The application of the HBM was developed in the 1950s by a group of psychologists — Irwin Rosenstock, Mayhew Derryberry, and Barbara Carriger — belonging to the Public Health Service of the United States (Green et al. 2020). This model has been extensively tested to explain adherence to health recommendations (Green et al. 2020; Moreno San Pedro & Gil Roales-Nieto 2003), and has recently gained renewed attention (Zewdie et al. 2022). The primary aim of this model is to understand why individuals often resist engaging in preventive behaviors (Moreno San Pedro and Gil Roales-Nieto 2003). It is grounded in the premise that individual health beliefs influence their behaviors (Champion and Skinner 2008; Janz and Becker 1984).

The original model contemplates four main dimensions: perceived susceptibility, perceived severity, perceived benefits, and perceived barriers (Champion and Skinner 2008; Moreno San Pedro & Gil Roales-Nieto 2003; Rosenstock 1966). This model has been expanded to include two additional dimensions: cues to action and self-efficacy (Bandura 1997; Champion & Skinner 2008; Green et al. 2020; Rosenstock et al. 1988). Susceptibility pertains to an individual's subjective perception of the risk of contracting diseases. The model posits that people are more motivated to take health-conscious actions if they believe they are susceptible to certain negative health outcomes (Rosenstock 1966). Severity encompasses beliefs about the seriousness of a given disease or the consequence of not treating it. This dimension includes assessments of medical–clinical consequences and possible social repercussions. The model predicts that the greater people's perception of the severity of negative health outcomes, the more motivated they are to act to take steps to avoid those outcomes. Benefits refer to the perceived effectiveness and efficacy of recommended actions or behaviors to reduce the risk or impact of the disease, while barriers relate to beliefs about the tangible and psychological costs of the recommended actions. Thus, the model suggests that if individuals perceive that a particular behavior will yield strong positive benefits, they are more likely to adopt it. Conversely, if individuals perceive barriers preventing them from engaging in preventive behavior, they are less likely to do so (Rosenstock 1966).

Cues to action refer to both internal factors (i.e., physical symptoms or bodily events) and external factors (i.e., advice from others, recommendations through the media or health services) that can trigger action. According to Hochbaum (1958), the activation of cues to action depends of perceived levels of susceptibility and severity. Therefore, this dimension has received limited attention in surveys (more so in content analysis, e.g., Tang and Park 2017) due to its challenging application in explanatory surveys (Champion and Skinner 2008). Self-efficacy refers to an individual’s confidence in their ability to perform a specific behavior and achieve certain results, and it is grounded in the concept of self-conviction in executing a behavior and attaining specific outcomes (Bandura 1997).

Through the use of quantitative methodological approaches, several studies have shown that HBM constructs can predict cancer detection behavior in certain groups within the population (Burak & Meyer 1997; Darvishpour et al. 2018; Lau et al. 2020). Research has shown that perceived barriers and severity of skin cancer (Støle et al. 2019) are significant, as reducing barriers and increasing perceived benefits enhances sun protection behaviors (Pearlman et al. 2021). However, factors such as the time between belief measurement and behavior, as well as types of behaviors, can moderate the predictive power of independent variables (Carpenter 2010). Thus, while the HBM may be useful in predicting cancer prevention behavior in certain populations, its predictive ability should be evaluated based on the specific health behavior context in which it is applied.

Age is considered a crucial factor in skin cancer prevention behavior (Çelik & Koç 2023), as the history of sunburns performs an influential role in the development of skin cancer (Cercato et al. 2015), particularly during the period of highest risk behaviors that could drive to the development of this disease — adolescence and young adulthood (Wu et al. 2018). Nevertheless, perceived susceptibility is inversely proportional to age (Çelik & Koç 2023; Grubbs & Tabano 2000). Although young people have knowledge about the risks of sun exposure and skin cancer, their perception of immortality (Davis et al. 2015) and perceived social norms (Glanz et al. 1999) act as barriers to preventive behavior (Carmel et al. 1994; Davati et al. 2013).

Critics of the HBM have noted several limitations, particularly with regard to its ability to predict actual behavior. While the model is useful for determining the intention of preventive behavior, it pays little attention to the individual variables that influence actual behavior (Janz & Becker 1984; Moreno San Pedro and Gil Roales-Nieto 2003). Furthermore, the absence of an individual experience focus has been considered another limitation of the model. As Davidhizar (1983) agues, a clear understanding of the cause of behavior is necessary for predicting change. Lastly, the model's significant emphasis on the risk and severity of the condition is another critical aspect to consider, as it may provide a simplistic view of health-related decision-making (Carpenter 2010).

This study is part of the Research and Development (R&D) project with reference PID2020-116487RB-100, funded by Spanish Ministry of Science and Innovation. The project involves high degrees of innovation, interdisciplinarity, and knowledge transfer, with the participation of five institutions, including universities and hospitals. This research focuses on a population sector identified in Spain as a group at the highest risk of improper photoprotection and susceptibility to the disease: college students.

A statistically representative sample of students from the diverse fields of studyFootnote 1—arts and humanities, sciences, health sciences, social and legal sciences, engineering and architecture — enrolled in Spanish universities through all the country, was recruited for this research through the databases of the Health Universities Network.

Data collection took place between October 2022 and March 2023. Before conducting data collection, the Research Ethics committee from [removed] reviewed and granted ethical approval of the instrument (registration number 1701202201422). The participants who completed the questionnaire adhered to the recommendations of the ethical committee at [removed]. They were not students or former students of any member of the research team. The decision to exclusively target university students aligns with the research project's objectives.

The universe of college students nationwide in Spain is 1,722,247.Footnote 2 The minimum sample figure of 384 students was calculated, with a confidence margin of 95% and a sampling error of 5%. The final sample was 496 completed surveys. Surveys lacking internal coherence, displaying anomalous durations, or being incomplete were excluded. The sample was selected based on predetermined criteria, including age, gender, habitat, and fields of study according to the most up-to-date data from the National Agency for Quality Assessment and Accreditation (ANECA).

The response rate for the online questionnaire was 52.4% of the sample.

The questionnaire included two sections containing the CHACES epidemiological questionnaire (Blázquez-Sánchez et al. 2020; De Troya-Martín et al. 2009), and the HBM construct. To develop an online questionnaire instrument for this research project, a review of instruments measuring the variables of HBM was conducted. The items on the scales were carefully examined to determinate whether their alignment with the research objectives and to assess their optimal psychometric properties.

Additionally, the Practices, Attitudes and Knowledge related to Sun Exposure (CHACES) epidemiological questionnaire, which has been validated for use with a population over 18 years of age (Blázquez-Sánchez et al. 2020) and to diverse contexts and populations in Spain, was applied.

The questionnaire included also sociodemographic characteristics such as age, gender, educational level, field of study, marital status, and habitat, together with information regarding self-reported phototype and sun-reactive skin type (Author3 n.d; Fitzpatrick 1988). The primary sections of the questionnaire focused on the frequency of sun exposure habits during outdoor activities in various scenarios (across different days of the year and daily hours). It also collected information on the number of sunburns experienced (redness and pain) within the last year. Additionally, the questionnaire included six items related to sun-protection practices and ten tems assessing attitudes toward sun exposure and photoprotection (Author3 n.d). The key variable of the CHACES questionnaire for this research focuses on sunburn experiences as the determinant consequence of sun exposure beliefs, attitudes, and practices. This variable of the CHACES is operationalized based on a single burn, i.e., in two categories: no burns in the last year and one or more burns in the last year.

The wording of some questions was modified to better align them with the research objectives. The final questionnaire underwent validation by a panel of health research experts from the [removed]. Subsequently, a pretest was carried out with 10% of the sample to assess the performance of the variables and the data production software.

Table 1 displays the variables used in this research, which include the HBM constructs used (perceived susceptibility, perceived severity, perceived benefits, perceived barriers, cues to action, and self-efficacy), along with the sunburn experiences.

A descriptive statistical analysis was performed to obtain information on each of the variables. To evaluate statistically significant associations among the HBM variables, Pearson correlations, one-way analysis of variance (ANOVA) and Cronbach's α were employed to calculate internal consistency reliability, together with independent samples t-tests.

To examine the relationship between sunburn in the last year (dependent variable) and the HBM constructs (independent variables), a chi-square analysis was performed. Subsequently, for determining the relative strength and significance of the independent variables concerning the dependent variable and among themselves, a hierarchical clustering based on the chi-square strength was performed.

For all statistical analyses, a level of statistical significance equal to or less than 0.05 was applied. All data analysis was carried out using IBM SPSS Statistics 26.