Network Analysis of Social Support and Anxiety Dimensions Among College Students

doi:10.21203/rs.3.rs-3450371/v1

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Research Article

Network Analysis of Social Support and Anxiety Dimensions Among College Students

https://doi.org/10.21203/rs.3.rs-3450371/v1

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Background

Previous research has suggested that transitioning from the level of impairment to the level of symptoms may provide a more nuanced understanding in psychopathology. This study aims to explore the relationships between the three dimensions of perceived social support (i.e., family support, friend support, and other support) and individual symptoms of anxiety among college students.

Methods

We conducted a network analysis of anxiety symptoms and perceived social support in a sample of 4105 college students. Two networks were constructed in this study, namely the Perceived Social Support-Anxiety network. Bridge centrality indices were calculated for variables within both networks.

Results

In this sample, perceived social support showed a significant association with anxiety symptoms. Bridge centrality analysis revealed that, in both networks, family support had negative bridge expected influence values, while friend support and other support had positive bridge expected influence values.

Conclusions

The results unveil specific roles of perceived social support in relation to anxiety at the symptom level.

Generalized anxiety disorder

Perceived social support scale

Network analysis

Perceived social support refers to an individual's subjective emotional experience of feeling supported, respected, and understood in their social interactions [1]. Various aspects of social support, including family and friend support, hold significant significance for college students [2, 3]. Generalized Anxiety Disorder (GAD) is a mental disorder characterized by excessive anxiety and worry about numerous activities and events [4, 5, 6, 7]. Along with the academic, personal, and employment challenges inherent in the college experience, individuals often manifest negative emotional experiences such as fear, unease, and apprehension [8, 9, 10], leading to cognitive, behavioral, and physiological changes [11, 12].

Previous research has shown relatively limited attention to the relationship between social support and anxiety [13, 14], leaving scholars with a less comprehensive understanding of their interplay. This study, departing from various dimensions of social support and anxiety disorders, employs latent variable methods, using anxiety severity as an indicator, to examine their relationship. Prior studies have often employed mediation analyses [15] to explore this relationship, potentially overlooking a more nuanced comprehension of their interaction at the symptom level. Emerging research indicates a significant negative correlation between perceived social support and employment anxiety among college students [16]. The more social support college students receive at the societal level, the lower their levels of anxiety [17]. Studies suggest that positive social evaluations exhibit distinct associations with individual anxiety symptoms [18]. Neglecting the heterogeneity of anxiety symptoms may obscure differential relationships between various clinical symptoms and levels of social support, potentially leading to inconsistent findings across studies.

To address the challenges encountered in the aforementioned research, we have employed a symptom-based analytical approach, namely network analysis. From a network analysis perspective, mental disorders stem from relationships among interacting variables, which include both symptomatic and non-symptomatic factors, rather than being mere consequences of an underlying common cause [19]. With this method, perceived social support and individual psychological symptoms, such as anxiety, serve as nodes, and the potential pathways connecting different symptoms are depicted as edges. Thus, the emphasis of network analysis lies in identifying specific pathways linking social support to individual symptoms of anxiety. This stands out from latent variable models that focus on how varying degrees of social support may affect latent psychopathological structures. In addition to describing the social support-symptom pathways, network analysis also provides empirical indices, such as bridge expected influence [20]. These novel metrics enable researchers to quantify to what extent family support, friend support, and other support may convey positive/negative influences (both risk and protective capabilities) to clusters of symptoms.

This study independently investigates the interplay between three dimensions of social support at the symptom level, namely family support, friend support, and other support, and anxiety disorders. The study has two primary objectives: 1) to explore the latent pathways linking perceived social support (i.e., family support, friend support, other support) with individual symptoms of anxiety, and 2) to measure the risk and protective capabilities of perceived social support on clusters of anxiety symptoms. 3) Exploring the fine-grained relations between PASS and anxiety. Using the bridge centrality index to examine the bridge variables in the PASS -GAD7 network.

Participants and Ethical Statement

Data were collected via paper-and-pencil tests between January 16, 2022, and April 18, 2022. A total of 4,142 college students from Xijing University in Shaanxi Province, China, participated in this study. Thirty-eight responses were excluded due to incomplete demographic information or the failure of two honesty-check items. Consequently, 4,105 questionnaires were included in the final sample, comprising 1,956 males and 2,149 females, with an average age of 22.74 (SD = 5.37). This study was approved by the Ethics Committee of the First Affiliated Hospital of the Air Force Medical University (Project Number: KY20234195-1.

Measures

Perceived social support scale. The Perceived Social Support Scale (PSSS) [21] is an instrument designed to measure an individual's self-perceived level of social support from various facets of their social environment. The scale comprises three dimensions with a total of 12 items. These dimensions are delineated as follows: Family Support: Assessing the subjective perception of support from one's family (items 3, 4, 8, and 11). Friend Support: Gauging the subjective perception of support from friends (items 6, 7, 9, and 12). Other Support: Evaluating the subjective perception of support from individuals other than family and friends (items 1, 2, 5, and 10).

The scale utilizes a 7-point rating system (1 point = "completely disagree," 7 points = "completely agree"). A higher total score on the PSSS reflects a heightened subjective perception of support from individuals within one's social network. The Cronbach's alpha coefficient for this scale is 0.90.

Generalized anxiety disorder 7-item. We employed the Generalized Anxiety Disorder 7-item (GAD-7) scale to assess anxiety symptoms [4]. This scale consists of seven items measuring the severity of anxiety. Participants were instructed to report the frequency of symptoms they have experienced over the past two weeks, with response options ranging from 0 (not at all) to 3 (nearly every day). Individual symptom scores serve as the focal metric of interest in this study. The scale demonstrated good internal consistency in this research (Cronbach's alpha = 0.90).

Network analysis

This study investigated two networks, namely the Perceived Social Support-Anxiety network. The network structure of the two undirected networks was estimated using graphical LASSO (Least Absolute Shrinkage and Selection Operator) methodology. In each network, edges represent the partial correlations between two nodes when adjusting for the effects from all other remaining nodes. The networks in this study were based on Spearman partial correlations. The graphical LASSO technique was employed to regularize the partial correlations within the represented network [22]. By penalizing very small partial correlation coefficients to zero, this technique aids in removing spurious edges, resulting in a more stable and sparse network [22]. The Extended Bayesian Information Criterion (EBIC) hyperparameter γ was set to 0.5 to balance sensitivity and specificity [23]. The networks presented herein were constructed using the Fruchterman-Reingold algorithm [20]. The qgraph package in R was utilized to compute the proposed networks [24, 25].

In this study, two communities were predefined, namely the Perceived Social Support community (comprising family support, friend support, and other support) and the Symptom community (anxiety symptoms). Bridge expected influence, which calculates the sum of edge weights connecting a given node to all nodes in the other community, was computed to identify bridge nodes connecting these communities. Bridge centrality measures (e.g., bridge strength and betweenness) are more suitable for identifying bridge nodes in negatively and positively connected networks [26]. Higher bridge expected influence values imply a higher likelihood of activating the opposite community. Bridge expected influence was calculated using the network tool package in R [27].

The accuracy of edge weights was assessed by plotting 95% confidence intervals for each edge using 2,000 bootstrap samples. To evaluate the stability of bridge expected influence, the Correlation Stability (CS) coefficient was calculated using the bootstrapping method with 2,000 bootstrap samples. Following recommendations [26], an ideal CS coefficient is higher than 0.5 and should not be less than 0.25. Further, bootstrapped difference tests were conducted for edge weights and bridge expected influence with 2,000 bootstrap samples. These procedures were carried out using the R software package bootnet [26].

We used R-package bootnet to examine the accuracy and reliability of the proposed network [28]. Edge weight accuracy was examined via a nonparametric bootstrap approach with 2000 bootstrap samples. The stability of centrality measures (i.e. node expected influence and bridge expected influence) were indexed by the correlation stability (CS) coefficient. CS coefficients for both measures were computed by a case-dropping bootstrap approach with 2000 bootstrap samples. The recommended value for CS-coefficient is above 0.5 and should not be lower than 0.25 [29]. Bootstrapped difference tests were conducted for edge weights, node expected influence and bridge expected influence.

Descriptive statistics

The range, average scores and standard deviations of individual symptoms are depicted in Table 1.

Table 1

Descriptive statistics for individual symptoms in overall sample (N = 4105)
Variables	Abbreviation	Bridge EI	Predictability	Mean	SD
PASS
Friend Support	FA	0.14	0.62	19.31	5.22
Family Support	FR	0.04	0.33	20.01	4.83
Other Support	SO	0.04	0.61	17.65	5.15
GAD
GAD-1	G1	0.02	0.51	1.72	0.69
GAD-2	G2	0.05	0.56	1.55	0.72
GAD-3	G3	0.04	0.58	1.65	0.75
GAD-4	G4	0.05	0.55	1.62	0.78
GAD-5	G5	0.03	0.55	1.49	0.71
GAD-6	G6	0.04	0.54	1.76	0.85
GAD-7	G7	0.05	0.48	1.49	0.71
Note: Abbreviations: M, mean; SD, standard deviation; Pre, predictability.

Perceived Social Support - Anxiety Network

The network structure of Perceived Social Support and Anxiety is illustrated in Fig. 1. The four strongest edges are observed between Family Support - Other Support (FA-SO, 0.61), G1-G2 (0.27), Family Support - Friend Support (0.26), and G4-G5 (0.26). Family Support exhibits negative associations with four specific anxiety symptoms: G6 (-0.05), G4 (-0.03), G3 (-0.02), and G7 (-0.02). Friend Support is positively associated with two anxiety symptoms: G6 (0.03) and G1 (0.01), and negatively associated with three anxiety symptoms: G2 (-0.04), G7 (-0.02), and G5 (-0.01). Other Support demonstrates positive associations with three anxiety symptoms: G6, G5, and G4, each with a partial correlation coefficient of 0.01.

The node expected influence is shown in Fig. 2. The bridge connection expected influence centrality (1-step) is highest for the nodes representing Anxiety or Tension (G1) and Restlessness (G5), with values of 0.02 and − 0.04, respectively. Within the PASS community, the bridge connection expected influence centrality for Friend Support (FR) is -0.04. Family Support (FA) and the Worry about Things Happening (G7) nodes exhibit the lowest bridge connection expected influence centrality (1-step), with values of -0.14 and − 0.05, respectively. The predictability for Family Support (FA), Other Support (SO), and Excessive Worry (G3) and Uncontrollable Worries (G2) nodes is the highest, at 62%, 61%, 58%, and 56%, respectively. Friend Support (FR) and Worry about Things Happening (G7) have the lowest predictability, at 33% and 48%, respectively, with an average predictability of 53%.

All edge weights in the Perceived Social Support-Anxiety Network can be found in Figure S2 (Supplementary Material). Bootstrap 95% confidence interval plots in Figure S1 (Supplementary Material) support the accuracy of edge weights in the Perceived Social Support-Anxiety Network. Supplementary Figure S3 (available in the Supplementary Material) displays bootstrapped difference tests for edge weights.

Figure 2 depicts the original values of bridge expected influence for each node in the Perceived Social Support-Anxiety Network. Family Support has a negative bridge expected influence value (-0.14), while Friend Support has a value of (-0.04). The CS coefficient for node bridge expected influence is 0.75, indicating that centrality indices, such as bridge expected influence, are sufficiently stable (see Supplementary Figure S3 in the Supplementary Material). Supplementary Figure S4 (available in the Supplementary Material) presents bootstrapped difference tests for node bridge expected influence.

***Fig. 1***

Figure 1. Descriptive Statistics of Network Structures for Perceived Social Support and Anxiety Disorder Among College Students. Blue edges represent positive correlations, red edges represent negative correlations. The thickness of the edge reflects the magnitude of the correlation. Cut value = 0.05.

***Fig. 2***

Figure 2. Centrality plot depicting the expected influence and bridge expected influence of each variable selected in the present network (z-score). The text of intolerance of uncertainty and depression can be seen in Fig. 1.

Applying network analysis, we aim to explore the fine-grained relations between various symptoms of perceived social support and anxiety. It is showed that various symptoms of anxiety are commonly but differentially connected with components of PASS.

This study reports research on perceived social support and anxiety disorders in a Chinese university student population, indicating that different aspects of social support have long-term psychological effects on the mental health of college students. To provide proactive early prevention and intervention measures for anxiety in college students and high-risk individuals, this study employed network analysis to explore how individual differences in social support can prevent/drive the risk of individual anxiety symptoms. Consistent with previous research [30, 31], we found that family support, friend support, and other support may play different roles in specific anxiety symptoms and identified some robust pathways. Furthermore, the results of bridge centrality analysis support the protective role of family and the detrimental effects of friend support and other support on anxiety.

Notably, we found that family support is negatively correlated with four anxiety symptoms (G6 "irritable," G4 "hard to relax," G3 "excessive worry," and G7 "worry about things happening"). In the era of artificial intelligence, college students' employment often diverges from reality. More students prioritize theoretical learning over swiftly translating theory into practice, potentially placing them in high-pressure and anxiety-inducing situations [32, 33, 34]. Existing research suggests that family support can help college students navigate these circumstances when faced with academic and employment challenges, boosting their self-efficacy to take appropriate actions, alleviate academic and employment stress, and further reduce anxiety symptoms [35, 36].

In the network, we observed a strong negative edge between family support and 'irritable' (G6), indicating that more frequent use of family support is associated with an increased risk of reducing anxiety emotions. Existing research on the relationship between family support and suicidal ideation has reported consistent results [37, 38]. Specifically, as good family parenting provides the next generation with more encouragement and support, it enhances college students' communication and emotional expression, reduces the accumulation of negative emotions, and lowers thoughts that trigger anxiety and depression. Therefore, this may be a protective factor for college students facing higher levels of anxiety disorders.

In the social support-anxiety network, we found that friend support was positively related to G6 'irritable' and G1 'tense or anxious,' and negatively related to G2 'uncontrollable worry,' G7 'worry about things happening,' and G5 'restlessness.' This finding can be explained in several ways. For instance, during the pandemic, being disconnected from offline environments and daily communication for an extended period may require college students to engage in academic and employment activities in an isolated and high-pressure environment (potentially involving ongoing stressful events). Intensive study until graduation in the face of uncertainty may objectively increase the perceived difficulty of events [39, 40]. In this scenario, college students' support or communication with peers or classmates may make them more sensitive or excessively concerned about the uncertainty of the future, given the uncertainty of external information [41, 42, 43, 44]. It is worth noting that family support may be more effective in alleviating somatic anxiety symptoms than friend support and other support. Future research needs to test these two hypotheses and determine the direction of these pathways.

To quantify the protective or risk capacity of social support on anxiety symptoms through network analysis, we calculated the bridge expected influence for each network separately. In the social support-anxiety network, family support had a negative bridge expected influence value, while friend support and other support had positive bridge expected influence values, suggesting that family support may act as a protective factor, whereas friend support and other support may function as risk factors for anxiety in college students [45]. These findings align with previous research that has considered family support and friend support as protective and risk factors for anxiety [46, 47].

The current research findings hold several significant implications for the development of early detection and intervention strategies aimed at safeguarding the psychological well-being of college students. First and foremost, by identifying the pathways between social support and anxiety, our study results suggest that friend support might be prioritized as a target for early detection and prevention. Specifically, when employing symptom-based questionnaires as screening tools, addressing the stigma around mental illnesses (e.g., the fear of repercussions from employers, health regulatory authorities, or medical boards) might introduce self-report biases. Conversely, items related to other support are more neutral and are less likely to raise concerns such as fear of retaliation during screening. Moreover, given that other support may exist independently of symptoms, measuring self-expression inhibition may help identify high-risk individuals and offer timely support before symptoms manifest. Therefore, stakeholders may consider incorporating the Perceived Social Support Scale into existing occupational health assessments.

Furthermore, by demonstrating the protective role of family support and the detrimental effects of friend support and other support in anxiety, our study results indicate that psychological education focusing on promoting the use of family support while reducing reliance on friend support and other support could be beneficial. Given the high prevalence of anxiety symptoms within the college student population, there is an urgent need for such programs to mitigate short-term and long-term adverse psychological impacts. Lastly, by elucidating the differential relationships between social support and psychological symptoms, our research findings may provide a theoretical basis for developing personalized prevention and intervention strategies tailored to identify symptom heterogeneity.

While the current findings are novel, there are also some limitations that should be considered. Firstly, the use of cross-sectional data restricts our ability to determine the direction of causal relationships. It remains unclear whether social support is influenced by anxiety symptoms, or if external circumstances simultaneously induce symptoms and affect social support. Secondly, the observed network structures in this study may be specific to the questionnaires we employed and may not be replicable when different measures are used to assess these structures. However, all the measures employed in this study have been widely adopted and validated in various contexts. Thirdly, the social support-symptom network at the group level reflects effects between individuals, which may not capture intra-individual processes. Diary network analysis using dense longitudinal data can assist in understanding the social support-symptom relationship within individuals. Fourthly, considering the differences in healthcare systems between China and other countries, future research should investigate university students from various countries to test the generalizability of the reported study results.

This study employed network analysis to investigate the symptom interactions between perceived social support and anxiety among college students during a pandemic. Our research findings underscore the protective role of family support and the detrimental effects of friend support and other support. We outline potential pathways through which various aspects of social support may interact with anxiety symptoms. These findings may contribute to the development of early detection and intervention strategies to mitigate the adverse impact of the pandemic on the mental health of college students.

GAD

Generalized anxiety disorder

GAD-7

Generalized Anxiety Disorder 7-Item Questionnaire

PASS

Perceived social support scale.

Acknowledgements

We would like to thank all the individuals who participated in the study. We also thank all the administrative staff and teachers in the university who help us with the recruitment.

Authors’ contributions

T.F., L.R. and D.L. proposed the facility idea and scheme of this project. T.F., B.W., D.L., M.M., L.R., L.W. and Y.W. conducted the research and collected the raw data. T.F. and L.R analyzed the data. T.F. and D.L. drafted the manuscript. H.W. and X.L. revised the manuscript and took responsibility for the integrity of the data and the accuracy of the manuscript. All authors have read and agreed to the published version of the manuscript.

Funding

XL’s involvement in this research was funded by the Year 2022 Major Projects of Military Logistic Research Grant and the Key Project of Air Force Equipment Comprehensive Research (KJ2022A000415).

Availability of data and materials

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

Ethics approval and consent to participate

This study was conducted in accordance with the ethical standards put forth in the Declaration of Helsinki. The questionnaire was completed online in the WeChat application after informed consent was obtained. The study design and procedures were reviewed and approved by the Independent Ethics Committee of the First Affiliated Hospital of the Fourth Military Medical University (No. KY20234195-1).

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

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No competing interests reported.

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Network Analysis of Social Support and Anxiety Dimensions Among College Students

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Abstract

Background

Methods

Results

Conclusions

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Background

Methods

Participants and Ethical Statement

Measures

Network analysis

Results

Descriptive statistics

Perceived Social Support - Anxiety Network

Discussion

Conclusions

List Of Abbreviations

Declarations

References

Additional Declarations

Supplementary Files

Status:

Version 1