health belief model

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CHAPTER 4 Health Belief Model

STUDENT LEARNING OUTCOMES

After reading this chapter the student will be able to: • Explain the original concept of the Health Belief Model (HBM). • Discuss how the constructs of perceived seriousness, susceptibility, benefits, and barriers might predict health

behavior. • Analyze the impact of the modifying variables on health behavior. • Identify cues to action and how they motivate behavior. • Use the model to explain at least one behavior.

THEORY ESSENCE SENTENCE Personal beliefs influence health behavior.

Health Belief Model Constructs Chart

Perceived susceptibility: An individual’s assessment of his or her chances of getting the disease

Perceived benefits: An individual’s conclusion as to whether the new behavior is better than what he or she is already doing

Perceived barriers: An individual’s opinion as to what will stop him or her from adopting the new behavior

Perceived seriousness: An individual’s judgment as to the severity of the disease

Modifying variables: An individual’s personal factors that affect whether the new behavior is adopted

Cues to action: Those factors that will start a person on the way to changing behavior

Self-efficacy: Personal belief in one’s own ability to do something

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IN THE BEGINNING

The Health Belief Model was developed by researchers at the U.S. Public Health Service in the late 1950s. At the time, a great emphasis was being placed on screening programs for disease prevention and early detection. Although public health practitioners were in favor of screenings, the public was not very receptive to being tested for diseases of which they didn’t have symptoms. This was particularly true for tuberculosis (TB) (Hochbaum, 1958; Rosenstock, 1960).

Although TB screening programs were attracting some people, they were not attracting the large numbers who were known to be at risk for the disease. Consequently, there was a need to understand why some people went for the screening, but why so many others did not (Hochbaum, 1958; Rosenstock, 1960). To find out, researchers at the U.S. Public Health Service conducted a study to identify the combination of psychological, social, and physical factors (observations) that determined whether a person wanted to be screened for TB, when, and at what type of facility (Hochbaum, 1958).

Because the researchers were all social psychologists, their approach was based on the idea that behavior is the result of how people perceive their environment. That is, individual beliefs or perceptions are what determine behavior. Using this as the foundation, it was reasoned that in order for people to take action to prevent a disease they didn’t have, or to be screened/tested for a disease they didn’t have symptoms of, certain beliefs or perceptions about the disease needed to exist.

The outcome of the study identified three sets of factors that determined participation in a voluntary screening program: psychological readiness, situational influences, and environmental conditions (Rosenstock, 1958). Factors identified as being indicative of people’s psychological readiness to be screened for TB included the belief that they had TB, were at risk of getting TB, or that they would benefit from being tested for TB. Situational influences included having bodily changes thought to be symptoms of TB and other people’s opinions of whether they should or shouldn’t be screened. And lastly, if the environmental conditions were provided an opportunity to be screened and if it was convenient (Rosenstock, 1958). The conclusions drawn from this study formed the basis of the Health Belief Model.

THEORETICAL CONCEPT

The Health Belief Model (HBM) is by far the most commonly used theory in health education and health promotion (Glanz, Rimer, & Viswanath, 2008; National Cancer Institute [NCI], 2005). The underlying concept of the HBM is that health behavior is determined by personal beliefs or perceptions about a disease and the strategies available to decrease its occurrence (Hochbaum, 1958). Personal perception is influenced by the whole range of intrapersonal factors affecting health behavior, including, but not limited to: knowledge, attitudes, beliefs, experiences, skills, culture, and religion.

THEORETICAL CONSTRUCTS

The following four perceptions serve as the main constructs of the model: perceived seriousness, perceived susceptibility, perceived benefits, and perceived barriers. Each of these perceptions, individually or in combination, can be used to explain health behavior. More recently, other constructs have been added to the HBM; thus, the model has been expanded to include cues to action, motivating factors, and self-efficacy.

PERCEIVED SERIOUSNESS

The construct of perceived seriousness speaks to an individual’s belief about the seriousness or severity of a disease. While the perception of seriousness is often based on medical information or knowledge, it may also come from beliefs a person has about the consequences an illness might have on him or her personally. For example, most of us perceive seasonal flu as a relatively minor ailment. We get it, stay home a few days, and get better. However, if you have asthma, contracting the flu could land you in the hospital. In this case, your perception of the flu might be that it is a serious disease. Or, if you are self-employed, having the flu might mean a week or more of lost wages. Again, this would influence your perception of the seriousness of this illness.

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Perception of seriousness can also be colored by past experience with the illness. No doubt, most people would consider skin cancer a serious disease. However, the perception of serious might be diminished in someone who had a cancerous lesion removed and recovered without much more than a sore area and a Band-Aid for a few days.

PERCEIVED SUSCEPTIBILITY

Personal risk or susceptibility is one of the more powerful perceptions in prompting people to adopt healthier behaviors. The greater the perceived risk, the greater the likelihood of engaging in behaviors to decrease the risk. This is what prompts men who have sex with men to be vaccinated against hepatitis B (de Wit, Vet, Schutten, & van Steenbergen, 2005) and to use condoms in an effort to decrease susceptibility to HIV infection (Belcher, Sternberg, Wolotski, Halkitis, & Hoff, 2005). Perceived susceptibility motivates people to be vaccinated for influenza (Chen, Fox, Cantrell, Stockdale, & Kagawa-Singer, 2007) to use sunscreen to prevent skin cancer, and to floss their teeth to prevent gum disease and tooth loss (Figure 4–1).

It is only logical that when people believe they are at risk for a disease, they will be more likely to do something to prevent it from happening. Unfortunately, the opposite also occurs. When people believe they are not at risk or have a low risk of susceptibility, unhealthy behaviors tend to result. This is exactly what has been found with older adults and HIV prevention behavior. Because older adults generally do not perceive themselves to be at risk for HIV infection, many do not practice safer sex (Rose, 1995; Maes & Louis, 2003). This same scenario was found with Asian American college students earlier in the HIV/AIDS epidemic. They tended to view epidemic as a non-Asian problem; thus, their perception of susceptibility to HIV infection was low and not associated with practicing safer sex behaviors (Yep, 1993).

Unfortunately, this lack of perceived susceptibility to sexually transmitted infections (STIs) is still alive and well on campuses, albeit not necessarily because of ethnicity as seen in the previous example. Rather, students under estimate their risk of contracting infections from their partners, because they underestimate their partners susceptibility. (They ignore the old adage that you are sleeping with everyone your partner has ever slept with.) Consequently, they do not protect themselves against STIs, especially when sexual activity is restricted to oral sex. This is particularly evident if they attend schools in a geographic area that has a low incidence of HIV/AIDS (Downing-Matibag & Geisinger, 2009).

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FIGURE 4–1 Risk avoidance is a powerful motivator for change © Michael D Brown/Shutterstock, Inc.

What we have seen so far is that a perception of increased susceptibility or risk is linked to healthier behaviors, and perception of decreased susceptibility to unhealthy behaviors. However, this is not always the case. In college students, perception of susceptibility is rarely linked to the adoption of healthier behaviors (Courtenay, 1998), even when the perception of risk is high. For example, even if college students consider themselves at risk for HIV because of their unsafe sex behaviors, they still do not practice safer sex (Lewis & Malow, 1997), nor do they stop tanning even though they perceive themselves to be at increased risk for skin cancer (Lamanna, 2004). Perception of susceptibility explains behavior in some situations, but not all.

When the perception of susceptibility is combined with seriousness, it results in perceived threat (Stretcher & Rosenstock, 1997). If the perception of threat is to a serious disease for which there is a real risk, behavior is likely to change. This is what happened in Germany in 2001 after an outbreak of bovine spongiform encephalitis (BSE), better known as mad cow disease. Although mad cow disease does not occur in people, research suggests that eating cattle with the disease can result in variant Creutzfeldt-Jakob disease (CJD). Variant CJD, like BSE, affects the brain, causing tiny holes that make it appear spongelike. Both diseases are untreatable and fatal (National Institute of Neurological Disorders and Stroke, 2007). The perception of threat of contracting this disease through eating beef was one factor related to declining meat consumption in Germany (Weitkunat et al., 2003). People changed their behavior based on the perception of threat of a fatal disease.

Another example in which perception of threat is linked to behavior change is found in colon cancer survivors. Colorectal cancer is a very serious disease with a high risk of recurrence. It is the perception of the threat of recurrence that increases the likelihood of behavior change in people previously treated for this disease. In particular, changes occur in their diets, exercise, and weight (Mullens, McCaul, Erickson, & Sandgren, 2003).

We see the same thing when people perceive a threat of developing non-insulin-dependent diabetes mellitus (NIDDM). Among people whose parents had or have the disease, the perception of threat of developing it themselves is predictive of more health-enhancing, risk-reducing behaviors. Most important, they are more likely than others to engage in behaviors to control their weight (Forsyth, 1997), given that obesity is a known risk factor for NIDDM.

Just as perception of increased susceptibility does not always lead to behavior change, as we saw earlier in the chapter with college students, neither does a perception of increased threat. This is the scenario with older adults and safe food- handling behaviors. Older adults are among the groups most vulnerable to foodborne illness (Gerba, Row, & Haas, 1996) and are among those for whom it can be particularly serious. Even though they perceive a threat of illness from foodborne sources, they still do not use safe food-handling practices (Hanson & Benedict, 2002) all of the time.

PERCEIVED BENEFITS

The construct of perceived benefits is a person’s opinion of the value or usefulness of a new behavior in decreasing the risk of developing a disease. People tend to adopt healthier behaviors when they believe the new behavior will decrease their chances of developing a disease. Would people strive to exercise if they didn’t believe it was beneficial? Would people quit smoking if they didn’t believe it was better for their health? Would people use sunscreen if they didn’t believe it worked? Probably not.

A prime example of this is seen among parents who either do not have their children vaccinated for vaccine preventable diseases (VPD), or delay having them vaccinated. Children whose parents do not see a benefit in vaccination—that is, are more likely to agree vaccines may have serious side effects and that too many vaccines overwhelm the immune system, tend to not to have their children vaccinated. Conversely, parents who see a benefit to vaccination—that is, agree vaccines are effective in preventing diseases and are safe and necessary to protect the health of the child, are more likely to have their children vaccinated. (Smith, et al., 2011).

Perceived benefits play an important role in the adoption of secondary prevention behaviors, as well. A good example of this is screening for colon cancer. One of the screening tests for colon cancer is a colonoscopy. It requires a few days of preparation prior to the procedure to completely cleanse the colon: a diet restricted to clear liquids followed by cathartics.

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The procedure involves the insertion of a very long, flexible tube instrument with a camera on the end into the rectum to view the length of the colon. The procedure itself is done under anesthesia, so it is not uncomfortable, but it does take time afterward to recover, and the preparation is time consuming. Regardless of the inconvenience, this is presently the best method for early detection of colon cancer, the third leading cause of cancer deaths in the United States. When colon cancer is found early, it has a 90% cure rate. However, only 36% of people over age 50 (who are most at risk) have this screening done (New York-Presbyterian Hospital, 2006). What makes some people undergo screening and others not? Among women, those who perceive a benefit from colonoscopy (early detection) are more likely to undergo screening than those who do not see the screening as having a benefit (Frank & Swedmark, 2004).

PERCEIVED BARRIERS

Since change is not something that comes easily to most people, the last construct of the HBM addresses the issue of perceived barriers to change. This is an individual’s own evaluation of the obstacles in the way of him or her adopting a new behavior. Of all the constructs, perceived barriers are the most significant in determining behavior change (Janz & Becker, 1984).

In order for a new behavior to be adopted, a person needs to believe the benefits of the new behavior outweigh the consequences of continuing the old behavior (Centers for Disease Control and Prevention, 2004). This enables barriers to be overcome and the new behavior to be adopted (Figure 4–2).

Even though there is much education on college campuses about HIV/AIDS risk reduction, and even though students demonstrate they are knowledgeable about HIV/AIDS, condom use among African American college students remains inconsistent (Winfield & Whaley, 2002) leaving them exposed to a greater risk of infection. There is obviously something else at play here. Using the HBM to explain what that something else might be reveals that perceived barriers may be a contributing factor. Barriers such as perceived difficulty in doing the things that need to be done to protect oneself, for example. Further, research has suggested that African American men use condoms more frequently when they perceive there are fewer barriers to their use. This is important to recognize as HIV/AIDS prevention often focuses on empowering women to negotiate safer sex rather than addressing it as a shared responsibility (Winfield & Whaley, 2002) and addressing the perceived barriers of both men and women.

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FIGURE 4–2 Barriers can be overcome © iQoncept/Shutterstock, Inc.

MODIFYING VARIABLES

The four major constructs of perception are modified by other variables, such as culture, education level, past experiences, skill, and motivation, to name a few. These are individual characteristics that influence personal perceptions. For example, if someone is diagnosed with basal cell skin cancer and successfully treated, he or she may have a heightened perception of susceptibility because of this past experience and be more conscious of sun exposure because of past experience. Conversely, this past experience could diminish the person’s perception of seriousness because the cancer was easily treated and cured.

In personal health classes on many campuses, students are required to complete a behavior change project. They choose an unhealthy behavior and develop a plan to change it and adopt a more healthy behavior. The modifying variable behind this is motivation. The motivation is a grade.

FIGURE 4–3 Cue to action—don’t drink and drive © Wendy M. Simmons/Shutterstock, Inc.

CUES TO ACTION

In addition to the four beliefs or perceptions and modifying variables, the HBM suggests that behavior is also influenced by cues to action. Cues to action are events, people, or things that move people to change their behavior. Examples include illness of a family member, media reports (Graham, 2002), mass media campaigns, advice from others, reminder postcards from a health care provider (Ali, 2002), or health warning labels on a product (Figure 4–3).

Knowing a fellow church member with prostate cancer is a significant cue to action for African American men to attend prostate cancer education programs (Weinrich et al., 1998). Hearing TV or radio news stories about foodborne illness and reading the safe handling instructions on packages of raw meat and poultry are cues to action associated with safer food- handling behaviors (Hanson & Benedict, 2002). Having displays on college campuses of cars involved in fatal crashes from drunk driving is an example of a cue to action—don’t drink and drive.

SELF-EFFICACY

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In 1988, self-efficacy was added to the original four beliefs of the HBM (Rosenstock, Strecher, & Becker, 1988). Self- efficacy is the belief in one’s own ability to do something (Bandura, 1977). People generally do not try to do something new unless they think they can do it. If someone believes a new behavior is useful (perceived benefit), but does not think he or she is capable of doing it (perceived barrier), chances are that it will not be tried.

FIGURE 4–4 Health Belief Model Adapted from Stretcher, V. & Rosenstock, I.M. (1997). The Health Belief Model. In Glanz, K., Lewis, F.M., & Rimer, B.K. (Eds). Health Behavior and Health Education Theory, Research and Practice. San Francisco: Jossey-Bass.

When we look at osteoporosis, exercise self-efficacy and exercise barriers are the strongest predictors of whether one practices behaviors known to prevent this disease. Women who do not engage in the recommended levels of weight- bearing exercise tend to have low exercise self-efficacy, meaning they do not believe they can exercise, and perceive there to be significant barriers to exercise (Wallace, 2002). As a result, these women do not exercise.

In summary, according to the Health Belief Model, modifying variables, cues to action, and self-efficacy affect our perception of susceptibility, seriousness, benefits, and barriers and, therefore, our behavior (Figure 4–4).

THEORY IN ACTION—CLASS ACTIVITY

Use the constructs of the HBM to explain your own daily physical activity. Then, think about how those same constructs might help you to increase your physical activity. Share this insight with others in your class. Now, read the following article and answer the questions at the end.

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Chapter 4 Article: A Community-wide Media Campaign to Promote Walking in a Missouri Town1

Wray, Ricardo J; Jupka, Keri; Ludwig-Bell, Cathy

Introduction Engaging in moderate physical activity for 30 minutes five or more times per week substantially reduces the risk of coronary heart disease, stroke, colon cancer, diabetes, high blood pressure, and obesity, and walking is an easy and accessible way to achieve this goal. A theory-based mass media campaign promoted walking and local community- sponsored wellness initiatives through four types of media (billboard, newspaper, radio, and poster advertisements) in St Joseph, Mo, over 5 months during the summer of 2003.

Methods The Walk Missouri campaign was conducted in four phases: 1) formative research, 2) program design and pretesting, 3) implementation, and 4) impact assessment. Using a postcampaign-only, cross-sectional design, a telephone survey (N = 297) was conducted in St Joseph to assess campaign impact. Study outcomes were pro-walking beliefs and behaviors.

Results One in three survey respondents reported seeing or hearing campaign messages on one or more types of media. Reported exposure to the campaign was significantly associated with two of four pro-walking belief scales (social and pleasure benefits) and with one of three community-sponsored activities (participation in a community-sponsored walk) controlling for demographic, health status, and environmental factors. Exposure was also significantly associated with one of three general walking behaviors (number of days per week walking) when controlling for age and health status but not when beliefs were introduced into the model, consistent with an a priori theoretical mechanism: the mediating effect of pro- walking beliefs on the exposure—walking association.

Conclusion These results suggest that a media campaign can enhance the success of community-based efforts to promote pro-walking beliefs and behaviors.

INTRODUCTION

Sedentary lifestyles contribute to chronic diseases, such as cardiovascular disease, cancer, and diabetes, and to risk factors including obesity (1). In 2003, the majority of U.S. adults (52.8%) were not physically active at levels that promote health (2). In Missouri, 54.9% of the adult population in 2003 failed to get enough physical activity to provide any health benefits (2). Healthy People 2010 includes several goals that seek to increase physical activity levels in the United States (3).

Walking is an easy and accessible way to achieve the recommended amount of daily activity (4,5). Thirty minutes of brisk walking five or more times per week substantially reduces the risk of developing or dying of coronary heart disease, stroke, colon cancer, diabetes, high blood pressure, and obesity (6).

Known determinants of physical activity participation fall into multiple categories, including demographics (7), psychosocial factors (8–10), social support (7), and neighborhood and other environmental factors (11–13). Media-based interventions have been implemented to promote physical activity in recent years, but three recent reviews of media-based programs have disagreed on their potential to change behavior (14–16). Our review of studies on eight media campaigns in five countries offers moderate evidence of impact on behavior change. The data suggest success in reaching audiences,

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with exposure rates (percentage of a population that reports seeing or hearing campaign messages) ranging from 38% to 90% (17–22) and campaign-message recall rates (the percentage of the population that is able to recall a specific campaign message) ranging from 23% (19) to 30% (17). These studies also report shifts in cognition related to moderate physical activity. For example, in a study in Australia, 62% of individuals exposed to media campaigns reported awareness of the benefits of moderate physical exercise (compared with 29% of control populations) (21). In a national study in NewZealand, the percentage of adults intending to be more active increased from 2% in 1999 to 9% in 2002, following a media campaign (22).

The data on behavior change, however, are mixed. Campaigns in the United Kingdom (18) and Scotland (23) resulted in no increase or a negligible increase in physical activity despite moderate levels of exposure to media campaigns. In Brazil, a citywide campaign achieved a campaign-message recall rate of 56% and was associated with a reduction in sedentary lifestyles (19). Two national campaigns in Australia in 1990 and 1991 were shown to increase intention to engage in physical activity and physical activity behavior in older adults in the first year, but physical activity levels reached a plateau in the second year (24). A national campaign in New Zealand was associated with a 5% increase in the proportion of walkers in a national survey, but the gain declined to baseline in the second year (22). A campaign in New South Wales, Australia, was shown to increase knowledge about the benefits of physical activity and lead to increases in self-efficacy for physical activity; residents were twice as likely to engage in physical activity as residents in other states (20). A 6% national decline in physical activity improved in New South Wales to 4.4% during the period of the campaign (21). Finally, in Wheeling, WVa, a campaign reaching 90% of the population affected stages of change as well as perceived behavioral control and intention. In this quasi-experimental study, the proportion of walkers in the intervention community increased after the campaign more than it increased in a comparison community where no campaign was implemented (25).

Some consensus for an integrated approach to increase physical activity—including environmental and policy changes, community-based programs, and media campaigns—has emerged (14–16, 26). For example, in its recent evaluation of interventions promoting physical activity, the Centers for Disease Control and Prevention’s (CDC’s) Guide to Community Preventive Services (Community Guide) strongly recommends community-wide initiatives that include informational components, but it finds insufficient evidence to recommend media-only approaches (16). It is not clear which, if any, informational elements should be included in an integrated campaign to promote physical activity. This gap in knowledge has led to research designed to address two important questions. Can mass media-based interventions support community- based activities? If so, how do media campaigns contribute to community-based interventions designed to promote physical activity? To answer these questions, we describe the design, development, implementation, and impact assessment of Walk Missouri, a mass media campaign designed to promote walking in a Missouri town.

METHODS

CAMPAIGN DESIGN AND DEVELOPMENT

The Walk Missouri campaign was conducted in four phases: 1) formative research, 2) program design and pretesting, 3) implementation, and 4) impact assessment. Elements of the health belief model (HBM) (27) provided a framework for the effort from formative research through assessment. Perceived susceptibility and perceived severity, two elements of the HBM, were not included in this study because their predictive ability has been questioned in other research (28,29); perceived barriers and perceived benefits were included in the framework because they have been shown to be strongly correlated with physical activity behavior (7).

PHASE 1: FORMATIVE RESEARCH

We conducted 24 focus groups in 2001 in both midsize and large metropolitan areas across Missouri. The demographic characteristics of the focus group members participating in the formative research are shown in Table 1. Focus group questions were designed to identify perceived benefits of and barriers to walking as well as possible cues to action. Focus

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group findings indicated that participants responded more readily to messages emphasizing ways to overcome barriers and the short-term benefits of walking rather than long-term health benefits. These findings informed messages that emphasized the short-term positive outcomes of walking and identified strategies to overcome obstacles.

Table 1 Demographic Characteristics of Focus Group Participants, Walk Missouri Campaign, 2001–2002

Characteristics Formative Research Focus Groups (N = 24) Pretest Focus Groups (N = 16) No. participants 174 118 Female, % Not collected 80 Age, median, y (range) 44 (18–83) 46 (18–83) Education, mean, y (range) 14.3 (8–16+) 14.5 (9–16+) Household income, mean, $ 30,000–39,999 30,000–39,999 Race and ethnicity, %

White 83 83 African American 15 14 Native American 1 1 Hispanic 1 1 Other 0 1

PHASE 2: MESSAGE DEVELOPMENT AND PRETESTING

Behavioral messages for the media campaign were developed as cues to action, reminding Missourians of the short-term health benefits of walking (e.g., losing weight), the social benefits (e.g., spending time with loved ones), and the pleasure benefits (e.g., having fun). Messages also communicated ways to overcome barriers (e.g., providing ideas on how to incorporate walking into a busy schedule). Messages included phrases and themes drawn from the formative research focus groups. Messages used a question-and-answer format, with a message answering one of four questions: when, where, why, or with whom do you walk? Answers were emphasized more than questions (i.e., were spoken first in radio advertisements or were set in larger typeface on billboards and posters and in newspaper advertisements). Messages also used the phrase “do it” to pique curiosity.

Print, radio, and television messages were developed and tested with 16 focus groups representing a range of audience segments across Missouri. Table 1 shows the characteristics of this second group of participants. The campaign strategy was received positively by the focus groups, and message materials were selected for the campaign.

PHASE 3: IMPLEMENTATION

The multimedia campaign took place during a 5-month period from May through September 2003. The media plan consisted of billboard, newspaper, radio, and poster advertisements. (Television spots were not used because of the expense of buying airtime.) The strategy for media placement was to achieve the greatest visibility at the outset, in May and June, followed by reduced numbers of advertisements from July through September. In a press conference to kick off the campaign, local political leaders and coalition partners announced the Walk Missouri campaign to local radio, television, and newspaper outlets. Table 2 shows the amount and cost of media space and time purchased. Table 3 presents a complete list of messages included in all types of media in the St Joseph, Mo, campaign.

Table 2 Media Purchases for Walk Missouri Campaign, St Joseph, Mo, 2003

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Type Amount Spent, $ No. Advertisements Billboards 2760 8 Newspapers 5862 16 Radio 9876 1296 Posters 800 200 Total 19,298 1520

Table 3 Messages Promoted in Walk Missouri Campaign, St Joseph, Mo, 2003

Type Messages Used Billboards • I like to do it with my best friend. Who do you walk with?

• Sunday afternoons are a family affair. When do you walk? • We like to do it in nature’s backyard. Where do you walk? • We do it because it’s better than television. Why do you walk? • It’s like recess for grown-ups. Why do you walk?

Radio • We do it as a church group. I do it with my coworkers on my lunch hour. We do it together. I do it with friends at the gym. Who do you walk with?

• I do it first thing in the morning to start the day off right. I do it on my lunch hour; it feels great. I do it on my way to work and on my way home. I do it after dinner to help me wind down. When do you walk?

• I do it around my neighborhood every morning. I do it at the mall. I do it at the gym. I do it at the park. I do it around the softball field while my daughter practices. Where do you walk?

• We do it because a family who plays together, stays together. I do it because it’s easy to fit into my busy schedule. I do it to feel better and have more energy. I do it to lose weight. I do it because it’s fun. Why do you walk?

Newspaper • My co-worker and I keep each other on track. Who do you walk with? • Sunday afternoons are a family affair. When do you walk? • We like to do it in nature’s backyard. Where do you walk? • I do it to set a good example. Why do you walk?

Posters • It’s great for catching up with my buddy. Who do you walk with? • We like to do it on cool, cloudy days. When do you walk? • We like to do it in nature. Where do you walk? • I do it for my health. Why do you walk?

The campaign was designed to reach adult residents of St Joseph, Mo. A midsize town with a population of 84,909 in 2003 (30), St Joseph is located about 45 miles north of Kansas City, Mo. The community was chosen because of its cooperation during the first two stages of the study and its commitment to physical activity initiatives. St Joseph had already developed local initiatives to increase physical activity, including an extensive network of walking trails and an active worksite wellness coalition (Get Movin’ St. Joe) led by the Buchanan County Health Department, the YMCA, and Heartland Health, the owner of a local hospital. The Get Movin’ St. Joe coalition was active in the community, working with worksites, schools, and athletic organizations for at least 1 year before the Walk Missouri campaign. Although the worksite wellness coalition advertised its events through its affiliated groups, it had not engaged in mass media advertising. Consistent with the Community Guide recommendation for community-wide campaigns (16), the Walk

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Missouri campaign tapped into these local initiatives, augmenting available resources and increasing campaign reach. The Get Movin’ St. Joe coalition organizers participated in implementing the Walk Missouri campaign. Local walking

resources and activities organized by Get Movin’ St. Joe were incorporated into Walk Missouri newspaper and radio advertisements. For example, the Get Movin’ St. Joe logo was incorporated into the Walk Missouri campaign advertisements. Local collaborators increased visibility of the Walk Missouri campaign by distributing and displaying Walk Missouri campaign posters in community centers, businesses associated with the worksite wellness program, and other locations across the town. In this way, the Walk Missouri media effort helped to advertise community-sponsored walking activities and resources while capitalizing on local efforts to expand Walk Missouri campaign reach.

PHASE 4: IMPACT ASSESSMENT

The objectives of the Walk Missouri campaign were as follows:

• To increase knowledge and positive beliefs about the social and short-term health benefits as well as pleasures of walking;

• To increase knowledge and positive beliefs about ways to overcome barriers to walking; • To increase participation in community walking and wellness activities; and • To increase amount and frequency of walking.

A community-wide campaign to promote walking in a Missouri town Source: Reproduced from Wray, R.J., Jupka, K., Ludwig-Bell, C. (2005). A community-wide media campaign to promote walking in a Missouri town. Preventing Chronic Disease, 2(4). Available from: http:/www.cdc.gov/pcd/issues/2005/oct/05_0010.htm.

The figure shows the conceptual framework of the evaluation, including the hypothesis that exposure to the Walk Missouri campaign could achieve a direct effect on walking behaviors, indicated by the arrow linking exposure to behaviors. Alternately, there might be an indirect effect of exposure on behaviors, mediated by pro-walking beliefs, indicated by the arrows linking exposure with beliefs and beliefs with behaviors. In testing for either of these effects, we controlled for likely moderating factors.

EVALUATION METHODS

SURVEY DESIGN AND SAMPLE

The Saint Louis University Institutional Review Board approved this study. A postcampaign-only design was used: phone numbers for residents living within the city of St Joseph were purchased from a market research firm, and a random-digit–

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dial telephone survey was conducted. Individuals were eligible to participate if they identified themselves as adult (aged 18 years or older) residents of St Joseph. Trained callers conducted the interviews between July 31 and October 31, 2003. The survey required an average of 15 minutes to complete. Individual numbers were dialed numerous times before being eliminated from the survey. A total of 297 interviews were completed with the funds available for evaluation.

MEASURES

Exposure. As in the evaluation of other media campaigns (31), various campaign exposure measures were used to evaluate the Walk Missouri campaign, including campaign-exposure questions, media-type–exposure questions, and dose-exposure questions. Both prompted and unprompted questions were asked. (The Appendix provides all survey items used in the analysis.) To discern media-type dose exposure, individuals were first asked if they had been exposed to any campaign advertisements through billboards, radio, or newspapers or if they had seen any campaign posters or news stories about the campaign. (News stories were initiated by local newspapers in response to the press conference and the campaign.) Individuals who answered in the affirmative for a media type were then asked how many times they had been exposed to that type. For example, respondents who answered yes for billboards were asked in how many locations they had noticed billboards sponsored by the campaign (with answers ranging from one to six billboards). Respondents who answered yes for radio were asked how many times they had heard radio advertisements sponsored by the campaign (with survey items offering ranges of 1 to 5, 6 to 10, 11 to 20, 21 to 50, 51 to 100, or more than 100 times).

Two variables were developed for analysis of exposure: a four-level dose-exposure scale and a dichotomous variable (exposed and unexposed). The four-level dose-exposure scale summed the five media-type dose-exposure items in the survey. A higher value on this scale signifies either more types of media through which the campaign was seen or heard or a greater number of messages seen or heard through fewer types of media. Because the scale was highly skewed toward no exposure, the scale was recoded as a four-category variable (none, low, medium, and high exposure) to make coefficients more stable. A value of 1 (low) signifies that the respondent reported seeing one billboard, newspaper advertisement, or newspaper story; heard only five or fewer radio advertisements; or saw only five or fewer posters. A value of 2 (medium) signifies that the respondent reported exposure to the campaign through two or three types of media or exposure to more advertisements through one or two types of media. A value of 3 (high) signifies that the respondent reported exposure on four or more types of media or exposure to more advertisements on fewer types of media. Because of varying ranges within survey items and different kinds of exposure to messages on different types of media, it was not practicable to convert the scale into levels of frequency of exposure. The four-level scale was used to assess associations of amount of exposure with study outcomes; it was also recoded into a new dichotomous variable (exposed and unexposed) to test for group differences.

Beliefs. The survey asked participants 12 questions about their opinions of exercise using a 5-point Likert scale. Four subscales were computed from nine survey items to measure theoretical belief constructs. Despite a small number of items in each subscale (only two or three), the Cronbach α calculated for each subscale was near or higher than minimum desired level of α = .70 for social benefits (α = .66), pleasure benefits (α = .58), health benefits (α = .73), and social support (α = .60). (A fifth subscale for overcoming barriers was dropped from the analysis because of an unacceptably low Cronbach α of .46.) Subscales were computed using belief items that were recoded to three levels because the individual items and the resulting scales had normal distributions and would provide more stable results in statistical analyses. Strongly disagree, disagree, and neutral were consolidated as one value coded as 1; agree was coded as 2; and strongly agree was coded as 3. To make them comparable, each subscale was computed to three value scales by dividing the summed scale by the original number of items. To test for a mediating effect of beliefs on the association between exposure and behaviors, a single all-beliefs scale was computed from all 12 belief items in the survey (Cronbach α = .75).

Behavior measures. The survey asked six walking-behavior questions. Three dichotomous (yes or no) measures inquired about walking and wellness activities sponsored by Get Movin’ St. Joe. One dichotomous (yes or no) and two continuous measures of walking behavior were adapted from physical activity measures in the 2000 Behavioral Risk Factor Surveillance System (BRFSS).

Moderators. Various measures were included to control for possible alternative explanations for evaluation results. Demographic measures included sex, age, race, and level of education. Health-related measures included health status,

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medical diagnosis of chronic disease or overweight, medical advice to walk more, and recent injury. Perceived safety of the participant’s walking environment was assessed using six Likert items. Cronbach α for the scale was .63.

Analysis. Our conceptual framework (Figure) offers the hypothesis that exposed individuals are more likely to hold beliefs consistent with campaign themes and more likely to engage in walking activities than individuals not exposed to the campaign and controlling for likely alternative explanations. In the multivariate analyses, we controlled for moderators that were significantly associated with outcomes. In addition, we hypothesized that beliefs had a mediating effect on the association between exposure and behaviors.

Group differences for the exposed and unexposed portions of the sample were assessed using two-tailed t tests for ordinal and continuous outcomes and chisquare tests for dichotomous outcomes. Associations between amount of exposure and outcomes were assessed using the Spearman rank correlation (ρ). For multivariate analyses, linear regression was used for ordinal and continuous outcomes, and logistic regression was used for dichotomous outcomes. When beliefs were significantly associated with behaviors, stepwise regression was used to test a mediating effect of beliefs on any associations between exposure and behaviors.

RESULTS

RESPONSE RATE

During data collection, 4668 phone numbers were used, 2866 of which were out of scope (e.g., businesses, out-of-service numbers, numbers failing to be answered after multiple calls). Of the remaining 1802 numbers, 1461 refused participation before we were able to determine eligibility. Of the remaining 341, five respondents were aged younger than 18 years, bringing our total number of eligible respondents to 336. The total number of completed interviews was 297, resulting in a cooperation rate of 88% (297/336). Compared with the Council of American Survey Research Organization (CASRO) response rates of 54.6% found for the BRFSS Missouri, which include estimates of eligible households among households for which eligibility was not determined, our response rate was low at 17% (32). However, our response rate proved to be better than the rate of 9.1% provided for random-digit–dial surveys tracked by the Market Research Association (33). Low response rates for random-digit–dial surveys are increasingly a problem for evaluators of health promotion interventions (34).

SURVEY FINDINGS

SAMPLE CHARACTERISTICS AND EXPOSURE LEVELS

The sample had more women and was older, better educated, and more diverse than the U.S. census indicates for this area (Table 4). Thirty-two percent of the sample reported exposure to the campaign through news and advertising media. Exposure levels by type of media ranged from 7% (newspaper advertisements) to 13% (newspaper articles and billboards). Among respondents reporting exposure to the campaign through different types of media, the median number of advertisements reported for each type of media was one to five posters, two newspaper advertisements, two newspaper stories, one billboard, and six to 10 radio advertisements. The exposed respondents (32%) were distributed evenly to low (11%), medium (10%), and high (11%) levels within the dose-exposure scale. A separate analysis found no demographic differences between exposed and unexposed groups, nor was any association found between dose exposure and demographic characteristics.

Table 4 Demographic Characteristics of Respondents and Results of Telephone Survey to Assess Walk Missouri Media Campaign Impact on Community, St Joseph, Mo, 2003

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BELIEFS

On a scale of 1 to 3, with 2 indicating agree, survey participants rated all beliefs as approximately 2 (Table 5). The mean for the all-beliefs scale was 4, also equivalent to agree, on the 5-point scale. The exposed group reported greater agreement with two of the four belief subscales (social benefits and pleasure benefits) than the unexposed group at a statistically significant level. Amount of exposure was associated with three of four subscales (social, pleasure, and health

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benefits) and the all-beliefs scale at a statistically significant level.

BEHAVIORS

The exposed group reported a greater level of participation in three of six wellness or walking behaviors than the unexposed group at a statistically significant level. Amount of exposure was associated with the same three behaviors at a statistically significant level. Two of the outcomes were wellness behaviors: participation in a community-sponsored walk or participation in a health fair. The third outcome was a general walking behavior: the number of days per week the respondent walked at least 10 minutes.

ASSOCIATION OF EXPOSURES, BELIEFS, AND BEHAVIORS

Beliefs and behaviors associated with campaign exposure at the bivariate level were selected for multivariate analysis, controlling for variables associated with the dependent variable in bivariate analyses. Campaign-dose exposure remained associated with two of the four belief subscales (social benefits and pleasure benefits) when controlling for likely confounding factors (Table 6). The association of campaign-dose exposure with the health benefits subscale and all- beliefs subscale was not statistically significant when controlling for other factors.

Table 5 Telephone Survey Results by Level of Exposure to Walk Missouri Media Campaign, St Joseph, Mo, 2003

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Table 6 Linear Regression Analysis for Belief Subscales, Walk Missouri Campaign, St Joseph, Mo, 2003

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Campaign-dose exposure remained associated with participation in a community-sponsored walk at a statistically significant level when controlling for educational level. In this analysis, the odds ratio of dose exposure was 2.14 (confidence interval, 1.04–4.41; P = .04); exposed respondents were more than twice as likely to participate in the community-sponsored walks than unexposed respondents. In another multivariate analysis (not shown), dose exposure was not associated with participation in community-sponsored health fairs when controlling for other factors.

Campaign-dose exposure was associated with the number of days per week walking at a statistically significant level when controlling for age and health status (Table 7). However, when the all-beliefs scale was introduced in the second step of the linear regression, the coefficient for campaign exposure lost statistical significance.

DISCUSSION

Impact assessment of media campaigns seeks to answer the question “Did exposure to the campaign lead to changes in beliefs and behavioral outcomes?” The evidence presented here shows that exposure to the Walk Missouri campaign had limited effects, producing small increases in positive walking beliefs and behaviors among residents of St Joseph. Effect sizes were small, with Spearman ρs of between 0.13 and 0.18 for statistically significant associations of beliefs and behaviors with campaign-dose exposure. Among exposed respondents, 4.3% reported participation in community- sponsored walks, compared with 0.5% of unexposed respondents. Exposed respondents reported walking for at least 10 minutes per day 5.2 days of the week, compared with 4.5 days per week for unexposed respondents.

Table 7 Stepwise Linear Regression for Number of Days Walked per Week, Walk Missouri Campaign, St Joseph, Mo, 2003a

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When an external control community and baseline measures are not possible for assessing the impact of media campaigns, alternative approaches can offer evidence of effects when they show 1) moderate levels of campaign reach; 2) associations of exposure with behaviors, controlling for alternative explanations; 3) and confirmation of an a priori hypothesis positing mediation of exposure–behavior associations by beliefs promoted by the campaign (35). The Walk Missouri evaluation set out to provide such evidence. First, survey respondents reported a moderate level of exposure to the campaign, with about one in three respondents reporting some exposure. This level of exposure was near the 36% average for health-behavior media campaigns found in a recent meta-analysis (36). Second, exposure was significantly associated with three of six walking-behavior measures, and one association remained when controlling for several known predictors: demographic characteristics, health status, perceptions of the walking environment, and health beliefs.

Third, in addition to differences between exposed and unexposed groups, there was a dose-response relationship between exposure and outcomes, with higher levels of agreement on beliefs and positive walking behavior corresponding to higher levels of exposure.

Fourth, the association of exposure and number of days walking was mediated by health beliefs, providing evidence of a theoretically informed causal mechanism. For one wellness behavior—participation in community-sponsored walking activities—beliefs did not mediate the association with exposure. We conclude that campaign information on walking opportunities increased knowledge about these activities, leading to a slight increase in walking.

The single-site, postcampaign-only design limited the power of the study in several ways. Lack of an external control community and baseline measures may have weakened the study’s internal validity. We cannot rule out the possibility of reverse causal direction—that walking adherents were more likely to pay attention to and recall the campaign. Nor can we conclude that the associations we found were not the result of other unmeasured third factors. The low response rate for our random-digit–dial survey may have introduced selectivity bias into the sample and limited our ability to generalize even to the medium-size midwestern town of St Joseph. Self-report measures are vulnerable to socially desirable responses, although there is no evidence that this necessarily contributed to differences between groups.

Acknowledging these limitations and caveats, our study provides information for public health communication researchers and practitioners about the potential for media interventions to promote physical activity. The study elucidates how a media campaign can contribute to a community-sponsored effort to promote walking behavior.

EVIDENCE DESPITE A SIMPLE DESIGN

Elements of this single-site, postcampaign-only, cross-sectional design support our claim of limited effect. Careful and multiple measurements of exposure allowed for the creation of an unexposed comparison group. Two measures of exposure confirmed both group differences and dose-response associations of exposure and outcomes. A dose-response

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relationship suggested a possible causal relationship between exposure to the media campaign and increased likelihood of undertaking walking behaviors (37). We account for likely alternative explanations for associations of exposure with outcomes by including moderating factors in multivariate analysis. Empirical support for a theoretical mechanism is established in a test of the mediating effect of pro-walking beliefs on the association of campaign exposure and walking behavior. Combined, these results strengthen our claim of limited effect by ruling out alternative explanations and supporting an a priori theoretical approach that underlies the campaign strategy and study design (35).

SUPPORT FOR THE MESSAGE STRATEGY

The association of exposure with social and pleasure benefits suggests that the campaign was most successful in communicating these ideas. Although health benefits, social support, and overcoming barriers were also included in the messages, they did not appear to have as much of an impact on the intended audience.

SUCCESSFUL INTEGRATION INTO LOCAL ACTIVITIES

Originally envisioned and designed as a stand-alone media campaign, Walk Missouri was successfully integrated into local community-sponsored activities, consistent with recommendations from the literature (14–16,26), including