Introduction

Smoking is a well-known risk factor for premature morbidity and mortality, responsible for over 175 million deaths. In 2020, more than one in ten deaths worldwide and nearly 142 million years of life lost (YLLs) were attributable to smoking 20211. In low- and middle-income countries such as Iran, where more than 80% of tobacco users live, the burden of tobacco-related mortality is disproportionately high2.

The Eastern Mediterranean Region, exhibits the highest global prevalence of waterpipe use3. This trend is particularly pronounced among the younger demographic within this region4,5,6,7.

Studies conducted during the early years of the 21 st century have revealed a significant trend of waterpipe smoking surpassing cigarette smoking in various contexts8,9,10. In a 2020 meta-analysis study, a 25% prevalence of lifetime water-pipe smoking was found among Iranian university students (37% among men and 17% among women)11.

Studies have shown that waterpipe smoking poses significant cardiovascular health risks. These include acute effects like increased heart rate and blood pressure12autonomic dysregulation13and reduced exercise capacity14as well as chronic issues such as coronary artery disease15heart disease13and metabolic syndrome16.

Various factors have contributed to the growing popularity of hookah smoking, especially among young people. Key contributors include the availability of flavored tobacco, the social acceptability fostered by café and restaurant culture, the influence of mass communication and social media, the absence of specific policies and regulations targeting waterpipe use, and the widespread misconception that hookah smoking is less harmful than cigarette smoking17,18,19. Moreover, the belief that occasional hookah use carries minimal risks and that quitting is a simple matter of choice further reinforces a low perceived risk17,20. This low perceived risk is a critical element in behavior change theories, as individuals are less likely to modify their behavior when they do not view it as harmful17,21.

The Health Belief Model (HBM) provides valuable insights into the factors influencing health behaviors, such as hookah use. According to this model, individuals are more likely to engage in health-promoting behaviors when they perceive a significant threat to their health22,23. The HBM identifies four key constructs: Perceived Susceptibility (an individual’s belief about their personal risk), Perceived Severity (the seriousness of a health threat), Perceived Benefits (the effectiveness of a health behavior in reducing the risk), and Perceived Barriers (factors that hinder the adoption of a health behavior)24. Understanding these constructs is essential for addressing hookah smoking and encouraging healthier behaviors.

The four constructs of the HBM are influenced by demographic factors like age, gender, and education level, as well as psychological characteristics, including personality and peer pressure25. The HBM has been extensively applied to various health behaviors, including smoking cessation, alcohol consumption, dietary habits, physical activity, and hookah use26,27,28,29,30.

Within the HBM, perceived susceptibility and perceived severity are essential components of perceived risk, a core construct of the model. Researchers frequently combine scores for perceived susceptibility and perceived severity to create a composite measure of perceived risk31,32. Although this approach offers a comprehensive measure, it may present challenges if perceived susceptibility and perceived severity exert distinct effects on behavior change, This highlights the importance of understanding the individual roles of these constructs in addressing hookah smoking.

Cluster analysis, a technique commonly used in fields such as health, psychology, and nutrition33,34,35,36offers a way to address the potential issue of differing impacts of perceived susceptibility and severity on behavior change in hookah smoking. This method enables researchers to group individuals based on similar levels of perceived susceptibility and severity37facilitating a deeper understanding of the factors associated with varying levels of perceived risk and their influence on behavior change specific to hookah use.

In this study, we utilized cluster analysis within the HBM to investigate the determinants of perceived risk for MI, stroke, and hypertension among hookah smokers. This approach enhances the understanding of these factors within the HBM framework for behavior change.

Materials and methods

Procedure

To examine the factors influencing the perceived risk of cardiovascular diseases among hookah users, a cross-sectional study was conducted between January and September 2023. Using a convenience sampling method, 245 participants were recruited from coffee houses; restaurants (places where people have coffee, other beverages, cakes, or small meals), and entertainment centers in six cities of Khuzestan Province, southwest of Iran. The coffee house, in its popular and public form, originated in Iran during the Safavid period. Since ancient times, these establishments have served as social spaces where people gathered to drink coffee and tea, smoke hookah, and discuss daily affairs. Participants were adults aged 18 or older who currently used hookah at least once a month or had done so in the past.

The study was conducted in compliance with the ethical guidelines and regulations established byAbadan University of Medical Sciences and the Declaration of Helsinki. The study protocol received approval from the Ethics Committee of Abadan University of Medical Sciences (Approval number: IR.ABADANUMS.REC.1401.167).

Informed consent

was obtained from all participants before they were included in the study. Participants were assured of privacy and confidentiality. They were provided with a detailed explanation of the study objectives and willingly completed a questionnaire after providing their consent.

Measures

The data were collected using four main questionnaires to assess: (1) risk perception, (2) stage of change, (3) attitudes and beliefs about hookah use, (4) demographic information. To evaluate the risk perception of hookah consumption, questionnaires adapted from those designed by Wong and Cappella38 and Kotz et al.39 for lung cancer, and previously used in Iran by Zarghami et al.40were supplemented with additional questions about the participants’ perceived risk of cardiovascular-related diseases.

The risk perception questionnaire included two sets of questions: the perceived risk of CVD including MI, HTN, and stroke, and participants’ assessments of the likelihood of surviving these diseases. The perceived risk section of the questionnaire contained of two questions assessing participants’ perceptions of developing MI, HTN, and stroke in the future compared to non-smokers of the same age. Respondents answered on a scale ranging from zero to ten. A score of zero indicates a belief that they are unlikely to develop these diseases and a score of ten reflects a high probability of developing one or more of these morbiditiesin the future.

The second category of questions asked how likely participants believed they were to survive in the next five years if they are diagnosed with any of these diseases. A score of zero indicates that they think it is very likely that they will be alive after 5 years while a score of ten reflects that they think they will not be alive after 5 years of after being diagnosed with any of these conditions (due to complications from these incidents). The sum of the scores of these two sections was considered their perceived risk, with the range of possible numerical scores spanning from 0 to 20.

Prochaska and DiClemente’s questionnaire of stages of change in cigarette consumption was used, to measure the stages of change in hookah consumption in this study41,42. It included five categories; (1) precontemplation stage; people who currently are smoking and do not intent to quit it the next six months, (2) contemplation stage; people who consider smoking abstinence in the next six months, (3) preparation stage; people who intend to quit smoking in the next month, (4) action stage; people who have quit smoking but for no more than six months, and (5) maintenance stage; people who have quit smoking but for no more than six months. This questionnaire was previously used in Iran in a study conducted by Davaji et al.43.

Attitude and beliefs about hookah were measured using three questions ranging from “completely agree” to “completely disagree”: (1) In my opinion, hookah smoking is not addictive, (2) I can stop smoking hookah whenever I decide, and (3) The harms of using hookah smoking are less than cigarette smoking. If a person acknowledges that hookah smoking is addictive, finds it difficult to stop smoking hookah whenever they decide, and believes the harms of hookah smoking are greater than those of cigarette smoking considered as negative attitude toward hookah smoking and vice versa. This questionnaire was previously used in Iran in a study conducted by Davaji et al.43.

Also, this questionnaire included demographic variables such as age, gender, level of education, occupation, duration of hookah consumption, and current patterns of hookah consumption.

Statistical analysis

Descriptive statistics were used to analyze the demographic characteristics of the participants and to assess their perceived susceptibility and severity to cardiovascular diseases. Perceived susceptibility and severity were then categorized as low (scores below 4), moderate (scores between 4 and 7), or high (scores above 7).

Cluster analysis was used to group participants into homogeneous clusters based on their perceived susceptibility and severity scores for the three cardiovascular diseases. The Euclidean distance metric and Wards.D2 method were applied to identify similar clusters of participants, considering both perceived susceptibility and severity. This approach allowed us to explore factors associated with varying levels of perceived risk for each of the three diseases.

For each cluster, the average perceived susceptibility and severity scores were calculated for MI, stroke, and hypertension. Based on these averages and the score ranges (0–4: low, 4–7: moderate, 7 and above: high), a cluster status was assigned for each of the three conditions (MI, stroke, and hypertension) in terms of perceived susceptibility and severity. The overall cluster status of the sample group in terms of perceived susceptibility and severity was determined based on the individual cluster statuses for each condition.

To identify potential differences in demographic, attitudinal, and behavioral factors between groups with varying levels of perceived risk, the clusters were compared using Chi-square tests with Bonferroni corrections for categorical variables and the Kruskal-Wallis test with Dunn’s post-hoc analysis (Dwass-Steel-Critchlow-Fligner method) for continuous variables.

For categorical variables, we reported Cramér’s V as a measure of association, with values ranging from 0 (no association) to 1 (perfect association). For continuous variables, we used Eta squared (η²) as a measure of association, calculated from the Kruskal-Wallis test results in Jamovi software. Eta squared values also range from 0 (no association) to 1 (perfect association), indicating the strength of the relationship between the variables.

Cluster analysis was performed using the snowCluster module in Jamovi 2.4 software at a significance level of 0.05 to identify groups with varying levels of perceived risk. All results are presented as tables or figures, where appropriate.

Results

The mean age of the study participants was 28.9 ± 9.2 years, with a mean age of 20.7 ± 6.8 years for initiation of smoking. The median (interquartile range) number of hookah consumption per month was 12 (27) sessions. Of the participants, 81.2% were male and 65.3% were single. In terms of educational attainment, 52.7% of participants had a high-school education, 44.5% held an academic degree, and 3.8% had other educational qualifications. Moreover, nearly a third (30.6%) of participants reported having no fixed income, while 30.2% reported a monthly income ranging from $120 to $240.

Nearly 69% of participants were in the precontemplation stage. Furthermore, 60.4% did not view hookah smoking as addictive, and 77.4% believed they could quit whenever they wanted. Despite these beliefs, 57.6% recognised the greater harm associated with hookah smoking compared to cigarette smoking (Table 1).

Table 1 Demographic characteristics of Hookah users in Southwest Iran (n = 245).
Full size table

As depicted in Fig. 1, perceived susceptibility scores ranged from 2.46 (low, < 4) for stroke to 3.07 (low, < 4) for hypertension, indicating a generally low levels of perceived susceptibility for all three cardiovascular diseases. Perceived severity scores ranged from 4.07 (moderate, 4–7) for hypertension to 5.07 (moderate, 4–7) for MI and stroke, showing moderate levels of perceived severity for all three cardiovascular diseases.

Fig. 1
figure 1

Mean (SD) perceived susceptibility and severity of cardiovascular diseases (Mi, Stroke, and Hypertension) among hookah users in southwest Iran (n = 245).

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Based on the cluster analysis, the subjects were divided into six clusters. The average profiles of these six clusters in terms of perceived susceptibility and perceived severity for the three diseases are presented in Fig. 2, depicted in different colors. Based on the cluster’s status (low, moderate, or high) for each of the three diseases, the overall perceived susceptibility and severity of the clusters were classified as low, moderate, or high.

In summary, the cluster analysis divided participants into six clusters. Figure 2 illustrates the average profiles based on perceived susceptibility and severity for MI, stroke, and hypertension. The clusters are:

Cluster 1 (n = 65, 26.5%; black line): Low perceived susceptibility and severity.

Cluster 2 (n = 42, 17.1%; red line): Low susceptibility, moderate severity.

Cluster 3 (n = 44, 17.9%; purple line): Moderate susceptibility and severity.

Cluster 4 (n = 54, 22%; yellow line): Low susceptibility, high severity.

Cluster 5 (n = 18, 7.3%; blue line): High susceptibility, low severity.

Cluster 6 (n = 22, 9%; green line): Moderate susceptibility, high severity.

This visualization highlights the distinct patterns of perceived cardiovascular disease risk across the identified clusters.

Fig. 2
figure 2

Average Cardiovascular Disease Risk Perceptions (Mi, Stroke, and Hypertension) across Clusters based on Susceptibility and Severity (n = 245).

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The comparison of the six clusters based on demographic variables is presented in Table 2. While no significant differences were observed regarding gender, marital status, or income level. A statistically significant difference in smoking status was found between the six clusters (χ2 = 12, p = 0.034, η² = 0.22). Pairwise comparisons revealed that Cluster 2 (50%) had a significantly higher proportion of smokers than Cluster 1 (20%) (p = 0.001).

Turning to age, a significant difference in age was identified between the clusters (χ2 = 15.03, p = 0.003, η² = 0.063). Comparing analysis revealed that participants in Cluster 6 (36 ± 10.01) were significantly older than those in Clusters 2 (26.7 ± 7.8), Cluster 3 (27.5 ± 7.0), and Cluster 4 (27.8 ± 8.8).

A significant difference was noted in the age at initiation of hookah use between the clusters (χ2 = 17.5, p = 0.004, η² = 0.072). Post-hoc comparisons indicated that participants in Cluster 6 began using hookah at a significantly older age (27.2 ± 8) compared to those in Clusters 2 (19.2 ± 6.5), 3 (19.7 ± 5.5), and 4 (20.5 ± 6.2) (p < 0.05).

Regarding the frequency of hookah use, Cluster 1 reported the highest average number of sessions per month (28.02), followed by Cluster 5 (23.06) and Cluster 3 (21.09). In contrast, Cluster 6 had the lowest average number of sessions per month (11.45). Statistical analysis revealed significant differences between the clusters (χ2 = 17.3, p = 0.004, η² = 0.07), with Cluster 1 demonstrating significantly higher hookah use compared to Clusters 6 and 4 (p < 0.05).

Table 2 Comparisons of demographic distributions of Hookah users across perceived susceptibility and severity clusters in Southwest Iran (n = 245).
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Turning to the attitudinal variables, Table 3 presents the comparison of the six clusters. Notably, a significant difference was found in the stages of change for quitting hookah use among the clusters (χ² = 44.2, p < 0.001, η = 0.24). Pairwise comparisons indicated that a significantly higher proportion of participants in Cluster 4 (85%) and Clusters 1, 2, and 3 (71% for each) were in the pre-contemplation stage (not intending to quit), compared to Cluster 6 (27%).

The clusters demonstrated a significant difference in attitudes toward the addictiveness of hookah use (χ² = 27, p = 0.003, η² = 0.23). When comparing the clusters, participants in Clusters 2 (74%) and 4 (80%) were more likely to perceive hookah as non-addictive compared to those in Cluster 6 (27%).

Additionally, there was a notable difference among the clusters regarding their perceived ability to quit hookah use (χ² = 46.8, p < 0.001, η² = 0.31). Participants in Clusters 1 (76%), 2 (81.7%), 3 (82%), and 4 (89%) demonstrated a greater belief in their ability to quit hookah whenever they wanted compared to those in Cluster 6 (36%).

Table 3 Comparisons of quitting behavior and attitudes towards Hookah addiction across perceived susceptibility and severity clusters in Southwest Iran (n = 245).
Full size table

Similarly, a significant difference was observed in the belief that hookah use is less harmful than smoking among clusters (χ² = 27.6, p = 0.002, η² = 0.23). In Cluster 1, 45% of participants agreed that hookah use is less harmful, compared to only 18% of participants in Cluster 6, with a statistically significant difference (p = 0.003).

Discussion

This study aimed to investigate the perceived risk of hookah use among individuals in Khuzestan Province, Iran, using cluster analysis and the Health Belief Model (HBM) as the theoretical framework. Participants were clustered based on their perceived susceptibility and severity, and demographic and attitudinal factors were compared across these clusters to identify potential influences on perceived risk.

Using cluster analysis, participants were divided into six distinct groups based on combinations of perceived susceptibility and severity, of which there are nine possible permutations (3 susceptibility levels x 3 severity states). To simplify interpretation, the clusters were labeled in ascending order based on perceived risk, starting with Cluster 1 (low susceptibility/low severity), representing the lowest perceived risk, and ending with Cluster 6 (moderate susceptibility/high severity), representing the highest perceived risk.

Although most participants (60.4%) did not view hookah use as addictive and 77.1% believed they could quit whenever they wanted, this study found that at least half of the participants smoked hookah an average of 12 times per month, indicating frequent use. Griffith and Ford suggest that the social nature of hookah smoking and its intermittent consumption may contribute to the perception of low addiction potential28,44. This disconnect between perceived addiction potential and actual usage patterns underscores the need for effective health education campaigns to clearly communicate the dangers of hookah smoking.

Several studies suggest that the low perceived risk of hookah smoking may stem from the belief that water filtration effectively removes harmful substances18as well as the assumption that hookah contains fewer harmful substances than cigarettes19,45.

The large proportion of participants in the precontemplation stage (69%) and the widespread misconceptions about hookah addiction and cessation, despite frequent use, indicate that interventions should focus not only on correcting risk perceptions but also on addressing the challenges of quitting. The low perceived risk among participants highlights the need for effective, targeted interventions to raise awareness about the health risks of hookah smoking and encourage cessation.

Perceived risk, a core concept in health behavior theories, is shaped by an individual’s perceived susceptibility and severity. Susceptibility refers to a person’s belief about their personal risk of facing a negative health outcome46. In this study, susceptibility was defined as the perceived likelihood of developing hypertension, stroke, or MI as a result of hookah use.

In the context of hookah use, susceptibility to cardiovascular diseases such as hypertension, stroke, and myocardial infarction is a significant concern due to the toxic substances present in tobacco47. However, despite this risk, participants in this study perceived a low susceptibility to these health outcomes. This may be due to their misconceptions about hookah addiction and cessation, as discussed earlier.

Perceived severity, defined in this study as the risk of death within five years from cardiovascular complications associated with hookah use, is a key component of perceived risk. In the present study, participants exhibited moderate levels of perceived severity. This result aligns with a previous study conducted in Bushehr Province, which reported similar levels of perceived susceptibility (2.62) and perceived severity (3.37) among female participants48.

The moderate levels of perceived severity and susceptibility observed among participants suggest that recognizing health risks associated with hookah use may be a crucial step toward promoting cessation. Research on smoking cessation indicates that health concerns often serve as a significant motivator for quitting49,50. This highlights the role of risk perception in promoting healthier behaviors and indicates that perceived risks and negative health consequences are important factors influencing the intention to quit hookah smoking51,52,53,54.

These findings are align with previous research on the connection between perceived risk and hookah use. Earlier studies have identified a direct connection between perceived risk and willingness to quit hookah use55,56as well as a link between perceived risk and the use of other substances, such as drugs57,58. In line with these observations, participants in Cluster 6 (moderate susceptibility/high severity) demonstrated a significantly higher intention to quit hookah or were already abstaining from use, compared to those in Clusters 1, 2, 3, and 4. Additionally, participants in Cluster 6 and Cluster 4 (low susceptibility/high severity) reported significantly lower frequencies of hookah use than those in Cluster 1 (low susceptibility/low severity).

The observed lower frequencies of hookah use among Cluster 6 and Cluster 4 participants can be better understood by examining participant demographics and quitting behaviors. Cluster 6 (moderate susceptibility/high severity) differed significantly from the other clusters in terms of age, with participants in this group being older and initiating hookah use at a later age. This suggests that while increased age may be associated with higher perceived risk, it does not necessarily deter individuals from starting hookah use. The observed relationship between age and perceived risk aligns with Romer and Jamieson’s59 suggestion that high perceived risk has a stronger influence on quitting behavior than on initiation. This may explain why, despite the older age of Cluster 6 participants, they began hookah use later in life but now exhibit a stronger intention to quit.

Building upon the unique characteristics of Cluster 6, it is noteworthy that Clusters 6 and 4 shared similar levels of perceived severity but differed in perceived susceptibility. Interestingly, this difference in perceived susceptibility appeared to have little impact on hookah use frequency of hookah use. Expanding the comparison to other clusters reveals that while Clusters 1, 2, and 4 exhibited comparable levels of perceived susceptibility, hookah use frequency declined as perceived severity increased. This trend is particularly evident when comparing Cluster 1, with the lowest perceived severity and the highest hookah use frequency (39.5 times per month), to Cluster 4, with the highest perceived severity and the lowest frequency (13.2 times per month). These findings suggest that perceived severity may play a more influential role in shaping hookah use behavior than perceived susceptibility.

The distinct characteristics of Cluster 4 provide valuable insights into the complexity of perceived risk and its impact on hookah use behavior. Despite exhibiting high perceived severity, individuals in this cluster exhibited low hookah use frequency and a low perceived addiction level. While they acknowlege the potential harm of hookah use, their low perceived susceptibility, high perceived severity, and lack of perceived addiction may stem from social norms and personal beliefs. This cluster’s underestimation of the likelihood of adverse health outcomes, coupled with their lack of intention to quit in the near future, suggests that perceived severity alone may insufficient to drive behavior change. The unique combination of low perceived susceptibility and high perceived severity in Cluster 4 underscores the intricate interplay between these factors, highlighting potential opportunities for targeted interventions that address both dimensions of perceived risk to encourage cessation.

In addition to Cluster 4, the distinct characteristics of Cluster 5 further highlight the complex interaction between perceived susceptibility and severity. Cluster 5’s combination of high perceived susceptibility and low perceived severity was linked to a high frequency of hookah use, suggesting that moderate perceived susceptibility alone may not be enough to drive behavior change. This observation supports the hypothesis that a combination of high perceived severity and moderate perceived susceptibility may be required to motivate individuals to modify their hookah use patterns. Furthermore, this result aligns with other theories proposing a multiplicative relationship between perceived susceptibility and perceived severity in shaping overall perceived risk46,60,61. Taken together, these insights underscore the importance of addressing both dimensions of perceived risk when designing targeted interventions to encourage hookah cessation.

Strengths and limitations

Cluster analysis, by identifying distinct groups based on specific combinations of perceived susceptibility and severity, offers valuable insights into the diversity of perceived risk among hookah users. If a simple summation of perceived risk had been used, Clusters 3 and 4, with distinct profiles, would have been incorrectly grouped, leading to misleading conclusions. Through cluster analysis, the average perceived risk scores for Clusters 1 to 6 were determined to be 2.52, 20.1, 27.7, 28.4, 37.1, and 46.2, respectively. These results highlight the importance of employing cluster analysis to capture the subtle variations in perceived risk among hookah users.

A notable strength of this study is the use of cluster analysis to identify distinct groups of hookah users based on their perceived susceptibility and severity. This method provided valuable insights into the diversity of perceived risk among hookah users, uncovering more nuanced variations than a simple summation of perceived risk could have captured. For example, Clusters 3 and 4, with their distinct profiles, would have been erroneously grouped under a simple summation method, potentially leading to misleading conclusions. In contrast, cluster analysis highlighted unique perceived risk profiles, with average scores for Clusters 1 to 6 calculated as being 2.52, 20.1, 27.7, 28.4, 37.1, and 46.2, respectively. These findings emphasize the importance of utilizing cluster analysis to accurately represent the diverse range of perceived risk among hookah users.

A limitation of this study is the relatively small sample size in Clusters 5 and 6, which may have restricted our ability to detect significant differences between these clusters and others. Although this constraint might have influenced the depth of insights derived gained from comparing these clusters, it is important to recognize that sample size limitations are a common challenge in cluster analysis. This challenge stems from the variability in the number of clusters generated by different clustering techniques57. Despite this limitation, our findings still provide valuable insights into the diverse profiles of perceived risk among hookah users.

The first potential limitation of this study is the use of a convenience sampling method, which may have introduced selection bias. Additionally, while we employed a questionnaire validated by previous researchers, its self-reported nature could still result in information bias. To explore the relationships between variables, we analyzed the effect of various covariates on attitudinal factors. Another limitation lies in the uncertainty surrounding the number of clusters identified in the analysis, as sample size limitations and variability in cluster numbers are inherent challenges in cluster analysis. Finally, the small sample sizes in Clusters 5 and 6 may have limited the depth of insights obtained.

Recommendation

To achieve a more comprehensive understanding of the determinants of perceived risk, future research should examine a wide range of factors. These factors could include psychological, social, and cultural dimensions that may shape an individual’s perception of susceptibility and severity.

Longitudinal studies would be valuable in clarifying the temporal relationship between perceived risk and actual behavior change. By investigating how perceptions evolve over time and their connection to behavioral changes, researchers can gain deeper insights into the role of perceived risk in health-related decision-making.

Incorporating additional constructs from the Health Belief Model (HBM), such as perceived benefits and barriers, into clustering methods could provide further insights into the factors that effectively drive behavior change. However, while integrating these additional HBM constructs in future clustering approaches may offer valuable findings, it is essential to recognize that larger sample sizes will likely be required to identify these factors with greater confidence.

Conclusion

Our study’s findings on the low levels of perceived addiction and susceptibility to cardiovascular diseases among hookah users underscore key factors contributing to the continued popularity of hookah smoking and the heightened risk of adverse health outcomes. Addressing these misconceptions and increasing awareness of the health risks associated with hookah use, particularly regarding cardiovascular diseases, could play a pivotal role in reducing hookah consumption and encouraging healthier behaviors. By leveraging the insights from our study, we can guide the development of targeted interventions and public health strategies to mitigate the harmful health effects linked to hookah smoking.