Developments in human societies have led to an increase in resources, facilities, and opportunities for life. With the increase in life expectancy, the number of elderly people has also increased. One of the major challenges that emerges in this regard is the increasing prevalence of osteoporosis in human populations. In fact, the aging of societies has contributed to the increase in osteoporosis [1], to the point that this disease has recently become one of the four major diseases affecting human life [2], and millions of people worldwide suffer from it [3]. In addition to age, gender also influences the risk of osteoporosis. According to international statistics, approximately 35% of women are at risk of osteoporosis and related fractures, while the risk for men is about 20% [4]. Among women, those approaching menopause are at significantly higher risk of developing osteoporosis [5, 6]. Some findings suggest that women are up to eight times more likely to develop osteoporosis than men, indicating the critical role of gender and age as risk factors [7].

Furthermore, osteoporosis prevalence varies across different countries and societies, with developing and underdeveloped nations exhibiting higher rates. Studies conducted in Iran show that the average bone density among Iranians is lower than global standards, and the prevalence of osteoporosis is higher than the global average [8]. Data from national programs for osteoporosis prevention, diagnosis, and treatment in Iran show that among individuals over 50 years old, approximately 70% of women and 50% of men suffer from osteoporosis [5].

Osteoporosis and its associated fractures result in significant morbidity, reduced productivity, premature mortality, and increased healthcare costs. The disease negatively impacts individuals’ quality of life, well-being, and physical and mental health. Some studies suggest that only 20% of people with hip fractures survive more than one year after the fracture [9]. With the increasing elderly population, osteoporosis is expected to place increasing pressure on healthcare systems worldwide in the coming years [10].

In spite of the numerous challenges and complications posed by osteoporosis, effective preventive and coping strategies exist [11]. The most effective approach to preventing and controlling this public health issue is for individuals in the community to adopt preventive behaviors. However, adherence to osteoporosis prevention behaviors remains suboptimal [1, 12]. Studies show that although women are most affected by osteoporosis, their participation in preventive behaviors is insufficient [13, 14]. Some studies even report that people who already have osteoporosis fail to modify their preventive behaviors. Findings from a study by Jahle-Kuns et al. (2022) indicate that awareness of osteoporosis is less than 40% [14]. Research focused on women also shows that in different countries, women lack sufficient awareness about osteoporosis preventive behaviors, follow inappropriate diets, and have limited awareness about osteoporosis and its prevention [15].

Understanding the factors that influence the adoption of osteoporosis preventive behaviors and modifying these factors can help improve preventive and health-promoting behaviors. In this regard, the use of health education and health promotion theories and models can be useful. One such model is the HBM [2]. The Health Belief Model was chosen for this study because of its particular relevance to osteoporosis prevention. As a symptomless “silent disease,” osteoporosis prevention relies heavily on an individual’s perception of personal threat. The core constructs of the HBM -perceived susceptibility and severity-address this primary motivation directly. Further-more, the model’s focus on overcoming perceived barriers (e.g., cost, time) to achieve long-term benefits is particularly relevant to the nature of osteoporosis preven-tive behaviors, making it more appropriate for this study than models that focus primarily on social influences.

The health belief model is one of the most common and cost-effective models used in educational interventions [16]. This model emphasizes the central role of individuals in disease prevention and positions them as key agents in effectively confronting osteoporosis. According to this model, individuals play a fundamental role in managing their health. By actively involving individuals in the prevention process, this approach facilitates the internalization of preventive behaviors, enabling individuals to comprehend the importance, benefits, and potential harms of preventive measures [17].

The HBM consists of several constructs, including perceived susceptibility, perceived severity, perceived benefits, perceived barriers, cues to action, and self-efficacy. Perceived susceptibility refers to an individual's assessment of their risk of developing the disease. Perceived severity reflects the extent to which an individual considers the disease to be serious and acknowledges its consequences. Perceived benefits indicate the advantages an individual associates with adopting preventive behaviors or reducing the risks associated with the disease. At this stage, individuals decide whether to take preventive action. Perceived barriers represent obstacles that hinder individuals from adopting preventive behaviors. A major perceived barrier can be the financial cost of treatment and prevention, which may diminish the perceived benefits. Cues to action serve as triggers that prompt individuals to recognize the need for action or increase their knowledge of potential health risks through reminders and warnings. Self-efficacy reflects an individual’s belief in their ability to perform the necessary actions [18, 19].

Numerous studies indicate that the HBM and its constructs can significantly influence osteoporosis preventive behaviors. Research conducted in various countries and cultural contexts, such as studies by Ercan et al. [20], Kolac et al. [21], and Elgzar et al. [22] demonstrates that the HBM and its components can enhance knowledge and health beliefs regarding osteoporosis and reduce the associated risks.

Based on the aforementioned introduction, osteoporosis is a growing health concern among women, particularly among menopausal women. The HBM can play a vital role in promoting preventive behaviors, thereby reducing the prevalence of osteoporosis and its associated complications. However, few studies in Iran have specifically applied the HBM to investigate osteoporosis preventive behaviors among menopausal women. Therefore, the present study was designed and conducted to address this gap by identifying the factors influencing osteoporosis preventive behaviors among postmenopausal women based on the HBM.

The present study is a descriptive correlational investigation. The study population consists of all menopausal women in the city of Urmia, northwest Iran. The research sample includes menopausal women who meet the eligibility criteria and have family records in the comprehensive health centers of Urmia. The inclusion criteria comprised menopausal women residing in Urmia, having been at least 1 year past their last menstrual period, aged 50 years or older, with the physical and mental capability to complete the questionnaire, and willing to participate in the study. Exclusion criteria included lack of willingness to participate, attendance in formal osteoporosis education sessions within the past six months, and incomplete questionnaire responses.

To determine the sample size, G*Power software was used with an effect size of 0.30, an alpha error probability of 0.05, and a confidence level of 0.99, yielding a sample size of 200. A multi-stage random sampling method was employed. Initially, Urmia was divided into four geographical regions: north, south, east, and west. One comprehensive health center was randomly selected from each region (totaling four centers). A list of eligible postmenopausal women covered by these centers was prepared, and 50 eligible women from each center were randomly selected using simple random sampling. Subsequently, telephone contact was made with the selected participants to explain the study’s purpose. The phone numbers of those who agreed to participate were recorded for follow-up. The entire study procedure, including sampling, participant selection, and data collection, was carried out between September and November 2023.

A three-part questionnaire was used for data collection. The first section covered demographic information, the second section included questions on osteoporosis-preventive behaviors (dietary behavior frequency and physical activity), and the third section contained a standardized questionnaire assessing Knowledge and constructs of the HBM.

The first section of the questionnaire collected demographic data, including age, education level, employment status, income level, and number of family members. The second section measured osteoporosis-preventive behaviors, specifically dietary behavior frequency and physical activity. To assess physical activity, the physical activity section of the Walker Lifestyle Questionnaire³ was employed. This questionnaire was validated and standardized in Iran by Mohammadi Zeidi et al. [23], with a reported Cronbach’s alpha coefficient of 0.79. The Walker Healthy Lifestyle Physical Activity Questionnaire consists of eight questions regarding physical activity, using a four-point Likert scale (never, sometimes, often, always) to indicate an individual’s level of physical activity. Each question is scored from 1 to 4 (1 = never, 2 = sometimes, 3 = often, 4 = always). The Walker questionnaire evaluates health-promoting behaviors across six dimensions, one of which is physical activity. In this study, participants were asked to record their physical activity and exercise status.

Dietary behavior frequency was assessed using a Food Frequency Questionnaire (FFQ) to measure calcium intake. This questionnaire consists of 19 items related to the consumption of calcium-rich foods (including skim milk, low-fat milk, full-fat milk, chocolate milk, strained yogurt, regular yogurt, full-fat yogurt, cream yogurt, buttermilk, cream, ice cream, whey, raw spinach, cooked spinach, and turnips). It was designed and validated by Ghaffari et al. in 2011 for an educational intervention on osteoporosis, with a reported Cronbach’s alpha coefficient of 0.76 [24].

To obtain dietary information, participants were asked to report their average consumption of each food item over the past two months based on the questionnaire’s options (almost never, 1–3 times per month, once per week, 2–4 times per week, 5–6 times per week, once per day, 2–3 times per day, 4–5 times per day, or 6 or more times per day). To score the completed questionnaires, food intake values were first converted to grams. Consequently, using food composition tables, the calcium content per 100 grams for each food item was calculated, and the total calcium intake for each participant was determined.

The third section consisted of a standardized questionnaire assessing knowledge and HBM constructs regarding calcium intake and physical activity, developed, validated, and approved by Beheiraei et al. in 2005 [25]. This questionnaire contains 36 questions covering perceived susceptibility, perceived severity, perceived benefits, perceived barriers, cues to action, and perceived self-efficacy, each with six items related to osteoporosis and its preventive behaviors (calcium intake and physical activity). Responses were rated on a five-point Likert scale from “strongly disagree” (score 1) to “strongly agree” (score 5). Perceived barriers were scored inversely. Thus, each construct had a minimum score of 6 and a maximum score of 30. In the present study, Confirmatory Factor Analysis (CFA) was not conducted because the construct validity of the instrument had already been established in a similar population by Baheiraei et al., and only internal consistency reliability was reassessed using Cronbach’s alpha. In this psychometric evaluation, Cronbach’s alpha coefficients were reported as follows: knowledge (0.70), perceived susceptibility (0.78), perceived severity (0.80), perceived benefits (0.77), perceived barriers (0.70), self-efficacy (0.75), and cues to action (0.72).

Data analysis was performed using SPSS software version 26, employing descriptive and analytical statistics. Results were considered statistically significant at P < 0.05.

Table 1 presents the demographic characteristics of the participants. The mean age of the sample was 57.1 ± 6.3 years. Regarding age groups, 33% of participants were 50–55 years old (n=66), 31% were 56–60 years old (n=62), 23% were 61–65 years old (n=46), and 13% were older than 65 years old (n=26). In terms of education level, 40.5% (n=81) had below high school education, 25.5% (n=51) held a high school diploma, 24.5% (n=49) had an associate or bachelor’s degree, and 9.5% (n=19) had a master’s degree or above. Most participants were homemakers (69.5%, n=139), while 5.5% (n=11) were employed and 25% (n=50) reported other employment statuses. Regarding income, 74.5% (n=149) reported a monthly income above 50 million Rials. Household size analysis showed that 10.5% lived alone (n=21), 54.5% lived in two-person households (n=109), 27% lived in three-person households (n=54), and 8% had more than three family members (n=16).

Table 2 presents the descriptive statistics of the study variables. The mean scores (± SD) were as follows: physical activity (10.19 ± 3.17), weekly calcium intake (6831.91 ± 1704.52 mg), perceived susceptibility (14.95 ± 3.35), perceived severity (15.26 ± 3.65), perceived benefits (14.88 ± 3.65), perceived barriers (14.36 ± 3.19), cues to action (15.04 ± 3.37), perceived self-efficacy (16.70 ± 4.14), and knowledge (9.13 ± 3.91). Skewness and kurtosis values indicated that all variables were within acceptable ranges for normality.

Correlation coefficients among the study variables are presented in Table 3. Preventive behaviors showed significant positive correlations with calcium intake (r = 0.548, p < 0.01), physical activity (r = 0.477, p < 0.01), perceived susceptibility (r = 0.338, p < 0.01), perceived severity (r = 0.260, p < 0.01), perceived benefits (r = 0.382, p < 0.01), cues to action (r = 0.305, p < 0.01), perceived self-efficacy (r = 0.384, p < 0.01), and knowledge (r = 0.576, p < 0.01). A small but significant correlation was also observed with perceived barriers (r = 0.168, p < 0.05). In addition, all Health Belief Model constructs generally demonstrated significant correlations with one another.

To examine the predictive power of HBM components, a simultaneous linear regression analysis was conducted (Table 4). The model predicting preventive behaviors was statistically significant, with R = 0.627, R² = 0.451, and Adjusted R² = 0.425, indicating that the predictor variables explained 42.5% of the variance in preventive behaviors (SE = 8.620; Durbin–Watson = 2.03).

Standardized regression coefficients are presented in Table 5. Among the predictors, perceived susceptibility (β = 0.137, p = 0.031), perceived benefits (β = 0.169, p = 0.006), perceived self-efficacy (β = 0.182, p = 0.005), knowledge (β = 0.329, p = 0.001), age (β = –0.178, p = 0.002), and education (β = 0.184, p = 0.002) significantly predicted preventive behaviors. Perceived severity, perceived barriers, and cues to action did not show significant predictive effects.

Among all variables, knowledge had the strongest predictive value, such that higher levels of knowledge were associated with greater adoption of osteoporosis preventive behaviors.

Overall, six variables-perceived susceptibility, perceived benefits, perceived self-efficacy, knowledge, age, and education-were identified as significant predictors of osteoporosis preventive behaviors.

The present study aimed to predict osteoporosis preventive behaviors among postmenopausal women in Urmia based on the HBM. The findings indicate a significant positive relationship between the HBM components and osteoporosis preventive behaviors among postmenopausal women. According to the regression model results, four components-perceived benefits, perceived susceptibility, perceived severity, and cues to action-remained in the model and were statistically significant. The model predicted 36.8% of the variance in osteoporosis preventive behaviors.

Among the HBM components, the highest predictive coefficients were related to Knowledge, perceived benefits, self-efficacy, and perceived susceptibility, respectively. A review of previous studies demonstrated that some research findings align with and confirm the results of the present study. For example, a study conducted by Solimanian et al. found that the HBM model predicted 48% of the variance in physical activity as a preventive behavior for osteoporosis [26]. Similarly, a study by Baghiani Moghadam et al. in Yazd revealed that the HBM variables collectively explained 36% of osteoporosis preventive behaviors [27]. Another study by Khani Jeihooni et al. showed that the HBM constructs explained approximately 30% of osteoporosis-preventive behaviors [28]. These findings suggest that the HBM can serve as a useful framework for designing interventions and educational programs aimed at encouraging women to adopt health-promoting behaviors.

According to the HBM, one of the key factors influencing the adoption of health-promoting behaviors is individuals' Knowledge and knowledge. The greater the individuals' Knowledge of health problems, the more effectively they can protect themselves. In the present study, Knowledge and knowledge were the most significant factors influencing osteoporosis preventive behaviors. Baghiani Moghadam et al. found a positive, significant correlation between women's Knowledge and their preventive behaviors regarding osteoporosis [27]. Similar findings were reported by Liza et al. [29] and Vered et al. [30], all of whom identified Knowledge as a crucial factor in osteoporosis prevention. These findings are consistent with the results of the present study.

Perceived benefits were another HBM component influencing osteoporosis preventive behaviors. Khani Jeihooni et al. reported that perceived benefits significantly improve osteoporosis preventive behaviors among women [28]. Ghaffari et al. also found that improvements in the perceived benefits component of the HBM were associated with better osteoporosis prevention behaviors [24]. Similarly, Khorsandi et al. demonstrated a positive correlation between perceived benefits and pregnant women's performance in osteoporosis prevention [31]. Additionally, research by Jang and Ahn indicated that perceived benefits enhance osteoporosis preventive behaviors in women [32]. Findings from Gammage and Klentrou also confirmed that self-efficacy, perceived benefits, and perceived barriers were significant predictors of preventive behaviors [33]. These studies collectively suggest that when individuals recognize the benefits and positive outcomes of health behaviors, they are more likely to adopt such behaviors. Therefore, improving this aspect of the model through appropriate educational methods could increase the likelihood of women adopting osteoporosis preventive behaviors.

Self-efficacy is another critical HBM factor influencing health behavior change, which was also confirmed in the present study. The findings suggest that when individuals believe they can perform recommended health behaviors despite potential obstacles, they are more likely to adopt those behaviors. Wallace found that self-efficacy was a significant predictor of exercise and calcium intake among postmenopausal women [34]. Similarly, Vahedian-Shahroodi et al. showed that self-efficacy significantly predicted physical activity [35]. Jang and Ahn also reported that self-efficacy and knowledge enhance osteoporosis preventive behaviors in women [32]. These results align with the findings of the present study, further reinforcing the importance of self-efficacy in adopting health-promoting behaviors.

Perceived susceptibility is another influential factor in adopting preventive behaviors. By strengthening individuals' perceptions of the seriousness and severity of a disease and its complications, perceived susceptibility plays a crucial role in promoting preventive actions. When individuals perceive themselves to be at risk, they are more likely to seek information and take preventive measures [36, 37]. The findings of the present study confirm that perceived susceptibility is significantly associated with osteoporosis preventive behaviors. Malak et al. found that improving perceived susceptibility among Jordanian women led to better osteoporosis prevention behaviors [38]. Ghaffari et al. similarly reported that improvements in perceived susceptibility were linked to better osteoporosis-preventive behaviors [24]. Jang and Ahn demonstrated that perceived susceptibility can enhance osteoporosis preventive behaviors in women [32]. Additionally, Gammage and Klentrou identified perceived susceptibility as a significant predictor of preventive behaviors [33]. These studies support the present study's findings, further confirming the role of perceived susceptibility in the adoption of health behaviors.

Overall, the HBM suggests that by enhancing perceived severity, perceived susceptibility, perceived benefits, and self-efficacy, and by reducing perceived barriers, women can gain greater knowledge about osteoporosis. With improved knowledge, individuals actively seek beneficial information to help them manage and reduce the complications of the disease.

Despite providing significant insights into the relationship between osteoporosis preventive behaviors and the Health Belief Model, this study has several methodological constraints. Firstly, its correlational nature means that while relationships can be identified, causal inferences cannot be made. Secondly, the generalizability of the results is limited to populations with characteristics similar to those of the sample. Lastly, the use of self-report measures for behaviors raises the possibility of response bias, which may have affected the data's accuracy.

Since preventive behaviors are fundamental to effective disease management, understanding the influential factors is crucial. In this study, the HBM components, along with Knowledge, explained 36.8% of the variance in osteoporosis preventive behaviors. Among these, knowledge, perceived benefits, perceived susceptibility, and self-efficacy were the primary determinants. Increasing knowledge, self-efficacy, perceived susceptibility, and perceived benefits encourages women to adopt more preventive behaviors, learn coping strategies, and become aware of the potential complications associated with osteoporosis. Therefore, understanding individuals’ preventive behaviors toward the disease can significantly influence their coping strategies. Using the HBM, it is possible to enhance women's knowledge and preventive behaviors, enabling more effective management of osteoporosis as a growing public health concern.