ORIGINAL RESEARCH article

Using the health belief model to understand age differences in perceptions and responses to the covid-19 pandemic.

\nLauren E. Bechard

  • Department of Kinesiology, University of Waterloo, Waterloo, ON, Canada

COVID-19 severity and mortality risk are greater for older adults whereas economic impact is deeper for younger adults. Using the Health Belief Model (HBM) as a framework, this study used a web-based survey to examine how perceived COVID-19 susceptibility and severity and perceived efficacy of recommended health behaviors varied by age group and were related to the adoption of health behaviors. Proportional odds logistic regression was used to examine the relationship between age group and perceived COVID-19 susceptibility, severity, impact, and health behavior efficacy and adoption. Structural equation modeling based on HBM constructs examined the relationships between health beliefs and behaviors. Data from 820 participants (Ontario, Canada) were analyzed (age: 42.7, 16.2 years; 79% women). Middle-aged and older adults reported greater concerns about the personal risk of hospitalization and mortality, economic impact, and social impact of COVID-19 than young adults. Middle-aged adults also reported greatest concern for other age groups. Adoption and perceived efficacy of health behaviors was similar across age groups with few exceptions. Both middle-aged and older-adults were more likely to perceive their own and each other's age groups as responding adequately to COVID-19 compared to young adults. Structural equation modeling indicated perceived benefits of health behaviors were the primary driver of behavior uptake, with socioeconomic factors and perceived severity and susceptibility indirectly associated with uptake through their influence on perceived benefits. Overall, these results suggest adoption of health behaviors is very high with few differences between age groups, despite differences in perceived impact of COVID-19. Public health communications should focus on the benefits of health behaviors to drive adoption.

Introduction

In December 2019, a cluster of pneumonia cases of unknown origin was reported in Wuhan, China ( Bogoch et al., 2020 ). The novel coronavirus SARS-COV-2, more commonly known as COVID-19, was later determined to be the cause ( Jiang et al., 2020 ). The World Health Organization (WHO) declared COVID-19 a global pandemic on March 11, 2020 ( World Health Organization, 2020 ). As of March 4, 2021, there were over 115 M global confirmed cases of COVID-19 and over 2.5 M deaths ( Johns Hopkins University of Medicine, 2021 ), with a case fatality rate estimated to be 0.25–3.0% ( Wilson et al., 2020 ). While all age groups are susceptible to COVID-19 infection, older age groups have increased risk of severe symptoms and mortality ( Onder et al., 2020 ; Ruan et al., 2020 ; Zhou et al., 2020 ). Case fatality among adults over 65 years is approximately 4 times that of young adults ( Guo et al., 2020 ).

Until herd immunity are achieved, containment of the COVID-19 pandemic continues to rely on reduction of exposure through personal health behaviors (e.g., physical distancing, hand-washing, avoidance of face touching) and governmental restrictions (e.g., mandatory isolation periods, boarder closures) ( Wilder-Smith and Freedman, 2020 ). Adherence to public health measures can limit the rate of transmission, thus reducing the number of cases and fatalities and the risk of overwhelming health systems ( Jarvis et al., 2020 ; Tuite et al., 2020 ). In the first 3-months of the COVID-19 pandemic, rates of COVID-19 and mortality across countries were highly dependant on the extent and timing of public health measures ( Ng et al., 2020 ; Pan et al., 2020 ; Rocklöv et al., 2020 ). Analysis of the public health measures in relation to COVID-19 incidence estimated that over 50 million cases were prevented in the first 26 days of the pandemic by implementation of public health measures in China, South Korea, Iran, Italy, France, and the United States alone ( Hsiang et al., 2020 ).

While public health restrictions are critical to containing the spread of COVID-19, infection prevention and control measures have had substantial repercussions for personal economic, physical, social, and mental well-being, which are likely to persist beyond the COVID-19 pandemic. Unemployment in the Canada has risen over the course of the pandemic from 5.6% in January 2020 pre-pandemic to 9.4% in January 2021 ( Statistics Canada, 2021 ). The number of people seeking care for other serious health conditions has decreased substantially (e.g., stroke, heart attacks, cancer), which will likely exacerbate management of these health conditions in the long-term ( de Pelsemaeker et al., 2020 ; De Rosa et al., 2020 ; Dinmohamed et al., 2020 ; Pessoa-Amorim et al., 2020 ). Social distancing and quarantine recommendations have disrupted important social connections, negatively impacting mental well-being by increasing levels of loneliness and psychological distress as observed following other virus outbreaks ( Hawryluck et al., 2004 ; Jeong et al., 2016 ; Brooks et al., 2020 ; Williams et al., 2020 ). For example, evidence indicates increased illicit drug-related overdoses during the COVID-19 pandemic ( American Medical Association, 2020 ; Canadian Centre on Sunbtance Use Addiction, 2020 ).

The type and severity of COVID-19's impacts vary across age groups. While the immediate health impact of COVID-19 infection is more severe among older adults, the economic impact of COVID-19 can be greater for young adults ( Popplewell, 2020 ). Loss of income was reported by 80% of young adults during the first wave of COVID-19, but only two-thirds of middle-aged adults and less than half of older adults (who are retired more often) reported loss of income during this same time period ( Popplewell, 2020 ). Furthermore, young adults are employed more often in sectors that have seen greater job loss due to COVID-19 disruptions (e.g., service sector, gig economy) ( Bell and Blanchflower, 2020 ).

The Health Belief Model (HBM) is an empirically-supported model of health behavior that provides a framework for understanding how the adoption of public health measures is driven by perceptions of COVID-19 risk and the benefits and barriers to recommended health behaviors for reducing COVID-19 transmission ( Rosenstock, 1974 ; Janz and Becker, 1984 ; Champion and Skinner, 2008 ). The HBM consists of six core constructs: perceived severity and susceptibility of the condition, perceived benefits and barriers to the recommended health behavior, and cues to action (immediate prompts for the behavior) and self-efficacy to uptake behavior ( Champion and Skinner, 2008 ). The HBM also considers factors that can moderate the relationships among key constructs (e.g., age group) ( Jones et al., 2014 ). The HBM was originally developed to understand acceptance of preventive measures or screening for early detection of asymptomatic disease ( Rosenstock, 1974 ) but has been used to study the uptake of and compliance to a range of health behaviors. The HBM has been used to specifically study the uptake of health behaviors recommended to avoid seasonal influenza in elderly adults and to characterize perceptions of physical distancing for COVID-19 prevention ( Kan and Zhang, 2018 ; Raamkumar et al., 2020 ).

Since the health, social, and economic impacts of COVID-19 varies by age group, the cost-to-benefit trade-offs of recommended health behaviors may be perceived as more severe among younger than older adults. An understanding of decision making related to the adoption of these health behaviors is imperative for managing the continuing COVID-19 pandemic response. As a result, the objectives of this study were to: (1) compare perceived COVID-19 susceptibility, severity, and personal, social, and economic impacts across age groups; (2) compare the perceived efficacy of COVID-19 infection control health behaviors amongst different age groups; (3) apply the HBM framework to understand the relationships between health beliefs and behaviors for preventing COVID-19 infection and transmission; (4) describe perceptions of how well various age groups are responding to the COVID-19 pandemic.

Materials and Methods

Study design.

This was a cross-sectional survey conducted from May 7, 2020 to June 5, 2020. Adults across all age groups were enrolled through convenience sampling. Participants were recruited by circulating a weblink to the survey using social media (Twitter, Facebook, LinkedIn) and email listservs at the authors' research institution. Secondary sharing of recruitment materials by word of mouth within personal networks of survey respondents supported broader dissemination of the survey. This study was reviewed and approved by the University of Waterloo Office of Research Ethics (ORE#42131). All participants provided informed consent by choosing “yes” after reviewing study information, acknowledging that they understood study information and potential risks.

A survey was developed by the research team to collect sociodemographic, perceived health and COVID-19-related health impacts, beliefs, and behaviors was delivered using Qualtrics (Qualtrics, Provo, UT). The full survey is provided in Supplementary File 1 . This survey was informed by the domains of the HBM (as well as theoretically confounding variables). Where possible, demographic questions and format of COVID-19 questions was based on Statistics Canada survey question. Where we did not identify any directly relevant survey questions, wording and responses were based on recommended Likert scale wording for similar questions. Although there was no formal validity or reliability evaluation, the survey was piloted among 6 local researchers not involved in the study to provide feedback on usability and face validity of questions prior to dissemination.

Demographic and Health-Related Characteristics

Participants reported demographics including age, gender, ethnicity, highest level of education, income, employment status, and country, province/state and city of residence. Using location data, participants were classified as residing in or not residing in large urban centers (≥100,000 people) based on the most recent census population data ( Raamkumar et al., 2020 ) or a web search of population for regions that were not included in Canadian census data. Participants also reported whether their occupation was health-related and, if so, their role (health care provider, health care administrator, public health professional, community health worker, health researcher, or other). Participants described self-rated physical and mental health and need for assistance with everyday activities. Participants also reported whether they: (1) had tested positive for COVID-19; (2) whether they suspected that they had COVID-19; (3) whether they suspected they had been exposed to COVID-19; and (4) whether they had a close family member or friend who had COVID-19.

Health Behaviors and Perceived Effectiveness

Participants reported frequency of adhering to recommended COVID-19 health behaviors. These included: (1) avoiding leaving home for non-essential reasons; (2) using social distancing when out in public; (3) avoiding crowds and large gatherings; (4) washing hands more frequently; (5) avoiding touching their face; (6) working from home; and (7) canceling non-essential travel; and (8) other (specified). Participants were also able to specify other health behaviors. Of note, wearing a face mask was not yet widely recommended at the start of the survey, so it was not specifically listed as a health behavior; however, participants could choose to report masks in the “other” category. Frequency of adherence to and perceived effectiveness of the health behaviors was reported using a 5-point Likert scale (never, rarely, sometimes, very often, always).

Perceived Severity and Susceptibility of COVID-19

Participants reported their level of concern about the impact of COVID-19 for their personal health, including being infected themselves, being hospitalized if infected, and dying if infected along a scale of five categories of increasing concern (not at all concerned, somewhat concerned, moderately concerned, very concerned, extremely concerned). Participants also reported their level of concern about the impact of COVID-19 on their social and financial well-being. These concerns included: ability to meet their financial obligations, their economic future, and maintaining social connections. Participants were also able to specify another impact that was not listed for each type of impact. All concerns were reported using the same 5-point Likert scale, as above.

Perceived Threat of COVID-19 to Others by Age Group

Participants reported their level of concern (using the 5-point Likert scale described above) about the impacts of COVID-19 to the health, economic future, and social well-being of youth (<18 years), young adults (18–34 years), middle-aged adults (35–64 years), and older adults (≥65 years).

Perception of Others' Health Behaviors

Participants reported whether youth (under 18 years old), young adults (18–39 years old), middle-aged adults (40–64 years old), and older adults (65 years and older) were doing enough to reduce the spread of COVID-19. Participants responded using a 5-point Likert scale (definitely not, probably not, might or might not be, probably yes, definitely yes).

Statistical Analysis

Of 1,105 submitted surveys, we restricted our analyses to complete surveys where participants were at least 18 years old and took at least a minute to complete the survey. Additionally, due to low response rates from other regions, we restricted our analyses to respondents who reported their province of residence as Ontario, Canada, leaving us with 820 participants. For analyses that controlled for gender, only those who answered male or female were included ( n = 813). Missing data ranged from 0 to 6.2% across variables of interest, with an average of 1%. missRanger v2.1.0 package was used to impute data ( Mayer, 2019 ). This package imputes data by chaining random forests in order to predict missing values based on the missForest package. Between the iterative model fitting it performs, this method also uses predictive mean matching to avoid imputation of values not present within the original data, and raises the variance in the resulting conditional distributions to more realistic levels. The random forest algorithm works well for datasets with mixed type data and accounts for complex interactions and non-linear relations. Conceptually, this is similar to the expectation maximization procedure in that it seeks to predict the most plausible value for missing items from other variables in the dataset. To simplify analyses, we used a single imputed dataset (i.e., we did not perform multiple imputation) since the missing data rate was very low so the effect of single imputation vs. multiple imputation was likely to be negligible.

All analyses were performed using R v4.0.1. Impact of age group (young adult, 18–39 years; middle-aged adult, 40–64 years; and older adult, ≥65 years) on outcome variables was analyzed using partial proportional odds logistic regression [clm() function of the ordinal package v2019.12-10] ( Christensen, 2019 ). Very few participants reported low frequency of adherence to health behaviors, resulting in a clustering of responses and unstable models. As a result, we collapsed the bottom 3 response categories for health behavior frequency (“Never,” “Rarely,” “Sometimes”) into a single category and maintained the other two response options separately (“Very Often,” “Always”). We regressed the outcome variables on age group (“young adult,” 18–39 years; “middle-aged adult,” 40–64 years; “older adult,” ≥65 years), our predictor of interest. We also included variables that were likely to influence health behaviors based on prior research but were not included in the HBM, including gender, ethnicity (Caucasian/other), residence in large urban center (yes/no), household income, employment status (employed vs. unemployed), self-reported physical health, self-reported mental health, education (post-secondary vs. no post-secondary), health-related occupation (yes/no) and exposure to COVID-19 (whether they had perceived self-exposure, a family member diagnosed, suspected that they had had COVID-19, or themselves were diagnosed with COVID-19). The proportional odds assumption for variables in the model was tested using the nominal_test function from the ordinal package. For the control variables, if the nominal_test indicated a violation of the proportional odds assumption (for this purpose a p -value of <0.1) we specified the variable as having a nominal effect, resulting in a partial proportional odds model. Some models experienced convergence difficulties when run with some of the control variables and so those control variables had to be dropped. Where this happened, we have indicated what variables had to be dropped. We made no changes to the specification of the age group variable based on results of the nominal_test; we provide all results along with the nominal_test p -value for this variable. Younger adults were the reference group in all analyses, as increased age was considered the added exposure for risk of COVID-19 severity.

Structural equation modeling was used to assess the relationship between perceived health benefits, perceived personal severity and susceptibility, socioeconomic barriers, and health behaviors using the sem() function from the lavaan package v0.6-6 ( Rosseel, 2012 ). The model was theoretically defined before examining the data. The model was registered online prior to analysis 1 . The Perceived Health Benefits latent variable was comprised of survey items corresponding to belief in the efficacy of 5 health behaviors: social distancing, staying home, avoiding crowds, canceling travel, and working from home. The Reported Health Behaviors latent variable was comprised of survey items reporting the frequency in which participants engaged in these same 5 health behaviors. The behaviors “washing hands” and “avoiding touching face” were not included as these were health behaviors which we did not believe to be related to socioeconomic barriers. The Socioeconomic Barriers latent variable was made up of participants' self reported concerns for their financial future, their financial obligation, their social connections, and their self reported income bracket (5 categories, higher numeric values correspond to lower incomes). The Perceived Personal Severity and Susceptibility latent variable was comprised of survey items corresponding to self-reported concerns of being infected, being hospitalized, dying, or other concerns defined by participants.

To assess the relationship between latent variables, the Health Behavior latent variable was regressed on the Socioeconomic Barriers, Perceived Personal Severity and Susceptibility , and Perceived Health Benefits latent variables. The Socioeconomic Barriers, Perceived Personal Severity and Susceptibility , and Perceived Health Benefits latent variables were allowed to correlate with each other. Latent variables were scaled by fixing a reference indicator, the lavaan default. To address issues of non-normality, a robust version of the maximum likelihood estimator was used (MLM). For our purposes, a “good” model fit is indicated by SRMR close to.08, RMSEA close to.06, and CFI close to 0.95 ( Hu and Bentler, 1999 ).

Study Sample

The average age of the study sample was 42.7 years (SD = 16.2 years), with an age range of 18–83 years. The study sample was predominantly women (79.3%) and Caucasian (81.4%), and most had at least some post-secondary education (94.7%). Almost a quarter of the study sample (24.0%) worked in health-related occupations. There were significant differences between age groups in terms of employment status and occupation, with more older adults being retired. Almost a quarter of the sample believed they had been exposed to COVID-19 ( n = 189, 23.3%), and more people believed they had been infected with COVID-19 ( n = 93, 11.5%) than reported receiving positive test result for COVID-19 ( n = 2, 0.2%). More older adults believed they had been exposed to COVID-19 at the time of the survey. Detailed participant characteristics by age group are provided in Table 1 .

www.frontiersin.org

Table 1 . Participant characteristics by age group [ n = 820; mean (sd) or % ( n )].

Health Beliefs About COVID-19

Table 2 shows perceived severity of the impact of COVID-19 to one's economic well-being, social well-being, and personal health. In brief, compared to young adults, middle-aged and older adults had greater concerns about COVID-19 infection (OR 2.85, 95% CI 1.96–4.16 and OR 2.30, 95% CI 1.26–4.16, respectively), hospitalization (OR 3.12, 95% CI 2.25–4.36 and OR 4.08, 95% CI 2.41–6.93, respectively), and death (OR 2.82, 95% CI 1.98–4.05 and OR 3.65, 95% CI 2.08–6.43, respectively). Older adult respondents were less likely to be concerned about meeting current financial obligations than were younger adults (OR 0.30, 95% CI 0.11–0.70) and less likely to be concerned about the impact of COVID-19 on their economic future (OR 0.52, 95% CI 0.27–0.97). Middle-aged adults were marginally more concerned (OR 1.43, 95% CI 0.99–2.08) about the impact of COVID-19 on their ability to maintain their social connections and older adults were significantly more concerned about this (OR 2.22, 95% CI 1.20–4.05).

www.frontiersin.org

Table 2 . Level of concerns about personal impact of COVID-19 infection [% ( n )].

Level of concern amongst age groups about the impact of COVID-19 on personal health, economic future, and social well-being for one's own and other age groups are reported in Table 3 . Middle-aged and older adults were more likely to report greater concern for the health impacts of COVID-19 for youth and young adults relative to young adults. Middle-aged adults were also more likely to report greater concern for other middle-aged adults. When it came to the health impact on older adults, middle-aged adults were marginally more concerned while older adults were marginally less concerned. Middle-aged and older adults also reported higher concern for the economic futures of youth, middle-aged adults, and older adults compared to young adults. Concerns about social impacts were more similarly rated across age groups, with the only differences being that middle-aged adults were more likely to report higher concern for older adults (1.56, 1.16–2.11) and older adults were more likely to report higher concern for youth (1.87, 1.12–3.13) than did young adults.

www.frontiersin.org

Table 3 . Level of concern about impact of COVID-19 on health, economic, and social well-being of age groups.

Adoption and Perceived Effectiveness of Health Behaviors

Survey respondents indicated their likelihood of engaging in specific health behaviors to reduce the risk of COVID-19 infection and transmission and their perceived effectiveness of these behaviors ( Table 4 ). Both middle-aged (OR 0.45, 95% CI 0.31–0.64) and older (OR 0.23, 95% CI 0.13–0.41) adults were less likely to report working from home than young adults. Middle-aged adults were less likely to report staying home (OR 0.72, 95% CI 0.53–0.98) but were more likely to report social distancing (OR 1.61, 95% CI 1.12–2.33), washing hands (OR 1.44, 95% CI 1.04–1.99), and avoiding touching their face (OR 1.43, 95% CI 1.06–1.93) than were young adults.

www.frontiersin.org

Table 4 . Likelihood of reporting health behaviors across age groups.

Across age groups, both middle-aged and older adults indicated that they believed the health behaviors queried were either similarly effective or more effective compared to young adults.

Perceptions of Adequacy of Health Behaviors Across Age Groups

There were no significant differences across age groups in perceived adequacy of youths' health behaviors to prevent COVID-19 transmission (middle-aged vs. young adults: OR 0.99, 95% CI 0.69–1.41; older vs. young adults: OR 0.96, 95% CI 0.51–1.77). It should be noted that the control variable for education was dropped due to convergence issues for this model. There were differences for perceived adequacy of health behaviors across all adult age groups. Middle-aged adults were less likely to believe that young adults were doing enough to prevent COVID-19 transmission compared to young adults themselves (OR 0.53, 95% CI 0.37–0.75). It should be noted that control variable for urban living was dropped due to convergence issues for this model. Both middle-aged and older-adults were more likely to perceive their own age groups (OR 2.88, 95% CI 2.08–3.99 and OR 2.35, 95% CI 1.73–3.20, respectively) and each other's age groups (OR 2.88, 95% CI 1.65–5.04 and OR 2.35, 95% CI 1.73–3.20, respectively) as responding adequately compared to young adults.

Model of COVID-19 Health Beliefs and Behaviors

Overall, the structural equation model had a RMSEA of 0.07, a CFI of 0.91, and a SRMR of 0.05. The SRMR value indicates a good fit. On the other hand, the RMSEA and CFI do not meet the Hu and Bentler (1999) definition of “good” fit but can be considered an “acceptable” fit ( Brown, 2015 ). Taken together, it appears that our model provides “acceptable” fit to the data and interpretation of the model is appropriate.

The structural equation model diagram is presented in Figure 1 , with all coefficients standardized. All factor loadings were significant at the 0.001 level. The only significant predictor of the adoption of health behaviors was the perceived benefits of health behaviors (standardized coefficient: 0.82, p < 0.001). Socioeconomic barriers (standardized coefficient: 0.01, p = 0.772) and perceived severity and susceptibility (standardized coefficient: 0.05, p = 0.132) were not significant predictors of health behavior adoption. Though not directly affecting health behavior adoption, the perceived severity and susceptibility latent variable was directly correlated with the perceived benefits of health behaviors (standardized coefficient: 0.29, p < 0.001). Socioeconomic barriers were not directly correlated with perceived benefits of health behaviors (standardized coefficient: 0.00, p = 0.001), though they were directly correlated with perceived severity and susceptibility (standardized coefficient: 0.18, p < 0.001).

www.frontiersin.org

Figure 1 . Structural equation modelling describing the relationship between perceived severity and susceptibility of COVID-19, barriers, perceived effectiveness of health behaviors, and socioeconomic variables on the uptake recommended health behaviors (all coefficients are standardized).

This study surveyed adults across young to older age groups to understand their beliefs and behaviors related to COVID-19 and perceptions of other age groups' responses to the COVID-19 pandemic. Our study yielded several findings that contribute to the evolving literature on health beliefs and behaviors surrounding COVID-19. Prior reports have not investigated the perceptions of COVID-19 impacts, health beliefs, and health behaviors of one's own age group as well as other age groups. Our results suggest that different age groups have distinct perceptions of the health, social, and economic impacts of COVID-19 for people of their own and other age groups. Despite differing perceptions of risk and impact, adoption and perceived effectiveness was largely similar across age groups with high perceived efficacy and high levels of adoption. However, there are some significant differences between age groups related to occupational, personal health, and social behaviors. As well, despite similar adoption of health behaviors across age groups, middle-aged and older adults both perceived members of their own and each others' age groups as more adequate to reduce the spread of COVID-19 compared to how they perceived the behaviors of young adults. In modeling the interaction between perceived benefits, perceived severity and susceptibility, and behavior using the HBM, our findings suggest belief in the benefits of health behaviors are the most important factor driving their adoption amongst all age groups. Our model also suggests perceived severity and susceptibility of COVID-19 to oneself and socioeconomic factors may indirectly affect adoption by altering perceived efficacy of the behaviors.

The perceived impacts of COVID-19 on the health, economic, and social well-being of oneself and others vary by age group in mostly expected ways. In the present study, both middle-aged and older adult respondents were more concerned about the risk of COVID-19 hospitalization and death than younger adult respondents but were not more concerned about COVID-19 infection despite increased susceptibility. This aligns with research showing an age-related increase in COVID-19 severity ( Guo et al., 2020 ), as well as with prior research from a sample of American older adults ( Bruine de Bruin, 2020 ) that found older age was not associated with higher perceived risk of getting COVID-19 but was associated with greater perceived risk of dying due to COVID-19 ( Bruine de Bruin, 2020 ). Another study using data from an American adult sample also found adults of all ages tend to underestimate their risk of being infected with and dying due to COVID-19 ( Niepel et al., 2020 ). Other research has had variable findings about the association between age and COVID-19 risk perception. A Turkish survey of health beliefs found age was negatively associated with perceived vulnerability, risk of, and fear of COVID-19 ( Yildirim et al., 2020 ). Another survey found that older American men were less worried about COVID-19 than younger adults despite perceiving higher COVID-19 risk for themselves ( Barber and Kim, 2020 ). While associations between age and different types of COVID-19 risk (e.g., infection, hospitalization, death) vary in the literature, findings from the present study considered in light of prior research confirm that concerns about the personal health impacts of COVID-19 vary with age.

The Socio-emotional Selectivity Theory (SST) proposed by Carstensen (1995) may provide some explanation into the discrepancies between the perceived susceptibility vs. the risk of death among middle-aged and older adults in our study. SST suggests that as one ages, most of the attention is focused on present-moment events, placing major emphasis on finding meaning and positive emotion, and less focus on future circumstances ( Carstensen, 1995 ). As such, older adults and, to a lesser extent, middle-aged adults, may have a decreased perception of susceptibility, while acknowledging their increased risk of death. This aligns with an Italian study in which older adults showed a more positive outlook and attitude toward the COVID-19 pandemic compared to other age groups ( Ceccato et al., 2020 ). In another study, older adults reported fewer negative emotions related to exposure to COVID-19, despite still reporting an increased perceived risk when compared to other age groups ( Carstensen et al., 2020 ). These findings suggest that older adults may indeed focus more on positive and less on negative aspects of the COVID-19 pandemic, aligning with the SST.

Our results suggest middle-aged adults have greater levels of concern about the impacts of COVID-19 on health, economic, and social well-being across age groups, while there were fewer differences between the beliefs of older and young adults. Middle-aged respondents were more concerned about the impacts of COVID-19 to both their own health and their ability to meet financial obligations during COVID-19 compared to young adults, whereas older adults were no more concerned than young adults. Only for impacts to maintaining social connections and economic futures were older adults also more concerned than young adults. The reason for peak concern among middle-aged adults is unclear. A possible explanation is that middle-aged adults are more likely to have dual care responsibilities, still caring for their children while also caring for aging parents. The combination of caregiving responsibilities with regular family stressors may be exacerbated in middle-aged adults when considering the possible impacts of COVID-19 on the family unit ( Prime et al., 2020 ). It is also possible that the higher concerns amongst middle-aged adults could be related to their specific age cohort. However, the “middle-aged” group as defined in this study spans several generational cohorts, including elder millennials (individuals born in 1980–1990's), Generation X (individuals born 1965–1979), and the youngest Baby Boomers (individuals born 1964–1946). Further research should specifically investigate the impacts of COVID-19 on middle-aged adults' mental well-being, as well as physical, economic, and social well-being, as our research suggests they have heightened concerns compared to older and younger adults.

Respondents in our study reported high uptake of health behaviors, with few differences across age groups. A Turkish study also found high adherence to health behaviors, with the sample reporting increases in all health behaviors assessed due to the COVID-19 pandemic, although it did not report frequency of health behaviors ( Yildirim et al., 2020 ). In the present study, the only significant difference in engagement in health behaviors between age groups was for working from home. This contrasts with findings of a survey of Turkish adults that found younger age was associated with higher frequency of self-reported health behaviors for preventing COVID-19 infection ( Yildirim et al., 2020 ). It is possible that adoption of health behaviors in this Canadian sample was driven by intrinsic factors (e.g., perceptions and beliefs) rather than extrinsic/situational factors (e.g., employment) as respondents reported similar perceived effectiveness of most health behaviors across age groups (with the exception of social distancing and avoiding crowds).

Confirming the importance of intrinsic beliefs, the structural equation model indicated that perceived benefits were the major (and only significant, direct) driver of uptake of COVID-19 health behaviors. This finding aligns with a prior study that used the HBM to assess uptake of a COVID-19 tracing app in a German sample ( Walrave et al., 2020 ). This study also found that perceived benefits of using the app was a greater driver of use of a COVID-19 tracing app than perceived COVID-19 risk. In a study of HBM constructs and uptake of health behaviors in a Chinese sample, perceived benefits of behaviors and rewards for engaging in behaviors were positively associated with practicing good hand hygiene, wearing a face mask, and social distancing ( Tong et al., 2020 ). Studies have also used different theories to understand health behaviors under COVID-19. The Extended Parallel Process Model is similar to HBM and describes behavior as a result of perceived threat (severity and susceptibility) of the outcome and self-efficacy for reducing the threat ( Lithopoulos et al., 2021 ), with additional moderators. This model was used as a framework in recent study of Canadian adults where both perceived threat and self-efficacy for behaviors predicted uptake of health behaviors. However, self-efficacy for reducing the threat (belief that their behaviors will be beneficial) was most strongly related to behavior uptake ( Kim and Crimmins, 2020a ). Protection Motivation Theory (PMT) ( Rogers, 1975 ) also includes perceived threat (severity and susceptibility) as a predictor of behavior and intentions as well as coping resources (efficacy of behavior and self-efficacy for the behavior). One study using PMT as a framework surveyed people in Wave 1 (March 2020) and Wave 2 (May 2020) ( Rogers, 1975 ). Results indicated that older adults and women were more likely to report that protective behaviors were effective in reducing COVID-19 spread and were more likely to adopt these behaviors ( Kowalski and Black, 2021 ). Another study compared behaviors between younger and older adults ( Kim and Crimmins, 2020a ). Similar to the current study, perceived coping resources (including perceived efficacy of the behavior) predicted behavior uptake among young adults. However, in contrast to the current study, behavior of older adults was more strongly predicted by perception of COVID-19 severity ( Kim and Crimmins, 2020a ). Other research (without a guiding framework), also found increased perceived risk of being infected with or dying from COVID-19 was associated with higher uptake of health behaviors (e.g., increased hand washing, avoiding crowds) in an American sample ( Niepel et al., 2020 ). A survey from Turkey also found that fear of COVID-19 infection, perceived risk, and vulnerability were significant predictors of engagement in recommended health behaviors ( Yildirim et al., 2020 ). However, neither of these latter studies assessed beliefs about the benefits of health behaviors, so it is unclear the relative importance of perceived benefits and risks in these studies.

The finding that benefits are the primary driver of behavior uptake has important implications for both designing public health communications and for interpreting why some health behaviors are not as well-adopted. In particular, the uptake of masks in Canada, which was not captured in this study as it was not a recommended health behavior for COVID-19 prevention at the time of data collection, is more controversial than other health behaviors. This may be caused by variable messaging around the benefits of masks, where early public health communications stated that there were few if any benefits of masks, and global mask-wearing recommendations have been multifarious ( Feng et al., 2020 ). While these messages have been updated, our study suggests that the inconsistent messaging regarding the benefits of masks may be a reason for poor uptake, rather than the perceived barriers to uptake (e.g., discomfort, cost). Public health messaging may be more effective at increasing the uptake of health behaviors if it highlights the benefits of health behaviors to prevent COVID-19, rather than focusing solely on communicating risk related to COVID-19.

Our study found differences in the perceptions of age groups about the adequacy of other age groups' health behaviors in responding to the COVID-19 pandemic. Specifically, each age group rated their own age group more highly than others age groups did. This finding may be the result of cognitive biases that cause one to perceive their own abilities as superior to others. The egocentric “better than average” or Illusory Superiority bias proposes that people tend to assume their own traits or abilities are better than the average ( Zell et al., 2020 ). In the context of COVID-19 health behaviors, egocentric bias could lead to one assuming that one is doing more than others to prevent COVID-19 transmission. Combined with group attribution error ( Mackie and Allison, 1987 ) and ingroup bias ( Taylor and Doria, 1981 ), this may lead one to assume that one's own age group is doing more than others to prevent COVID-19 transmission. Group attribution error posits that individuals assume that all members of a group share the same traits, and ingroup bias posits that individuals view others more positively when they have shared characteristics, which may further lead to people rating their own age group more highly.

Our study also found that younger adults' behaviors were more poorly perceived by older age groups, with middle-aged and older adults more likely to perceive their own and each other's health behaviors as adequate to reduce COVID-19 transmission compared to younger adults. News and social media reports documenting specific instances where health behaviors were not adhered to by specific age groups early in the COVID-19 pandemic (e.g., young adults participating in large social gatherings with lack of social distancing during spring break and St. Patrick's Day festivities) could have led to the early formation of biased perceptions amongst other age groups. Group attribution error may then cause this to become a stereotypical belief about poor COVID-19 health behaviors among younger adults as a whole. While younger adults were less likely to report belief in the efficacy of some health behaviors and adhering to social and occupational recommendations to prevent COVID-19 than middle-aged and older adults, it's important to note that reported levels of adherence were high across all age groups. With the exception of avoiding touching one's face, more than 80% of younger adults who responded reported engaging in all health behaviors queried “very often” or “always.”

This study has limitations that must be considered when interpreting these findings and their applicability to other populations. First, the participants of our study are not representative of the broader population; ours is a highly educated, predominantly white, and largely female sample residing in urban areas in Ontario, Canada. This is likely due in part to the use of a convenience sampling approach to survey recruitment conducted over social media, but there is also a systemic trend in research for participation to be biased toward people with higher socioeconomic status and Caucasian ethnicity ( Oh et al., 2015 ). COVID-19 has had vastly different effects in different countries, and disproportionately affects ethnic minority groups and people with lower socioeconomic status ( Hawkins et al., 2020 ; Sze et al., 2020 ). As such, findings from this study are not representative of the population as a whole or groups that are most vulnerable to COVID-19. In addition, the survey was cross-sectional. As a result, these findings depict the health beliefs and behaviors of a specific subset of the population at a single time, which may or may not be reflected by other socioeconomic or ethnic groups or changing mindset over the pandemic. Further research should seek to explore the health beliefs and behaviors of more vulnerable socioeconomic groups to understand how this may affect uptake of COVID-19 health behaviors, as a significant but indirect association of socioeconomic factors was associated with health behavior uptake in our study.

These results reflect a snapshot of COVID-19 health beliefs and behaviors, which will continue to evolve as we live with the global COVID-19 pandemic. Data collection occurred from May to June 2020, which was relatively early in the pandemic, and thus does not necessarily reflect how health behavior adherence evolves over time. One study from a sample representative of the American population found older adults were more likely to sustain uptake of health behaviors for preventing COVID-19 transmission, but reduction in “risky” health behaviors did not vary as the pandemic progressed ( Hawkins et al., 2020 ). These results suggest that ongoing assessment of health perceptions and behaviors in the COVID-19 pandemic, including those related to vaccine efficacy, is important to tailoring health behavior interventions and messaging.

Though the perceived impact of COVID-19 varied across age groups in predictable ways, our study indicates that adoption of health behaviors to contain COVID-19 is high with few differences across age groups, at least among our limited sample. Of specific importance for public health communications, the perceived benefits of the health behaviors, and not the risk of COVID-19, appeared to be the only significant, direct driver of adoption. Public health communications regarding health behaviors should be designed with this in mind. Future research should probe the impact of COVID-19 on middle-aged adults more deeply, as this age group was most concerned about the impact of COVID-19 among all age groups.

Data Availability Statement

The original contributions presented in the study are publicly available. This data can be found here: https://osf.io/q28mh/ .

Ethics Statement

This study was reviewed and approved through a University of Waterloo Research Ethics Committee. Written informed consent for participation was not required for this study in accordance with the national legislation and the institutional requirements.

Author Contributions

LB, MB, BN, TD, and LM contributed to the research concept development, research design, data collection, data analysis, interpretation, and manuscript preparation for this research study. All authors contributed to the article and approved the submitted version.

LB was supported by the Alzheimer Society of Canada through an Alzheimer Society Research Program Quality of Life Doctoral Research Award (#19-28).

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Acknowledgments

The authors would like to acknowledge and thank all the participants who participated in this research study. The authors would like to acknowledge and thank Alicia Nadon for supporting administration of this study and Jaashing He for contributing to data processing for this study.

Supplementary Material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpsyg.2021.609893/full#supplementary-material

1. ^ https://osf.io/q28mh/

American Medical Association (2020). Issue Brief: Reports of Increases in Opioid-Related Overdose and Other Concerns During COVID Pandemic . Available online at: https://www.ama-assn.org/system/files/2020-07/issue-brief-increases-in-opioid-related-overdose.pdf (accessed September 18, 2020).

Google Scholar

Barber, S. J., and Kim, H. (2020). COVID-19 worries and behavior changes in older and younger men and women. J. Gerontol. Ser. B 76, e17–e23. doi: 10.1093/geronb/gbaa068

PubMed Abstract | CrossRef Full Text | Google Scholar

Bell, D. N. F., and Blanchflower, D. G. (2020). US and UK labour markets before and during the COVID-19 crash. Natl. Inst. Econ. Rev . 252, R52–R69. doi: 10.1017/nie.2020.14

CrossRef Full Text | Google Scholar

Bogoch, I. I., Watts, A., Thomas-Bachli, A., Huber, C., Kraemer, M. U. G., and Khan, K. (2020). Pneumonia of unknown aetiology in Wuhan, China: potential for international spread via commercial air travel. J. Travel Med. 27:taaa008. doi: 10.1093/jtm/taaa008

Brooks, S. K., Webster, R. K., Smith, L. E., Woodland, L., Wessley, S., Greenberg, N., et al. (2020). The psyhcological impact of quarantine and how to reduce it: rapid review of the evidence. Lancet . 395, 912–920. doi: 10.1016/S0140-6736(20)30460-8

Brown, T. A. (2015). Confirmatory Factor Analysis for Applied Research , 2nd Edn. New York, NY: Guilford Publications.

Bruine de Bruin, W. (2020). Age differences in COVID-19 risk perceptions and mental health: evidence from a national US survey conducted in March 2020. J. Gerontol. Ser B 76, e24–e29. doi: 10.1093/geronb/gbaa074

Canadian Centre on Sunbtance Use Addiction (2020). CCENDU Alert: Changes Related to COVID-19 in the Illegal Drug Supply and Access to Services, and Resulting health Harms . Available online at: https://www.ccsa.ca/sites/default/files/2020-05/CCSA-COVID-19-CCENDU-Illegal-Drug-Supply-Alert-2020-en.pdf (accessed September 18, 2020).

Carstensen, L. L. (1995). Evidence for a life-span theory of socioemotional selectivity. Curr. Direct. Psychol. Sci. 4, 151–156. doi: 10.1111/1467-8721.ep11512261

Carstensen, L. L., Shavit, Y. Z., and Barnes, J. T. (2020). Age advantages in emotional experience persist even under threat from the COVID-19 pandemic. Psychol. Sci . 31, 1374–1385. doi: 10.1177/0956797620967261

Ceccato, I., Palumbo, R., Di Crosta, A., La Malva, P., Marchetti, D., Maiella, R., et al. (2020). Age-related differences in the perception of COVID-19 emergency during the Italian outbreak. Aging Ment. Health 1–9. doi: 10.1080/13607863.2020.1856781

Champion, V. L., and Skinner, C. S. (2008). “The health belief model,” in health behavior and health education: theory, research, and practice,” in 4th Edn . eds K. Glanz, B. K. Rimer, and K. Viswanath (San Francisco: John Wiley & Sons), 45–65.

Christensen, R. H. B. (2019). Ordinal - Regression Models for Ordinal Data . Available online at: https://CRAN.R-project.org/package=ordinal (accessed October 12, 2019).

de Pelsemaeker, M. C., Guiot, Y., Vanderveken, J., Galant, C., and Van Bockstal, M. R. (2020). The impact of the COVID-19 pandemic and the associated Belgian governmental measures on cancer screening, surgical pathology and cytopathology. Pathobiology 88, 46–55. doi: 10.1159/000509546

De Rosa, S., Spaccarotella, C., Basso, C., Calabrò, M. P., Curcio, A., Filardi, P. P., et al. (2020). Reduction of hospitalizations for myocardial infarction in Italy in the COVID-19 era. Eur. Heart J . 41, 2083–2088. doi: 10.1093/eurheartj/ehaa409

Dinmohamed, A. G., Visser, O., Verhoeven, R. H. A., Louwman, M. W. J., van Nederveen, F. H., Willems, S. M., et al. (2020). Fewer cancer diagnoses during the COVID-19 epidemic in the Netherlands. Lancet Oncol . 21, 750–751. doi: 10.1016/S1470-2045(20)30265-5

Feng, S., Shen, C., Xia, N., Song, W., Fan, M., and Cowling, B. J. (2020). Rational use of face masks in the COVID-19 pandemic. Lancet Respir Med . 8, 434–436. doi: 10.1016/S2213-2600(20)30134-X

Guo, Y., Liu, X., Deng, M., Liu, P., Li, F., Xie, N., et al. (2020). Epidemiology of COVID-19 in older persons, Wuhan, China. Age Ageing 49, 706–712. doi: 10.1093/ageing/afaa145

Hawkins, R. B., Charles, E. J., and Mehaffey, J. H. (2020). Socio-economic status and COVID-19-related cases and fatalities. Public Health 189, 129–134. doi: 10.1016/j.puhe.2020.09.016

Hawryluck, L., Gold, W. L., Robinson, S., Pogorski, S., Galea, S., and Styra, R. (2004). SARS control and psychological effects of quarantine, Toronto, Canada. Emerg. Infect. Dis. 10, 1206–1212. doi: 10.3201/eid1007.030703

Hsiang, S., Allen, D., Annan-Phan, S., Bell, K., Bolliger, I., Chong, T., et al. (2020). The effect of large-scale anti-contagion policies on the COVID-19 pandemic. Nature 584, 262–267. doi: 10.1038/s41586-020-2404-8

Hu, L. T., and Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Struct. Eq. Model. 6, 1–55. doi: 10.1080/10705519909540118

Janz, N. K., and Becker, M. H. (1984). The health belief model: a decade later. Health Educ. Q . 11, 1–47. doi: 10.1177/109019818401100101

Jarvis, C. I., Van Zandvoort, K., Gimma, A., Prem, K., CMMID COVID-19 Working Group, Klepac, P., et al. (2020). Quantifying the impact of physical distance measures on the transmission of COVID-19 in the UK. BMC Med . 18:124. doi: 10.1186/s12916-020-01597-8

Jeong, H., Yim, H. W., Song, Y. J., Ki, M., Min, J. A., Cho, J., et al. (2016). Mental health status of people isolated due to Middle East Respiratory Syndrome. Epidemiol. Health 38:e2016048. doi: 10.4178/epih.e2016048

Jiang, S., Xia, S., Ying, T., and Lu, L. (2020). A novel coronavirus (2019-nCoV) causing pneumonia-associated respiratory syndrome. Cell. Mol. Immunol . 17:554. doi: 10.1038/s41423-020-0372-4

Johns Hopkins University of Medicine (2021). COVID-19 Dashboard by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University. Coronavirus Resource Center . Available online at: https://coronavirus.jhu.edu/map.html (accessed March 4, 2021).

Jones, C. J., Smith, H., and Llewellyn, C. (2014). Evaluating the effectiveness of health belief model interventions in improving adherence: a systematic review. Health Psychol. Rev . 8, 253–269. doi: 10.1080/17437199.2013.802623

Kan, T., and Zhang, J. (2018). Factors influencing seasonal influenza vaccination behaviour among elderly people: a systematic review. Public Health 156, 67–78. doi: 10.1016/j.puhe.2017.12.007

Kim, J. K., and Crimmins, E. M. (2020a). Age differences in the relationship between threatening and coping mechanisms and preventive behaviors in the time of COVID-19 in the United States: protection motivation theory. Res. Psychother . 23:485. doi: 10.4081/ripppo.2020.485

Kim, J. K., and Crimmins, E. M. (2020b). How does age affect personal and social reactions to COVID-19: results from the national understanding American study. PLoS ONE . 15:e0241950. doi: 10.1371/journal.pone.0241950

Kowalski, R. M., and Black, K. J. (2021). Protection Motivation and the COVID-19 Virus. Health Commun . 36, 15–22. doi: 10.1080/10410236.2020.1847448

Lithopoulos, A., Liu, S., Zhang, C.-Q., and Rhodes, R. E. (2021). Predicting physical distancing in the context of COVID-19: a test of the extended parallel process model among Canadian adults. Can. Psychol . doi: 10.1037/cap0000270. [Epub ahead of print].

Mackie, D. M., and Allison, S. T. (1987). The group attribution error and the illusion of group attitude change. J. Exp. Soc. Psychol . 23, 460–480. doi: 10.1016/0022-1031(87)90016-3

Mayer, M. (2019). missRanger: Fast Imputation of Missing Values (2.1.0) . Available online at: https://cran.r-project.org/package=missRanger (accessed September 23, 2020).

Ng, Y., Li, Z., Chua, Y. X., Chaw, W. L., Zhao, Z., Er, B., et al. (2020). Evaluation of the effectiveness of surveillance and containment measures for the first 100 patients with COVID-19 in Singapore - January 2-February 29, 2020. Morb. Mortal. Wkly. Rep. 69, 307–311. doi: 10.15585/mmwr.mm6911e1

Niepel, C., Kranz, D., Borgonovi, F., Emslander, V., and Greiff, S. (2020). The coronavirus (COVID-19) fatality risk perception of US adult residents in March and April 2020. Br. J. Health Psychol . 25, 883–888. doi: 10.1111/bjhp.12438

Oh, S. S., Galanter, J., Thakur, N., Pin-Yanes, M., Barcelo, N. E., white, M. J., et al. (2015). Diversity in clinical and biomedical research: a promise yet to be fulfilled. PLoS Med . 12:e1001918. doi: 10.1371/journal.pmed.1001918

Onder, G., Rezza, G., and Brusaferro, S. (2020). Case-fatality rate and characteristics of patients dying in relation to COVID-19 in Italy. J. Am. Med. Assoc. 323, 1775–1776. doi: 10.1001/jama.2020.4683

Pan, A., Liu, L., Wang, C., Guo, H., Hao, X., Wang, Q., et al. (2020). Association of public health interventions with the epidemiology of the COVID-19 outbreak in Wuhan, China. J. Am. Med. Assoc . 323, 1915–1923. doi: 10.1001/jama.2020.6130

Pessoa-Amorim, G., Camm, C. F., Gajendragadkar, P., De Maria, G. L., Arsac, C., Laroche, C., et al. (2020). Admission of patients with STEMI since the outbreak of the COVID-19 pandemic: a survey by the European Society of Cardiology. Eur. Heart J. Qual. Care Clin. Outcomes 6, 210–216. doi: 10.1093/ehjqcco/qcaa046

Popplewell, B. (2020, May 21). Carleton researchers find younger canadians hardest hit financially by COVID-19. Carleton Newsroom . Available online at: https://newsroom.carleton.ca/2020/carleton-researchers-find-younger-canadians-hardest-hit-financially-by-covid-19/ (accessed September 18, 2020).

Prime, H., Wade, M., and Browne, D. T. (2020). Risk and resilience in family well-being during the COVID-19 pandemic. Am. Psychol . 75, 631–643. doi: 10.1037/amp0000660

Raamkumar, A. S., Tan, S. G., and Wee, H. L. (2020). Use of health-belief model-based learning classifiers for COVID-19 social media content to examine public perceptions of physical distancing: model development and case study. JMIR Public Health Surveil . 6:e20493. doi: 10.2196/20493

Rocklöv, J., Sjödin, H., and Wilder-Smith, A. (2020). COVID-19 outbreak on the Diamond Princess cruise ship: estimating the epidemic potential and effectiveness of public health countermeasures. J Travel Med . 27:taaa030. doi: 10.1093/jtm/taaa030

Rogers, R. W. (1975). A protection motivation theory of fear appeals and attitude change. J Psychol . 9, 93–114. doi: 10.1080/00223980.1975.9915803

Rosenstock, I. M. (1974). The health belief model and preventive health behavior. Health Educ. Monogr . 2, 54–386. doi: 10.1177/109019817400200405

Rosseel, Y. (2012). Lavaan: an R package for structural equation modeling. J. Stat. Softw . 48:2. doi: 10.18637/jss.v048.i02

Ruan, Q., Yang, K., Wang, W., Jiang, L., and Song, J. (2020). Clinical predictors of mortality due to COVID-19 based on an analysis of data of 150 patients from Wuhan, China. Intens. Care Med. 46, 846–848. doi: 10.1007/s00134-020-05991-x

Statistics Canada (2016). Population Centre and Rural Area Classification 2016 . Available online at: https://www.statcan.gc.ca/eng/subjects/standard/pcrac/2016/introduction (accessed September 18, 2020).

Statistics Canada (2021). Table 14-10-0287-03 Labour Force Characteristics by Province, Monthly, Seasonally Adjusted. Ottawa, ON: Statistics Canada.

Sze, S., Pan, D., Nevill, C. R., Gray, L. J., Martin, C. A., Nazareth, J., et al. (2020). Ethnicity and clinical outcomes in COVID-19: a systematic review and meta-analysis. EClinicalMedicine 29:100630. doi: 10.1016/j.eclinm.2020.100630

Taylor, D. M., and Doria, J. R. (1981). Self-serving and group-serving bias in attribution. J. Soc. Psychol . 113, 201–211. doi: 10.1080/00224545.1981.9924371

Tong, K. K., Chen, J. H., Yu, E. W., and Wu, A. M. S. (2020). Adherence to COVID-19 precautionary measures: applying the health belief model and generalized social beliefs to a probability community sample. Appl. Psychol. Health Well Being . 12, 1205–1223. doi: 10.1111/aphw.12230

Tuite, A. R., Fisman, D. N., and Greer, A. L. (2020). Mathematical modelling of COVID-19 transmission and mitigation strategies in the population of Ontario, Canada. Can. Med. Assoc. J . 192, E497–E505. doi: 10.1503/cmaj.200476

Walrave, M., Waeterloos, C., and Ponnet, K. (2020). Tracing the COVID-19 virus: a health belief model approach to the adoption of a contact tracing app. JMIR Public Health Surveil . 6:e20572. doi: 10.2196/20572

Wilder-Smith, A., and Freedman, D. O. (2020). Isolation, quarantine, social distancing and community containment: pivotal role for old-style public health measures in the novel coronavirus (2019-nCoV) outbreak. J. Travel Med . 27:taaa20. doi: 10.1093/jtm/taaa020

Williams, S. N., Armitage, C. J., Tampe, T., and Dienes, K. (2020). Public perceptions and experiences of social distancing and social isolation during the COVID-19 pandemic: a UK-based focus group study. BMJ Open . 10:e039334. doi: 10.1136/bmjopen-2020-039334

Wilson, N., Kvalsvig, A., Barnard, L. T., and Baker, M. G. (2020). Case-fatality risk estimates for COVID-19 calculated by using a lag time for fatality. Emerg. Infect. Dis . 26, 1339–1441. doi: 10.3201/eid2606.200320

World Health Organization (2020). Timeline of WHO's Response to COVID-19 . Available online at: http://www.who.int/news-room/detail/29-06-2020-covidtimeline (accessed September 18, 2020).

Yildirim, M., Geçer, E., and Akgül, O. (2020). The impacts of vulnerability, perceived risk, and fear on preventive behaviours against COVID-19. Psychol. Health Med . 26, 35–43. doi: 10.1080/13548506.2020.1776891

Zell, E., Strickhouser, J. E., Sedikides, C., and Alicke, M. D. (2020). The better-than-average effect in comparative self-evaluation: a comprehensive review and meta-analysis. Psychol. Bull . 146, 118–149. doi: 10.1037/bul0000218

Zhou, F., Yu, T., Du, R., Fan, G., Liu, Y., Liu, Z., et al. (2020). Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: a retrospective cohort study. Lancet 395, P1054–1062. doi: 10.1016/S0140-6736(20)30566-3

Keywords: COVID-19, health belief model, health behavior, health communication, aging, public health practice

Citation: Bechard LE, Bergelt M, Neudorf B, DeSouza TC and Middleton LE (2021) Using the Health Belief Model to Understand Age Differences in Perceptions and Responses to the COVID-19 Pandemic. Front. Psychol. 12:609893. doi: 10.3389/fpsyg.2021.609893

Received: 16 December 2020; Accepted: 19 March 2021; Published: 15 April 2021.

Reviewed by:

Copyright © 2021 Bechard, Bergelt, Neudorf, DeSouza and Middleton. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Laura E. Middleton, laura.middleton@uwaterloo.ca

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

  • Research article
  • Open access
  • Published: 14 November 2018

Using the Health Belief Model to explore why women decide for or against the removal of their ovaries to reduce their risk of developing cancer

  • Anne Herrmann   ORCID: orcid.org/0000-0002-8855-4176 1 ,
  • Alix Hall 1 &
  • Anthony Proietto 2  

BMC Women's Health volume  18 , Article number:  184 ( 2018 ) Cite this article

25k Accesses

16 Citations

11 Altmetric

Metrics details

Women at an increased risk of ovarian cancer often have to decide for or against the surgical removal of their healthy ovaries to reduce their cancer risk. This decision can be extremely difficult. Despite this, there is a lack of guidance on how to best support women in making this decision. Research that is guided by theoretical frameworks is needed to help inform clinical practice. We explored the decision-making process of women who are at an increased risk of developing ovarian cancer and had to decide for or against the removal of their ovaries.

A qualitative study of 18 semi-structured interviews with women who have attended a cancer treatment centre or cancer counselling and information service in New South Wales, Australia. Data collection and analysis were informed by the Health Belief Model (HBM). Data was analysed using qualitative content analysis.

The paper describes women’s decision making with the help of the four constructs of the HBM: perceived susceptibility, perceived severity, perceived benefits, and perceived barriers. The more anxious and susceptible women felt about getting ovarian cancer, the more likely they were to have an oophorectomy. Women’s anxiety was often fuelled by witnessing family members suffer or die from cancer. Women considered a number of barriers and potential benefits to having the surgery but based their decision on “gut feeling” and experiential factors, rather than statistical risk assessment. Age, menopausal status and family commitments seemed to influence but not determine women’s decisions on oophorectomy. Women reported a lack of decision support and appreciated if their doctor explained their treatment choice, provided personalised information, involved their general practitioner in the decision-making process and offered a second consultation to follow-up on any questions women might have.

Conclusions

These findings suggest that deciding on whether to have an oophorectomy is a highly personal decision which can be described with the help of the HBM. The results also highlight the need for tailored decision support which could help improve doctor-patient-communication and patient-centred care related to risk reducing surgery in women at an increased risk of ovarian cancer.

Peer Review reports

Challenges of medical decision making

Cancer is the largest cause of death in Australia and worldwide, surpassing cardiovascular disease. On average one in two men and one in three women will be diagnosed with a form of cancer during their lifetime [ 1 , 2 ]. Cancer incidence rates have been increasing over the last decades [ 3 , 4 ]. Simultaneously, medical progress has resulted in a growing number of cancer prevention, screening and treatment options. Many patients have to make difficult decisions regarding the various options available to them [ 5 , 6 , 7 , 8 , 9 , 10 ]. More and more of these decisions involve options which show similar medical effectiveness but hold various side-effects and impacts that each patient may value differently. Such decisions are called “preference-sensitive” [ 11 , 12 ]. Patients have to weigh-up the risks and benefits of the options available to them. The “best choice” cannot be pre-defined. It depends on patients’ preferences.

Deciding for or against having an oophorectomy can be particularly difficult

Women at an increased risk of developing ovarian cancer may be offered a bilateral salpingo-oophorectomy, a surgical procedure to remove apparently normal ovaries and fallopian tubes in order to decrease their risk of developing cancer [ 13 ]. It is a particularly difficult decision to make, involving numerous risks and potential benefits which need to be taken into account [ 14 ]. The risks and benefits, and clinical recommendations can also vary depending on whether women carry certain gene mutations, their age and family history of cancer. Bilateral salpingo-oophorectomy has been shown to decrease women’s risk of developing ovarian cancer [ 15 ]. This is crucial for many patients as early ovarian cancer usually causes no or only very few symptoms. Ovarian cancer is often diagnosed at a stage where the cancer has spread beyond the ovaries [ 16 ]. The 5-year relative survival rate for Australian ovarian cancer patients is only 43% [ 1 , 17 ]. As there is no proven screening method for ovarian cancer, having a bilateral salpingo-ophorectomy can significantly decrease patients’ anxiety and depression related to their perceived cancer risk [ 18 ]. Conversely, having an oophorectomy has been associated with short-term and long-term health risks, including surgical complications such as bleeding or infections, abrupt onset of menopausal symptoms such as hot flushes, and symptoms associated with menopause such as depression or anxiety [ 17 ]. Having the surgery may also cause an increased risk of cognitive impairment, osteoporosis and hip fracture [ 17 ]. Many patients experience a significant decrease in their sexual function and have to decide on whether to undergo hormone replacement therapy after having their ovaries surgically removed [ 19 ]. However, there is a lack of data on the effects of the long-term use of hormone replacement therapy and its psychological influences on women who underwent an oophorectomy [ 19 ].

Research is needed to help women decide on this surgery

In order to adequately support women in deciding on whether or not to undergo the surgical removal of their ovaries, we need to understand why and how they make this decision. However, a recent review found a lack of research examining ovarian cancer treatment decision making from the perspective of the patient [ 20 ]. Uncertainty remains regarding the range and complexity of contextual factors that may impact on patients’ decisions [ 21 , 22 , 23 ]. Further, most research has been undertaken outside of Australia, and findings may not generalise given differences in healthcare delivery and social norms [ 23 , 24 ]. Most studies failed to use a theoretical framework to guide research on decision making regarding oophorectomy. For example, a review of 43 studies on women’s decision making about risk-reducing strategies in the context of hereditary breast and ovarian cancer found that only two employed a theoretical framework to guide their research [ 23 ]. Employing a theoretical framework is likely to advance our understanding of women’s decisions about risk reducing strategies by helping to organise and integrate existing knowledge on preventive health behaviours. This can provide important guidance for research and clinical practice specific to the area of decision making on oophorectomy [ 23 ]. One model that may be particularly well suited to study women’s decisions on oophorectomy is the Health Belief Model (HBM). It was developed by a group of psychologists as a systematic method to explain and predict preventive health behaviour [ 25 , 26 ]. The HBM is one of the most widely used theoretical frameworks for understanding health behaviour [ 27 ]. It is a psychosocial model that is designed to help understand health behaviours which prevent disease, or detect disease when a patient has little or no symptoms [ 28 ]. Unlike other models used to describe and predict health behaviour, such as the Theory of Planned Behaviour, the HBM focuses on intra-personal factors, including risk-related beliefs which influence individuals’ health-related decision making [ 29 ]. This was considered particularly important for this current study which aimed to explore women’s attitudes and beliefs regarding deciding on oophorectomy, with the aim to provide suggestions for clinical practice on how to better support this decision-making process.

Conducting qualitative research which is guided by theoretical frameworks can help provide valuable in-depth insights into patients’ perceptions of the decision-making process and thus enhance our understanding of existing quantitative data on patients’ views and experiences [ 30 , 31 ]. Findings of qualitative research can further inform future studies by providing suggestions for how to design and implement decision support strategies [ 32 ]. Conducting qualitative research in this area will enable us to better help patients make difficult decisions and improve their outcomes.

To explore why women who are at an increased risk of developing ovarian cancer decide for or against the surgical removal of their ovaries.

A qualitative study of 18 semi-structured interviews.

Setting and sample

Eighteen women who have attended a cancer treatment centre or cancer counselling and information service in New South Wales, Australia, took part in this study. Participants who were at an increased risk of developing ovarian cancer and had to make a decision regarding the surgical removal of their ovaries were included in this study. Women were defined as being at an increased risk of developing ovarian cancer if they have had at least one first degree relative diagnosed with ovarian cancer and/or carried a BRCA gene mutation [ 33 ]. A purposeful sampling frame was used to allow for recruitment of women who decided for removing their ovaries and those who decided against removing their ovaries. Data collection was stopped when data saturation was perceived to be reached and further data gathering was not considered to reveal additional findings to answer the research question [ 34 , 35 ].

Inclusion criteria

Eligible patients: (i) were determined by their treating clinician as being at an increased risk of developing ovarian cancer in the future (according to the criteria defined above); (ii) have decided for or against the surgical removal of their ovaries to reduce their cancer risk within the last two years (the outcome of this decision and when it was made was informed by the electronic patient management system described below); (iii) were aged 18 years or over; (iv) have attended a cancer treatment centre or cancer counselling and information service in New South Wales at least once in the past; (v) were determined by their treating clinician as physically and mentally capable of taking part in this study; and (vi) were English speaking.

Recruitment

Potentially eligible patients were identified by their treating clinician through a state wide genetic services patient management system. It lists the names, decision for or against oophorectomy, BRCA gene status and contact details of women who have attended a cancer treatment centre or cancer counselling and information service in New South Wales. Eligible patients were mailed a study package by their treating clinician which included a study information sheet, a study consent form and a reply paid envelope. Patients who were willing to participate in the study returned the consent form to the research team by posting it in the provided reply paid envelope. Consenting patients were required to indicate their name, contact details and preferred contact time on the consent form.

Data collection

Consenting patients were contacted by a member of the research team via telephone to arrange a time for a face-to-face interview, telephone interview or videoconferencing. Patients were given the choice of interview mode to reduce research related burden. The location of face-to-face interviews was determined according to what was most convenient for the patient. To further reduce research related burden, particularly for patients from remote areas, patients were given the option to conduct the interview via the free Avaya Scopia Mobile app. This software has been used by clinic staff for the transmission of clinical consultations by videoconference over the internet. Scopia offers patients the opportunity to receive some of their healthcare at or close to home through telehealth. It is completely secure, encrypted and confidential.

Before the interview commenced, patients were asked for permission to audio-record and transcribe the interview. They were informed that all data would be de-identified and thus remain confidential. Patients were further told that the interview would last approximately 30 min and that they could stop the interview or skip questions they did not feel comfortable answering any time. The interviewer then encouraged patients to tell their story about how they made the decision for or against the removal of their ovaries, in the way they prefer, with as little interruption as possible from the interviewer. This narrative approach helped elicit the range and interplay of potential reasons for or against having an oophorectomy [ 36 , 37 ].

At the end of the narrative section, a set of semi-structured questions was used to explore particular issues further. These issues included reasons for why women decided for or against the surgery as well as factors which might have influenced their decision. The question guide was informed by the HBM which includes four constructs which were used to develop the questions patients were asked: perceived susceptibility, perceived severity, perceived benefits, and perceived barriers [ 38 ]. The questions were further informed by discussions amongst the research team which included experts in the areas of health behaviour, gynaecological oncology and qualitative research. Questions explored patients’:

○ Perceived susceptibility to ovarian cancer by asking patients how likely they thought it was that they will develop cancer in the future, how worried they were about developing cancer, how they felt about the lack of proven screening methods for ovarian cancer;

○ Perceived severity of the situation by asking patients how worried they were about the consequences of developing cancer (e.g. risk of dying, treatment side-effects, fear of outcomes, not being able to support their family while receiving treatment), whether they have had personal experiences with cancer themselves and/or in the family, whether this influenced their decision and if so, how;

○ Perceived benefits of an oophorectomy by asking patients whether they believed that the surgery would prevent cancer and if so, how much it would decrease their risk of developing cancer, whether they perceived it would decrease their worries related to developing cancer, whether they trusted the information they had received regarding their cancer risk and the surgery’s benefits, whether they felt the information was applicable to them and how it factored into their decision;

○ Perceived barriers to an oophorectomy by asking patients how they felt about potential complications of the operation (e.g. bleedings, infections), long-term effects of the surgery (e.g. abrupt onset of menopause), its impact on their sexuality, its impact on their ability to have children, its impact on “feeling like a woman”, its impact on social relationships (e.g. with their partner, family), financial issues (e.g. whether to take time of work, costs of the surgery), what impact the surgery may have on their family and other social obligations;

○ Decision-making process by asking patients what support they had in dealing with the consequences of the surgery and what else could have helped them make this decision, how much time they spent thinking about the surgery’s benefits and risks, and how long it took them to make the decision, who made the decision in the end and what happened after the decision was made.

Patients were also asked about the following sociodemographic and disease-related characteristics: highest level of education completed, occupation and marital status. To reduce research related burden for patients, further patient characteristics were sought with the help of their medical records, including date of birth, results of previous genetic testing and date of surgery (if applicable). Wherever possible standardised questions were used. All study materials were reviewed by the research team and pilot tested prior to finalisation with a group of health behaviour scientists and clinicians.

Data analysis

All interviews were transcribed verbatim. Transcripts were double-checked for accuracy by a member of the research team (AH1). Text passages were read by members of the research team to familiarize themselves with the data and prepare the assignment of codes and categories (AH1 and AH2). A qualitative content analysis approach was chosen to identify and investigate key factors patients considered as influencing their decision regarding surgically removing their ovaries. Qualitative content analysis has been used frequently in nursing research, and is rapidly becoming more prominent in the medical and bioethics literature to systematically describe the meaning of qualitative data [ 39 , 40 ]. This approach is particularly suited to multifaceted, sensitive phenomena, such as decision making on risk reducing surgery. Qualitative content analysis is recommended when there is no or only fragmented knowledge on the critical social process to be studied and meanings, intentions, consequences and context related to this process need to be investigated [ 30 ].

When conducting qualitative content analysis, text passages were coded following a systematic, interpretative act. Techniques used included a) summarising the data where the aim of the analysis was to reduce the material in such a way that the essential contents remain, b) explication of data where the aim of the analysis was to provide additional material on individual doubtful text components to increase understanding and interpreting particular passages of text, as well as c) structuring the data where the aim of the analysis was to filter out particular aspects of the material. These techniques were employed and results of the coding process were documented to help construct a coherent category system. This documentation contributed to the intersubjectivity of the procedure and would allow others to reconstruct or repeat the analysis [ 32 ].

The early stages of the coding process followed a conventional, inductive qualitative content analysis approach to minimise bias and ensure all relevant codes were captured. Initially, the transcripts were read line by line, and their content was examined, compared and categorized in order to apply a paraphrase or label (a “code”) that described what was interpreted in the passage as important. Codes were then grouped around the domains of the HBM to develop more abstract categories [ 30 ]. A category in this sense was a group of codes that shared a commonality [ 41 ]. If a code could not be linked to any of the domains, a separate category was developed to ensure all data was captured, regardless of whether it fitted in the existing model. This helped us validate and extend conceptually the underlying theoretical framework [ 42 ]. Based on the emerging categories, we generated threads of meaning across categories. Consequently, we analysed both latent and manifest content and chose each whole interview as unit of analysis [ 41 ]. Initial coding was conducted by one researcher (AH1). Conclusions drawn from the data were discussed amongst all three members of the research team (AH1, AH2, AP). In accordance with the principle of constant comparison, the robustness of the developed hypotheses was tested on different levels. As suggested by Przyborski and Wohlrab-Sahr, conclusions made by the research team were questioned on the basis of each single case as well as independently of individuals and thus beyond single cases [ 43 ]. Patient characteristics are presented using summary statistics. Chi-square tests were used to assess consent bias.

Women were interviewed between March and November 2017. Eighty-six patients were invited to participate. Of these, 18 women (21%) consented to participate and were interviewed. Most interviews were conducted via telephone. Only one participant preferred to be interviewed face-to-face. Women had a mean age of 57 years, ranging from 22 to 81 years (SD = 15, see Table  1 ). Sixty seven percent of study participants had not been diagnosed with a gene mutation associated with an increased risk of developing ovarian cancer ( n  = 12). Of these, ten women (83%) had been tested for relevant gene mutations. Sixty one percent of women had previously been diagnosed with cancer ( n  = 11). Most of these women had been diagnosed with breast cancer ( n  = 9, 50%). Eleven participants underwent an oophorectomy, seven decided against the surgery. A mean of 23 days elapsed between study consent and interview (SD = 12). There were no statistical significant differences between consenters and non-consenters in terms of age and prevalence of relevant gene mutations ( p > 0.05 ).

Perceived severity

Many women witnessed family members suffering and dying from breast or ovarian cancer. For most women who decided to have an oophorectomy, this was the single most important reason for getting their ovaries surgically removed. Several participants perceived undergoing cancer treatment to be worse than receiving a cancer diagnosis. They felt that having cancer treatment would have a strong impact on patients’ quality of life and lead to a lack of control over one’s health and wellbeing, even when a patient was in remission, due to the potential long-term treatment side-effects and fear of cancer recurrence.

I don't have a problem with getting cancer, I have a problem with the treatment of cancer. From my own personal experience of seeing other people go through it, the treatment is worse than the disease. […] They're spending their whole life of not living their life, just thinking, oh, I've just to get to the next doctors visit and the next doctors visit. (Participant 7, 34 years, BRCA2 carrier, no oophorectomy)
Well, it's not easy for any woman but I guess maybe when you've been through something you make your decisions and you see people, you're losing people with ovarian and breast cancer, you do everything in your power to help yourself and help your family. (Participant 2, 68 years, BRCA2 carrier, oophorectomy)

A number of women were concerned about the lack of symptoms of early-stage ovarian cancer and the fact that it is often detected at a late stage. Many women also worried about the non-specificity of symptoms of ovarian cancer. Most were aware of the low survival rates of ovarian cancer which highlighted to them the seriousness of the disease. This knowledge increased women’s fear of getting ovarian cancer and made them reflect on the potential benefits of having an oophorectomy. For many women, having their ovaries surgically removed seemed to be the only effective option of taking control over their perceived cancer risk.

Basically if I felt bloated, if I was gaining weight or losing weight, or if I didn't feel right in my abdominal area then I should get professional medical help. I'm thinking, that's every other day I have these symptoms. It [=identifying relevant symptoms and seeking timely medical help based on self-assessment only] is not really realistic. (Participant 7, 34 years, BRCA2 carrier, no oophorectomy)

Perceived susceptibility

All women realised that they were at an increased risk of developing ovarian cancer. However, women’s perceived cancer risk often differed from their actual risk. Women who had cancer in the past or who had witnessed or heard of family members being diagnosed with a form of cancer often felt more susceptible to getting ovarian cancer than women without a personal or family history of cancer.

Well it all started with my sister. She had cervical cancer. Yeah, so that’s how it all started. She had a gene test. We didn’t have the mutant gene, but I still felt that it would be less risk if I did have the op and to have them taken. I was particularly worried, my sister passed away at the beginning of this year, and to see her go through that wasn’t a good thing. That was certainly something that I never want to put myself through. So that was my main reason, I’d seen her go through it all, and so if I could just reduce that risk, I thought it would be a good start at least. (Participant 3, 68 years, BRCA2 carrier, oophorectomy)

Also, women coped very differently with their perceived cancer risk. Many of the women who decided for the surgery did not carry a BRCA gene mutation. They were thus at a relatively low risk of developing ovarian cancer but could not cope with the anxiety related to their cancer risk. They felt that the cancer was “floating around” in their family (patient, 70 years) and thought that having the surgery would be the logical consequence to reduce their cancer risk. They considered the decision as a “no brainer” (patient, 57 years). In contrast, some of the women who had a BRCA2 gene mutation decided against the surgery, despite their relatively high risk of getting ovarian cancer. These women also felt susceptible to getting ovarian cancer but they felt less anxious and worried about their cancer risk. Women often trusted their “gut feeling” and based their decisions on experiences and beliefs, rather than statistical risk assessment. Although their decisions may seem to be emotive, women perceived their decisions to be perfectly logical and reasonable within the context of their experiences.

I don't know whether some people would think I was crazy for making the decisions I’ve made, but I just feel – I don't know, my innate feeling – it's just a feeling that it's the right thing to do at the moment. (Participant 1, 60 years, no known gene mutations, no oophorectomy)

Perceived benefits

Reducing their risk of developing ovarian cancer and decreasing their worries related to their cancer risk were seen to be the most important benefits of having the surgery. Many women felt that taking away their worries would significantly improve their quality of life. This was although the data also revealed a lack of knowledge related to the potential benefits of undergoing an oophorectomy as some women felt that by having the surgery they would reduce their risk of ovarian cancer, whereas others thought it would prevent them from getting the cancer.

But if I was living in fear and I was worried every day of my life that I may get cancer again then I probably would have had surgery. (Participant 1, 60 years, no known gene mutations, no oophorectomy) I've had a lot of friends say that to me why would you want to have all that surgery if there's nothing wrong? I'm just like well, it's a ticking time bomb really. (Participant 7, 34 years, BRCA2 carrier, no oophorectomy)

Women reported that a lot of the benefits of having an oophorectomy involved social factors. Many women decided for the surgery because they wanted to be able to be there for their children or grandchildren in the future. Some women also mentioned a need to be healthy in order to fulfil their family commitments, such as looking after their children or elderly parents and running a household. Some women said that they underwent the surgery to reduce the burden on others as the time and help they would need while recovering from the surgery would be much less than the time and help they would need following a cancer diagnosis and treatment.

Probably one of my biggest fears, especially having a kid, that I wouldn’t be there for him. (Participant 9, 34 years, BRCA2 carrier, no oophorectomy)
As I said, I live on my own. I look after grandchildren during the school holidays. I’ve got my mum still alive, she’s 92. I’ve got to be fit and healthy. (Participant 4, 70 years, BRCA2 carrier, oophorectomy)

Perceived barriers

Women identified a number of barriers which they felt made it harder to opt for the surgery. For instance, several women feared the sudden onset of menopause and relating symptoms. They reflected on the potential intensity of symptoms, how long it would take them to physically, emotionally and mentally adjust to having early menopause and for how many years they may have to cope with menopausal symptoms.

I don’t want to be this scarred cranky bitch, dry old woman in the body of a 40-year-old. And not just that, I've then got another 15 years on top of the normal woman who does that [= having menopause]. […] But I just want you to understand that I'm not a 67-year-old woman, I'm a 40-odd-year-old woman, so I've got to think of my long term. I'm fighting this to be here for a longer period of time, but I've got to think about these things so that I can manage in those years ahead. (Participant 14, 47 years, no known gene mutations, oophorectomy)

Some women were concerned about the side-effects of hormone replacement therapy and that having this treatment could increase their risk of getting breast cancer. All women made their treatment decision based on what they thought would be the best long-term option for them.

The risk factors of osteoporosis and that sort of thing that was relevant to me. I thought, oh, well maybe it's better if there's something else that we can do, as opposed to [having surgery and] then needing to take hormone replacement and all that sort of stuff, which puts you at higher risk of breast cancer once again anyway. (Participant 7, 34 years, BRCA2 carrier, oophorectomy)

Some women were worried about potential surgical complications, such as bleeding or infections. Many women above the age of 60 felt more susceptible to such complications. Women tried to counteract these risks by choosing a clinic they found trustworthy, maintaining physical activity, planning sufficient recovery time and by seeking additional information through family, friends or support groups. Some women perceived the lack of an accurate risk assessment and the preventive (rather than curative) purpose of the procedure as a barrier to having the surgery. These women did not want to undergo an elective invasive procedure although they may never develop ovarian cancer. Others pointed out that there may be a chance that screening for ovarian cancer will be improved in the near future which could make the surgery redundant.

There’s a difference between doing something for a purpose, but just in case seems to be subjecting your mind. Also when you get older anaesthetics aren't very good for your mental condition. It takes ages for you to get over anaesthetics, you know, as you get older as well. I didn't want my – how would I say? I didn't want my body violated, just in case. (Participant 5, 81 years, no known gene mutations, no oophorectomy)
When I had breast cancer, you just go, well you've just got to get the breast off. There's not a choice. It's got cancer, it's sick. It's an easy decision to make. I think I would be the same if I knew there was something wrong with my uterus or my ovaries or whatever. I'd say, yeah, absolutely take it. But when there's nothing wrong with them, as far as I know, it's harder for me to make that decision to have them out." (Participant 1, 60 years, no known gene mutations, no oopohrectomy)
And what if next year they find a test for it [=ovarian cancer]? Yes, that runs through your head. (Participant 11, 57 years, BRCA2 carrier, oophorectomy)

Some women were concerned that the surgery might affect their femininity or their sexuality. They feared to have a decreased libido or may feel less like a woman. Women’s thoughts on this were also influenced by how supportive their partner was perceived to be in terms of having their ovaries surgically removed. However, for most women, impacts on their femininity or sexuality were of little or no concern as they felt that removing their ovaries would not change their appearance and thus be less obvious than other procedures, such as having a mastectomy. Some women even felt empowered by having an oophorectomy because they perceived that they made their own decision on their health and well-being. These women reported that deciding to get their ovaries removed made them realise that their femininity was linked to their attitudes and feelings, rather than to the presence of their ovaries.

So for me that was a difficult decision because I was a woman that had gone into my 40s and reached my peak of … our children were now late teens, so my husband and I were getting more time to be intimate and all that sort of stuff. (Participant 14, 47 years, no known gene mutations, oophorectomy)
I mean you can't stand there at the café and say, oh, she's had a hysterectomy. I'd be more conscious if my breasts and things like that had to be removed. I mean, I haven't changed, my hair hasn't changed, my skin hasn’t changed. My husband hasn't said to me I'm getting crankier or not. (Participant 6, 55 years, no known gene mutations, oophorectomy)
I felt quite empowered because it just made me believe that all those things [=femininity and sexuality] are very much an inner thing and more of an emotional thing. (Participant 1, 60 years, no known gene mutations, no oophorectomy)

Some women said that not being able to have children in the future was a barrier to undergoing the surgery. This was particularly relevant for younger women. Many of these women were advised by their treating doctor to have their children first and then undergo the surgery. Consequently, some women struggled with the pressure of having to finish their family plans in a timely manner in order to be able to have their ovaries removed. Some of the women who had a BRCA2 gene mutation decided against having further children because they were concerned that they may pass on the gene mutation.

My next birthday I’ll be 34 and we’ve been trying to have a second child now for maybe seven or eight months and it still hasn’t happened yet. So in the back of my mind I feel like there is a bit of a deadline; I need to hurry up, but yeah. (Participant 9, 34 years, BRCA2 carrier, no oophorectomy)

A number of women who their doctor considered to be too young to have surgery reported that they had not been offered a choice of whether to have their ovaries removed. They felt a lack of control over the decision-making process and wished that their doctor had explained the reason for this lack of choice more in detail. These women felt that this would have allowed them to feel more involved in and confident with their decision.

I really kind of feel that I didn't really get a choice to tell you the truth because basically it was you're too young. (Participant 7, 34 years, BRCA2 carrier, no oophorectomy)

Only a few women felt that financial barriers impacted on their decision. These women were single, on the pension or had to travel long distances to the clinic which meant that some of them had to take a considerable amount of time off work. However, for most women financial factors were not relevant as the costs for the surgery were covered by their health insurance and they lived close to the clinic where the surgery was done. Some women said that reducing their risk of having ovarian cancer would be invaluable which made the costs of having the surgery of little relevance to their decision.

We had a big trip home, but then we stayed up there a couple of extra days so that it wouldn’t be as bad travelling home. You just try and plan. […] It is very, very difficult when you live in the country. (Participant 2, 68 years, BRCA2 carrier, oophorectomy)
Well, I'm on the pension, so I couldn't afford to pay for it. Yeah, because like I've been on the pension for a while and I just don't have that money to go ahead with it if it's going to cost me anything. (Participant 18, 74 years, no known gene mutations, no oophorectomy)
No it was all done free. I think the clinic covered the bills. I don't think money would have made a difference either. It was a long-term decision. (Participant 6, 55 years, no known gene mutations, oophorectomy)

Perceived self-efficacy

Women’s confidence in their ability to take action seemed to be an important factor for their decision on whether or not to have an oophorectomy. Women who decided for the surgery tended to feel the need to take control over their situation by having their ovaries surgically removed. They wished “to do everything they can” to decrease their cancer risk. These women felt that screening was invasive, strenuous and would not be effective in picking up ovarian cancer. They perceived the surgery to be the only option offering sufficient efficacy in risk reduction. In contrast, women who decided against the surgery felt that their ability to judge their health and wellbeing, to undergo regular screening and contact their specialist whenever symptoms arise or their personal circumstances change would give them sufficient peace of mind. This made having the surgery redundant.

So I had to have checks all the time to see whether anything was going on. They’re really invasive tests, and they don’t guarantee, they’re not 100 per cent clear. So there was always that doubt. So in the end I was just sick of going through the tests that couldn’t really tell me conclusively anyway, so I decided to have everything removed. (Participant 11, 57 years, BRCA2 carrier, oophorectomy)
I thought, okay, well I'm feeling really good. I'm not seeing any history of ovarian cancer. I'll be keeping an eye on it. (Participant 1, 60 years, no known gene mutations, no oophorectomy)

Modifying factors and cues for action

A number of factors influenced women’s decisions on whether to have their ovaries surgically removed. For instance, age and menopausal status seemed to modify the decision-making process in many ways. All women felt that increasing age and being post-menopausal made it easier to decide for the surgery. This was linked to the perceived “uselessness” of ovaries once women had had children. However, there was no clear threshold indicating from what age women were more or less likely to undergo the surgery. For example, some women who were in their 30s decided for the surgery since they felt very anxious about their risk of developing ovarian or breast cancer. Some women were close to menopause or were post-menopausal but decided against the surgery because they feared that removing their ovaries could have a negative impact on their sexuality. Women’s medical history also influenced their decision, but as with age and menopausal status, this factor did not determine women’s decision. Some women who had been diagnosed with breast cancer in the past felt more alert and anxious about their cancer risk and decided for the surgery. Others wished not to have another invasive procedure since they had undergone breast cancer treatment and perceived this to be very burdensome. For example, some women had a mastectomy and feared that removing their ovaries would cause more scars on their body and further damage their feeling of being a woman.

I've have my breasts taken […] but it has damaged my femininity, you know in my head space in what I see in the mirror it still affects me emotionally, and now you want to take away sort of my last part of my femininity. (Participant 14, 47 years, no known gene mutations, oophorectomy)

Women reported a number of cues of action which promoted awareness and initiated the decision-making process. Some women reported that having family members recently undergoing genetic testing or passing away from cancer made them undergo genetic testing themselves. This often led to discussions with their doctor about whether or not to have the surgery. Other women reported that having their family plans completed would provide the cue to go ahead with the surgery.

I mean it sort of put a bit more pressure on me, in that to finish having a family. It sort of put a bit of a timeline on it all. (Participant 9, 34 years, BRCA2 carrier, no oophorectomy)

The decision-making process

Most women reported that they made the final decision about whether to have an oophorectomy, after considering their healthcare providers’ opinions and involving their supportive others. The time women took to make the decision differed considerably, depending on women’s informational needs and preferences: While some women said they made their decision during or immediately following the initial consultation with their doctor, others took weeks or months to seek additional information and weigh-up the potential risks and benefits of having an oophorectomy. Consequently, not only the decision on whether to have an oophorectomy but also the decision-making process was highly personal and differed considerably depending on the needs and preferences of each individual patient.

I think my husband and I more or less made it [=the decision] together. We’ve been together that long that we think the same anyway. (Participant 8, 62 years, no known gene mutations, oophorectomy)
It was just always in the back of my mind that that’s probably the better option to go. But I just needed to find more information about it before I actually made that decision because, yeah, it just affects, it does affect you, because all your hormones change and everything. So yeah, it’s a big change to get used to. (Participant 11, 57 years, BRCA2 carrier, oophorectomy)

Many women could not remember exactly how high their risk of developing ovarian cancer was. Most women recalled being provided with this information but rather than basing their decision on numerical values, they focused on broad verbal categories (e.g. “high” or “low” risk) and experiential factors, such as their family history of cancer.

We did have the general worry that we could get it [=ovarian cancer] because of so much cancer in the family. The doctor did give me some percentages, but I could not remember them now, on our chances of getting it. (Participant 8, 62 years, no known gene mutations, oophorectomy)

A number of women reported asking other women who underwent the surgery about their experiences, and searching for additional information online. Some found online social networks helpful as they allowed them to read other patients’ experiences and ask questions. Thus, searching information online not only helped women overcome their perceived lack of knowledge but also provided some comforting by enabling them to share their experiences and thoughts with other women, whenever and wherever they wished to do so.

There’s a few Facebook groups […] you sort of see more, you can read people’s circumstances or how their surgeries went. Because I don’t have family to talk really candidly about it. A lot of the stories on there are pretty open and honest and I find that helpful to read through them. […] You can post any questions you have on there and actually people that have gone through it or are thinking about going through it [answer], so that’s quite useful. (Participant 9, 34 years, BRCA2 carrier, no oophorectomy)
I wanted to be able to talk about it when I wanted to talk about it, when I could talk about it, and I didn't want to talk about it when I didn't want to talk about it. So I really think that that social component is really important. […] Rather than saying here's the medical reason why you need to do this, you know what I mean. (Participant 14, 47 years, no known gene mutations, oophorectomy)

Many women felt a lack of support with choosing the right treatment option for them. Women perceived that compared with breast cancer, ovarian cancer received insufficient attention by researchers and policy makers, leading to a lack of public awareness and reliable information on ovarian cancer. Many women wished that they had had a better documentation of what had been discussed during the consultation with their doctor to help them “digest” and use the abundance and complexity of information they received. Women also indicated that this would have provided them with tailored and personalised information which they could not find by searching other information sources. Women appreciated having a follow-up consultation or the opportunity to call their specialist after their initial consultation.

Everything seems to be the breast cancer. There’s not near as much with the other cancers and they really do need it. (Participant 15, 22 years, no known gene mutations, no oophorectomy)
Yeah, it was very difficult to find anything that really was representative of me. Like I could pull things out of different articles but there wasn't anything for the full-time student, single parent who didn't have a private health insurance. (Participant 7, 34 years, BRCA2 carrier, no oophorectomy)
If you can read the information and take it in yourself, then if you've got queries, ring back. Because sometimes you go to ring up and ask questions and they'll say something and it throws you and you forget the next question. So, I think if you can get your information and then ring them back, it's helpful. (Participant 2, 68 years, BRCA2 carrier, oophorectomy)

Some women said it would have been helpful to see a short description of their specialists’ professional interests and experiences prior to the consultation. This would have allowed them to be reassured that their doctor has the expertise required to guide them through this preference-sensitive decision-making process. Many women found it very helpful to have their GP involved in the decision-making process in order to get a second opinion of someone they trust. This was also seen as an additional opportunity to ask questions women may have.

Maybe a blurb or a CV on the doctor might, just a paragraph or two, might be handy. It might make people feel better if they know, I've done X amount of surgeries, I operate this often on blah, blah, blah, might put nervous people at rest. (Participant 6, 55 years, no known gene mutations, oophorectomy)
I have spoken about it with my own GP who is very, very thorough and she's supportive of everything I do. She researches it and she explained why it was that he had said that basically it wasn't an option for me at this point in time and I'm grateful. I know, a lot of people don't have access to a really good GP, and if I had any advice for anybody who was going through what I've essentially been through, it would be get yourself a good GP who knows what they're talking about or who is willing to investigate genetic issues. (Participant 7, 34 years, BRCA2 carrier, no oophorectomy)

Applicability and limitations of the Health Belief Model

Numerous studies have examined women’s decisions for or against having preventive surgery to reduce their cancer risk [ 23 ]. However, despite considerable research efforts in this area, there is a lack of theoretically guided studies that could help advance our understanding of patient decision making on oophorectomy [ 23 ]. Our study findings suggest that the HBM provides a structured approach to guide both data collection and analysis of research in this area, and helps describe women’s decision making on oophorectomy. As suggested by the HBM, women conducted an internal assessment of the benefits and barriers to undergoing the surgery, and then decided whether or not to act [ 29 ]. In line with research conducted in other geographical areas, women in our sample reflected on an array of factors before deciding on whether to undergo an oophorectomy, including potential complications of the surgery and long-term side-effects [ 23 ]. However, most women’s decisions were driven by their anxiety about developing ovarian cancer sometime in the future. Women’s decisions on whether or not to have an oophorectomy were thus often based on “gut feeling” and experiential factors, rather than statistical risk assessment. This is in line with previous studies describing the importance of experiential factors for individual decision making [ 44 , 45 , 46 ]. Our data suggest that witnessing a family member suffer or die from cancer can have a strong impact on women’s decision making who often feel more anxious about their own cancer risk and may thus be more likely to opt for the surgery. This also highlights the preference-sensitive nature of this decision and the need to provide appropriate support which can assist women in dealing with their familial experiences and choosing the option that is in line with their preferences.

Numerous studies have provided empirical evidence to support the dimensions of the HBM as important factors when explaining and predicting individuals’ health-related behaviours [ 27 ]. However, given its focus on attitudes and beliefs of individuals, the HBM does not involve all potential determinants that may dictate a person’s acceptance of a health behaviour [ 27 ]. Our study findings reflect this. For example, we also focused on the decision-making process to account for and explain data on decisional factors which were not captured by the original domains of the model. This approach also allowed us to make suggestions for clinical practice. It helped compensate for another limitation for the HBM which is more descriptive than explanatory, and does not suggest a strategy for changing health-related actions [ 27 ].

Implication for clinical practice

Women appreciated being provided with a choice of whether to have an oophorectomy. Some clinicians may perceive that having their ovaries surgically removed is not an option for some patients, given that guidelines recommend oophorectomy to be considered for all women who are around the age of 40 and at an increased risk of ovarian cancer due to a confirmed BRCA1/2 gene mutation [ 47 ]. However, all women in our study indicated that they would still like to receive comprehensive information on the risks and potential benefits of the surgery, as well as details on why their doctor thinks they were not eligible for this procedure. This is in line with studies suggesting that the choice of treatment has an intrinsic value to patients, even if they decide to follow their doctor’s treatment recommendation [ 48 ]. Also, as suggested by previous research in this area, many women felt empowered by deciding on whether or not to get their ovaries removed. Not being offered a treatment choice made women perceive a lack of control over their situation and impacted negatively on their decisional confidence and satisfaction with the consultation with their doctor [ 49 ]. Consequently, it is important that clinicians try to guide women through this preference-sensitive decision-making process by explaining their choices, eliciting their preferences and tailoring care accordingly.

Many women reported a lack of information on the risks and benefits of having an oophorectomy. A documentation of what had been discussed during the consultation could help women recall and use the information provided to them. It would also allow women to access personalised and tailored information which they could not find elsewhere. Women appreciated if their doctor offered a second consultation to discuss their questions or concerns. This is in line with best practice guidelines on how to provide complex and potentially distressing information to patients [ 50 , 51 ]. Having a follow-up consultation and being provided with additional information to consider in-between these consultations may help patients understand and weigh-up the information they received, seek further information, involve their support persons and increase their ability to participate in the decision-making process [ 52 ].

Given that it may not always be possible to cover all risks and potential benefits of all available treatment options during each consultation, tailored decision support, for example in the form of a decision aid, could supplement the above mentioned documentations of what has been discussed during the consultation and further support women with deciding on whether to have an oophorectomy. Patient decision aids present specific, evidence-based information on the healthcare options available to patients and aim to assist patients with clarifying and communicating the value they associate with each option [ 53 ]. They explicitly state the decision to be made and explain in detail the risks and potential benefits of the available options. Thus, decision aids help patients comprehend and weigh-up the risks and benefits of the options available to them and support patients in clarifying their preferences [ 54 ]. A decision aid on oophorectomy could include firsthand accounts of women who made this decision in the past since many women in our sample found it helpful to read about other women’s experiences. Decision aids on oophorectomy have been shown to improve a number of patient outcomes, such as increased knowledge of oophorectomy, decreased uncertainty related to their treatment options and decreased decisional conflict [ 55 , 56 , 57 ]. However, such strategies are still not commonly used in clinical practice which means that their benefits are unlikely to reach the intended patient populations [ 58 ]. This is in line with our findings indicating that many women perceived a lack of decision support when deciding on whether to have an oophorectomy. Further research on the influence of information experiences on women’s decision making, with particular focus on online information, may help provide valuable suggestions for how to design and test appropriate decision support strategies.

Also, women felt it would have been helpful to receive information on their doctor’s experiences and professional interests prior to the consultation. They thought that this would allow them to learn more about their clinician’s expertise, and help them choose the right healthcare provider for them. Previous studies have reported on the benefits of such information for doctor-patient communication and suggested that clinicians consider their online profile as an extension of their practice within reasonable limits [ 50 , 59 ]. Additional clinician information provided online may facilitate healthcare decision making by increasing the trust patients place in their doctor [ 51 , 52 ].

Women also indicated they appreciated the involvement of their general practitioners (GPs) in the decision-making process. This follows the principle of shared care which is the joint coordination and delivery of healthcare by a patient’s specialist and their GP [ 60 ]. Shared care has a number of potential benefits, including improved delivery and access to recommended healthcare [ 61 ], and improved coordination and continuity of care [ 62 ]. Involving women’s GPs in the decision-making process could help women feel more confident to ask any questions they may have, and get a second opinion from a healthcare provider who may be more familiar with their health and personal circumstances. Specialists could consider suggesting to patients that they see their GP prior to making the final decision.

Limitations

Our results are not intended to be numerically representative. They rather provide in-depth insights into women’s decision-making process. We used a qualitative content analysis approach which was guided by the HBM. This may be criticized due to its potential to bias the analysis and thus the study findings [ 42 ]. However, to ensure a non-biased approach to coding which allows to identify and categorize all instances and dimensions of women’s decision-making process, we coded all transcripts inductively first and sought the HBM only to inform the later stages of the analysis. Consequently, we also developed codes and categories which did not fit into the model. Also, most interviews in our study were telephone interviews. Some authors may argue that this mode of data collection could be less valuable than face-to-face interviews. However, there is a lack of evidence on whether telephone interviews produce lower quality data [ 63 , 64 ]. Also, most patients in our study preferred to be interviewed via telephone. They may feel more relaxed and able to disclose sensitive information when being interviewed on the telephone, and may find it easier to rearrange a telephone interview by calling back at a more convenient time, rather than having to rearrange a face-to-face interview [ 65 ].

Some women participated in the interview months after deciding on oophorectomy. This introduces the possibility of recall bias that could lead to inaccurate narratives. Also, most study participants had either a BRCA2 or no BRCA gene mutation. Women with a BRCA1 or other gene mutation have to consider different risks and potential benefits of having an oophorectomy and might thus have different experiences with deciding on oophorectomy [ 23 ]. Only few women mentioned CA125 screening as further option for monitoring their health. Future research should further explore the differences in women’s decision making related to specific gene mutations, as well as the role of CA125 screening in women’s decision-making process. Also, clinicians’ communication skills and styles may have influenced how women decided on whether to have an oophorectomy. For example, clinicians’ skills in communicating risks might have had an impact on patients’ understanding of their options [ 66 , 67 ]. We did not record the consultations where the decision on oophorectomy was discussed. Thus, we do not know how clinicians’ communication skills and styles may have influenced patient decision making.

This study identified and examined reasons for why women decided for or against the surgical removal of their healthy ovaries to reduce their risk of developing cancer. The results of this study suggest that deciding on whether to have an oophorectomy is a highly personal decision which can be described and explained with the help of the HBM. Many women trusted their “gut feeling” and based their decision on experiential factors, rather than statistical risk assessment. Our findings also emphasise the need for further decision support. This could be achieved by explaining women’s treatment choice, and providing them with both a documentation of what had been discussed during their consultation and a tailored decision aid. Women also appreciated the involvement of their GP in the decision-making process and found it helpful to be offered a follow-up conversation with a healthcare provider to address any concerns or answer any questions women may have. These strategies could help improve doctor-patient-communication and patient-centred care related to risk reducing surgery in women at an increased risk of ovarian cancer.

Abbreviations

General practitioners

Health Belief Model

Australian Institute of Health and Welfare & Australasian Association of Cancer Registries: Cancer in Australia: in brief 2014. Cancer series no 91 Cat no CAN 89. Canberra: AIHW; 2014.

Ferlay J, Soerjomataram I, Dikshit R, Eser S, Mathers C, Rebelo M, Parkin DM, Forman D, Bray F. Cancer incidence and mortality worldwide: sources, methods and major patterns in GLOBOCAN 2012. Int J Cancer. 2015;136(5):E359–86.

Article   CAS   Google Scholar  

TW CD, Lu H. Cancer in NSW: incidence and mortality report 2010. Sydney: Cancer Institute NSW; 2010.

Google Scholar  

Kamangar F, Dores GM, Anderson WF. Patterns of cancer incidence, mortality, and prevalence across five continents: defining priorities to reduce cancer disparities in different geographic regions of the world. J Clin Oncol. 2006;24(14):2137–50.

Article   Google Scholar  

Brundage MD, Davidson JR, Mackillop WJ. Trading treatment toxicity for survival in locally advanced non-small cell lung cancer. J Clin Oncol. 1997;15(1):330–40.

Davis EL, Oh B, Butow PN, Mullan BA, Clarke S. Cancer patient disclosure and patient-doctor communication of complementary and alternative medicine use: a systematic review. Oncologist. 2012;17(11):1475–81.

Goldhirsch A, Winer EP, Coates AS, Gelber RD, Piccart-Gebhart M, Thürlimann B, Senn H-J, Members P. Personalizing the treatment of women with early breast cancer: highlights of the St Gallen international expert consensus on the primary therapy of early breast cancer 2013. Ann Oncol. 2013;24(9):2206–23.

Mauri D, Pavlidis N, Ioannidis JPA. Neoadjuvant versus adjuvant systemic treatment in breast cancer: a meta-analysis. J Natl Cancer Inst. 2005;97(3):188–94.

Okawara G, Rusthoven J, Newman T, Findlay B, Evans W. Unresected stage III non-small-cell lung cancer. Cancer Prevent Control. 1997;1(3):249–59.

CAS   Google Scholar  

Smith RA, Cokkinides V, Brooks D, Saslow D, Brawley OW. Cancer screening in the United States, 2010: a review of current American Cancer Society guidelines and issues in cancer screening. CA Cancer J Clin. 2010;60(2):99–119.

Brown R, Butow P, Wilson-Genderson M, Bernhard J, Ribi K, Juraskova I. Meeting the decision-making preferences of patients with breast cancer in oncology consultations: impact on decision-related outcomes. J Clin Oncol. 2012;30(8):857–62.

Politi MC, Lewis CL, Frosch DL. Supporting shared decisions when clinical evidence is low. Med Care Res Rev. 2013;70(1 Suppl):113S–28S.

Das N, Kay VJ, Mahmood TA. Current knowledge of risks and benefits of prophylactic oophorectomy at hysterectomy for benign disease in United Kingdom and Republic of Ireland. Eur J Obstet Gyn R B. 2003;109(1):76–9.

Klitzman R, Chung W. The process of deciding about prophylactic surgery for breast and ovarian cancer: patient questions, uncertainties, and communication. Am J Med Genet. 2010;152A(1):52–66.

Kauff ND, Satagopan JM, Robson ME, Scheuer L, Hensley M, Hudis CA, Ellis NA, Boyd J, Borgen PI, Barakat RR, et al. Risk-reducing salpingo-oophorectomy in women with a BRCA1 or BRCA2 mutation. N Engl J Med. 2002;346(21):1609–15.

Jacobs IJ, Menon U. Progress and challenges in screening for early detection of ovarian cancer. Mol Cell Proteomics. 2004;3(4):355–66.

Erekson EA, Martin DK, Ratner ES. Oophorectomy: the debate between ovarian conservation and elective oophorectomy. Menopause. 2013;20(1):110–4.

Hickey M, Ambekar M, Hammond I. Should the ovaries be removed or retained at the time of hysterectomy for benign disease? Hum Reprod Update. 2010;16(2):131–41.

Madalinska JB, van Beurden M, Bleiker EM, Valdimarsdottir HB, Hollenstein J, Massuger LF, Gaarenstroom KN, Mourits MJ, Verheijen RH, van Dorst EB, et al. The impact of hormone replacement therapy on menopausal symptoms in younger high-risk women after prophylactic salpingo-oophorectomy. J Clin Oncol. 2006;24(22):3576–82.

Pozzar RA, Berry DL. Patient-centered research priorities in ovarian cancer: a systematic review of potential determinants of guideline care. Gynecol Oncol. 2017;147(3):714–22.

Stacey D, Legare F, Col NF, Bennett CL, Barry MJ, Eden KB, Holmes-Rovner M, Llewellyn-Thomas H, Lyddiatt A, Thomson R, et al. Decision aids for people facing health treatment or screening decisions. Cochrane Database Syst Rev. 2014;1:CD001431.

Kinnersley P, Edwards A, Hood K, Cadbury N, Ryan R, Prout H, Owen D, MacBeth F, Butow P, Butler C. Interventions before consultations for helping patients address their information needs. Cochrane Database Syst Rev. 2007;(3):CD004565.

Howard AF, Balneaves LG, Bottorff JL. Women’s decision making about risk-reducing strategies in the context of hereditary breast and ovarian cancer: a systematic review. J Genet Couns. 2009;18(6):578–97.

Hallowell N, Baylock B, Heiniger L, Butow PN, Patel D, Meiser B, Saunders C, Price MA. Looking different, feeling different: women's reactions to risk-reducing breast and ovarian surgery. Familial Cancer. 2012;11(2):215–24.

Champion VL, Skinner CS: The Health Belief Model. Health behavior and Health Educ: Theory, research, and Practice Jossey-Bass 2008, 4:45–65.

Hochbaum G, Rosenstock I, Kegels S: Health belief model. United States Public Health Service 1952.

Janz NK, Becker MH: The health belief model: a decade later. Health Educ Behav 1984, 11(1):1–47.

Stein JA, Fox SA, Murata PJ, Morisky DE. Mammography usage and the health belief model. Health Educ Q. 1992;19(4):447–62.

Green EC, Murphy E. Health belief model. In: The Wiley Blackwell encyclopedia of health, illness, behavior, and society: John Wiley & Sons, Ltd; 2014.

Elo S, Kyngäs H. The qualitative content analysis process. J Adv Nurs. 2008;62(1):107–15.

O'Donnell S, Cranney A, Jacobsen MJ, Graham ID, O'Connor AM, Tugwell P. Understanding and overcoming the barriers of implementing patient decision aids in clinical practice. J Eval Clin Pract. 2006;12(2):174–81.

Mayring P. Qualitative content analysis. Forum Qual Soc Res. 2000;1(2):105–14.

National Breast and Ovarian Cancer Centre. Advice about familial apsepcts of breast cancer and ephelial ovarian cancer. A guide for health professionals. Sydney: Surry Hills; 2010.

Coyne IT. Sampling in qualitative research. Purposeful and theoretical sampling; merging or clear boundaries? J Adv Nurs. 1997;26(3):623–30.

Draucker CB, Martsolf DS, Ross R, Rusk TB. Theoretical sampling and category development in grounded theory. Qual Health Res. 2007;17(8):1137–48.

Groleau D, Young A, Kirmayer LJ. The McGill illness narrative interview (MINI): an interview schedule to elicit meanings and modes of reasoning related to illness experience. Transcult Psychiatry. 2006;43(4):671–91.

Mishler EG. Research interviewing: context and narrative: Harvard University Press; 1991.

Strecher VJ, Champion VL, Rosenstock IM: The health belief model and health behavior. Handbook of health behavior research 1: Personal and social determinants. New York: Plenum Press 1997:71–91.

Forman J, Damschroder L. Qualitative content analysis. In: Empirical Research for Bioethics: A Primer. London: Elsevier Publishing; 2008. p. 39–62.

Chapter   Google Scholar  

Schreier M. Qualitative content analysis. In: The Sage handbook of qualitative data analysis. Oxford: Sage; 2014. p. 170–83.

Graneheim UH, Lundman B. Qualitative content analysis in nursing research: concepts, procedures and measures to achieve trustworthiness. Nurse Educ Today. 2004;24(2):105–12.

Hsieh H-F, Shannon SE. Three approaches to qualitative content analysis. Qual Health Res. 2005;15(9):1277–88.

Przyborski A, Wohlrab-Sahr M. Qualitative Sozialforschung: Ein Arbeitsbuch: De Gruyter; 2014.

Holmberg C, Waters EA, Whitehouse K, Daly M, McCaskill-Stevens W. My lived experiences are more important than your probabilities:the role of individualized risk estimates for decision making about participation in the study of tamoxifen and raloxifene (STAR). Med Decis Mak. 2015;35(8):1010–22.

Hallowell N. Varieties of suffering: living with the risk of ovarian cancer. Health Risk Soc. 2006;8(1):9–26.

Hesse-Biber S, An C. Genetic testing and post-testing decision making among brca-positive mutation women: a psychosocial approach. J Genet Couns. 2016;25(5):978–92.

Cancer Australia. Recommendations for management of women at high risk of ovarian cancer. Surry Hills, NSW; 2011.

Coulter A. Do patients want a choice and does it work? BMJ. 2010;341:c4989.

Hallowell N, Mackay J, Richards M, Gore M, Jacobs I. High-risk premenopausal women's experiences of undergoing prophylactic oophorectomy: a descriptive study. Genet Test. 2004;8(2):148–56.

Mostaghimi A, Crotty BH, Landon BE. The availability and nature of physician information on the internet. J Gen Intern Med. 2010;25(11):1152–6.

Faber M, Bosch M, Wollersheim H, Leatherman S, Grol R. Public reporting in health care: how do consumers use quality-of-care information? A systematic review. Med Care. 2009;47(1):1–8.

Ball MJ, Lillis J. E-health: transforming the physician/patient relationship. Int J Med Inform. 2001;61(1):1–10.

International Patient Decision Aid Standards (IPDAS) Collaboration: What are Patient Decision Aids? URL: http://ipdas.ohri.ca/what.html .

Lenz M, Buhse S, Kasper J, Kupfer R, Richter T, Mühlhauser I. Decision aids for patients. Dtsch Arztebl Int. 2012;109(22–23):401–8.

PubMed   PubMed Central   Google Scholar  

Hooker GW, Leventhal K-G, DeMarco T, Peshkin BN, Finch C, Wahl E, Joines JR, Brown K, Valdimarsdottir H, Schwartz MD. Longitudinal changes in patient distress following interactive decision aid use among BRCA1/2 carriers:a randomized trial. Med Decis Mak. 2011;31(3):412–21.

Metcalfe KA, Poll A, O’Connor A, Gershman S, Armel S, Finch A, Demsky R, Rosen B, Narod SA. Development and testing of a decision aid for breast cancer prevention for women with a BRCA1 or BRCA2 mutation. Clin Genet. 2007;72(3):208–17.

Tiller K, Meiser B, Gaff C, Kirk J, Dudding T, Phillips K-A, Friedlander M, Tucker K. A randomized controlled trial of a decision aid for women at increased risk of ovarian cancer. Med Decis Mak. 2006;26(4):360–72.

Elwyn G, Scholl I, Tietbohl C, Mann M, Edwards AG, Clay C, Legare F, van der Weijden T, Lewis CL, Wexler RM, et al. “Many miles to go ...”: a systematic review of the implementation of patient decision support interventions into routine clinical practice. BMC Med Inf Decis Mak. 2013;13(Suppl 2):S14.

Edgman-Levitan S, Cleary PD. What information do consumers want and need? Health Aff. 1996;15(4):42–56.

Smith SM, Allwright S, O'Dowd T. Effectiveness of shared care across the interface between primary and specialty care in chronic disease management. Cochrane Database Syst Rev. 2007;3:CD004910.

Earle CC, Neville BA. Under use of necessary care among cancer survivors. Cancer. 2004;101(8):1712–9.

Aubin M, Giguere A, Martin M, Verreault R, Fitch MI, Kazanjian A, Carmichael PH. Interventions to improve continuity of care in the follow-up of patients with cancer. Cochrane Database Syst Rev. 2012;7:CD007672.

Holt A. Using the telephone for narrative interviewing: a research note. Qual Res. 2010;10(1):113–21.

Irvine A. Duration, dominance and depth in telephone and face-to-face interviews: a comparative exploration. Int J Qual Methods. 2011;10(3):202–20.

Novick G. Is there a bias against telephone interviews in qualitative research? Res Nurs Health. 2008;31(4):391–8.

Jansen J, van Weert JC, Wijngaards-de Meij L, van Dulmen S, Heeren TJ, Bensing JM. The role of companions in aiding older cancer patients to recall medical information. Psychooncology. 2010;19(2):170–9.

Wills CE, Holmes-Rovner M. Patient comprehension of information for shared treatment decision making: state of the art and future directions. Patient Educ Couns. 2003;50(3):285–90.

Australian Department of Home Affairs: What a de facto relationship is. URL: https://www.homeaffairs.gov.au/visas/supporting/Pages/partner/what-de-facto-relationship-is.aspx .

Download references

Acknowledgements

We would like to thank our study participants for their time and effort, and Laureate Professor Rob Sanson-Fisher for his advice on the question guide.

AH has received funding support from the Priority Research Centre for Health Behaviour/University of Newcastle and the Hunter Cancer Research Alliance Implementation Science Flagship Program as part of the 2017 and the 2018 RHD Student Award initiative. The funding agreements ensured the authors’ independence in designing the study, interpreting the data, writing, and publishing the report.

We acknowledge funding support from a Strategic Research Partnership Grant [CSR 11-02] from the Cancer Council New South Wales to the Newcastle Cancer Control Collaborative [New-3C], and infrastructure funding from the University of Newcastle and Hunter Medical Research Institute.

Availability of data and materials

All transcripts were de-identified and saved electronically. Electronic copies of recordings and transcripts are kept in password protected files on the University of Newcastle server, with access available to the research team only. Data will be stored for seven years in accordance with the Australian National Health and Medical Research Council’s (NHMRC) guidelines. After this time, electronic computer files will be deleted from the computer system. The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.

Author information

Authors and affiliations.

Priority Research Centre for Health Behaviour, Health Behaviour Research Collaborative, University of Newcastle and Hunter Medical Research Institute, University Drive, Callaghan, 2308, Australia

Anne Herrmann & Alix Hall

Cancer Services and Cancer Network, Hunter New England Local Health District, Newcastle, Australia

Anthony Proietto

You can also search for this author in PubMed   Google Scholar

Contributions

All authors conceived of this paper together. AH1 and AP conducted the recruitment of study participants. AH1 collected the data and developed the initial coding scheme. All conclusions drawn from the data were double-checked by AH2 and AP. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Anne Herrmann .

Ethics declarations

Ethics approval and consent to participate.

Ethical approval for this study was sought by the local Human Research Ethics Committee (approval number: 16/10/19/5.12). All study participants provided voluntary, informed, written consent to take part in the study.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/ ), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/ ) applies to the data made available in this article, unless otherwise stated.

Reprints and permissions

About this article

Cite this article.

Herrmann, A., Hall, A. & Proietto, A. Using the Health Belief Model to explore why women decide for or against the removal of their ovaries to reduce their risk of developing cancer. BMC Women's Health 18 , 184 (2018). https://doi.org/10.1186/s12905-018-0673-2

Download citation

Received : 31 May 2018

Accepted : 28 October 2018

Published : 14 November 2018

DOI : https://doi.org/10.1186/s12905-018-0673-2

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

  • Communication
  • Decision making
  • Oophorectomy
  • Patient-centred care
  • Qualitative research
  • Semi-structured interviews
  • Qualitative content analysis

BMC Women's Health

ISSN: 1472-6874

health belief model research paper

An extended health belief model for COVID-19: understanding the media-based processes leading to social distancing and panic buying

  • Original Empirical Research
  • Published: 16 May 2022
  • Volume 51 , pages 132–152, ( 2023 )

Cite this article

  • Marie Louise Radanielina Hita 1 ,
  • Yany Grégoire   ORCID: orcid.org/0000-0001-6939-4798 2 ,
  • Bruno Lussier 2 ,
  • Simon Boissonneault 2 ,
  • Christian Vandenberghe 2 &
  • Sylvain Sénécal 2  

6871 Accesses

11 Citations

1 Altmetric

Explore all metrics

Building on the health belief model (HBM), this research tests, over six months, how the exposure to COVID-related information in the media affects fear, which in turn conditions beliefs about the severity of the virus, susceptibility of getting the virus, and benefits of safety measures. These health beliefs ultimately lead to social distancing and panic buying. As a first contribution, we find that fear is not directly triggered by the objective severity of a crisis, but rather formed over time by the way individuals are exposed to media. Second, we show that fear affects behaviors through the components of the HBM which relate to the risks/benefits of a situation. Last, we find that critical thinking about media content amplifies the “adaptive” responses of our model (e.g., health beliefs, social distancing) and reduces its “maladaptive” responses (e.g., panic buying). Interestingly, we note that the beneficial effect of critical thinking about media content disappears as the level of fear increases over time. The implications of these findings for policymakers, media companies, and theory are further discussed.

Similar content being viewed by others

health belief model research paper

Social Media Use and Mental Health among Young Adults

Chloe Berryman, Christopher J. Ferguson & Charles Negy

health belief model research paper

Fake news on Social Media: the Impact on Society

Femi Olan, Uchitha Jayawickrama, … Shaofeng Liu

Social Media Effects on Young Women’s Body Image Concerns: Theoretical Perspectives and an Agenda for Research

Richard M. Perloff

Avoid common mistakes on your manuscript.

As of March 2022, COVID-19 resulted in more than six million deaths worldwide (World Health Organization, 2022 ). The ripple effect of the pandemic has been colossal. For instance, unemployment rates soared in April 2020 at 14.7%, the highest in the USA since the late 1940s (Bureau of Labor Statistics, 2020 ). Similarly, unemployment rates in Canada peaked to 13.7% in May 2020 (Statistics Canada, 2021 ). In addition to these economic outcomes, there were significant unquantifiable human consequences related to this crisis. Even though lockdowns can help curb the spread of the virus, research notes their detrimental effects on individuals’ anxiety and mental health (Pew Research Center, 2020 ). Although the situation has improved in the last years, people are now learning to live with the omnipresence of COVID. Despite the vaccines, this virus is unlikely to disappear as new variants (e.g., Omicron) keep emerging (CDC, 2022 ; Das et al., 2021 ). Living with COVID will likely become the “new normal” with seasonal propagation waves that will require cyclical preventive measures. Given the constant evolution of the virus, the effectiveness of vaccines could greatly vary, and the implementation of physical preventive measures (e.g., social distancing, hand washing, and mask wearing) will remain relevant in years to come.

Policymakers have an important role to play in managing the consequences of the new COVID variants or the next pandemic (Das et al., 2021 ). We argue that policymakers would be in a better position to manage the next waves if they have a deeper understanding of the processes that lead individuals to adopt adaptive (e.g., social distancing) or maladaptive (e.g., panic buying) behaviors. We focus on the prediction of social distancing and panic buying because of their importance for policymakers at the beginning of a pandemic or a new variant (see Table 1 for the definitions of our core italicized concepts). At its outbreak, social distancing is among the most effective measures to curb a virus’s propagation and to reduce the number of deaths and hospitalizations (e.g., Matrajt & Leung, 2020 ). Then, for effective management of a pandemic, policymakers need to ensure that people stay calm and do not succumb to any form of panic. In this research, we pay special attention to panic buying given its implications for consumers and retailers (e.g., Islam et al., 2021 ).

To predict our two core behaviors, we propose a framework (Fig.  1 ) that combines the health belief model (HBM; Keller & Lehmann, 2008 ) with elements from marketing-related literatures, such as crisis in marketing (e.g., Khamitov et al., 2020 ) and health communication (e.g., Austin et al., 2015 ; Pinkleton et al., 2010 ; Radanielina Hita et al., 2018 ). We anchor our framework by starting with the HBM, which has been widely used in prevention and medical contexts (e.g., Chin & Mansori, 2019 ; Keller & Lehmann, 2008 ). The HBM predicts that people are more likely to adopt healthy behaviors when they develop appropriate beliefs related to the susceptibility of getting a disease , severity of getting a disease , and benefits of a preventive measure (Table 1 ). Despite the popularity of the HBM to predict behaviors in a private health context (e.g., cancer prevention), it has rarely been applied to an ongoing public health crisis. In this research, we extend the HBM by adding three crucial components. To the best of our knowledge, these variables have never been combined with the HBM, and these additions are necessary given the particularities of the pandemic.

figure 1

Conceptual framework

First, the severity of a pandemic keeps changing according to its different propagation waves and their levels (e.g., top, decreasing, bottom). We incorporate this key notion because it affects the measures put in place by policymakers and the attention given by media (Das et al., 2021 ). Second, people’s responses to a pandemic are largely explained by their exposure to mass media content and their ability to critically assess such content. As a result, we add to the HBM two key variables from the communication literature: media exposure (e.g., de Vreese & Neijens, 2016 ) and critical thinking about media conten t (e.g., Austin et al., 2015 ; Pinkleton et al., 2010 ; Radanielina Hita et al., 2018 ). Third, pandemics are characterized by a high level of fear of the virus (Peters et al., 2013 ); in our model, this emotion activates the HBM, which ultimately leads to our core behaviors. Building on the above, our core contribution is to propose a unique theoretical assemblage that is specifically designed for a global health crisis. Importantly, this framework is tested with six surveys, collected over six months (May to October 2020), with a homogenous population (Quebec, Canada). We explain our framework by dividing it into three distinct parts, which correspond to three specific contributions for theory and policymakers.

As a first contribution, we focus on the role of mass media in transmitting governments’ information about crisis severity and in creating a sense of fear (Campbell et al., 2020 ; Olagoke et al., 2020 ). Although governments have accurate information about the severity and propagation of a pandemic, such an information is not necessarily the direct cause leading to fear. We rather argue that people’s fear is mainly conditioned by their exposure to media (traditional and online) and the COVID-related information obtained through this exposure. Here, we posit that media exposure mediates over time the effect of crisis severity on fear of COVID; this sequence corresponds to the longitudinal process “crisis severity/propagation wave → media exposure → fear.” Since the HBM has rarely been applied to a global health crisis, this sequence adds two key components (i.e., crisis severity and media exposure) that have been under-researched in the HBM literature.

As a second contribution, we explain the fear-based processes that lead to our two behaviors. We argue that fear of COVID is the emotional drive that conditions both adaptive and maladaptive behaviors through its specific effects on health beliefs (Earl & Albarracín, 2007 ; Meadows, 2020 ); this logic is reflected in the longitudinal processes “fear→ components of the HBM → social distancing, panic buying.” Although fear is an important consideration to account for in a pandemic, there is surprisingly little HBM research on the matter. For instance, the meta-analyses and reviews on the HBM do not consider fear as a potential driver (e.g., Jones et al., 2014 ; Sulat et al., 2018 ). We address this gap by proposing two different processes depending on the behaviors. For social distancing, all three beliefs—reflecting both the threat of the virus (i.e., severity and susceptibility) and the benefits of the measure—should play a mediating role. For panic buying, only the beliefs associated with the threat of COVID should play a mediating role in the sequence of interest.

After integrating media exposure and fear into the HBM, our third contribution refers to the evaluation of media content. It is not sufficient to account for mere “media exposure” to add a strong “media and communication” component to the HBM; we also need to account for people’s ability to critically assess media content (Austin et al., 2006 , 2015 ; Pinkleton et al., 2010 ). Different media have different views on the pandemic, and individuals need to distinguish between false and truthful information. Accordingly, we examine the beneficial effects that critical thinking about media content may have on our model. In terms of main effects, we argue that critical thinking should amplify adaptive responses (e.g., social distancing), and mitigate maladaptive ones (i.e., panic buying). Importantly, we argue that critical thinking moderates the paths involving the notion of fear (i.e., “media exposure → fear” and “fear → HBM components”). The amplifying effect of critical thinking is expected to decrease as the levels of media exposure or fear increase. Understanding the beneficial effect of critical thinking is important for policymakers and media companies because this skill can be taught and learned.

Research background

Two behaviors of interest for policymakers.

In this section, we justify the selection of our two behaviors and present the HBM. When a pandemic erupts, policymakers are primarily concerned about 1) slowing down the virus and 2) ensuring that individuals do not get overwhelmed by panic. Accordingly, we focus on predicting two key outcomes: social distancing and panic behavior.

When no vaccine is available, social distancing is the most effective measure to slow down the propagation of the virus; it is the most adapted initial response for societies and individuals (Das et al., 2021 ; Greer, 2013 ). The concept of social distancing comprises a set of simple precautionary measures that share the common goal of limiting community transmission of a virus (World Health Organization, 2021 ). At a societal level, policymakers can implement measures such as quarantines, travel limitations, and workplace closures. In this research, our focus is on the individual level, which includes measures such as physical distancing with any unrelated individual as well as avoiding public places (Greer, 2013 ). Other forms of physical measures include hand washing and mask wearing, and all these measures aim to create a distance or barriers between people and the virus (World Health Organization, 2021 ).

It should be noted that vaccination is often viewed as the preventive measure of choice to counter a pandemic (World Health Organization, 2021 ). Unfortunately, the development of vaccines can take months before being available, and their effectiveness may decrease over time, as new variants emerge (Centers for Disease Control and Prevention, 2022 ). Accordingly, some form of social distancing and other physical preventive measures keep their relevance over time, and they almost always accompany vaccination efforts.

Along with social distancing, policymakers are also concerned that people do not succumb to panic. Here, panic buying is one of the most observable movements of collective fear, and this behavior reflects well a population’s state of mind (Ahmadi et al., 2021 ; Omar et al., 2021 ). Policymakers hope that people will not fall into such a trap, and that they will keep trusting governments. Consistent with recent marketing research, panic buying is described as a specific form of stockpiling of food and non-food items, which is driven by impulsiveness and insecurity about the future (Ahmadi et al., 2021 ; Herjanto et al., 2021 ; Omar et al., 2021 ). In line with this literature, we define panic buying as an impulsive buying behavior that leads consumers to stockpile food and non-food items in times of uncertainty to face a potential threat. Because panic buying threatened retailers’ operations in times of crisis, it is viewed as a maladaptive response (Ahmadi et al., 2021 ).

The health belief model

The HBM is the cornerstone of our research, and we posit that its core components can predict our two behaviors of interest. Substantial empirical evidence has supported the predictive ability of the HBM for a variety of preventive and medical behaviors, such as cancer screening or the adoption of a medical treatment (see Chin & Mansori, 2019 ; Jones et al., 2014 ; Sulat et al., 2018 for reviews). Although the HBM is typically used to predict health interventions for specific targets in a private context (e.g., the elderly, youth), we argue that it can also be employed in the context of a public and ongoing global pandemic.

Three beliefs of the HBM are relevant for the prediction of our two behaviors (Birmingham et al., 2015 ; Fall et al., 2018 ). Specifically, (1) susceptibility of getting COVID represents an individual belief about the likelihood of getting infected by the virus; (2) severity of getting COVID is defined as a belief about the medical and social seriousness of contracting the virus for oneself and others; and (3) benefits of social distancing reflect an individual belief about the advantages of engaging in social distancing to reduce the threat of the virus for oneself and others. The combination of susceptibility and severity forms the “costs” that people try to minimize by accounting for the “benefits” of a measure (Glanz et al., 2008 ). At its core, the HBM includes divergent considerations (i.e., threats/costs vs. opportunities/benefits) that individuals try to weigh when making health decisions.

Our research is not simply a replication of the HBM with new behaviors, in a new context. We add to the HBM new conceptual elements to capture the particularities of the current pandemic (Fig. 1 ), which is characterized by high levels of severity, media influence, and fear. This research proposes a new theoretical assemblage that captures the fear-based process leading to social distancing and panic buying in the context of a heavily mediatized pandemic. Thus, we incorporate into the HBM new conceptual aspects that relate to the severity of a crisis (e.g., Khamitov et al., 2020 ), media involvement (e.g., Austin et al., 2015 ; Radanielina Hita et al., 2018 ), and the role of fear (e.g., Meadows, 2020 ). The linkage between these variables and the HBM are explained in detail when we formulate our hypotheses in the next section.

Our framework is developed in a longitudinal manner (Bauer et al., 2006 ; Singer & Willett, 2003 ) by using a mix of repeated variables (level 1) and trait variables measured at baseline (level 2; see Table 1 ). Our multi-level framework has implication for theory development. Indeed, our initial hypotheses propose longitudinal, within-individual processes (in contrast with cross-sectional, between-individuals processes) through which predictors, mediators, and outcomes travel together over time, as we see next. Such a longitudinal approach is recommended to better understand individual responses to crises (Khamitov et al., 2020 ), and to establish causal inferences in the HBM literature (Sulat et al., 2018 ).

Development of hypotheses

The process “crisis severity/propagation wave → media exposure → fear of covid”.

  • Crisis severity

Consistent with recent research on crises and pandemics (Campbell et al., 2020 ; Islam et al., 2021 ), the variable “severity” is our logical starting point. The severity of a pandemic is the triggering variable that conditions “macro” considerations (e.g., economic contraction, uncertainty, scarcity), which then influence marketers’ and policymakers’ decisions (Das et al., 2021 ). For instance, governments were much more active in managing COVID compared to SARS-1 (2002–2003), which was less costly and deadly. Being one of the most studied variables in the crisis literature (Khamitov et al., 2020 ), severity is one of the first factors considered by all actors (policymakers, marketers, consumers) before taking actions.

We define the severity of the COVID crisis as the loss in terms of human lives and the efforts required by the population to face the crisis (Khamitov et al., 2020 ; Laufer et al., 2005 ). Here, the severity of the COVID crisis is estimated by referring to four indicators, or proxies. The number of deaths captures the “loss” dimension of severity (Das et al., 2021 ; Islam et al., 2021 ). The “effort” dimension is assessed by three other proxies: the number of hospitalizations, confinement orders, and deconfinement orders. The hospitalizations capture the pressure of a pandemic on the healthcare system. In turn, the order of confinement (vs. deconfinement) represents temporary policies requesting people to decrease (vs. allowing people to increase ) their contacts with others. Such orders include the closure of schools, retailers, and workplaces.

By referring to these proxies, we determine the level of severity for each month of interest. To capture the longitudinal aspect of crisis severity, we refer to the notion of propagation wave , defined as the oscillating pattern (top, decreasing, bottom, increasing, and so forth) showing the spread and severity of a virus over time. Pandemics are characterized by a series of waves, and this notion is widely used to qualify the severity of a pandemic (World Health Organization, 2021 ). People are particularly restricted at the top of a wave and much less constrained at the bottom of it (see Table 2 for our assessment per month).

The role of media exposure

Governments possess accurate information about the severity of a pandemic. However, they are not well equipped to quickly bring this information to the population; mass media typically plays this crucial role. In marketing, crises typically affect only specific customers that a firm can contact by using personal information. Because of the localized nature of most crises in marketing, prior research did not see the necessity of integrating the role of mass media (Cleeren et al., 2017 ; Khamitov et al., 2020 ). This situation is different for a global pandemic in which policymakers rely on mass media to alert and update the population. We address this issue by examining the effects of crisis severity on media exposure.

We define media exposure as the extent to which viewers have encountered and engaged with messages about COVID in all types of media (de Vreese & Neijens, 2016 ; Slater, 2004 ). There are different ways to measure media exposure by using aggregated archival data or individual perceptions. Given the purpose of our research, we rely on self-reported measures in which consumers assess the extent to which they watched, read, and shared COVID-related information (de Vreese & Neijens, 2016 ; Slater, 2004 ); using perceptual, panel data is a common way to test longitudinal processes (Bauer et al., 2006 ). Although such perceptions have limitations, Footnote 1 they are regularly used in communication; about 94% of communication research used self-reported measures of media exposure (de Vreese & Neijens, 2016 ). Given the centrality of media exposure in our model, we elaborate on three of its core attributes.

First, we use a generic measure of media exposure that includes exposure to all types of media: traditional, online, or social. Given the convergence of media on different platforms (traditional or online), consumers now use a blend of media to get information. In this context, it can be challenging to dissociate the influence of different media source when measuring exposure (Ohme et al., 2016 ). Second, we directly ask the participants to remember and report their active exposure to different media (de Vreese & Neijens, 2016 ). Accordingly, the current research does not account for involuntary exposure to information and unconscious processes. We rather measure consumers’ perception of their willful exposure to COVID-related information and their level of engagement with this information (i.e., sharing it). Third, our measure of media exposure does not reflect the evaluation that consumers make about media content. This evaluative part is discussed later when we introduce critical thinking (H4-H6).

H1 Governments critically need the support of mass media to make their populations aware of and careful about the pandemic. The current research pays special attention to the notion of fear, which has been prevalent in the current pandemic. Here, fear of COVID refers to an intense and unpleasant emotion that is triggered by the anticipation of getting infected by the virus (Ruiter et al., 2014 ; Tannenbaum et al., 2015 ). In H1, the level of propagation wave affects over time the extent to which consumers expose themselves to media feeds about COVID, which in turn generates fear. H1 argues that fear is not directly created by the characteristics of a crisis (e.g., severity, level of wave) as described by governments. It is rather created over time by the way that individuals expose themselves to media and the content they have collected through this exposure. Because of the deadly nature of the disease, COVID-related information should create some fear (Olagoke et al., 2020 ). For instance, Sacerdote et al. ( 2020 ) reported that major media were overwhelmingly negative when reporting on COVID even when the cases were declining. This situation made the population fearful even if the initial wave was decreasing.

The effect of propagation wave (i.e., crisis severity) on fear of COVID is mediated over time by media exposure. Specifically, H1 involves the longitudinal indirect effect: level of propagation wave → media exposure → fear.

The process “fear of covid → HBM components → behaviors”

Understanding the longitudinal effects of fear of COVID is important for policymakers. Fear creates an urge to reduce the threat at the origin of the emotion (Campbell et al., 2020 ). This urge, in turn, motivates the target audience to revise their beliefs and behaviors to protect themselves from the threat (Meadows, 2020 ; Tannenbaum et al., 2015 ). On the one hand, this intense emotion can be functional in the context of a pandemic when it leads to appropriate actions (e.g., social distancing). On the other hand, it can also produce maladaptive responses (e.g., panic buying) if individuals do not have the resources to correctly assess the threat. Although prior research has highlighted the action-oriented nature of fear (Campbell et al., 2020 ; Meadows, 2020 ), its longitudinal mechanisms leading to behavioral responses, in the context of a pandemic, still need to be documented.

The link between fear and health beliefs is surprisingly under-researched (Ort & Fahr, 2018 ). Indeed, fear is absent from meta-analyses and systematic reviews about the HBM (Carpenter, 2010 ; Jones et al., 2014 ; Sulat et al., 2018 ). To fill this gap, fear is viewed as the emotional drive that leads to the formation of health beliefs, which in turns leads to our two behaviors. As their level of fear increases, individuals become highly motivated to appraise the pandemic, and to develop appropriate health beliefs that correspond to their assessment (Meadows, 2020 ; Tannenbaum et al., 2015 ). Based on these explanations, we can reasonably expect that fear of COVID will lead to the formation of the three health beliefs of interest. Depending on the beliefs that are activated, individuals then develop a propensity to engage in social distancing and/or panic buying. We suggest two slightly different processes depending on the behavior.

First, we argue for the longitudinal sequence: fear → severity, susceptibility, and benefits → social distancing. This sequence highlights the role of all three HBM components in predicting social distancing (Chin & Mansori, 2019 ; Sulat et al., 2018 ; Tannenbaum et al., 2015 ). Fear on its own is not sufficient to predict this prevention measure, and it needs to go through all the beliefs related to its costs (i.e., severity and susceptibility) and benefits. As long as consumers perceive that COVID could have severe consequences for them, that they are susceptible to getting the virus, and that the benefits of social distancing are important, they should engage in social distancing. Accordingly, the effect of fear on social distancing should be mediated in parallel by all three HBM components. Formally:

The effect of fear of COVID on social distancing is mediated over time by the three components of the HBM. Specifically, H2 involves three parallel longitudinal indirect effects:

Fear of COVID → severity of getting COVID → social distancing;

Fear of COVID → susceptibility of getting COVID → social distancing;

Fear of COVID → benefits of social distancing → social distancing.

Our other mediation effect links, in a longitudinal manner, the variables: fear → severity, susceptibility → panic buying. We argue that only two of the HBM components—those related to the costs of COVID—are relevant in explaining the effect of fear on panic buying. If consumers perceive the virus as causing severe consequences and if they feel susceptible to getting it, they should engage in panic buying. Here, recent research explains that panic buying is driven by negative motivations, such as urgency or insecurity about the future (Ahmadi et al., 2021 ; Omar et al., 2021 )—which seem related to the beliefs about severity and susceptibility. For these reasons, we expect that severity and susceptibility are relevant, parallel mediators in explaining the sequence between fear and panic buying. The belief about the benefits of social distancing refer to a different behavior; as a result, it does not play a role in H3. Footnote 2

The effect of fear of COVID on panic buying is mediated over time by the two components of the HBM. Specifically, H3 involves two parallel longitudinal indirect effects:

Fear → severity of getting COVID → panic buying;

Fear → susceptibility of getting COVID → panic buying.

The beneficial effects of critical thinking about media content

So far, we account for the effect of media exposure and fear without considering viewers’ ability at evaluating media content. Given that different media have different views on the pandemic, our model also integrates individuals’ ability to critically think about media content (Austin et al., 2006 , 2015 ; Pinkleton et al., 2010 ; Radanielina Hita et al., 2018 ). Policymakers are concerned about this issue; the effectiveness of their policies relies on people’s ability to distinguish truthful from false information.

C ritical thinking about media content is an individual, inquiry-based competence that captures viewers’ ability to analytically assess the information heard in the media before accepting it as believable (e.g.: Pinkleton et al., 2010 ; Radanielina Hita et al., 2018 ). It is viewed as a core component of the broader concept of media literacy (National Association for Media Literacy Education (NAMLE), 2020 ). Footnote 3 Critical viewers actively reflect on media content and seek more information before developing their own opinion. After being exposed to content of all types (traditional or online), they think twice about the intent of the media and its credibility. Critical thinking is viewed as an individual trait (e.g.: Radanielina Hita et al., 2018 ), and it constitutes a level 2 variable in our model (see Table 1 ). This competence was originally intended to increase youth’s understanding of persuasive commercials and to prevent risky behaviors on their part. We are not aware of any research examining, in a longitudinal manner, the effects of critical thinking to promote health behaviors during an ongoing global pandemic.

A critical orientation operates by activating viewers’ logic-based processing to help them resist persuasive appeals, which could take advantage of people’s lack of understanding (e.g.,Austin et al., 2006 ; Pinkleton et al., 2010 ; Radanielina Hita et al., 2018 ). Extending this logic to our context, we argue that critical thinking has different effects on the components of our models depending on their nature as adaptive or maladaptive. On the one hand, we predict that critical thinking amplifies all the adaptive responses of our models—such as fear, the HBM components, and social distancing. Critical thinkers can recognize the extreme danger associated with COVID. As a result, they become fearful, develop appropriate health beliefs, and engage more intensively in social distancing. On the other hand, critical thinking should decrease panic buying because of its maladaptive nature. Individuals with a strong critical orientation should recognize that this behavior is somewhat unreasonable and unproductive (Ahmadi et al., 2021 ; Herjanto et al., 2021 ).

Critical thinking about media content (level 2 variable) has positive effects over time on (a) fear of COVID, the three HBM components, and on social distancing. In turn, (b) critical thinking has a negative effect on panic buying over time.

We also expect critical thinking to moderate the paths involving fear. We focus on these paths because prior research suggests that critical thinking could mitigate the emotional route of the persuasion process (Austin et al., 2006 ; Pinkleton et al., 2010 ; Radanielina Hita et al., 2018 ). For the path “media exposure → fear,” we expect that the amplifying effect of critical thinking (H4) will decrease as the level of media exposure increases. On the one hand, we should find the amplification effect of critical thinking for individuals who were less exposed to media and less informed about the crisis. In the context of low media exposure, individuals who possess lower critical thinking could dismiss the danger of the crisis and feel little fear. On the other hand, heavy users of media should experience a higher level of fear regardless of their level of critical thinking. The pandemic has been described as dangerous in most media; well-informed consumers should feel afraid irrespective of their critical thinking.

Critical thinking about media content (level 2 variable) interacts with media exposure to predict fear of COVID over time. As the level of media exposure increases, the amplifying effect of critical thinking is reduced.

We expect the same pattern of interaction for the three paths “fear → three HBM components.” Again, we expect that the amplifying effect of critical thinking mainly holds at low levels of fear. In a situation of low fear, consumers with low critical thinking may not understand the importance of developing appropriate health beliefs about the pandemic (Austin et al., 2006 ; Pinkleton et al., 2010 ; Radanielina Hita et al., 2018 ). However, as the fear increases, all consumers develop appropriate health beliefs, regardless of their level of critical thinking. Their high level of fear becomes the main driver conditioning their beliefs; critical thinking has little effect when consumers feel vivid fear.

Critical thinking about media content (level 2 variable) interacts with fear of COVID to predict over time a) severity of getting COVID, b) benefits of social distancing, and c) susceptibility of getting COVID. As the level of fear increases, the amplifying effect of critical thinking on these health beliefs is reduced.

Sample and study design

We conducted a longitudinal study over a six-month period in Quebec, Canada. We collaborated with the firm Delvinia, which operates an online panel called “Asking Canadians.” The panel consists of about one million Canadians, with 22% Quebecois. This panel matches the characteristics of the population on a set of relevant variables such as age, sex, language, income, education, and regions (Statistics Canada, 2021 ). The data collection comprised six measurement periods. At the beginning of each month, from May to October 2020, participants were asked to think about the last month and answer questions related to our variables.

For the first measurement period, Delvinia sent the link to 14,702 panelists, from whom the firm obtained 2333 first clicks, for an initial response rate of 15.87%. We excluded incomplete questionnaires and participants who missed the attention checks. In addition, Delvinia used an in-house procedure to eliminate “straightliners” and “racers” (about 5%). After eliminating these participants, the baseline data included 881 complete questionnaires, for a final response rate of 5.99%. Although lower, this level of response Footnote 4 appears reasonable compared to recent response rates (10%–15%) (Chen, 2021 ; Wielgos et al., 2021 ).

After time 1, we obtained the following numbers of participants (and response rates): 631 (71.6%), 378 (59.9%), 190 (50.3%), 123 (64.7%), and 84 (68.3%) for times 2–6, respectively. In prior work using repeated surveys (e.g., Bolander et al., 2017 ; Palmatier et al., 2007 ), the response and attrition rates varied greatly according to different factors, such as the type of participants (consumers vs. employees), the intervals between surveys (ranging from two weeks to a year), and the number of measurement periods (ranging from three to 24). Among the reviewed articles, the response rate across periods tended to vary between 55% (Palmatier et al., 2007 ) and 80% (Grégoire et al., 2018 ). After balancing these considerations and the costs of collecting data, we consider that our response rates across periods are generally satisfactory. Research firms, such as Delvinia, typically aim for an average response rate of 60% (50% being the lowest acceptable threshold). Accordingly, the response rates appear high for times 2 and 6 (71.6% and 68.3%), satisfactory for times 3 and 5 (59.9% and 64.7%) and acceptable for time 4 (50.3%).

In our sample, 49.5% were female; the mean age was 48 years (SD = 11.23); and 76.7% were francophone. In terms of locations, 52.2% came from Montreal, 19.1% from Quebec City, and 28.7% from other areas. To assess the possibility of non-response bias (Hulland et al., 2018 ), we compared the socio-demographics (i.e., gender, region, and language) and the scores of our key variables between a random sample of early and late participants (for time 1). We did not find any significant differences on any of these variables ( p’s  > .09 and χ 2  > .05).

Longitudinal designs in marketing

Longitudinal surveys are rare in marketing; Hulland et al. ( 2018 ) report that only 7.9% of the surveys published in JAMS , between 2006 and 2015, use longitudinal designs. Similarly, Khamitov et al. ( 2020 ) find that only 3.8% of the methods are longitudinal surveys in the service failure literature. A longitudinal design fits our research well since we are interested in testing longitudinal, within-individual processes (Bolander et al., 2017 ; Rindfleisch et al., 2008 ). Longitudinal designs are appropriate to capture the evolution of self-reported and internally oriented variables, which are consistently measured over time. This method is also adapted to capture the particularities of a phenomenon that evolves over time, such as a pandemic. As documented in Table 2 , our research covers most of the first propagation wave (April to July 2020) and the beginning of the second wave in Quebec (August and September 2020).

For longitudinal designs, a major threat for internal validity is the presence of intervening events (Bolander et al., 2017 ; Rindfleisch et al., 2008 ). Such a threat is important in the current COVID context, as the situation was changing almost daily. To account for this threat, our analyses integrate the level of propagation wave associated with each period. In addition, the endogeneity related to the COVID situation is somewhat reduced by the fact that we study a specific population with localized media habits (Quebec).

Common method bias (CMB)

We used three sets of remedies to account for CMB. First, we implemented two procedural remedies. As a first remedy of this sort, our model contains three types of variables that are measured in different manners (Fig. 1 and Table 1 ), including one objective variable of level 1 (i.e., propagation wave), eight perceptual variables of level 1 (our seven core variables and job insecurity), and three variables of level 2 (critical thinking, age, and gender). These three types of variables involve different sources of variance. As a second procedural remedy, the merits of longitudinal data in minimizing CMB has regularly been noted by survey researchers (e.g., MacKenzie & Podsakoff, 2012 ; Rindfleisch et al., 2008 ). Most of these researchers argue that panel data is an effective strategy to reduce CMB, which tends to be stable across time (Jakobsen & Jensen, 2015 ).

Second, in terms of statistical remedy, we conducted a series of post hoc analyses with a marker variable (MacKenzie & Podsakoff, 2012 ). See our Post Hoc Analyses and Web Appendix F that show that the presence of a marker (i.e., neo-racism) does not affect our results.

Third, this research relies on linear mixed modeling (LMM), which enables the estimation of both fixed and random effects. To account for the violation of the assumption of independence in longitudinal designs, we specify random effects for the residuals of the repeated dependent variables (West et al., 2007 ). We use a special application of LMM for repeated measure in SPSS 23.0 that models “repeated covariance type” specifying “the covariance structure for the residuals” (IBM SPSS Statistics, 2021 ). In sum, the estimation of this covariance structure represents a remedy that accounts for the correlated nature of our repeated variables.

Measurements and scales

Crisis severity and propagation wave (level 1).

To assess crisis severity and propagation wave per period, we collected four proxies from the Institut National de Santé Publique du Québec ( 2021 ) . According to our definition, we use the number of deaths to capture the “loss” dimension and the number of hospitalizations, confinement orders, and deconfinement orders to capture our population’s “efforts” (Table 2 ). By referring to these four proxies, we associate each period with a level of crisis severity and propagation wave. We use three levels for crisis severity per period (high, moderate, and weak), which correspond to four levels of propagation wave (i.e., top, decreasing, bottom, growth). Both April and May were on top of wave 1, June was the decreasing period of wave 1, July and August were the bottom of waves 1 and 2, and September was the beginning of the growth of wave 2. In our analyses, we use two operationalizations for propagation wave. First, we account for the individual effects of each period; we use August as the reference category because it corresponds to a time of relative normality with a low level of severity. Second, we transform “propagation wave” into an ordinal and continuous variable by associating the value “3” with the top of a wave, “2” with the middle of the wave (i.e., decreasing or growth), and “1” with the bottom of the wave.

Repeated variables (level 1)

Most of our repeated constructs (level 1) rely on a seven-point Likert scale (1 = strongly disagree and 7 = strongly agree), unless otherwise indicated. Most of our measures are established by and adapted to a COVID context. The scales and their psychometric properties are presented in Web Appendix A . The means per period for our seven core repeated variables are presented in Fig.  2 . Overall, we measure six times these seven variables for a total of 42 variables, to which we add six measures for job insecurity (a control variable). Web Appendix B shows the correlation matrix including these 48 variables and critical thinking about media content (a total of 49 variables).

figure 2

Evolution of our repeated variables according to the periods (observed means)

To measure media exposure , we used a three-item scale adapted from the original work of de Vreese and Neijens ( 2016 ). The items were measured on a seven-point scale, with 1 = never and 7 = all the time. This scale includes items such as “I watch television programs/shows about COVID-19.” The index was reliable for the six periods with alphas from .73 to .86.

Fear of COVID was measured using a three-item scale adapted from Birmingham et al. ( 2015 ). The scale includes items such as “Thinking about getting COVID-19 makes me afraid.” This index was reliable over time with alphas ranging from .93 to .97.

The three components of the HBM were all measured with established scales adapted from prior work (Fall et al., 2018 ). Susceptibility of getting COVID was measured using a three-item scale including “I am at risk for COVID-19” (alphas ranging from .76 to .85). In turn, severity of getting COVID was measured using a three-item scale including “Getting COVID-19 would make my daily activities more difficult” (alphas ranging from .85 to .93). Finally, benefits of social distancing were measured using a four-item scale including “Practicing social distancing will prevent me from getting COVID-19” (alphas ranging from .88 to .95).

For social distancing , we used a three-item scale developed by Greer ( 2013 ). This scale includes items such as “I avoid public places” (alphas ranging from .64 to .75). To capture panic buying , we developed a five-item scale that combined the items of two scales capturing the notions of food hoarding (Janssens et al., 2019 ) and urgency buying (Beatty & Ferrell, 1998 ). This newly developed scale comprises items such as “I buy food items that I did not plan to buy” and “I buy too many products (other than food) more than I need when I go shopping.” The index is reliable with alphas ranging from .83 to .89.

We controlled for job insecurity , which was repeatedly measured over time. Given that economic hardships may exacerbate fear during a major crisis, it seemed important to control for variables that reflect participants’ economic stress as the economic downturns deepens or lengthens. This four-item scale adapted from past research (Vander Elst et al., 2014 ) includes items such as “Because of the coronavirus pandemic, I now feel insecure about the future of my job” (alphas ranging from .90 to .92).

Critical thinking about media content (level 2)

Critical thinking about media content is an individual difference (level 2) that was measured only at baseline. Footnote 5 This variable was measured with a four-item scale adapted from prior research (e.g., Austin et al., 2013 ; Radanielina Hita et al., 2018 ), including items such as “It is important to think twice about what you hear in the media.” This construct is reliable, and its descriptive statistics are within typical ranges (alpha = .78; M =  5.67; SD  = 1.09). Finally, in terms of other individual differences measured at time 1 (level 2), we also controlled for age (measured in years) and gender.

Measurement validation

We took a series of measures to validate our scales. First, we performed six cross-sectional CFAs, one for each period. Second, we conducted four longitudinal CFAs to establish the equivalence of our repeated constructs across periods. Third, we conducted additional tests for panic buying because of the novelty of this scale. Given space constraints, we only summarize our results here. Please see Web Appendices A and C for details.

All our six cross-sectional CFA models, one for each period, provide acceptable fit indices (see Web Appendix A ). The average variance extracted (AVE) was close to or greater than .5 for all constructs for each period. All alphas and composite reliabilities were close to or above .7 for all constructs for all periods. All the loadings were significant and substantial.

We conducted four longitudinal CFAs (see Web Appendix C ) to establish the equivalence of our repeated constructs across periods 1–4 (e.g., Steenkamp & Maydeu-Olivares, 2021 ). We used only the first four waves for these analyses because the periods 5 and 6 have a limited number of participants (respectively, 123 and 84). As presented in Web Appendix C , all four unconstrained models represent a good fit with the data. Then, we constrained to equality the loadings of a given item in all four structures/periods and conducted a chi-square difference test between both models (constrained vs. unconstrained). None of the differences was significant for any of the models. Overall, these results indicate that our repeated constructs achieve configural and metric invariances across periods.

Given the novelty of the topic, we conducted additional tests for panic buying (see Web Appendix C ). First, in terms of face validity, we note that our items are highly consistent with items used in recently developed scales (see Table C2). We also conducted a CFA with panic buying and a scale measuring social anxiety. Overall, this model fits the data well. Consistent with theory, both constructs are significantly correlated, which provides evidence of convergent validity. However, they are also distinct; when we constrain their correlation to equality, we note a significant increase in chi-square, which provides evidence of discriminant validity.

Linear mixed modeling (LMM)

Our analyses are conducted with a multi-level framework by using LMM for three key reasons (Diggle et al., 2002 ). First, LMM allows combining the fixed effects of variables that are measured at different levels (level 1 or 2). This analysis allows the incorporation of individual differences that are measured at baseline (level 2) and repeated responses that are nested within individuals (level 1). Second, as previously noted, LMM accounts for the correlated and dependent nature of our longitudinal data by specifying random effects for the residuals (West et al., 2007 ). Third, LMM accounts for missing responses at each period, and it relies on all the observations collected over time (i.e., 2269 observations through six measurement periods). In our LMM analyses, we control for job insecurity (level 1) and the three trait variables (level 2): gender, age, and critical thinking. All variables are standardized. We also control for propagation wave by using one of our two operationalizations. Finally, we specify a random intercept for all the analyses using LMM.

Overview of our results

We conducted a first series of models (see Web Appendix D ) to test the effects of the six periods—characterized by different levels of propagation wave—on the evolution of our seven core repeated variables (Fig. 2 ). When operationalized as a nominal variable, the five different periods are associated with different levels of media exposure, which differ from the reference category (August). As shown in Fig. 2 , Panel A, the level of media exposure followed the level of a propagation wave; high media exposure at the top of wave 1 (April and May), moderate exposure in the middle of waves 1 (June) and 2 (September), and low exposure in the bottom of waves 1 and 2 (July and August). Interestingly, the periods have limited effect on the six other repeated variables. Footnote 6 As shown in Fig. 2 , Panels A and B, the patterns for the other variables tend to be relatively flat. Consistent with these findings, when propagation wave is a continuous variable, we find a strong significant effect of this variable on media exposure but little or nonsignificant effects on the other variables (see Web Appendix D ).

Figure 3 provides an overview of the main effects of our framework. Footnote 7 Most main effects are significant and in the expected direction. As previously noted, the main effect of propagation wave/period on media exposure is significant ( F  = 126.66, p <  .001); all the different periods differ from the reference category (Fig.  3 ). In turn, the continuous version of propagation wave also has a positive effect over time on media exposure (β = .26, p  < .001), which in turn enhances fear of COVID over time (β = .25, p  < .001). Next, this fear positively affects the three components of HBM over time (all p’s  < .001). Then, the three health beliefs positively influence the practice of social distancing over time (all p’s  < .001), whereas only “susceptibility of getting COVID” has an impact on panic buying over time (β = .20, p  < .001). Finally, critical thinking about media content has a positive effect on most responses ( p’s  < .001) and a negative effect only on panic buying (β = −.10, p  < .001). We formally test our hypotheses next.

figure 3

Presentation of the main effects

Tests of hypotheses

Mediation analyses (h1-h3).

Our mediation analyses were conducted by using the MLmed macro—i.e., Multi-Level Mediation—which relies on LMM (Rockwood, 2019 ; Rockwood & Hayes, in press ). This macro can handle different types of multilevel mediation analyses, including longitudinal and within-individual mediations. In our research, H1-H3 take this form of mediation—that is, a sequence of three variables of level 1 (Rockwood, 2019 ). This macro automatically conducts the different linear mixed models necessary to test the longitudinal mediation effects, and it calculates the indirect effects by following the procedures of Bauer et al. ( 2006 ). The significance of the indirect effects is determined by using Monte-Carlo simulations (i.e., 10,000 resamples) that produce 95% confidence intervals (CI). MLmed is one of the rare available options to test the significance of longitudinal indirect effects with LMM.

MLmed automatically decomposes indirect effects into the within-individual and between-individuals indirect effects (Bauer et al., 2006 ). The within indirect effects are the focus of our research. Footnote 8 It should be noted that the within indirect effects reported in Table 3 are systematically lower than the total indirect effects—which contain both the within- and between-individuals indirect effects. We standardized all variables before entering them in MLmed. We also controlled for job insecurity (level 1), propagation wave (level 1), critical thinking (level 2), age (level 2), and gender (level 2) in all our models. In sum (see Table 3 ), MLmed supports most of our hypothesized mediations (H1-H3); H3a is the only exception.

For H1, the indirect effect “level of propagation wave → media exposure → fear” is significant as the confidence intervals do not contain zero (H1: ind. Effect = .05; CI: .03, .06). In turn, H2 is tested with a single MLmed model that simultaneously incorporates three parallel mediators. Here, the three sequences of interest—i.e., “fear → severity → social distancing” (H2a: ind. Effect = .05; CI: .02, .07), “fear → susceptibility → social distancing” (H2b: ind. Effect = .05; CI: .02, .08), and “fear → benefits → social distancing” (H2c: ind. Effect = .05; CI: .03, .07)—are all significant because the confidence intervals do not contain zero. Finally, H3 is tested with a single MLmed model that simultaneously incorporates two parallel mediators. Only the sequence “fear → susceptibility → panic buying” (H3b: ind. Effect = .05; CI: .02, .08) is significant. The sequence “fear → severity → panic buying” (H3a: ind. Effect = −01.; CI: −.03, .02) does not achieve significance because the confidence intervals contain zero.

We conducted three additional MLmed models to calculate the three indirect paths “media exposure → fear → three HBM components.” Although we do not have hypotheses for these paths, we tested them because they are part of our model. All the three longitudinal sequences—that is, “media exposure → fear → severity” (ind. Effect = .06; CI: .04, .08), “media exposure → fear → susceptibility” (ind. Effect = .07; CI: .05, .09), and “media exposure → fear → benefits” (ind. Effect = .04; CI: .03, .06)—are significant.

We conducted additional LMM models to test H4. As shown in Fig. 3 and Table 4 , the direct effects of critical thinking about media content on fear (β = .06, p  < .05; model 1), severity (β = .14, p  < .001; model 3), susceptibility (β = .05, p  < .05; model 5), benefits (β = .17, p  < .001; model 7), and social distancing (β = .09, p  < .001; model 9) are all positive and significant. H4a is supported. In turn, critical thinking has a significant, negative effect on panic buying (β = −.10, p  < .001; model 10), which is consistent with H4b. Overall, these results support H4.

For H5, we examine the interaction “media exposure X critical thinking” in the model predicting fear of COVID (model 2, Table 4 ). Both media exposure (β = .25, p  < .001) and critical thinking (β = .06 p  < .05) positively influence fear of COVID. Although the interaction appears in the right direction (β = −.03, p  = .13), it does not achieve significance; H5 is not supported.

For H6, we test the interaction “fear X critical thinking” in three different models predicting the three HBM components. Since these three tests follow the same procedure, they are presented together. Please see models 4, 6, and 8 (Table 4 ) for the results of severity, susceptibility, and benefits, respectively. First, we note main positive effects of fear on the three HBM components (severity: β = .42, p  < .001; susceptibility: β = .63, p  < .001; benefits: β = .29, p  < .001) and main positive effects of critical thinking on the same variables (severity: β = .14, p  < .001; susceptibility: β = .05, p  < .05; benefits: β = .16, p  < .001). Second, the interaction “fear X critical thinking” was significant for severity (β = −.04, p  < .05) and benefits (β = −.05, p  < .01) but not significant for susceptibility (β = −.01, p  > .6). H6ac are supported but not H6b.

We plotted the significant interactions by showing the predicted means of the dependent variables for different values (−1 and + 1 standard deviation) of fear and critical thinking. The patterns of interaction for H6a,c are consistent with our predictions (Fig.  4 ). In the context of low fear, we note a substantial difference across critical thinking conditions: individuals having low critical thinking about media content perceive an especially low level of severity of COVID (Panel A) and benefits of social distancing (Panel B). In contrast, we note that the effect of critical thinking is reduced as the level of fear increases. Individuals reporting a high level of fear recognize the severity of getting COVID and the benefits of social distancing; the effect of critical thinking for these two variables is reduced for heightened level of fear.

figure 4

Interaction with critical thinking

Effects of our control variables

Given the particularities of the pandemic (see Table 4 ), we pay special attention to our control variables. First, age has an amplifying effect on media exposure, severity/COVID, and susceptibility/COVID (all p’s  < .05), as well as a negative effect on panic buying (β = −.18, p  < .001). Second, we find that women, compared to men, are associated with greater scores on fear and social distancing (all p’s  < .01). Third, job insecurity has a positive effect on media exposure, fear, and panic buying ( p ’s < .05) as well as a negative effect on severity, benefits of social distancing, and practice of social distancing ( p ’s < .01).

Post hoc analyses

We conducted four additional analyses. First, we replicated the trajectories of perceived media exposure with archival media data. Then, we did three robustness checks; see details in Web Appendices E, F, and G.

Archival data for media exposure

Because individuals’ perceptions of media exposure may not reflect their actual behavior, we complemented this perceptual measure with archival data (Fig.  5 ). We used two datasets to estimate the press coverage of COVID and the level of word-of-mouth in social media. First, to estimate the press coverage, we used the Eureka database, which tracks million of news items published in Canadian and international press outlets. For our timeframe, there were 215,983 COVID-related articles published in Quebec in 437 generalist news outlets (Fig. 5 , Panel A). Second, we estimated the level of electronic word-of-mouth about COVID by examining the reactions to COVID-related posts (i.e., likes, retweets, and comments) on Twitter (Fig. 5 , Panel B). As most communications from the government were made through the Premier of Quebec, we focused on his Twitter account (@francoislegault), which has 278,200 followers. This account is much larger than the Government’s official account with only 17,200 followers. As shown in Fig. 5 (Panels A and B), the evolutions of our objective variables (i.e., media coverage, and Twitter-related word-of-mouth) are consistent with the evolution of our variable perceived media exposure (Fig. 2 , Panel A). Here, two chi-square tests (all p’s  < .001) indicates that the frequencies are generally different from each other in Panels A and B. In sum, there were more media coverage and tweet reactions during the first two months (top of wave 1). These statistics decrease during June and July (middle of wave 1) and reach their lowest level in August (bottom of wave 2). Then, we note a slow increase in September (growth of wave 2).

figure 5

Objective data about media exposure in Quebec. Note: Results mentioning “COVID” within the text from the local, provincial, and regional generalist press outlets for Quebec (n = 437) during the period of interest (retrieved from Eureka.cc database on July 28, 2021). Note: Total reactions (i.e., likes, comments and retweets) associated to COVID- related posts from the Premier of Quebec, Francois Legault Twitter account (@francoislegault)

Three robustness checks

First, we conducted a series of LMM models in which we account for the effects of the variable “negative evaluation of media content” ( Web Appendix E ). We did this to test the robustness of the effect of media exposure on fear. Although “negative evaluation of media content” tends to reduce the level of fear, its inclusion does not affect the significance and nature of any of our prior results.

Second, we also employed the marker-variable technique as a post hoc remedy to account for the presence of any remaining common method bias ( Web Appendix F ). We used “neo-racism” as the marker variable. In sum, the presence of this marker does not change the direction, amplitude, or significance of any of our prior results.

Third, we replicated our results with two smaller samples ( Web Appendix G ). In the first sample, since the number of participants becomes small at times 5 and 6, we replicated all our analyses with only periods 1–4. We have 2065 observations in this first sample (see Tables G1-G3). In the second sample, we use only the 190 participants who completed the four measurement periods for a total of 756 observations (see Tables G4-G6). Despite some minor differences, almost all our hypotheses remain supported with smaller samples.

General discussion

We propose a unique theoretical assemblage that maps the processes that lead to the adoption of social distancing and panic buying, two crucial behaviors that policymakers try to manage in a pandemic. Using the HBM as our cornerstone, we inject to this theory three new conceptual elements that are needed in the context of a pandemic. First, we start our model with the notion of propagation wave, which reflects the severity of a pandemic. Second, we incorporate variables capturing the influence of mass media given their crucial role in a pandemic. Third, we study the adaptive role of fear, which allows the development of health beliefs. Our framework was tested with 2269 observations collected over a six-month period in Quebec. Our hypotheses were tested by using LMM and MLmed (Diggle et al., 2002 ; Rockwood, 2019 ). Out of six hypotheses, five are supported (H5 is the exception). Next, we discuss the theoretical implications of the three core processes of our framework.

The crucial role of mass media in communicating crisis severity and generating fear

By adopting a longitudinal approach, we show that the need for information evolves during a pandemic by following the shape of a propagation wave. Indeed, media exposure peaks at the top of a wave, and it fluctuates by following waves’ oscillations. As a core result (H1), we find that media exposure mediates over time the effects of propagation wave on fear of COVID. We conclude that people’s fear is not directly triggered by governments’ objective description of the severity of the virus. Such a fear is rather created over time by the information obtained through media exposure. This result is important for policymakers and media companies; it shows the crucial role that media play in conditioning individual responses to the pandemic.

This first process combines two literatures—crisis marketing and health communication—which have been rarely integrated in the past. The marketing crisis literature typically focuses on crises of a different nature than pandemics (e.g., product harm crisis). For instance, Khamitov et al. ( 2020 ) found that little research on crisis (2.3%) had been conducted in a health context. As a typical marketing crisis usually affects a small part of a population, this literature did not see the necessity of studying the role of mass media (Cleeren et al., 2017 ). We address this issue by testing the relationship between crisis severity and media coverage and by integrating the variable critical thinking about media content (e.g., Radanielina Hita et al., 2018 ).

The fear-based processes leading to social distancing and panic buying

Our results suggest that fear is adaptive in a pandemic because this emotion leads to the development of appropriate beliefs, which in turn condition adaptive behaviors. The linkage between fear and the development of health beliefs is an under-researched topic (Sulat et al., 2018 ) that needs to be considered in a pandemic context. In support of H2, we find that the effects of fear on social distancing are mediated by all three health beliefs. As per H3, only “susceptibility” mediates the path between fear and panic buying. This finding is consistent with prior research suggesting that threat appeals combined with perceived susceptibility are effective at generating strong responses (e.g., Tannenbaum et al., 2015 ).

In contrast to some prior research that shows the ineffectiveness of fear appeals in advertising (Hammond, 2011 ), we find a beneficial effect of fear, which can be explained by two reasons. First, this effect can occur because the threat of COVID was viewed as being more believable and imminent that the threat suggested in social advertising (cigarettes, HIV-AIDS, alcohol, or car speeding; Hammond, 2011 ; Earl & Albarracín, 2007 ). We believe that people are less resistant to believe the threat of a pandemic, given its immediate impact on everybody. Second, our context is different from prior work that examines fear generated by advertising (Keller & Lehmann, 2008 ; Tannenbaum et al., 2015 ). In our research, the starting points are “real” indices of the severity of a pandemic (e.g., deaths) rather than scenarioized advertising.

Our results suggest that the effects of critical thinking about media content are rich and complex. In terms of main effects, this form of critical thinking simultaneously “amplifies” adaptive responses and “reduces” maladaptive responses (H4). In terms of moderation effects, critical thinking does not influence the path between media exposure and fear; H5 is not supported. However, consistent with H6ac, this competence moderates the linkage between fear and two health beliefs (severity and benefits). The virtuous amplifying effect of critical thinking is especially important among less fearful consumers (see Fig. 4 ). In the end, our results suggest that less fearful individuals having low critical thinking are especially at risk. They are less likely to develop the beliefs leading to safety measures.

To the best of our knowledge, no research has ever examined the longitudinal effect of critical thinking in the context of an ongoing pandemic. Most of the research on critical thinking about media content uses cross-sectional data (e.g., Austin et al., 2015 ; Radanielina Hita et al., 2018 ). Our research is arguably the first to show the long-term beneficial effects of critical thinking to increase adaptive responses during an ongoing pandemic crisis. This last contribution is important because this competence can be taught and learned, as we see next.

Implications for policymakers and marketing

The crucial role of media exposure (h1).

In the context of a pandemic, mass media have much power, as they become people’s main source of information; policymakers only have an indirect influence on their populations. This finding speaks to the importance for policymakers and media companies to collaborate and build strong relationships with one another. As far as possible, governments need to ensure that mass media report accurate information, especially at the peak of a wave. This finding also highlights the danger associated with some media that provide a biased view of the pandemic, which could misguide individuals. Given the dangers associated with unverified information, policymakers need to put in place measures to help individuals develop their ability to identify misinformation. Because of free speech, policymakers cannot easily control media content through regulations. However, they can help people develop stronger critical thinking about media content (as we see next).

In addition, policymakers should capitalize on people’s willingness to listen at the tops of propagation waves . At such moments, effective communication could influence the trajectory of the crisis. For instance, the Twitter account of Quebec’s prime minister was being actively followed during the top of wave 1 when many COVID-related policies and safety measures were implemented. Effective communication at the peak of a wave is even more important in the era of digital communication when policymakers’ voices are just one among many.

Fear-based processes (H2-H3)

Building on the fear-based processes depicted in H2 and H3, we suggest that policymakers and media companies frame their messages by eliciting a reasonable level of fear and by referring to the three components of the HBM (see Appendix H for examples of COVID ad campaigns). Here, the role of media companies is especially important given their influence (see prior point). First, fear appears adaptive in our context; this emotion is the strongest predictor leading to the development of health beliefs. However, it is also important not to go overboard with the generation of fear. Policymakers and mass media need to strike the proper tone. Research on fear appeals suggests that when fear appeals are too extreme, they become ineffective and even counterproductive (Feinberg & Willer, 2011 ). Second, in framing their messages, mass media need to highlight both the threats associated with the virus (in terms of severity and susceptibility) and the benefits of a protective measures (e.g., social distancing). Our results suggest that a communication needs both components—highlighting the threat and explaining the virtues of a measure—to lead to the adoption of adaptive behaviors. Too much emphasis on the threat is likely to lead to maladaptive behavior, such as panic buying.

The beneficial effects of critical thinking (H4-H6)

An important implication refers to the development of critical thinking about media content . Our findings indicate that fear, health beliefs, and social distancing are positively influenced by consumers’ ability to critically evaluate media content. Additionally, critical thinking leads to less panic buying. In the weeks following the outbreak of the pandemic, scenes of empty shelves were common in the media, which led to a form of panic and “herd” mentality. This example shows the influence of media as a “super peer” (Strasburger & Wilson, 2002 ). If consumers do not possess a high level of critical thinking, they are more likely to internalize this “super-influence” and give in to panic.

In the prevention literature, critical thinking was developed by media literacy programs delivered offline or via web-based trainings (Austin et al., 2020 ). Media literacy trainings have been incorporated in a variety of prevention programs (e.g., Food in a Marketing-Driven World , Austin et al., 2020 ). By getting inspired by these existing programs, policymakers could develop web-based media literacy training to increase the general population’s critical thinking about COVID-related content. By collaborating with national media literacy organizations (National Association for Media Literacy Education (NAMLE), 2020 ), governments could offer short web lessons about media literacy (e.g., “how to discriminate between accurate/inaccurate information”, etc.). Improved critical thinking should lead to more information seeking behaviors with the purpose of validating the quality of information and debunking false claims.

To maximize the effectiveness of such programs, it is important to adapt the training for different segments of consumers. For instance, our research shows that in the context of low media exposure, people with low critical thinking experience little fear, which could lead to lower adaptive beliefs. For these individuals, their media literacy training should focus on promoting a more systematic processing of information and a more intensive use of trustworthy media. They should become reasonably aware of the dangers of the virus. In turn, when people are highly afraid of the virus, their training should focus on maximizing the cognitive route of decision making so that they will not act on emotion and fear alone.

Limitations and future research

As in all work, our research includes a series of limitations. First, the use of retrospective methods may create recall bias; therefore, future research could test our model with other methods (e.g., social networks). Second, the decision to focus on Quebec makes it difficult to generalize the findings to all populations. Future research could generalize our results to other populations with different attributes (e.g., rural vs. urban, liberal vs. conservative, developed vs. developing). Third, our longitudinal design does not allow establishing causal inference; experiments are more appropriate for this. Fourth, given the collective aspect of the pandemic, it would have been interesting to understand how community-level variables (such as social pressure or social compliance) could have influenced our outcomes, especially social distancing.

Fifth, despite our efforts to combine perceptual measures with archival data, future research could benefit from additional objective measures, such as other social media metrics or physio-neurological measures. In turn, one of our post hoc analyses measures a key construct with a single item (i.e., negative evaluation of media content). Given the importance of this construct, future research should use a stronger scale for this notion. In addition, future research should incorporate other outcome of interest including vaccination and mask wearing.

Sixth, it would be important to understand the independent contribution of social vs. traditional media on the components of our model. Future research could capture media exposure separately for traditional and online media. In addition, our perceptual measure of media exposure does not differentiate between “unvoluntary” vs. “voluntary” media exposure, and it does not capture participants’ level of elaboration when processing a message. Further research could benefit from studying these elements.

Seventh, given the relative complexity of our model, we test only one moderator of level 2—that is, critical thinking about media content. Since we tried to inject a “mass media” component to the HBM, the selection of this trait seems logical. However, there is place for the exploration of other moderators, such as perceived efficacy, medical literacy, or political orientation. These moderators would potentially play a role in our model. Eighth, given the current context, further research would benefit from the inclusion of a variable related to misinformation or conspiracy beliefs which could affect individuals’ health beliefs. It would be interesting to analyze the responses to a pandemic by using other theories than the HBM. For instance, theories related to message compliance appear promising.

Consumers’ perception of their own media exposure may not reflect their actual exposure. To address this limitation, we collected objective media variables (i.e., COVID press coverage and Twitter reactions) over the same period, which will be compared to perceptual media exposure in the results section.

Our model also includes three additional longitudinal processes: “media exposure → fear → HBM components.” Although we test for these indirect effects, we do not present them in the theory section to avoid redundancies. These effects rely on the same logic and main effects that we use to build H1-H3.

Other dimensions of media literacy education include media skills, intercultural dialogue, media participation, and civic engagement (National Association for Media Literacy Education (NAMLE), 2020 ).

Hulland et al. ( 2018 ) note that reporting the initial response rate is not critical when the general purpose of a research study is theory driven. Consistent with this view, the initial response rate of panel data is usually not reported in academic research (e.g., Baehre et al., 2022 ; Bolander et al., 2021 ; Lamey et al., 2021 ). That being said, we gladly do so in this research for the sake of completeness and transparency.

We measured critical thinking over our six periods to validate that this variable is truly a stable individual difference. To do so, we conducted a linear mixed model that examines the effects of the six periods on the evolution of critical thinking. Consistent with a conceptualization as a trait variable, the level of critical thinking remains the same over the six periods; none of the periods differs from the reference category (all p ’s > .17).

We also find a significant effect of the first period (April, top of wave 1) on the practice of social distancing. However, this effect is more localized (only the first period) and of lesser importance compared to media exposure.

These different coefficients come from the different LMM models conducted in this research.

The between-individuals indirect effect is calculated by mean-centering all the repeated values, and by conducting traditional mediation analyses (Bauer et al., 2006 ); this indirect effect reflects a static perspective of the process.

Ahmadi, I., Habel, J., Jia, M., Lee, N., & Wei, S. (2021). Consumer stockpiling across cultures during the COVID-19 pandemic. Journal of International Marketing , forthcoming

Austin, E. W., Austin, B., Kaiser, C. K., Edwards, Z., Parker, L., & Power, T. G. (2020). A media literacy-based nutrition program fosters parent-child food marketing discussions, improves home food environment, and youth consumption of fruits and vegetables. Childhood Obesity, 16 (S1), S33–S43.

Article   Google Scholar  

Austin, E. W., Chen, M., & Grube, J. W. (2006). How does alcohol advertising influence underage drinking? The role of desirability, identification and skepticism. Journal of Adolescent Health, 38 (4), 376–384.

Austin, E.W., Pinkleton, B., Beam, M. & Porismita, B. (2013). Celebrities and media literacy:

Google Scholar  

Austin, E. W., Pinkleton, B. E., Chen, Y., & Austin, B. W. (2015). Processing of sexual media messages improve due to media literacy effects on perceived message desirability. Mass Communication and Society, 18 (4), 399–421.

Baehre, S., O’Dwyer, M., O’Malley, L., & Lee, N. (2022). The use of net promoter score (NPS) to predict sales growth: Insights from an empirical investigation. Journal of the Academy of Marketing Science, 50 (1), 67–84.

Bauer, D. J., Preacher, K. J., & Gil, K. M. (2006). Conceptualizing and testing random indirect effects and moderated mediation in multilevel models: New procedures and recommendations. Psychological Methods, 11 (2), 142–163.

Beatty, S. E., & Ferrell, M. E. (1998). Impulse buying: Modeling its precursors. Journal of Retailing, 74 (2), 169–191.

Birmingham, W. C., Hung, M., Boonyasiriwat, W., Kohlmann, W., Walters, S. T., Burt, R. W., & Kinney, A. Y. (2015). Effectiveness of the extended parallel process model in promoting colorectal cancer screening. Psycho-Oncology, 24 (10), 1265–1278.

Bolander, W., Chaker, N. N., Pappas, A., & Bradbury, D. R. (2021). Operationalizing salesperson performance with secondary data: Aligning practice, scholarship, and theory. Journal of the Academy of Marketing Science, 49 (3), 462–481.

Bolander, W., Dugan, R., & Jones, E. (2017). Time, change, and longitudinally emergent conditions: understanding and applying longitudinal growth modeling in sales research. Journal of Personal Selling & Sales Management, 37 (2), 153–159.

Bureau of Labor Statistics, U.S. Department of Labor (2020) The Economics Daily : Unemployment rate rises to record high 14.7 percent in April 2020. Retrieved March 9, 2020 from https://www.bls.gov/opub/ted/2020/unemployment-rate-rises-to-record-high-14-point-7-percent-in-april-2020.htm?view_full

Campbell, M. C., Inman, J. J., Kirmani, A., & Price, L. L. (2020). In times of trouble: A Framework for Understanding Consumers’ Responses to Threats. Journal of Consumer Research, 47 (3), 311–326.

Carpenter, C. J. (2010). A meta-analysis of the effectiveness of health belief model variables in predicting behavior. Health Communication, 25 (8), 661–669.

Centers for Disease Control and Prevention. (2022). What We Know about the Variants. Retrieved on March 14, 2022 from https://www.cdc.gov/coronavirus/2019-ncov/variants/about-variants.html .

Chen, J. (2021). A structural model of purchases, returns, and return-based targeting strategies, A structural model of purchases, returns, and return-based targeting strategies Journal of the Academy of Marketing Science , forthcoming

Chin, J. H., & Mansori, S. (2019). Theory of planned behaviour and health belief model: Females’ intention on breast cancer screening. Cogent Psychology, 6 (1), 1–12.

Cleeren, K., Dekimpe, M. G., & van Heerde, H. J. (2017). Marketing research on product-harm crises: A review, managerial implications, and an agenda for future research. Journal of the Academy of Marketing Science, 45 (5), 593–615.

Das, G., Jain, S. P., Maheswaran, D., Slotegraaf, R. J., & Srinivasan, R. (2021). Pandemics and marketing: Insights, impacts, and research opportunities. Journal of the Academy of Marketing Science, 49 (5), 835–854.

De Cuyper, N., Schreurs, B., Elst, T., Vander, Baillien, E., & De Witte, H. (2014). Exemplification and perceived job insecurity: Associations with self-rated performance and emotional exhaustion. Journal of Personnel Psychology, 13 (1A), 1–10.

de Vreese, C. L., & Neijens, P. (2016). Measuring media exposure in a changing communications environment. Communication Methods and Measures, 10 (2–3), 69–80.

Diggle, P. J., Heagerty, P., Liang, K. Y., & Zeger, S. (2002). Analysis of Longitudinal Data . Oxford University Press.

Earl, A., & Albarracín, D. (2007). Nature, decay, and spiraling of the effects of fear-inducing arguments and HIV counseling and testing: A meta-analysis of the short- and long-term outcomes of HIV-prevention interventions. Health Psychology, 26 (4), 496–506.

Fall, E., Izaute, M., & Chakroun-Baggioni, N. (2018). How can the health belief model and self-determination theory predict both influenza vaccination and vaccination intention? A longitudinal study among university students. Psychology and Health, 23 (6), 746–764.

Feinberg, M., & Willer, R. (2011). Apocalypse soon? Dire messages reduce belief in global warming by contradicting just-world beliefs. Psychological Science, 22 (1), 34–38.

Glanz, K., Rimer, B. K., & Viswanath, K. (2008). Health behaviour and health education (4th Edition). In Health Behavior and Health Education: Theory, Research, and Practice . John Wiley & Sons.

Greer, A. L. (2013). Can informal social distancing interventions minimize demand for. antiviral treatment during a severe pandemic? BMC Public Health, 13 (1), 1–9.

Grégoire, Y., Ghadami, F., Laporte, S., Sénécal, S., & Larocque, D. (2018). How can firms stop customer revenge? The effects of direct and indirect revenge on post-complaint responses. Journal of the Academy of Marketing Science, 46 (6), 1052–1071.

Hammond, D. (2011). Health warning messages on tobacco products: A review. Tobacco Control, 20 (5), 327–337.

Herjanto, H., Amin, M., & Purington, E. F. (2021). Panic buying: The effect of thinking style And situational ambiguity. Journal of Retailing and Consumer Services, 60 (5), 1–10.

Hulland, J., Baumgartner, H., & Smith, K. M. (2018). Marketing survey research best practices: Evidence and recommendations from a review of JAMS articles. Journal of the Academy of Marketing Science, 46 (1), 92–108.

IBM SPSS Statistics (2021) “Linear Mixed Models” Documentation for SPSS 23.0.0, Retrieved on August 3rd 2021 from https://www.ibm.com/docs/en/spss-statistics/23.0.0?topic=option-linear-mixed-models

Institut National de Santé Publique du Québec. (2021). Données COVID-19 au Québec. Retrieved March 9, 2021 from https://www.inspq.qc.ca/covid-19/donnees

Islam, T., Pitafi, A. H., Arya, V., Wang, Y., Akhtar, N., Mubarik, S., & Xiaobei, L. (2021). Panic buying in the COVID-19 pandemic: A multi-country examination. Journal of Retailing and Consumer Services, 59 (3), 1–13.

Jakobsen, M., & Jensen, R. (2015). Common method bias in public management studies. International Public Management Journal, 18 (1), 3–30.

Janssens, K., Lambrechts, W., van Osch, A., & Semeijn, J. (2019). How consumer behavior in daily food provisioning affects food waste at household level in the Netherlands. Foods, 8 (10), 1–19.

Jones, C. J., Smith, H., & Llewellyn, C. (2014). Evaluating the effectiveness of health belief model interventions in improving adherence: A systematic review. Health Psychology Review, 8 (3), 253–269.

Keller, P. A., & Lehmann, D. R. (2008). Designing effective health communications: A meta-analysis. Journal of Public Policy & Marketing, 27 (2), 117–130.

Khamitov, M., Grégoire, Y., & Suri, A. (2020). A systematic review of brand transgression, service failure recovery and product-harm crisis: Integration and guiding insights. Journal of the Academy of Marketing Science, 48 (3), 519–542.

Lamey, L., Breugelmans, E., Vuegen, M., & ter Braak, A. (2021). Retail service innovations and their impact on retailer shareholder value: Evidence from an event study. Journal of the Academy of Marketing Science, 49 (4), 811–833.

Laufer, D., Silver, D. H., & Meyer, T. (2005). Exploring differences between older and younger consumers in attributions of blame for product harm crises. Academy of Marketing Science Review, 7 , 1–24.

MacKenzie, S. B., & Podsakoff, P. M. (2012). Common method bias in marketing: Causes, mechanisms, and procedural remedies. Journal of Retailing, 88 (4), 542–555.

Matrajt, L., & Leung, T. (2020). Evaluating the effectiveness of social distancing interventions to delay or flatten the epidemic curve of coronavirus disease. Emerging Infectious Diseases, 26 (8), 1740–1748.

Meadows, C. Z. (2020). The effects of fear appeals and message format on promoting skin cancer prevention behaviors among college students. Societies, 10 (1), 1–12.

National Association for Media Literacy Education (NAMLE). (2020). Core Principles of Media Literacy Education in the United States . Retrieved December 20, 2021 from https://namle.net/wp-content/uploads/2020/09/Namle-Core-Principles-of-MLE-in-the-United-States.pdf

Ohme, J., Albaek, E., de Vreese, H., & C. (2016). Exposure research going mobile: A smartphone-based measurement of media exposure to political information in a convergent media environment. Communication Methods and Measures, 10 (2–3), 135–148.

Olagoke, A. A., Olagoke, O. O., & Hughes, A. M. (2020). Exposure to coronavirus news on mainstream media: The role of risk perceptions and depression. British Journal of Health Psychology, 25 (4), 865–874.

Omar, N. A., Nazri, M. A., Ali, M. H., & Alam, S. S. (2021). The panic buying behavior of consumers during the COVID-19 pandemic: Examining the influences of uncertainty, perceptions of severity, perceptions of scarcity, and anxiety. Journal of Retailing and Consumer Services, 62 (9), 1–12.

Ort, A., & Fahr, A. (2018). Using efficacy cues in persuasive health communication is more effective than employing threats–An experimental study of a vaccination intervention against Ebola. British Journal of Health Psychology, 23 (3), 665–684.

Palmatier, R. W., Dant, R. P., & Grewal, D. (2007). A comparative longitudinal analysis of theoretical perspectives of interorganizational relationship performance. Journal of Marketing, 71 (4), 172–194.

Peters, G. J. Y., Ruiter, R. A. C., & Kok, G. (2013). Threatening communication: A critical re-analysis and a revised meta-analytic test of fear appeal theory. Health Psychology Review, 7 (sup1), S8–S31.

Pew Research Center. (2020). A third of Americans experienced high levels of psychological distress during the coronavirus outbreak . Retrieved March 9, 2020 from https://www.pewresearch.org/fact-tank/2020/05/07/a-third-of-americans-experienced-high-levels-of-psychological-distress-during-the-coronavirus-outbreak

Pinkleton, B., Austin, E., & Van de Vord, R. (2010). The role of realism, similarity and expectancies in adolescents’ interpretation of abuse-prevention messages. Health Communication, 25 (3), 258–265.

Radanielina Hita, M. L., Kareklas, I., & Pinkleton, B. E. (2018). Parental mediation in the digital era: Increasing children’s critical thinking may help decrease positive attitudes toward alcohol. Journal of Health Communication, 23 (1), 98–108.

Rindfleisch, A., Malter, A. J., Ganesan, S., & Moorman, C. (2008). Cross-sectional versus longitudinal survey research: Concepts, findings, and guidelines. Journal of Marketing Research, 45 (3), 261–279.

Rockwood, Nicholas J. (2019). MLMED User Guide: Beta Version 2.0 . Accessed at https://njrockwood.com/mlmed/ on August the 4th.

Rockwood, N. J., & Hayes, A. F. (in press). Multilevel mediation analysis. In A. A. O’Connell, D. B. McCoach, & B. Bell (Eds.), Multilevel Modeling Methods with Introductory and Advanced Applications . Information Age Publishing.

Ruiter, R. A. C., Kessels, L. T. E., Peters, G. J. Y., & Kok, G. (2014). Sixty years of fear appeal research: Current state of the evidence. International Journal of Psychology, 49 (2), 63–70.

Sacerdote, B., Sehgal, R., & Cook, M. (2020). Why Is All COVID-19 News Bad News? (Vol. No. w28110). National Bureau of Economic Research.

Book   Google Scholar  

Satirical news and critical thinking about politics (2013). APSA 2013 Annual Meeting Paper . American Political Science Association 2013 Annual Meeting.

Singer, J. D., & Willett, J. B. (2003). Applied longitudinal data analysis: Modeling Change and Event Occurrence . Oxford University Press.

Slater, M. D. (2004). Operationalizing and analyzing exposure: The foundation of media effects research. Journalism & Mass Communication Quarterly, 81 (1), 168–183.

Statistics Canada. (2021). Census Profile, 2016 Census . Retrieved March 23, 2021 from https://www12.statcan.gc.ca/census-recensement/2016/dp-pd/prof/index.cfm?Lang=E .

Steenkamp, J. B. E., & Maydeu-Olivares, A. (2021). An updated paradigm for evaluating measurement invariance incorporating common method variance and its assessment. Journal of the Academy of Marketing Science, 49 (1), 5–29.

Strasburger, V. C., & Wilson, B. J. (2002). Youth and media: Opportunities for development or lurking dangers? Children, Adolescents, and the Media . SAGE.

Sulat, J. S., Prabandari, Y. S., Sanusi, R., Hapsari, E. D., & Santoso, B. (2018). The validity of health belief model variables in predicting behavioral change: A scoping review. Health Education, 118 (6), 499–512.

Tannenbaum, M. B., Hepler, J., Zimmerman, R. S., Saul, L., Jacobs, S., Wilson, K., & Albarracín, D. (2015). Appealing to fear: A meta-analysis of fear appeal effectiveness and theories. Psychological Bulletin, 141 (6), 1178–1204.

Vander Elst, T., Van den Broeck, A., De Cuyper, N., & De Witte, H. (2014). On the reciprocal relationship between job insecurity and employee well-being: Mediation by perceived control? Journal of Occupational and Organizational Psychology, 87 , 671–693.

West, B. T., Welch, K. B., & Gałlechki, A. T. (2007). Linear mixed models. A practical guide using statistical software . Chapman & Hall/CRC.

Wielgos, D. M., Homburg, C., & Kuehnl, C. (2021). Digital business capability: its impact on firm and customer performance. Journal of the Academy of Marketing Science, 49 (4), 762–789.

World Health Organization (2021). Coronavirus disease (COVID-19) advice for the public. Retrieved on March 9, 2021, from https://www.who.int/emergencies/diseases/novel-coronavirus-2019/advice-for-public .

World Health Organization (2022). WHO Coronovirus (COVID-19) Dashboard. Retrieved on March 15, 2022. https://covid19.who.int/

Download references

Author information

Authors and affiliations.

HEC Montréal RBC Groupe Financier, 3000 Chemin de la Côte-Sainte-Catherine, Montréal, QC, H3T 2A7, Canada

Marie Louise Radanielina Hita

HEC Montréal, 3000 Chemin de la Côte-Sainte-Catherine, Montréal, QC, H3T 2A7, Canada

Yany Grégoire, Bruno Lussier, Simon Boissonneault, Christian Vandenberghe & Sylvain Sénécal

You can also search for this author in PubMed   Google Scholar

Corresponding author

Correspondence to Marie Louise Radanielina Hita .

Ethics declarations

Conflict of interest.

The authors declare that they have no conflict of interest.

Additional information

Publisher’s note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Brent McFerren served as Article Editor for this article.

Supplementary Information

(PDF 1352 kb)

Rights and permissions

Reprints and permissions

About this article

Hita, M.L.R., Grégoire, Y., Lussier, B. et al. An extended health belief model for COVID-19: understanding the media-based processes leading to social distancing and panic buying. J. of the Acad. Mark. Sci. 51 , 132–152 (2023). https://doi.org/10.1007/s11747-022-00865-8

Download citation

Received : 16 April 2021

Accepted : 04 April 2022

Published : 16 May 2022

Issue Date : January 2023

DOI : https://doi.org/10.1007/s11747-022-00865-8

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

  • Media exposure
  • Critical thinking about media content
  • Fear appeals
  • Social distancing
  • Panic buying
  • Health belief model
  • Public policy
  • Mixed linear model
  • Longitudinal analyses
  • Find a journal
  • Publish with us
  • Track your research

Critique of the health-belief model

  • PMID: 6558081
  • DOI: 10.1111/j.1365-2648.1983.tb00473.x

Health-related behaviour is an important issue for providers for health care. Understanding and being able to predict and influence health behaviour are all essential if client co-operation and participation is to be obtained. Since nurses spend more time with patients than any other health care professional, they are in a position to influence health behaviour. Optimal interaction with patients and potential patients is the result of careful application of theory. The health-belief model offers an approach to understanding health-related behaviour. A clear understanding of the cause of behaviour is necessary in order to predict change. A clear understanding of cause is also necessary for determining methods to influence health behaviour. As a new model and one developed for the healthy, the model needs development and testing for applicability in understanding health and other behaviour. Nurses can make a significant contribution in this development by testing aspects of the model. This paper summarizes the model and assesses the model using specific criteria for a theory.

  • Attitude to Health*
  • Evaluation Studies as Topic
  • Models, Psychological*
  • Psychological Theory

U.S. flag

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

  • Publications
  • Account settings
  • Advanced Search
  • Journal List
  • Online J Public Health Inform
  • v.4(3); 2012

Logo of ojphi

Towards an Effective Health Interventions Design: An Extension of the Health Belief Model

1 Department of Computer Science, University of Saskatchewan, Canada

Julita Vassileva

Regan mandryk, introduction.

The recent years have witnessed a continuous increase in lifestyle related health challenges around the world. As a result, researchers and health practitioners have focused on promoting healthy behavior using various behavior change interventions. The designs of most of these interventions are informed by health behavior models and theories adapted from various disciplines. Several health behavior theories have been used to inform health intervention designs, such as the Theory of Planned Behavior, the Transtheoretical Model, and the Health Belief Model (HBM). However, the Health Belief Model (HBM), developed in the 1950s to investigate why people fail to undertake preventive health measures, remains one of the most widely employed theories of health behavior. However, the effectiveness of this model is limited. The first limitation is the low predictive capacity (R 2 < 0.21 on average) of existing HBM’s variables coupled with the small effect size of individual variables. The second is lack of clear rules of combination and relationship between the individual variables. In this paper, we propose a solution that aims at addressing these limitations as follows: (1) we extended the Health Belief Model by introducing four new variables: Self-identity, Perceived Importance, Consideration of Future Consequences, and Concern for Appearance as possible determinants of healthy behavior. (2) We exhaustively explored the relationships/interactions between the HBM variables and their effect size. (3) We tested the validity of both our proposed extended model and the original HBM on healthy eating behavior. Finally, we compared the predictive capacity of the original HBM model and our extended model.

To achieve the objective of this paper, we conducted a quantitative study of 576 participants’ eating behavior. Data for this study were collected over a period of one year (from August 2011 to August 2012). The questionnaire consisted of validated scales assessing the HBM determinants – perceived benefit, barrier, susceptibility, severity, cue to action, and self-efficacy – using 7-point Likert scale. We also assessed other health determinants such as consideration of future consequences, self-identity, concern for appearance and perceived importance. To analyses our data, we employed factor analysis and Partial Least Square Structural Equation Model (PLS-SEM) to exhaustively explore the interaction/relationship between the determinants and healthy eating behavior. We tested for the validity of both our proposed extended model and the original HBM on healthy eating behavior. Finally, we compared the predictive capacity of the original HBM model and our extended model and investigated possible mediating effects.

The results show that the three newly added determinants are better predictors of healthy behavior. Our extended HBM model lead to approximately 78% increase (from 40 to 71%) in predictive capacity compared to the old model. This shows the suitability of our extended HBM for use in predicting healthy behavior and in informing health intervention design. The results from examining possible relationships between the determinants in our model lead to an interesting discovery of some mediating relationships between the HBM’s determinants, therefore, shedding light on some possible combinations of determinants that could be employed by intervention designers to increase the effectiveness of their design.

Conclusion:

Consideration of future consequences, self-identity, concern for appearance, perceived importance, self-efficacy, perceived susceptibility are significant determinants of healthy eating behavior that can be manipulated by healthy eating intervention design. Most importantly, the result from our model established the existence of some mediating relationships among the determinants. The knowledge of both the direct and indirect relationships sheds some light on the possible combination rules.

The growing increase in lifestyle-related health problems has motivated a shift from treatment-and-prescription centric (reactive) healthcare system to a patient-centric (proactive) system that is based on prevention and promotion of healthy behavior around the world. A variety of health behavior interventions have been designed with a preventive standpoint toward diseases in mind. A typical example is RightWay Café, a game designed to encourage healthy eating in young adult ( Peng, W., 2009 ). Similarly, LunchTime is persuasive health application designed to teach people how to make healthy eating choices when eating in the restaurants ( Orji et al., 2012 ). The designs of most of these interventions are informed by health behavior models and theories adapted from various disciplines. This is because interventions that are informed by theories and models tend to be more successful than those based on intuition ( Glanz et al., 1997 ). Several health behavior theories have been used to inform health intervention designs, such as the Theory of Planned Behavior ( Ajzen, I., 1991 ), the Transtheoretical Model ( Prochaska et al. 1992 ), and the Health Belief Model ( Rosenstock, 1966 ). However, the Health Belief Model (HBM), developed in the 1950s to investigate why people fail to undertake preventive health measures, remains one of the most widely employed theories in the design and evaluation of health behavior interventions ( Glanz and Lewis, 2002 ; National Cancer Institute, 2003 ). The HBM was developed to address problem behaviors that evoke health concerns. It postulates that an individual’s likelihood of engaging in a health related behavior is determined by his/her perception of the following six variables: Perceived susceptibility (perceived risk for contracting the health condition of concern); Perceived severity (perception of the consequence of contracting the health condition of concern); Perceived benefit (perception of the good things that could happen from undertaking specific behaviors); Perceived barrier (perception of the difficulties and cost of performing behaviors); Cue to action (exposure to factors that prompt action); and Self-efficacy (confidence in one’s ability to perform the new health behavior). These six health determinants identified by HBM together provide a useful framework for designing both long and short-term health behavior interventions (Glanz, 1995). HBM focuses mainly on health determinants; therefore, it is most suitable for addressing problem behaviors that have health consequences (e.g., unhealthy eating and physical inactivity). HBM has been adapted and successfully applied in the design of health interventions (for example see Peng, W., 2009 ; Orji et al., 2012 ). However, despite the success of HBM in informing and predicting a range of behaviors with health outcomes, previous research shows that HBM’s determinants are insufficient predictors of behavior ( Norman & Brain, 2005 ). This is due to two main limitations of HBM: the low predictive capability of the determinants; their small effect size; and the lack of clear rules for combination of the variables and the relationships between them. On average, HBM’s determinants predict approximately 20% (R 2 < 0.21, see Cohen, 1988, 1992) of variance in healthy behavior, leaving 80% of the variance unaccounted for. This points to the need to investigate other determinants that were not accounted for by HBM. In addition, most HBM researchers assume that the individual determinants are only directly related with healthy behavior and no indirect or mediating effects exist between the variables.

In response to these two limitations, many researchers have extended the original HBM to increase its predictive capacity. For instance, self-efficacy was added as an extension to the original HBM ( Rosenstock et al., 1988 ). In recent years, there has been renewed interest in adapting and extending the HBM. For example, Reece (2003) , in a study of HIV-related mental health care extended the HBM to include ‘ HIV-related stigma ’ variable. However despite these extensions, the average predictive capacity of HBM remains considerably low, ranging from 20% to 40% ( Conner & Sparks, 1996 ; Conner & Armitage, 1998 ). Moreover, most of the new variable(s) added to the model are application area specific and thus only suitable for a particular health behavior domain under investigation. Therefore, the extended models may not be applicable in other health domains. Investigating other determinants that affect a range of health behaviors is essential for improving the effectiveness of health promotion intervention designs based on HBM.

To address these limitations and also further our effort towards developing an effective persuasive technological intervention for behavior change, we extend the HBM to include four new variables as determinants of healthy behavior: Self-identity, Perceived Importance, Consideration of Future Consequences, and Concern for Appearance. To test the suitability of our model, we validated it on healthy eating domain. We conducted a quantitative study on 576 participants and employed Structural Equation Modeling (SEM) to exhaustively explore the interaction/relationships between the variables of the extended HBM. As a secondary research objective, we validated the predictive capacity of the primary HBM (with susceptibility, severity, benefit, and barrier as main determinants) on healthy eating behavior. We also examined the predictive utility of self-efficacy alongside cue to action to the HBM. Finally, we compared the results from the three models (the primary HBM, the primary HBM with self-efficacy and cue to action added, and our extended HBM).

The results from our models show that the four new variables we added to the HBM are in fact better predictors of healthy behavior than all the previously proposed variables ( susceptibility, severity, benefit, barrier, and cue to action ). Self-efficacy however, remains the strongest determinant of healthy behavior in all the models, confirming its predictive utility. Our extended HBM model led to approximately 78% increase (from 40 to 71%) in predictive capacity in comparison with the old models. Most importantly, the result from our model established the existence of some mediating relationships among the variables. The knowledge of both the direct and indirect relationships sheds some light on the possible combination rules. These findings have both practical and theoretical implications which we discuss later.

The remainder of this paper is organized as follows: Section 2 reviews the Health Belief Model and some other determinants of healthy behavior. In section 3, we describe our research methodology: research approach and measurement instrument, research participants, and data validation. Section 4 contains the result analysis: test of the old model and our proposed model. In Section 5, we discuss our results and its implications. Finally Section 6 concludes the paper with future research directions.

Related Work

Behavioral and social science theories provide a basis for understanding health behavior. The Health Belief Model (HBM) has been widely adapted and applied in various health domains. Despite its popularity, the HBM has limitations, which stems from its low predictive capability. We begin this section with an overview of the Health Belief Model (HBM) and its limitations. We then discuss some other relevant health behavior determinants that could possibly extend the HBM and improve its predictive capability. This work is an effort towards designing an effective persuasive technological intervention for healthy behavior motivation.

1.1. Health Belief Model

The HBM is the first theory that was developed exclusively to explain health-related behaviors. As one of the oldest and the most widely used theories of health behavior ( Glanz and Lewis, 2002 ; National Cancer Institute, 2003 ), it is regarded as the origin of systematic and theory-based research in health behavior ( Hochbaum, 1992 ; Kharrazi, 2009 ). HBM was developed as a systematic method to identify, explain, and predict preventive health behavior ( Janz and Becker, 1984 ; Rosenstock, 1974 ). According to Rosenstock (1966) , the original goal of the developers of the HBM was to focus the effort of researchers who aim to improve public health by understanding why people do not take preventive measures to health promotion.

Since its development, HBM has been employed in a variety of public health settings over the years. For example, HBM has been applied to help increase voluntary screening rates for cervical cancer, e.g., undergoing Pap-test ( Hay et al., 2003 ) and breast cancer, e.g., mammography;( Simon and Das, 1984 ), breast self-examination ( Umeh and Rogan-Gibson, 2001 ), for smoking cessation ( Li et al., 2003 ), contraceptive use ( Lowe and Radius, 1987 ), osteoporosis prevention Wallace (2002) , Dental-care ( Chen and Land, 1986 ), and healthy eating (Deshpande et al., 2009). The model’s ability to explain and predict variety of health related behaviors has been validated across various domains and among wide range populations ( Janz & Becker, 1984 ; Carpenter, 2010 ). The model has also been used in designing many successful health interventions ( Arik and Boeijen, 2009 ; Kharrazi, 2009 ).

The HBM postulates that an individual’s likelihood of engaging in a health-related behavior is determined by his/her perception of the four variables: Perceived Susceptibility; Perceived Severity; Perceived Benefit; Perceived Barrier. Each of these variables, individually or in combination, has been used to explain health behavior. These four variables have been broadly categorized into two main aspects of individuals’ representations of health and health behaviors: Perceived Threat and Behavioral Evaluation ( Abraham and Sheeran, 2005 ). We discuss each of these categories and their associated variables in detail below.

1.1.1. The Perceived Threat

The HBM posits that an individual is likely to perform a behavior if he/she perceives a threat from a disease or health condition. The threat perception is based on two beliefs: the perceived susceptibility of the individual to the disease and the perceived severity of the consequences of the disease for the individual.

Perceived Susceptibility refers to the probability that an individual assigns to personal vulnerability to developing the health condition. In order words, it is the subjective belief a person has regarding the likelihood of acquiring a disease or harmful state as a result of indulging in a particular behavior. Perceived susceptibility explains that people will be more motivated to behave in healthy ways if they believe they are vulnerable to a particular negative health outcome ( Rosenstock, 1966 ). The personal perception of risk or vulnerability has been found to be an important perception in promoting the adoption of healthier behaviors ( Abraham and Sheeran, 2005 ). Individuals vary widely in their perception of susceptibility to ill health condition or disease. Often, the higher the perceived risk, the higher the likelihood of an individual engaging in behaviors that decrease the risk. For example, the likelihood that an individual will engage in precautionary behavior to prevent weight gain (e.g. exercise and low calorie diet) may depend on how much they believe that they are at risk of obesity. Perceived susceptibility has been found to be predictive of a number of health-promotion behaviors including smoking cessation, breast self-examination, healthy dental behaviors, and healthy diet and exercise ( Abraham and Sheeran, 2005 ). However, in general, it has been found that people often underestimate their own susceptibility to disease ( Redding and Rossi, 2000 ).

Perceived Severity refers to how serious an individual believes the consequences of developing the health condition will be. It deals with an individual’s subjective belief in the extent of harm that can be caused from acquiring the disease or unhealthy state, as a result of a particular behavior. An individual is more likely to take an action to prevent gaining weight if s/he believes that the possible negative physiological, psychological and social effects resulting from becoming obese pose serious consequences (e.g., death, physical impairment leading to other health condition, financial burden, pain and discomfort, and difficulties with family and social relationships). Specifically, if the undesirable health outcome will not have a large impact on individual’s life, s/he will not be motivated to act to avoid it even when s/he is at risk. Although the perception of seriousness of any health condition may be based on medical knowledge, it may also come from one’s belief about the difficulties a disease would create or the effects it would have on his or her life in general ( McCormick-Brown, 1999 ).

1.1.2. Behavioral Evaluation

HBM also proposes that an individual is likely to perform a behavior if s/he perceives that performing the behavior will supposedly reduce the negative health outcome. The behavioral evaluation is based on two beliefs: the perceived benefit or efficacy of the target health behavior and the perceived costs or barrier to performing the target behavior.

Perceived Benefit refers to an individual’s subjective opinion of the value or usefulness of enacting a health behavior to offset the perceived threat. Under perceived benefit, motivation to take action to change a behavior requires the belief that the precautionary behavior will effectively prevent the condition. The individual must perceive that the target behavior will provide strong positive benefits. Specifically, the target behavior must have the tendency of preventing the negative health outcome. For instance, individuals who are not convinced that there is a relationship between eating and gaining weight are unlikely to adopt a healthier eating behavior for the mere purpose of reducing their chances of getting obese.

Perceived Barrier refers to an individual’s subjective evaluation of the difficulties or the hindrances associated with the target behavior. With perceived barrier, an individual may not perform a behavior despite his/her belief about the effectiveness (benefit) of taking the action in reducing the threat if the barrier outweighs the benefit ( Rosenstock, 1966 ). The barrier often relates to the characteristics of the health promotion measure. It may be expensive, painful, inconvenient, and unpleasant. These characteristics may lead one away from adopting the behavior. To adopt the new healthy behavior, people have to believe that the benefits by far outweigh the consequences of continuing the old behavior ( Center for Disease Control and Prevention, 2004 ).

1.2. Extensions to the original HBM

The original HBM consisting of the four primary variables ( susceptibility, severity, benefit, and barrier ) has been modified my researchers. In this subsection, we discuss how the original HBM has been extended over the years with new variables

Cue to Action:

In addition to the four primary variables mentioned above, Rosenstock (1966) suggests that a combination of threat and behavioral evaluation variables could reach a considerable level of intensity without resulting in overt action unless an event occurs to trigger action in an individual. Thus, cue to action determinant was added to the model to denote a trigger for health behavior when appropriate beliefs are held ( Rosenstock, 1966 ). In Rosenstock’s original formulation, cues to action could include external cues such as a mass media campaign, social influence, or internal cues such as a negative change in bodily state or perception of symptoms. More generally, cues to action can be events, people, or things that spur people to change their behavior. Although, cue to action have been identified as an important behavioral determinant, it is the most underdeveloped and rarely measured or researched variable of the model ( Janz & Becker, 1984 ; Rosenstock, 1974 )

Self-Efficacy was added to the HBM in 1988 by Rosenstock et al. It is a term that is used to describe an individual’s belief about his/her ability to perform the behavior in question ( Bandura, 1977 ). Generally, people may not want to attempt to do something new unless they think that they can do it. For instance, if someone believes that a new behavior is useful (high perceived benefit), but does not think that s/he is capable of doing it (low self-efficacy), chances are that s/he will not try the new behavior. While it seems intuitively clear that self-efficacy is a significant determinant of health-behavior following the wide adoption by health-promotion researchers, it is necessary to examine its impact in relation to other health determinants.

Table 1 presents a summary of the primary HBM constructs and the later extensions with possible strategy for applying them.

Health Belief Model variable summary and related intervention strategies

1.3. Strengths and Weaknesses of HBM

The original HBM has some recognized strengths and weaknesses which we discuss below.

The main strength of the HBM is its use of simplified health-related constructs that make it easy to implement, apply, and test ( Conner, 2010 ). The HBM has provided a useful theoretical framework for investigating the cognitive determinants of a wide range of behaviors for over three decades. Again, it has focused researchers’ and health care professionals’ attention on variables that are prerequisites for health behavior. Hence, it has formed a basis for many practical interventions across a range of behaviors ( Jones et al., 1987 ). However, it’s not without some limitations.

There are two main criticisms of HBM: first, the model did not explicitly spell out the relationships between the variables and no clear rules for combining the formulated variables ( Armitage and Conner, 2000 ; Sheeran and Abraham, 1996 ). However, this weakness can also be viewed as strength, because lack of strict rules of combination offers flexibility that makes the HBM adaptable and applicable to many health behavior and population groups.

The second and a major weakness of HBM is its predictive capability. The results from quantitative reviews of the HBM, suggest that the primary variables ( susceptibility, severity, benefits, and barriers ) were significant predictors of health-related behavior in most cases. However, their effect sizes are usually very small ( Harrison et al., 1992 ; Abraham and Sheeran, 2005 ). This suggests that there are other important variables that determine healthy behavior that have not been accounted for by HBM. Thus, the model is incomplete, despite its high adoption by healthy behavior promotion researchers.

In response to this limitation, researchers have identified other variables that are probably stronger determinants of health behavior than those identified by the HBM. For instance, Rosenstock et al. (1966 , 1988) extended the HBM with cue to action and self-efficacy , which generally improved the predictive power of the model. Similarly, several other researches have adapted and extended HBM in various contexts. For example, King (1982) , in a study of screening for hypertension extended the HBM to include a measure of individuals understanding of high blood pressure. Similarly, Reece (2003) extended the HBM to access HIV-related mental health care with the addition of ‘ HIV-related stigma ’ variable. According to Reece (2003) , the addition of ‘HIV-related stigma’ significantly increased the model’s predictive capacity from R 2 = 0.29 to R 2 = 0.63 showing again that there exist some room for improving the predictive effectiveness of HBM.

Although these researchers have attempted to improve the predictive capability HBM, most of the extended variable(s) are application area specific (only suitable for that particular health behavior under investigation). Therefore, the extended model may not be suitable for application in a range of other health behavior. Our work therefore, aims to develop an extended HBM model that can be applied across several health domains.

In summary, the HBM provides a useful framework for investigating health behaviors. In general, all the model’s components are seen as independent predictors of health behavior ( Armitage and Conner, 2000 ). High-perceived threat, low barriers, and high perceived benefits to action increase the likelihood of engaging in the recommended behavior ( Berker and Maiman, 1979 ). However, according to Bandura (1977 as cited in Munro et al., 2007 ), perceived severity might have a weak correlation with health action and might even result in avoidance of protective action. The perceived severity therefore, may not be as important as perceived susceptibility. Similarly, in a review by Harrison et al (1992) susceptibility and barrier were the strongest predictors of behavior.

An individual’s perception of perceived susceptibility and seriousness provide the motivation to act while benefits (minus barriers) provide the path of action. However, it may require a cue to action for the desired behavior to occur. The HBM differs from other models (e.g., the Theory of Planned Behavior (TPB)) in that there are no strict guidelines on how the different variables combine to predict behaviors. Instead, the HBM proposes that the individual independent variables are likely to contribute to the prediction of health behaviors ( Sheeran & Abraham, 1996 ).

Thus, HBM has been widely employed in predicting a range of behaviors with health implications. For example, Janz and Becker (1984) found that across 18 studies, the 4 primary HBM variables were nearly always significant predictors of health behavior. Susceptibility, severity, benefits, and barrier significantly predicted health behavior for 82, 65, 81, and 100% of the studies respectively. This results show that barriers and susceptibility are the most reliable predictor of behavior followed by benefits, and finally severity. This finding was further supported by the review conducted by Harrision et al. (1992) with more stringent inclusion criteria. Harrison et al. (1992) reported that susceptibility and barriers were the strongest predictors of behavior.

1.4. Other Important Health Behavior Determinants

Researchers have identified some other important variables that affect the tendency of performing a behavior. Most of these variables have not been examined in the context of any existing theoretical framework and therefore, have not been widely employed by health behavior intervention designers. In this section, we review some of these determinants that could possibly improve the predictive capability of existing health behavior theories.

Consideration of Future Consequences

One of the main difficulties one encounters when attempting to motivate people to adopt a healthy behavior is the invisible immediate and short-term benefit and consequences of many health behaviors. Health-related behaviors are often characterized by immediate effort for possible future gain. A rational decision to adopt healthy behavior may require that the value attached to the future health benefits outweigh the immediate cost in terms of time, money, or pleasure foregone to achieve longer-term health benefit ( Adams, 2012 ). How one considers the future outcomes of the present behavior may also play a role in adoption of healthy behaviors. Given this, Strathman et al. (1994) proposed a new variable: Consideration of Future Consequences (CFC). CFC is used to measure “the extent to which people consider the potential distant outcomes of their current behavior and the extent to which they are influenced by these potential outcomes” ( Strathman et al., 1994 ). Research suggests that CFC is a reliable, stable, and a valid predictor of a range of significant behaviors. Strathman et al. (1994) validated the CFC variable and reported internal reliability for the 12-item scale ranging from .80 to .86. Furthermore, Joireman et al. (2006) examined the relationship between CFC and some other related constructs and provided evidence for its convergent and discriminant validity. For instance, people who scored high in CFC also scored higher in delayed gratification and higher levels of conscientiousness ( Strathman et al., 1994 ). Since its development, CFC has successfully predicted a range of behaviors, including health concern, environmental behavior, and cigarette or alcohol use ( Strathman et al., 1994 ). Research has shown that individuals high on the CFC scale generally reported greater concern for health and lower use of alcohol and cigarettes. Similarly, subsequent research has demonstrated that individuals scoring high in CFC reported that they exercised more frequently ( Uuellette et al., 2005 ), are more likely to get an HIV test, and less likely to engage in risky sexual practice (Dorr et al., 1999). While the validity of CFC to predict range of behaviors has been examined, promising past studies have not tested the validity of CFC within any well-known health theoretical framework. A study by Orbell et al. (2004) was one of the few that studied CFC under an existing theoretical framework. They utilized the Theory of Planned Behavior (TPB) framework to illustrate the mediating role of CFC on TPB variables.

We propose to examine whether the CFC will affect the adoption of a healthy behavior and whether the interaction between CFC and healthy behavior is mediated by HBM determinants by integrating it into the HBM model as one of the health behavior determinants.

Self-Identity

Self-identity is another predictor of behavioral intention and actual behavior that has been suggested by identity theorists and has been empirically tested by researchers (Stryker, 1987). Self-identity is a term used in describing some salient and enduring part of one’s self-perception in relation to a particular behavior (e.g., “I think of myself as a health conscious person”) (Sparks, 2000). Research has shown that self-identity plays a role in motivating human behavior. For instance, according to Sparks and Gutherie (1998) , individuals who perceive themselves as health conscious tend to positively associate with health behavior. There are other evidences linking self-identity to actual behavior or behavioral intention in several domains, including exercising ( Theodorakis, 1994 ). In relation to healthy behavior, some work has identified that self-identity influences the tendency of one adopting a healthy behavior. According to Szalavitz (2012) , one of the best ways to change health behavior is to change a person’s self-identity. “” Szalavitz (2012) , When a smoker begins to view herself as a nonsmoker or a teen sees binge-drinking as something people like me don’t do, behavior change is typically more lasting than if the person’s sense of identity is not invoked.

It has been argued that measures of self-identity can enhance models of the cognitive antecedents of behavior ( Eagley and Chaiken, 1993 ). For example, Spark and Shepherd (1992) examined the role of identity in relation to the Theory of Planned Behavior and found that individuals who see themselves as green consumers (i.e., green identity) had stronger intentions to consume organic vegetables, and their self-identities contributed significantly to the prediction of intention over and above other TPB variables. Furthermore, Spark et al (1995) reported that self-identity had an independent predictive effect on intentions in relation to five dietary changes associated with reducing the amount of fat in the diet. Thus, self-identity has been shown in many studies to be a useful addition to TPB variables in predicting different dietary behaviors. Several other research works in domains ranging from exercise, eating behavior, to sexuality, and drug use suggests that having one’s identity wrapped up in a particular behavior is a crucial motivating factor to sustaining the behavior ( Szalavitz, 2012 ). The reverse is also possible. For example, a person whose identity and self-sense are tied directly to unhealthy behavior will likely continue performing the behavior.

Concern for Appearance

Several research findings have shown that people who are concerned about their health believe that they are responsible to engage in protective health behavior ( Orji et al., 2012 ). However, other processes may also be operating. People may eat properly, not smoke, exercise, watch their weight, and practice other preventive health behavior for reasons unrelated to health concern. Research has shown that people are motivated by their concern for appearance, attractiveness, and popularity more than by the health consequences of their behavior ( Hayes and Ross, 1987 ). The society tends to attach a lot of importance on an individual’s physical appearance. This is evident in public media and advertising sectors where several actions and products are symbolized with physically attractive models, actors and actresses suggesting that they are the ideal that the public should seek to achieve ( Hayes and Ross, 1987 ). According to Kai-Yan (2002) people believe that physical attractiveness is linked to life of happiness, success, and social acceptance while fatness is associated with laziness, stupidity, and chaos. In general, physically attractive people have more positive social contacts and more success in manipulating their social environment than unattractive people ( Barocas and Karoly, 1972 ). Concern with appearance has had a long-term research history. Several research findings have shown that concern with appearance exerts a great influence on human behavior and decision making. For example, as early as 1960’s Walster et al. (1966) found in a study of 752 students that physical attractiveness emerged as the only predictor of an individual’s liking for and desire to subsequently date a potential partner. Similarly, concern with appearance has successfully predicted health behavior in many domains including dieting ( Hayes and Ross, 1987 ). Similarly, increased physical activity seems to be associated with concern about appearance. According to the analysis by Hausenblas and Fallon (2006), exercisers have a more positive body image than non-exercisers. Despite the increasing evidence of the widespread impact of appearance concern, it has not been widely adopted by health behavior promotion researchers. However, concern for appearance ought to be taken more seriously because people’s feelings about their appearance can have significant effect on their self-perception, their health behavior, well-being, and even adherence to treatment. Thus concern with appearance may be a motivating factor in preventive health behaviors.

Perceived Importance

Research has shown that perceived importance could also be a significant predictor of behavior (Deshpande et al., 2009). Perceived importance, a term suggested and validated by Robin at al. (1996) , describes how much value a person attaches to the outcomes of a particular behavior. It is different from perceived benefit in that benefit is more concerned with the good things that will happen as a result of performing the behavior in question to avert the threat. Perceived important on the other hand is more about the value an individual attaches to the outcomes of a particular behavior. These outcomes can be either positive (as a result of performing the desired health behavior) or negative as a result of not performing the behavior (or indulging in the unhealthy behavior). Perceived importance unlike benefit is not necessary evaluated based on its ability to prevent the threat. For example, exercising 30 minutes daily may be perceived by an individual to be of benefit (because of its ability to keep one from gaining excessive weight), however, s/he may likely not exercise if s/he perceive that the various benefits accrue to exercising as unimportant to him/her. Perceived importance has been shown to successfully predict ethical behavior intention (moral judgment) ( Robin et al., 1996 ), dietary behavior (Deshpande et al., 2009) and Honjo and Siegel (2003) showed that perceived importance impact on weight concern and smoking initiation. The impact of perceived importance on health behavior is underexplored. Therefore further research is needed to investigate the impact of this variable on various health behaviors both independently and in the context of other known theoretical frameworks.

In summary, behavioral or social science theories provide the basis for understanding health behavior. These theories have been proposed as a framework for designing interventions, understanding how the interventions work to promote change in behavior, and for evaluating the effectiveness of interventions. However, the theories are limited by the percentage of variance in behavior they explain. Therefore, there is a need for research on other variables to account for the missing variance. On the other hand, the variables discussed above ( consideration of future consequences, self-identity, concern for appearance, and perceived importance ) have been validated as independent predictors of various behaviors. However, they have not been examined in context of any known health theories to know their relationships with other variables and their ability to increase the predictive capacity of these theories. This work therefore seeks to examine the predictive capacity of these variables in the context of HBM, one of the widely employed theories of health behavior

Research Method

The data reported in this paper is part of a project aimed at designing theory-driven technological interventions for promoting healthy behavior that was approved by University of Saskatchewan ethics board. Research Approach and Measurement Instrument

This study employed a quantitative method of data collection which involved the collection of primary survey data from a large number of participants. To collect data for our model, we developed an online survey version of the HBM scale, concern for appearance; consideration of future consequences; self-identity; and perceived importance scales posted announcements in high traffic websites and forums. The survey was developed after an extensive review of HBM, their application areas and their effectiveness and was pilot tested (n=10) for refinement. We chose dietary behavior as a case study to validate our research instrument and to test our model because healthy eating is a desirable behavior with wide range of both mental and physical health implications. Good dietary behavior can delay or even prevent the onset of many diseases, including diabetes 2 diabetes and obesity. As a result, interventions aimed at modifying dietary behavior have been identified as the cornerstone treatment for these health conditions ( Lau et al., 2007 ). Accordingly, several health promotion and diseases control programs (for example see Peng, 2009 ; Fujiki et al., 2008 ; Orji et al., 2012 ) are focused on promoting healthy eating and physical activity.

The survey instrument consists of questions assessing (1) participant demography; (2) perceived benefit of healthy eating; (3) perceived barrier to healthy eating; (4) perceived susceptibility ; (5) perceived severity ; (6) cue to action ; (7) self-efficacy (8) likelihood of healthy eating behavior (9) concern for appearance; (10) consideration of future consequences (11) self-identity; (12) and perceived importance , where (9), (10), (11) and (12) are new variables, that we propose as extension to the HBM model.

The questions used in measuring the HBM variables (questions (2) to (8) listed above) were derived from Abraham and Sheeran (2005) and most of the questions have been validated on healthy eating by Deshpande (2009) and Sapp and Jensen (1998). All the HBM variables were measured using a 7-point Likert scale ranging from “1 = Strongly disagree” to “7 = Strongly agree”. An example of a question in the perceived susceptibility variable category is requesting the participants to state their level of agreement with the statement “If I don’t stick to a healthy diet, I will be at high risk for some diet-related diseases”,

Extending HBM Variable

Following from the discussion in section 2.4, we extended the HBM model by including C onsideration of Future Consequences , Concern for Appearance , Self-identity , and Perceived Importance .

Consideration of Future Consequences (CFC) has been increasingly acknowledged as being important behavior determinant. The effect that the current health behavior and attitudes has on future health and well-being can be profound and, research has shown that consideration of future consequences impacts health behavior ( Uuellette et al., 2005 ; Joireman et al., 2006 ). CFC was measured using 12-item questions developed and validated by ( Strathman et al., 1994 ). Respondents were required to indicate to what extent each item characterized them on a 5-point Likert scale ranging from “1 = Not at all” to “5 = Extremely well”. Some examples of questions are “I often consider how things might be in the future and try to influence those things with my day to day behavior,” “I only act to satisfy immediate concerns, figuring the future will take care of itself,” and “I think that sacrifice now is usually unnecessary since future outcomes can be dealt with at a later time.”

Concern for Appearance is included based on previous research findings that people are motivated by their concern for appearance, attractiveness, and popularity more than by the health consequences of their behavior ( Hayes & Ross, 1987 ). Concern for appearance was measured using validated scales adapted from Hayes & Ross (1987) . Typical questions for this variable ask the participants to rate how important it is for them to: “look attractive” and “have good posture”. The questions were measured using a 5-point Likert scale ranging from “1 = Not at all important” to “5 = Very important”.

Self-identity is used to describe one’s perception about him/herself. Research has shown that self-identity plays a role in motivating human behavior. Individuals who perceive themselves as health conscious tend to positively associate with healthy behaviors ( Sparks & Gutherie, 1998 ). We measured self-identity using a validated scale adapted from Sparks & Gutherie (1998) . An example of a question in this category is “I think of myself as someone who is concerned with healthy eating”. The participant states their level of agreement with each item using a 5-point Likert scale, ranging from “1 = Strongly agree” to “5 = Strongly disagree”.

Perceived Importance: Research has shown that perceived importance could also be a significant predictor of behavior. Perceived importance, a term suggested by Robin et al. (1996) , describes how much value a person attaches to the outcomes of a particular behavior. It was added following research from Deshpande et al. (2009) that showed that perceived importance is a determinant of healthy eating. Perceived importance was measured using a validated scale adapted from Deshpande et al. (2009). A typical question is “How important is it for you to eat a diet high in nutrition?” The questions were measured using a 5-point Likert scale ranging from “1 = Not at all important” to “5 = Very important”.

1.5. Research Participants

The participants consisted of 576 adults recruited from the Internet. There were 559 usable responses (responses from participants that are at least 18 years of age). The data were gathered over a period of eleven months in from August 2011 to August 2012. The eligibility criterion was that the participants were at least 18 years old at the time of data collection. The eligibility criterion was in compliance with the study ethics approval that ensured that the participants were of legal age to make decisions independently (including decisions on what to eat). The participants’ demographics is as summarized in Table 2 .

Summary of participants’ profile

1.6. Data Validation

To ensure reliability and validity, we selected an analytical method that explicitly models the linear and quadratic effect (non-linear relationships) between the measured variables. We used both SPSS 19 and SmartPLS 2 ( Ringle et al., 2005 ) Structural Equation Modeling (SEM) tool to exhaustively explore the interaction between the variables and to simultaneously solve the multiple equations.

Instrument Validation:

To determine the validity of the survey instrument we conducted Principal Component Analysis (PCA) using SPSS 19. Before conducting PCA, the Kaiser-Meyer-Olkin (KMO) and Bartlett sphericity tests were used to measure the sample adequacy ( Kaiser, 1970 .). The KMOs were all greater than the recommended threshold of 0.5 and the result of Bartlett sphericity tests were significant at <0.001. Thus, the data was suitable to conduct factor analysis ( Guo, 1999 ). The factor loadings and the corresponding factor scores (weights) for each variable were generated. The factor loading resulted in removal of some questions and the remaining questions have larger loading on their corresponding factor (≥0.7) than cross-loadings on other factors (≤0.4) ( Gefen et al., 2000 ). Thus, these questions could effectively reflect factors because they have good validity including convergent and discriminant validity.

Reliability of the Variables and Indicators:

We examined the data for reliability using both SPSS and SmartPLS tool. To check the reliability, we used Cronbach’s α, which ranges from 0 to 1. According to Peter (1997) , Cronbach’s α should be ≥ 0.7, but for 2–3 indicator variables, a Cronbach’s α ≥ 0.4 is acceptable. As shown in Tables 3 (column 4), the Cronbach’s α of the variables satisfies these conditions (susceptibility and severity have two indicators each, therefore, their Cronbach’s α are within the acceptable range of ≥ 0.4)

Scale reliabilities

Results Analysis

After the validation of the data, we developed and tested the path model presented in Figures 1 , ​ ,2, 2 , and ​ and3 3 using SEM in SmartPLS tool, which allows for simultaneous measurement (of indirect and direct influences of the variables) and structural models. In contrast to previous work in which SEM was used to confirm or test hypotheses, our goals are:

An external file that holds a picture, illustration, etc.
Object name is ojphi-04-27f1.jpg

The extended health belief model predicting healthy eating behavior. The ‘ ’denotes the interactions and the associated no. represents the β values. The ‘ ’

An external file that holds a picture, illustration, etc.
Object name is ojphi-04-27f2.jpg

Intermediate Model Predicting Healthy Eating Behavior

An external file that holds a picture, illustration, etc.
Object name is ojphi-04-27f3.jpg

Baseline Model Predicting Healthy Eating Behavior

  • to validate our extended HBM in the healthy eating behavior domain;
  • to test the predictive capability of the extended HBM model by generating a predictive model of healthy eating;
  • to exhaustively examine the interactions between the extended HBM variables and the original HBM variables;
  • to validate the old HBM and confirm its performance on healthy eating behavior, and
  • to compare the predictive capability of the original HBM variables and the extended HBM variables.

To achieve these aims, we systematically examined the interactions and the impact of the 10 variables ( susceptibility, severity, benefit, barrier, cue to action, self-efficacy, perceived importance, consideration of future consequences, appearance concern, and self-identity ) on healthy behavior. This enabled us to exhaustively explore the importance of each variable in determining healthy eating behavior. We chose to validate our model on eating behavior because it is associated with many health implications.

1.7. Test of Proposed Path Model

Partial Least Square (PLS) model analysis essentially proceeds through two stages. The first stage deals with reliability and discriminants validity analysis of the indicator items and their associated independent variables in the outer model. In the second stage, the relationships between the dependent variables in the inner model are estimated through bootstrapping procedures. Our analysis rigorously followed these two stages to confirm both discriminate and convergent validity and internal consistency. The model fit indices of the structural equation model are presented in Table 3 . The square root of Average Variance Extraction (AVE) coefficients from the SmartPLS output is a key statistic at the first stage of the path analyses as it represents the variance extracted by the variable from its indicator items. As shown in Table 3 , the AVE indices for all the variables are above the ideal value of 0.5. The Cronbach’s α values and the composite reliability that analyzes the strength of each indicator’s correlation with their variables are all higher than their threshold values. Specifically, the high Cronbach’s α values of our newly added variables of consideration for future consequence, perceived importance, self-identity, and appearance concern (0.81. 0.87, 0.88, and 0.80 respectively) shows suitability. Similarly, redundancy values are greater or equal to “0”. The t-values that measure the significance of the path coefficient are all greater than the recommended threshold value of 1.96. All the interactions (path models) presented in the models are statistically significant at p≤ 0.01. Overall, our proposed model’s variables predict 71% of variance in healthy eating behavior (see Figure 1 ). This shows the high predictive relevance and the suitability of the extended HBM.

To measure the shared variance between the variables and their measures, we evaluated the discriminate validity of the model. The discriminate validity further confirmed that the diagonal values were significantly higher than the off diagonal values (i.e., correlation values) as shown in Table 4 . All variables had diagonal elements (AVE) greater than the recommended value of 0.5, and greater than the correlation values; the data demonstrates successful discriminate validation.

AVE and latent variables correlation matrix

APP = Appearance, BAR = Barrier, BEN = Benefit, CFC = Concern of Future Consequences, CUA = Cue to Action, EFF = Self-efficacy, IMP = Importance, LOB = Likelihood of Behavior, SEI = Self-identification, SEV = Severity, SUS = Susceptibility

1.8. Proposed Model

Figure 1 represents the extended HBM proposed by us, Figure 2 represents the primary HBM path model with added variables self-efficacy and cue to action (henceforth referred to as the “intermediate model”) and Figure 3 represents the primary HBM path model (henceforth referred to as the “baseline model”). The baseline model consists only of the four primary determinants ( benefit, barrier, susceptibility, and severity ) of HBM. In the intermediate model the six variables: benefit, barrier, susceptibility, severity, self-efficacy and cue to action are the determinants of the healthy eating behavior. In the extended HBM models ( Figure 1 ), perceived importance, consideration of future consequences, self-identity, and appearance concern were added as an extension to the intermediate model’s variables ( benefit, barrier, susceptibility, severity, self-efficacy and cue to action). The variables ( benefit, barrier, susceptibility, severity, self-efficacy, cue to action, perceived importance, consideration of future consequences, self-identity, and appearance concern ) serve as independent variables that influence (i.e., are the determinants of) healthy eating behavior in our extended model.

1.8.1. The Influence of the Models’ Variables on Healthy Eating Behavior

The structural model determines the relationships between the determinants in the models. Important criteria for evaluating the structural model are the coefficient of determination (R 2 ) - measures the percentage of variance that is explained by the independent variable of a model, as well as the path coefficients (β) and their corresponding significance level (p-value), which were derived from the t-test ( Hair and Ringle, 2011 ). The structural models and their corresponding are R 2, β are as shown in Figures 1 , ​ ,2 2 and ​ and3 3 and summarized in Table 5 . The extended HBM model, the intermediate, and the baseline model yield R 2 value of 71, 40, and 20% respectively. The p-values as shown in Table 3 are all ≤ 0.01.

Summary of the Interactions between the determinants and healthy eating behavior

BAR = perceived barrier, BEN = perceived benefit, SUS = perceived susceptibility, SEV = perceived severity, IMP = perceived importance, CUA = cue to action, EFF= Self-efficacy, APP = appearance, SEI = self-identity, CFC = consideration of future consequences, R 2 = coefficient of determination

As can be seen from the three figures, numerous interactions exist among the many variables involved in the extended model. However, in designing theory-driven interventions for health promotion and disease prevention designers often need to select from the various variables of HBM since it may not be feasible to implement all the variables in a particular intervention. The practical question, therefore, is which of the variables or which combinations of variable from the HBM will provide the most effective result?

To answer this question, we explored the effect of each variable on healthy behavior by exploring the performance of our model with and without each of the ten variables from our extended HBM. This gives an insight on the proportion of the variance in the dependent variable that is predictable from each independent variable f 2 as shown in Table 6 .

Magnitude of variance on behavior accounted by each independent variable (effect Size)

We also tested for mediating effect in PLS-SEM and tested for significant mediation using sobel test ( Sobel, 1982 ). The test establishes that the effect consideration for future consequences, self-efficacy, and self-identity on healthy eating behavior are partially mediated by HBM determinants.

1.8.1.1. The Performance of the Baseline and the Intermediate HBM

The old HBM model comprises of the baseline and the intermediate model.

The structural models of the intermediate and the baseline model are as shown in Figures 2 and ​ and3 3 respectively and summarized in Table 5 . In the baseline model, perceived barrier emerged as the strongest determinant of healthy behavior (β = −.42, p<0.01). It is followed by susceptibility with only a weak effect (β = 0.11, p<0.01). The independent variables in the baseline model predicted only 20% of the variance in healthy eating behavior.

In the intermediate model, with the exception of self-efficacy and perceived barrier , all the HBM variables show only weak association with healthy eating behavior (with β value ranging from 0.02 to 0.08). Susceptibility (β = 0.06, p≤ 0.01), severity (β = 0.05, p≤ 0.01) , benefit (β = 0.02, p≤ 0.01) , and cue to action (β = 0.08, p≤ 0.01). Self-efficacy emerged as the only strong positive and significant determinant of healthy behavior with β = 0.53 and p≤ 0.01. On the other hand, perceived barrier remains the only variable that influences healthy behavior negatively with β = −0.20 and p≤ 0.01. The independent variables in the intermediate model accounted for 40% of the variance in healthy eating behavior.

1.8.1.2. Performance of the extended HBM

The new variables ( perceived importance, consideration of future consequences, self-identity, and appearance concern ) added as an extension to the HBM passed both the validity and reliability test. Their test scores exceeded the recommended threshold for all the measured components (see Table 3 and ​ and4). 4 ). This shows that they are adequate to be used as behavior determinants with the HBM.

The structural model of the new extended HBM is as shown in Figure 1 and summarized in Table 5 . All the new added variables to the HBM are positively and significantly associated with healthy behavior with path coefficient (β) value ranging from 0.10 to 0.37. Surprisingly, our model shows that the newly added variables are better predictors of healthy behavior than the variables from the baseline and the intermediate HBM, with the exception of self-efficacy (β = 0.39, p≤ 0.01, , f 2 = 21%) - which is part of the intermediate HBM. The contributing effect of each individual variable (effect size) f 2 of the newly added variables ranges from 15% to 20% (see Table 6 ). Perceived importance (β = 0.32, p≤ 0.01, f 2 = 15%), consideration of future consequences (β = 0.20, p≤ 0.01, f 2 = 15%), self-identity (β = 0.37, p≤ 0.01, f 2 = 20%), and appearance concern (β = 0.10, p≤ 0.01, f 2 = 16%). Perceived Barrier emerged as the only variable that influences healthy behavior negatively. Again, in addition to the direct impact of the variables on healthy behavior, our model also shows that the primary HBM variables ( susceptibility, severity, benefit, and barrier) mediate the effect of consideration of future consequences and self-identity on healthy behavior .

Discussion and Implications

A major limitation of HBM as identified by research and confirmed by our model (see Figure 3 ) is the low predictive capacity of its primary variables ( Abraham and Sheeran, 2005 ). The HBM variables predict approximately 20% of the variance in healthy behavior on average. This suggests that there are other important determinants of healthy behavior not yet accounted for by HBM. Our work responds to these shortcomings by discovering new variables ( consideration of future consequences, perceived importance, appearance concern, and self-identity that could extend the capability of HBM. Again, as can be seen from Figures 2 and ​ and3, 3 , the majority of research involving HBM assumes the existence of only direct relationships between the variables. This is as a result of lack of clear rule of combination of variables and their relationships. Our model established the existence of both direct and indirect (mediating) relationships among the variables in the HBM.

1.9. The Baseline and Intermediate HBMs

From the SEM results shown in Figures 3 , among the four primary variables ( susceptibility, severity, benefit, and barrier ) in the baseline model, barrier emerged as the strongest predictor of behavior (with path coefficient β = −0.42 and p ≤0.01). This is followed by susceptibility (with β = 0.11 and p ≤0.01), finally followed by severity and benefit with β = 0.08 and p ≤0.01 each as shown in Figure 3 . This is in agreement with previous results that have identified barrier and susceptibility as the best predictor of healthy behavior ( Harrison et al., 1992 ; Janz and Becker, 1984 ). The baseline model predicted a total of 20% (R 2 = 0.20) of variance in healthy behavior. This is again comparable to earlier research focusing on a variety of health behaviors. The variance explained has ranged from 20% to 40% ( Conner and Sparks, 1996 ; Conner and Armitage, 1998 ). A sizable amount of variance (approx. 80%) could not be explained by the baseline model.

However, the addition of the two variables cue to action and self-efficacy in the intermediate model tremendously increased the predictive capability of the model by 100% (from 20% to 40%) as shown in Figure 2 . This shows that these two variables account for as much variance as the four primary HBM variables combined. Interestingly, self-efficacy emerged as both the strongest and the most significant determinant of healthy behavior with β = 0.53, and p ≤0.01. It is followed by barrier with β = −0.20, and p ≤0.01. This shows that health intervention designers should pay more attention to designing interventions that increase the user’s feeling of self-efficacy.

1.10. The Extended HBM

To improve the predictive capability of the HBM which is the major limitation of HBM, we added the four variables consideration of future consequences; self-identity; perceived importance; and appearance . As shown in Figure 1 , including these four variables significantly increased the predictive capacity of the model by approximately 78 % (R 2 increased from 40% to 71%). The statistical finding shows that the four new variables added to the HBM have their place as determinants in HBM model. The model shows that self-identity, appearance, consideration of future consequences and perceived importance (listed in decreasing order of magnitude of effect) yield substantial improvements (see Table 6 ). Thus we expect that healthy behavior intervention based on our extended HBM variables will be more effective.

In addition to that, within the extended HBM, self-efficacy still emerged as the strongest and most significant determinant of healthy behavior (with β = 0.39, effect size f 2 = 21% and p ≤0.01) (see Figure 1 and Table 6 ). This again confirms the importance of designing to promote self-efficacy. However, benefit, severity, and cue to action have weak association with behavior with effect size f 2 = 1%, 1%, and 0% respectively. This shows that severity, benefit, and cue to action may not be as important as the other variables in promoting the health behavior (e.g., perceived susceptibility ). This is in line with Bandura (1977 as cited in Munro et al., 2007 ) “ perceived severity might have a weak correlation with health action and might even result in avoidance of protective action.”

1.10.1. Variables Interactions

Interaction with cue to action:.

Cue to action was introduced by Resenstock (1966) based on the assumption that certain cues would activate/stimulate an individual’s perception of threat from certain health condition by influencing the perceived severity, susceptibility, or both. With more powerful cues or accumulation of cues (especially those with more personal relevance), a person is stimulated to take action. Surprisingly, from both our extended model and the intermediate model (see Figures 1 and ​ and2 2 respectively), it was found that cue to action has weak or no effect (with = 0.03 and 0.08, and p≤ 0.01) on health behavior. As shown in Table 6 cue to action has very weak effect (f 2 = 0%) on health behavior. This finding was unexpected. However, we observed that the addition of the four new determinants in our extended model decreased the association between cue to action and healthy behavior (i.e., β from 0.08 to 0.03, see Figures 1 and ​ and2). 2 ). One possible explanation is that the introduction of the variables self-identity, consideration of future consequences, importance, and appearance reduce the tendency of any form of cues (both external and internal) to trigger behavior performance. Another possible explanation according Baranowski et al. (2003) is that people may not rate the importance of cue to change accurately. Little research has been focused on the impact of cue to action. However, internal cues, such as feeling better physically or mentally after adopting a healthy behavior were rated as the most likely to prompt action ( Baranowski, et al., 2003 ).

Interaction with Perceived Threat:

Research on HBM and its applications so far has focused mainly on manipulating an individual’s perception of threat ( susceptibility and severity ). This is because perceived susceptibility and severity have been considered as the primary motivation to change for most individuals. Our model, however, shows that perceived severity has only a weak relationship with health behavior (with β = 0.08, f 2 = 1%, and p≤ 0.01) and susceptibility shows only a moderate association with the behavior (β = 0.17, f 2 = 5%, and p≤ 0.01) as shown in Figure 1 and Table 6 . This ordering confirms previous findings that have identified susceptibility as a stronger predictor of healthy behavior when compared with severity ( Harrison et al., 1992 ; Janz and Becker, 1984 ) and severity as weak predictor that might even lead to avoidance of the health behavior ( Bandura, 1977 as cited in Munro et al., 2007 ). This implies that perceived severity may not be as important as perceived susceptibility in motivating behavior change. This also implies that health intervention developers focusing on only these key variables ( susceptibility and severity ) have only a limited chance of being successful (approximately 6% effect size). It also means that perception of threat alone is not enough to motivate healthy behavior adoption.

Interaction with Perceived Benefit and Barrier:

The perceived benefit and barrier are among the primary variables of the HBM that have been reasonably well researched. The HBM posits that an individual is likely to perform a behavior if s/he perceives that performing the behavior will reduce the negative health outcome (perceived threat). Our data shows that among the four primary variables of HBM ( susceptibility, severity, barrier, and benefit ), barrier is the strongest and most significant determinant of behavior (with β = −0.42 and p≤ 0.01). However, the addition of self-efficacy and cue to action in the intermediate modelreduces the inhibiting effect of barrier (from β = −0.42 to β = −0.20), as shown in Figure 2 . This can be explained by the fact that increasing an individual’s feeling of confidence about a particular behavior will reduce the perceived difficulty associated with the performance of that particular behavior and increase the tendency of performing the behavior.

Our model shows that self-efficacy, self-identity, and consideration for future are mediated by perceived barrier (see Figure 1 ). Therefore, health intervention designers aiming at reducing the inhibiting effect of perceived barrier associated with a particular health behavior should design an application that increases the individual’s self-identity, concern of future, and self-efficacy about his/her ability to perform a behavior. On the other hand, benefit , just like severity , has a weak relationship health behavior (with β = 0.08, f 2 = 1%, and p< 0.01). However, it mediates the relationships between self-identify and consideration for future on behavior (see Figure 1 ). Similarly, any intervention designed to increase self-identity and consideration for future will increase the perceived benefit associated with a particular health behavior.

Interaction with Self-efficacy:

Self-efficacy describes an individual’s confidence in his/her ability to perform the health behavior. The HBM proposes that an individual is more likely to perform a behavior if s/he believes that s/he is able to perform it. Our extended model shows that among all the ten variables that emerged as determinants of healthy behavior, self-efficacy is the strongest and the most significant determinant (β = 0.39, f 2 = 21%, and p≤ 0.01) as shown in Figure 1 . Interestingly, self-efficacy has both a direct relationship with healthy behavior and an indirect relationship via barrier (with β = −0.45 and p≤ 0.01) as shown in Figure 1 . This means that self-efficacy not only increases an individual’s tendency of adopting a healthy behavior but also reduces the inhibiting effect of barrier on behavior performance. The strong and significant negative association of self-efficacy with barrier (β = −0.45 and p≤ 0.01) means that designing intervention to increase the feeling of self-efficacy might be the most effective way to reduce an individual’s perceived difficulties associated with a certain behavior. This highlights the need for both health behavior intervention designers and behavioral theorists to pay special attention on self-efficacy and to design their interventions to emphasize self-efficacy.

Interaction with Self-Identity:

Self-identity , which describes one’s perception about him/herself, emerged as the second strongest and significant determinant of healthy behavior (with β = 0.37, f 2 = 20%, and p≤ 0.01), following self-efficacy . Among the four new variables that we added to extend the HBM self-identity is the best determinant. The strong relationship between self-identity and healthy behavior is in line with the Cognitive Dissonance Theory ( Festinger, 1957 ), which suggests that people try to be consistent with their existing views to reduce dissonance. Our results show that people value consistency. Therefore, if an intervention can be designed in a way that associate individuals with certain health behavior and make them commit to a behavior, they are likely to stick to it. Therefore, designers aiming at increasing health behavior can use commitment, consistency, and goal setting to make the user identify with the desired health behavior. Tracking and comparing behavior with stated goals and commitments makes the deviations observable, and will cause dissonance, which could motivate the desired behavior performance.

It is also worth noting that self-identity has both direct and indirect relationship with the healthy behavior. Susceptibility, severity, benefit, barrier, and cue to action mediate the relationship between self-identity and healthy behavior (with β = 0.17, β = 0.16, β = 0.21, β = −0.24, and β = 0.16 respectively) as shown in Figure 1 . This implies that designing to increase self-efficacy will invaluably increase the perceived susceptibility, severity, benefits, and cue to action while reducing the perceived barrier

Interaction with Perceived Importance:

Perceived Importance describes how much value a person attaches to the outcomes of a particular health behavior. Perceived importance is a significant determinant of healthy behavior from our model (with β = 0.32, f 2 = 15%, and p≤ 0.01). This is in line with findings in previous work that stated that importance is positively associated with healthy behavior (Deshpande et al., 2009). This positive association of importance with healthy behavior shows that the value an individual attached to outcomes associated with a particular behavior is a better determinant of behavior performance than the ability of the behavior to avert perceived threat (benefit). Perceived importance is not necessarily associated with threat. However, since several health behaviors (e.g., healthy eating) often have multiple benefits, a prerequisite to designing to increase the perceived importance should be to identify the outcome that is of important to an individual or group of individuals. Designers should, therefore, design personalized interventions that motivate individuals by linking behavior performance to outcomes that are of important to each individual.

Interaction with Consideration of Future Consequences:

A major challenge in motivating people to adopt healthy behavior is the invisible immediate and short-term benefit of many health behaviors. Adopting and maintaining a healthy behavior is a difficult task that has almost no immediate health effect. Therefore, consideration of future consequences measures the extent to which people consider the potential distant outcomes of their current behavior. Our model shows that consideration of future consequence is a significant determinant of healthy behavior (with β = 0.20, f 2 = 15%, and p≤ 0.01). The positive relationship between consideration of future consequences and healthy behavior confirms previous study results that consideration for future plays a role in the adoption of healthy behavior ( Strathman et al., 1994 ). Thus, to motivate an individual to adopt a healthy behavior, intervention designers should make the long-term effects of healthy behavior observable.

Another interesting finding from our model is that consideration of future consequences has both direct and indirect relationship with healthy eating. The relationship between consideration of future consequences and healthy behavior is mediated by susceptibility, severity, benefit, and barrier (with β = 0.08, β = 0.13, β = 0.09, and β = −0.07 respectively) as shown in Figure 1 . This implies that any intervention that is designed to increase consideration of future consequences will also increase the perceived susceptibility, severity, and benefits while reducing the perceived barrier .

Interaction with Appearance:

People may adopt healthy behavior for reasons that are unrelated to health. For instance, concern for appearance has been identified as one of those reasons. From our model, appearance is positively associated with healthy behavior (β = 0.10, f 2 = 16%, and p≤ 0.01). This confirms previous research that shows that people are motivated by their concern for appearance and attractiveness ( Hayes and Ross, 1987 ). This is because people believe that attractiveness is linked to life of happiness. Our results also confirm the previous study that shows that weight concern (which can lead to reduced attractiveness) is an important consideration in peoples’ decision to adopt a healthy behavior ( Orji et al., 2012 ). Therefore, physical self-presentation is important for motivating healthy behavior change. Designers could emphasize reduced attractiveness as a potential risk of unhealthy behavior.

The Mediating Roles on Consideration of Future Consequences, Self-Identity, and Self-Efficacy:

One of the limitations of HBM is the lack of clear rules of combination ( Armitage and Conner, 2000 ). The model did not explicitly spell out the relationships between the variables. A novel contribution of this work is the establishment of the mediating role played by the five HBM’s variables ( susceptibility, severity, benefit, barrier, and cue to action ) on consideration of future consequences, self-identity, and self-efficacy . As shown in Figure 1 , the four variables susceptibility, severity, benefit, and barrier partially mediate the relationship between consideration for future consequences and healthy behavior. Introducing consideration for future consequence variable will increase an individual’s perception about susceptibility, severity, benefit and reduces the perceived barrier associated with behavior performance. For example, an intervention that increases an individual’s consideration of future is more likely to make the individual have a favorable evaluation of the perceived susceptibility, severity, benefit, and barrier associated with a particular behavior.

Similarly, as shown in Figure 1 , along with perceived susceptibility, severity, benefit, barrier , and cue to action partially mediates the relationship between self-identity and healthy behavior. This implies that any intervention that causes one to associate him/herself positively with a certain health behavior (e.g., “I am a healthy eater or health conscious person”) will be more likely to increase the likelihood of responding to various cues to action with regards to that particular behavior. For example, an individual who encounters a health message that associates him/her with certain health behavior may be more likely to respond to various cues to action with respect to that particular behavior; see more of the benefits as opposed to the barriers associated with performing the target behavior; and finally have an increased evaluation of perceived threat of the unhealthy behavior. This will lead to an increased adoption of healthy behavior.

Also worth noting is the fact that barrier mediates the relationship between self-efficacy and healthy behavior. As shown in Figure 1 , self-efficacy reduces the negative influence of barrier on healthy behavior. This implies that designing to increase the feeling of self-efficacy using various technological intervention strategies such as role-playing, incremental goal setting, and modeling will reduce the hindering effect of barrier on healthy behavior adoption.

Establishing the existence of mediating relationship contributes to both the theory and the practical application of the theory in intervention design. Theoretically, it provides a holistic understanding of the interaction between the variables (both direct and indirect interactions). In practice, it gives intervention designers an idea of how to combine the variables of the extended HBM to amplify their effect.

Limitations

Although this study enhances our understanding of the factors determining the healthy behavior, there are some limitations that warrant further research.

Most of the survey participants (approximately 60%) in the present evaluation are in the age range of 18–35 years; therefore, care should be taken in generalizing our result for all age groups. A further bias might be caused by the relatively high level of education of the participants – approximately 70% of our participants are at least a bachelor’s degree holder. Future work will try to expand the participants group so it is representative of all age groups. Including younger participants would ensure also a wider representation of people with lower education levels (e.g. not yet completed high-school, or middle school), where healthy eating interventions would be particularly important.

The present study tested our proposed model in the healthy eating domain only; there is still the need to validate the extended HBM model in other health behavior domains. In principle, our model could be applied to various health behavior domains (e.g. exercising, smoking cessation, dealing with various addictions). Future studies can also expand this model by adding some other variables that can account for the remaining 29% variance in healthy behavior.

In the future, we would like to conduct a more comprehensive evaluation of the proposed model. We will use the model to develop an application to motivate healthy eating behavior and evaluate its effectiveness.

Behavioral theories play an important role in the design, implementation, and evaluation of health behavior interventions. In recent years, a number of behavior theories have emerged. The HBM is of particular interest, because of its wide adoption and application in several health domains. Several health interventions have been developed that are based solely on the primary variables ( susceptibility, severity, benefit, and barrier ) proposed by the HBM. Despite the widespread adoption of HBM, it has failed to provide consistent evidence of success in health behavior promotion. Therefore, it has been argued that the existing HBM’s variables are limited in guiding the design of health behavior interventions for two main reasons: (1) the predictive capacity of HBM is low. The primary variables account for less than 21% (R 2 < 0.21) of variance in healthy behavior change and the effect sizes of the individual variables are very small. (2) There no is clear rule of combination of the HBM variables. The relationships between the individual variables are not specified. These limitations are the cause (at least to some extent) of the inability of HBM to provide substantial health behavior improvements.

In this paper, we took a first step towards overcoming these limitations. We improved the predictive capability of HBM by extending it to include four other healthy behavior determinants: self-identity, perceived importance, consideration of future consequences , and concern for appearance. We also explored the interactions between the variables and established some mediated relationships. In the process of ascertaining the suitability of each variable to act as a health determinant within HBM, several theoretical questions were answered and some other findings validated

To show the suitability of our proposed HBM extension, we validated our model in the healthy eating domain. The statistical findings show that the four new variables ( self-identity, perceived importance, consideration of future consequences , and concern for appearance ) added to the HBM do in fact all have their place as determinants within the HBM by showing some significant association with healthy behavior. Our newly added variables appeared to be better predictors of healthy behavior than all the previously proposed variables with exception of self-efficacy with remains the strongest determinant of healthy behavior. Our extended HBM model led to approximately 78% increase (from 40 to 71%) in predictive capacity from the old model. This shows the suitability of our extended HBM to predict healthy eating behavior and to direct health intervention design.

Finally, to give some insight on the possible combination and relationship between the HBM variables, we systematically explored the model for possible interactions between the variables. The results show that there exist some mediating relationships between some variables of HBM. The primary HBM variables - susceptibility, severity, benefit, and barrier do in fact mediate the relationship between consideration of future and self-identity alongside with cue to action . Another novel finding is that perceived barrier mediates the relationship between self-efficacy and healthy behavior.

This work contributes to both health behavior theory and health intervention design domains. In the theory front, we extended the HBM, validated the extended model and showed that the extended model predicted 71% variance in health behavior, in contrast to the 21% variance predicted by the original HBM. We also compared the old models (the baseline and the intermediate models) with our extended model and showed that our four proposed variables along with self-efficacy are better predictors of healthy behavior than all the original variables of the HBM. Our extended HBM significantly increased the predictive capacity. We hope that this new model will spur research on investigating the influence of our proposed variables within other theoretical frameworks (e.g., the TPB).

On the practical side, our discovery of mediating relationships between the extended HBM variables is intended to allow for a more straightforward and informed combination of variables in health intervention design, leading to higher effectiveness of interventions. The mediating effect shows that some variables function as an antecedent to others and, therefore, will produce a better effect when applied together either in succession or simultaneously. For instance, self-identity can be applied alongside susceptibility, severity, benefit, and cue to action to increase their effect. Barrier can be significantly reduced following successful implementation of self-identity and self-efficacy. The increased predictive capacity of our extended HBM also means that interventions designed based on this model have a greater chance of success than those based on the original HBM.

Although most of the variables are important, some variables like self-efficacy, self-identity, and perceived importance are obviously more important determinants of healthy behavior. Calculating the effect size f 2 (as shown in Table 6 ) ensures that intervention designers can easily make an informed decision on the choice of variable or a combination of variables to implement in a design.

Acknowledgments

The first author of this paper is being sponsored by the NSERC Vanier Canada Graduate Scholarship. Many thanks to Dr. Ebele Osita, Fidelia Orji, and Fr Patrick Ampani for their assistance with the data collection and to the reviewers for their insightful comments.

health belief model research paper

Academia.edu no longer supports Internet Explorer.

To browse Academia.edu and the wider internet faster and more securely, please take a few seconds to  upgrade your browser .

  •  We're Hiring!
  •  Help Center

Health Belief Model

  • Most Cited Papers
  • Most Downloaded Papers
  • Newest Papers
  • Save to Library
  • Last »
  • Self Efficacy Theory Follow Following
  • Tai Chi Exercise Follow Following
  • Fear of falling in older adults Follow Following
  • Gerontological Nursing Follow Following
  • Public/Social Policy Follow Following
  • Health Promotion and Education Follow Following
  • Busines plan Follow Following
  • Manegement and Business Administration Follow Following
  • Health Literacy Follow Following
  • Health Management Follow Following

Enter the email address you signed up with and we'll email you a reset link.

  • Academia.edu Publishing
  •   We're Hiring!
  •   Help Center
  • Find new research papers in:
  • Health Sciences
  • Earth Sciences
  • Cognitive Science
  • Mathematics
  • Computer Science
  • Academia ©2024

IMAGES

  1. 1 The Health Belief Model

    health belief model research paper

  2. Health belief model

    health belief model research paper

  3. | Extended Health Belief Model.

    health belief model research paper

  4. (PDF) The Health Belief Model

    health belief model research paper

  5. Health Belief Model And Smoking

    health belief model research paper

  6. The Health Belief Model

    health belief model research paper

VIDEO

  1. health belief model

  2. Introduction to research methodology for health sciences-Second day 2023

  3. PUBH 606: Application of the Health Belief Model on Opioid Use

  4. Individual Research

  5. Medical sociology : Health belief model , Social health model , Biomedical model

  6. AeroVertical: VTOL convertiplane RC model research. Part 1

COMMENTS

  1. The Health Belief Model as an Explanatory Framework in Communication Research: Exploring Parallel, Serial, and Moderated Mediation

    As one of the most widely applied theories of health behavior (Glanz & Bishop, 2010), the Health Belief Model (HBM) posits that six constructs predict health behavior: risk susceptibility, risk severity, benefits to action, barriers to action, self-efficacy, and cues to action ( Becker, 1974; Champion & Skinner, 2008; Rosenstock, 1974 ).

  2. (PDF) The Health Belief Model

    The health belief model is the basis of or is incorporated into interventions to increase knowledge of health challenges, enhance perceptions of personal risk, encourage actions to reduce...

  3. Testing the Effectiveness of the Health Belief Model in Predicting

    We use a cultural psychology approach to examine the relevance of the Health Belief Model (HBM) for predicting a variety of behaviors that had been recommended by health officials during the initial stages of the COVID-19 lockdown for containing the spread of the virus and not overburdening the health system in Europe.

  4. The health belief model: How public health can address the

    This paper proposes an intervention into health misinformation that relies upon the health belief model as a means to bridge the risks associated with health misinformation and the impact on individual health, beyond the current recommendations for fact checking and information literacy. Study design This is a short theoretical paper. Methods N/A.

  5. (PDF) THEORY AT A GLANCE: HEALTH BELIEF MODELS IN ...

    The Health Belief Model is a dynamic framework for guiding health promotion and illness prevention initiatives. The Health Belief Model was created by social scientists at the US...

  6. (PDF) Usage of Health Belief Model (HBM) in Health Behavior: A

    ... To prevent and control the condition and to increase community knowledge of its health risks, it is crucial to comprehend the current attitudes and ideas about it (13). Beliefs and opinions...

  7. Using the Health Belief Model to Understand Age Differences in

    The Health Belief Model (HBM) is an empirically-supported model of health behavior that provides a framework for understanding how the adoption of public health measures is driven by perceptions of COVID-19 risk and the benefits and barriers to recommended health behaviors for reducing COVID-19 transmission (Rosenstock, 1974; Janz and Becker ...

  8. The Health Belief Model: A Decade Later

    This article presents a critical review of 29 HBM-related investigations published during the period 1974-1984, tabulates the findings from 17 studies conducted prior to 1974, and provides a summary of the total 46 HBM studies (18 prospective, 28 retrospective).

  9. Using the Health Belief Model to explore why women decide for or

    Data collection and analysis were informed by the Health Belief Model (HBM). Data was analysed using qualitative content analysis. Results The paper describes women's decision making with the help of the four constructs of the HBM: perceived susceptibility, perceived severity, perceived benefits, and perceived barriers.

  10. PDF Using the Health Belief Model to design a questionnaire aimed at

    We designed an online survey including an adaptation of the following Health Belief Model constructs: perceived COVID-19 susceptibility, perceived COVID-19 severity, perceived benefits of using immunity certificates, perceived barriers from using immunity certificates, perceived severity of not using immunity certificates, and perceived vaccinat...

  11. Illness Attributions and The Health Belief Model

    Abstract. This paper proposes an attributional approach to the traditional Health Belief Model (HBM). It is argued that this approach has two purposes: (1) health beliefs might themselves be determined by attribution and (2) the prediction of health behavior might be sig nificantly improved by combining health beliefs with illness attribu tions.

  12. The health belief model's ability to predict COVID-19 preventive

    Abstract Objective: The health belief model specifies that individuals' perceptions about particular behavior can predict the performance of respective behavior. So far, the model has been used to explain why people did not follow COVID-19 preventive behavior.

  13. Using the Health Belief Model to explain patient involvement in patient

    Original Research Paper. Open Access. Using the Health Belief Model to explain patient involvement in patient safety. Andrea C. Bishop PhD, ... This research used the Health Belief Model (HBM) as a framework to understand how patients' perceptions of benefits, threats, cues to action, and self-efficacy play a role in the likelihood of patients ...

  14. An extended health belief model for COVID-19: understanding ...

    Building on the health belief model (HBM), this research tests, over six months, how the exposure to COVID-related information in the media affects fear, which in turn conditions beliefs about the severity of the virus, susceptibility of getting the virus, and benefits of safety measures. These health beliefs ultimately lead to social distancing and panic buying. As a first contribution, we ...

  15. The Revised Champion's Health Belief Model Scale: Predictive Validity

    In 1999, these were combined and revised to become Champion's Health Belief Model Scale (CHBMS), a 19-item, 3-factor scale to measure beliefs about breast cancer and mammography screening (Champion, 1999). Although Champion's study provided evidence of internal consistency, test-retest stability, and confirmation of its factor structure ...

  16. (PDF) The Health Belief Model

    (PDF) The Health Belief Model The Health Belief Model Authors: Charles Abraham University of Exeter Paschal Sheeran University of North Carolina at Chapel Hill Figures .4 Evaluations of...

  17. How Well the Constructs of Health Belief Model Predict Vaccination

    This systematic review synthesizes the findings of quantitative studies examining the relationships between Health Belief Model (HBM) constructs and COVID-19 vaccination intention. We searched PubMed, Medline, CINAHL, Web of Science, and Scopus using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines and identified 109 eligible studies.

  18. Using the Health Belief Model to Explain the Patient's Compliance to

    This study is guided by the Health Belief Model (HBM) to investigate the influence of treatment compliance using HBM constructs among elderly hypertensive patients in 3 regional hospitals in Dar es Salaam, Tanzania. Methods: An analytical cross-sectional study was conducted in 3 region hospitals in Dar es Salaam from April to May 2012.

  19. Critique of the health-belief model

    The health-belief model offers an approach to understanding health-related behaviour. A clear understanding of the cause of behaviour is necessary in order to predict change. A clear understanding of cause is also necessary for determining methods to influence health behaviour. As a new model and one developed for the healthy, the model needs ...

  20. The Health Belief Model and Preventive Health Behavior

    Becker M, Kaback M, Rosenstock I, Ruth M: Some influences on public participation in a genetic screening program. (In press) Community Health 1, 1975. Google Scholar. 35. Heinzelmann F, Bagley RW: Response to physical activity programs and their effects on health behavior. Public Health Rep 85:905-911, October 1970.

  21. Expanding the Health Belief Model for exploring inpatient fall risk

    RESEARCH METHODOLOGY: DISCUSSION PAPER - METHODOLOGY. Expanding the Health Belief Model for exploring inpatient fall risk perceptions: A methodology paper. ... Based on philosophical nursing underpinnings, the Health Belief Model (HBM) was selected as the theoretical model. The limitations of the model led to expansion of the model with ...

  22. Towards an Effective Health Interventions Design: An Extension of the

    In this paper, we propose a solution that aims at addressing these limitations as follows: (1) we extended the Health Belief Model by introducing four new variables: Self-identity, Perceived Importance, Consideration of Future Consequences, and Concern for Appearance as possible determinants of healthy behavior.

  23. Health Belief Model Research Papers

    The Effect of Home Care Education to Parents based on Health Belief Model on Recurrent Urinary Tract Infection of Children. Introduction: Regarding to the importance of educational interventions to increase the preventive behaviors of urinary tract infection in children, this study was conducted aimed to investigate the effect of home care ...