The network and interactive pattern of social adjustment and psychological symptoms in patients with spinal cord injury: A network analysis

doi:10.21203/rs.3.rs-4306663/v1

Background: The network approach offers a novel framework for conceptualizing depression, anxiety and social adaptability as a causal system, which helps for the understanding of what may be relevant targets for treatment.

Objective：To find the network and interactive pattern of social adjustment and psychological symptoms in patients with spinal cord injury(SCI).

Methods: A cross-sectional parent-reported study was conducted to use an online survey disseminated from February to April 2023. The survey including Medical Coping Modality Questionnaire (MCMQ), Social Spuport Rate Scale (SSRS), Patient Health Questionnaire-9(PHQ-9), and Generalized Anxiety Disorder-7(GAD-7). 251 subjects were completed . Graphical lasso networks and directed acyclic graph (DAG) were used to visualize and analyze the associations between symptom levels corresponding to the four scales.

Results: The average age of patients with SCI was 54.96 years, 61.4% were male. Network analysis results showed that "Concentration" in depression symptoms and "Trouble relaxing" in anxiety symptoms had the highest centrality strength. Anxiety symptom "Nervousness" and depression symptom "Failure" exhibited the highest bridge strength. The result of directed acyclic graph indicated a key triggering role for "Nervousness" symptoms.

Conclusion: "Concentration" in depression symptoms and "Trouble relaxing" in anxiety symptom play a key role in psychological and social adjustment of patients with SCI. It is essential to pay attention to "Nervousness" as it is at the upper end of the symptom response cascade.

Depression

Anxiety

Spinal cord injury

Network analysis

DAG

Spinal cord injury (SCI) commonly results from road traffic crashes, falls or violence, that cause permanent changes in strength, sensation and body functions. There were approximately 700,000 new cases of SCI worldwide each year(Kumar et al., 2018). In China, the random pooled incidence of SCI was estimated to be 65.15 per million(Hu et al., 2023). The symptoms and outcomes after SCI depend on where the injury occurred and its severity. In recent years, the mortality rate of patients with SCI during the acute phase had been significantly decreased due to the advancements in emergency and critical care. However, patients with SCI are associated with a risk of developing secondary conditions that can be debilitating and even life-threaten.

As an adverse life event, patients with SCI would encompass significant physiological, psychological, and social changes. Researches have shown that people with sudden onset of SCI exhibit extreme negative emotions, which is generally known as post-traumatic stress disorder. Even in a stable or recovering condition, the patients with SCI are at significantly higher risk of depression, anxiety, and poor quality of life (QoL) comparing with their healthy peers. Data from researches showed that the prevalence of depression and anxious after SCI was around 20% and 30% respectively. The emotional, cognitive, and social sequalae following SCI are highly individualized, fluctuate across time and setting, and are influenced by cultural and contextual factors. It is essential to gain a better understanding of the factors associated with the mental health of individuals with SCI. Nevertheless, most existing studies primarily focus on whether patients have mental disorders, without delving into the underlying connections between emotional symptoms in detail.

There is a great deal of variation in the post-injury coping responses of patients with SCI, and their appraisal of illness and coping behaviours are related to their psychological states(Kennedy et al., 2010, 2012) . For individuals with SCI, the presence and severity of mental disorders are influenced by various factors, including the acceptance of the injury and the response to it, as well as the quantity and quality of social support^. Acceptance is defined as viewing the injury as an integral part of one's future life and reevaluating life values, providing a coping strategy that is particularly important when circumstances cannot be changed(Zvi & Cohen-Louck, 2023). Empirical research shows that coping resources and strategies are significantly related to psychosocial adaptation(Livneh & Martz, 2014), and the level of acceptance is associated with the severity of depression and anxiety disorders. Coping with injury is an important mediating factor in post-injury life adjustment, and maladjustment can lead to depression in at-risk individuals(Kang & Bullman, 2008; Ryerson Espino et al., 2022).

A systematic review suggests that social support, both in terms of quantity (network size, presence, and/or availability of sources of support) and quality (evaluation of social support resources), is associated with better psychological health and quality of life(Zhao et al., 2022). Social support may have a mediating or moderating role in mental health. Cross-sectional studies have shown that perceived social support is related to depression and subjective well-being in individuals with physical disabilities(M et al., 2022; Zhao et al., 2022). The strongest association is particularly found between social support and depression(Tough et al., 2017). Evidence indicates that patients with SCI experience higher levels of loneliness and lower evaluations of the social support they receive(Ryerson Espino et al., 2022). Lack of social support is associated with depressive symptoms, anxiety, sadness, and social withdrawal(Huang et al., 2015). Having adequate social support resources can alleviate spinal cord injury(Huang et al., 2015) and mental health issue. In conclusion, these research findings indicate that social support and coping strategies are crucial for the psychological well-being of individuals with spinal cord injury. However, most existing studies on these three core dimensions have primarily focused on the relationship between two of them or analyzed them as risk factors for mental states. There is relatively limited research exploring the specific connections between social support, coping strategies, and anxiety or depressive disorders. This limitation restricts the ability to draw conclusive evidence on the potential causal impact of social support on psychological health outcomes.

The level of association between psychological disorder symptoms and social adaptation can be explored through network analysis in psychopathology(Jones et al., 2021). It offers powerful computational methods to model and visualize diseases as symptom networks, which can be used to identify potential causal relationships that may influence the etiology and maintenance of the disease. Network analysis methods have been widely used in the network structure of anxiety and depressive disorders and social adaptation in the general population, such as children(Bartels et al., 2019; Li et al., 2023), the elderly, firefighters(Yuan et al., 2022), etc., but no studies have been conducted on individuals with spinal cord injuries. Considering the unique characteristics of individuals with spinal cord injuries and the high prevalence of psychological disorders, this study investigates the network structure of social support, coping styles, anxiety, and depressive symptoms in individuals with spinal cord injuries. The following research questions are proposed:

1. What are the differences in social adaptation and psychological status between individuals with spinal cord injuries and the general population?

2. What are the most important ant symptoms in the network of social adaptation and psychological status?

3. What are the bridges of anxiety and depression symptoms network? What symptoms connect social adaptation and psychological status?

4. What are the potential causal relationships between anxiety, depression, medical coping styles, and social support?

Participants and study design

The cross-sectional survey was conducted in Shanghai, China from January to March 2022. The survey included patients with SCI who were over 18 years old and excluded those with a history of mental illness.

A QR code was generated through QuestionStar e-mail to voluntarily complete a self-made questionnaire. patients with SCI were invited to participate while waiting for clinical consultations or during rehabilitation. Each participant received information on the study's purpose, content, procedures, and anonymity. After signing an informed consent form, patients completed the questionnaire. A total of 255 questionnaires were collected, excluding those with incomplete or erroneous responses.

A total of 251 valid questionnaires were included from patients with SCI for subsequent statistical analysis. These respondents completed the survey from February to April 2023. They were surveyed on demographic characteristics, social adaptation, social support, depression, and anxiety symptoms. This study had been reviewed and approved by blinded for review.

Measures

Sociodemographic and clinical characteristics

This survey collected the following information about patients' sociodemographic and clinical characteristics using a self-made questionnaire: gender, age, education level, duration of spinal cord injury, and location of spinal cord injury.

Medical Coping Styles

The coping styles of patients were evaluated using the Medical Coping Modality Questionnaire (MCMQ). MCMQ includes three dimensions of avoidance, submission, and confrontation. Each item on the MCMQ has a four-point likert scale response format, ranging from "never" to "always," indicating the frequency of the coping strategy used. The questionnaire consists of 20 items, with 8 of them being reverse scored. The total score is calculated by summing the scores of each dimensions, with higher scores indicating greater use of coping strategies. Actually, three dimensions of avoidance, submission, and confrontation are approached as individual entity to analyzed in our article.

Social support level

The Social Spuport Rate Scale (SSRS) is a tool used to assess an individual's perceived social support, and it is used to measure the level of social support of the survey participants. It consists of 10 items that measure three dimensions of social support: subjective support, objective support, and utilization of support. The subjective support dimension assesses the individual's perception of the availability of social support, while the objective support dimension measures the actual availability of social support. The utilization of support dimension assesses the individual's use of social support. In our article, we analyze each of the three dimensions of social support as a separate entity.

Depression symptoms

Depressive symptoms in patients with SCI will be assessed using the Patient Health Questionnaire-9(PHQ-9). The PHQ-9 is a self-administered screening tool used to assess the severity of depression in adults. It consists of nine questions that ask about the frequency and severity of depressive symptoms over the past two weeks, such as feelings of sadness, loss of interest in activities, changes in appetite or sleep patterns, and thoughts of self-harm. Each question is scored on a scale of 0 to 3, with higher scores indicating more severe symptoms. The total score ranges from 0 to 27, with scores of 5, 10, 15, and 20 representing mild, moderate, moderately severe, and severe depression, respectively.

Anxiety symptoms

The severity of anxiety symptoms is assessed by the Generalized Anxiety Disorder-7(GAD-7) scale. GAD-7 is a widely used screening tool for generalized anxiety disorder. It consists of seven questions that assess the severity of anxiety symptoms over the past two weeks. These questions cover common symptoms of generalized anxiety disorder, such as excessive worry, restlessness, difficulty concentrating, and more. Each question has four answer options that range from "not at all" to "nearly every day" indicating the severity of the symptom. Based on the scores, a total score is calculated to determine whether an individual has mild, moderate, or severe generalized anxiety disorder symptoms.

Data analysis

Graphical LASSO network

This model is built upon a Gaussian graphical model (GGM) based on partial correlation analysis. The GGM is estimated using a non-parametric Spearman correlation matrix(Epskamp & Fried, 2018). By introducing a penalty factor and employing the graphical least absolute shrinkage and selection operator (LASSO), weaker connections are removed, resulting in a clearer display of important connections within the network(Isvoranu & Epskamp, 2021). To select the LASSO regularization parameter using the extended Bayesian information criterion (EBIC), the hyperparameter gamma was set to the default value of 0.50 for model selection(Epskamp et al., 2018), and the network is constructed and visualized using the R-package qgraph (Epskamp et al., 2012).

In the network, we have pre-divided the nodes into four communities, corresponding to four different scales. The thickness of the edges represents the strength of the correlations, with green shading indicating positive correlations and red shading indicating negative correlations between nodes. Central strength and bridge strength are calculated using the R-package qgraph. Central strength is defined as the sum of the values of all edges connected to a given node. The greater the central strength, the more important the node is within the network. Bridge strength is defined as the sum of the values of all edges connecting a given node to nodes in other communities(Vanzhula et al., 2021). A higher bridge strength value implies a greater risk of spreading contagion to other communities.

We assessed the robustness of the network using the R package bootnet(Epskamp et al., 2018). The accuracy of edge weights was evaluated by calculating 95% confidence intervals using a non-parametric bootstrap method (10,000 bootstrap samples) and performing a bootstrap difference test for edge weights. The stability of node expected influence and node bridging expected influence was assessed by calculating the correlation stability (CS) coefficient, using a case-dropping bootstrap method (10,000 bootstrap samples), and performing their bootstrap difference tests. The CS coefficient values should not be lower than 0.25 and are preferably higher than 0.5(Epskamp et al., 2018).

Directed acyclic graph

Directed acyclic graph (DAG) is a directed network that can reflect the predicted direction of probability dependence between nodes. It is an intuitive yet rigorous tool used in clinical and epidemiological research to communicate about causal questions, inform study design, and guide statistical analysis(Digitale et al., 2022).We used the R package bnlearn and the Bayesian hill-climbing algorithm(Scutari, 2010) to compute the DAG for depression and social anxiety symptoms. The algorithm continuously adds, deletes, and reverses the direction of edges until the best fit is obtained based on the Bayesian information criterion (BIC). This involves an iterative process, where different possible edges connecting different pairs of nodes are randomly restarted, perturbing the structure, and applying 100 perturbations for each of the 50 different random restarts to avoid local maxima.

To ensure the stability of the DAG, we used a bootstrapping method (10,000 bootstrap samples with replacement) to obtain the final DAG structure. First, we determined the frequency of a given edge appearing in 10,000 bootstrap DAGs. Then, we used the optimal breakpoint method to retain edges, resulting in a DAG with high sensitivity and specificity(Scutari & Nagarajan, 2013). Next, when determining the direction of each edge, if 51% or more of the edges were in the same direction in 10,000 bootstrap DAGs, the directional edge was represented as a solid line in the final DAG, while if the value was between 50% and 51%, it was represented as a dashed line. To make the DAG more interpretable, we generated two visualizations as output results. In the first visualization, the thickness of the edge represents the arc strength of the connections in the network. In the second visualization, the thickness of the edge represents the probability of direction.

Descriptive statistics

A total of 251 patients with SCI were enrolled in this study, with 154 being male and 97 female. The mean age was 54.96 (SD = 13.04) years, and the mean of onset was 22.89 (SD = 14.10) years. Further basic information about the participants can be found in table S1. The average "Facing" score in the MCMQ was significantly lower than the norm while the average "Yielding " score was higher. The social scores were lower than the norm.. The incidence rates of depression and anxiety were 30.7% and 15.9%, and the comorbidity rate was 14.34%. The overall scores on the scales for all symptoms are listed in Table 1, and all symptom items and codes are displayed in Table 2.

Table 1

descriptive statistics of symptoms
Categorisation	Chinese Normals/proportions		t	p
Medical coping style
Facing	17.01 ± 3.54	19.48 ± 3.82	-11.05	<0.01
Avoiding	14.61 ± 2.74	14.44 ± 2.97	0.98	0.328
Yielding	12.15 ± 3.14	8.81 ± 3.17	16.83	<0.01
Social support
Subjective support	21.41 ± 5.96	23.81 ± 4.75	-6.37	<0.01
Objective support	8.47 ± 3.39	12.68 ± 3.47	-19.64	<0.01
Support availability	7.07 ± 1.99	9.38 ± 2.40	-18.37	<0.01
Social support	36.96 ± 8.82	44.34 ± 8.38	-13.26	<0.01
Depression
0–4	89	35.5%
5–9	85	33.8%
10–19	65	25.9%
20–27	12	4.8%
Anxiety
0–4	125	49.8%
5–9	86	34.3%
10–18	37	14.7%
19–21	3	1.2%

Table 2

Abbreviation of depression, anxiety, medical coping style, social support symptoms
Symptoms	Abbreviation
Depression symptoms
Little interest or pleasure in doing things	P1: Anhedonia
Feeling down, depressed, irritable, or hopeless	P2: Sadness
Trouble falling asleep, staying asleep, or sleeping too much	P3: Sleeping problems
Feeling tired, or having little energy	P4: Fatigue
Poor appetite, weight loss, or overeating	P5:Appetite
Feeling bad about yourself, or that you are a failure or have let yourself or your family down	P6: Failure
Trouble concentrating on things like school work, reading, or watching TV	P7: Concentration
Moving or speaking so slowly that other people could have noticed	P8: Motor
Thoughts that you would be better off dead or of hurting yourself in some way	P9: Suicidal ideation
Anxiety Symptoms
Feeling nervous, anxious or on edge	G1: Nervousness
Not being able to stop or control worrying	G2: Uncontrollable worry
Worrying too much about different things?	G3: Excessive worry
Trouble relaxing?	G4: Trouble relaxing
Being so restless that it is hard to sit still?	G5: Restlessness
Becoming easily upset or irritated	G6: Irritability
Feeling afraid as if something awful might happen?	G7: Feeling afraid
Medical coping style
Facing	Fac
Avoiding	Avo
Yielding	Yie
Social support
Subjective support	Sbs
Objective support	Obs
Utilization of social support	UsS

Graphical LASSO network

The graphical LASSO-regularized partial correlation network obtained from survey data is shown in Fig. 1. From the network analysis, it can be seen that depression and anxiety symptoms show strong associations in the four communities corresponding to the four scales, while the three dimensions of social support are closely related.

The centrality indices of z-scores are displayed in Fig. 2 (a), Fig. 2 (b) and Fig. 2 (c). Depression symptom P7 (Concentration) and anxiety symptoms G4 (Trouble relaxing) and G6 (Irritability) exhibit the highest centrality strength, indicating that these three symptoms have the closest associations within the network and may play particularly important roles in maintaining depression and anxiety. The CS coefficient of node strength is 0.673, suggesting that the estimation of node strength is sufficiently stable (figure S1 in the supplementary materials). The bootstrap difference test for node strength is shown in figure S2 of the supplementary materials.

The bridge strength of node on the network is illustrated in Fig. 2(d) and Fig. 2(e). Anxiety symptom G1 (Nervousness) and depression symptom P6 (Failure) exhibit the highest bridge stength, indicating that these two symptoms serve as bridging symptoms connecting the anxiety and depression symptom communities. Therefore, the two symptoms may possess the strongest ability to activate/deactivate the co-occurrence of depression and anxiety symptoms. The CS coefficient of node bridge stength is 0.59, suggesting that the estimation of node bridge stength is sufficiently stable (figure S3 in the supplementary materials). The bootstrap difference test for node bridge stength is shown in figure S4 of the supplementary materials.

Green edges represent positive correlations, and red edges represent negative correlations. The thickness of the edge reflects the magnitude of the correlation. The cut-off value is set to be 0.5. Label names can be found in Table 2.

Directed acyclic graph

Figure 3(a) displays the importance of each edge to the DAG structure (edge thickness represents arc strength). Thicker edges imply greater arc strength and are more critical for model fitting. The most important edges in the DAG structure are “ObS-SuS” and “SuS-UsS”, with an arc strength of 0.999 and0.998, respectively. Following closely are “P1-P2”(arc strength: 0.995) and“P6-P7” (arc strength: 0.992). The least important edges are “G1-P6” (arc strength: 0.509) and “G3-G7”(arc strength: 0.527). The arc strength of each edge in the network can be found in figure S5 of the supplementary materials. Figure 3(b) shows the directional probability of each edge. Thicker edges imply a larger proportion of the current direction in 10,000 bootstrap DAGs. The thickest edge connects G4 to P8 (0.89; i.e., 8,900 out of 10,000 bootstrap DAGs have G4 pointing to P8). The exact directional probabilities of each edge in the DAG network can be found in table S2 of the supplementary materials.

From the structure of DAG graph, anxiety symptom G1 (Nervousness) appears upstream, directly affecting other anxiety symptoms (i.e., G2: [Uncontrollable worry], G3: [Excessive worry], G6: [Irritability]) and other depression symptoms (i.e., P4: [Fatigue], P6: [Failure]). Anxiety and depression symptoms are generally upstream of the other two communities, with P5 (Appetite) and P6 (Failure) serving as symptom nodes connecting the anxiety and depression communities to the social support and coping style communities. Additionally, depression symptom P4 (Fatigue) appears upstream in the depression symptoms, and SuS (Subjective support) from the SSRS scale appears at the very upstream of the social support and coping style communities.

This study set out to explore the network and interactive pattern of social adjustment and psychological symptoms in patients with spinal cord injury. The results revealed that among depression symptoms, "Concentration" had the highest centrality strength, while "Trouble relaxing" topped the list for anxiety symptoms in terms of centrality strength. Meanwhile, the anxiety symptom "Nervousness" along with the depression symptom "Failure" distinguished themselves by exhibiting the highest bridge strength. Our findings indicated that "Nervousness" in anxiety symptoms was upstream of all other symptoms. To the best of our knowledge, few previous studies have focused on the network relationships and patterns of interaction between social adaptation and psychological symptoms in patients with spinal cord injury.

As a special population, the social adaptability of patients with SCI is lower than that of the general population after the pandemic, with higher rates of anxiety, depression, and comorbidity. Our research validates this viewpoint. We found that the prevalence of depression among patients with SCI mirrors the results of a previous study conducted on the same patient group during the pandemic (Mesa et al., 2022). However, under the same conditions, the prevalence of depression in the general population is significantly lower than that in patients with spinal cord injuries. In addition, the prevalence of anxiety in patients is significantly higher compared to previous research conducted during the pandemic (Mesa et al., 2022). The combined occurrence rate of anxiety and depression in patients with SCI is 14.34%, but there is limited research on this topic, and further study of the relationship between depression and anxiety in patients with SCI is of great significance. Our research findings support previous studies, indicating that patients with SCI have lower scores in total social support, subjective support, objective support, and support availability compared to the norm. This suggests that compared to the general population, more attention needs to be given to providing support for patients with SCI. The MCMQ scale scores show that the average "Facing" score is significantly lower than the norm, and the " Yielding " score is significantly higher than the norm. Due to the nature of their condition, patients with SCI may be more negative and prone to avoidance than the general population.

Across the lasso network, depressive symptom P7 (Concentration) showed the highest centrality intensity, followed closely in intensity by anxiety symptoms G4 (Trouble relaxing) and G6 (Irritability). This suggests that the above three symptoms are most closely associated in the network and may play a particularly important role in maintaining depression and anxiety in people with spinal cord injury. However, this result differs from many network analyses, where previous studies have suggested that "Irritability" or "Fatigue"(Garabiles et al., 2019)have the highest centrality strength in the network, and other studies have found "Sadness", "Uncontrollable worry" and other symptoms(Kaiser et al., 2021; Ventura-León et al., 2022). Interestingly, it has been shown that 'Concentration' appears as an important bridging symptom in the nodes(Chen et al., 2023; Jones et al., 2021). This suggests the uniqueness of depression and anxiety symptoms in the SCI population. Anxiety symptoms 'Trouble relaxing' and 'Irritability' are also more central in other anxiety-depression networks(Garabiles et al., 2019; Wang et al., 2020; Wei et al., 2021). In conclusion, more attention should be paid to the symptoms with higher centrality in the lasso network in patients with spinal cord injury, which may be helpful in managing the development of adverse psychological symptoms.

Anxiety symptom G1 (Nervousness) and depressive symptom P6 (Failure) showed the highest bridge strengths in the network, suggesting that they are bridge symptoms connecting the depressed and anxious communities. Bridging symptoms increase the risk of interactions between different disorders(R. J. McNally et al., 2017). The identification of bridging symptoms can help to elucidate the complexity of comorbidity and provide potential targets for intervention for comorbidity. This finding has led to the hypothesis that reducing the symptoms of “Nervousness”in people with SCI who have anxiety disorders may reduce the likelihood of their developing co-morbid depressive symptoms, and similarly, the “Failure” in depressive symptoms may could also play a similar role. The bridge symptoms shown in this study were psychological rather than physical, in contrast to previous studies(Bi et al., 2021; Kaiser et al., 2021). This may be due to the poor physical condition of the patients with SCI, who exhibit a lower than normal physical condition despite their normal psychological condition.

To further investigate the relationship between symptoms of depressive and anxiety disorders and their relationship with social support, and coping styles, we used the DAG to explore the predictive (and potentially causal) prioritisation of these symptoms. Although the current study is a cross-sectional investigation that ignores the effects of time, the DAG is able to provide some hypotheses about acceptable causal relationships. We can see from the overall structure of the DAG that the most upstream of the network in both communities is anxiety symptom G1 (Nervousness) while the most downstream are the two depressive symptoms P5 (Appetite) and P6 (Failure). And anxiety symptoms as a whole appear upstream of depressive symptoms. Above imply that when anxiety and depression occur together, anxiety always precedes depression. This is consistent with previous research findings. Interestingly, the anxiety and depression communities as a whole were located upstream of the social support and coping community, suggesting that changes in mental health in people with SCI may precede changes in coping and social support, although previous research has shown that positive and negative coping styles influence mental health through the mediation between social support and coping styles as well as through direct pathways(Dai et al., 2023; Mao et al., 2015; Zhang et al., 2020). Insufficient research exists for specific populations in this area. As a result, we can't determine if the inconsistent outcomes are exclusive to patients with SCI or stem from other factors. Further study is necessary.

Anxiety symptom G1 (Nervousness) appears upstream of the entire DAG, which is inconsistent with some of the previous findings. Their research supports the idea that "Anhedonia" activates other symptoms and leads to comorbidity(Chen et al., 2023; Winer et al., 2017). Within social support, subjective support is in the uppermost of the three support measures. Subjective social support reflects the individual's experience and feelings of social support (Gyasi et al., 2019). Patients' support is mainly provided by family members such as spouses, parents and children, indicating that family members' support plays an important role in social support. In conclusion, narrowing the range of symptoms caused by anxiety may block the risk of other comorbidities, and giving special attention to upstream symptoms that are in various communities may play a key role in preventing anxiety and depressive symptoms and giving effective social support to people with spinal cord injury(R. McNally et al., 2014).

Limitations

Our study has several limitations. Firstly, our study used cross-sectional data and it was not possible to determine the temporal causality of anxiety and depression. Secondly, Our study used self-rating scales so the results are susceptible to the influence of socially desirable answers. Besides, The age structure of our respondents was skewed and the sample size was not large enough. Finally, we analyzed the centrality of the nodes based on their strength, which may have resulted in a bias towards evaluating a small subset of nodes.

Compared to the general population, SCI patients exhibit lower social adaptation ability after the epidemic, along with a higher prevalence of anxiety, depression, and the comorbidity of these two symptoms. The network analysis results show that depression symptom P7 (concentration) exhibits the highest central strength; anxiety symptom G1 (tension) and depression symptom P6 (failure) demonstrate the highest bridge strength in the network; G1 (tension) has also been identified as a key prioritized symptom for the entire DAG, as it is located upstream of all symptoms. Attention should be paid to the "focus" of depression symptoms, the "trouble relaxing" of anxiety symptoms, and the "tension" of patients. By implementing timely and effective intervention measures to control the development of adverse psychological symptoms, the incidence of psychological disorders following various emergencies can be reduced.

Funding: Health Management Outstanding Talents of Shanghai Association of Rehabilitation Medical(2023JGYQ08); Clinical Research Plan of SHDC(No. SHDC2022CRS049); National clinical key specialty construction project of China(Z155080000004); Shanghai Research Center of Rehabilitation Medicine (Top Priority Research Center of Shanghai) (2023ZZ02027); Shanghai Hospital Development Center Foundation—Shanghai Municipal Hospital Rehabilitation Medicine Specialty Alliance(SHDC22023304)

Ethics statements

This retrospective study was approved by the Ethics Committee of Shanghai YangZhi Rehabilitation Hospital (Shanghai Sunshine Rehabilitation Center)(NO.2023-010). The methods were carried out in accordance with the with the Declaration of Helsinki. All the patients have been informed and signed informed consent before the experiments.

Availability of data and material

The datasets generated and analysed during the current study are available.

Conflict of Interest

The authors declare that they have no known competing financial interests or personalrelationships that could have appeared to influence the work reported in this paper. All authors disclosed no relevant relationships.

Author Contributions

H.L. wrote the main manuscript text and prepared all figures; H.W. and J.W. conceived and designed the experiments; H.L. and Y.L. analyzed the data; H.Z. , Y.N. , H.X. , Y.X. , C.F. ,C.J. ,and W.S. Y.N.collected and provided the sample for this study.

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No competing interests reported.

SupplementaryMaterials.docx

The network and interactive pattern of social adjustment and psychological symptoms in patients with spinal cord injury: A network analysis

Status:

Version 1

Abstract

Figures

Introduction

Methods

Participants and study design

Measures

Sociodemographic and clinical characteristics

Medical Coping Styles

Social support level

Depression symptoms

Anxiety symptoms

Data analysis

Graphical LASSO network

Directed acyclic graph

Results

Descriptive statistics

Graphical LASSO network

Directed acyclic graph

Discussion

Limitations

Conclusion

Declarations

References

Additional Declarations

Supplementary Files

Status:

Version 1