Perceived Risk of Cardiovascular Diseases in the Italian population: Insights from the INNOPREV trial

Perceived Risk of Cardiovascular Diseases in the Italian population: Insights from the INNOPREV trial | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Perceived Risk of Cardiovascular Diseases in the Italian population: Insights from the INNOPREV trial Chiara de Waure, Ilaria Valentini, Maddalena Arcelli, Francesca Volpi, and 27 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9385767/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 8 You are reading this latest preprint version Abstract Objective Cardiovascular disease (CVDs) is the leading cause of death globally. This study, within the Italian INNOPREV trial, explored cardiovascular risk perception in adults aged 40–69 years old at moderate-to-high risk of CVDs and its association with socio-demographic, behavioural, and clinical characteristics. Methods Cardiovascular risk perception was measured using the Health Beliefs Related to Cardiovascular Disease Scale (HBCVD; 25 items; four subscales: susceptibility, severity, benefits, barriers). Mean HBCVD scores were compared across socio-demographic variables (sex, age, marital status, education level, employment status) and risk factors (smoking status, BMI, family history of cardiovascular conditions, SCORE2, and Life Essential, 8 score–LE8). Non-parametric tests (Wilcoxon or Kruskal–Wallis, as appropriate) were used. Results were summarized using forest plots, reporting mean scores with 95% confidence intervals and p-values. Results A total of 1,019 participants (52.11% males; mean age: 55 years old), and 988 questionnaires were successfully collected and analyzed. The mean overall HBCVD score was 62.30 ± 6.15 (out of 100). Employment status was the only sociodemographic variable associated with HBCVD (p = 0.035), with higher scores among homemakers/unemployed and lower scores among retired participants. HBCVD was also associated with BMI class (p = 0.003), family history of hypercholesterolaemia (p = 0.023), and LE8 score (p = 0.006). Scores increased with higher BMI in people with a family history of hypercholesterolaemia, and with poorer cardiovascular health based on LE8 score. Conclusions Risk perception of CVD was overall moderate and varied by socio-demographic characteristics and risk factors. These findings might support the development of tailored preventive strategies to enhance risk awareness and promote healthy behaviours. Cardiovascular risk perception Health Belief Model HBCVD Scale Life’s Essential 8 Preventive behaviours Italy Cardiovascular prevention. Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Cardiovascular diseases (CVDs) remain the leading cause of death and disability across Europe, accounting over 1.68 million deaths in 2022 or 32.7% of all deaths in the WHO European Region [ 1 ]. In Italy, despite a significant decline since 1990 to nowadays in age-standardised prevalence (-12.7%), mortality rate (-53.8%), and disability-adjusted life years rate (-55.5%), CVDs continue to represent the primary cause of death, being responsible for 30.8% of overall mortality [ 3 ]. The burden of CVDs remains high, particularly due to population aging [ 4 ] and the persistence of modifiable risk factors, such as hypertension, unhealthy diet, obesity, and dyslipidaemia [ 5 – 7 ]. In addition to direct healthcare costs, CVDs are also associated with significant indirect costs due to decreased productivity as well as reduced quality of life [ 8 ]. More than 80% of the cardiovascular burden is attributable to known modifiable risk factors [ 2 ], many of which are unevenly distributed across socioeconomic groups [ 9 ]. Preventive strategies, including lifestyle changes (e.g., diet, physical activity, smoking cessation) [ 10 ] and medical interventions for at-risk individuals, have proven effective in reducing CVD incidence [ 11 ]. However, individuals’ health-related behaviours are not driven by clinical risk alone, but are influenced by their beliefs and perceptions about risk, illness, and prevention [ 12 ]. Risk perception is often assessed to better understand people’s thoughts about diseases and their attitudes toward preventive health behaviours. However, risk perception is a highly complex concept and the relationship between risk perception and behaviour change can be inconsistent [ 13 ]. Several factors may influence one’s perception of risk, including individual characteristics, perceptions of general health, knowledge of the illness or disease, risk factor status, family history of disease, perceived control, personal experience, and media exposure [ 13 ]. Individual risk perceptions may also vary based on current behaviours and behavioural intentions [ 14 ]. A bidirectional relationship exists between risk perception and behaviour change over time, such that individuals who engage in risky behaviours may perceive a higher risk, and risk perception may influence individuals to make behaviour changes, but once the protective behaviour is adopted, perception of risk is likely to decrease [ 15 ]. Consequently, underestimations of perceived risk can hinder the decision-making, underscoring the need for continuous assessment and reinforcement [ 15 ]. To explain and predict health behaviours, several models have been proposed. One of them is the Health Belief Model (HBM), a socio-psychological model that attempts to explain and predict health behaviours in terms of certain belief patterns and by focusing on the attitudes and beliefs of individuals [ 16 ]. Developed in the 1950s as part of an effort by social psychologists in the United States Public Health Service to explain the lack of public participation in health screening and prevention programmes, it has been adapted to explore a variety of long and short-term health behaviours, including cardiovascular risk behaviours [ 17 ]. The HBM addresses the individual’s perceptions of the threat posed by a health problem (susceptibility, severity), the benefits of avoiding the threat, and factors influencing the decision to act (barriers, cues to action, and self-efficacy). It states that perceptions of general health values, specific health beliefs related to the health problem and recommended health actions influence the likelihood of taking recommended health actions. This study aimed to assess how Italian individuals at moderate-to-high risk of CVDs perceive their risk using the Health Belief related to CVD (HBCVD) scale, and to explore how these perceptions vary according to socio-demographic characteristics, behavioural factors, and clinical characteristics. Methods 1.1. Inclusion and exclusion criteria The study embeds in the INNOPREV trial, which is a four-arm randomized controlled trial conducted in Italy [ 18 ]. This multicenter prospective study was conducted among individuals aged 40 to 69 years old without established CVDs, diabetes mellitus, or familial hypercholesterolemia. Participants were selected based on being at moderate-to-high cardiovascular risk, defined as a 10-year risk of cardiovascular events between 2.5% and 10%, as estimated using the SCORE2 risk charts [ 19 ]. 1.2. Study design and sample recruitment The study was carried out in three centres: Rome (Central Italy), Catania, and Palermo (Southern Italy). Recruitment was conducted in various healthcare settings, depending on the location: primary care ambulatories in Rome, Occupational Health Physicians and specialist physicians in Catania, and a vaccination centre in Palermo [ 18 ]. All eligible individuals accessing the identified recruitment settings were invited to participate and received detailed information both orally and through an informative leaflet. Those who gave consent were formally enrolled and received the planned interventions. The recruitment phase lasted 8 months and has been completed. Participants are being followed for a total period of 12 months. Upon enrolment (T0), participants underwent a comprehensive baseline assessment, including cardiovascular evaluation and structured questionnaires. 1.3. Data collection instrument and process Data collection at T0 was conducted through structured interviews and standardized clinical assessments and all data were recorded and managed electronically within REDCap [ 20 ]. The baseline data collection process included also a combination of validated self-administered questionnaires, including the HBCVD 25-item scale [ 17 ]. The latter was divided into four subscales: perceived susceptibility (5 items), perceived severity (5 items), perceived benefits (6 items), and perceived barriers (9 items). A four-point Likert scale (from 1 - Strongly Disagree to 4 - Strongly Agree) was used to collect information. Higher scores on the susceptibility dimension reflect a stronger perception of personal risk of developing CVD. Elevated severity scores indicate that participants view the condition as more serious. The benefit subscale captures the extent to which individuals believe in the efficacy of preventive behaviours, while the barriers subscale measures perceived obstacles to adopt such behaviours, with higher scores indicating greater perceived difficulty. Socio-demographic data (e.g., age, sex, education, area of residence) and lifestyle behaviours were evaluated using the Life Essential 8 score, (LE8) score [ 21 ], a validated tool developed by the American Heart Association to categorize individuals into favourable, intermediate, or unfavourable cardiovascular lifestyle profiles [ 22 ]. This tool incorporates eight domains: physical activity, Body Mass Index (BMI), blood pressure, diet, nicotine exposure, cholesterol levels, blood glucose, and sleep health. Each component is scored on a 0–100 scale, and scores are averaged (unweighted) to generate an overall cardiovascular health score, also ranging from 0 to 100. Higher LE8 scores are associated with a substantially lower risk of CVD incidence [ 23 ]. 1.4. Data management and statistical analysis Data cleaning procedures were implemented to oversee missing values, which were excluded from the relevant analyses. Normality of continuous variables was evaluated using the Shapiro–Wilk test; the distributions were bell-shaped with moderate skewness. Responses to the HBCVD scale were managed as follows. Each of the four subscales ( susceptibility, severity, benefits, barriers ) was scored by summing the responses to the items belonging to that specific subscale. The overall HBCVD score was calculated by summing the scores of all items included in the questionnaire, with higher scores indicating a higher level of cardiovascular risk perception. Participants were grouped into three age classes (40–49, 50–59, 60–69 years old) based on their reported age. BMI was categorized into three groups: <25 kg/m² (underweight/normal), 25–29.9 kg/m² (overweight), and ≥ 30 kg/m² (obesity) [ 24 – 26 ]. Family history of CVD and stroke was combined into a single variable. Participants were classified as “At least one yes” if they reported a positive family history for either CVD or stroke, “No” if they reported no family history for both conditions, and “Don’t know” otherwise. SCORE2 was summarized as mean ± standard deviation (SD). In addition, SCORE2 was categorized into tertile-based ranges using the 33rd and 66th percentile cut-offs (3.1 and 5.2): lower range (Score2 ≤ 3.1), mid range (3.1 5.2). Based on LE8 scores, cardiovascular health (CVH) was classified into three categories according to established thresholds [ 21 ]: low (0–49), moderate (50–79), and high (80–100). The statistical analyses aimed to evaluate associations between the different independent variables and the HBCVD score overall and for each HBCVD subscale. Non-parametric tests were used, specifically the Wilcoxon rank-sum test (for comparing two groups) and Kruskal-Wallis test (for comparing three or more groups). Responses marked as ‘Don’t know’ were excluded from these analyses to ensure the accuracy and reliability of the results. All statistical analyses were performed using RStudio (version 2025). The level of statistical significance was set at p < 0.05. Results Of the 1,019 participants enrolled (52.10% male), nearly half were aged 50–59 years old (46.71%), while 22.08% were 40–49 and 31.21% were 60–69 (Table 1 ). Most were married or partnered (71.84%), with 9.52% never married, and 16.58% widowed, divorced or separated. Table 1 Baseline Demographics and health and family history. Variable Category FemaleS n. (%) MaleS n. (%) Total n. (%) Sex Male / Female 488 (47.89%) 531 (52.11%) 1019 (100%) Age (years old) 40–49 85 (8.34%) 140 (13.74%) 225 (22.08%) 50–59 228 (22.37%) 248 (24.34%) 476 (46.71%) 60–69 175 (17.17%) 143 (14.03%) 318 (31.21%) Marital status Never married 56 (5.50%) 41 (4.02%) 97 (9.52%) Married or Partnered 319 (31.31%) 413 (40.53%) 732 (71.84%) Widowed/ Divorced/ Separated 102 (10.01%) 67 (6.58%) 169 (16.58%) Missing 11 (1.08%) 10 (0.98%) 21 (2.06%) Education level Elementary/ Intermediate 48 (4.71%) 35 (3.43%) 83 (8.15%) High School 196 (19.23%) 201 (19.73%) 397 (38.96%) College Degree or Postgraduate 235 (23.06%) 284 (27.87%) 519 (50.93%) Missing 9 (0.88%) 11 (1.08%) 20 (1.96%) Occupational status Employed 337 (33.07%) 421 (41.32%) 758 (74.39%) Homemaker/ Unemployed 71 (6.97%) 11 (1.08%) 82 (8.05%) Retired 44 (4.32%) 58 (5.69%) 102 (10.01%) Other 25 (2.45%) 27 (2.65%) 52 (5.10%) Missing 11 (1.08%) 14 (1.37%) 25 (2.45%) Smoking Never smoked 269 (26.40%) 275 (26.99%) 544 (53.39%) Ex-smoker 113 (11.09%) 153 (15.01%) 266 (26.10%) Current smoker 101 (9.91%) 96 (9.42%) 197 (19.33%) Missing 5 (0.49%) 7 (0.69%) 12 (1.18%) BMI Underweight/Normal 296 (29.05%) 179 (17.57%) 475 (46.61%) Overweight 135 (13.25%) 267 (26.20%) 402 (39.45%) Obese 57 (5.59%) 84 (8.24%) 141 (13.84%) Missing 0 (0.0%) 1 (0.10%) 1 (0.10%) Family history: CVD or STROKE At least one yes 205 (20.12%) 183 (17.96%) 388 (38.08%) No 271 (26.59%) 338 (33.17%) 609 (59.76%) Don't know 8 (0.79%) 6 (0.59%) 14 (1.37%) Missing 4 (0.39%) 4 (0.39%) 8 (0.79%) Family history: High cholesterol Yes 240 (23.55%) 210 (20.61%) 450 (44.16%) No 225 (22.08%) 282 (27.67%) 507 (49.75%) Don't know 19 (1.86%) 33 (3.24%) 52 (5.10%) Missing 4 (0.39%) 6 (0.59%) 10 (0.98%) Family history: Diabetes Yes 141 (13.84%) 150 (14.72%) 291 (28.56%) No 333 (32.68%) 357 (35.03%) 690 (67.71%) Don't know 8 (0.79%) 16 (1.57%) 24 (2.36%) Missing 6 (0.59%) 8 (0.79%) 14 (1.37%) Family history: Hypertension (parents) Yes 283 (27.7%) 272 (26.69%) 555 (54.47%) No 182 (17.86%) 225 (22.08%) 407 (39.94%) Don't know 18 (1.77%) 27 (2.65%) 45 (4.42%) Missing 5 (0.49%) 7 (0.69%) 12 (1.18%) Females Males Total SCORE2 (mean ± sd) 3.91 (± 1.67) 5.37 (± 2.28) 4.67 (± 2.14) SCORE2 Cluster n. (%) Lower range (Score2 ≤ 3.1) 224 (21.98%) 112 (10.99%) 336 (32.97% Mid-range (3.1 5.2) 89 (8.73%) 236 (23.16%) 325 (31.89%) Missing 9 (0.88%) 15 (1.47%) 24 (2.35%) Life's Essential 8 (mean ± sd) Female Male Total Diet 51.41 (± 13.11) 50.32 (± 13.24) 50.84 (± 13.18) Physical Active 55.34 ± 42.34 61.47 ± 40.47 58.53 ± 41.47 Nicotine 70.91 ± 40.13 72.09 ± 38.18 71.52 ± 39.11 Sleep health 81.3 ± 23.55 84.41 ± 20.4 82.92 ± 22.01 BMI 83.16 ± 24.43 73.37 ± 24.28 78.06 ± 24.83 Cholesterol level 61.19 ± 28.52 55.62 ± 25.93 58.29 ± 27.33 Blood Sugar 22.39 ± 20.79 26.77 ± 21.47 24.67 ± 21.25 Blood Pressure 86.12 ± 19.06 87.27 ± 18.65 86.72 ± 18.85 Mean LE8 (± SD) (mean ± sd) 63.91 ± 11.44 63.92 ± 10.45 63.92 ± 10.93 LE8 scores based CVH n. (%) Low (0–49) 57 (5.50%) 46 (4.44%) 103 (9.94%) Moderate (50–79) 383 (36.97%) 446 (43.05%) 829 (80.02%) High (80–100) 39 (3.76%) 29 (2.80%) 68 (6.56%) Missing 9 (0.87%) 10 (0.97%) 19 (1.64) Regarding education, over half had a college degree or higher (50.93%), 38.96% completed high school, and 8.15% had elementary or intermediate education. The majority were employed (74.39%), with 8.05% homemakers or unemployed, and 10.01% retired. More than half had never smoked (53.39%), while 26.10% were ex-smokers, and 19.33% current smokers. Based on BMI, 46.61% were underweight/normal, 39.45% overweight, and 13.84% obese. Family history revealed that 38.08% reported at least one first-degree relative with CVD or stroke, 44.16% with hypercholesterolemia, 28.56% with diabetes, and 54.47% with hypertension. The mean SCORE2 was 4.67 (± 2.14) overall. In the overall sample, the mean LE8 score was 63.92 (± 10.93) with virtually identical values in females and males. According to LE8 based CVH categories, 9.94% of participants were classified as low (0–49), 80.02% as moderate (50–79), and 6.56% as high (80–100). 2.1. HBCVD answers overview The total number of questionnaires collected was 988 out of 1,019 enrolled patients (96.96%). The overall HBCVD score was 62.30 (± 6.15) out of 100. Table S1 provides an overview of the distribution of responses with the mean for each item of the HBCVD subscale. Regarding susceptibility (maximum score = 20), the mean score was 10.90 and item means ranged from 1.98 to 2.42 on a 1–4 scale, indicating a generally low perceived likelihood of developing a CVD. Most participants disagreed with statements suggesting high personal vulnerability (“It is likely that I will suffer from a CVD…”, “My chances of suffering from a CVD… are great”). The only item with relatively higher endorsement concerned short-term worry (“I am concerned about the likelihood of having a CVD in the near future”). The severity subscale (maximum score = 20) yielded a mean of 11.30, reflecting a moderately high perception of the seriousness of CVD. The items with the highest means (“My whole life would change if I had a CVD”, “A CVD would have a very bad effect on my sex life”) indicate that participants perceive CVDs as conditions with substantial impact on quality of life. The benefits subscale (maximum score = 24) showed the highest overall endorsement, with a mean of 21.00. This reflects a strong and nearly unanimous belief in the benefits of healthy diet and exercise in reducing CVD risk and improving personal well-being. All items scored above 3.35 on average, with the strongest agreement observed for “When I eat healthy, I am doing something good for myself” and “When I exercise, I am doing something good for myself.” The barriers subscale (maximum score = 36) had a mean of 19.10 and displayed substantial variability across items. Several barriers were rated as minimal, including physical limitations (“It is painful for me to walk for more than 5 minutes”, M = 1.47) and financial constraints related to buying healthy food (M = 1.62). In contrast, more salient barriers were predominantly logistical or social in nature: limited access to exercise facilities/equipment (M = 2.91), lack of someone to exercise with (M = 2.67), and, to a lesser extent, insufficient time for exercise (M = 2.2). At the socio-demographic level, a significant association with the HBCVD score was observed only for employment status (p = 0.035) (Fig. 1). The highest mean score was found among homemakers/unemployed individuals (63.08), followed by employed (62.36) and other employment categories (62.00), whereas the lowest value was observed among retired participants (61.05). Figure 1. Overall HBCVD score by a) socio-demographic and b) CV risk factor variables. Regarding risk factors, significant differences emerged for BMI class (p = 0.003), cholesterol family history (p = 0.023), and LE8 score (p = 0.006). Specifically, the HBCVD score increased across BMI categories, being lowest in normal-weight individuals (61.62), intermediate in those overweight (62.49), and highest among the obese (63.69). Participants with a family history of hypercholesterolemia showed higher scores compared with those without such history (62.70 vs 61.77). Finally, an inverse gradient was observed for the LE8 based CVH, with higher HBCVD scores in the low category (63.94) and lower scores in the high group (60.81). 2.2. Perceived susceptibility Figure 2 displays the forest plots illustrating perceived susceptibility to CVDs across socio-demographic and risk factors variables. No significant association between socio-demographic characteristics and perceived susceptibility was found. On the contrary, several significant associations were shown in respect to risk factors. Current smokers reported a significant highest level of perceived susceptibility compared to ex-smokers, never smoker, and ex-smokers (M = 11.31, 10.87 and 10.71, respectively; p = 0.030). BMI class was strongly associated with susceptibility (p < 0.001), with participants classified as obese reporting a higher score (M = 11.71) compared to those with normal (M = 10.65) or overweight (M = 10.82) BMI. A similar trend was observed among individuals with a family history of CVD, who reported significantly higher perceived susceptibility (M = 11.19) than those without such history (M = 10.64) (p < 0.001). Figure 2. Perceived susceptibility to CVDs by a) socio-demographic and b) CV risk factor variables. Significant associations were also found for family history of hypertension (p < 0.001), whereas participants with a positive family history reported higher perceived susceptibility (M = 11.11) than those without (M = 10.41). A similar pattern was observed for family history of high cholesterol level (p < 0.001), with higher susceptibility among those with a positive family history (M = 11.22) compared with those without (M = 10.41). In contrast, no significant difference was found for diabetes family history. Finally, CVH as measured by the LE8 score showed a strong inverse relationship with perceived susceptibility (p < 0.001). Participants with a low CVH reported the highest susceptibility (M = 12.04), while those with a high CVH score reported the lowest (M = 10.34). 2.3. Perceived severity The analysis of the perceived severity subscale revealed significant differences across demographic variables, while no relevant associations were observed in relation to other socio-demographic characteristics and risk factors (Fig. 3). Figure 3. Perceived severity to CVDs by a) socio-demographic and b) CV risk factor variables. Females reported significantly higher perceived severity scores compared to males (M = 11.49 vs 11.10; p = 0.003). Age class was also significantly associated with severity perception (p < 0.001), with mean scores progressively increasing from younger (30–39 years old: M = 10.75) to older individuals (60–69 years old: M = 11.66). 2.4. Perceived benefits Perceived barriers showed noteworthy differences across socio-demographic characteristics but not in respect to risk factors. As shown in the top panel of Fig. 4, sex was a significant factor (p = 0.005), with males reporting higher perceived benefits (M = 21.25) than females (M = 20.74). Age class also showed a significant association (p = 0.003), with perceived benefits increasing slightly from older (60–69 years old: M = 20.49) to middle-aged groups (50–59 years old: M = 21.26). A strong association was observed with education level (p < 0.001), with participants having a college degree or postgraduate education reporting the highest perceived benefits (M = 21.37), while those with elementary/intermediate education having the lowest scores (M = 20.01). Figure 4. Perceived benefits to CVDs by a) socio-demographic and b) CV risk factor variables. Employment status was also significantly related to perceived benefits (p = 0.01). Employed individuals reported higher mean scores (M = 21.16) than those unemployed/homemakers (M = 20.29) or retired (M = 20.49). 2.5. Perceived barriers As shown in the forest plots (Fig. 5), perceived barriers to CVD prevention showed significant associations with a limited number of socio-demographic and risk factor variables. Figure 5. Perceived barriers to CVDs by a) socio-demographic and b) CV risk factor variables. Among socio-demographic variables, the only significant difference was observed for education level (p = 0.027): individuals with an elementary or intermediate education reported the highest perceived barriers (M = 19.43), compared to those with a high school diploma (M = 18.86) and those with a college degree or postgraduate education (M = 19.37). Regarding risk factor variables, significant differences in perceived barriers were observed across several variables. BMI class was associated with perceived barriers (p = 0.002): participants with obesity reported the highest scores (M = 19.76), compared to those overweight (M = 19.29) or underweight/normal weight (M = 18.75). A significant association emerged with the LE8 based CVH (p < 0.001). Individuals with a low CVH reported the highest perceived barriers (M = 19.82), followed by those with moderate CVH (M = 19.08), while participants with a high CVH score had the lowest (M = 18.03). Discussion This study represents the first Italian investigation exploring CVD risk perception among adults aged 40–69 years old, classified as moderate-to-high risk based on the SCORE2 algorithm but free from clinically diagnosed CVDs. Using the HBCVD we observed a moderate overall awareness and engagement in health beliefs related to CVDs. Furthermore, significant differences were found across socio-demographic and risk factors. Our findings showed that women demonstrated significantly higher perceived severity of CVDs than men. This finding aligns with previous research suggesting that women may exhibit greater health awareness and emotional responsiveness to disease threats [ 27 , 28 ]. However, women also reported lower perceived benefits from prevention, potentially indicating a lack of confidence on preventive measures. These findings are consistent with those reported in a systematic review by Betai et al. (2024) [ 29 ], which underscored significant gender-based differences in how individuals perceive cardiovascular risk and engage in preventive behaviours. An upward trend in perceived severity was observed with increasing age, supporting the hypothesis that older adults may develop heightened concern due to cumulative exposure to illness, either personally or through peers [ 30 , 31 ]. Despite this, age was not significantly associated with perceived susceptibility or benefits, suggesting that awareness of disease severity alone may not motivate behaviour change. This suggests that interventions for older adults should not solely rely on age-related vulnerability but focus on reinforcing personal perception and empowerment. This suggests that interventions for older adults should not solely rely on age-related vulnerability but focus on reinforcing personal perception and empowerment. In our sample higher education levels and employment status were both positively associated with perceived benefits of preventive behaviours. Participants with a college degree or higher scored significantly higher than those with lower education, in line with literature showing that health literacy and education are strong predictors of health engagement [ 32 – 34 ]. Similarly, employed individuals reported higher perceived benefits than unemployed or retired participants. This is consistent with prior findings that link employment with better access to health resources, social support, and preventive care [ 33 ]. In contrast, across the other scales, higher values were observed among unemployed participants; however, these differences did not reach statistical significance. Regarding risk factors, smokers and individuals with obesity reported higher perceived obstacles to adopting preventive measures. This pattern is aligned with previous research [ 35 ] which showed that individuals engaged in risky behaviours often recognize their vulnerability but simultaneously feel constrained, psychologically, socially, or practically, from making changes [ 35 ]. High perceived barriers in these groups may stem from addiction, failed past attempts at behaviour change, or limited access to supportive environments. All these findings suggest the need for behaviourally targeted strategies that address both motivation and external limitations. Perceived susceptibility was also significantly higher among participants with a family history of hypertension and hypercholesterolemia. Prior research showed that family history increases personal risk appraisal primarily when the condition is perceived as common, observable, and directly linked to modifiable cardiovascular risk factors [ 36 – 38 ]. Hypertension and hypercholesterolemia are typically managed through routine monitoring and long-term preventive treatment, making them more visible within families and more readily interpreted as signals of future cardiovascular risk. In contrast, diabetes, overt CVD, or stroke may be perceived as conditions affecting older relatives or as outcomes rather than modifiable precursors, which may attenuate their impact on perceived personal susceptibility [ 37 ]. Finally, participants in the LE8 high-value category showed higher mean overall HBCVD scores. This finding is consistent with prior evidence indicating that higher LE8 levels are associated with a lower risk of cardiovascular disease, with an inverse relationship between healthier behaviours/factors and CVD that remains robust even after adjustment for potential confounders [ 39 ]. The association between LE8 scores and perceptions of benefits and barriers reinforces the reciprocal relationship between objective health status and psychological readiness for change. Individuals with lower CVH based on LE8 scores reported both higher barriers and lower perceived benefits. These results confirm the bidirectional relationship between objective health and perceived ability to take preventive action [ 16 , 21 ]. This pattern suggests that those in worse health may feel discouraged or powerless to change their habits, while those in better health may be more confident and optimistic about prevention. This underscores the need for tailored interventions that not only address clinical risk but also actively work to reshape beliefs and build self-efficacy in vulnerable populations. A major strength of this study lies in the first application of the well-established conceptual framework of the HBM within the Italian healthcare context to investigate CVDs risk perception and provide a theoretical foundation for designing preventive interventions. Other strengths of this study include the multicentre design across two diverse Italian regions, the use of validated instruments (HBCVD Scale and LE8), and the comprehensive assessment of demographic, behavioural, and clinical variables. Furthermore, the large sample size enhances the generalizability of the findings to the Italian population. However, this study has also several limitations. First, although participants were recruited as part of a randomized controlled trial, the present analysis is based on baseline cross-sectional data collected prior to randomization. As such, causality cannot be inferred between cardiovascular risk perception and participants’ characteristics. Second, data were self-reported, introducing potential for recall bias and social desirability bias, particularly in relation to sensitive behaviours such as smoking, diet, and physical activity. Third, the sample was recruited in healthcare settings, which may overrepresent individuals who are more health-conscious or more engaged with the health system. This may limit the generalizability of findings to community-dwelling adults. Finally, the HBM, while useful for capturing individual cognitive constructs, does not account for broader contextual, emotional, or structural determinants of behaviour, such as health system barriers, social support, or cultural norms. Future research should consider longitudinal designs, and mixed method approaches to better understand causal relationships and contextual influences on cardiovascular risk perception and preventive behaviour. Overall, our results emphasize the need for multilevel interventions that consider demographic, behavioural, and clinical profiles to tailor cardiovascular risk awareness initiatives. Public health programs should move beyond standardized approaches and instead adopt differentiated strategies based on individual characteristics. Conclusions This study is among the first ones to investigate cardiovascular risk perception using a validated theoretical framework within in a population of Italian adults aged 40–69 years, free from overt disease but identified as clinically at moderate-to-high risk. Overall, participants reported a mean overall score of 62.30 (± 6.15) out of 100. Mean subscale scores provide a quantitative description of participants’ perceptions across the measured domains and can inform the development of interventions targeting cardiovascular prevention and lifestyle behaviours. Health beliefs varied meaningfully across both socio-demographic characteristics and risk factors. More in depth, differences by sex, age, education, and employment status, indicate that social determinants may shape how individuals interpret CVD threat and value preventive actions. At the same time, behavioural and clinical risk factors, including smoking, BMI class, family history, and LE8, were associated with specific belief domains: most consistently with perceived susceptibility and perceived barriers, and to a lesser extent with perceived severity and perceived benefits. This domain-specific pattern indicates that, even among adults clinically classified as at moderate-to-high risk, risk perception does not simply mirror objective risk: individuals with different risk profiles differ in how they appraise personal vulnerability and the practical constraints to prevention. Importantly, subjective perceptions are not always aligned with cardiovascular health based on well-known risk factors, underscoring the limitations of prevention strategies based solely on clinical risk stratification. These results call for more comprehensive preventive interventions that can integrate psychosocial dimensions of risk perception to effectively engage at-risk individuals and reduce the burden of CVDs. Abbreviations Abbreviation Full Term AHA American Heart Association BMI Body Mass Index CVD / CVDs Cardiovascular Disease / Cardiovascular Diseases CVH Cardiovascular Health DALY Disability-Adjusted Life Years HBM Health Belief Model HBCVD Health Beliefs Related to Cardiovascular Disease Scale INNOPREV INNOvative personalized cardiovascular disease PREVention LE8 Life's Essential 8 NCT National Clinical Trial PNRR Piano Nazionale di Ripresa e Resilienza RCT Randomized Controlled Trial REDCap Research Electronic Data Capture SCORE2 Systematic COronary Risk Evaluation 2 SD Standard Deviation WHO World Health Organization Declarations Acknowledgements For the INNOPREV Unit in Perugia, we gratefully acknowledge the contribution of the medical of the Section of Hygiene of University of Perugia who participated in the Unit’s activities: Dr. Pier Luigi Russo, Dr. Lorenzo Conciarelli and Dr. Marta Caminiti. For the INNOPREV Unit in Rome, we gratefully acknowledge the contribution of the study coordinator, Dr. Giulia Antonini, and of the physicians who participated in the study by recruiting participants, Dr. Daniela Pedicino, Dr. Alessia D'Aiello, Dr. Antonio De Vita, Dr. Lorenzo Genuardi, Dr. Eleonora Santucci, Dr. Aureliano Ruggio, Dr. Simone Filomia, Dr. Maria Chiara Grimaldi, and Dr. Eugenia De Marco. We also sincerely thank the medical doctors of the Section of Hygiene of Università Cattolica del Sacro Cuore who participated in the Unit’s activities: Dr. Angelo Maria Pezzullo, Dr. Sara Farina, Dr. Alessandra Maio, Dr. Martina Porcelli, Dr. Matteo Di Pumpo, Dr. Diego Tona. For the INNOPREV Unit in Catania, we sincerely thank the physicians who participated in the study by recruiting participants and by contributing to the organization of the Rome event, the production of the project video, and the dissemination event held in Catania: Dr. Salvatore Rubulotta, Dr. Francesco Laudicina, Dr. Fabrizio Rapisarda, Dr. Domenico Arcoria (Clinica Arcoria), Dr. Alessandra Lussi, Prof. Venerando Rapisarda, Dr. Vito Borzì, Dr. Salvatore Bellia, and Dr. Leonardo Serafino. We also acknowledge the following physicians for their valuable collaboration in participant recruitment: Dr. Nuccia Spada, Dr. Gaetano Mannino, Dr. Annalisa Vetri, Dr. Grazia Gambera, Dr. Sabrina Polidoro, Dr. Andrea Barbagallo, Dr. Giacomo Pampallona, Dr. Federica Costanzo, Dr. Giorgio Pulvirenti, Dr. Stella Gangi, and Dr. Marco Messina. For the INNOPREV Unit in Palermo, we gratefully acknowledge the contribution of the teams from the Dipartimento PROMISE, Università degli Studi di Palermo, and the UOC Epidemiologia Clinica, Azienda Ospedaliera Universitaria Policlinico (AOUP) di Palermo: Santo Fruscione, Claudio Tripodo, Fabio Tramuto, Valeria Guzzetta, Sabina Paolizzo, Giorgio Grazie, Katia Spinelli, Andrea Oddo, Luca Sparacino, Andrea Guarcello, Martina Mormino, Serena Ragusa, Davide Costanza, Alessandra Savatteri, Salvatore Pipitone, Dafne Riina, Tommaso Mancuso, Chiara Norrito, Miriam Belluzzo, Maria Chiara Lo Porto, Veronica Messina, Anna Maria Ciaccio, Marco Mazzola, Martina Profita, Mariagiovanna Cuffaro, Andrea Salvo, Antonino Marchese, Rachele Malfitano, Lavinia Leone, Mariarita Bona, Filippo Vutano, Rosalia Tambuzzo, and Jessica Burzilleri. Finally, we are deeply grateful to all study participants whose involvement made this research possible. Declaration of financial/other relationships The authors declare that financial support was received for the research. This study was conducted as part of the project INNOvative personalized cardiovascular disease PREVention in high-risk adults: protocol of a randomized controlled trial, funded by the Italian Ministry of Health (contract number: PNRR-MAD-2022-1237579. Conflict of interests None to declare. Author contributions All authors contributed to the study design. C.d.W., I.V., M.A., and F.V. drafted the main manuscript text. I.V. performed the data analysis and prepared the figures. All authors contributed to the interpretation of the results, critically reviewed the manuscript, and approved the final version for submission. Ethics approval This trial was approved by the Ethics Committee of the Fondazione Policlinico Universitario Agostino Gemelli (approval number 5506). Furthermore, the trial has been approved by the Local Ethics Committee Catania 2 with the protocol number: 149 C.E. (101/CECT2) and by the Local Ethics Committee Palermo 1 with the approval number CE 150109. All procedures adhered to the Declaration of Helsinki and relevant ethical standards. This trial has been registered on ClinicaTrials.gov, with the identifier: NCT05883878. Patient consent The participants signed an informed consent form. All information taken from the subjects was coded and kept confidential. Consent for publication Not applicable. Data availability statement The data are available from the corresponding author on reasonable request. References Eurostat. Causes of death - deaths by country of residence and occurrence. 2025. Cortesi PA, Fornari C, Madotto F, et al. Trends in cardiovascular diseases burden and vascular risk factors in Italy: The Global Burden of Disease study 1990–2017. Eur J Prev Cardiol. 2021;28:385–96. Fondazione Ambrosetti. Meridiano Sanità. 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2","display":"","copyAsset":false,"role":"figure","size":1759905,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003ePerceived susceptibility to CVDs by a) socio-demographic and b) CV risk factor variables.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-9385767/v1/84dedaf6f29fc44b86c1e4d6.png"},{"id":107659784,"identity":"3ff93bf5-b29c-4b83-a585-4b3645a0a2ff","added_by":"auto","created_at":"2026-04-23 16:50:54","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1726304,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003ePerceived severity to CVDs by a) socio-demographic and b) CV risk factor variables.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-9385767/v1/259d88c7cfcb82d84e20043b.png"},{"id":107707415,"identity":"afa220bd-6dbf-43cb-9d0a-65444cb4b2e7","added_by":"auto","created_at":"2026-04-24 09:20:14","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1795193,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003ePerceived benefits to CVDs by a) socio-demographic and b) CV risk factor variables.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-9385767/v1/cff838d8d74f8677959bb461.png"},{"id":107659783,"identity":"ee974d60-7032-489e-bf06-db0980657403","added_by":"auto","created_at":"2026-04-23 16:50:54","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1796643,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003ePerceived barriers to CVDs by a) socio-demographic and b) CV risk factor variables.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-9385767/v1/c75e76d447e94e036ea041ec.png"},{"id":107709288,"identity":"ff43dd21-0cee-4c77-9785-94f3afc33710","added_by":"auto","created_at":"2026-04-24 09:35:21","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":8336580,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9385767/v1/085a2e51-817c-41ca-afd7-5ceab420f879.pdf"},{"id":107659779,"identity":"58a4619a-ebae-487f-89a0-4980d638faea","added_by":"auto","created_at":"2026-04-23 16:50:54","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":21017,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementarymaterialsTableS1.docx","url":"https://assets-eu.researchsquare.com/files/rs-9385767/v1/5f6dbb4aa730706e256fbe9d.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Perceived Risk of Cardiovascular Diseases in the Italian population: Insights from the INNOPREV trial","fulltext":[{"header":"Introduction","content":"\u003cp\u003eCardiovascular diseases (CVDs) remain the leading cause of death and disability across Europe, accounting over 1.68\u0026nbsp;million deaths in 2022 or 32.7% of all deaths in the WHO European Region [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. In Italy, despite a significant decline since 1990 to nowadays in age-standardised prevalence (-12.7%), mortality rate (-53.8%), and disability-adjusted life years rate (-55.5%), CVDs continue to represent the primary cause of death, being responsible for 30.8% of overall mortality [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. The burden of CVDs remains high, particularly due to population aging [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e] and the persistence of modifiable risk factors, such as hypertension, unhealthy diet, obesity, and dyslipidaemia [\u003cspan additionalcitationids=\"CR6\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. In addition to direct healthcare costs, CVDs are also associated with significant indirect costs due to decreased productivity as well as reduced quality of life [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eMore than 80% of the cardiovascular burden is attributable to known modifiable risk factors [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e], many of which are unevenly distributed across socioeconomic groups [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Preventive strategies, including lifestyle changes (e.g., diet, physical activity, smoking cessation) [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e] and medical interventions for at-risk individuals, have proven effective in reducing CVD incidence [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. However, individuals\u0026rsquo; health-related behaviours are not driven by clinical risk alone, but are influenced by their beliefs and perceptions about risk, illness, and prevention [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eRisk perception is often assessed to better understand people\u0026rsquo;s thoughts about diseases and their attitudes toward preventive health behaviours. However, risk perception is a highly complex concept and the relationship between risk perception and behaviour change can be inconsistent [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Several factors may influence one\u0026rsquo;s perception of risk, including individual characteristics, perceptions of general health, knowledge of the illness or disease, risk factor status, family history of disease, perceived control, personal experience, and media exposure [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Individual risk perceptions may also vary based on current behaviours and behavioural intentions [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. A bidirectional relationship exists between risk perception and behaviour change over time, such that individuals who engage in risky behaviours may perceive a higher risk, and risk perception may influence individuals to make behaviour changes, but once the protective behaviour is adopted, perception of risk is likely to decrease [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Consequently, underestimations of perceived risk can hinder the decision-making, underscoring the need for continuous assessment and reinforcement [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTo explain and predict health behaviours, several models have been proposed. One of them is the Health Belief Model (HBM), a socio-psychological model that attempts to explain and predict health behaviours in terms of certain belief patterns and by focusing on the attitudes and beliefs of individuals [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Developed in the 1950s as part of an effort by social psychologists in the United States Public Health Service to explain the lack of public participation in health screening and prevention programmes, it has been adapted to explore a variety of long and short-term health behaviours, including cardiovascular risk behaviours [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. The HBM addresses the individual\u0026rsquo;s perceptions of the threat posed by a health problem (susceptibility, severity), the benefits of avoiding the threat, and factors influencing the decision to act (barriers, cues to action, and self-efficacy). It states that perceptions of general health values, specific health beliefs related to the health problem and recommended health actions influence the likelihood of taking recommended health actions.\u003c/p\u003e \u003cp\u003eThis study aimed to assess how Italian individuals at moderate-to-high risk of CVDs perceive their risk using the Health Belief related to CVD (HBCVD) scale, and to explore how these perceptions vary according to socio-demographic characteristics, behavioural factors, and clinical characteristics.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e1.1. Inclusion and exclusion criteria\u003c/h2\u003e \u003cp\u003eThe study embeds in the INNOPREV trial, which is a four-arm randomized controlled trial conducted in Italy [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. This multicenter prospective study was conducted among individuals aged 40 to 69 years old without established CVDs, diabetes mellitus, or familial hypercholesterolemia. Participants were selected based on being at moderate-to-high cardiovascular risk, defined as a 10-year risk of cardiovascular events between 2.5% and 10%, as estimated using the SCORE2 risk charts [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e1.2. Study design and sample recruitment\u003c/h2\u003e \u003cp\u003eThe study was carried out in three centres: Rome (Central Italy), Catania, and Palermo (Southern Italy). Recruitment was conducted in various healthcare settings, depending on the location: primary care ambulatories in Rome, Occupational Health Physicians and specialist physicians in Catania, and a vaccination centre in Palermo [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAll eligible individuals accessing the identified recruitment settings were invited to participate and received detailed information both orally and through an informative leaflet. Those who gave consent were formally enrolled and received the planned interventions. The recruitment phase lasted 8 months and has been completed. Participants are being followed for a total period of 12 months.\u003c/p\u003e \u003cp\u003eUpon enrolment (T0), participants underwent a comprehensive baseline assessment, including cardiovascular evaluation and structured questionnaires.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e1.3. Data collection instrument and process\u003c/h2\u003e \u003cp\u003eData collection at T0 was conducted through structured interviews and standardized clinical assessments and all data were recorded and managed electronically within REDCap [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. The baseline data collection process included also a combination of validated self-administered questionnaires, including the HBCVD 25-item scale [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. The latter was divided into four subscales: \u003cem\u003eperceived susceptibility\u003c/em\u003e (5 items), \u003cem\u003eperceived severity\u003c/em\u003e (5 items), \u003cem\u003eperceived benefits\u003c/em\u003e (6 items), and \u003cem\u003eperceived barriers\u003c/em\u003e (9 items). A four-point Likert scale (from 1 - Strongly Disagree to 4 - Strongly Agree) was used to collect information. Higher scores on the susceptibility dimension reflect a stronger perception of personal risk of developing CVD. Elevated severity scores indicate that participants view the condition as more serious. The benefit subscale captures the extent to which individuals believe in the efficacy of preventive behaviours, while the barriers subscale measures perceived obstacles to adopt such behaviours, with higher scores indicating greater perceived difficulty.\u003c/p\u003e \u003cp\u003eSocio-demographic data (e.g., age, sex, education, area of residence) and lifestyle behaviours were evaluated using the Life Essential 8 score, (LE8) score [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], a validated tool developed by the American Heart Association to categorize individuals into favourable, intermediate, or unfavourable cardiovascular lifestyle profiles [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. This tool incorporates eight domains: physical activity, Body Mass Index (BMI), blood pressure, diet, nicotine exposure, cholesterol levels, blood glucose, and sleep health. Each component is scored on a 0\u0026ndash;100 scale, and scores are averaged (unweighted) to generate an overall cardiovascular health score, also ranging from 0 to 100. Higher LE8 scores are associated with a substantially lower risk of CVD incidence [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e1.4. Data management and statistical analysis\u003c/h2\u003e \u003cp\u003eData cleaning procedures were implemented to oversee missing values, which were excluded from the relevant analyses. Normality of continuous variables was evaluated using the Shapiro\u0026ndash;Wilk test; the distributions were bell-shaped with moderate skewness.\u003c/p\u003e \u003cp\u003eResponses to the HBCVD scale were managed as follows. Each of the four subscales (\u003cem\u003esusceptibility, severity, benefits, barriers\u003c/em\u003e) was scored by summing the responses to the items belonging to that specific subscale. The overall HBCVD score was calculated by summing the scores of all items included in the questionnaire, with higher scores indicating a higher level of cardiovascular risk perception. Participants were grouped into three age classes (40\u0026ndash;49, 50\u0026ndash;59, 60\u0026ndash;69 years old) based on their reported age. BMI was categorized into three groups: \u0026lt;25 kg/m\u0026sup2; (underweight/normal), 25\u0026ndash;29.9 kg/m\u0026sup2; (overweight), and \u0026ge;\u0026thinsp;30 kg/m\u0026sup2; (obesity) [\u003cspan additionalcitationids=\"CR25\" citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Family history of CVD and stroke was combined into a single variable. Participants were classified as \u0026ldquo;At least one yes\u0026rdquo; if they reported a positive family history for either CVD or stroke, \u0026ldquo;No\u0026rdquo; if they reported no family history for both conditions, and \u0026ldquo;Don\u0026rsquo;t know\u0026rdquo; otherwise.\u003c/p\u003e \u003cp\u003eSCORE2 was summarized as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD). In addition, SCORE2 was categorized into tertile-based ranges using the 33rd and 66th percentile cut-offs (3.1 and 5.2): lower range (Score2\u0026thinsp;\u0026le;\u0026thinsp;3.1), mid range (3.1\u0026thinsp;\u0026lt;\u0026thinsp;Score2\u0026thinsp;\u0026le;\u0026thinsp;5.2), and above range (Score2\u0026thinsp;\u0026gt;\u0026thinsp;5.2).\u003c/p\u003e \u003cp\u003eBased on LE8 scores, cardiovascular health (CVH) was classified into three categories according to established thresholds [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]: low (0\u0026ndash;49), moderate (50\u0026ndash;79), and high (80\u0026ndash;100).\u003c/p\u003e \u003cp\u003eThe statistical analyses aimed to evaluate associations between the different independent variables and the HBCVD score overall and for each HBCVD subscale. Non-parametric tests were used, specifically the Wilcoxon rank-sum test (for comparing two groups) and Kruskal-Wallis test (for comparing three or more groups). Responses marked as \u0026lsquo;Don\u0026rsquo;t know\u0026rsquo; were excluded from these analyses to ensure the accuracy and reliability of the results.\u003c/p\u003e \u003cp\u003eAll statistical analyses were performed using RStudio (version 2025). The level of statistical significance was set at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eOf the 1,019 participants enrolled (52.10% male), nearly half were aged 50\u0026ndash;59 years old (46.71%), while 22.08% were 40\u0026ndash;49 and 31.21% were 60\u0026ndash;69 (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Most were married or partnered (71.84%), with 9.52% never married, and 16.58% widowed, divorced or separated.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline Demographics and health and family history.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCategory\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFemaleS\u003c/p\u003e \u003cp\u003en. (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMaleS\u003c/p\u003e \u003cp\u003en. (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003cp\u003en. (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSex\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale / Female\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e488 (47.89%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e531 (52.11%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1019 (100%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge (years old)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e40\u0026ndash;49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e85 (8.34%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e140 (13.74%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e225 (22.08%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e50\u0026ndash;59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e228 (22.37%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e248 (24.34%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e476 (46.71%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e60\u0026ndash;69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e175 (17.17%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e143 (14.03%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e318 (31.21%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003eMarital status\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNever married\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e56 (5.50%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e41 (4.02%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e97 (9.52%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMarried or Partnered\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e319 (31.31%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e413 (40.53%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e732 (71.84%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWidowed/ Divorced/ Separated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e102 (10.01%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e67 (6.58%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e169 (16.58%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMissing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11 (1.08%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10 (0.98%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e21 (2.06%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003eEducation level\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eElementary/ Intermediate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e48 (4.71%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e35 (3.43%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e83 (8.15%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh School\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e196 (19.23%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e201 (19.73%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e397 (38.96%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCollege Degree or Postgraduate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e235 (23.06%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e284 (27.87%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e519 (50.93%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMissing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9 (0.88%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11 (1.08%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e20 (1.96%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e\u003cb\u003eOccupational status\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEmployed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e337 (33.07%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e421 (41.32%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e758 (74.39%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHomemaker/ Unemployed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e71 (6.97%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11 (1.08%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e82 (8.05%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRetired\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e44 (4.32%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e58 (5.69%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e102 (10.01%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25 (2.45%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e27 (2.65%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e52 (5.10%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMissing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11 (1.08%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14 (1.37%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e25 (2.45%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003eSmoking\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNever smoked\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e269 (26.40%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e275 (26.99%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e544 (53.39%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEx-smoker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e113 (11.09%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e153 (15.01%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e266 (26.10%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCurrent smoker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e101 (9.91%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e96 (9.42%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e197 (19.33%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMissing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5 (0.49%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7 (0.69%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12 (1.18%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003eBMI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnderweight/Normal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e296 (29.05%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e179 (17.57%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e475 (46.61%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOverweight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e135 (13.25%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e267 (26.20%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e402 (39.45%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eObese\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e57 (5.59%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e84 (8.24%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e141 (13.84%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMissing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (0.10%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1 (0.10%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003eFamily history: CVD or STROKE\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAt least one yes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e205 (20.12%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e183 (17.96%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e388 (38.08%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e271 (26.59%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e338 (33.17%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e609 (59.76%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDon't know\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8 (0.79%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6 (0.59%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14 (1.37%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMissing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 (0.39%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4 (0.39%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8 (0.79%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003eFamily history: High cholesterol\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e240 (23.55%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e210 (20.61%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e450 (44.16%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e225 (22.08%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e282 (27.67%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e507 (49.75%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDon't know\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19 (1.86%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e33 (3.24%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e52 (5.10%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMissing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 (0.39%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6 (0.59%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10 (0.98%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003eFamily history: Diabetes\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e141 (13.84%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e150 (14.72%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e291 (28.56%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e333 (32.68%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e357 (35.03%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e690 (67.71%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDon't know\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8 (0.79%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16 (1.57%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e24 (2.36%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMissing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6 (0.59%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8 (0.79%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14 (1.37%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003eFamily history: Hypertension (parents)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e283 (27.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e272 (26.69%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e555 (54.47%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e182 (17.86%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e225 (22.08%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e407 (39.94%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDon't know\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18 (1.77%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e27 (2.65%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e45 (4.42%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMissing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5 (0.49%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7 (0.69%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12 (1.18%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eFemales\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eMales\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSCORE2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(mean\u0026thinsp;\u0026plusmn;\u0026thinsp;sd)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.91 (\u0026plusmn;\u0026thinsp;1.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.37 (\u0026plusmn;\u0026thinsp;2.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.67 (\u0026plusmn;\u0026thinsp;2.14)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSCORE2 Cluster\u003c/b\u003e\u003c/p\u003e \u003cp\u003en. (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLower range (Score2\u0026thinsp;\u0026le;\u0026thinsp;3.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e224 (21.98%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e112 (10.99%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e336 (32.97%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMid-range (3.1\u0026thinsp;\u0026lt;\u0026thinsp;Score2\u0026thinsp;\u0026le;\u0026thinsp;5.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e166 (16.29%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e168 (16.49%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e334 (32.78%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAbove range (Score2\u0026thinsp;\u0026gt;\u0026thinsp;5.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e89 (8.73%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e236 (23.16%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e325 (31.89%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMissing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9\u003c/p\u003e \u003cp\u003e(0.88%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15 (1.47%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e24 (2.35%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLife's Essential 8\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(mean\u0026thinsp;\u0026plusmn;\u0026thinsp;sd)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eFemale\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eMale\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDiet\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e51.41 (\u0026plusmn;\u0026thinsp;13.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e50.32 (\u0026plusmn;\u0026thinsp;13.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e50.84 (\u0026plusmn;\u0026thinsp;13.18)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePhysical Active\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e55.34\u0026thinsp;\u0026plusmn;\u0026thinsp;42.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e61.47\u0026thinsp;\u0026plusmn;\u0026thinsp;40.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e58.53\u0026thinsp;\u0026plusmn;\u0026thinsp;41.47\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNicotine\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e70.91\u0026thinsp;\u0026plusmn;\u0026thinsp;40.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e72.09\u0026thinsp;\u0026plusmn;\u0026thinsp;38.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e71.52\u0026thinsp;\u0026plusmn;\u0026thinsp;39.11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSleep health\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e81.3\u0026thinsp;\u0026plusmn;\u0026thinsp;23.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e84.41\u0026thinsp;\u0026plusmn;\u0026thinsp;20.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e82.92\u0026thinsp;\u0026plusmn;\u0026thinsp;22.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBMI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e83.16\u0026thinsp;\u0026plusmn;\u0026thinsp;24.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e73.37\u0026thinsp;\u0026plusmn;\u0026thinsp;24.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e78.06\u0026thinsp;\u0026plusmn;\u0026thinsp;24.83\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCholesterol level\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e61.19\u0026thinsp;\u0026plusmn;\u0026thinsp;28.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e55.62\u0026thinsp;\u0026plusmn;\u0026thinsp;25.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e58.29\u0026thinsp;\u0026plusmn;\u0026thinsp;27.33\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBlood Sugar\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22.39\u0026thinsp;\u0026plusmn;\u0026thinsp;20.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e26.77\u0026thinsp;\u0026plusmn;\u0026thinsp;21.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e24.67\u0026thinsp;\u0026plusmn;\u0026thinsp;21.25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBlood Pressure\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e86.12\u0026thinsp;\u0026plusmn;\u0026thinsp;19.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e87.27\u0026thinsp;\u0026plusmn;\u0026thinsp;18.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e86.72\u0026thinsp;\u0026plusmn;\u0026thinsp;18.85\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMean LE8 (\u0026plusmn;\u0026thinsp;SD)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(mean\u0026thinsp;\u0026plusmn;\u0026thinsp;sd)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e63.91\u0026thinsp;\u0026plusmn;\u0026thinsp;11.44\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e63.92\u0026thinsp;\u0026plusmn;\u0026thinsp;10.45\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e63.92\u0026thinsp;\u0026plusmn;\u0026thinsp;10.93\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLE8 scores based CVH\u003c/b\u003e\u003c/p\u003e \u003cp\u003en. (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLow (0\u0026ndash;49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e57\u003c/p\u003e \u003cp\u003e(5.50%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e46\u003c/p\u003e \u003cp\u003e(4.44%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e103\u003c/p\u003e \u003cp\u003e(9.94%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModerate (50\u0026ndash;79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e383\u003c/p\u003e \u003cp\u003e(36.97%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e446\u003c/p\u003e \u003cp\u003e(43.05%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e829\u003c/p\u003e \u003cp\u003e(80.02%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh (80\u0026ndash;100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e39\u003c/p\u003e \u003cp\u003e(3.76%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e29\u003c/p\u003e \u003cp\u003e(2.80%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e68\u003c/p\u003e \u003cp\u003e(6.56%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMissing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9\u003c/p\u003e \u003cp\u003e(0.87%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10\u003c/p\u003e \u003cp\u003e(0.97%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e19\u003c/p\u003e \u003cp\u003e(1.64)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eRegarding education, over half had a college degree or higher (50.93%), 38.96% completed high school, and 8.15% had elementary or intermediate education. The majority were employed (74.39%), with 8.05% homemakers or unemployed, and 10.01% retired.\u003c/p\u003e \u003cp\u003eMore than half had never smoked (53.39%), while 26.10% were ex-smokers, and 19.33% current smokers. Based on BMI, 46.61% were underweight/normal, 39.45% overweight, and 13.84% obese.\u003c/p\u003e \u003cp\u003eFamily history revealed that 38.08% reported at least one first-degree relative with CVD or stroke, 44.16% with hypercholesterolemia, 28.56% with diabetes, and 54.47% with hypertension.\u003c/p\u003e \u003cp\u003eThe mean SCORE2 was 4.67 (\u0026plusmn;\u0026thinsp;2.14) overall. In the overall sample, the mean LE8 score was 63.92 (\u0026plusmn;\u0026thinsp;10.93) with virtually identical values in females and males. According to LE8 based CVH categories, 9.94% of participants were classified as low (0\u0026ndash;49), 80.02% as moderate (50\u0026ndash;79), and 6.56% as high (80\u0026ndash;100).\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.1. HBCVD answers overview\u003c/h2\u003e \u003cp\u003eThe total number of questionnaires collected was 988 out of 1,019 enrolled patients (96.96%). The overall HBCVD score was 62.30 (\u0026plusmn;\u0026thinsp;6.15) out of 100. Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e provides an overview of the distribution of responses with the mean for each item of the HBCVD subscale.\u003c/p\u003e \u003cp\u003eRegarding susceptibility (maximum score\u0026thinsp;=\u0026thinsp;20), the mean score was 10.90 and item means ranged from 1.98 to 2.42 on a 1\u0026ndash;4 scale, indicating a generally low perceived likelihood of developing a CVD. Most participants disagreed with statements suggesting high personal vulnerability (\u0026ldquo;It is likely that I will suffer from a CVD\u0026hellip;\u0026rdquo;, \u0026ldquo;My chances of suffering from a CVD\u0026hellip; are great\u0026rdquo;). The only item with relatively higher endorsement concerned short-term worry (\u0026ldquo;I am concerned about the likelihood of having a CVD in the near future\u0026rdquo;).\u003c/p\u003e \u003cp\u003eThe severity subscale (maximum score\u0026thinsp;=\u0026thinsp;20) yielded a mean of 11.30, reflecting a moderately high perception of the seriousness of CVD. The items with the highest means (\u0026ldquo;My whole life would change if I had a CVD\u0026rdquo;, \u0026ldquo;A CVD would have a very bad effect on my sex life\u0026rdquo;) indicate that participants perceive CVDs as conditions with substantial impact on quality of life.\u003c/p\u003e \u003cp\u003eThe benefits subscale (maximum score\u0026thinsp;=\u0026thinsp;24) showed the highest overall endorsement, with a mean of 21.00. This reflects a strong and nearly unanimous belief in the benefits of healthy diet and exercise in reducing CVD risk and improving personal well-being. All items scored above 3.35 on average, with the strongest agreement observed for \u0026ldquo;When I eat healthy, I am doing something good for myself\u0026rdquo; and \u0026ldquo;When I exercise, I am doing something good for myself.\u0026rdquo;\u003c/p\u003e \u003cp\u003eThe barriers subscale (maximum score\u0026thinsp;=\u0026thinsp;36) had a mean of 19.10 and displayed substantial variability across items. Several barriers were rated as minimal, including physical limitations (\u0026ldquo;It is painful for me to walk for more than 5 minutes\u0026rdquo;, M\u0026thinsp;=\u0026thinsp;1.47) and financial constraints related to buying healthy food (M\u0026thinsp;=\u0026thinsp;1.62). In contrast, more salient barriers were predominantly logistical or social in nature: limited access to exercise facilities/equipment (M\u0026thinsp;=\u0026thinsp;2.91), lack of someone to exercise with (M\u0026thinsp;=\u0026thinsp;2.67), and, to a lesser extent, insufficient time for exercise (M\u0026thinsp;=\u0026thinsp;2.2).\u003c/p\u003e \u003cp\u003eAt the socio-demographic level, a significant association with the HBCVD score was observed only for employment status (p\u0026thinsp;=\u0026thinsp;0.035) (Fig.\u0026nbsp;1). The highest mean score was found among homemakers/unemployed individuals (63.08), followed by employed (62.36) and other employment categories (62.00), whereas the lowest value was observed among retired participants (61.05).\u003c/p\u003e \u003cp\u003e \u003cem\u003eFigure 1. Overall HBCVD score by a) socio-demographic and b) CV risk factor variables.\u003c/em\u003e \u003c/p\u003e \u003cp\u003eRegarding risk factors, significant differences emerged for BMI class (p\u0026thinsp;=\u0026thinsp;0.003), cholesterol family history (p\u0026thinsp;=\u0026thinsp;0.023), and LE8 score (p\u0026thinsp;=\u0026thinsp;0.006). Specifically, the HBCVD score increased across BMI categories, being lowest in normal-weight individuals (61.62), intermediate in those overweight (62.49), and highest among the obese (63.69). Participants with a family history of hypercholesterolemia showed higher scores compared with those without such history (62.70 vs 61.77). Finally, an inverse gradient was observed for the LE8 based CVH, with higher HBCVD scores in the low category (63.94) and lower scores in the high group (60.81).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Perceived susceptibility\u003c/h2\u003e \u003cp\u003eFigure 2 displays the forest plots illustrating perceived susceptibility to CVDs across socio-demographic and risk factors variables.\u003c/p\u003e \u003cp\u003eNo significant association between socio-demographic characteristics and perceived susceptibility was found. On the contrary, several significant associations were shown in respect to risk factors. Current smokers reported a significant highest level of perceived susceptibility compared to ex-smokers, never smoker, and ex-smokers (M\u0026thinsp;=\u0026thinsp;11.31, 10.87 and 10.71, respectively; p\u0026thinsp;=\u0026thinsp;0.030). BMI class was strongly associated with susceptibility (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), with participants classified as obese reporting a higher score (M\u0026thinsp;=\u0026thinsp;11.71) compared to those with normal (M\u0026thinsp;=\u0026thinsp;10.65) or overweight (M\u0026thinsp;=\u0026thinsp;10.82) BMI. A similar trend was observed among individuals with a family history of CVD, who reported significantly higher perceived susceptibility (M\u0026thinsp;=\u0026thinsp;11.19) than those without such history (M\u0026thinsp;=\u0026thinsp;10.64) (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003e \u003cem\u003eFigure 2. Perceived susceptibility to CVDs by a) socio-demographic and b) CV risk factor variables.\u003c/em\u003e \u003c/p\u003e \u003cp\u003eSignificant associations were also found for family history of hypertension (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), whereas participants with a positive family history reported higher perceived susceptibility (M\u0026thinsp;=\u0026thinsp;11.11) than those without (M\u0026thinsp;=\u0026thinsp;10.41). A similar pattern was observed for family history of high cholesterol level (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), with higher susceptibility among those with a positive family history (M\u0026thinsp;=\u0026thinsp;11.22) compared with those without (M\u0026thinsp;=\u0026thinsp;10.41). In contrast, no significant difference was found for diabetes family history.\u003c/p\u003e \u003cp\u003eFinally, CVH as measured by the LE8 score showed a strong inverse relationship with perceived susceptibility (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Participants with a low CVH reported the highest susceptibility (M\u0026thinsp;=\u0026thinsp;12.04), while those with a high CVH score reported the lowest (M\u0026thinsp;=\u0026thinsp;10.34).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Perceived severity\u003c/h2\u003e \u003cp\u003eThe analysis of the perceived severity subscale revealed significant differences across demographic variables, while no relevant associations were observed in relation to other socio-demographic characteristics and risk factors (Fig.\u0026nbsp;3).\u003c/p\u003e \u003cp\u003e \u003cem\u003eFigure 3. Perceived severity to CVDs by a) socio-demographic and b) CV risk factor variables.\u003c/em\u003e \u003c/p\u003e \u003cp\u003eFemales reported significantly higher perceived severity scores compared to males (M\u0026thinsp;=\u0026thinsp;11.49 vs 11.10; p\u0026thinsp;=\u0026thinsp;0.003). Age class was also significantly associated with severity perception (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), with mean scores progressively increasing from younger (30\u0026ndash;39 years old: M\u0026thinsp;=\u0026thinsp;10.75) to older individuals (60\u0026ndash;69 years old: M\u0026thinsp;=\u0026thinsp;11.66).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e2.4. Perceived benefits\u003c/h2\u003e \u003cp\u003ePerceived barriers showed noteworthy differences across socio-demographic characteristics but not in respect to risk factors. As shown in the top panel of Fig.\u0026nbsp;4, sex was a significant factor (p\u0026thinsp;=\u0026thinsp;0.005), with males reporting higher perceived benefits (M\u0026thinsp;=\u0026thinsp;21.25) than females (M\u0026thinsp;=\u0026thinsp;20.74). Age class also showed a significant association (p\u0026thinsp;=\u0026thinsp;0.003), with perceived benefits increasing slightly from older (60\u0026ndash;69 years old: M\u0026thinsp;=\u0026thinsp;20.49) to middle-aged groups (50\u0026ndash;59 years old: M\u0026thinsp;=\u0026thinsp;21.26). A strong association was observed with education level (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), with participants having a college degree or postgraduate education reporting the highest perceived benefits (M\u0026thinsp;=\u0026thinsp;21.37), while those with elementary/intermediate education having the lowest scores (M\u0026thinsp;=\u0026thinsp;20.01).\u003c/p\u003e \u003cp\u003e \u003cem\u003eFigure 4. Perceived benefits to CVDs by a) socio-demographic and b) CV risk factor variables.\u003c/em\u003e \u003c/p\u003e \u003cp\u003eEmployment status was also significantly related to perceived benefits (p\u0026thinsp;=\u0026thinsp;0.01). Employed individuals reported higher mean scores (M\u0026thinsp;=\u0026thinsp;21.16) than those unemployed/homemakers (M\u0026thinsp;=\u0026thinsp;20.29) or retired (M\u0026thinsp;=\u0026thinsp;20.49).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e2.5. Perceived barriers\u003c/h2\u003e \u003cp\u003eAs shown in the forest plots (Fig.\u0026nbsp;5), perceived barriers to CVD prevention showed significant associations with a limited number of socio-demographic and risk factor variables.\u003c/p\u003e \u003cp\u003e \u003cem\u003eFigure 5. Perceived barriers to CVDs by a) socio-demographic and b) CV risk factor variables.\u003c/em\u003e \u003c/p\u003e \u003cp\u003eAmong socio-demographic variables, the only significant difference was observed for education level (p\u0026thinsp;=\u0026thinsp;0.027): individuals with an elementary or intermediate education reported the highest perceived barriers (M\u0026thinsp;=\u0026thinsp;19.43), compared to those with a high school diploma (M\u0026thinsp;=\u0026thinsp;18.86) and those with a college degree or postgraduate education (M\u0026thinsp;=\u0026thinsp;19.37).\u003c/p\u003e \u003cp\u003eRegarding risk factor variables, significant differences in perceived barriers were observed across several variables. BMI class was associated with perceived barriers (p\u0026thinsp;=\u0026thinsp;0.002): participants with obesity reported the highest scores (M\u0026thinsp;=\u0026thinsp;19.76), compared to those overweight (M\u0026thinsp;=\u0026thinsp;19.29) or underweight/normal weight (M\u0026thinsp;=\u0026thinsp;18.75). A significant association emerged with the LE8 based CVH (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Individuals with a low CVH reported the highest perceived barriers (M\u0026thinsp;=\u0026thinsp;19.82), followed by those with moderate CVH (M\u0026thinsp;=\u0026thinsp;19.08), while participants with a high CVH score had the lowest (M\u0026thinsp;=\u0026thinsp;18.03).\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study represents the first Italian investigation exploring CVD risk perception among adults aged 40\u0026ndash;69 years old, classified as moderate-to-high risk based on the SCORE2 algorithm but free from clinically diagnosed CVDs.\u003c/p\u003e \u003cp\u003eUsing the HBCVD we observed a moderate overall awareness and engagement in health beliefs related to CVDs. Furthermore, significant differences were found across socio-demographic and risk factors.\u003c/p\u003e \u003cp\u003eOur findings showed that women demonstrated significantly higher perceived severity of CVDs than men. This finding aligns with previous research suggesting that women may exhibit greater health awareness and emotional responsiveness to disease threats [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. However, women also reported lower perceived benefits from prevention, potentially indicating a lack of confidence on preventive measures. These findings are consistent with those reported in a systematic review by Betai et al. (2024) [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e], which underscored significant gender-based differences in how individuals perceive cardiovascular risk and engage in preventive behaviours.\u003c/p\u003e \u003cp\u003eAn upward trend in perceived severity was observed with increasing age, supporting the hypothesis that older adults may develop heightened concern due to cumulative exposure to illness, either personally or through peers [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Despite this, age was not significantly associated with perceived susceptibility or benefits, suggesting that awareness of disease severity alone may not motivate behaviour change. This suggests that interventions for older adults should not solely rely on age-related vulnerability but focus on reinforcing personal perception and empowerment. This suggests that interventions for older adults should not solely rely on age-related vulnerability but focus on reinforcing personal perception and empowerment.\u003c/p\u003e \u003cp\u003eIn our sample higher education levels and employment status were both positively associated with perceived benefits of preventive behaviours. Participants with a college degree or higher scored significantly higher than those with lower education, in line with literature showing that health literacy and education are strong predictors of health engagement [\u003cspan additionalcitationids=\"CR33\" citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Similarly, employed individuals reported higher perceived benefits than unemployed or retired participants. This is consistent with prior findings that link employment with better access to health resources, social support, and preventive care [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. In contrast, across the other scales, higher values were observed among unemployed participants; however, these differences did not reach statistical significance.\u003c/p\u003e \u003cp\u003eRegarding risk factors, smokers and individuals with obesity reported higher perceived obstacles to adopting preventive measures. This pattern is aligned with previous research [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e] which showed that individuals engaged in risky behaviours often recognize their vulnerability but simultaneously feel constrained, psychologically, socially, or practically, from making changes [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. High perceived barriers in these groups may stem from addiction, failed past attempts at behaviour change, or limited access to supportive environments. All these findings suggest the need for behaviourally targeted strategies that address both motivation and external limitations.\u003c/p\u003e \u003cp\u003ePerceived susceptibility was also significantly higher among participants with a family history of hypertension and hypercholesterolemia. Prior research showed that family history increases personal risk appraisal primarily when the condition is perceived as common, observable, and directly linked to modifiable cardiovascular risk factors [\u003cspan additionalcitationids=\"CR37\" citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Hypertension and hypercholesterolemia are typically managed through routine monitoring and long-term preventive treatment, making them more visible within families and more readily interpreted as signals of future cardiovascular risk. In contrast, diabetes, overt CVD, or stroke may be perceived as conditions affecting older relatives or as outcomes rather than modifiable precursors, which may attenuate their impact on perceived personal susceptibility [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFinally, participants in the LE8 high-value category showed higher mean overall HBCVD scores. This finding is consistent with prior evidence indicating that higher LE8 levels are associated with a lower risk of cardiovascular disease, with an inverse relationship between healthier behaviours/factors and CVD that remains robust even after adjustment for potential confounders [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe association between LE8 scores and perceptions of benefits and barriers reinforces the reciprocal relationship between objective health status and psychological readiness for change. Individuals with lower CVH based on LE8 scores reported both higher barriers and lower perceived benefits. These results confirm the bidirectional relationship between objective health and perceived ability to take preventive action [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. This pattern suggests that those in worse health may feel discouraged or powerless to change their habits, while those in better health may be more confident and optimistic about prevention. This underscores the need for tailored interventions that not only address clinical risk but also actively work to reshape beliefs and build self-efficacy in vulnerable populations.\u003c/p\u003e \u003cp\u003eA major strength of this study lies in the first application of the well-established conceptual framework of the HBM within the Italian healthcare context to investigate CVDs risk perception and provide a theoretical foundation for designing preventive interventions. Other strengths of this study include the multicentre design across two diverse Italian regions, the use of validated instruments (HBCVD Scale and LE8), and the comprehensive assessment of demographic, behavioural, and clinical variables. Furthermore, the large sample size enhances the generalizability of the findings to the Italian population.\u003c/p\u003e \u003cp\u003eHowever, this study has also several limitations. First, although participants were recruited as part of a randomized controlled trial, the present analysis is based on baseline cross-sectional data collected prior to randomization. As such, causality cannot be inferred between cardiovascular risk perception and participants\u0026rsquo; characteristics. Second, data were self-reported, introducing potential for recall bias and social desirability bias, particularly in relation to sensitive behaviours such as smoking, diet, and physical activity. Third, the sample was recruited in healthcare settings, which may overrepresent individuals who are more health-conscious or more engaged with the health system. This may limit the generalizability of findings to community-dwelling adults. Finally, the HBM, while useful for capturing individual cognitive constructs, does not account for broader contextual, emotional, or structural determinants of behaviour, such as health system barriers, social support, or cultural norms. Future research should consider longitudinal designs, and mixed method approaches to better understand causal relationships and contextual influences on cardiovascular risk perception and preventive behaviour.\u003c/p\u003e \u003cp\u003eOverall, our results emphasize the need for multilevel interventions that consider demographic, behavioural, and clinical profiles to tailor cardiovascular risk awareness initiatives. Public health programs should move beyond standardized approaches and instead adopt differentiated strategies based on individual characteristics.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThis study is among the first ones to investigate cardiovascular risk perception using a validated theoretical framework within in a population of Italian adults aged 40\u0026ndash;69 years, free from overt disease but identified as clinically at moderate-to-high risk. Overall, participants reported a mean overall score of 62.30 (\u0026plusmn;\u0026thinsp;6.15) out of 100. Mean subscale scores provide a quantitative description of participants\u0026rsquo; perceptions across the measured domains and can inform the development of interventions targeting cardiovascular prevention and lifestyle behaviours.\u003c/p\u003e \u003cp\u003eHealth beliefs varied meaningfully across both socio-demographic characteristics and risk factors. More in depth, differences by sex, age, education, and employment status, indicate that social determinants may shape how individuals interpret CVD threat and value preventive actions. At the same time, behavioural and clinical risk factors, including smoking, BMI class, family history, and LE8, were associated with specific belief domains: most consistently with perceived susceptibility and perceived barriers, and to a lesser extent with perceived severity and perceived benefits. This domain-specific pattern indicates that, even among adults clinically classified as at moderate-to-high risk, risk perception does not simply mirror objective risk: individuals with different risk profiles differ in how they appraise personal vulnerability and the practical constraints to prevention.\u003c/p\u003e \u003cp\u003eImportantly, subjective perceptions are not always aligned with cardiovascular health based on well-known risk factors, underscoring the limitations of prevention strategies based solely on clinical risk stratification. These results call for more comprehensive preventive interventions that can integrate psychosocial dimensions of risk perception to effectively engage at-risk individuals and reduce the burden of CVDs.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"624\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAbbreviation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eFull Term\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAHA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAmerican Heart Association\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eBMI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eBody Mass Index\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCVD / CVDs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCardiovascular Disease / Cardiovascular Diseases\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCVH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCardiovascular Health\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDALY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDisability-Adjusted Life Years\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHBM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHealth Belief Model\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHBCVD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHealth Beliefs Related to Cardiovascular Disease Scale\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eINNOPREV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eINNOvative personalized cardiovascular disease PREVention\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLE8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLife's Essential 8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNCT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNational Clinical Trial\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePNRR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePiano Nazionale di Ripresa e Resilienza\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRCT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRandomized Controlled Trial\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eREDCap\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eResearch Electronic Data Capture\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSCORE2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSystematic COronary Risk Evaluation 2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eStandard Deviation\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eWHO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eWorld Health Organization\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFor the INNOPREV Unit in Perugia, we gratefully acknowledge the contribution of the medical of the Section of Hygiene of University of Perugia who participated in the Unit’s activities: Dr. Pier Luigi Russo, Dr. Lorenzo Conciarelli and Dr. Marta Caminiti.\u003c/p\u003e\n\u003cp\u003eFor the INNOPREV Unit in Rome, we gratefully acknowledge the contribution of the study coordinator, Dr. Giulia Antonini, and of the physicians who participated in the study by recruiting participants, Dr. Daniela Pedicino, Dr. Alessia D'Aiello, Dr. Antonio De Vita, Dr. Lorenzo Genuardi, Dr. Eleonora Santucci, Dr. Aureliano Ruggio, Dr. Simone Filomia, Dr. Maria Chiara Grimaldi, and Dr. Eugenia De Marco. We also sincerely thank the medical doctors of the Section of Hygiene of Università Cattolica del Sacro Cuore who participated in the Unit’s activities: Dr. Angelo Maria Pezzullo, Dr. Sara Farina, Dr. Alessandra Maio, Dr. Martina Porcelli, Dr. Matteo Di Pumpo, Dr. Diego Tona.\u003c/p\u003e\n\u003cp\u003eFor the INNOPREV Unit in Catania, we sincerely thank the physicians who participated in the study by recruiting participants and by contributing to the organization of the Rome event, the production of the project video, and the dissemination event held in Catania: Dr. Salvatore Rubulotta, Dr. Francesco Laudicina, Dr. Fabrizio Rapisarda, Dr. Domenico Arcoria (Clinica Arcoria), Dr. Alessandra Lussi, Prof. Venerando Rapisarda, Dr. Vito Borzì, Dr. Salvatore Bellia, and Dr. Leonardo Serafino. We also acknowledge the following physicians for their valuable collaboration in participant recruitment: Dr. Nuccia Spada, Dr. Gaetano Mannino, Dr. Annalisa Vetri, Dr. Grazia Gambera, Dr. Sabrina Polidoro, Dr. Andrea Barbagallo, Dr. Giacomo Pampallona, Dr. Federica Costanzo, Dr. Giorgio Pulvirenti, Dr. Stella Gangi, and Dr. Marco Messina.\u003c/p\u003e\n\u003cp\u003eFor the INNOPREV Unit in Palermo, we gratefully acknowledge the contribution of the teams from the Dipartimento PROMISE, Università degli Studi di Palermo, and the UOC Epidemiologia Clinica, Azienda Ospedaliera Universitaria Policlinico (AOUP) di Palermo: Santo Fruscione, Claudio Tripodo, Fabio Tramuto, Valeria Guzzetta, Sabina Paolizzo, Giorgio Grazie, Katia Spinelli, Andrea Oddo, Luca Sparacino, Andrea Guarcello, Martina Mormino, Serena Ragusa, Davide Costanza, Alessandra Savatteri, Salvatore Pipitone, Dafne Riina, Tommaso Mancuso, Chiara Norrito, Miriam Belluzzo, Maria Chiara Lo Porto, Veronica Messina, Anna Maria Ciaccio, Marco Mazzola, Martina Profita, Mariagiovanna Cuffaro, Andrea Salvo, Antonino Marchese, Rachele Malfitano, Lavinia Leone, Mariarita Bona, Filippo Vutano, Rosalia Tambuzzo, and Jessica Burzilleri.\u003c/p\u003e\n\u003cp\u003eFinally, we are deeply grateful to all study participants whose involvement made this research possible.\u003c/p\u003e\u003ch2\u003e\u003cstrong\u003eDeclaration of financial/other relationships\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eThe authors declare that financial support was received for the research. This study was conducted as part of the project INNOvative personalized cardiovascular disease PREVention in high-risk adults: protocol of a randomized controlled trial, funded by the Italian Ministry of Health (contract number: PNRR-MAD-2022-1237579.\u003c/p\u003e\n\u003ch2\u003e\u003cstrong\u003eConflict of interests\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eNone to declare.\u003c/p\u003e\n\u003ch2\u003e\u003cstrong\u003eAuthor contributions\u0026nbsp;\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eAll authors contributed to the study design. C.d.W., I.V., M.A., and F.V. drafted the main manuscript text. I.V. performed the data analysis and prepared the figures. All authors contributed to the interpretation of the results, critically reviewed the manuscript, and approved the final version for submission.\u003c/p\u003e\n\u003ch2\u003e\u003cstrong\u003eEthics approval\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eThis trial was approved by the Ethics Committee of the Fondazione Policlinico Universitario Agostino Gemelli (approval number 5506). Furthermore, the trial has been approved by the Local Ethics Committee Catania 2 with the protocol number: 149 C.E. (101/CECT2) and by the Local Ethics Committee Palermo 1 with the approval number CE 150109. All procedures adhered to the Declaration of Helsinki and relevant ethical standards. This trial has been registered on ClinicaTrials.gov, with the identifier: NCT05883878.\u003c/p\u003e\n\u003ch2\u003e\u003cstrong\u003ePatient consent\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eThe participants signed an informed consent form. \u0026nbsp;All information taken from the subjects was coded and kept confidential.\u003c/p\u003e\n\u003ch2\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003ch2\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eThe data are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eEurostat. Causes of death - deaths by country of residence and occurrence. 2025.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCortesi PA, Fornari C, Madotto F, et al. 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Nutr Metab (Lond). 2025;22:38.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Cardiovascular risk perception, Health Belief Model, HBCVD Scale, Life’s Essential 8, Preventive behaviours, Italy, Cardiovascular prevention.","lastPublishedDoi":"10.21203/rs.3.rs-9385767/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9385767/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjective\u003c/h2\u003e \u003cp\u003eCardiovascular disease (CVDs) is the leading cause of death globally. This study, within the Italian INNOPREV trial, explored cardiovascular risk perception in adults aged 40\u0026ndash;69 years old at moderate-to-high risk of CVDs and its association with socio-demographic, behavioural, and clinical characteristics.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eCardiovascular risk perception was measured using the Health Beliefs Related to Cardiovascular Disease Scale (HBCVD; 25 items; four subscales: susceptibility, severity, benefits, barriers). Mean HBCVD scores were compared across socio-demographic variables (sex, age, marital status, education level, employment status) and risk factors (smoking status, BMI, family history of cardiovascular conditions, SCORE2, and Life Essential, 8 score\u0026ndash;LE8). Non-parametric tests (Wilcoxon or Kruskal\u0026ndash;Wallis, as appropriate) were used. Results were summarized using forest plots, reporting mean scores with 95% confidence intervals and p-values.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eA total of 1,019 participants (52.11% males; mean age: 55 years old), and 988 questionnaires were successfully collected and analyzed. The mean overall HBCVD score was 62.30\u0026thinsp;\u0026plusmn;\u0026thinsp;6.15 (out of 100). Employment status was the only sociodemographic variable associated with HBCVD (p\u0026thinsp;=\u0026thinsp;0.035), with higher scores among homemakers/unemployed and lower scores among retired participants. HBCVD was also associated with BMI class (p\u0026thinsp;=\u0026thinsp;0.003), family history of hypercholesterolaemia (p\u0026thinsp;=\u0026thinsp;0.023), and LE8 score (p\u0026thinsp;=\u0026thinsp;0.006). Scores increased with higher BMI in people with a family history of hypercholesterolaemia, and with poorer cardiovascular health based on LE8 score.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eRisk perception of CVD was overall moderate and varied by socio-demographic characteristics and risk factors. These findings might support the development of tailored preventive strategies to enhance risk awareness and promote healthy behaviours.\u003c/p\u003e","manuscriptTitle":"Perceived Risk of Cardiovascular Diseases in the Italian population: Insights from the INNOPREV trial","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-23 16:50:48","doi":"10.21203/rs.3.rs-9385767/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"243481661582047241242198907760881311674","date":"2026-05-10T10:16:37+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-21T14:52:12+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"85903541487301614806201795079858530577","date":"2026-04-21T13:04:38+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-15T18:10:18+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-04-15T06:05:19+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-14T01:59:41+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-04-14T01:59:00+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Public Health","date":"2026-04-11T07:41:10+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"7f8bcebc-e0cb-4a34-86c9-b0edddd6c75a","owner":[],"postedDate":"April 23rd, 2026","published":true,"recentEditorialEvents":[{"type":"reviewerAgreed","content":"243481661582047241242198907760881311674","date":"2026-05-10T10:16:37+00:00","index":37,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-23T16:50:48+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-23 16:50:48","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9385767","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9385767","identity":"rs-9385767","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}