Introduction

Recent advances in health and medical technology, socioeconomic development, and improved living standards have qualitatively and quantitatively enhanced the overall living environment of modern societies. Consequently, as human life expectancy increases, interest in quality of life (QoL) and subjective well-being has expanded as central topics of public health and social research [1]. According to the World Health Organization (WHO), health is defined not merely as the absence of disease or infirmity but as “a state of complete physical, mental, and social well-being” [2]. This holistic definition underscores that health must be understood in connection with broader constructs such as QoL, happiness, and well-being [3].

Within this context, QoL is conceptualized as an individual’s perception of their position in life in the context of their culture and value systems [4]. It is typically operationalized using multidimensional tools such as the WHOQOL-BREF, encompassing physical, psychological, social, and environmental domains. In contrast, happiness and life satisfaction represent core components of subjective well-being (SWB)—with life satisfaction reflecting the cognitive evaluation of one’s life circumstances and happiness reflecting the affective or experiential dimension [3,5]. The World Happiness Report (WHR) operationalizes happiness through the Cantril Ladder life-evaluation scale, while life satisfaction is often assessed through the Satisfaction With Life Scale (SWLS) [7,8]. Distinguishing between these constructs strengthens conceptual validity by allowing for differentiated interpretation of cognitive and affective aspects of well-being.

Theoretically, QoL functions as a determinant or contextual domain influencing SWB, while happiness and life satisfaction represent subjective evaluations of these life conditions [9]. The OECD Well-Being Framework further illustrates that objective indicators such as income, social connections, environmental quality, and health collectively contribute to subjective life evaluations [10]. This multidimensional perspective suggests that promoting happiness and life satisfaction requires an integrated understanding of psychological, social, and structural determinants.

Moreover, happiness extends beyond the prevention of individual diseases; it is intrinsically tied to population health and the sustainability of society [11]. Empirical studies demonstrate that higher levels of individual happiness are associated with better physical and mental health outcomes, greater social trust, and stronger community cohesion [12,13]. In this sense, happiness operates as both an indicator and a determinant of societal well-being.

Despite the abundance of prior research examining relationships among stress, depression, and QoL, relatively few studies have clearly differentiated the conceptual domains of happiness and life satisfaction or have contextualized them within specific regional and social environments. Therefore, further investigation is warranted to clarify the theoretical boundaries between happiness and life satisfaction and to explore how regional characteristics—such as socioeconomic context, community support systems, and local health policies—interact with psychological factors to influence subjective well-being. Such clarification is essential to enhance the construct validity of key variables and to establish evidence-based regional strategies for improving the happiness and mental health of community residents.

This study was conducted to analyze the various factors influencing the happiness of residents in Gyeongsangbuk-do, with the aim of providing foundational data for promoting mental health within local communities. Through this, the study seeks to contribute to the establishment of effective health policies and the development of community-based mental health promotion programs.

Methods

Research Design

This study is a secondary data analysis using community health survey data collected in 2022 to identify factors affecting the happiness of residents of Gyeongsangbuk-do communities aged 19 to 65 years.

Participants

This study included adults aged > 19 years in North Gyeongsang Province who participated in a community health survey conducted by the Korea Centers for Disease Control and Prevention from August to October 2022. Community health surveys are conducted annually at public health centers nationwide. Trained investigators visit the final selected sample households in person through a system extraction method to conduct a 1:1 interview (electronic survey).

The 2022 Community Health Survey used in this study included 211 questions on demographic characteristics, health behavior, psycho-emotional characteristics, stress, depression, and happiness. Raw data were provided by submitting a pledge of raw data and a data use plan for the community health survey on the community health survey website of the Korea Centers for Disease Control and Prevention. This analysis utilized high-quality, outlier-excluded data from 21,490 Gyeongsangbuk-do residents (96.9% of the original 22,171 participants from the 2022 Community Health Survey), following systematic outlier detection and removal of 651 cases with missing values, ensuring robust and meaningful statistical analyses.

Measurements

1. Sociodemographic factors

Residential area, sex, age, education, employment status, and household income were used as variables to understand demographic and sociological characteristics. Residential areas were classified into Dong, Eup, and Myeon; sex was classified into men and women; and age was classified into 20s, 30s, 40s, 50s, and 60s or older [14]. Education was classified into 'middle school graduation or lower,’ 'high school graduation,' and 'university graduation or higher.’ Employment status has been classified into “yes” and “no," depending on whether they worked for income in the past week. Household income was classified into income quintiles and classified into 'low,' 'medium,' 'medium,' and 'high' [15].

2. Health behavior

Smoking, drinking, physical activity, and subjective health levels were classified as variables to understand health behaviors [15]. Smoking status was classified as ‘yes’ and ‘no,’ and drinking was classified as ‘yes’ and ‘no,’ depending on whether the participant drank alcohol in the past year.

Physical activity was considered intense if it was very difficult or resulted in shortness of breath than usual for the past week. If exercise, such as walking for more than 10 minutes, stretching, or bare-handed gymnastics, was practiced for more than 1 day, it was classified as yes,’ and if it was less than 1 day, it was classified as ’no.’ Subjective health level was classified as 'very good,’ 'good,’ 'normal,’ 'bad,’ and 'very bad.’

3. Psycho-emotional characteristics

Among the mental health items of the community health survey, psychological and emotional characteristics were classified as suicidal thoughts, stress-counseling experiences, and depression experiences. As for the risk of suicide, those who answered "yes" to the question of whether they had attempted suicidal thoughts for 1 year were classified into suicide risk groups. The experience of counseling due to mental problems for 1 year was categorized into "yes" and "no.”

4. Stress, Depression, and Happiness

For stress perception, items, such as 'feeling very much,’ 'feeling a lot,’ 'feeling a little,’ and 'feeling little,' were used. For depression, the PHQ-9, a self-report tool for the diagnosis of mental illness designed to meet the criteria for diagnosing depression in the Diagnostic and Statistical Manual of Mental Disorders-IV, was used based on the contents of the National Health and Nutrition Survey. There are nine questions in the questionnaire, and the higher the total score, the more severe the symptoms of depression. For happiness, the variable of satisfaction with life (10-point scale, very dissatisfied to very satisfied) from the community health survey was used [15]. Although happiness and life satisfaction are theoretically distinct concepts, life satisfaction was used as a proxy measure of happiness in this study because it is widely employed in empirical research and is considered to reflect an individual’s overall level of subjective well-being in a relatively stable manner.

5. Ethical considerations

This study was approved by the Institutional Bioethics Committee (IRB No. 1040191-202310-HR-014-01).

6. Analysis

Data were analyzed using SPSS version 27 (SPSS Inc, Chicago, IL, USA). The analysis was performed using a t-test and analysis of variance to confirm stress, depression, and happiness according to the general characteristics of the participants. The correlation between stress, depression, and happiness was analyzed using Pearson's correlation coefficient. Multiple regression analysis was performed to identify the factors affecting happiness.

Results

General characteristics of the participants

The demographic and sociological characteristics of the 21,490 study participants are presented in Table 1. Regarding community type, the majority of participants resided in dong areas (urban neighborhoods, 27.1%), while 72.9% lived in eup/myeon areas (rural towns and villages). The sex distribution showed a slight female predominance, with 53.2% females and 46.8% males.

The age distribution revealed that participants in their 50s comprised the largest group (43.7%), followed by those aged 60 years and older (29.0%), participants in their 40s (12.8%), 30s (7.7%), and 20s (6.8%). Regarding educational attainment, 41.5% had completed middle school education or lower, 28.6% had completed high school education, and 29.9% had attained college-level education or higher.

With respect to economic characteristics, the majority of participants (65.6%) reported being economically active, while 34.4% were not engaged in economic activities. The Income distribution showed 44.9% in the lowest quartile, 22.4% in the middle quartile, 13.9% in the middle high quartile, and 18.8% in the highest quartile.

Health behaviors demonstrated that 76.4% of participants consumed alcohol, while 23.6% were non-drinkers. Regarding smoking status, 84.3% were non-smokers and 15.7% were current smokers. Physical activity engagement was reported by 79.3% of participants, while 20.7% reported no regular exercise.

Subjective health status assessment revealed that 43.8% of participants rated their health as average, 28.9% as good, 17.9% as bad, 5.2% as very good, and 4.3% as very bad.

Psycho-emotional characteristics indicated that the majority of participants (92.4%) reported no suicidal ideation, while 7.6% had experienced suicidal thoughts. Additionally, 97.9% had not received stress counseling, and 92.7% reported no prior experience with depression-related issues (Table 1).

Stress, depression, and happiness according to sociodemographic factors, health behaviors, and psycho-emotional characteristics of the participants

The associations between participant characteristics and the three outcome variables (stress, depression, and happiness) are presented in Table 1. Statistical analyses revealed significant differences in stress, depression, and happiness levels across all sociodemographic, health behavioral, and psycho-emotional variables.

1. Demographic and Sociological Factors

Community type demonstrated significant differences across all three outcomes. Residents in dong (urban) areas reported higher levels of stress (t=7.58, p<.001), depression (t=4.77, p<.001), and happiness (t=7.88, p<.001) compared to those living in eup/myeon (rural) areas. Sex differences were statistically significant in stress, depression, and happiness. Females exhibited significantly higher stress (t=-8.72, p<.001), depression (t=-18.82, p<.001), and happiness levels (t=3.78, p<.001) than males. while did not differ significantly between sexes. Age-related patterns revealed distinct profiles across the three variables. Stress levels were highest among participants in their 30s and 40s (F=242.84, p<.001), while depression was most prevalent in those aged 60 years and older (F=91.79, p<.001). Conversely, happiness levels were lowest in the oldest age group (≥60 years) (F=104.42, p<.001). Educational attainment showed a complex relationship with mental health outcomes. Higher education was associated with increased stress levels (F=196.23, p<.001) but also greater happiness (F=409.64, p<.001). Depression was most prevalent among participants with middle school education or lower (F=177.21, p<.001). Economic activity status revealed contrasting patterns. Participants who were economically active showed significantly lower levels of depression (t=19.14, p<.001) and higher levels of stress (t=-12.71, p<.001) and happiness (t=-15.21, p<.001).

Income level demonstrated differential effects across outcomes. Higher-income participants experienced greater stress than those with lower incomes (F=56.11, p<.001). Conversely, depression was more prevalent among participants with lower incomes (F=63.72, p<.001), while happiness increased with higher income levels (F=129.95, p<.001).

2. Health Behaviors

Drinking patterns indicated that non-drinkers had lower stress levels than drinkers (t=-7.62, p<.001), but higher levels of depression (t=5.40, p<.001) and happiness (t=-5.18, p<.001), demonstrating significantly differenced. Smoking status revealed significant differences across all variables. Current smokers exhibited higher stress levels (t=-7.37, p<.001), while non-smokers reported higher depression (t=2.25, p<.05) and happiness (t=9.28, p<.001). Participants who exercised regularly showed higher stress levels (t=-5.43, p<.001), and happiness (t=-31.74, p<.001, as well as lower levels of depression (t=30.41, p<.001) ) compared to those who did not engage in regular exercise.

3. Health Status and Psycho-emotional Characteristics

Subjective health status was strongly associated with all outcomes. Participants rating their health as moderate or poor reported higher stress (F=52.13, p<.001) and depression (F=849.46, p<.001) compared to those with good subjective health. Participants with good or very good self-rated health demonstrated higher happiness levels. Suicidal ideation was associated with significantly elevated stress (t=-26.75, p<.001) and depression (t=-41.27, p<.001), along with reduced happiness (t=39.26, p<.001) compared to those without suicidal thoughts. Stress counseling history was linked to higher stress levels (t=-38.87, p<.001) and lower happiness (t=18.04, p<.001) among participants who had received such services. Depression experience showed strong associations across all variables, with participants having depression history reporting elevated stress (t=-28.31, p<.001) and depression levels (t=-35.90, p<.001), alongside diminished happiness (t=28.92, p<.001).

Correlation between the participants’ stress, depression, and happiness index

Pearson product-moment correlations were computed to examine the relationships among the three primary outcome variables: subjective stress level, depression, and happiness index. The correlation matrix is presented in Table 2.

The analysis revealed significant intercorrelations among all three variables (all p<.001). Stress and depression demonstrated a significant positive correlation (r= p<.001), indicating that participants with higher stress levels tended to report greater depressive symptoms.

Stress and happiness showed a significant negative correlation (r=-.25, p<.001), suggesting that increased stress was associated with decreased happiness levels. Similarly, depression and happiness exhibited a significant negative correlation (r=-.37, p<.001), representing the strongest association among the three variables and indicating that higher depressive symptoms were associated with substantially lower happiness levels.

The magnitude of these correlations suggests moderate relationships among the variables. The strongest correlation was observed between depression and happiness (r=-.37), followed by the stress-happiness relationship (r=-.25), and the stress-depression relationship (r=.24). These findings support the theoretical framework that stress, depression, and happiness represent interconnected but distinct dimensions of psychological well-being.

All correlations were statistically significant at the p<.001 level, indicating robust associations that are unlikely to have occurred by chance. The pattern of relationships aligns with established psychological theories suggesting that stress and depression are positively associated, while both negatively impact happiness and overall well-being.

Influencing factors on happiness

As shown in Table 3, all variance inflation factor (VIF) values were below 10, indicating no evidence of multicollinearity among the independent variables. The Durbin-Watson statistic was 1.77, which is also reported in Table 3, and is close to the ideal value of 2. This suggests that there was no significant autocorrelation of residuals and that the assumption of independence was satisfied. The overall regression model was statistically significant, supporting the validity of the model in explaining the variation in happiness.

As a result of the regression analysis, the most significant positive predictors of happiness were subjective health status (β=.07 to .27), having a college-level or higher education (β=.10), exercise (β=.04), and high income (β=.04). Additionally, increasing age(specially 50's) was associated with a significant rise in happiness levels. In contrast, perceived stress levels (β=–.19), depression scores (β=–.17), suicidal thoughts (β=–.11), and smoking (β=–.08) emerged as key negative predictors of happiness. The explanatory power of the model was 25.6% (F=337.76, p<.001), indicating that the included variables significantly explained the variance in happiness levels.

Discussion

Using the 2022 community health survey data, this study aims to identify the stress, depression, and happiness of residents living in the Gyeongsangbuk-do community and identify factors that affect their happiness to provide basic data to improve the condition of residents’ lives. A comparison of the drinking, smoking, exercise habits, and subjective health levels of Korean people in 2022 with those of Gyeongsangbuk-do showed that the monthly drinking rate in Gyeongsangbuk-do was 76.4%, much higher than the national average (57.4%) [16]. In South Korea, drinking is often embedded in social rituals (e.g., after-work gatherings), and drinking behaviours have been associated with stress relief in some study contexts. Recent research also suggests that normative attitudes toward drinking remain relatively permissive, although trends vary by demographic group [17,18]. However, residents of Gyeongsangbuk-do communities drink more frequently than the national average, suggesting that Gyeongsangbuk-do communities have more tolerant local cultural characteristics regarding drinking than the entire country. In 2022, the national average smoking rate was 17.7%, while it was 15.7% in Gyeongsangbuk-do [19], suggesting that smoking cessation policies or health-education programmes implemented in Gyeongsangbuk-do may have had some effect. For example, the region’s “Creating a smoke-free living environment” initiative offers 1:1 tailored cessation counselling [20], provides nicotine-replacement aids and behaviour-reinforcement items, and runs outreach programmes in schools, workplaces and low-income housing via the Gyeongbuk Smoking-Cessation Support Centre [21]. In addition, the provincial government expanded smoke-free zones around kindergartens, children’s daycare centres and schools (extending from 10 m to 30 m) and strengthened regional tobacco-control regulations [22]. In terms of exercise habits, 79.3% of Gyeongsangbuk-do say they were exercising, which is considerably higher than the national average of 45.5% [23], indicating that local residents are engaged in physical activities. Thus, Gyeongsangbuk-do practices health promotion programs well in consideration of community characteristics and is thought to be a result of the positive effect of the accessibility of having a lot of space to exercise, as it is the largest administrative district in Korea [24].

The analysis of subjective health status revealed significant regional disparities among study participants. As presented in Table 1, only 34.1% of Gyeongsangbuk-do residents rated their health status as either "good" (28.9%) or "very good" (5.2%), representing a substantial deviation from the national average of 53.1% reported in the Community Health Survey. This 19.0 percentage point difference indicates that residents in Gyeongsangbuk-do demonstrate significantly more negative health perceptions compared to the general Korean population, thereby highlighting pronounced regional disparities in self-rated health status. These observed disparities may be attributed to regional characteristics including accelerated demographic aging and challenging living conditions characterized by limited healthcare accessibility [25]. The ratio of depression risk groups was 7.3% lower in Gyeongsangbuk-do than the national average of 16.9% [26]. This might have affected Gyeongsangbuk-do because there are many farming and fishing villages [25] with high access to the natural environment, providing psychological stability.

These results show that the health-related behaviors and perceptions of Gyeongsangbuk-do residents differ from those of other regions and can be used as important basic data for future policy development and health promotion programs. It is necessary to analyze the factors related to these differences in greater depth through additional research. The analysis of the effects of demographic factors, health behaviors, and psychological and emotional characteristics of residents in Gyeongsangbuk-do on stress, depression, and happiness revealed that the type of residential area, among the demographic factors, had a significant influence on all three outcomes. Specifically, residents living in urban areas exhibited higher levels of stress, depression, and happiness than those residing in town or myeonareas. This finding aligns with the results of a recent analysis using data from the 2017 Korean Community Health Survey, which reported that the prevalence of depressive symptoms was approximately 1.29 times higher among urban residents compared with those living in rural areas [27]. The elevated stress and depression levels among city residents may be attributed to overcrowded and polluted urban environments that increase exposure to stressors [28], as well as to greater social isolation, relative deprivation, and competitive social climates that undermine social support [27] . Nevertheless, the paradoxical coexistence of higher happiness levels among urban residents can be explained by the broader range of opportunities and resources available in cities. Urban environments provide greater access to leisure, cultural, and social activities [29] enhanced educational, employment, and consumption opportunities [30] and superior transportation accessibility [31]. These conditions may foster higher life satisfaction and expectations, thereby contributing to increased happiness despite elevated stress and depression. In other words, although city residents are more susceptible to stress and depressive symptoms, they may simultaneously experience greater positive emotions and higher levels of subjective well-being due to enriched activity opportunities and lifestyle satisfaction compared with residents of towns or villages. Furthermore, a recent study confirmed that happiness, while inversely related to depression and suicidal ideation, is also influenced by a variety of contextual and environmental factors [32]. Therefore, the coexistence of “higher stress and depression along with higher happiness” observed among urban residents in this study should not be interpreted as a contradiction but rather as a reflection of the complex realities of urban life.

In addition, significant sex differences were observed, with females reporting higher levels of stress and depression, and lower levels of happiness compared to males. This is consistent with the result of a previous study, which showed that females’ mental suffering tends to be more severe than males’ [33]. Perceived stress levels reported by participants were higher among individuals in their 30s and 40s, whereas depression scores were notably higher among those aged 60 and above. This age-related pattern suggests that younger adults may be more vulnerable to external stressors related to employment and social competition, while older adults may be more susceptible to depressive symptoms potentially due to increased social isolation and age-related health issues [34].

Regarding educational level, highly educated people had high levels of stress and happiness, but depression was higher in those who had graduated from middle school or lower. This demonstrates that educational level can positively influence mental well-being [35]. Depending on whether or not they are economically active, stress and happiness were higher in the economically active group, but depression was higher in the non-economically active group. This finding is consistent with previous studies showing that economic stability positively affects mental health [35]. In terms of health behaviors, the effects of drinking and smoking on stress and depression were confirmed.

Stress was higher in the drinking group, whereas depression was higher in the nondrinking group. This suggests that drinking may provide temporary stress relief [36] but may have a long-term negative impact on mental health in the long run. Exercise positively affected stress and happiness, whereas depression was higher in the non-exercise group. This is consistent with previous studies [37] showing that physical activity plays an important role in mental well-being. When subjective health was poor, stress and depression tended to be high, and happiness tended to be low. This implies that health conditions have a significant impact on an individual's mental well-being [38]. In addition, the effects of suicidal ideation and stress counseling experiences, which are psychological and emotional factors, on stress and depression were found to be significant. In the suicidal ideation group, stress and depression were high, and happiness was low, emphasizing the need for social support in mental crisis situations [39]. These results provide important basic data for understanding the various stressors and causes of mental pain experienced by residents of Gyeongsangbuk-do and can be used for policy development and mental health promotion programs in the future.

Table 2 demonstrates significant intercorrelations among stress, depression, and happiness variables. Stress showed positive correlation with depression (r=.24, p<.001) and negative correlation with happiness (r=-.25, p<.001), consistent with established research identifying stress as a major depression risk factor [40]. The strongest negative association was observed between depression and happiness (r=-.37, p<.001). These findings emphasize the critical role of stress and depression management in maintaining psychological well-being [10].

Since happiness is closely associated with quality of life, an effective management program is needed to alleviate mental burden and promote well-being. There was also a significant negative correlation between depression and happiness, which means that the higher the depression, the lower the individual's happiness [41], confirming that depression negatively affects an individual's overall life satisfaction. These results highlight the need for psychological interventions to treat and prevent depression and suggest that various programs are needed to promote well-being. Therefore, stress, depression, and happiness are correlated and can provide important basic data for mental health management. In future studies, it is necessary to analyze these correlations in more depth and apply mental health management programs to solve them.

The factor with the greatest influence on happiness was the subjective level of good health. This is significant because health status has an important influence on mental well-being. In poor health, stress and depression may increase, while happiness may decrease [42].

Both depression and subjective stress levels demonstrated significant negative associations with happiness. Effective depression and stress management represents a critical component in promoting psychological well-being, while suicidal ideation showed particularly detrimental effects on happiness [39]. These findings highlight the clinical significance of psycho-emotional problems and underscore the need for comprehensive mental health interventions.

Low happiness in groups with suicidal thoughts indicates that mental support is important for residents and the need for state and social intervention. Additionally, normal health levels and very good health cases affect happiness. Considering the effect of health levels on happiness, health promotion programs can contribute to improving happiness. Participants with poor subjective health conditions, stress, and depression had higher suicidal thoughts than those who did not [43], which affects happiness; therefore, it is considered necessary to prepare and apply a plan to focus on managing participants’ stress and depression. Stress counseling and smoking were identified as factors influencing happiness. In particular, the finding that smoking negatively affects happiness is consistent with previous research demonstrating a significant association between smoking and mental health [44,45].

Furthermore, higher educational attainment, regular physical activity, and higher income were identified as significant predictors of happiness. Both emgagement in physical activity and economic stability were positively contribute to individuals' subjective well-being. Consequently, it is imperative for the government to support underserved populations, such as low-income individuals, by providing continuous and comprehensive economic assistance. Additionally, the development and widespread implementation of exercise programs for the general population should be prioritized to enhance overall happiness.

However, some studies, have reported that education level and smoking status were not significant predictors of depression or happiness [43], so it is necessary to analyze the results of national surveys in the future to identify factors influencing happiness according to the general characteristics, health behavior, and mental health of our people.

This study's methodological strengths include a large representative sample (N=21,490) and comprehensive variable assessment. Multiple regression analysis identified significant happiness predictors including health status, depression, stress, suicidal ideation, education, smoking, exercise, and income. The model explained 25.6% of happiness variance (adjusted R²=.256, p<.001), demonstrating substantial predictive validity.

To enhance the well-being of residents in Gyeongsangbuk-do, maintaining their health is of paramount importance. This can be achieved by providing health-related education and intervention programs that promote and sustain health through activities such as stress, depression, and suicide management, as well as encouraging regular exercise and smoking cessation. Active support should be extended to all residents to enable them to lead healthy and fulfilling lives. Furthermore, it is essential to continuously identify populations characterized by low educational attainment, low income, and smoking habits, and to connect these groups with community resources to ensure a minimum standard of living and access to economic support.

Measuring happiness using quality of life–based indicators has several limitations. As this study relied on secondary data, the measure does not sufficiently capture the emotional and experiential dimensions of happiness and may be restricted to interpretations centered on cognitive evaluation. Accordingly, the happiness variable in this study should be interpreted as “subjective well-being based on overall life satisfaction” rather than emotional happiness in the strict sense. Future research should employ multidimensional instruments that directly assess emotional happiness in conjunction with life satisfaction. Despite these limitations, this study is meaningful in that it identified the overall level of subjective well-being among community residents and analyzed the factors influencing happiness.

Conclusions

Residents of Gyeongsangbuk-do exhibited high rates of alcohol consumption, smoking, and exercise participation; however, their subjective health status was lower than the national average. These findings suggest that regional characteristics and cultural factors influence residents' health behaviors. A significant positive correlation was observed between stress and depression, indicating that higher stress levels are associated with increased depressive symptoms. The most influential factors affecting happiness were health status, stress, depression, suicidal ideation, educational attainment (e.g., college graduation or higher), smoking, exercise, and income level.

The final model demonstrated robust predictive validity, accounting for 25.6% of the variance in happiness scores (adjusted R²=.256, F=337.76, p<.001).

These findings underscore the complex interplay between health behaviors, mental health, and subjective well-being among residents of Gyeongsangbuk-do. Despite relatively high levels of health-related behaviors such as exercise participation, the lower subjective health status compared to the national average highlights the influence of regional and cultural contexts on health perceptions. The strong positive correlation between stress and depression, along with the identification of multiple psychosocial and behavioral factors—such as health status, stress, depression, suicidal ideation, education, smoking, exercise, and income—as key predictors of happiness, suggests that a multidimensional approach is essential for promoting well-being. Therefore, regionally tailored public health strategies that address both mental health and socioeconomic disparities are needed to effectively enhance the happiness and quality of life of local residents.

In future research, it will be necessary to analyze various factors that can be used as basic data for policy development to improve the mental health of Gyeongsangbuk-do residents. In particular, efforts are needed to reduce stress and depression and promote happiness by strengthening the mental health support system and developing customized health promotion programs for local residents. In addition, it is important to support residents in maintaining healthy behaviors by implementing health education and prevention programs that reflect the characteristics of local communities.

References

• 1. Kim YB, Lee SH. Effects of the elderly’s health statuses, health behavior, and social relations on their health-related quality of life: Focusing on family types. Journal of Korean Academy of Community Health Nursing. 2018;29(3):310–321. https://dx.doi.org/10.12799/jkachn.2018.29.3.310ArticlePDF
• 2. World Health Organization. A conceptual framework for action on the social determinants of health [Internet]. Geneva: World Health Organization; 2010 [cited 2010 Jul 13]. Available from: https://iris.who.int/bitstream/handle/10665/44489/9789241500852_eng.pdf?sequence=1
• 3. Helliwell JF, Layard R, Sachs JD, De Neve JE, Aknin LB, Wang S. World happiness report 2024 [Internet]. Oxford: Wellbeing Research Centre; 2024 [cited 2025 Nov 2]. Available from: https://worldhappiness.report/ed/2024/
• 4. Choon SW, Yong CC, Tan SK, Tan SH. A proposed integrated happiness framework to achieve sustainable development. Heliyon. 2022;8(10):e10813. https://doi.org/10.1016/j.heliyon.2022.e10813ArticlePubMedPMC
• 5. Lyubomirsky S, Sheldon KM, Schkade D. Pursuit happiness: The architecture of sustainable change. Review of General Psychology. 2005;9(2):111–131. https://doi.org/10.1037/1089-2680.9.2.111Article
• 6. Beutel ME, Klein EM, Brähler E, Reiner I, Jünger C, Michal M, et al. Loneliness in the general population: Prevalence, determinants and relations to mental health. BMC Psychiatry. 2017;17(1):97. https://doi.org/10.1186/s12888-017-1262-xArticlePubMedPMC
• 7. Cho AJ. The significant and way forward of the 7th community health plan [Internet]. Seoul: Korea Health Promotion Institute (KHPI); 2018 [cited 2025 06 20]. Available from: https://www.khepi.or.kr/kps/publish/view?board_idx=10064&menuId=MENU00889&pageNum=2&page_no=B2017002
• 8. Cha NH. An in-depth analysis of health issues and uncovering health disparties in the Gyeongbuk region based on community health surveys. Research Report. Sejong: Centers for Disease Control and Prevention North Gyeongsang Province; 2023. December;Report No.: 2023-16-002.
• 9. Koo S, Lee MS. Chronic disease morbidity and management status in Gyeongsangbuk-do: A summary of the 2018-2022 community health survey. Public Health Weekly Report. 2024;17(7):241–261. https://doi.org/10.56786/PHWR.2024.17.7.1ArticlePubMedPMC
• 10. Bahk JW, Lynch J, Khang YH. Forty years of economic growth and plummeting mortality: The mortality experience of the poorly educated in South Korea. Journal of Epidemiology and Community Health. 2017;71(3):282–288. https://doi.org/10.1136/jech-2016-207707ArticlePubMed
• 11. Oswald AJ, Proto E, Sgroi D. Happiness and productivity. Journal of Labor Economics. 2015;33(4):789–822. https://doi.org/10.1086/681096Article
• 12. Zhao M, Li Y, Lin J, Fang Y, Yang Y, Li B, Dong Y. The relationship between trust and well-being: A meta-analysis. Journal of Happiness Studies. 2024;25:56. https://doi.org/10.1007/s10902-024-00737-8Article
• 13. Holt-Lunstad J. Social connection as a critical factor for mental and physical health: Evidence, trends, challenges, and future implications. World Psychiatry. 2024;23(3):312–332. https://doi.org/10.1002/wps.21224ArticlePubMedPMC
• 14. Lee J, Bahk JW, Kim IH, Kim YY, Yun SC, Kang HY, et al. Geographic variation in morbidity and mortality of cerebrovascular diseases in Korea during 2011-2015. Journal of Stroke and Cerebrovascular Diseases. 2018;27(3):747–757. https://doi.org/10.1016/j.jstrokecerebrovasdis.2017.10.011ArticlePubMed
• 15. Kim DJ. English Indices of multiple deprivation: review and implications. Global Social Security Review. 2018;5:108–119. http://dx.doi.org/10.23063/2018.06.9Article
• 16. Kim I, Bahk J, Kim YY, Lee J, Kang HY, Lee J, et al. Prevalence of overweight and income gaps in 245 districts of Korea: Comparison using the national health screening database and the community health survey, 2009-2014. Journal of Korean Medical Science. 2018;33(1):e3. https://doi.org/10.3346/jkms.2018.33.e3ArticlePubMed
• 17. Kang J, Han H, Kim H, Park A, Kim D, Hwang R, et al. Alcohol consumption norms and the favored alcohol consumption policies of citizens of Seoul. International Journal of Environmental Research and Public Health. 2024;21(7):834–845. https://doi.org/10.3390/ijerph21070834ArticlePubMedPMC
• 18. Yoo JJ, Lee DH, Chang Y, Jo HG, Cho YY, Lee SH, Kim LY, Jang JY. Trends in alcohol use and alcoholic liver disease in South Korea. BMC Public Health. 2024;24(1):1841. https://doi.org/10.1186/s12889-024-19321-zArticlePubMedPMC
• 19. Korea Disease Control and Prevention Agency. Trends in current smoking rates, 2013-2022. Public Health Weekly Report. 2024;17(18):787–788. https://doi.org/10.56786/PHWR.2024.17.18.4ArticlePubMedPMC
• 20. Gyeongbuk Smoking Cessation Support Center. Description of the “creating a smoke-free living environment” project [Internet]. Andong: Andong Medical Center public health care page; 2024 [cited 2025 Nov 2]. Available from: https://www.amc.or.kr/front/page.do?pageView=%2Fpublic_health_care%2Fsmoking_cessation
• 21. Gyeongbuk Smoking Cessation Support Center. Mungyeong university campaign and anti-smoking education implemented [Internet]. Andong: Gyeongbuk Smoking Cessation Support Center; 2024 [cited 2025 Nov 2]. Available from: https://www.mkc.ac.kr/?kboard_content_redirect=12082
• 22. Gyeongsangbuk-do Provincial Office. Notice on expansion of non-smoking areas around kindergartens, daycare centers, and schools [Internet]. Andong: Gyeongsangbuk-do Provincial Office; 2024 Aug 5 [cited 2025 Nov 2]. Available from: https://www.gb.go.kr/Main/page.do?BD_CODE=bbs_gongji&B_NUM=436487001&B_STEP=436487000&cmd=2&mnu_uid=6786
• 23. Lee SS. Current state and challenges of policy response to low fertility and population aging. Health and Welfare Policy Forum. 2016;231:51–65. http://dx.doi.org/10.23062/2016.01.6
• 24. Korea Disease Control and Prevention Agency. Announcement of the results of the 2023 community health survey [Internet]. Osong: Korea Disease Control and Prevention Agency; 2023 [cited 2024 Oct 19]. Available from: https://www.kdca.go.kr/board/board.es?mid=a20501010000&bid=0015&list_no=724113&cg_code=&act=view&nPage=1
• 25. Korea Society for the Study of Obesity. The cause of obesity [Internet]. Seoul: Korea society for the study of obesity; 2023 [cited 2024 Jan 23]. Available from: https://general.kosso.or.kr/html/?pmode=obesityCause
• 26. Yoon JY. Table cultures changed by corona. Korea foodservice news. 2021 Feb 11;sect.03. Available from: https://www.kfoodtimes.com/news/articleView.html?idxno=14729#
• 27. Jeong JA, Kim SA, Yang JH, Shin MH. Urban-rural differences in the prevalence of depressive symptoms in Korean adults. Chonnam Medical Journal. 2023;May;59(2):128–133. https://doi.org/10.4068/cmj.2023.59.2.128ArticlePubMedPMC
• 28. Lim AJ. A study on factors influencing obesity in Korean adults before and after the covid-19 pandemic (2019-2020)-using the national health and nutrition survey (2019-2020) [master’s thesis]. [Seoul]: Chung-Ang University; 2023. 37-39pp.
• 29. Yoo J. Attitude toward leisure, satisfaction with leisure policy, and happiness are mediated by satisfaction with leisure activities. Scientific Reports. 2023;12(1):11723. https://doi.org/10.1038/s41598-022-16012-w
• 30. Lee C, Choi S, Yoon J. The influence of access to urban amenities on urban environment satisfaction: A case study of four new towns in the vicinity of Seoul, South Korea. Applied Research in Quality of Life. 2023;18(6):3111–3139. https://doi.org/10.1007/s11482-023-10223-7
• 31. Lee KY. Factors influencing urban livability in Seoul, Korea: Urban environmental satisfaction and neighborhood relations. Social Sciences. 2021;10(4):138. https://doi.org/10.3390/socsci10040138Article
• 32. Choi M, Sempungu JK, Kim MH, Han J, Lee YH. Happiness and its association with suicide ideation and attempt in Korea: The roles of socio-environmental, psychological, and health-related factors. Journal of Korean Medical Science. 2024;Aug;39(44):e283. https://doi.org/10.3346/jkms.2024.39.e283ArticlePubMedPMC
• 33. Park YR. Association between social isolation and depressive symptoms following the COVID-19 pandemic in Korean adult: Analysis of community health survey sata for 2020 [master’s thesis]. [Seoul]: Yonsei University; 2022. 62 p.
• 34. Ettman CK, Abdalla SM, Cohen GH, Sampson L, Viver PM, Galea S. Prevalence of depression symptoms in US adults before and during the COVID-19 pandemic. JAMA Network Open. 2020;3(9):e2019686. https://doi.org/10.1001/jamanetworkopen.2020.19686ArticlePubMedPMC
• 35. Leigh-Hunt N, Bagguley D, Bash K, Turner V. Turnbull S, Vaitorta N. An overview of systematic reviews on the public health consequences of social isolation and loneliness. Public Health. 2017;152:157–171. https://doi.org/10.1016/j.puhe.2017.07.035ArticlePubMed
• 36. American Diabetes Association. 1. Strategies for improving care. Diabetes Care. 2015;38(1):S5–S7. https://doi.org/10.2337/dc15-S004ArticlePubMed
• 37. Rodriguez-Ayllon M, Cadenas-Sánchez C, López FE, Muñoz NE, Gonzalez JM, Migueles JH, et al. Role of physical activity and sedentary behavior in the mental health of preschoolers, children and adolescents: A systematic review and meta‑analysis. Sports Medicine. 2019;49:1383–1410. https://doi.org/10.1007/s40279-019-01099-5Article
• 38. Silva C, McGovern C, Gomez S, Beale E, Overholser J, Ridley J. Can I count on you? Social support, depression and suicide risk. Clinical Psychology & Psychotherapy. 2023;30(6):1407–1415. https://doi.org/10.1002/cpp.2883Article
• 39. Yang Y. The study on the factors affecting depression experiences of local residents in Gwangju. Journal of the Korea Entertainment Industry Association. 2022;16(4):243–251. https://doi.org/10.21184/jkeia.2022.6.16.4.243Article
• 40. Ahn SA, Sim MY. A study on the mediation effects of depression and self-efficacy on college students’ stress and subjective happiness. Journal of the Korea Academia-Industrial cooperation Society. 2015;16(10):7021–7033. https://doi.org/10.5762/KAIS.2015.16.10.7021Article
• 41. Chun HJ, Kim HY, Chun KK. The effects of objective characteristics and subjective evaluation of community on depression of adults-The case of Seoul city-. Journal of Community Welfare. 2019;71:1–39. https://doi.org/10.15300/jcw.2019.71.4.1Article
• 42. Kim KS. Factors affecting the depressive mood experience in local residents: Focusing on Gangseo-gu, Seoul. Health Policy and Management. 2023;33(4):432–439. https://doi.org/10.4332/KJHPA.2023.33.4.432Article
• 43. Kwon HJ, Kim YJ, Kim HS. The association between smoking amount and mental health. Journal of the Korean society for Wellness. 2016;11(3):297–305. http://dx.doi.org/10.21097/ksw.2016.08.11.3.297Article
• 44. Shin M, Park J. The factors affecting the depression in adult after COVID-19. The Journal of Humanities and Social Science. 2022;13(3):1677–1692. https://doi.org/10.22143/HSS21.13.3.117Article
• 45. Park M, Min B, Lee M. A study on the happiness of future generations: Focusing on MZ generations. Journal of the Korean Urban Management Association,. 2021;34(4):121–147. https://doi.org/10.36700/KRUMA.2021.12.34.4.121Article

Figure & Data

References

Citations

Citations to this article as recorded by