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Article

Health-Promoting Behaviors in Bulgaria: A Cross-Sectional Study on Non-Communicable Diseases and Lifestyle

by
Sophia Lazarova
1,* and
Dessislava Petrova-Antonova
1,2
1
GATE Institute, Sofia University “St. Kliment Ohridski”, 1164 Sofia, Bulgaria
2
Faculty of Mathematics and Informatics, Sofia University “St. Kliment Ohridski”, 1164 Sofia, Bulgaria
*
Author to whom correspondence should be addressed.
Societies 2025, 15(1), 15; https://doi.org/10.3390/soc15010015
Submission received: 21 November 2024 / Revised: 7 January 2025 / Accepted: 15 January 2025 / Published: 17 January 2025
Versions Notes

Abstract

:
(1) Background: Non-communicable diseases (NCDs) are a global health issue causing millions of deaths annually. Bulgaria has one of the highest rates of premature mortality due to NCDs in the European Union, mostly attributed to poor lifestyle habits. While adopting a healthy lifestyle is beneficial to preventing NCDs and managing existing conditions, research on health-promoting behaviors among individuals with NCDs remains limited. (2) Methods: This study investigates lifestyle disparities between individuals diagnosed with common NCDs and healthy individuals, and the sociodemographic determinants of healthy lifestyles among a nationally representative sample of 2017 adult Bulgarian citizens. We analyzed sociodemographic characteristics, health status (weight, height, existing diagnoses—diabetes, cardiovascular disease, insulin resistance, hypertension, and depression), and lifestyle data. (3) Results: Only 18.8% of the participants maintained a healthy lifestyle and 65% had a moderately healthy lifestyle. Over one-third of the respondents were pre-obese, and many reported having at least one chronic condition. Multinomial logistic regression revealed individuals with chronic conditions were less likely to have healthy or moderately healthy lifestyles compared to healthy participants, except those diagnosed with cardiovascular disease or depression, who were more likely to maintain healthy habits. (4) Conclusions: Considering the discovered discrepancies in lifestyle quality, more research should be directed toward identifying the barriers to healthy living for individuals diagnosed with NCDs.

1. Introduction

Non-communicable diseases (NCDs), also known as chronic diseases, are responsible for over 41 million deaths each year, making them the world’s leading cause of death [1]. While the NDCs causing the highest number of deaths are cardiovascular diseases (17.9 million deaths annually), cancers (9.0 million), respiratory diseases (3.9 million), and diabetes (1.6 million), the term has been extended to include a wide range of health problems including mental disorders [2]. Despite the high prevalence, NCDs are considered preventable as the main risk factors contributing to their onset involve unhealthy diets, physical inactivity, smoking, and alcohol misuse [2]. An overwhelming amount of evidence shows that practicing a healthy lifestyle lowers the risk of developing cardiovascular disease [3,4], cancer [5,6], diabetes [7,8], dementia and cognitive decline [9,10], and all-cause mortality [11,12,13]. Similarly, sustaining a healthy lifestyle is vital in managing NCDs as it has been associated with improved health outcomes and reduced mortality [14,15,16,17].
Bulgaria has one of the highest rates of premature mortality due to NCDs in the European Union (EU), with the probability of premature death (between the ages of 30 and 70 years) being 24% [18]. The high risk of premature mortality can be, at least partially, explained by the poor lifestyle habits among the Bulgarian population. According to the 2023 health profile report by the European Commission, adult and adolescent smoking rates in Bulgaria are the highest in Europe with three in every ten adults and one-third of adolescents smoking daily [19]. Similarly, alcohol consumption per capita in 2019 was 12% higher than in the EU, with alcohol rates among adolescents being among the highest in Europe [19]. Furthermore, the proportion of adults reporting consumption of at least five portions of fruits and vegetables daily (5.0%) is the lowest in the EU and far from the average for the European region (12.4%) [19]. The share of adults engaging in at least 150 min of moderate physical activity per week is 11.3%—the smallest in the EU [19]. Similar trends were reported by several other studies [20,21,22].
Although the lifestyle patterns of the Bulgarian population have been previously researched, there is a severe lack of studies investigating the determinants of healthy lifestyles, especially in populations diagnosed or at high risk of NCDs. Thus, the present study aims to examine the lifestyle disparities between individuals diagnosed with common NCDs and healthy individuals as well as the sociodemographic determinants of healthy lifestyles among a nationally representative sample of Bulgarian adults.
The rest of the study is organized as follows: Section 2 describes the methods and materials used to conduct the present investigation; Section 3 reports the obtained results; Section 4 discusses the implications of our findings in the context of existing literature; and Section 5 concludes the paper.

2. Materials and Methods

2.1. Design and Procedure

An online survey was conducted between 23 February and 28 March 2024. Lifestyle and health status were assessed as a part of a larger study, investigating health-promoting behaviors and health literacy among the Bulgarian population. Participant recruitment was outsourced to an agency for social surveys and public opinion polls. All communication with the respondents was handled by the agency. Questionnaire completion was possible only once from a single IP address and only fully completed survey forms were recorded. Respondents were provided with a study informational sheet and only consenting respondents were forwarded to the survey. The survey forms were available only in the Bulgarian language.

2.2. Participants

Quota sampling methodology was used to obtain a nationally representative sample of Bulgaria’s adult population in terms of age groups (18–34; 35–54; 55+), residency region, and gender. Respondents were sourced from the contracted agency’s database and were contacted via email. A total of 2017 participants completed the survey. All participants were Bulgarian citizens, currently residing in Bulgaria.

2.3. Data

2.3.1. Demographic Characteristics

The following demographic data were collected as part of this study: age, gender, ethnicity, education, type of living area, marital status, employment status, and living arrangements. All sociodemographic characteristics were encoded as categorical variables (age was collected as a continuous variable and then encoded categorically). Additionally, each participant was asked to rate their satisfaction with the Bulgarian healthcare system on a scale from 0 to 10, where 10 signified high satisfaction and 0 low.

2.3.2. General Health Status

To estimate the current health status of each participant, we collected height, weight, and additional information regarding metabolic, cardiovascular, and mental disorders. The body mass index (BMI) of each participant was calculated based on their height and weight. BMI scores were categorized according to the World Health Organization’s recommendations for healthy lifestyle [23] as follows: underweight (BMI < 18.5); normal weight (BMI 18.5–24.9); pre-obesity (BMI 25.0–29.9); obesity class I (BMI 30.0–34.9); obesity class II (BMI 35.0–39.9); and obesity class III (BMI ≥ 40). Participants were asked to note if they had any of the following conditions: type I diabetes, type II diabetes, insulin resistance, hypertension, cardiovascular disease, and depression. The presence/absence of each condition was encoded with a binary variable.

2.3.3. Diet

The Mediterranean diet (MedDiet) is one of the most researched dietary patterns globally, known for its numerous health benefits. For example, adherence to the Mediterranean diet has been linked to a lower risk of non-communicable diseases, including cardiovascular diseases, cancer, neurodegenerative diseases, and overall mortality [24,25,26,27,28,29,30,31]. Research suggests that the diet’s protective effects are due to its strong anti-inflammatory properties and mechanisms such as improved lipid profiles, and reductions in blood pressure and insulin resistance [32].
Adherence to the MedDiet was evaluated with the MDSPyr, a diet score based on the Mediterranean diet pyramid [33]. The MDSPyr includes 15 items, adopting the indication for optimal food frequency from the 2011 version of the MedDiet pyramid [34]. The MDSPyr was found to be the most reliable score among 8 others, having the least number of flaws and a strong body of theoretical and scientific support [35]. By design, the MDSPyr score considers alcohol consumption; however, since we are calculating a lifestyle score that includes alcohol consumption as a separate component, we did not consider the MDSPyr question regarding alcohol. Thus, we used 14 out of the 15 items, accounting for a maximum possible result of 14 points instead of 15. The total score was calculated according to the original formulation of the scale—each item was scored continuously, with possible values between 0 and 1, according to the participant’s degree of adherence to the recommendation. For items with recommended high intake, 0 to 1 point were assigned proportionally from no consumption to the recommended level of consumption. For low-intake items, the scoring was reversed. For components with moderate intake recommendations, a score of 1 was assigned for consumption within the recommendation and 0 for no consumption. Overconsumption two-fold higher than the mid-point of the recommended intake was penalized and received a maximum of 0.5 points [33]. Table 1 shows the recommendations for each component as well as the respective scores according to the reported consumption.

2.3.4. Physical Activity

The physical activity of each participant was estimated with the question “Do you exercise regularly?” (yes/no). Answers were encoded with a binary variable.

2.3.5. Alcohol and Tobacco Consumption

Participants were asked to estimate the usual number of drinks they would have in one sitting considering the last month. Smoking was assessed with the question “Do you smoke cigarettes?” (yes/no/used to smoke). However, for the purpose of the present investigation, only their current smoking status was considered. Thus, all “used to smoke” answers were treated as “no” and the responses were coded as a binary variable.

2.3.6. Lifestyle Score

We estimated the level of adherence to a healthy lifestyle by taking into account the following lifestyle factors—diet, physical activity, alcohol consumption, and smoking. Each of these factors was awarded 1, 0, or 0.5 points according to the behavioral patterns of each participant. For this purpose, MDSPyr scores were classified as follows: MDSPyr < 6 points = low adherence (0 points); MDSPyr ≥ 6 points and MDSPyr < 10 points = moderate adherence (0.5 points); MDSPyr ≥ 10 points high adherence (1 point). Exercising regularly and not smoking were awarded 1 point each. Not consuming alcohol or consuming less than 2 glasses of alcohol per sitting received 1 point as well. Not exercising, smoking, and drinking received 0 points each. Thus, the final score ranged between 0 and 4, with a 0.5 step. To obtain a categorical representation of the final lifestyle score we established the following classification: lifestyle score ≤ 1 point was classified as an unhealthy lifestyle; 2 points ≤ lifestyle score < 3 points was considered a moderately healthy lifestyle; and 3 points ≤ lifestyle score ≤ 4 points signified a healthy lifestyle.

2.4. Data Analysis

Data were analyzed using R Studio (v2023.03.1 Build 446). The default level of statistical significance was 0.05. Descriptive analyses of participants’ characteristics were conducted, providing frequencies and percentages for each variable. Pearson’s chi-square test was conducted to investigate associations between sociodemographic and health status characteristics, and healthy lifestyle behaviors (expressed as the categorical lifestyle score). Cramer’s V was calculated to estimate the strength of the discovered associations. Multinomial logistic regression was used to determine the association of sociodemographic factors and health status with moderately healthy and healthy lifestyle behaviors, using the unhealthy lifestyle group as the reference group. All predictors were included in the baseline model and then a stepwise backward elimination procedure was applied to refine it. Elimination was based on p-values, starting with the predictors having the highest p-values. Finally, odds ratios and 95% confidence intervals were estimated and reported.

2.5. Ethical Approval

This study was performed according to the ethical standards outlined in the Declaration of Helsinki. All participants participated voluntarily after being presented with an information sheet. Access to the questionnaire was granted only to participants who provided their explicit consent. The General Data Protection Regulation (GDPR) in a research context [36] was respected through the confidentiality and anonymity of the data. Ethical approval was obtained from the Ethics Committee of the Sofia University “St. Kliment Ohridski” (no. 70-123-96/19.01.2024).

3. Results

A total of 2017 participants took part in this study (N = 2017; m = 44.89 ± 12.83 years; 50.9% female). Most participants had at least secondary-level education (44.9%). Furthermore, more participants were employed full-time (65.1%), ethnic Bulgarians (85.7%), urban residents (78.6%), and shared their living space with other individuals (83.9%). Half of the participants had a normal BMI (50.5%), 34.5% were pre-obese, and nearly 13% were obese. Nearly 30% had a metabolic disorder (diabetes type 1, diabetes type 2, or insulin resistance), 16% had cardiovascular disease, and 23.3% had hypertension. Finally, there was a small number of participants diagnosed with depression (2.9%). The overall satisfaction with the Bulgarian healthcare system was low, receiving an average rating of 3.70 (SD = 1.63). Table 2 summarizes the socio-demographic and health characteristics of the studied sample.
Participants were predominantly non-smokers (61.9%) and were moderately adhering to the Mediterranean diet (47.1%). Less than half of the participants exercised regularly, and nearly 50% consumed two or more drinks (per serving) regularly. Based on the classification used in the present study, 65.1% were in the moderately healthy lifestyle group (Table 3).
Lifestyle behaviors were associated with age, education, employment status, marital status, living arrangement, and health status. BMI, diabetes type 2, insulin resistance, and hypertension were also associated with lifestyle (Table 4). Hypertension was the only variable with a moderate effect size (Cramer’s V = 0.216); all other associations had a weak effect size (Cramer’s V ≤ 0.2).
Table 5 shows the sociodemographic and health status characteristics affecting lifestyle behaviors assessed with a multinomial logistic regression. The unhealthy group was considered a reference category. All sociodemographic and health characteristics were included as predictors in the base model. A backward elimination strategy was employed to incrementally reduce the number of predictors based on their p-values, starting with the highest p-values. Thus, in the final model, significant predictors of a moderately healthy lifestyle were employment status, living arrangement, BMI, diabetes type 2, insulin resistance, cardiovascular disease, hypertension, and depression (p < 0.05). Unemployed individuals had 53% lower odds of leading a moderately healthy lifestyle compared to those working full-time jobs (OR = 0.474; 95% CI = 0.264, 0.853). Those living with others had 44% higher odds of living moderately healthily compared to those living alone (OR = 1.438; 95% CI = 1.042, 1.985). Pre-obese individuals had 56% lower odds and obese individuals had 60% lower odds of leading a moderately healthy life compared to those in the normal BMI range. Similarly, obesity class III was associated with 88% lower odds of living a moderately healthy lifestyle. Individuals with type 2 diabetes had 43% lower odds of a moderately healthy lifestyle. Those with insulin resistance or hypertension had about 57% lower odds of being classified as moderately healthy. In contrast, having a cardiovascular disease was associated with 59% higher odds of living a healthy lifestyle compared to those without such conditions. Finally, individuals with depression had 2.5 times the odds of being classified in the moderately healthy group compared to those without depression.
Predictors of a healthy lifestyle were marital status, living arrangement, BMI, diabetes type 2, insulin resistance, cardiovascular disease, hypertension, and healthcare satisfaction (p < 0.05). Married individuals had 36% lower odds (OR = 0.639; 95% CI = 0.409, 0.997) of being in the healthy group compared to single individuals and widowed individuals had over four times the odds (OR = 4.364; 95% CI = 1.426, 13.355) of having a healthy lifestyle compared to singles. Once again, living with others was associated with higher odds of living a healthier lifestyle (OR = 1.707; 95% CI = 1.113, 2.619). Similarly, pre-obesity and obesity were associated with more than 20% lower odds of leading a healthy lifestyle (Table 5). In contrast, those underweight had over 10 times the odds of having a healthy lifestyle compared to those with a normal BMI. Individuals with type 2 diabetes had 82% lower odds and those with insulin resistance had 61% lower odds of living healthily. Similarly, hypertension was associated with lower odds of achieving a healthy lifestyle (OR = 0.202; 95% CI = 0.128, 0.318). Individuals diagnosed with cardiovascular disease had 71% higher odds of living healthily compared to those without such conditions. Finally, higher satisfaction with the healthcare system was associated with 14% higher odds of achieving a healthy lifestyle.

4. Discussion

In this cross-sectional study, we discovered that only 18.8% of all participants exercised a healthy lifestyle and 65% had moderately healthy habits. Additionally, our results confirmed the significant prevalence of unhealthy choices among Bulgarians, namely a pronounced lack of physical activity, smoking, and high consumption of alcohol. In contrast to the low obesity rates within our sample (about 12%), more than one-third of the respondents were pre-obese, more than one-fourth had hypertension, about 16% were diagnosed with cardiovascular disease, and 19% had diabetes. Similarly to our findings, previous studies have reported high rates of hypertension [37,38] and proximal estimations of diabetes burden in Bulgaria with an increasing number of cases in the last 20 years [39]. Finally, our results showed that individuals with chronic conditions were less likely to have healthy or moderately healthy lifestyles compared to healthy participants except those diagnosed with CVD or depression.
We found weak-to-moderate associations between lifestyle behaviors and age, education, employment, marital status, living arrangement, BMI, type 2 diabetes, insulin resistance, hypertension, and satisfaction with the healthcare system. We found that married individuals were less likely to lead a healthy lifestyle compared to singles. Having a family may be associated with more responsibilities and less time to devote to a healthy lifestyle. However, certain behavioral and psychological aspects of marriage should be considered regarding health behaviors. For instance, couples tend to become more similar or influence each other’s health behaviors and co-occurring activities such as sports, diet, and sleep [40,41,42]. Thus, marriage quality may exert a considerable influence over the quality of health behaviors of the partners. Despite widowhood being associated with worse health outcomes, depression, and psychological distress [43,44,45], we found that widowers were more likely to live a healthier life compared to singles. It might be the case that losing a loved one has triggered these individuals to become more vigilant of their ways of living. In any case, further research is needed to elucidate the role of these findings.
In line with previous research [46], we found that pre-obese and obese individuals were less likely to have a healthy lifestyle compared to individuals with physiological BMI. While healthy living is usually discussed in terms of obesity prevention, maintaining a healthy diet, exercising regularly, and refraining from alcohol and smoking are associated with improved health outcomes for obese individuals, including a decreased risk of cardiovascular disease, kidney failure, gout, sleep disorders, and mood disorders [47,48]. Thus, there is a need for a broader discussion of the barriers to a healthier lifestyle for overweight and obese individuals in Bulgaria. Previous studies have identified insufficient self-control, physical pain, time constraints, and lack of support as barriers to losing weight [49,50]. However, the barriers in the context of Bulgaria remain unknown, granting future investigations of this matter.
Next, our findings showed that underweight individuals were more likely to maintain a healthy lifestyle compared to individuals with physiological BMI. In Western countries, being underweight is a less discussed type of unhealthy weight as it is not very common. However, just like obesity, being underweight is associated with elevated health risks and mortality [51]. Our results can be explained by the fact that some eating disorders are associated with strong fixations on healthy life choices. For instance, orthorexia nervosa is a condition in which the patient becomes overly focused on maintaining a healthy diet, so much so that this fixation often leads to malnourishment, loss of relationships, and a worsened social environment [52]. Similarly, dieting and weight loss are major factors that predispose to anorexia nervosa [53]. Thus, our results may portray the lifestyle dynamics of a small group of individuals affected to some degree by eating disorders (3% of the sample). However, future research is needed to confirm the validity of this finding.
We found that individuals diagnosed with type 2 diabetes, insulin resistance, or hypertension were less likely to have a healthy or moderately healthy lifestyle compared to individuals without such conditions. Conversely, those with cardiovascular disease were significantly more likely to have a moderately healthy or healthy lifestyle compared to individuals without the condition. Similarly, individuals diagnosed with depression were two times more likely to have a moderately healthy lifestyle with a similar (but non-significant) trend for a healthy lifestyle versus an unhealthy one. Cardiovascular diseases are a major health problem for the Bulgarian population, accounting for more than 60% of all deaths in 2019 [54]. Thus, it might be the case that those diagnosed with CVD are more aware of their lifestyle following the diagnosis. This can be partially factored by the strict lifestyle management prescription given to CVD patients as well as the health-related fears stemming from receiving a diagnosis. Compared to hypertension and diabetes the fear of receiving a CVD diagnosis might be more pronounced as the repercussions of these conditions tend to be sudden instead of unfolding over time. Regardless, our results provide evidence for a positive trend among CVD patients in terms of disease management. Future research may investigate the motivations of CVD patients for maintaining a healthy lifestyle compared to individuals diagnosed with other conditions. Finding the determinants of motivation and drivers of these behaviors will be informative for translating this example into the management of other chronic conditions.
Our results confirm the need for further dialogue and extensive promotion of healthy lifestyles among the general population of Bulgaria. Modification of health behaviors is not trivial and requires intervention at individual, community, healthcare, and policy levels [55]. Health awareness campaigns are a valuable tool in reducing disease burden with demonstrated efficacy [56]. However, the success of awareness campaigns is usually not uniform across different groups and communities. Thus, public health initiatives should be tailored to specific target groups to ensure optimal effects. The vast majority of public campaigns and health-promoting initiatives in Bulgaria are currently oriented toward young people and school-aged children, with many of them emphasizing the importance of physical activity and healthy nutrition. However, our results demonstrate that adults from all age groups would greatly benefit from similar efforts targeted at them. More initiatives should target adult individuals, especially older adults as they are often subjected to stigmatizing views and stereotypes which may pose additional barriers to healthy living. Another solution may be the proactive promotion of healthy lifestyles by clinicians. This approach may be especially suitable for chronically ill patients as a lot of them pay regular visits to their doctors. Accumulating evidence suggests that clinicians are well positioned to influence the lifestyle practices of their patients [55]. However, a survey in the USA showed that, in 2016, only 13.4% of physician visits made by children or adults included counseling about nutrition or diet and 5.3% included counseling about physical activity. Similarly, only 7.7% of physician visits made by patients diagnosed with CVD, diabetes, or dyslipidemia included counseling about physical activity [55]. While such statistics for Bulgaria are not available, we believe that our results suggest that some clinicians may put more emphasis on lifestyle than others. Namely, our results showed that individuals diagnosed with CVD were more likely to make healthy lifestyle choices, in contrast to those diagnosed with other conditions. Thus, a more proactive approach in advising patients about their lifestyle may prove effective in behavior modification, ultimately improving prevention and disease management.
The results of the present study should be considered alongside several limitations. While our sample is nationally representative of the Bulgarian adult population, it may be prone to selection bias as our participants were volunteers sourced from the database of the contracted agency. Also, our estimates are based on self-reported data which may affect the overall results. For instance, our data may be prone to social desirability bias as our questions portray everyday behaviors that have well-defined norms of what is healthy and what is not. Thus, participants may distort their responses to bring them closer to what is expected. Nevertheless, the fact that our results are close to the figures reported in other studies shows that the effects of such bias remain minimal.
Our results provide evidence of an existing discrepancy in the behavioral habits of healthy individuals and individuals with NCDs. Future work should focus on incorporating longitudinal designs, capable of assessing the direct effects of lifestyle choices on health status. While lifestyle is heavily studied in the primary prevention of NCDs, far fewer studies are focused on its effects on the management of chronic conditions as well as the barriers to implementing healthy behaviors in day-to-day life. The scientific community will greatly benefit from more in-depth explorations of these topics, especially in the context of different communities and cultures.

5. Conclusions

The prevalence of NCDs and poor lifestyles in Bulgaria continues to be a serious health issue for the country. Most adult Bulgarians have a moderately healthy lifestyle with a lot of them consuming high amounts of alcohol, smoking, and/or leading a sedentary life. More research should be directed towards identifying the barriers to healthy living for different social groups including individuals diagnosed with NCDs. Additionally, Bulgarian society should be further educated on the importance of maintaining a healthy lifestyle in the prevention and management of NCDs.

Author Contributions

Conceptualization, S.L.; Methodology, S.L.; Investigation, S.L.; Writing—Original Draft Preparation, S.L.; Writing—Review & Editing, S.L. and D.P.-A.; Supervision, D.P.-A.; Project Administration, D.P.-A.; Funding Acquisition, D.P.-A. All authors have read and agreed to the published version of the manuscript.

Funding

This research work was supported by the GATE project, funded by the Horizon 2020 WIDESPREAD-2018-2020 TEAMING Phase 2 programme under grant agreement no. 857155; by the European Union-NextGenerationEU, through the National Recovery and Resilience Plan of the Republic of Bulgaria, project № BG-RRP-2.004-0008.

Institutional Review Board Statement

The study was conducted in accordance with the Declaration of Helsinki, and approved by the Institutional Review Board (or Ethics Committee) of Sofia University “St. Kliment Ohridski” (70-123-96/19.01.2024).

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

The raw data supporting the conclusions of this article will be made available by the authors on request.

Conflicts of Interest

The authors declare no conflicts of interest.

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Table 1. Pyramid-based Mediterranean diet score (PyrMDS) scoring criteria [33]. All recommendations are in number of servings per day or per week. Continuous scoring was used for all components except olive oil.
Table 1. Pyramid-based Mediterranean diet score (PyrMDS) scoring criteria [33]. All recommendations are in number of servings per day or per week. Continuous scoring was used for all components except olive oil.
ComponentRecommended IntakeScore of 0Score of 1
Vegetables 1≥6/d0/d≥6/d
Legumes 1≥2/wk0/wk≥2/wk
Fruits 23–6/d0/d3–6/d
Nuts 21–2/d0/d1–2/d
Cereals 23–6/d0/d3–6/d
Dairy 22/d0/d1.5–2.5/d
Fish 1≥2/wk0/wk≥2/wk
Red meat 3˂2/wk≥4/wk˂2/wk
Processed meat 3≤1/wk≥2/wk≤1/wk
White meat 22/wk0/wk1.5–2.5/wk
Egg 22–4/wk0/wk2–4/wk
Potato 3≤3/wk≥6/wk≤3/wk
Sweets 3≤2/wk≥4/wk≤2/wk
Olive oilPrincipal source ofdietary lipidsNon-consumersConsumers
1 High-consumption components. 2 Moderate-consumption components. 3 Low-consumption components.
Table 2. Socio-demographic and health status characteristics of the sample.
Table 2. Socio-demographic and health status characteristics of the sample.
Categorical VariablesCount (n)Percentage of Total, %
Gender
      Male99149.1
      Female102650.9
Age
      18–35 years62531.0
      36–54 years79139.2
      55+ years60129.8
Education
      Primary education20.1
      Secondary education90644.9
      Bachelor’s degree59929.7
      Master’s degree50825.2
      PhD20.1
Type of living area
      Urban46923.3
      Rural27013.4
      City—administrative center127863.4
Employment
      Full time131365.1
      Part-time23711.8
      Unemployed743.7
      Retired1085.4
      Other28514.1
Marital Status
      Single42020.8
      In a relationship60830.1
      Married71935.6
      Divorced20410.1
      Widowed663.3
Ethnicity
      Bulgarian172985.7
      Turkish22811.3
      Roma160.8
      Other402.0
      Will not answer40.2
Living arrangement
      Alone32516.1
      With others169283.9
BMI
      Underweight (<18.5)603.0
      Normal (18.5–24.9)100550.5
      Pre-obesity (25.0–29.9)67634.0
      Obesity class I (30.0–34.9)20010.1
      Obesity class II (35.0–39.9)452.3
      Obesity class III (>39.9)40.2
Health conditions
      Type 1 diabetes23311.6
      Type 2 diabetes1497.4
      Insulin resistance21410.6
      Cardiovascular disease32516.1
      Hypertension46923.3
      Depression592.9
Continuous variables Mean ± SD
Healthcare satisfaction 3.70 ± 1.63
Table 3. Lifestyle characteristics of the sample.
Table 3. Lifestyle characteristics of the sample.
Count (n)Percentage of Total, %
Smoking (Currently)
        Yes76838.1
        No124961.9
Alcohol consumption
        <2 drinks109954.5
        2 or more drinks91845.5
Exercising regularly
        Yes76838.1
        No 94947.1
Adherence to MD
        Low adherence (PyrMDS < 6)1497.4
        Moderate adherence (6 ≥ PyrMDS < 10)171147.1
        High adherence (PyrMDS ≥ 10)1577.8
Lifestyle Behaviors
        Unhealthy32516.1
        Moderately Healthy131365.1
        Healthy37918.8
Table 4. Socio-demographic and health status characteristics according to lifestyle behaviors. Statistical significance is indicated as follows: *** p < 0.001; * p < 0.05.
Table 4. Socio-demographic and health status characteristics according to lifestyle behaviors. Statistical significance is indicated as follows: *** p < 0.001; * p < 0.05.
Categorical VariablesUnhealthy
n (%)		Moderately Healthy
n (%)		Healthy
n (%)		p-Value
(Pearson’s χ2)		Cramer’s V
Gender 0.323
      Male172 (52.9)634 (48.3)185 (48.8)
      Female153 (47.1)679 (51.7)194 (51.2)
Age 0.011 *0.057
      18–35 years92 (28.3)394 (30.0)139 (36.7)
      36–54 years121 (37.2)519 (39.5)151 (39.8)
      55+ years112 (34.5)400 (30.5)89 (23.5)
Education <0.001 ***0.155
      Primary education0 (0.0)2 (0.2)0 (0.0)
      High school degree201 (61.8)579 (44.1)126 (33.2)
      Bachelor’s degree51 (15.7)373 (28.4)175 (46.2)
      Master’s degree73 (22.5)357 (27.2)78 (20.6)
      PhD0 (0.0)2 (0.2)0 (0.0)
Type of living area 0.594
      Urban77 (23.7)300 (22.8)92 (24.3)
      Rural45 (13.8)184 (14.0)41 (10.8)
      City—administrative center203 (62.5)829 (63.1)246 (64.9)
Employment <0.001 ***0.084
      Full time203 (62.5)865 (65.9)245 (64.6)
      Part-time37 (11.4)142 (10.8)58 (15.3)
      Unemployed25 (7.7)38 (2.9)11 (2.9)
      Retired23 (7.1)71 (5.4)14 (3.7)
      Other37 (11.4)197 (15.0)13.5 (51)
Marital Status <0.001 ***0.096
      Single59 (18.2)250 (19.0)111 (29.3)
      In a relationship80 (24.6)423 (32.2)105 (27.7)
      Married127 (39.1)482 (36.7)110 (29.0)
      Divorced48 (14.8)117 (8.9)39 (10.3)
      Widowed11 (3.4)41 (3.1)14 (3.7)
Ethnicity 0.056
      Bulgarian275 (84.6)1116 (85.0)338 (89.2)
      Turkish35 (10.8)159 (12.1)34 (9.0)
      Roma6 (1.8)10 (0.8)0 (0.0)
      Other7 (2.2)27 (2.1)6 (1.6)
      Will not answer2 (0.6)1 (0.1)1 (0.3)
Living arrangement 0.018 *0.063
      Alone68 (20.9)207 (15.8)50 (13.2)
      With others257 (79.1)1106 (84.2)329 (86.8)
BMI <0.001 ***0.164
      Underweight (<18.5)1 (0.3)37 (2.8)22 (5.8)
      Normal (18.5–24.9)106 (32.6)676 (51.5)243 (64.1)
      Pre-obesity (25.0–29.9)158 (48.6)443 (33.7)82 (21.6)
      Obesity class I (30.0–34.9)50 (15.4)123 (9.4)27 (7.1)
      Obesity class II (35.0–39.9)8 (2.5)32 (2.4)5 (1.3)
      Obesity class III (>39.9)2 (0.6)2 (0.2)0 (0.0)
Health conditions
      Type 1 diabetes33 (10.2)165 (12.6)35 (9.2)0.140
      Type 2 diabetes44 (13.5)95 (7.2)10 (2.6)<0.001 ***0.122
      Insulin resistance61 (18.8)123 (9.4)30 (7.9)<0.001 ***0.117
      Cardiovascular disease50 (15.4)221 (16.8)54 (14.2)0.448
      Hypertension135 (41.5)293 (22.3)41 (10.8)<0.001 ***0.216
      Depression7 (2.2)45 (3.4)7 (1.8)0.183
Continuous variablesUnhealthy
Mean ± SD		Moderately Healthy
Mean ± SD		Healthy
Mean ± SD		p-value
(ANOVA)
Healthcare satisfaction3.51 ± 1.623.70 ± 1.663.84 ± 1.500.031 *
Table 5. Characteristics associated with healthy lifestyle patterns—results from a multinomial logistic regression. The unhealthy lifestyle group was used as a reference group. Statistical significance is indicated as follows: *** p < 0.001; ** p < 0.01; * p < 0.05.
Table 5. Characteristics associated with healthy lifestyle patterns—results from a multinomial logistic regression. The unhealthy lifestyle group was used as a reference group. Statistical significance is indicated as follows: *** p < 0.001; ** p < 0.01; * p < 0.05.
Variables
Moderately HealthyHealthy
Odds Ratio (95% CI)p-ValueOdds Ratio (95% CI)p-Value
Employment (ref. “full-time”)
      Part-time0.945 (0.624–1.431)0.7891.368 (0.837–2.236)0.211
      Unemployed0.474 (0.264–0.853)0.013 *0.443 (0.193–1.018)0.055
      Retired0.870 (0.467–1.619)0.6600.602 (0.248–1.461)0.262
      Other1.517 (1.008–2.283)0.455 *1.454 (0.881–2.401)0.143
Marital Status (ref. “single”)
      In a relationship1.359 (0.922–2.005)0.1220.769 (0.487–1.216)0.262
      Married1.094 (0.755–1.587)0.6340.639 (0.409–0.997)0.048 *
      Divorced0.927 (0.568–1.511)0.7601.012 (0.557–1.839)0.968
      Widowed1.889 (0.774–4.613)0.1624.364 (1.426–13.355)0.009 **
Living arrangement (ref. “alone”)
      With others1.438 (1.042–1.985)0.027 *1.707 (1.113–2.619)0.142
BMI (ref. “normal”)
      Underweight (<18.5)5.527 (0.741–41.230)0.09510.087 (1.313–77.473)0.026 *
      Pre-obesity (25.0–29.9)0.441 (0.332–0.586)<0.001 ***0.220 (0.152–0.317)<0.001 ***
      Obesity class I (30.0–34.9)0.402 (0.269–0.602)<0.001 ***0.253 (0.147–0.436)<0.001 ***
      Obesity class II (35.0–39.9)0.570 (0.251–1.297)0.1800.220 (0.067–0.717)0.012 *
      Obesity class III (>39.9)0.119 (0.016–0.877)0.037 *--
Health conditions (ref. “no condition”)
      Type 2 diabetes0.575 (0.348–0.951)0.031 *0.182 (0.076–0.436)<0.001 ***
      Insulin resistance0.437 (0.303–0.632)<0.001 ***0.392 (0.233–0.660)<0.001 ***
      Cardiovascular disease1.593 (1.082–2.344)0.018 *1.714 (1.054–2.788)0.030 *
      Hypertension0.432 (0.318–0.587)<0.001 ***0.202 (0.128–0.318)<0.001 ***
      Depression2.515 (1.043–6.063)0.040 *1.869 (0.587–5.955)0.290
Healthcare satisfaction1.082 (0.999–1.172)0.0541.143 (1.033–1.263)0.009 **
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Lazarova, S.; Petrova-Antonova, D. Health-Promoting Behaviors in Bulgaria: A Cross-Sectional Study on Non-Communicable Diseases and Lifestyle. Societies 2025, 15, 15. https://doi.org/10.3390/soc15010015

AMA Style

Lazarova S, Petrova-Antonova D. Health-Promoting Behaviors in Bulgaria: A Cross-Sectional Study on Non-Communicable Diseases and Lifestyle. Societies. 2025; 15(1):15. https://doi.org/10.3390/soc15010015

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Lazarova, Sophia, and Dessislava Petrova-Antonova. 2025. "Health-Promoting Behaviors in Bulgaria: A Cross-Sectional Study on Non-Communicable Diseases and Lifestyle" Societies 15, no. 1: 15. https://doi.org/10.3390/soc15010015

APA Style

Lazarova, S., & Petrova-Antonova, D. (2025). Health-Promoting Behaviors in Bulgaria: A Cross-Sectional Study on Non-Communicable Diseases and Lifestyle. Societies, 15(1), 15. https://doi.org/10.3390/soc15010015

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