Stages of Change Model.
Individual behavior change has been conceptualized as linear by many theoretical models. The Stages of Change Model (popular name for the Transtheoretical Model of Behavior Change) is based on the premise that change is not linear (20-23). There are discrete stages of motivational readiness to change representing long periods of equilibrium interspersed with repeated cycles of progression and regression. The stages of change are anchored by the relatively stable Precontemplation and Maintenance. The other stages tend to be relatively labile with considerable forward and backward movement as people cycle through change. The distinct stages allows interventions tailored to stage of change to be more effective than a "one size fits all" approach.
During the last two decades, the model has become one of the most influential theoretical models for health related behavior change (30). The model consists of three dimensions: the central organizing construct, stages of change, which is the temporal dimension and the three additional dimensions of decisional balance, self-efficacy or temptation, and processes of change (20-23). Of these three dimensions, stage is the most widely researched dimension and during the last decade decisional balance and self-efficacy have received considerable attention. The least investigated dimension is the one that delineates the processes of change.
Stage of change is the temporal dimension of motivational readiness to change a health behavior, and the stages are as follows: Precontemplation (no intention of changing in the foreseeable future), Contemplation (intending to change, but not soon), Preparation (intending to change within the next few weeks), Action (recent change) and Maintenance (maintaining change). Stage change defines when transitions occur, and stage of change can be used in interventions to show a person where he/she is in the change process. Like all dimensions of the model, stage is a dynamic variable and people are expected to move from one stage to another. Although stage change is sometimes conceptualized as linear, a spiral appears to best represent the dynamic nature of change. For example, regressing from Preparation or Action to an earlier stage is just as likely as progressing to the next stage (21, 31). In the area of smoking cessation, self-changers averaged three serious quit attempts (Action stage) over seven years before succeeding (21).
Developing a valid and practical staging algorithm is critical to all applications of the model. In order to measure stage, it is necessary to begin by defining the target behavior and the criterion for effective action (31). Once the criterion is defined, the algorithm should be constructed to allow self-assessment of whether or not it has been met. If met, has it been met for greater than 6 months (Maintenance) or less (Action)? If the criterion has not been met, individuals need to have an idea of the amount of change necessary to meet the criterion (behavioral distance). Subsequent questions assess whether a person intends to change in the next month (Preparation), in the next 6 months (Contemplation), or they have no intention of changing to meet the criterion in the next 6 months (Precontemplation) (30, 31, 33, 34). We developed a staging algorithm in NC 219 (based on Laforge, Greene, and Prochaska, 1994)(35) that assesses stage separately for fruits and vegetables and uses the guidelines (3) criteria of ³ 2 servings fruit and ³ 3 servings vegetables. This provides a clear criterion, behavioral distance, and is based on self-assessed number of servings.
The NC 219 algorithm is similar in overall structure to the one used in the 5 A Day studies (13) but there are two critical differences. The 5 A Day studies assessed stage for fruits and vegetables combined. However stage is different for fruits and vegetables (see Figure 1). Second, the question for classification into Precontemplation, Contemplation or Preparation did not provide the behavioral distance, i.e., it asked, "Do you intend to increase?" instead of the question we used which asked, "Do you intend to increase to 2 servings (fruit) or 3 servings (vegetables)?" In the NC 219 pilot study young adults underestimated intake of these foods by at least ½ serving comparing self-assessment to dietary recall, but 70 to 80% of those perceiving they met the guidelines did meet them according to dietary recall data.
Figure 1 illustrates stage distribution for the NC 219 sample
(N=1545), a representative sample of young adults proactively
recruited by telephone in 10 different states. We used the definition
of stage described above. This survey represents the largest study
in this population to date. Although the majority were in Action
or Maintenance for fruit, the majority were in pre Action stages
for vegetables and only 35% met the criteria for both fruits and
vegetables. It is clear that young adults are at a greater level
of motivational readiness to consume fruits than vegetables. This
must be taken into consideration in intervention development.
The typical intervention combines fruits and vegetables with a
goal of five servings a day but has had greater success with fruits
than vegetables (36). We will continue to assess stage for these
foods separately and will tailor our interventions to be sensitive
to differences in motivational readiness to change for fruits
and vegetables.
Table 1 - A copy is available through the Executive Director's
Office.
The Transtheoretical Model of Behavior Change (TTM) originated from an analysis of 18 systems of psychotherapy which identified common processes of change (20). Processes are the covert and overt activities that people use to progress through the stages; they are how people change. Experiential processes focus on thoughts, feelings, and experiences while behavioral processes focus on behaviors, social support, and reinforcement. In a study of dietary fat reduction, Greene and colleagues found all process use was low in Precontemplation (30). Experiential process use increased sharply through Preparation, peaked in Action, then decreased in Maintenance. Behavioral process use remained low through Contemplation then rose sharply and linearly through Action before decreasing in Maintenance.
Decisional balance measures the balance or relative importance to the individual of the pros (advantages or benefits) and the cons (disadvantages, barriers, or costs) of change. Prochaska and colleagues (37) found that for 12 health behaviors, including dietary fat reduction, pros had to outweigh cons for all behaviors before Action. Progress from Precontemplation to Action required an increase in the pros of approximately 1 standard deviation (38). This progress was associated with a decrease in the cons of approximately ½ a standard deviation. Shifting decisional balance so pros outweigh cons appears to be important in explaining why people make a commitment to change behavior in the near future. We have developed a valid long form of an instrument for assessing decisional balance in NC 219 (18 items) and RI is working on shortening the instrument for the proposed intervention.
The self-efficacy construct represents situation specific confidence people have that they can engage in the desired behavior change and is usually operationalized in interventions as confidence (39). This construct was adapted from Bandura’s self-efficacy theory (40). The converse of confidence is situation specific temptation, e.g., how tempted a person feels to eat high-fat foods across different situations. Both confidence and temptation have the same measurement structure with three distinct factors, i.e., positive social, negative affect and, challenging situations (situations in which it is difficult to obtain low-fat foods) (41). In a smoking cessation study, temptation predicted which self-changers would relapse and start smoking again (39). For dietary fat reduction, Greene and colleagues found that temptation was low in Precontemplation, rose sharply to a peak in Contemplation, dipped slightly in Preparation, then declined somewhat in Action and sharply in Maintenance (30). The low values in Precontemplation may be typical for dietary restriction; people only perceive temptation as a problem if they are trying to avoid eating something. The sharp decline in Maintenance illustrates the reduced effort needed to maintain the change after six months. We have developed a brief instrument (10 items) assessing self-efficacy in NC 219 which is ready for use with interventions. In our survey (N=1545), we found self-efficacy for increasing fruit and vegetable consumption rose linearly from Precontemplation through Maintenance. The table below shows how self-efficacy differentiates between young adults meeting and failing to meet guidelines for fruits and vegetables.
Self-efficacy1 by Meeting Guidelines for Fruit (2
servings) and Vegetables (3 servings)
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| Efficacy Score Fruit | 20.4+3.9*** | 17.2+4.6 |
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20.6+4.0*** | 18.4+4.6 |
| Efficacy Score Vegetables |
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19.5+4.3*** | 17.3+4.5 | 19.8+4.2*** | 17.5+4.5 |
***p <.001
Using the Stage of Change Model to develop interventions.
Stage tailored interventions have been effective in increasing motivational readiness to change for those who are not ready for action-oriented interventions. Traditional action-oriented interventions can be equally effective as stage tailored interventions for 25% of the population that is ready for action (Preparation and Action stages). However, stage tailored interventions that reassess stage of change and provide feedback related to change in stage can accelerate the process of recycling back to action for those who relapse. Because it takes many people repeated cycles of action and relapse before behavior change becomes established, a sustained, stage tailored intervention has the potential for accelerating the rate of change in an entire population.
Stages of change-based interventions using computer-generated stage-tailored messages have been successful in promoting dietary fat reduction in adult clinic patients (42). However, effectiveness in improving fruit and vegetable intake was not demonstrated. Possible reasons for nonsupport include use of a brief (ten item) food frequency questionnaire to assess fruit and vegetable intake, inappropriate statistical collapse of stage categories, summing fruit and vegetable data and providing only one intervention incident. The authors added season-related fruit and vegetable unavailability and personalized messages in the control group as possible explanations for lack of movement in fruit and vegetable consumption. A later study by Campbell and colleagues found stage tailored materials, along with an intensive multicomponent intervention in African American churches, were effective in increasing fruit and vegetable consumption by 0.85 servings over the control group, however, the increase was almost entirely in fruit consumption (36).
Lutz and colleagues studied 573 health maintenance organization clients to examine the effects of three newsletter interventions on fruit and vegetable intake (43). The interventions included nontailored nutrition newsletters, tailored newsletters without a goal-setting component, and those with a tailored goal-setting component. Although fruit and vegetable intake increased significantly for all groups receiving the newsletter, the computer-generated stage-tailored newsletters were no more effective than the nontailored newsletters. Suggested explanations for the findings include a small sample size with only 50% power to detect group intake differences as small as 0.2 servings and a food frequency questionnaire with an inadequate number and variety of items. The sample size concerns are especially noteworthy because a nonsignificant trend toward increase in variety and intake was observed along the continuum: nontailored, tailored, tailored-with-goal-setting.
A computer-generated, stage-tailored newsletter intervention with a second personalized feedback report was found to increase fruit and vegetable intake over both a single stage-tailored newsletter and an action oriented, information-based newsletter for 762 adult Dutch persons (44). However, unlike the Lutz (43) study, a 32 item food frequency questionnaire was used, study length was four weeks longer with an additional evaluation, and, perhaps most importantly, the intervention included a second feedback report with personalized information (iterative feedback). Subjects receiving the information-based newsletter decreased fruit servings by .07 per day and increased vegetable intake by 0.6 servings per day. This contrasts with the stage tailored newsletter group being able to maintain fruit intake and increasing vegetable intake by .09 servings. An increased intake of .32 serving for fruits and .14 serving for vegetables occurred in the group receiving tailored newsletters featuring an iterative feedback component.
Although generally in the studies above individual tailoring for stage of change is effective, there is less agreement about the efficacy of either adding additional levels of tailoring or what variables are important for tailoring. Interventions often provide feedback about specific food behaviors (42, 46), perhaps because most people can not accurately quantify servings of food and even fewer are aware of their nutrient intake. Campbell and colleagues found self-efficacy was important for tailored interventions (36). Decisional balance is rarely assessed, but the somewhat related construct of barriers has also been found to be important for tailoring (36). Finally, few interventions have tailored for processes of change due to the difficulty in measuring the construct and to the difficulty in providing meaningful feedback because of the number of processes. Another consideration in deciding on the levels of tailoring is the complexity of the feedback generation process. Manual selection of materials that have been individually tailored on one or two variables is relatively simple. Computer generated materials are required to tailor additional variables and the programming costs are considerable. This project will determine which variables beyond stage will be included in the tailoring in Years 01 and 02. However, the success of the individualized stage tailored materials with a second iterative feedback report in the Brug study (44) suggests that a single newsletter is less powerful than a series of newsletters with individualized feedback.
A related issue is the method of delivery of the feedback. Multimedia feedback must be computer generated and production costs are enormous. Although Campbell and colleagues found that participants liked a multimedia intervention and learned more than controls, there was no difference in behavior (47). Most interventions have relied upon print material delivered by mail. Print-based interventions are relatively economical and tailored print materials are effective (48). It is possible that they may be more effective for sustained behavior change than multimedia interventions, but research is lacking in this area. The most effective method may also vary by age, gender, education, and ethnicity. The first objective of this renewal of NC 219 will be to systematically investigate the optimal delivery method for young adults in diverse populations.
Results of CRIS search November, 2000.
"Influences of Fruit and Vegetable Consumption of Low-Income Families" - University of Idaho, 1994-97
Loker, Swanson and Keim conducted a series of focus groups to identify fruit and vegetable consumption decision factors for low-income Hispanic and Caucasian participants in the Expanded Food and Nutrition Education Program (EFNEP) and Youth EFNEP. Behavioral factors included cost, food preferences, food preparation, meals, and shopping. Personal characteristic factors included appearance, quality, and nutrition. Environmental factors included availability and time constraints. Children cited taste, juiciness, and texture as personal characteristics and the behavioral factor of "dressing up" their fruits and vegetables.
"Nutritional Risk and Antioxidant Status in the Elderly" - NE-172, 1999-2004
Kantor et al are evaluating biomarkers of nutritional risk with an emphasis on antioxidant status to help identify risk for age-related macular degeneration and its risk factors and encourage greater consumption of good dietary sources of antioxidants through nutrition education programs.
"Dietary Assessment of Selected population Groups" - Washington State University, 1993-1999
Beerman et al identified factors affecting fruit and vegetable consumption among university students and determined the relationship between nutrient intake, meal patterns, and anthropometric measurements of college students. Findings of this study are not yet available.