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Healthy lifestyle promote Chinese university students’ well-being: a cross-sectional network analysis

BMC Public Health volume 25, Article number: 1770 (2025) Cite this article

Abstract

Background

Previous studies have revealed associations between lifestyle factors and well-being. However, few studies have examined these relationships within a network framework that accounts for their interconnections. The present study utilized network analysis to explore the promoting and inhibiting effects of various lifestyle behaviors on well-being.

Methods

Using convenience sampling, we surveyed 1,542 college students from seven universities in southern China between July and August 2023. We employed a network model to examine the structure of restorative behaviors (healthy eating and physical activity), deteriorative behaviors (alcohol consumption, cigarette use, and screen time), and well-being (psychological, social, and emotional well-being). This study explored the protective and risk effects of various restorative and deteriorative behaviors on well-being.

Results

The results revealed that healthy eating was positively associated with psychological well-being (PWB; edge weight = 0.14), social well-being (SWB; edge weight = 0.05), and emotional well-being (EWB; edge weight = 0.09); physical activity was positively associated with PWB (edge weight = 0.04), and SWB (edge weight = 0.04); screen time was negatively associated with SWB (edge weight = -0.04). Healthy eating (bridge expected influence = 1.48) was identified as essential bridge nodes in maintaining the structure of the network.

Conclusions

The present study offered a new perspective on the relationship between lifestyle behaviors and well-being using network analysis for the first time. These findings offer preliminary insights for public health strategies aimed at improving students’ well-being.

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Background

Well-being has become a critical public health concern, with a growing interest in its promotion [1,2,3]. An increasing number of studies have shown that well-being plays a significant role in enhancing resilience [4], attenuating depression [5], and facilitating prosocial behavior [6]. The university period is a critical stage for the development of students’ personalities as well as their physical and mental growth [7, 8]. It is worth noting that university students are considered a high-risk population for developing mental health issues due to maladaptive health behaviors and the stress associated with their academic programs [9,10,11]. A comprehensive global survey of university students revealed that 35% reported experiencing at least one mental health issue [12]. China has the largest population of college students globally, and mental health issues represent a significant public health challenge [13, 14]. Approximately 28.4% of college students in China have experienced depression or other mental health problems [15]. Therefore, identifying the determinants of university students’ well-being is crucial for developing effective public health interventions to enhance their mental health.

According to the World Health Organization (WHO), well-being refers to an optimal state of functioning in which individuals recognize their potential, manage daily stressors, work productively, and actively contribute to their community [16]. Keyes posited well-being is a multifaceted construct, distinguishable into three primary components, consisting of emotional, psychological, and social well-being [17]. Emotional well-being encompasses life satisfaction, positive feelings, and interest in life; social well-being involves social contribution, integration, realization, acceptance, and consistency; and psychological well-being includes self-acceptance, environmental mastery, positive relationships, personal growth, autonomy, and a meaningful life [18, 19]. In recent years, a number of empirical studies suggested that positive emotions, nostalgia, and creativity are positively associated with well-being [20,21,22].

In addition to the aforementioned factors, lifestyle factors are increasingly recognized as influential in shaping and maintaining well-being [23]. Lifestyle factors refer to modifiable habits that influence individuals’ daily lives, such as dietary behavior and physical activity [24]. A healthy lifestyle encompasses a series of behavior patterns aimed at promoting and maintaining good health, driven by individuals’ motivations and abilities [25]. Previous studies have shown that adopting a healthy lifestyle can enhance well-being [26,27,28]. However, university students often face various pressures, including academic demands, daily life challenges, and social interactions, which increase their susceptibility to health-risk behaviors [29, 30]. Numerous international survey studies have demonstrated that university students frequently engage in unhealthy lifestyle behaviors, such as smoking, excessive alcohol consumption, and poor dietary habits [30,31,32]. Consequently, further investigation into the impact of lifestyle behaviors on university students’ well-being is warranted to inform strategies that promote healthier lifestyles.

The relationship between lifestyle and well-being can be understood through the lens of lifestyle medicine [33, 34]. Lifestyle medicine aims to enhance physical and mental well-being through the application of various lifestyle behaviors, thereby optimizing the quality of life for all individuals [24, 35]. Health lifestyle are generally classified into two categories based on their health effects [36]: restorative behaviors (e.g., exercise, healthy diet) and deteriorative behaviors (e.g., heavy alcohol consumption, tobacco use, and excessive screen time). On the one hand, restorative behaviors were associated with better levels of well-being [37, 38]. Previous studies have shown that engaging in physical activity and maintaining healthy dietary habits can effectively promote well-being [39,40,41]. On the other hand, deteriorative behaviors had a strong link with poor mental and psychosocial well-being [42]. Previous research have indicated that alcohol consumption, prolonged screen time, and cigarette consumption were the negative predictors of well-being [43,44,45].

However, previous analysis have primarily focused on the relationship between individual health behaviors and well-being [46, 47], while largely neglecting the multidimensional of well-being and the interactions between different lifestyle behaviors. Given that well-being is multidimensional and complex, a comprehensive analysis could effectively capture its association with lifestyle behaviors [48]. Additionally, integrating individual health behaviors can accurately reflect real-world situations, as these behaviors often occur simultaneously and may produce synergistic effects [49]. Therefore, we employed the network analysis to examine the relationship between well-being and lifestyle behaviors.

The present study aims to explore the relationship between lifestyle behaviors and well-being using a network analysis approach, with a particular focus on restorative behaviors (e.g., physical activity, healthy diet) and deteriorative behaviors (e.g., alcohol consumption, cigarette consumption, and screen time). Importantly, this approach enables a comprehensive investigation of how various health-related lifestyle behaviors concurrently relate to well-being among college students. Furthermore, prior research suggests that the relationship between lifestyle behaviors and well-being may differ by gender [50, 51]. Therefore, this study also conducted a gender-based comparison of the network structure linking lifestyle behaviors to well-being. The findings may contribute to the development of more effective strategies for enhancing well-being.

Methods

Participants and procedures

A cross-sectional study providing the data for this analysis was conducted at seven universities in southern China between July and August 2023, including two universities in Shantou, Guangdong Province, and five universities in Kunming, Yunnan Province. The study used convenience sampling to collect data through an online survey. Investigators underwent standardized training to ensure proficiency in guiding language, questionnaire content, and procedural precautions. Surveys were then conducted online and by assigned personnel at each school. Participants completed the questionnaires through online links hosted on www.wjx.cn.

A total of 1,611 college students voluntarily participated in the study and completed the questionnaire. Data from 66 participants were deemed invalid and excluded prior to analysis, resulting in a final sample of 1,542 participants (see Fig. 1). According to Epskamp and Fried [52], network analysis typically requires a minimum sample size of 250 participants to ensure accuracy and reliability. Therefore, this sample size was deemed adequate for this study. The age of participants ranged from 16 to 33 years, with an average age of 20.10 ± 1.46 years, and included 468 males and 1074 females. All participants provided informed consent before completing the questionnaire and permitting the use of their data for research purposes. The study design received approval from the Ethics Review Board of Yunnan University of Chinese Medicine.

Fig. 1
figure 1

Flow chart showing the steps involved in establishing the study sample

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Measures

Sociodemographic information

The sociodemographic information included gender, age and residence.

Restorative behaviors and deteriorative behaviors

Two categories of behaviors are identified [36]: restorative behaviors (including healthy eating and physical activity) and deteriorative behaviors (including alcohol consumption, cigarette consumption, and screen time).

Restorative behaviors

Following prior studies [53, 54], healthy eating was assessed by the weekly frequency of fruit and vegetable consumption. Participants responded on a 7-point scale ranging from 1 (never) to 7 (several times a day). The item should serve as an indicator of overall healthy eating, with higher scores indicating better diet quality [53]. Physical activity was quantified by the weekly frequency of exercise sessions, each lasting a minimum of 60 min, adhering to WHO guidelines [55]. Participants reported the number of days they engaged in these activities on a scale from 0 (never) to 7 (every day). High physical activity (PA) was defined as participation on at least three days per week (PA ≥ 3 days) [51]. This item has been widely used in established student surveys and has demonstrated good reliability and validity [51, 56].

Deteriorative behaviors

Participants alcohol and cigarette consumption habits were assessed by asking about their frequency of consumption over the past 30 days [53]. Responses were recorded using a 7-point scale ranging from 1 (never) to 7 (30 days or more). In this study, a response of “never” indicated no alcohol or cigarette consumption, whereas any other response reflected varying frequencies of consumption, with higher scores indicating more frequent use. These two items have been extensively utilized in previous research and have shown strong reliability and validity [53, 57]. Screen time was measured by recording participants’ screen viewing duration over both weekdays (Monday to Friday) and weekends (Saturday and Sunday). Average daily screen time hours were calculated using the formula [58]: Average daily screen time hours = ((weekday screen time hours × 5) + (weekend screen time hours × 2)) / 7. Screen time was classified into three categories: high (> 4 h per day), medium (2–4 h per day), and low (≤ 2 h per day) [51].

Well-being

Well-being was measured by the Chinese version of the Mental Health Continuum-Short Form (MHC-SF) [59]. The MHC-SF comprised 14 items divided into three subscales: Emotional Well-being (EWB; e.g. During the past month, how often did you feel happy.), Psychological Well-being (PWB; e.g. During the past month, how often did you feel that you liked most parts of their personality), and Social Well-being (SWB; During the past month, how often did you feel that you had something to contribute to society). The structure, validity, and reliability of the questionnaire have been validated in previous research [60, 61]. Items were rated on a 6-point Likert scale from 0 (Never) to 5 (Every day), and higher scores indicate better positive mental health. The Cronbach’s α of MHC was 0.94, and the Cronbach’s α of its PWB, SWB and EWB are 0.91, 0.83, and 0.92, respectively.

Statistical analysis

Statistical analyses were conducted using SPSS version 26.0 and R version 4.3.1. In this study, the independent variables were restorative behaviors (including healthy eating and physical activity) and deteriorative behaviors (including alcohol consumption, cigarette consumption, and screen time); the dependent variables were different components of well-being (including emotional well-being, psychological well-being, and social well-being). First, descriptive statistics for all variables were calculated using SPSS 26. This included the computation of means and standard deviations for continuous variables, as well as counts and percentages for categorical variables.

The network analysis was conducted using R version 4.3.1. Network analysis serves as a statistical and theoretical framework that facilitates the integration of various factors into a cohesive network. This approach enables the estimation of which factors are most “central” or “bridged” [62]. To construct and visualize the network, calculated bridge expected influence, assessed the predictability of each node, and estimated the robustness of network characteristics, the following packages were utilized: qgraph [63], mgm [64], igraph [65], bootnet [66], networkcomparisontest [67], and networktools [68]. The analysis incorporated all participants and was based on nodes and undirected edges, with each node representing an inventory item and edges denoting partial correlations. The network was generated using the EBICglasso function from the qgraph package [63], which computed Spearman’s rank correlations and created a Graphical Gaussian Model (GGM). Regularization of the GGM was achieved through the graphical Least Absolute Shrinkage and Selection Operator (LASSO), which facilitated the formation of a sparse network by omitting weak correlations and minimizing the risk of false positives [69]. Correlation strength was evaluated using EBIC with a tuning parameter (γ = 0.5) to ensure a conservative network in the qgraph package. The Fruchterman-Reingold algorithm was employed to determine the network layout based on the connections between nodes [63]. Positive relationships were indicated by blue edges, while red edges denoted negative relationships. Node predictability was assessed using the mgm package [64], which refered to the extent to which the variance of a node is explained by its adjacent nodes in the network. In this study, we predefined two communities: lifestyle behaviors (restorative behaviors and deteriorative behaviors) and well-being.

The bridge function from the Networktools package was employed to identify key bridge variables linking lifestyle to well-being within the network [70]. Given the presence of both positive and negative edges in our network model, we selected the bridge centrality index known as bridge expected influence (BEI). The BEI quantified the overall connectivity of a node to other communities, assisting in the identification of potential intervention points or targets for prevention strategies. Higher values suggested that these nodes are likely to activate nearby communities, indicating their crucial role in connecting groups of nodes [70].

The network stability evaluations were conducted using the bootnet package [66]. First, edge weight accuracy was assessed by estimating the 95% confidence interval through nonparametric bootstrapping with 1,000 samples. A narrower 95% confidence interval indicates greater network reliability. The stability of node bridge centrality indices and node BEI was assessed using the correlation stability (CS) coefficient through a case-dropping bootstrap approach with 1,000 samples. A CS coefficient greater than 0.5 indicates ideal stability, while a CS coefficient exceeding 0.25 signifies acceptable stability [66]. Finally, bootstrapping with 1,000 samples was utilized to test the differences in node BEIs and edge weights among node pairs (α = 0.05).

In adidition, we examined differences in network characteristics between male and female participants using the Network Comparison Test (NCT), a permutation test designed to assess differences between two networks (e.g., male participants vs. female participants). The NCT was applied to subsamples categorized by gender, employing 1,000 permutations as recommended [71]. This procedure evaluated the global network strength by comparing the absolute sum of all edge weights across the networks. Subsequently, we compared the distributions of edge weights within each network to characterize the network structure. Finally, we analyzed edge strength differences between the two networks, with multiple testing corrections applied using the Holm–Bonferroni method.

Results

Descriptive statistics

Table 1 presented the demographic profile and the mean scores, along with their respective standard deviations, for each variable analyzed in the current network study.

Table 1 Sociodemographic and study variables characteristics
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Network structure of restorative, deteriorative behaviors, and well-being

The structure of the psychological network is illustrated in Fig. 2. There were 8 nodes and 18 non-zero edges out of the 28 possible between-community edges (64.28%) in the network. Several notable characteristics of this network are outlined as follows. First, the mean predictability was 0.393, indicating that 39.3% of the variation in nodes is explained by variations among other nodes. Secondly, the network structure involved restorative, deteriorative behaviors, and well-being includes both positive and negative edges. Notably, several strong between-community edges in different communities were identified, including: psychological well-being (PWB) with healthy eating (RB1; edge weight = 0.14), physical activity (RB2; edge weight = 0.04); Social well-being (SWB) with healthy eating (RB1; edge weight = 0.05), screen time (DB3; weights = -0.04), and physical activity (RB2; edge weight = 0.04); Emotional well-being (EWB) with healthy eating (RB1; edge weight = 0.09). The 95% confidence intervals for all edge weights demonstrated good accuracy (Figure S1), and the network structure was accurately identified through the edge-weight difference test (Figure S2).

Fig. 2
figure 2

Network structure for restorative, deteriorative behaviors, and well-being. Note: Edges between nodes signify associations. Blue edges indicate positive associations, while red edges denote negative associations. Predictability (i.e. the proportion of variance explained for a specific node by variance in nodes to which it is connected) was depicted as a filled part of a circle surrounding each node

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The nodes representing BEIs in the network are illustrated in Fig. 3. According to the BEI index, healthy eating (RB1; BEI = 1.48) showed the highest BEI in the network, followed by screen time (DB3; BEI = -1.32) and physical activity (RB2; BEI = 1.08). According to the critical criteria of previous research on the expected impact of bridges [70], we selected the top 20% scoring nodes on agiven statistic and selected these as observation node that bridges the expected influence. Therefore, these results indicated that healthy eating were the most crucial factor in linking predictors to well-being (Figure S3). Furthermore, the CS coefficient for BEI was computed to be 0.75. This value surpasses the recommended threshold, indicating robust centrality indices (Figure S4). Detailed results of bootstrapped difference tests for the BEI node values were presented in Figure S5.

Fig. 3
figure 3

Bridge expected influence values for restorative, deteriorative behaviors, and well-being. Note: DB1, Alcohol consumption; DB2, Cigarette consumption; DB3, Screen time; RB1, Healthy eating; RB2, Physical activity; PWB, Psychological well-being; SWB, Social well-being; EWB, Emotional well-being

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Network and symptom mean levels comparisons between the two genders

We conducted a comparative analysis of network models between male and female participants (Fig. 4). In the Figure S6 and Figure S7, our findings revealed no significant gender differences in network global strength (males: 2.76 vs. females: 2.52; global strength difference = 0.24, p = 0.392) or in the distribution of edge weights (M = 0.157, p = 0.09).

Fig. 4
figure 4

Bridge expected influence models for male and female participants. Note: DB1, Alcohol consumption; DB2, Cigarette consumption; DB3, Screen time; RB1, Healthy eating; RB2, Physical activity; PWB, Psychological well-being; SWB, Social well-being; EWB, Emotional well-being

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Covariates

Previous research has shown associations between age and residence status with the well-being [72, 73]. Therefore, the network model and local structural indexes were re-estimated after controlling for age and residence status as covariates. The results remained similar after adjusting for these covariates (Figure S8 and S9).

Discussion

The present study is the first to investigate the component-level relationships between lifestyle behaviors (including both restorative and deteriorative behaviors), and well-being among university students using a network analysis approach. By constructing cross-sectional networks, this study identified strong associations between restorative and deteriorative behaviors and various components of university students’ well-being. Notably, healthy eating emerged as the trait with the most substantial bridge expected influence. These findings offer new insights into the interrelationship between lifestyle behaviors and well-being, and clarify potential priorities for improving university students’ well-being.

Our findings identified several specific associations between restorative behaviors and well-being. Specifically, our results indicated that healthy eating is positively associated with psychological, social, and emotional well-being. Consistent with our findings, previous studies on university student populations have indicated that healthy eating habits are associated with lower levels of depressive symptoms, stress, and anxiety [74], as well as higher quality of life [75]. One possible explanation is that a healthy diet, particularly increased consumption of fruits and vegetables, may reduce inflammation and oxidative stress, thereby enhancing mental health [76]. Moreover, the results indicated that physical activity is positively associated with psychological and social well-being. In line with this, a prior study on university students reported an inverse relationship between physical activity and anxiety and depression [77]. Although the exact mechanisms remain unclear, the protective effects of physical activity on well-being may be attributed to its physiological and psychological benefits. On the one hand, physical activity can promote the release of endorphins, leading to feelings of pleasure and stress relief [78]. On the other hand, physical exercise can enhance well-being by facilitating the venting of anger and hostility [79]. Unexpectedly, the network analysis revealed no association between physical exercise and emotional well-being. Although this finding contrasts with previous research [80], this result reinforces the importance of assessing multiple lifestyle behaviors simultaneously, as analyzing them in isolation may overestimate their associations.

Regarding the relation between deteriorative behaviors and well-being, our findings indicated that only screen time demonstrated a direct negative relationship with social well-being. This aligned with prior research indicating that individuals with excessive screen time are more likely to experience poor well-being [81]. A possible explanation is that, for university students, excessive screen time reduced social interactions with peers [82], potentially leading to a decline in social well-being. Additionally, our findings reveal no significant association between screen time and psychological or emotional well-being. One possible explanation is that screen time may no longer be perceived as problematic but rather as a normalized behavior widely accepted among younger generations [83, 84].

Notably, bridge expected influence indices identified healthy eating as the most central factor linking lifestyle behaviors to well-being, suggesting its pivotal role in promoting well-being. Consistent with our findings, previous research has shown that among various lifestyle behaviors, healthy eating consistently plays an effective and stable role in promoting university students’ well-being [25, 42]. Therefore, interventions aimed at enhancing healthy eating behaviors may yield greater improvements in university students’ well-being compared to those targeting other lifestyle factors. Furthermore, compared to classical regression analysis, network analysis provides deeper insights into the complex interconnections among variables identified in this study [62]. Consequently, bridge expected influence indices suggest that healthy eating not only impact well-being but also shaped by other lifestyle behaviors. Despite earlier studies demonstrating a significant relationship between smoking or drinking and well-being [85], this association was not observed in our network model. It’s worth noting that the network analysis revealed a negative correlation between alcohol and cigarette consumption and healthy eating. Similarly, previous research suggested that excessive smoking or alcohol consumption is associated with poorer dietary habits [86,87,88]. Hence, alcohol and cigarette consumption may indirectly affect well-being by impairing healthy eating behaviors. By leveraging network analysis, our study provides a more nuanced understanding of the complex interplay between lifestyle behaviors and well-being.

Considering network differences between males and females, the analysis conducted did not reveal any significant gender differences in the modulation of the network or in the centrality of bridge connections. Consequently, the theoretical insights derived from the network structure in our study appear to be equally applicable across both male and female populations, provided evidence that the network has relatively high stability and generalizability.

Limitations and future directions

This study has several limitations that suggest directions for future research. First, the reliance on cross-sectional data prevents determining the directionality of the observed associations. Longitudinal studies are needed to clarify the potential causal relationships between lifestyle and well-being. Second, the sample comprised only college students in southern China, potentially limiting the generalizability of the findings to other populations. Thus, future research should replicate these findings with a more diverse and broader students’ sample. Third, our study did not account for all relevant lifestyle behaviors. Prior research has shown that other lifestyle factors, such as sedentary time and sleep, also influence well-being [89, 90]. Future studies should incorporate these variables to enhance the validity of the findings. Finally, as healthy eating demonstrated the strongest association with well-being, future research should conduct more detailed assessments of dietary habits, including specific food categories and portion sizes.

Conclusions

Lifestyle behaviors play a crucial role in maintaining and enhancing university students’ mental health. This study is the first to delineate the specific pathways linking lifestyle behaviors to well-being through network analysis. Network analysis identified specific associations between various restorative and deteriorative behaviors and different components of university students’ well-being. Notably, our findings highlight that healthy eating plays a central role in connecting lifestyle behaviors to well-being. Overall, maintaining positive lifestyle habits, particularly a healthy diet, could enhance university students’ well-being. These findings contribute to a deeper understanding of the relationships among restorative behaviors, deteriorative behaviors, and well-being, while also offering preliminary insights for public health strategies aimed at improving student well-being.

Data availability

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

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Acknowledgements

The authors wish to thank all of the participants involved in this study.

Funding

This study was supported by the Joint Key Project of Yunnan Federation of Social Sciences and Yunnan University of Traditional Chinese Medicine (LHZX202403), the Key Project of the National Traditional Chinese Medicine Examination Research Fund (TA2024001), and the Yunnan Provincial Department of Education Research Fund (2024J0412).

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Author notes

  1. Fuhua Yang and Kaiqing Xu contributed equally to this work.

Authors and Affiliations

  1. School of Information, Yunnan University of Chinese Medicine, Kunming, 650500, China

    Fuhua Yang & Kaiqing Xu

  2. Department of Student Affairs, Kunming Medical University, Kunming, 650500, China

    Ying Huang

  3. Department of Psychology, Faculty of Education, Yunnan Normal University, Lianda Street, Kunming, Yunnan, 650500, China

    Kunhua Pu & Jiaci Lin

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  1. Fuhua Yang
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Contributions

Conceptualisation/design: L.J. and Y.F. Data analysis/interpretation: X.K., H.Y., and P.K. Supervision: L.J. Drafting article: Y.F. and X.K. Critical revision of the article: Y.F. and L.J. All authors have read and agreed to the published version of the manuscript.

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Correspondence to Jiaci Lin.

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The study design was approved by Yunnan University of Chinese Medicine Ethics Review Board (IRB number: XXLW-2024-003). Written informed consent was obtained from all participants. All procedures conducted in this study adhered to the ethical standards of the institutional and regional committees on human experimentation and conformed to the principles outlined in the 1964 Declaration of Helsinki and its subsequent revisions.

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The authors declare no competing interests.

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Yang, F., Xu, K., Huang, Y. et al. Healthy lifestyle promote Chinese university students’ well-being: a cross-sectional network analysis. BMC Public Health 25, 1770 (2025). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12889-025-22894-y

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ISSN: 1471-2458

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