for smokers and their impact on cessation of smoking over 18 months (Prochaska et al., 1993). The study recruited 756 volunteers using newspaper advertisements. The participants were randomized to 1 of 4 conditions: (1) package of three standard smoking cessation manuals; (2) smoking cessation manual matched to smoker’s “stage of readiness” to quit smoking (stage-matched manual); (3) stage-matched manual plus tailored computer feedback report; and (4) stage-matched manual, tailored report, and personalized counselor call. Intervention materials were mailed to participants at varying intervals between baseline and 6 months, and were tailored based upon data gathered through mailed surveys. The feedback reports were tailored on variables from the TTM—including stage of readiness to quit smoking, decisional balance (pros and cons) for quitting smoking, self-efficacy to quit, and use of change processes for quitting. Results indicated that at 18-month follow-up, condition 3 (stage-matched manual plus tailored feedback report) significantly outperformed all other conditions, with approximately a 25% quit rate. This can be compared with conditions 2 and 4 (18% quit rate) and condition 1 (10% quit rate).

Another study aimed to improve dietary behaviors in 558 adult patients recruited from primary care settings (Campbell et al., 1994). Participants were randomized to 1 of 3 conditions: (1) nutrition information newsletter tailored on stage of readiness to change dietary behaviors, dietary intake, and psychosocial variables based upon the Health Belief Model (Janz & Becker, 1984); (2) nontailored nutrition information newsletter consisting of standard risk information on the relationship of diet to disease; and (3) no-treatment control group (assessment only). Participants were assessed using a survey and were subsequently mailed the tailored (or nontailored) print materials. At a 4-month follow-up, 73% of the tailored group recalled receiving the nutritional information compared with 33% of the nontailored and 15% of the control group (no information was sent to the control group). Those in the tailored group were also significantly more likely to have read all of the information that was sent. Results also indicated that total fat intake decreased by 23% in the tailored group compared to 11% in the nontailored and 3% in the control group. No differences on fruit and vegetable intake were found.

A third study aimed to increase mammography use among 497 women recruited in family practice settings (Skinner et al., 1994). Participants were randomized to 1 of 2 conditions: (1) letter tailored on stage of readiness to change mammography, beliefs about breast cancer and mammography, risk status and perceived barriers; and (2) standardized letter consisting of an adaptation of the surgeon general’s advice on mammography. Both conditions were mailed letters 5 months after the baseline survey, and a follow-up telephone interview took place 3 months after the materials were sent. Results indicated that those receiving tailored letters were significantly more likely to both remember and read them. Moreover, more individuals in the tailored condition (44%) had received mammograms as compared to the comparison condition (31%), although this difference was not statistically significant. There was also evidence that the tailored letters were particularly effective with certain subgroups, including both Black and low-income women.

Later Studies

Building on the success of these seminal studies, TTM-based computer-tailored interventions have been replicated within behaviors, as well as extended across behaviors. For instance, building on the early successful work of TTM-based computer-tailored interventions for smoking cessation, several subsequent trials replicated these effects, showing that these interventions have robust effects on smoking cessation on the magnitude of a 25% cessation rate (Velicer et al., 2006). Moreover, similar interventions were developed and tested and shown to be successful across several additional health behaviors, including condom use (Redding, Prochaska, et al., 2015), teen dating violence (Levesque, Johnson, Welch, Prochaska, & Paiva, 2016), and stress management (Evers et al., 2006). Also, TTM-based interventions that attempt to change multiple health behaviors have been developed and tested, and many have shown efficacious results (Prochaska et al., 2004; Prochaska et al., 2005; Velicer, Redding, et al., 2013).

Meta-Analyses of Computer-Tailored Interventions

As the literature on computer-tailored interventions has grown exponentially, narrative reviews have concluded that such intervention approaches are successful in affecting health behavior change in diverse areas (Skinner, Campbell, Rimer, Curry, & Prochaska, 1999), including smoking cessation (Strecher, 1999), nutrition (Brug, Campbell, & van Assema, 1999) and cancer prevention (Rimer & Glassman, 1999). There have also been systematic reviews that have concluded that tailored interventions are generally efficacious (Kroeze, Werkman, & Brug, 2006; Neville, O’Hara, & Milat, 2009; Richards et al., 2007). Perhaps most important, three meta-analytic projects have examined the literature and provided a more fine-grained analysis of efficacy, and those are now briefly described (Table 3).

Table 3. Summary of Meta-Analyses of Computer-Tailored Interventions.

Authors

Summary of Studies

Mean ES

Key Findings

Noar et al. (2007)

57 studies of print-tailored behavior change interventions—primarily smoking cessation, diet, and mammography screening.

d=.15

Effects greater when a) demographic and particular psychosocial variables used in tailoring, b) particular print formats used, c) more than one intervention session included.

Krebs et al. (2010)

88 studies of print, computer, and automated telephone-tailored interventions, primarily smoking, physical activity, and diet.

d=.17

Effects greater over time when dynamic tailoring (where participant reassessed before each feedback report) applied rather than static tailoring (multiple reports tailored from baseline data). Intervening on multiple behaviors did not undermine intervention efficacy. No significant difference by tailoring channels.

Lustria et al. (2013)

40 studies of web-tailored interventions, primarily physical activity, diet, and smoking.

d=.14

Effects of interventions sustained at longer-term follow-up (d=.16). Intervening on multiple behaviors did not undermine intervention efficacy.

Note: ES = effect size.

Noar et al. (2007) conducted a meta-analysis of 57 studies that tested the ability of tailored print materials to affect health behavior change. The studies primarily consisted of smoking cessation (26%), diet (23%), and mammography screening (21%) interventions, and the majority were based on the TTM or stages of change (62%). The overall effect size in this study was r = .074 for tailored interventions compared with no-treatment control and alternative interventions, which converts to d = .15. Perhaps more important, a subsequent analysis that excluded studies containing only no-treatment control comparison conditions revealed that tailored interventions still outperformed generic or targeted interventions (r = .058 or d = .12).

Across behaviors, smoking and diet had the largest effect sizes, followed by mammography screening and then exercise. Also, a variety of intervention characteristics moderated intervention efficacy. For example, studies that generated tailored reports in the form of pamphlets/leaflets and newsletters/magazines had significantly larger effect sizes than those generating letters or manuals. In addition, those interventions with more than one contact with participants had significantly larger effects than those with just one contact. Finally, studies that tailored on stages of change and other TTM variables had larger effect sizes than those not tailoring on those variables (Noar et al., 2007).

More recently, Krebs et al. (2010) conducted a meta-analysis of 88 studies testing tailored interventions delivered using print (75%), computer (22%), and automated telephone (3%) channels. Behaviors examined in this review were diet, smoking, physical activity, and mammography screening, and some studies intervened on more than one behavior at a time. Many of the studies in this meta-analysis were based on the TTM or stages of change. Overall, there was a statistically significant effect of tailored interventions on health behavior change (d = .17), and this effect did not vary significantly across tailoring channels. Effects were similar across behaviors, with dietary fat reduction being most efficacious (d = .22). In addition, interventions that focused on multiple behaviors did not have smaller effects than those focusing on a single behavior.

Krebs et al.’s (2010) meta-analysis also examined tailoring effects over time. Results revealed that effects tended to peak between 4–12 months and then gradually decline over time. Interestingly, there was also evidence that dynamically tailored interventions (those reassessing individuals before providing new tailored feedback) had significantly larger effects at most time points (including 13–24 month follow-up) than statically tailored interventions (those providing new tailored feedback based on the same baseline assessment). Indeed, only dynamically tailored interventions demonstrated statically significant effects at long-term follow-up.

Most recently, Lustria and colleagues conducted a meta-analysis of web-delivered, tailored health behavior change interventions (Lustria et al., 2013). The 40 studies primarily consisted of physical activity (42%), diet (25%), and smoking (18%) interventions, and 50% were based on the TTM or stages of change. The overall weighted mean effect size in this study was d = .14. Studies that contained a longer-term follow-up time point (53% of studies) also exhibited a significant effect at the follow-up time point, d = .16. This bodes well for maintenance of intervention effects using tailored interventions, as it suggests no decay of intervention effects over the course of the study. Another key finding is the fact that multiple behavior interventions had similar effects as single behavior interventions, suggesting that intervening on multiple behaviors may not undermine behavior change.

Overall, these meta-analyses suggest that tailored interventions have often been successful in stimulating behavior change, and they each contribute to our knowledge about what may make efficacious interventions. The evidence to date suggests that messages that are more customized to an individual are more successful in influencing health behavior change (Noar et al., 2007) and that carefully constructed interventions can maintain changes over the longer term (Krebs, Prochaska, & Rossi, 2010; Lustria et al., 2013). Effect sizes across all of these meta-analyses were similar (ranging from d = .14–.21), giving us some indication of what the “typical” effect of a tailored intervention may be. Other important findings from these meta-analyses indicate that tailoring channel does not in and of itself appear to make a difference, but how tailoring is carried out (e.g., choice of theoretical constructs, dynamic vs. static tailoring, design of print materials) does appear to have a measurable impact on the efficacy of a tailored intervention. Finally, many interventions based upon the TTM and stages of change have been efficacious, suggesting that the TTM can provide a strong theoretical basis for a successful tailored intervention.

Criticisms and Future Directions

While the TTM and other stage theories have been touted for their intuitive appeal, they have also received criticism in recent years. Criticisms have focused on the conceptualization of the TTM itself (Brug et al., 2005; Herzog, Abrams, Emmons, Linnan, & Shadel, 1999), methodological issues with staging algorithms (Adams & White, 2003; Brug et al., 2005) and theory-testing methods (Weinstein, Rothman, & Sutton, 1998), and the effectiveness of interventions based on the model (Adams & White, 2005; Bridle et al., 2005). Those seeking to apply the TTM should familiarize themselves with these criticisms.

Increasingly, TTM research is moving in new directions. One direction has been applying the model to new behaviors—such as HPV vaccination (Fernandez, Amoyal, Paiva, & Prochaska, 2016) and sustainable transportation behaviors (Redding, Mundorf, et al., 2015). Another direction has been to move away from a focus on single health behaviors to examine multiple behavior change interventions (Johnson et al., 2008) and mechanisms of change (Johnson et al., 2014). Newer research is also examining subtypes within stages of change (Santiago-Rivas, Velicer, Redding, Prochaska, & Paiva, 2013), as well as testing the TTM using effect size predictions rather than traditional significance testing (Velicer, Brick, Fava, & Prochaska, 2013). There have also been an increasing number of message-oriented studies examining stage-matched vs. mismatched messages (Godinho, Alvarez, Lima, & Schwarzer, 2015), effects of message framing within stages (Cornacchione & Smith, 2012), and fear appeal effectiveness across stages of change (Cho & Salmon, 2006).

Concluding Thoughts on the TTM

The current chapter has discussed the TTM and stages of change. It has discussed principles of this approach and provided an overview of how it has been applied in both health communication campaigns and computer-tailored interventions, as well as specific guidance on applying this approach to message design. The TTM approach offers much to the message design area in terms of theoretical guidance for designing effective health-related messages in a variety of campaign and intervention contexts. Importantly, this approach emphasizes that behavioral change is a complex and step-by-step process, and that change may not happen right away in response to any particular intervention or campaign message. It is thus not surprising that so many researchers have found the TTM and stages of change to be useful not only in providing a basis for campaign and intervention messages but also in evaluating the impact of those efforts.

Further Reading

Hall, K. L., & Rossi, J. S. (2008). Meta-analytic examination of the strong and weak principles across 48 health behaviors. Preventive Medicine, 46, 266–274.Find this resource:

Johnson, S. S., Paiva, A. L., Mauriello, L., Prochaska, J. O., Redding, C., & Velicer, W. F. (2014). Coaction in multiple behavior change interventions: Consistency across multiple studies on weight management and obesity prevention. Health Psychology, 33, 475–480.Find this resource:

Krebs, P., Prochaska, J. O., & Rossi, J. S. (2010). A meta-analysis of computer-tailored interventions for health behavior change. Preventive Medicine, 51, 214–221.Find this resource:

Noar, S. M., Benac, C. N., & Harris, M. S. (2007). Does tailoring matter? Meta-analytic review of tailored print health behavior change interventions. Psychological Bulletin, 133, 673–693.Find this resource:

Noar, S. M., Harrington, N. G., Van Stee, S. K., & Aldrich, R. S. (2011). Tailored health communication to change lifestyle behaviors. American Journal of Lifestyle Medicine, 5, 112–122.Find this resource:

Noar, S. M., & Van Stee, S. K. (2012). Designing messages for individuals in different stages of change. In H. Cho (Ed.), Health communication message design: Theory and practice (pp. 209–229). Thousand Oaks, CA: SAGE.Find this resource:

Prochaska, J. O., DiClemente, C. C., & Norcross, J. C. (1992). In search of how people change: Applications to addictive behaviors. American Psychologist, 47, 1102–1114.Find this resource:

Prochaska, J. O., Redding, C. A., & Evers, K. (2008). The transtheoretical model and stages of change. In K. Glanz, B. K. Rimer, & K. V. Viswanath (Eds.), Health behavior and health education: Theory, research, and practice (4th ed., pp. 170–222). San Francisco: Jossey-Bass.Find this resource:

Slater, M. D. (1999). Integrating application of media effects, persuasion, and behavior change theories to communication campaigns: A stages-of-change framework. Health Communication, 11, 335.Find this resource:

Velicer, W. F., Brick, L. A., Fava, J. L., & Prochaska, J. O. (2013). Testing 40 predictions from the transtheoretical model again, with confidence. Multivariate Behavioral Research, 48, 220–240.Find this resource:

Velicer, W. F., Prochaska, J. O., & Redding, C. A. (2006). Tailored communications for smoking cessation: Past successes and future directions. Drug & Alcohol Review, 25, 49–57.Find this resource:

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