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CHAPTER 5 THE HEALTH BELIEF MODEL...

CHAPTER 5 THE HEALTH BELIEF MODEL Celette Sugg Skinner Jasmin A. Tiro Serena A. Rodriguez FS O O Vignette: “I Thought This Wouldn’t Be a Problem Once KEY POINTS We Had a Vaccine” PR This chapter will: Suggest health problems that can be The development of effective vaccines has allowed us to address some of the most ­addressed by the Health Belief Model (HBM). serious health threats to humans and animals alike. To health professionals and Introduce the HBM’s historical development, many others, receiving a shot might appear to be a fairly straightforward and dis- key components, supporting evidence, and crete behavior, particularly in comparison to more difficult behavior changes, such how its constructs are measured and used in D as weight loss and stopping smoking, that require repeated actions to achieve and maintain desired outcomes. However, vaccines’ potential for prevention has not interventions. Present applications of the HBM to perceptions TE of diabetes risk and receipt of COVID-­19 always translated into swift or sustained acceptance. Adoption by US adolescents vaccination. of the first and only cancer-­prevention HPV vaccine—­first introduced in 2006 and requiring two doses for full coverage—­was only 75% for initial vaccination by 2020, EC with only 58.6% of adolescents fully up-­to-­date in that year (Pingali et al., 2021). As of September 2022, when COVID-­19 has taken the lives of over a million people in the United States and 6.5 million worldwide, only 67% of people eligible in the United States and 61% of the world population had received the first two recom- R mended doses of the vaccine (Mathieu et al., 2022). Health behavior theory can help us understand what’s going on here by elucidating key factors that influence R whether people take recommended actions to protect their health. We may feel just as frustrated today explaining these behaviors as the developers O of the first widely used health behavior theory—­the Health Belief Model (HBM)—­ back in the 1950s. In those years, tuberculosis (TB) was still a major health threat in the United States. Toward the goal of reducing morbidity and mortality, the US C Public Health Service rolled out a program for neighborhood-­based screening via mobile vans. The plan was innovative, and the potential benefits seemed obvious. N But even when vans were deployed into people’s neighborhoods, many fewer people than expected came to be screened. That put millions of people at risk for contract- U ing TB—­a highly contagious and potentially deadly and debilitating disease. Health Behavior: Theory, Research, and Practice, Sixth Edition. Edited by Karen Glanz, Barbara K. Rimer, and K. Viswanath. © 2024 John Wiley & Sons, Inc. Published 2024 by John Wiley & Sons, Inc. 0005800229.INDD 1 03-09-2024 08:05:49 PM  2 Chapter 5: The Health Belief Model The Public Health Service, wisely, realized a need to involve behavioral scientists who could help to explain the complex decisions influencing adoption of TB screening and, ultimately, other health-­related behaviors. Thus was born the field of modern health behavior theory, and its first product was the HBM (Hochbaum, 1958). Introduction The HBM remains one of the most widely used conceptual frameworks in health behavior research and practice. Over the decades, it has been expanded, compared, and contrasted to other theories and frameworks, and used to inform FS behavior-­change interventions—­both alone and in combination with other theories. In this chapter, we review the HBM’s historical development, core constructs, hypotheses, and relationships of ­constructs to each other and to specific health behaviors, as well as the empirical evidence supporting it. Of the many health behaviors to which researchers have applied the HBM, we provide examples relevant to diabetes risk and O COVID-­19 mitigation measures, using the first example to focus on how the constructs of this model are measured, and the second on how they can be used in interventions to change health behavior. O Origins of the Health Belief Model PR The HBM was originally developed by Dr. Godfrey Hochbaum, a social psychologist who immigrated to the United States from Austria following World War II, and his colleagues at the US Public Health Service to explain the wide- spread failure of people to participate in programs to prevent and detect disease (Hochbaum, 1958; Rosenstock, 1960). It was then extended to study people’s behavioral responses to opportunities for early detection of potentially curable diseases, and their response to illnesses, with particular focus on adherence to medical regimens (Becker, 1974; D Kirscht, 1974). HBM constructs were built on tenets of Cognitive Theory briefly discussed below. During the first half of the 20th century, social psychologists had developed two major approaches for explaining TE ­behavior: Stimulus-­Response (S-­R) Theory (Watson, 1925) and Cognitive Theory (Lewin, 1951). S-­R Theory posited that events (termed reinforcements) affect physiological drives that, in turn, activate behavior. American psychologist B. F. Skinner (1938) hypothesized that behavior is determined by its consequences, or reinforcements, and that the mere EC temporal ­association between a behavior and a reward or punishment immediately following that behavior was suffi- cient to increase the probability that the behavior would be repeated or avoided. According to S-­R Theory, behavior is automatic and does not require mental processes, such as reasoning or thinking. Researchers now regard behavior as more complicated in most cases. R Conversely, cognitive theorists argued that reinforcements operated by influencing expectations rather than by influencing behavior directly. Mental processes—­thinking, reasoning, hypothesizing, or expecting—­are critical compo- R nents of cognitive theories, which are often termed value-­expectancy models because they propose that behavior is a function of the degree to which individuals value an outcome of a specific behavior and their assessment of the probability, O or expectation, that a particular action will achieve that outcome (Lewin et al., 1944). For health-­related behaviors, the value is avoiding illnesses and staying or getting well. The expectation is that a specific health action may prevent C (or ameliorate) an illness or condition for which people believe they may be at risk. N Key Components of the HBM Findings from Hochbaum’s initial study of participation in TB screening were striking (Hochbaum, 1958). Among U ­individuals who believed that they were more susceptible to TB, and who believed that early detection offered benefits, 82% had at least one voluntary chest X-­ray. In contrast, only 21% of those who perceived lower personal susceptibility and benefits obtained X-­rays. Some recent studies have found more modest associations between perceived susceptibility and health behavior. In addition to TB screening, the HBM can be applied to many other health behaviors (such as medication a­ dherence) with potential to reduce risk of developing a health condition and/or the effects of an existing disease or condition. As Charles Abraham and Paschal Sheeran explain, the cognitive theorists who developed and refined this model did not ignore social factors that influence health behaviors. They understood that demographic and socioeconomic factors 0005800229.INDD 2 03-09-2024 08:05:49 PM  Introduction 3 were associated with both preventive behaviors and accessing health services. However, because their goal was to develop a framework that could be used to influence behavior change, they focused on specifying a set of “common-­ sense beliefs” that might be modified via health education interventions (Abraham & Sheeran, 2015). In our 21st-­century conceptualization of multi-­level determinants of both health and health behaviors, the HBM should be understood as primarily applicable to individual-­level factors. However, it can be combined with constructs from other theories that operate at different levels. Other chapters in this book address some of those theories. The HBM contains several primary components (or constructs) associated with whether and why people act to prevent, detect, or control illness condition. The model’s overall premise is that people are likely to engage in health FS behavior if they believe: 1. They are at risk for (or susceptible to) a condition. 2. The condition could have potentially serious or severe consequences. O 3. A course of action (behavior) available to them could reduce their susceptibility to and/or the severity of the condition. 4. There are benefits to taking action. O 5. The perceived barriers (or costs) would not be strong enough to prevent action. Other internal or external experiences (“cues”) may also prompt action, either working through or outside of PR these beliefs. The more precise definitions of these HBM constructs are summarized below: Perceived Susceptibility is a belief about the likelihood of developing a disease or condition. For instance, people must believe they are at risk of getting colon cancer or breast cancer before they are willing to get screened for D these ­cancers. Those being asked to take statins to prevent heart disease and stroke must believe they are at risk of these serious health problems if their high cholesterol is not managed. TE Perceived Severity is a belief about the seriousness of contracting a condition or of leaving it untreated, including health consequences (e.g., death, disability, and pain) and social consequences (e.g., ability to work, maintain relationships, or feeling stigmatized). EC Perceived Threat is sometimes described as the construct formed by the combination of susceptibility and severity. Perceived Benefits are beliefs about positive effects or advantages of a recommended action to reduce threat of a disease, health condition, or its consequences. Other nonhealth-­related benefits might be tangible (“I’ll save money by quitting smoking”) or social {“I’ll feel better about myself if I follow my doctor’s recommendation” or R “my family member concerned about my cancer risk will be satisfied if I have a colonoscopy”). Perceived Barriers are possible negative consequences associated with an action. They may impede initial action or R subsequent repeat of the behavior, and may be tangible (“If I quit, I’ll be ridiculed by my still-­smoking friends”) or psychological (“trying to quit might cause me anxiety”). O Cues to Action. In 1958, Hochbaum originally proposed that perceived susceptibility and perceived benefits were relevant only if activated by other factors that he termed “cues” to instigate action (Hochbaum, 1958). These C cues could be internal (such as noticing a symptom that increased perceived threat), or external (such as media publicity; receiving a recommendation from a doctor, a free sample, or an individualized reminder from a N health center; or even learning about a friend’s diagnosis). In the 1990s, Victor Strecher, Victoria Champion, and Irwin Rosenstock suggested that cues operate mainly U as perceived threats, such as a painful sunburn increasing perceived risk of skin cancer and prompting people to add sunscreen to their shopping list (Strecher et al., 1997). However, Hochbaum (1958) also suggested sce- narios through which the cue directly prompts behaviors without operating through beliefs. His often-­used classroom example was a point-­of-­purchase display at the drugstore counter that prompts a person to add a tube of sunscreen to the cart even if her perceptions do not change: she already believes in the benefits of sun- screen but would have left the store without it were it not for the prompt and easy access at the cash register. This construct of a cue resembles more recent suggestions by behavioral economists that a simple “nudge” may prompt a person to change behavior (Thaler & Sunstein, 2009) (Also see Chapter Twenty). 0005800229.INDD 3 03-09-2024 08:05:49 PM  4 Chapter 5: The Health Belief Model Efficacy Expectations. Years after the HBM was developed, Albert Bandura (2005) introduced the constructs of ­self-­efficacy and outcome efficacy expectations in his Social Cognitive Theory, postulating that behavior is guided by cognitive and affective (emotional) factors as well as biological and external events (see Chapter Nine). Outcome efficacy—­beliefs about the extent to which a particular behavior will lead to a certain outcome (Bandura, 1997, 1999)—­resembles the HBM construct of perceived benefits. The construct of self-­efficacy—­ the conviction that one can successfully execute a behavior—­was not clearly represented by an HBM construct (although lack of self-­efficacy was sometimes noted as a barrier to taking action) (Mahoney et al., 1995). In 1988, Rosenstock and colleagues suggested that self-­efficacy be added to the HBM as a separate construct FS (Rosenstock et al., 1988). Other Variables. An early assumption of the HBM was that demographic, structural, and psychosocial factors may affect beliefs and indirectly influence health behaviors. For example, sociodemographic factors, such as educa- tional attainment, can indirectly influence behaviors by altering perceptions of susceptibility, severity, benefits, O and barriers (Rosenstock, 1974; Salloway et al., 1978). However, the model does not specify how such factors operate or interact with other constructs. This remains a major gap in the HBM. O Operationalization of the HBM (Critical Assumptions and Hypotheses) PR Figure 5.1 depicts components of the HBM, with arrows indicating pathways through which the model’s constructs are linked to each other and to health behaviors. As shown, sociodemographic variables, such as age, sex/gender, race/­ ethnicity, education, income, and insurance status, may moderate relationships between health beliefs and health behaviors. For example, because cancer is more prevalent among older people, a person’s age may moderate the rela- D tionship between perceived threat and cancer screening behavior such that older individuals believe themselves to be at a greater risk for cancer and rate cancer as a more severe disease than younger adults. Gender may moderate the effects TE Sociodemographic factors: e.g., age, sex/gender, race/ethnicity, education, EC income, rural/urban residence, insurance status Health beliefs R Perceived benefits R Perceived barriers O Perceived threat C Health action/behavior Perceived susceptibility N Perceived U severity Self-efficacy Cues to action Figure 5.1 Components of the Health Belief Model 0005800229.INDD 4 03-09-2024 08:05:50 PM  Introduction 5 of perceived susceptibility and benefits regarding HPV vaccination because more attention has been drawn to the link between HPV infection and cervical cancer than to anal, penile, and oropharyngeal cancers. In addition, cues to action may affect health behaviors either directly or indirectly through their influence on health beliefs. The HBM clearly specifies that health beliefs collectively affect behaviors, but it does not delineate precise combi- nations, weights, and relationships among variables (Abraham & Sheeran, 2015). This ambiguity has led to variations in how the HBM is applied in research. For example, while many studies have evaluated the direct path between beliefs and a given health behavior, others have tested constructs by using mathematical combinations that were not part of the model’s original specification. FS In the 1970s, Marshall Becker and Lois Maiman (1975) evaluated whether barriers should be subtracted from ben- efits. Conceptually, they argued that a kind of unconscious cost-­benefit analysis occurs wherein individuals weigh the behavior’s expected benefits against perceived barriers: “It could help me,” the person might think, “but it may be expen- sive, painful, unpopular, unpleasant, or inconvenient.” This combination of benefits and barriers is similar to the O Transtheoretical Model’s later proposition that people weigh pros and cons of a given behavior against each other to form a single decisional balance score (see Chapter Seven). However, Neil Weinstein (1988) later argued that benefits O and barriers are qualitatively different and should be treated as distinct constructs with the potential to be linked to other HBM constructs and behavior through different pathways. Weinstein’s position has been borne out in the HBM PR context through psychometric testing of barrier and benefit scales, showing that they act as separate factors influencing behavior (Tiro et al., 2005). Studies using structural equation modeling to test multiple pathways have also shown that perceived benefits and barriers have independent associations with behavior (Gerend & Shepherd, 2012; Murphy, et al., 2013). The HBM was developed long before these statistical models were available to conduct such analyses. Empirical Evidence Supporting the HBM D TE Over the years, critical reviews of the HBM’s predictive validity have combined or analyzed results from many studies to assess its performance. Generally, the model’s constructs have been found consistently to predict health behaviors (Carpenter, 2010; Harrison et al., 1992; Janz & Becker, 1984; Jones et al., 2014; Zimmerman & Vernberg, 1994). Researchers’ interest in the relative predictive value of individual HBM constructs has waxed and waned over time. EC Studies that have assessed individual contributions of various constructs have found that the construct perceived barriers seem to be the most powerful single predictor of behavior (Carpenter, 2010; Harrison et al., 1992), followed by perceived benefits, with the magnitude of effect higher for prevention and risk-­reduction behaviors (such as vaccination, child safety restraints) than for treatment behaviors (such as adherence to a drug or medical regimen). Perceived R ­susceptibility was found to follow the same pattern as benefits, being a stronger predictor of preventive health behaviors (Janz & Becker, 1984), some of which are one-­time or periodic actions, like having a vaccine (although the need for R multiple vaccine doses for HPV and COVID-­19 adds complexity) or screening test, rather than behaviors like smoking cessation or physical activity, that must be practiced daily. In some meta-­analyses, perceived severity (Harrison et al., 1992; O Janz & Becker 1984) and perceived susceptibility (Carpenter, 2010) have been the weakest behavioral predictors. In the 1990s, some researchers (such as Lewis, 1994) suggested that susceptibility and severity should be combined C additively or multiplicatively to create the overarching construct of perceived threat (threat = susceptibility + [suscepti- bility × severity]). Others suggested that low variance in perceived severity for some health conditions—­such as the N almost universal perception that lung cancer is very severe—­empirically leads to small effect sizes (Harrison et al., 1992). No reviews have evaluated the contribution of cues to action due, in part, to the fact that few studies have explained U whether or how cues were measured or used for interventions (Abraham & Sheeran, 2015). Finally, no systematic reviews have evaluated whether the addition of self-­efficacy increases the HBM’s predictive validity. Measurement of HBM Constructs One of the most important limitations in both observational and intervention research using the HBM has been variabil- ity in measurement of its central constructs (Carpenter, 2010; Harrison et al., 1992; Janz & Becker, 1984). If researchers do not measure people’s HBM-­related perceptions consistently, they will be unable to understand the extent to which those perceptions are related to health behaviors and whether interventions are effective in changing these perceptions. 0005800229.INDD 5 03-09-2024 08:05:50 PM  6 Chapter 5: The Health Belief Model Several important principles should thus be used to guide HBM measurement. First, construct definitions should be consistent with the HBM as originally conceptualized. Measures also should be specific to the behavior being addressed (e.g., barriers to mammography may be quite different from barriers to colonoscopy) and relevant to the population among whom they will be used (e.g., groups with lower versus higher health literacy may respond differently to certain measures). Further, to ensure content validity of each HBM construct, it is important to measure the full range of factors that may influence behaviors, especially where the constructs are inherently complex. For example, measuring a single barrier to a health behavior would be insufficient if a person perceived multiple barriers to a health behavior. Formative research can identify factors perceived as particular benefits, barriers, and susceptibility beliefs for FS particular health behaviors, among particular populations, and in particular settings. Once identified, these beliefs may be incorporated into scales that include multiple items for each construct (King et al., 2012; Rawl et al., 2000, 2001; Russell, et al., 2003; Vernon et al., 1997). O Applications of the Health Belief Model O In this section, we discuss how the HBM has been applied to research related to two very different health behaviors. First, we examine how perceived risk for diabetes has been measured, realizing that those who do not perceive they are PR at risk will see no reason to engage in risk-­reducing behaviors. Next, we discuss how HBM constructs have been employed in studies of COVID-­19 vaccinations. HBM and Diabetes Risk D Type 2 diabetes is a chronic condition characterized by high blood sugar, occurring when the pancreas cannot effec- tively use the hormone insulin that regulates blood sugar levels. Resulting complications include hypoglycemia, kidney TE disease, nerve damage, poor circulation, heart disease, and vision problems. Globally, type 2 diabetes ranks among the top ten leading causes of death. Screening individuals at high risk for developing diabetes (including those who are ­overweight/obese and/or have a family history of the disease); detecting diabetes at an early stage; and connecting the newly diagnosed to treatment can reduce diabetes-­related morbidity and mortality (Herman et al., 2015; US EC Preventive Services Task Force, 2021). However, many people at high risk for developing type 2 diabetes—­and even many people who already have the condition—­are unaware of their status. In the United States, more than 20% of ­individuals with diabetes remain undiagnosed (Centers for Disease Control and Prevention, 2022). Multiple studies have used the HBM to examine beliefs and attitudes, such as perceived susceptibility, among R ­undiagnosed individuals. These studies are important to determine if these perceptions influence individual decisions to participate in screening for diabetes and in risk-­lowering lifestyle interventions, such as diet and physical activity R programs (Esquives et al., 2021). For example, Dorman and colleagues sought to assess perceived susceptibility in indi- viduals without a diagnosis of diabetes (Dorman et al., 2012). Researchers employed a single-­item measure: “Compared O to most people your age and sex, what would you say your chances are for developing diabetes?” among three groups with increasingly elevated risk: (1) no family history of diabetes risk factors (e.g., diabetes, coronary heart disease, and C stroke); (2) family history of diabetes alone; and (3) family history of multiple risk factors. Perceived susceptibility varied among the three groups, and there was a stronger association with risk factors as they increased from none to diabetes N alone, to multiple risk factors. However, most people within each group perceived themselves as being at or below “­average risk.” Therefore, despite differences in actual risk factors, most participants with elevated risk of developing U diabetes due to familial history still perceived themselves at a risk level that, according to the HBM, would not induce behavior change. In 2023, Serena Rodriguez and colleagues published a systematic review of studies assessing perceived susceptibility of developing diabetes among people without a diagnosis (Rodriguez et al., 2022). They found multiple ways in which the construct had been measured in different studies: (1) multi-­item scales with composite scores; (2) multiple items with no composite score; and (3) a single item. An example of a multi-­item scale includes the Perception of Risk Factors for Type 2 Diabetes (PRF-­T2DM) scale that sums the score of six questions about personal and behavioral risk factors (e.g., “What is the effect of your exercise habits on your risk for type 2 diabetes?”) and six questions about environmental risk factors (Sousa et al., 2010). In contrast, the Risk Perception Survey for Developing Diabetes (RPS-­DD) is a 0005800229.INDD 6 03-09-2024 08:05:50 PM  Applications of the Health Belief Model 7 multi-­item measure that assesses different aspects of perceived susceptibility—­internal and external control, worry, and optimistic bias—­but does not create a composite score (Walker et al., 2003). Even among studies using a single item to measure perceived susceptibility, Rodriguez et al. (2022) found a variety of ways to operationalize it. As noted above, Dorman et al.’ (2012) item asked a respondent to compare their own sus- ceptibility to others using a Likert scale ranging from less to more likely (Ranby et al., 2010). Other researchers ask about absolute perceived susceptibility—­perception of one’s own risk without comparison (Ranby et al., 2010). Response options for these items often use numerical scales such as “on a scale from 0 to 100, how likely are you to develop dia- betes at some point in your life?” Interpreting numerical responses to absolute perceived susceptibility questions can be FS challenging (Dillard et al., 2012). Overall, variations in the operationalization of perceived susceptibility make it difficult to summarize the direction and magnitude of association across studies. Increasing use of reliable and validated scales such as the PRF-­T2DM (Rodriguez et al., 2022), can help researchers synthesize findings across studies by helping to make researchers more O confident in their measurement of constructs and to guide manipulations of perceived susceptibility to, in turn, ­influence health-­promoting behaviors such as diabetes screening or treatment. Finally, high-­quality measurement of perceived O susceptibility can help enable researchers to determine whether interventions, such as lifestyle interventions to increase physical activity or change diet, are effective in changing behavior because they first influence this hypothesized inter- PR mediate variable. HBM and COVID-­19 Vaccination With the onset of the COVID-­19 pandemic in 2020, the HBM would have been a useful way for researchers to examine D response to vaccination. In fact, there has been a general increase in the application of HBM constructs to various health behaviors since 2000 by researchers working in international settings. A Medline search of peer-­reviewed TE ­articles from 2001 to 2021 with “Health Belief Model” as a search term identified more than 2,000 articles with over 600 ­published in a language other than English, an international journal, or conducted outside the United States. In addition, several studies have found significant associations between HBM constructs and vaccination for infectious diseases that have pandemic potential, including swine flu, H1N1, SARS, and MERS (Bish et al., 2011). EC To better understand the application of HPM to COVID-­19 vaccination, we conducted a systematic Medline search that identified 109 articles, published in English from January 2020 to December 2021, that applied the HBM to COVID-­19 behaviors. Here, we focus on the 29 studies that measured COVID-­19 vaccination or vaccination intentions. As described below, this approach allowed us to: (1) describe quality and completeness of measuring HBM constructs; R (2) summarize patterns of association between HBM constructs and a COVID-­19 vaccine outcome (either actual behav- ior or vaccination intention); and (3) describe whether constructs from other health behavior theories were integrated R into the multivariate models examining correlations between these constructs and vaccination. Most of the studies (21 of 29) were conducted in non-­US settings during 2020 (before vaccines became available to O the public) and measured the five main HBM constructs: susceptibility, severity, benefits, barriers, and cues to action. Self-­efficacy was rarely measured (Table 5.1). Studies found strong, consistent support for perceived benefits being C positively associated and perceived barriers negatively associated with vaccination intentions. For perceived suscepti- bility, severity, and cues to action, findings were mixed; some showed statistically significant positive associations and N others found no association (Table 5.1). Measurement of cues to action varied greatly across studies. Some only defined cues as recommendations from healthcare providers, the government, and family/friends, whereas others acknowl- U edged that reminders in one’s environment could include personal or family experiences of COVID-­19 disease and exposure to information via various media channels or public education campaigns. Such variability in operational ­definitions for HBM constructs continues to weaken our ability to draw conclusions of what might be driving differences in findings. A few of these recent studies employing the HBM found that susceptibility and severity may conceptually combine to denote an individual’s perceived threat, but only one study (Zampetakis & Melas, 2021) sought to test this through a factorial survey experiment; this approach highlighted interactions between HBM constructs, associating an interac- tion between high perceived susceptibility and severity with positive intentions to be vaccinated. The lack of significant main effects more generally may suggest that susceptibility and severity are more distal predictors of vaccination 0005800229.INDD 7 03-09-2024 08:05:50 PM  8 Chapter 5: The Health Belief Model Table 5.1 Summary of Patterns of Association for Health Belief Model Constructs and COVID-­19 Vaccine Intentions or Uptakea HBM N Associations: Construct Studies N Studies Studies Perceived 22 Positive: 12 Positive: Ahmed et al. (2021), Banik et al. (2021), Chen et al. (2021), Guillon and Kergall (2021), Susceptibility/Risk Negative: 0 Hossain et al. (2021), Lopez-­Cepero et al. (2021), Tao et al. (2021), Toth-­Manikowski et al. (2022), No significant Tsutsui et al. (2021), Wijesinghe et al. (2021), Wong et al. (2020), Zampetakis and Melas (2021) association: 9 Mixed: 1 No significant association: Al-­Metwali et al. (2021), Berg and Lin (2021), Chu and Liu (2021), Jiang et al. (2021), Not measured: 1 Mir et al. (2021), Shmueli (2021), Wong et al. (2020), Yu et al. (2021) FS Mixed: Lin et al. (2020) Not measured: Al-­Hasan et al. (2021) Perceived Severity 21 Positive: 11 Positive: Al-­Hasan et al. (2021), Hossain et al. (2021), Lopez-­Cepero et al. (2021), Shmueli (2021), O Negative: 0 Toth-­Manikowski et al. (2022), Tsutsui et al. (2021), Wong et al. (2020), Wong et al. (2021), Ye et al. (2021), No significant Yu et al. (2021), Zampetakis and Melas (2021) O association: 9 No significant association: Ahmed et al. (2021), Al-­Metwali et al. (2021), Banik et al. (2021), Berg and Lin (2021), Mixed: 1 Chen et al. (2021), Chu and Liu (2021), Jiang et al. (2021), Tao et al. (2021), Wijesinghe et al. (2021) Not measured: 2 PR Mixed: Lin et al. (2020) Not measured: Guillon and Kergall (2021), Mir et al. (2021) Perceived Benefits 22 Positive: 21 Positive: Ahmed et al. (2021), Al-­Hasan et al. (2021), Al-­Metwali et al. (2021), Banik et al. (2021), Chen et al. (2021), Negative: 0 Chu and Liu (2021), Guillon and Kergall (2021), Hossain et al. (2021), Jiang et al. (2021), Lin et al. (2020), No significant Lopez-­Cepero et al. (2021), Mir et al. (2021), Shmueli (2021), Tao et al. (2021), Toth-­Manikowski et al. (2022), association: 1 Mixed: 0 D Wijesinghe et al. (2021), Wong et al. (2020, 2021), Ye et al. (2021), Yu et al. (2021), Zampetakis and Melas (2021) No significant association: Berg and Lin (2021) TE Not measured: 1 Not measured: Tsutsui et al. (2021) Perceived Barriers 20 Positive: 0 Positive: Ahmed et al. (2021), Al-­Hasan et al. (2021), Al-­Metwali et al. (2021), Banik et al. (2021), Negative: 18 Berg and Lin (2021), Chen et al. (2021), Chu and Liu (2021), Guillon and Kergall (2021), Hossain et al. (2021), EC No significant Jiang et al. (2021), Lin et al. (2020), Lopez-­Cepero et al. (2021), Tao et al. (2021), Toth-­Manikowski et al. (2022), association: 2 Wong et al. (2020), Wong et al. (2021), Ye et al. (2021), Zampetakis and Melas (2021) Mixed: 0 Not measured: 3 No significant association: Shmueli (2021), Yu et al. (2021) R Not measured: Mir et al. (2021), Tsutsui et al. (2021), Wijesinghe et al. (2021) Cues to Action 17 Positive: 12 Positive: Al-­Hasan et al. (2021), Al-­Metwali et al. (2021), Chen et al. (2021), Jiang et al. (2021), Lin et al. (2020), Negative: 0 Lopez-­Cepero et al. (2021), Shmueli (2021), Tao et al. (2021), Toth-­Manikowski et al. (2022), R No significant Wong et al. (2020, 2021), Yu et al. (2021) association: 4 O Mixed: 1 No significant association: Banik et al. (2021), Berg and Lin (2021), Guillon and Kergall (2021), Hossain et al. (2021) Not measured: 6 Mixed: Ahmed et al. (2021) C Not measured: Chu and Liu (2021), Mir et al. (2021), Tsutsui et al. (2021), Wijesinghe et al. (2021), Ye et al. (2021), Zampetakis and Melas (2021) N Self-­Efficacy 3 Positive: 2 Positive: Chen et al. (2021), Yu et al. (2021) Negative: 0 U No significant No significant association: Chu and Liu (2021) association: 1 Not measured: Ahmed et al. (2021), Al-­Hasan et al. (2021), Al-­Metwali et al. (2021), Banik et al. (2021), Mixed: 0 Berg and Lin (2021), Guillon and Kergall (2021), Hossain et al. (2021), Jiang et al. (2021), Lopez-­Cepero et al. (2021), Not measured: 20 Mir et al. (2021), Tao et al. (2021), Toth-­Manikowski et al. (2022), Tsutsui et al. (2021), Wijesinghe et al. (2021), Ye et al. (2021) a Review of the full text from the 29 articles revealed reasons to exclude: two studies used nontraditional measures of HBM constructs (Kalam et al. 2021; Mercadante & Law, 2021), two studies did not quantitatively examine associations between HBM constructs and the target health behavior: vaccination or vaccination intention (Marquez et al., 2021; Williams et al. 2021), one study analyzed qualitative interviews (Walker et al., 2021), and one study had difficult-­to-­interpret multivariate models (Cerda & Garcia, 2021). As a result of these exclusions, the table summarizes patterns of associations for measured HBM constructs for the remaining 23 studies. Some studies included each survey item measuring the particular HBM construct in the multivariate model instead of an aggregate scale score. Thus, it was possible for a construct to have a positive, negative, and no association when looking across the set of survey items. 0005800229.INDD 8 03-09-2024 08:05:50 PM  References 9 acceptance (Brewer et al., 2017; Carpenter, 2010). Almost all the studies in our narrative review were cross-­sectional, which precludes discussion of how health beliefs, vaccination intentions, and vaccination behaviors evolved as informa- tion and misinformation about COVID-­19 and the vaccines were disseminated. Longitudinal studies and experiments are critical to better understand mechanisms of influence among the HBM constructs, intention, and actual behavior. Twelve studies integrated constructs from another theory along with the HBM, with Theory of Planned Behavior (TPB, Chapter Six) being the predominant addition. Studies that examined both HBM and TPB constructs did not address the conceptual overlap between perceived benefits and barriers and attitude towards the behavior, nor did they consider overlap of cues to action and subjective norms. More rigorous conceptualization and competitive testing of FS these two theories may be helpful in understanding their unique or overlapping contributions to understanding behav- ior. In addition, future work might include studies, not considered in our review, that examined HBM constructs, such as perceived susceptibility, but did not specify that this construct came from the HBM (Viswanath et al., 2021). O Discussion and Summary O In this chapter, we described origins of the HBM, reviewed and defined its key components and hypothesized relation- ships, summarized critical reviews, and provided examples of applications—­most recently in the study of COVID-­19-­ PR related behaviors. The model, which has been used for more than 60 years to predict health-­related behaviors and inform development of behavior-­change interventions, is still being used in health behavior research today. The model’s intuitive concepts have made its modifiable beliefs popular for designing interventions (e.g., messages to strengthen perception of benefits and weaken perceived barriers). As the chapter describes, this approach has been exceptionally popular in global studies. D However, we still know relatively little about relationships among HBM constructs, such as whether they all directly predict behavior or whether some beliefs have indirect effects or mediate relationships to behaviors. Notable exceptions TE are studies by Gerend and Shepherd (2012), comparing the value of HBM and the TPB in predicting HPV vaccine uptake, and Murphy et al. (2013), comparing the value of HBM and Theory of Reasoned Action in evaluating mam- mography behavior. 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