This study had 134 participants for every 1 item, with a sample size of 2,820 and 21 variables included. According to the N: q ratio rule of thumb, this sample size is sufficient to produce reliable results. Our study shows that physical activity levels present a significant health concern, as a troubling 62.1% of participants admit to being sedentary, outnumbering the 37.9% active. This suggests a potential need for targeted health interventions to promote physical activity, although research is required to confirm these findings.
Further, this study explored the relationship between socioeconomic status (SES), lifestyle, and cardiometabolic diseases (CMD). Results from the structural equation model (SEM) showed significant associations and gave insights into the mediation effects of lifestyle on the relationship between SES and CMD.
Regarding the mediation results, it was discovered that lifestyle didn’t fully mediate the relationship between SES and CMD. Although there was a significant direct effect of SES on CMD, there was no significant indirect effect through lifestyle. This suggests that lifestyle factors may not be the primary mechanism through which SES is associated with CMD risk, as measured in this study. These findings align with previous studies that have reported mixed results regarding the mediation effects of lifestyle between SES and health outcomes [21,22,23]. However, due to the self-reported nature of the data, these results should be interpreted with caution. The complexity of the relationship between these variables, with lifestyle factors influenced by various social, economic, and cultural factors, makes it challenging to establish a clear mediating role [10, 24]. Additionally, unmeasured variables may contribute to the relationship between SES and CMD, thus attenuating the mediating effect of lifestyle [1, 25].
Hicks et al. (2021) studied the relationship between lifestyle factors, and cardiovascular disease risk [26]. They found that lifestyle factors may partially mediate the association between SES and cardiovascular disease risk, supporting the idea that lifestyle behaviours explain some socioeconomic disparities in health outcomes. In contrast, Liu et al. (2023) conducted a similar study and reported no significant mediating effect of lifestyle on the relationship between SES and health outcomes [27]. These contrasting findings highlight the complexity of the relationship and suggest that other factors beyond lifestyle may also play a role in the socioeconomic disparities in CMD [28,29,30].
Furthermore, the present study revealed significant associations between SES, lifestyle, and CMD individually. SES was significantly associated with CMD, suggesting that higher SES is associated with an increased risk of CMD, contrary to the common belief that higher SES is generally linked to better health outcomes [19, 31, 32]. However, it aligns with previous studies that reported similar associations between higher SES and increased CMD risk [33]. The prevalence of risk factors such as a sedentary lifestyle and psychosocial stressors among individuals with higher SES may contribute to this association. In our research, we define higher socioeconomic status (SES) as individuals who score higher on the composite measure of SES, which takes into account their educational level, household income, and occupation status. Specifically, people with higher SES typically have higher levels of education, greater household income, and are more likely to have prestigious or stable occupations.
Lifestyle was significantly associated with CMD, suggesting that individuals with unhealthier lifestyles have a higher risk of CMD. This result is consistent with a vast body of literature linking unhealthy behaviours, such as physical inactivity, smoking, and excessive alcohol consumption, with increased risk of CMD [34,35,36,37]. Numerous studies have consistently demonstrated the detrimental effects of unhealthy lifestyle factors on cardiovascular health and CMD outcomes [34,35,36,37].
The results of this study provide important insights into the associations between lifestyle, SES, and CMD. The findings suggest that while lifestyle factors play a role in CMD risk, they may not fully mediate the relationship between SES and CMD. This suggests that other factors, such as psychosocial stressors, access to healthcare, environmental factors, and genetic predispositions, may contribute to the socioeconomic disparities in CMD [21, 38,39,40]. Future research should explore these additional factors to understand better the complex associations between SES, lifestyle, and CMD, using more robust methods to mitigate the limitations of self-reported data.
The findings of this study have significant policy implications for addressing cardiometabolic diseases and reducing socioeconomic disparities in health outcomes. The findings of this study emphasize the relevance of public health by highlighting the complex relationship between socioeconomic status (SES), lifestyle factors, and cardiometabolic diseases (CMD). Using a robust structural equation model (SEM) to analyze data from a large, nationally representative sample, our research offers new insights into the associations between SES, CMD, and lifestyle behaviors. These insights are essential for designing specific public health interventions to address the disparities in CMD prevalence associated with SES. Significantly, this study adds to the existing body of research by showing that while lifestyle factors are significantly associated with CMD risk, they do not fully mediate the SES-CMD relationship. This suggests the need for comprehensive strategies that consider multiple health determinants. The novelty of this paper lies in its thorough examination of the role of lifestyle in the SES-CMD link, its focus on the complexity of these relationships, and its implications for creating comprehensive public health policies that address both behavioral and structural health determinants.
Although the mediation analysis did not support lifestyle as the primary mechanism that explains the relationship between SES and CMD, it does not undermine the importance of lifestyle interventions in preventing and managing CMD [41,42,43,44]. Public health policies and interventions may consider promoting healthy lifestyles and addressing the risk factors associated with CMD while acknowledging the preliminary nature of these findings due to the reliance on self-reported data. However, it is crucial to recognize that multifaceted factors beyond lifestyle may influence socioeconomic disparities in CMD [24, 45]. Policymakers should consider implementing broader structural interventions to tackle the underlying socioeconomic determinants of health. This could involve improving access to healthcare services, addressing social inequalities, reducing poverty, and providing educational and employment opportunities to individuals from disadvantaged socioeconomic backgrounds [46, 47]. Additionally, efforts should be made to raise awareness about the complex relationship between lifestyle, SES, and CMD among healthcare professionals, policymakers, and the general population. To develop effective and fair strategies for preventing, early detecting, and managing cardiovascular and metabolic diseases, it may be necessary to take a holistic approach that considers the various factors contributing to socioeconomic disparities. However, it is essential to make these suggestions cautiously and support them with further research.
The study has several strengths that contribute to its validity. Firstly, structural equation modelling allowed for a comprehensive analysis of the complex relationships among socioeconomic status, lifestyle, and cardiometabolic diseases. This approach provides a robust statistical framework to evaluate the hypothesized associations, although caution is warranted in interpreting the findings due to reliance on self-reported data. Additionally, the introduction of multiple measures and using validated scales enhance the reliability and validity of the study’s findings.
However, it is essential to acknowledge some limitations. Firstly, the study relied on self-report measures, which might introduce response biases and recall errors. Future research could incorporate objective measures, such as clinical assessments and biomarkers, to enhance the accuracy of the data. Secondly, the study’s cross-sectional design limits the ability to establish causal relationships among the variables. Longitudinal or interventional studies would provide more robust evidence regarding the directionality of the associations. Lastly, the study was conducted in a specific population, which may limit the generalizability of the findings to other demographics or cultural contexts.
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