Network Analysis of Stress, Anxiety, and Depression During Pregnancy: An Integrated Perspective on Topics and Dimensions

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This study employed network analysis to construct and compare pregnancy stress-anxiety-depression networks with topics and dimensions as nodes. Centrality analysis identified core nodes, and the network's stability was assessed using the bootstrap method. Results revealed that pregnancy stress, anxiety, and depression form a tightly interwoven interactive network. At the item level, "Worry about not receiving sufficient psychological support" emerged as the most central node within the pregnancy stress network. At the dimension level, the role dimension exhibited the highest centrality in the pregnancy stress network, while the depression dimension served as the core of the hospital anxiety and depression network. Further analysis showed that the centrality stability coefficients of all networks exceeded 0.5, indicating robust and reliable network structures. This study offers new insights into the interactive mechanisms of negative emotions during pregnancy, and the identified core symptoms provide key targets for precise clinical interventions. Pregnancy-related stress (PPS) Anxiety Depression Hospital Anxiety and Depression Scale (HADS) Network analysis Second trimester of pregnancy Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 1. Introduction Pregnancy refers to the stage during which the fertilized egg develops into a fetus and grows within the mother's uterus (Bjelica et al., 2018 ). As a unique period in a woman's life, pregnancy progresses through distinct phases, during which expectant mothers experience significant changes in their physical health, daily life, and work. Coupled with heightened psychological sensitivity, this period increases susceptibility to various negative emotions (Zhang, Yang, Li, Wang, Liu, & Zhao, 2022). In China, the prevalence rates of depression, anxiety, and pregnancy-related stress among expectant mothers have reached 19.4%, 26.6%, and 92.3%, respectively (Meng et al., 2025 ). These figures reveal the high prevalence and widespread occurrence of psychological issues during pregnancy in China. Depression, anxiety, and stress are the most common mental health issues during pregnancy (Chauhan & Potdar, 2022 ), with their development involving the combined effects of physiological, psychological, and social environmental factors. Physiologically, expectant mothers often experience changes in body shape and appearance, such as breast tenderness, nausea and vomiting, weight gain, abdominal enlargement, and the emergence of melasma and stretch marks (Aguilera-Martín et al., 2021 ). These physical transformations can easily trigger discomfort and anxiety. Psychologically, difficulties adapting to the new parental role, persistent concerns about fetal health, and fear of the delivery process further exacerbate anxiety and tension (Lin et al., 2022 ). Socially, factors like family conflicts, lack of social support, poor interpersonal relationships, and financial pressures have been shown to significantly correlate with high prevalence rates of prenatal emotional disorders (Smythe et al., 2022 ). Despite the prevalence of these issues, current clinical and research attention remains largely focused on the physical health of pregnant women and their postpartum psychological state, with insufficient systematic attention given to prenatal mental health (Zeng et al., 2015 ).For pregnant women, the onset of anxiety and depression is often closely linked to hormonal changes within the body. Specifically, when the hypothalamic-pituitary-adrenal (HPA) axis is affected by steroid hormone imbalances, the risk of developing these emotional disorders is significantly elevated (Shea et al., 2007 ).Notably, due to the high similarity between clinical symptoms of anxiety and depression and normal pregnancy reactions, emotional disorders in pregnant women are often under-recognized and under-addressed in clinical practice (Sidebottom et al., 2012 ; Al-Sabah et al., 2024 ).However, research confirms that approximately two-thirds of postpartum emotional issues actually originate during pregnancy (Insan et al., 2022 ). Neglecting prenatal psychological status may miss critical windows for intervention, allowing stress, anxiety, and depression to accumulate throughout pregnancy. This can trigger a cascade of adverse effects on the physical and mental health of expectant mothers. Specifically, persistent negative emotions during pregnancy increase the risk of fetal developmental abnormalities, excessive maternal weight gain (Voerman et al., 2019 ), delivery complications, and postpartum depression (Gao et al., 2019 ).Regarding the underlying mechanisms, emotional states such as stress and anxiety can trigger elevated levels of specific hormones in the body, potentially affecting the physical and brain structure and function of the developing fetus (Cardwell, 2013 ).Regarding pregnancy outcomes, studies indicate that pregnant women experiencing high stress levels face a 25% to 60% higher risk of preterm birth compared to those with low stress levels, even after controlling for other known risk factors (Hobel et al., 2008 ). If these risks stemming from negative emotions are not addressed promptly, they may lead to more severe long-term consequences. Clinically, prenatal stress, anxiety, and depression often coexist and intertwine, mutually reinforcing each other to form a vicious cycle that is difficult to break independently (Feng et al., 2023 ). Specifically, anxiety and depression exhibit significant comorbidity, with most individuals experiencing depression also presenting anxiety symptoms (Zelkowitz & Papageorgiou, 2012 ).while stress, as a key predictor for both, further exacerbates their behavioral manifestations and neurobiological symptoms (Lemus et al., 2022 ). However, existing research exhibits notable limitations: most studies focus solely on pairwise associations among the three (e.g., stress and depression, anxiety and depression), with the core exploration centered on the interrelationship between anxiety and depression (Lancaster et al., 2010 ; Liu et al., 2017 ; O’Hara & McCabe, 2013 ). Research systematically examining their interactive mechanisms within a unified framework remains scarce. A deeper examination reveals dual limitations in current research on the relationship between prenatal stress, prenatal depression, and prenatal anxiety: On one hand, most studies remain at the superficial level of correlational analysis, failing to uncover the underlying pathways and mechanisms linking these three factors; On the other hand, studies based on latent variable assumptions often simplify these three constructs into a single concept, analyzing variable associations solely through total scale scores. This approach overlooks the essential characteristics of each construct—that they all encompass multiple subdimensions and specific symptoms, and that these subdimensions may exhibit distinct interactive patterns. These limitations obscure the true relational structure within constructs, preventing clarification of causal links and dynamic interactions among variables. Consequently, they hinder the development of targeted intervention entry points and impede the formulation of precise, tiered strategies for promoting mental health during pregnancy. Given the limitations of traditional statistical methods in revealing complex multivariable relationship structures, this study introduces network analysis to systematically explore the intrinsic relationships among prenatal stress, prenatal depression, and prenatal anxiety. This methodology has been widely applied in psychology, medicine, and various population studies, with its validity and applicability supported by empirical evidence (Li et al., 2022 ; Guo et al., 2023 ; Shalayiding et al., 2024 ). Network analysis offers three distinct advantages in this research context: First, unlike latent variable models, it constructs relational networks directly from observed variables, enabling intuitive visualization of interactions among specific symptoms or sub-dimensions; Second, this method permits bidirectional influence between variables, aligning more closely with the dynamic patterns of symptom emergence and development observed in practice. This facilitates the identification of key intervention targets, while centrality metrics quantify the importance of nodes within the network, providing a basis for prioritizing intervention strategies (Hevey, 2018 ; Broda et al., 2023 ); Third, network analysis integrates all observed variables to reveal the dynamic associative structure underlying complex psychological phenomena from a systemic perspective, thereby offering novel insights and methodological pathways for understanding the co-occurrence mechanisms of negative emotions during pregnancy. 2. Methodology 2.1 Participants The data for this study were drawn from the Maternal Mental Health Database (Version 5) compiled by Zhang, Cui, Niu, Li, Wang, and Zhao (2024), which has been archived in a scientific repository. A survey was conducted among pregnant women in their second trimester in a province of China, with 619 questionnaires distributed. After excluding outliers and poorly completed questionnaires, 604 valid responses were retained, yielding a response rate of 97.6%. Participants ranged in age from 20 to 42 years, with a mean age of (29.96 ± 3.90) years. The demographic characteristics of the sample can be summarized as follows: Regarding marital status, 94.87% of respondents were in their first marriage. In terms of educational attainment, 72.35% held a college degree. Natural conception was the predominant method of conception, accounting for 87.75%. Additionally, 65.40% of participants had no history of adverse pregnancy outcomes. These findings indicate a high degree of homogeneity within this group of pregnant women regarding marital status, educational background, and mode of conception. 2.2 Measurement Tools 2.2.1 Pregnancy Pressure Scale (PPS) The Pregnancy Pressure Scale (PPS), developed by Chen Zhanghui et al. in 1989, is employed as the pregnancy stress assessment tool. This scale is specifically designed for Chinese pregnant women to measure psychological stress levels during pregnancy. The PPS comprises 30 items across three dimensions: Role Dimension (items 1–15), Health Dimension (items 16–23), and Body Shape Dimension (items 24–27). The final three items, not assigned to a specific dimension, represent other stress sources. The scale employs a 0–3 point rating system across four levels, yielding a total score range of 0–90 points. Specific grading criteria are as follows: 0–13 points indicate no pregnancy stress; 14–36 points indicate mild pregnancy stress; 36–72 points indicate moderate pregnancy stress; >72 points indicate severe pregnancy stress. A higher total score indicates greater pregnancy stress levels among pregnant women. 2.2.2 Hospital Anxiety and Depression Scale (HADS) Anxiety and depression were assessed using the Hospital Anxiety and Depression Scale (HADS), developed by Zigmond and Snaith in 1983. This scale is widely used for screening anxiety and depression in patients across general hospitals. The HADS comprises 14 items: 7 assess depressive symptoms and 7 assess anxiety symptoms. All items use a 0–3 point rating scale. The scale score provides a preliminary assessment of emotional state. The grading criteria for the anxiety and depression subscales are consistent: 0–7 indicates no symptoms, 8–10 suggests possible anxiety or depression, and 11–21 confirms the presence of anxiety or depression symptoms. 2.3 Data Analysis This study employs network analysis methods to construct the pregnancy stress-anxiety-depression network structure among the maternal population. The research unfolds on two levels: first, analyzing the complex interrelationships among pregnancy stress, prenatal anxiety, and prenatal depression based on core themes; second, focusing on the dimensional level to explore the interactive relationships within the respective sub-dimensions of pregnancy stress, anxiety, and depression. Through this dual analytical perspective, the study systematically reveals the psychological characteristics of pregnant women regarding pregnancy stress and emotional symptoms. Finally, by identifying core nodes within the network via centrality analysis, the research pinpoints key targets for maintaining maternal mental health, providing a theoretical foundation for subsequent prevention practices and precision interventions. 2.3.1 Network Construction Method This study employed the qgraph package in R software, utilizing the ggmModSelect method to establish a Gaussian graphical model for maternal anxiety, depression, and pregnancy stress. Within the graph, node colors distinguish different items or dimensions, with blue edges indicating positive correlations and orange edges denoting negative correlations. The strength of associations between nodes is visually represented by edge thickness and color intensity: stronger associations are indicated by thicker edges and darker colors. 2.2 Centrality Analysis To identify key nodes within the network, centrality analysis was conducted. Centrality metrics measure the extent, strength, and closeness of a node's connections to others in the network. Altering highly central nodes can influence a larger number of other nodes. In the estimated network, nodes with more connections occupy central positions, while those with fewer connections reside peripherally. All centrality metrics are presented in z-score standardized form and sorted by strength to facilitate core node identification. 2.3 Stability Testing Finally, the bootnet package was employed to validate the accuracy and stability of the network. This validation comprised two key aspects: First, the stability of node edge weights. Nonparametric bootstrap methods were used to compute 95% confidence intervals (CI) for each edge weight in the network. Generally, lower overlap within confidence intervals indicates higher estimation accuracy for edge weights. Second, stability analysis of node strength and centrality. Node strength reflects the total weight of connections between a node and others, serving as a key metric for measuring node centrality. Stability testing assesses the reliability of node strength values by calculating the centrality stability coefficient (CS coefficient). A coefficient greater than 0.5 indicates good stability of network centrality metrics, meaning node rankings or values remain consistent across different samples or resampling. 3. Results 3.1 Network Structure Within the network where items serve as nodes, diverse and intricate patterns of associations emerge between entries across different dimensions. In terms of association strength, the anxiety dimension item "I feel tense or distressed (31)" exhibits a strong positive correlation with the pregnancy stress health dimension item "Worrying about the baby's safety (16)"; conversely, the depression dimension item "I still enjoy things that used to interest me (38)" shows a weak negative correlation with the pregnancy stress role dimension item "Difficulty preparing baby clothes (1)".Notably, within the role dimension of pregnancy stress, the item "Worrying about possible complications during delivery or needing a cesarean section (21)" showed a strong positive correlation with "Worrying that the doctor might not arrive in time during delivery (22)." Simultaneously, the anxiety item "My mind is filled with worries (33)" exhibited a weak negative correlation with the depression item "I can sit comfortably and relax (40) ". Further observation reveals that within the subnetworks formed by each dimension, certain anxiety dimension items (e.g., "I feel tense or distressed (31)", "My mind is filled with worries (33)") frequently connect with items from other dimensions (pregnancy stress, depression), exhibiting a high number of edges. This indicates these items serve as key nodes, playing a significant role in linking different psychological states and influencing network structure and information transmission (as shown in Fig. 1 ). Within the network where dimensions serve as nodes, the connections between the dimensions of pregnancy stress, anxiety, and depression are equally strong (Fig. 2 ). Specifically, all dimensions within pregnancy stress (role dimension, health dimension, body shape dimension, other stressors) are interconnected. The association between the role dimension and the other stressors dimension is relatively stronger, indicating that the role dimension occupies a more central position within the internal structure of pregnancy stress. Simultaneously, the anxiety dimension exhibits a very strong association with the depression dimension. Furthermore, the role dimension and health dimension are not only closely linked to both the anxiety and depression dimensions but also serve as crucial bridging elements between pregnancy stress and anxiety/depressive emotions overall. This reflects their pivotal role as hubs within the cross-construct network connections. 3.3 Centrality Analysis In network analysis, the relative importance of nodes within a network can be assessed through their connectivity patterns. Centrality serves as a set of key metrics for revealing node characteristics, reflecting the degree to which a node is directly connected to other nodes (Costantini, Epskamp, et al., 2015 ).Common centrality measures include Strength, Closeness, Betweenness, and Expected Influence, collectively used to identify core nodes within a network. Specifically: Strength centrality measures the quantity and strength of a node's direct connections to other nodes; Closeness centrality reflects the total path length from a node to all other nodes in the network—shorter paths indicate greater susceptibility to network changes; Betweenness centrality characterizes how frequently a node lies on the shortest paths between other nodes, with higher values indicating greater criticality in information flow and group connectivity; Expected Influence aggregates the sum of weights across all edges connected to a node, proving particularly useful in networks with both positive and negative associations, as it reflects a node's potential influence on the network as a whole. The centrality analysis results based on item nodes (Fig. 3 ) reveal that within the network structure of the Pregnancy Stress Scale, all centrality metrics—including strength, betweenness centrality, and expected influence—consistently indicate that Item 9 ("Worry about not receiving sufficient psychological support") is the most central node in the entire network. In the Hospital Anxiety and Depression Scale, Item 10 ("I feel cheerful") and Item 11 ("I can sit quietly and easily") both exhibit high levels across multiple centrality measures, indicating that both are key nodes within this scale's network. At the dimensional level, centrality metrics for each dimension are presented in Fig. 4 . Within the Hospital Anxiety and Depression Scale, the depression dimension exhibited the highest centrality values across all items, indicating its status as the core node within this scale's network. Conversely, in the Pregnancy Stress Scale, the role adaptation dimension demonstrated the highest centrality values, suggesting its pivotal role within the internal structure of pregnancy stress. 3.4 Stability Analysis The stability of the pregnancy stress, anxiety, and depression network was assessed using the self-organizing method. Regarding node centrality stability, results indicated: In the network with items as nodes, the centrality stability coefficient (CS coefficient) was 0.672 (range: 0.594–0.75); In the network with dimensions as nodes, the CS coefficient was 0.594 (range: 0.517–0.672). Both networks exhibited CS coefficients above the critical threshold of 0.50, indicating robust stability in the relative importance ranking of nodes across different sampling conditions and overall reliability of the network centrality metrics (see Figs. 5 and 7 for results). Second, analysis of edge weight estimation accuracy indicates that most connections in the network are estimated robustly. As shown in Figs. 6 and 8 , red lines represent edge weights calculated from the original sample, while black lines denote the average edge weights estimated via the nonparametric Bootstrap method. The gray areas correspond to the 95% confidence intervals (CI) for edge weights under the Bootstrap method. The results show that the red line representing the original estimates overlaps to some extent with the black line indicating the average values from the self-help method, and their 95% confidence intervals (gray areas) are generally narrower. This indicates that the estimated values of edge weights are less affected by sampling variability and exhibit higher precision. Considering the stability results for both node strengths and edge weights, the overall structure of the current pregnancy stress, anxiety, and depression network can be deemed reliable and credible. 4. Discussion This study employed network analysis methods to systematically examine the internal structural relationships among pregnancy stress, anxiety, and depression within a mid-pregnancy cohort in China. Results indicate tightly interwoven network relationships at both item and dimension levels. Specifically, within the item network, "Worry about not receiving sufficient psychological support" (Item 9) in pregnancy stress serves as a pivotal hub connecting diverse psychological states. In the dimension network, depression occupies a central position, while role adaptation stress exhibits close connections with both anxiety and depression. Notably, the aforementioned key items themselves belong to the role adaptation dimension. These findings reveal core targets within the psychological structure of pregnant women and postpartum mothers at multiple levels, providing empirical evidence and directional guidance for future precise and targeted psychological interventions. In the network analysis where items served as nodes, Item 9 of the Pregnancy Stress Scale—"Worry about not receiving sufficient psychological support"—was identified as the most central node in the overall network. This indicates that lack of psychological support is a key mechanism affecting the emotional health of women in mid-pregnancy. This finding aligns strongly with existing research: low social support shows significant positive correlations with both prenatal depression and anxiety (Vidhan, Rohilla, Dhiman, & Khoiwal, 2025 ). When pregnant women lack social resources for confiding, obtaining information, or alleviating negative emotions, they become more vulnerable to stress, potentially developing depressive or anxious disorders. Conversely, robust social support systems exert a protective effect on mental health during pregnancy. Pregnant women receiving adequate social support typically exhibit superior psychological well-being and emotional health (Herbell & Zauszniewski, 2019 ; Qi et al., 2022 ).Therefore, clinical and community interventions should prioritize strengthening social support—encompassing informational, emotional, and instrumental support—as a core strategy to alleviate negative maternal emotions, thereby effectively interrupting the pathway from stress to anxiety and depression. Within the dimensional network, the role adaptation dimension was identified as a core node within pregnancy stress, indicating that stress arising from role transitions may be a key pathway triggering maternal emotional symptoms. Once activated, role stress at the network's central position may further influence and exacerbate other psychological symptoms through its extensive connections, thereby amplifying overall emotional burden. This finding aligns strongly with existing research: Xiabidan Tuxunjiang et al. ( 2023 ) noted that role transition stress is the primary stressor during pregnancy, as expectant mothers often experience significant psychological burden due to anxieties about the unknown while undergoing physical changes and transitioning into motherhood. Ladekarl et al. (2022) further revealed from an identity construction perspective that pregnant women must integrate their new maternal identity while maintaining their original individual identity. This identity transition creates an "Identity Limbo," which is the core cause of maternal role stress. It manifests as sadness over leaving behind past life experiences and anxiety about the responsibilities of motherhood. Therefore, psychological interventions during pregnancy and childbirth should prioritize supporting role adaptation. By enhancing role identification and maternal competence development, these interventions structurally alleviate core stressors within the psychological network, effectively blocking the diffusion pathways of negative emotions. This study employs network analysis to systematically reveal the intricate internal connections between pregnancy stress, anxiety, and depression in mid-pregnancy women. It not only deepens theoretical understanding of the underlying mechanisms of maternal psychological issues but also provides crucial practical foundations for early identification and targeted interventions. Theoretically, this study overcomes the limitations of traditional factor analysis by examining the dynamic relationships between nodes and network structures. It identifies core targets such as "psychological support concerns" and "role adaptation stress," providing an empirical foundation for constructing a multidimensional theoretical model of maternal mental health. Practically, the findings suggest that clinical interventions should move beyond single-symptom management toward multi-level, structured approaches centered on strengthening social support and promoting role adaptation. This shift enables effective prevention and systematic alleviation of psychological issues during the critical perinatal period. 5. Conclusions This study systematically analyzed the interactive relationships among pregnancy stress, anxiety, and depression in women during mid-pregnancy.Results revealed a tightly interconnected psychological network among these three factors at both the item and dimension levels. At the granular item level, "Worrying about insufficient psychological support" (Item 9) emerged as a pivotal node linking diverse psychological concerns. At the dimension level, depression occupied a central position within the network, while role adaptation stress exhibited significant connections with both anxiety and depression.Notably, the key item itself belongs to the role adaptation stress dimension, further highlighting this dimension's pivotal role in psychological regulation during mid-pregnancy. This study reveals core targets within the maternal mental health network at multiple levels, providing theoretical foundations and practical guidance for implementing precise and effective psychological interventions in the future. Declarations Ethics approval and consent to participate Not applicable. This study is a secondary analysis of publicly available, anonymized data and did not involve direct interaction with human subjects or the collection of new personal data. Therefore, new ethical approval was not required for this analysis. Consent for publication Not applicable. (This manuscript does not contain any individual person's data in any form.) Availability of data and materials The dataset analyzed during the current study is publicly available and can be accessed as follows: Zhang, D., Cui, J., Niu, Y., et al. (2024, December 4). Maternal mental health dataset (Version 5) [Data set]. Science Data Bank. https://doi.org/10.57760/sciencedb.11251 Competing interests The authors declare that they have no competing interests. Funding The authors received no specific funding for this work. Authors' contributions Hu.H: Writing - Original Draft, Methodology, Software, Validation, Data Curation, Writing - Review & Editing, Funding acquisition, Project administration, Supervision Acknowledgements Not applicable. Authors' information Hong Hu* (School of Psychology, Guizhou Normal University, Guiyang, Guizhou, China, 550025) Email: [email protected] Address: School of Psychology, Guizhou Normal University, Guiyang 550025, China References Aguilera-Martín, Á., Gálvez-Lara, M., Blanco-Ruiz, M., et al. (2021). Psychological, educational, and alternative interventions for reducing fear of childbirth in pregnant women: A systematic review. Journal of Clinical Psychology, 77 (3), 525–555. https://doi.org/10.1002/jclp.23071 Al-Sabah, R., Al-Taiar, A., Ziyab, A. H., Akhtar, S., & Hammoud, M. S. (2024). Antenatal depression and its associated factors: findings from Kuwait birth cohort study. Journal of Epidemiology and Global Health, 14 (3), 847–859. https://doi.org/10.1007/s44197-024-00223-7 Bjelica, A., Cetkovic, N., Trninic-Pjevic, A., et al. (2018). The phenomenon of pregnancy -- a psychological view. Ginekologia Polska, 89 (2), 102–106. Broda, M. D., Granger, K., Chow, J., & Ross, E. (2023). Using social network analysis in applied psychological research: A tutorial. Psychological Methods, 28 (4), 791–805. https://doi.org/10.1037/met0000451 Cardwell, M. S. (2013). Stress: Pregnancy considerations. Obstetrical & Gynecological Survey, 68 (2), 119–129. https://doi.org/10.1097/OGX.0b013e31827203a3 Chauhan, A., & Potdar, J. (2022). Maternal mental health during pregnancy: A critical review. Cureus, 14 (10), e30656. https://doi.org/10.7759/cureus.30656 Costantini, G., Epskamp, S., Borsboom, D., Perugini, M., Mõttus, R., Waldorp, L. J., & Cramer, A. O. J. (2015). State of the aRt personality research: A tutorial on network analysis of personality data in R. Journal of Research in Personality, 54 , 13–29. https://doi.org/10.1016/j.jrp.2014.07.003 Feng, G., Xu, X., & Lei, J. (2023). Tracking perceived stress, anxiety, and depression in daily life: a double-downward spiral process. Frontiers in Psychology, 14 , 1114332. https://doi.org/10.3389/fpsyg.2023.1114332 Gao, M., Hu, J., Yang, L., et al. (2019). Association of sleep quality during pregnancy with stress and depression: A prospective birth cohort study in China. BMC Pregnancy and Childbirth, 19 (1), 444. https://doi.org/10.1186/s12884-019-2564-3 Guo, W., Zhao, Y., Chen, H., et al. (2023). The Bridge symptoms of childhood trauma, sleep disorder and depressive symptoms: a network analysis. Child and Adolescent Psychiatry and Mental Health, 17 (1), 88. https://doi.org/10.1186/s13034-023-00635-6 Hevey, D. (2018). Network analysis: a brief overview and tutorial. Health Psychology & Behavioral Medicine, 6 (1), 301–328. https://doi.org/10.1080/21642850.2018.1521283 Herbell, K., & Zauszniewski, J. A. (2019). Stress experiences and mental health of pregnant women: The mediating role of social support. Issues in Mental Health Nursing, 40 (7), 613–620. https://doi.org/10.1080/01612840.2019.1565873 Hobel, C. J., Goldstein, A., & Barrett, E. S. (2008). Psychosocial stress and pregnancy outcome. Clinical Obstetrics & Gynecology, 51 (2), 333–348. https://doi.org/10.1097/GRF.0b013e31817397b8 Insan, N., Weke, A., Forrest, S., & Rankin, J. (2022). Social determinants of antenatal depression and anxiety among women in South asia: A systematic review & meta-analysis. PLoS ONE, 17 (2), e0263760. https://doi.org/10.1371/journal.pone.0263760 Lancaster, C. A., Gold, K. J., Flynn, H. A., Yoo, H., Marcus, S. M., & Davis, M. M. (2010). Risk factors for depressive symptoms during pregnancy: a systematic review [published correction appears in Am J Obstet Gynecol. 2011;205(3):236]. American Journal of Obstetrics and Gynecology, 202 (1), 5–14. https://doi.org/10.1016/j.ajog.2009.09.007 Lemus, A., Vogel, S. C., Greaves, A. N., & Brito, N. H. (2022). Maternal anxiety symptoms associated with increased behavioral synchrony in the early postnatal period. Infancy, 27 (4), 821–835. https://doi.org/10.1111/infa.12473 Li, W., Zhao, N., Yan, X., et al. (2022). Network analysis of depression, anxiety, posttraumatic stress symptoms, insomnia, pain, and fatigue in clinically stable older patients with psychiatric disorders during the COVID-19 outbreak. Journal of Geriatric Psychiatry and Neurology, 35 (2), 196–205. https://doi.org/10.1177/08919887221078559 Lin, I. H., Huang, C. Y., Chou, S. H., & Shih, C. L. (2022). Efficacy of prenatal yoga in the treatment of depression and anxiety during pregnancy: A systematic review and Meta-Analysis. International Journal of Environmental Research and Public Health, 19 (9), 5368. https://doi.org/10.3390/ijerph19095368 Liu, S., Yan, Y., Gao, X., et al. (2017). Risk factors for postpartum depression among Chinese women: path model analysis. BMC Pregnancy and Childbirth, 17 (1), 133. https://doi.org/10.1186/s12884-017-1320-x Meng, W., Shalayiding, S., Wang, X., Sailike, B., & Jiang, T. (2025). Relationship between prenatal anxiety, depression, pregnancy stress and their social determinants. BMC Psychology, 13 , 905. https://doi.org/10.1186/s40359-025-03237-3 O’Hara, M. W., & McCabe, J. E. (2013). Postpartum depression: current status and future directions. Annual Review of Clinical Psychology, 9 , 379–407. https://doi.org/10.1146/annurev-clinpsy-050212-185612 Qi, W., Liu, Y., Lv, H., Ge, J., Meng, Y., Zhao, N., & Hu, J. (2022). Effects of family relationship and social support on the mental health of Chinese postpartum women. BMC Pregnancy and Childbirth, 22 (1), 65. https://doi.org/10.1186/s12884-022-04392-w Shalayiding, S., Meng, W., Wang, X., Sailike, B., & Jiang, T. (2024). Symptom network differences in school adjustment and anxiety-depression-stress in adolescents: a gender-based perspective. BMC Public Health, 24 (1), 3189. https://doi.org/10.1186/s12889-024-20718-zh Shea, A. K., Streiner, D. L., Fleming, A., Kamath, M. V., Broad, K., & Steiner, M. (2007). The effect of depression, anxiety and early life trauma on the cortisol awakening response during pregnancy: preliminary results. Psychoneuroendocrinology, 32 (8–10), 1013–1020. https://doi.org/10.1016/j.psyneuen.2007.07.006 Sidebottom, A. C., Harrison, P. A., Godecker, A., & Kim, H. (2012). Validation of the patient health questionnaire (PHQ)-9 for prenatal depression screening. Archives of Women's Mental Health, 15 (5), 367–374. https://doi.org/10.1007/s00737-012-0295-x Smythe, K. L., Petersen, I., & Schartau, P. (2022). Prevalence of perinatal depression and anxiety in both parents: A systematic review and Meta-analysis. JAMA Network Open, 5 (6), e2218969. https://doi.org/10.1001/jamanetworkopen.2022.18969 Vidhan, D., Rohilla, J., Dhiman, V., & Khoiwal, K. (2025). Associations between maternal anxiety and depression during pregnancy and obstetric outcomes: A cross-sectional study. Cureus . https://doi.org/10.7759/cureus.86541 Voerman, E., Santos, S., Inskip, H., et al. (2019). Association of gestational weight gain with adverse maternal and infant outcomes. JAMA, 321 (17), 1702–1715. https://doi.org/10.1001/jama.2019.4667 Xiabidan Tuxunjiang, Li Ling, Zhang Wei, Bahadana Selik, Gulijanati Wumaier, Jiang Ting. (2023). The mediating effect of psychological resilience between pregnancy stress and prenatal depression in pregnant women. Journal of Central South University (Medical Edition), 48(4), 557–564. https://doi.org/10.11817/j.issn.1672-7347.2023.220338 Zelkowitz, P., & Papageorgiou, A. E. (2012). Easing maternal anxiety: an update. Women's Health (London, England), 8 (2), 205–213. https://doi.org/10.2217/whe.11.96 Zeng, Y., Cui, Y., & Li, J. (2015). Prevalence and predictors of antenatal depressive symptoms among Chinese women in their third trimester: a cross-sectional survey. BMC Psychiatry, 15 , 66. https://doi.org/10.1186/s12888-015-0452-7 Zhang, D., Cui, J., Niu, Y., et al. (2024, December 4). Maternal mental health dataset (Version 5) [Data set]. Science Data Bank. https://doi.org/10.57760/sciencedb.11251 Zhang, L., Yang, Y. T., Li, M. D., et al. (2022). The prevalence of suicide ideation and predictive factors among pregnant women in the third trimester. BMC Pregnancy and Childbirth, 22(1), 266. Zigmond, A. S., & Snaith, R. P. (1983). The hospital anxiety and depression scale. Acta Psychiatrica Scandinavica, 67(6), 361–370. https://doi.org/10.1111/j.1600-0447.1983.tb09716.x Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 26 Feb, 2026 Reviewers agreed at journal 10 Feb, 2026 Reviews received at journal 03 Dec, 2025 Reviewers agreed at journal 30 Nov, 2025 Reviewers agreed at journal 26 Nov, 2025 Reviewers invited by journal 25 Nov, 2025 Editor assigned by journal 22 Oct, 2025 Submission checks completed at journal 22 Oct, 2025 First submitted to journal 20 Oct, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7903480","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":552993841,"identity":"046702b7-96e8-4c9f-9f13-bcb5ea23e118","order_by":0,"name":"Hong Hu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAzUlEQVRIiWNgGAWjYBACfmb+Dwc+GPyTY2xvIFKLZHuD4cMZFQeMmXsOEKnF4MwBY2OeMwcS22ckEGvLjIQ0Cd62O4m9Mx9vvMFQYxNNUAu/RMIxCcm2Z8YzZ6cVWzAcS8ttIGxLYpuEYRuz7MbZOWYSjA2HCWsxuJHMJpHYxsy4/+YZYrWcOcZscODMYcXGGTxEapFs72F82FCRZszYA/RLAjF+4WfmYTj8x8AGGJWHN974UGNDWAuKIyUSSFEO0UKqjlEwCkbBKBgZAAAvzUZHlfxLSgAAAABJRU5ErkJggg==","orcid":"","institution":"Guizhou Normal University","correspondingAuthor":true,"prefix":"","firstName":"Hong","middleName":"","lastName":"Hu","suffix":""}],"badges":[],"createdAt":"2025-10-20 08:08:34","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7903480/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7903480/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":97166584,"identity":"806a6407-0fe9-42c8-a280-9faadfa2c1dc","added_by":"auto","created_at":"2025-12-01 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13:52:37","extension":"html","order_by":20,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":93272,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7903480/v1/90262c2c69d1eba0f123e4b3.html"},{"id":97166579,"identity":"599f91b0-4667-4025-89c1-7308be76ec2e","added_by":"auto","created_at":"2025-12-01 13:52:36","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":408537,"visible":true,"origin":"","legend":"\u003cp\u003eNetwork Diagram of Pregnancy Stress, Anxiety, and Depression Based on the Subject\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7903480/v1/b6ba36d86ced50e856780556.jpeg"},{"id":97166578,"identity":"ae3a9bd6-07a0-40c2-b719-8465b4e85f08","added_by":"auto","created_at":"2025-12-01 13:52:36","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":85384,"visible":true,"origin":"","legend":"\u003cp\u003eNetwork Diagram of Pregnancy Stress, Anxiety, and Depression Based on Dimensions\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7903480/v1/fbab684cc94f2d144734b73d.jpeg"},{"id":97166582,"identity":"7b52440b-07e7-4439-9947-69ca319bc3a2","added_by":"auto","created_at":"2025-12-01 13:52:36","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":19838,"visible":true,"origin":"","legend":"\u003cp\u003eCentrality Measures for Each Item in the Network of Pregnancy Stress, Anxiety, and Depression\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7903480/v1/d37696bb575d000919233d8e.png"},{"id":97248914,"identity":"d0bd56d3-1add-4669-9430-9f5e4828479b","added_by":"auto","created_at":"2025-12-02 13:08:10","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":14284,"visible":true,"origin":"","legend":"\u003cp\u003eCentrality Measures for Each Dimension in the Network of Pregnancy Stress, Anxiety, and Depression\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-7903480/v1/b0d64bf7ed4fc93b522f25ea.png"},{"id":97166580,"identity":"ac0689d6-52bf-4b30-84d9-e5b31b393fc2","added_by":"auto","created_at":"2025-12-01 13:52:36","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":11397,"visible":true,"origin":"","legend":"\u003cp\u003eCentral Stability Test for the Network of Pregnancy Stress, Anxiety, and Depression (Topic Nodes)\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-7903480/v1/b51b1d40225f4708262f5471.png"},{"id":97250353,"identity":"747bbcd4-2760-4153-b7bf-795a9cf2685c","added_by":"auto","created_at":"2025-12-02 13:14:20","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":9235,"visible":true,"origin":"","legend":"\u003cp\u003eEdge Weight Stability Test for the Pregnancy Stress, Anxiety, and Depression Network (Topic Nodes)\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-7903480/v1/b21675a7a98de8782087dee1.png"},{"id":97249933,"identity":"6ae4af61-e6bb-4a6c-9da5-139ac03f8d7a","added_by":"auto","created_at":"2025-12-02 13:13:40","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":11357,"visible":true,"origin":"","legend":"\u003cp\u003eCentral Stability Test of the Pregnancy Stress, Anxiety, and Depression Network (Dimensional Nodes)\u003c/p\u003e","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-7903480/v1/f470a8c430ba7f3b183aea87.png"},{"id":97248958,"identity":"3ea75f63-df89-4c96-94b3-cdff6ca3675d","added_by":"auto","created_at":"2025-12-02 13:08:54","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":11365,"visible":true,"origin":"","legend":"\u003cp\u003eEdge Weight Stability Test for the Pregnancy Stress, Anxiety, and Depression Network (Dimensional Nodes)\u003c/p\u003e","description":"","filename":"floatimage8.png","url":"https://assets-eu.researchsquare.com/files/rs-7903480/v1/0f480a41541d5aa1a3c115fa.png"},{"id":97252550,"identity":"f32afb0c-7b00-4a49-b984-955d32b94823","added_by":"auto","created_at":"2025-12-02 13:22:21","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1202245,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7903480/v1/3be31ab6-5ec8-487d-8264-fcd5c791ab04.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Network Analysis of Stress, Anxiety, and Depression During Pregnancy: An Integrated Perspective on Topics and Dimensions","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003ePregnancy refers to the stage during which the fertilized egg develops into a fetus and grows within the mother's uterus (Bjelica et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). As a unique period in a woman's life, pregnancy progresses through distinct phases, during which expectant mothers experience significant changes in their physical health, daily life, and work. Coupled with heightened psychological sensitivity, this period increases susceptibility to various negative emotions (Zhang, Yang, Li, Wang, Liu, \u0026amp; Zhao, 2022). In China, the prevalence rates of depression, anxiety, and pregnancy-related stress among expectant mothers have reached 19.4%, 26.6%, and 92.3%, respectively (Meng et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). These figures reveal the high prevalence and widespread occurrence of psychological issues during pregnancy in China.\u003c/p\u003e\u003cp\u003eDepression, anxiety, and stress are the most common mental health issues during pregnancy (Chauhan \u0026amp; Potdar, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), with their development involving the combined effects of physiological, psychological, and social environmental factors. Physiologically, expectant mothers often experience changes in body shape and appearance, such as breast tenderness, nausea and vomiting, weight gain, abdominal enlargement, and the emergence of melasma and stretch marks (Aguilera-Mart\u0026iacute;n et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). These physical transformations can easily trigger discomfort and anxiety. Psychologically, difficulties adapting to the new parental role, persistent concerns about fetal health, and fear of the delivery process further exacerbate anxiety and tension (Lin et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Socially, factors like family conflicts, lack of social support, poor interpersonal relationships, and financial pressures have been shown to significantly correlate with high prevalence rates of prenatal emotional disorders (Smythe et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eDespite the prevalence of these issues, current clinical and research attention remains largely focused on the physical health of pregnant women and their postpartum psychological state, with insufficient systematic attention given to prenatal mental health (Zeng et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).For pregnant women, the onset of anxiety and depression is often closely linked to hormonal changes within the body. Specifically, when the hypothalamic-pituitary-adrenal (HPA) axis is affected by steroid hormone imbalances, the risk of developing these emotional disorders is significantly elevated (Shea et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2007\u003c/span\u003e).Notably, due to the high similarity between clinical symptoms of anxiety and depression and normal pregnancy reactions, emotional disorders in pregnant women are often under-recognized and under-addressed in clinical practice (Sidebottom et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Al-Sabah et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).However, research confirms that approximately two-thirds of postpartum emotional issues actually originate during pregnancy (Insan et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Neglecting prenatal psychological status may miss critical windows for intervention, allowing stress, anxiety, and depression to accumulate throughout pregnancy. This can trigger a cascade of adverse effects on the physical and mental health of expectant mothers.\u003c/p\u003e\u003cp\u003eSpecifically, persistent negative emotions during pregnancy increase the risk of fetal developmental abnormalities, excessive maternal weight gain (Voerman et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), delivery complications, and postpartum depression (Gao et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).Regarding the underlying mechanisms, emotional states such as stress and anxiety can trigger elevated levels of specific hormones in the body, potentially affecting the physical and brain structure and function of the developing fetus (Cardwell, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2013\u003c/span\u003e).Regarding pregnancy outcomes, studies indicate that pregnant women experiencing high stress levels face a 25% to 60% higher risk of preterm birth compared to those with low stress levels, even after controlling for other known risk factors (Hobel et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). If these risks stemming from negative emotions are not addressed promptly, they may lead to more severe long-term consequences.\u003c/p\u003e\u003cp\u003eClinically, prenatal stress, anxiety, and depression often coexist and intertwine, mutually reinforcing each other to form a vicious cycle that is difficult to break independently (Feng et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Specifically, anxiety and depression exhibit significant comorbidity, with most individuals experiencing depression also presenting anxiety symptoms (Zelkowitz \u0026amp; Papageorgiou, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2012\u003c/span\u003e).while stress, as a key predictor for both, further exacerbates their behavioral manifestations and neurobiological symptoms (Lemus et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). However, existing research exhibits notable limitations: most studies focus solely on pairwise associations among the three (e.g., stress and depression, anxiety and depression), with the core exploration centered on the interrelationship between anxiety and depression (Lancaster et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Liu et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; O\u0026rsquo;Hara \u0026amp; McCabe, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Research systematically examining their interactive mechanisms within a unified framework remains scarce.\u003c/p\u003e\u003cp\u003eA deeper examination reveals dual limitations in current research on the relationship between prenatal stress, prenatal depression, and prenatal anxiety: On one hand, most studies remain at the superficial level of correlational analysis, failing to uncover the underlying pathways and mechanisms linking these three factors; On the other hand, studies based on latent variable assumptions often simplify these three constructs into a single concept, analyzing variable associations solely through total scale scores. This approach overlooks the essential characteristics of each construct\u0026mdash;that they all encompass multiple subdimensions and specific symptoms, and that these subdimensions may exhibit distinct interactive patterns. These limitations obscure the true relational structure within constructs, preventing clarification of causal links and dynamic interactions among variables. Consequently, they hinder the development of targeted intervention entry points and impede the formulation of precise, tiered strategies for promoting mental health during pregnancy.\u003c/p\u003e\u003cp\u003eGiven the limitations of traditional statistical methods in revealing complex multivariable relationship structures, this study introduces network analysis to systematically explore the intrinsic relationships among prenatal stress, prenatal depression, and prenatal anxiety. This methodology has been widely applied in psychology, medicine, and various population studies, with its validity and applicability supported by empirical evidence (Li et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Guo et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Shalayiding et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Network analysis offers three distinct advantages in this research context: First, unlike latent variable models, it constructs relational networks directly from observed variables, enabling intuitive visualization of interactions among specific symptoms or sub-dimensions; Second, this method permits bidirectional influence between variables, aligning more closely with the dynamic patterns of symptom emergence and development observed in practice. This facilitates the identification of key intervention targets, while centrality metrics quantify the importance of nodes within the network, providing a basis for prioritizing intervention strategies (Hevey, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Broda et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2023\u003c/span\u003e); Third, network analysis integrates all observed variables to reveal the dynamic associative structure underlying complex psychological phenomena from a systemic perspective, thereby offering novel insights and methodological pathways for understanding the co-occurrence mechanisms of negative emotions during pregnancy.\u003c/p\u003e"},{"header":"2. Methodology","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Participants\u003c/h2\u003e\u003cp\u003eThe data for this study were drawn from the Maternal Mental Health Database (Version 5) compiled by Zhang, Cui, Niu, Li, Wang, and Zhao (2024), which has been archived in a scientific repository. A survey was conducted among pregnant women in their second trimester in a province of China, with 619 questionnaires distributed. After excluding outliers and poorly completed questionnaires, 604 valid responses were retained, yielding a response rate of 97.6%.\u003c/p\u003e\u003cp\u003eParticipants ranged in age from 20 to 42 years, with a mean age of (29.96\u0026thinsp;\u0026plusmn;\u0026thinsp;3.90) years. The demographic characteristics of the sample can be summarized as follows: Regarding marital status, 94.87% of respondents were in their first marriage. In terms of educational attainment, 72.35% held a college degree. Natural conception was the predominant method of conception, accounting for 87.75%. Additionally, 65.40% of participants had no history of adverse pregnancy outcomes. These findings indicate a high degree of homogeneity within this group of pregnant women regarding marital status, educational background, and mode of conception.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Measurement Tools\u003c/h2\u003e\u003cdiv id=\"Sec5\" class=\"Section3\"\u003e\u003ch2\u003e2.2.1 Pregnancy Pressure Scale (PPS)\u003c/h2\u003e\u003cp\u003eThe Pregnancy Pressure Scale (PPS), developed by Chen Zhanghui et al. in 1989, is employed as the pregnancy stress assessment tool. This scale is specifically designed for Chinese pregnant women to measure psychological stress levels during pregnancy. The PPS comprises 30 items across three dimensions: Role Dimension (items 1\u0026ndash;15), Health Dimension (items 16\u0026ndash;23), and Body Shape Dimension (items 24\u0026ndash;27). The final three items, not assigned to a specific dimension, represent other stress sources. The scale employs a 0\u0026ndash;3 point rating system across four levels, yielding a total score range of 0\u0026ndash;90 points. Specific grading criteria are as follows: 0\u0026ndash;13 points indicate no pregnancy stress; 14\u0026ndash;36 points indicate mild pregnancy stress; 36\u0026ndash;72 points indicate moderate pregnancy stress; \u0026gt;72 points indicate severe pregnancy stress. A higher total score indicates greater pregnancy stress levels among pregnant women.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section3\"\u003e\u003ch2\u003e2.2.2 Hospital Anxiety and Depression Scale (HADS)\u003c/h2\u003e\u003cp\u003eAnxiety and depression were assessed using the Hospital Anxiety and Depression Scale (HADS), developed by Zigmond and Snaith in 1983. This scale is widely used for screening anxiety and depression in patients across general hospitals. The HADS comprises 14 items: 7 assess depressive symptoms and 7 assess anxiety symptoms. All items use a 0\u0026ndash;3 point rating scale. The scale score provides a preliminary assessment of emotional state. The grading criteria for the anxiety and depression subscales are consistent: 0\u0026ndash;7 indicates no symptoms, 8\u0026ndash;10 suggests possible anxiety or depression, and 11\u0026ndash;21 confirms the presence of anxiety or depression symptoms.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e2.3 Data Analysis\u003c/h2\u003e\u003cp\u003eThis study employs network analysis methods to construct the pregnancy stress-anxiety-depression network structure among the maternal population. The research unfolds on two levels: first, analyzing the complex interrelationships among pregnancy stress, prenatal anxiety, and prenatal depression based on core themes; second, focusing on the dimensional level to explore the interactive relationships within the respective sub-dimensions of pregnancy stress, anxiety, and depression. Through this dual analytical perspective, the study systematically reveals the psychological characteristics of pregnant women regarding pregnancy stress and emotional symptoms. Finally, by identifying core nodes within the network via centrality analysis, the research pinpoints key targets for maintaining maternal mental health, providing a theoretical foundation for subsequent prevention practices and precision interventions.\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section3\"\u003e\u003ch2\u003e2.3.1 Network Construction Method\u003c/h2\u003e\u003cp\u003eThis study employed the qgraph package in R software, utilizing the ggmModSelect method to establish a Gaussian graphical model for maternal anxiety, depression, and pregnancy stress. Within the graph, node colors distinguish different items or dimensions, with blue edges indicating positive correlations and orange edges denoting negative correlations. The strength of associations between nodes is visually represented by edge thickness and color intensity: stronger associations are indicated by thicker edges and darker colors.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Centrality Analysis\u003c/h2\u003e\u003cp\u003eTo identify key nodes within the network, centrality analysis was conducted. Centrality metrics measure the extent, strength, and closeness of a node's connections to others in the network. Altering highly central nodes can influence a larger number of other nodes. In the estimated network, nodes with more connections occupy central positions, while those with fewer connections reside peripherally. All centrality metrics are presented in z-score standardized form and sorted by strength to facilitate core node identification.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e2.3 Stability Testing\u003c/h2\u003e\u003cp\u003eFinally, the bootnet package was employed to validate the accuracy and stability of the network. This validation comprised two key aspects: First, the stability of node edge weights. Nonparametric bootstrap methods were used to compute 95% confidence intervals (CI) for each edge weight in the network. Generally, lower overlap within confidence intervals indicates higher estimation accuracy for edge weights. Second, stability analysis of node strength and centrality. Node strength reflects the total weight of connections between a node and others, serving as a key metric for measuring node centrality. Stability testing assesses the reliability of node strength values by calculating the centrality stability coefficient (CS coefficient). A coefficient greater than 0.5 indicates good stability of network centrality metrics, meaning node rankings or values remain consistent across different samples or resampling.\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e3.1 Network Structure\u003c/h2\u003e\u003cp\u003eWithin the network where items serve as nodes, diverse and intricate patterns of associations emerge between entries across different dimensions. In terms of association strength, the anxiety dimension item \"I feel tense or distressed (31)\" exhibits a strong positive correlation with the pregnancy stress health dimension item \"Worrying about the baby's safety (16)\"; conversely, the depression dimension item \"I still enjoy things that used to interest me (38)\" shows a weak negative correlation with the pregnancy stress role dimension item \"Difficulty preparing baby clothes (1)\".Notably, within the role dimension of pregnancy stress, the item \"Worrying about possible complications during delivery or needing a cesarean section (21)\" showed a strong positive correlation with \"Worrying that the doctor might not arrive in time during delivery (22).\" Simultaneously, the anxiety item \"My mind is filled with worries (33)\" exhibited a weak negative correlation with the depression item \"I can sit comfortably and relax (40) \".\u003c/p\u003e\u003cp\u003eFurther observation reveals that within the subnetworks formed by each dimension, certain anxiety dimension items (e.g., \"I feel tense or distressed (31)\", \"My mind is filled with worries (33)\") frequently connect with items from other dimensions (pregnancy stress, depression), exhibiting a high number of edges. This indicates these items serve as key nodes, playing a significant role in linking different psychological states and influencing network structure and information transmission (as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eWithin the network where dimensions serve as nodes, the connections between the dimensions of pregnancy stress, anxiety, and depression are equally strong (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Specifically, all dimensions within pregnancy stress (role dimension, health dimension, body shape dimension, other stressors) are interconnected. The association between the role dimension and the other stressors dimension is relatively stronger, indicating that the role dimension occupies a more central position within the internal structure of pregnancy stress. Simultaneously, the anxiety dimension exhibits a very strong association with the depression dimension. Furthermore, the role dimension and health dimension are not only closely linked to both the anxiety and depression dimensions but also serve as crucial bridging elements between pregnancy stress and anxiety/depressive emotions overall. This reflects their pivotal role as hubs within the cross-construct network connections.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003e3.3 Centrality Analysis\u003c/h2\u003e\u003cp\u003eIn network analysis, the relative importance of nodes within a network can be assessed through their connectivity patterns. Centrality serves as a set of key metrics for revealing node characteristics, reflecting the degree to which a node is directly connected to other nodes (Costantini, Epskamp, et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).Common centrality measures include Strength, Closeness, Betweenness, and Expected Influence, collectively used to identify core nodes within a network.\u003c/p\u003e\u003cp\u003eSpecifically: Strength centrality measures the quantity and strength of a node's direct connections to other nodes; Closeness centrality reflects the total path length from a node to all other nodes in the network\u0026mdash;shorter paths indicate greater susceptibility to network changes; Betweenness centrality characterizes how frequently a node lies on the shortest paths between other nodes, with higher values indicating greater criticality in information flow and group connectivity; Expected Influence aggregates the sum of weights across all edges connected to a node, proving particularly useful in networks with both positive and negative associations, as it reflects a node's potential influence on the network as a whole.\u003c/p\u003e\u003cp\u003eThe centrality analysis results based on item nodes (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) reveal that within the network structure of the Pregnancy Stress Scale, all centrality metrics\u0026mdash;including strength, betweenness centrality, and expected influence\u0026mdash;consistently indicate that Item 9 (\"Worry about not receiving sufficient psychological support\") is the most central node in the entire network. In the Hospital Anxiety and Depression Scale, Item 10 (\"I feel cheerful\") and Item 11 (\"I can sit quietly and easily\") both exhibit high levels across multiple centrality measures, indicating that both are key nodes within this scale's network.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eAt the dimensional level, centrality metrics for each dimension are presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. Within the Hospital Anxiety and Depression Scale, the depression dimension exhibited the highest centrality values across all items, indicating its status as the core node within this scale's network. Conversely, in the Pregnancy Stress Scale, the role adaptation dimension demonstrated the highest centrality values, suggesting its pivotal role within the internal structure of pregnancy stress.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003e3.4 Stability Analysis\u003c/h2\u003e\u003cp\u003eThe stability of the pregnancy stress, anxiety, and depression network was assessed using the self-organizing method. Regarding node centrality stability, results indicated: In the network with items as nodes, the centrality stability coefficient (CS coefficient) was 0.672 (range: 0.594\u0026ndash;0.75); In the network with dimensions as nodes, the CS coefficient was 0.594 (range: 0.517\u0026ndash;0.672). Both networks exhibited CS coefficients above the critical threshold of 0.50, indicating robust stability in the relative importance ranking of nodes across different sampling conditions and overall reliability of the network centrality metrics (see Figs.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e and \u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e for results).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eSecond, analysis of edge weight estimation accuracy indicates that most connections in the network are estimated robustly. As shown in Figs.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e and \u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e, red lines represent edge weights calculated from the original sample, while black lines denote the average edge weights estimated via the nonparametric Bootstrap method. The gray areas correspond to the 95% confidence intervals (CI) for edge weights under the Bootstrap method.\u003c/p\u003e\u003cp\u003eThe results show that the red line representing the original estimates overlaps to some extent with the black line indicating the average values from the self-help method, and their 95% confidence intervals (gray areas) are generally narrower. This indicates that the estimated values of edge weights are less affected by sampling variability and exhibit higher precision. Considering the stability results for both node strengths and edge weights, the overall structure of the current pregnancy stress, anxiety, and depression network can be deemed reliable and credible.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThis study employed network analysis methods to systematically examine the internal structural relationships among pregnancy stress, anxiety, and depression within a mid-pregnancy cohort in China. Results indicate tightly interwoven network relationships at both item and dimension levels. Specifically, within the item network, \"Worry about not receiving sufficient psychological support\" (Item 9) in pregnancy stress serves as a pivotal hub connecting diverse psychological states. In the dimension network, depression occupies a central position, while role adaptation stress exhibits close connections with both anxiety and depression. Notably, the aforementioned key items themselves belong to the role adaptation dimension. These findings reveal core targets within the psychological structure of pregnant women and postpartum mothers at multiple levels, providing empirical evidence and directional guidance for future precise and targeted psychological interventions.\u003c/p\u003e\u003cp\u003eIn the network analysis where items served as nodes, Item 9 of the Pregnancy Stress Scale\u0026mdash;\"Worry about not receiving sufficient psychological support\"\u0026mdash;was identified as the most central node in the overall network. This indicates that lack of psychological support is a key mechanism affecting the emotional health of women in mid-pregnancy. This finding aligns strongly with existing research: low social support shows significant positive correlations with both prenatal depression and anxiety (Vidhan, Rohilla, Dhiman, \u0026amp; Khoiwal, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). When pregnant women lack social resources for confiding, obtaining information, or alleviating negative emotions, they become more vulnerable to stress, potentially developing depressive or anxious disorders. Conversely, robust social support systems exert a protective effect on mental health during pregnancy. Pregnant women receiving adequate social support typically exhibit superior psychological well-being and emotional health (Herbell \u0026amp; Zauszniewski, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Qi et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).Therefore, clinical and community interventions should prioritize strengthening social support\u0026mdash;encompassing informational, emotional, and instrumental support\u0026mdash;as a core strategy to alleviate negative maternal emotions, thereby effectively interrupting the pathway from stress to anxiety and depression.\u003c/p\u003e\u003cp\u003eWithin the dimensional network, the role adaptation dimension was identified as a core node within pregnancy stress, indicating that stress arising from role transitions may be a key pathway triggering maternal emotional symptoms. Once activated, role stress at the network's central position may further influence and exacerbate other psychological symptoms through its extensive connections, thereby amplifying overall emotional burden. This finding aligns strongly with existing research: Xiabidan Tuxunjiang et al. (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) noted that role transition stress is the primary stressor during pregnancy, as expectant mothers often experience significant psychological burden due to anxieties about the unknown while undergoing physical changes and transitioning into motherhood. Ladekarl et al. (2022) further revealed from an identity construction perspective that pregnant women must integrate their new maternal identity while maintaining their original individual identity. This identity transition creates an \"Identity Limbo,\" which is the core cause of maternal role stress. It manifests as sadness over leaving behind past life experiences and anxiety about the responsibilities of motherhood. Therefore, psychological interventions during pregnancy and childbirth should prioritize supporting role adaptation. By enhancing role identification and maternal competence development, these interventions structurally alleviate core stressors within the psychological network, effectively blocking the diffusion pathways of negative emotions.\u003c/p\u003e\u003cp\u003eThis study employs network analysis to systematically reveal the intricate internal connections between pregnancy stress, anxiety, and depression in mid-pregnancy women. It not only deepens theoretical understanding of the underlying mechanisms of maternal psychological issues but also provides crucial practical foundations for early identification and targeted interventions. Theoretically, this study overcomes the limitations of traditional factor analysis by examining the dynamic relationships between nodes and network structures. It identifies core targets such as \"psychological support concerns\" and \"role adaptation stress,\" providing an empirical foundation for constructing a multidimensional theoretical model of maternal mental health. Practically, the findings suggest that clinical interventions should move beyond single-symptom management toward multi-level, structured approaches centered on strengthening social support and promoting role adaptation. This shift enables effective prevention and systematic alleviation of psychological issues during the critical perinatal period.\u003c/p\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003eThis study systematically analyzed the interactive relationships among pregnancy stress, anxiety, and depression in women during mid-pregnancy.Results revealed a tightly interconnected psychological network among these three factors at both the item and dimension levels. At the granular item level, \"Worrying about insufficient psychological support\" (Item 9) emerged as a pivotal node linking diverse psychological concerns. At the dimension level, depression occupied a central position within the network, while role adaptation stress exhibited significant connections with both anxiety and depression.Notably, the key item itself belongs to the role adaptation stress dimension, further highlighting this dimension's pivotal role in psychological regulation during mid-pregnancy. This study reveals core targets within the maternal mental health network at multiple levels, providing theoretical foundations and practical guidance for implementing precise and effective psychological interventions in the future.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable. This study is a secondary analysis of publicly available, anonymized data and did not involve direct interaction with human subjects or the collection of new personal data. Therefore, new ethical approval was not required for this analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable. (This manuscript does not contain any individual person\u0026apos;s data in any form.)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe dataset analyzed during the current study is publicly available and can be accessed as follows: Zhang, D., Cui, J., Niu, Y., et al. (2024, December 4). Maternal mental health dataset (Version 5) [Data set]. Science Data Bank. https://doi.org/10.57760/sciencedb.11251\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors received no specific funding for this work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHu.H: Writing - Original Draft, Methodology, Software, Validation, Data Curation, Writing - Review \u0026amp; Editing, Funding acquisition, Project administration, Supervision\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHong Hu* (School of Psychology, Guizhou Normal University, Guiyang, Guizhou, China, 550025)\u003c/p\u003e\n\u003cp\u003eEmail: [email protected]\u003c/p\u003e\n\u003cp\u003eAddress: School of Psychology, Guizhou Normal University, Guiyang 550025, China\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAguilera-Mart\u0026iacute;n, \u0026Aacute;., G\u0026aacute;lvez-Lara, M., Blanco-Ruiz, M., et al. (2021). Psychological, educational, and alternative interventions for reducing fear of childbirth in pregnant women: A systematic review. \u003cem\u003eJournal of Clinical Psychology, 77\u003c/em\u003e(3), 525\u0026ndash;555. https://doi.org/10.1002/jclp.23071\u003c/li\u003e\n\u003cli\u003eAl-Sabah, R., Al-Taiar, A., Ziyab, A. H., Akhtar, S., \u0026amp; Hammoud, M. S. (2024). Antenatal depression and its associated factors: findings from Kuwait birth cohort study. \u003cem\u003eJournal of Epidemiology and Global Health, 14\u003c/em\u003e(3), 847\u0026ndash;859. https://doi.org/10.1007/s44197-024-00223-7\u003c/li\u003e\n\u003cli\u003eBjelica, A., Cetkovic, N., Trninic-Pjevic, A., et al. (2018). The phenomenon of pregnancy -- a psychological view. \u003cem\u003eGinekologia Polska, 89\u003c/em\u003e(2), 102\u0026ndash;106.\u003c/li\u003e\n\u003cli\u003eBroda, M. D., Granger, K., Chow, J., \u0026amp; Ross, E. (2023). Using social network analysis in applied psychological research: A tutorial. \u003cem\u003ePsychological Methods, 28\u003c/em\u003e(4), 791\u0026ndash;805. https://doi.org/10.1037/met0000451\u003c/li\u003e\n\u003cli\u003eCardwell, M. S. (2013). Stress: Pregnancy considerations. \u003cem\u003eObstetrical \u0026amp; Gynecological Survey, 68\u003c/em\u003e(2), 119\u0026ndash;129. https://doi.org/10.1097/OGX.0b013e31827203a3\u003c/li\u003e\n\u003cli\u003eChauhan, A., \u0026amp; Potdar, J. (2022). Maternal mental health during pregnancy: A critical review. \u003cem\u003eCureus, 14\u003c/em\u003e(10), e30656. https://doi.org/10.7759/cureus.30656\u003c/li\u003e\n\u003cli\u003eCostantini, G., Epskamp, S., Borsboom, D., Perugini, M., M\u0026otilde;ttus, R., Waldorp, L. J., \u0026amp; Cramer, A. O. J. (2015). State of the aRt personality research: A tutorial on network analysis of personality data in R. \u003cem\u003eJournal of Research in Personality, 54\u003c/em\u003e, 13\u0026ndash;29. https://doi.org/10.1016/j.jrp.2014.07.003\u003c/li\u003e\n\u003cli\u003eFeng, G., Xu, X., \u0026amp; Lei, J. (2023). Tracking perceived stress, anxiety, and depression in daily life: a double-downward spiral process. \u003cem\u003eFrontiers in Psychology, 14\u003c/em\u003e, 1114332. https://doi.org/10.3389/fpsyg.2023.1114332\u003c/li\u003e\n\u003cli\u003eGao, M., Hu, J., Yang, L., et al. (2019). Association of sleep quality during pregnancy with stress and depression: A prospective birth cohort study in China. \u003cem\u003eBMC Pregnancy and Childbirth, 19\u003c/em\u003e(1), 444. https://doi.org/10.1186/s12884-019-2564-3\u003c/li\u003e\n\u003cli\u003eGuo, W., Zhao, Y., Chen, H., et al. (2023). The Bridge symptoms of childhood trauma, sleep disorder and depressive symptoms: a network analysis. \u003cem\u003eChild and Adolescent Psychiatry and Mental Health, 17\u003c/em\u003e(1), 88. https://doi.org/10.1186/s13034-023-00635-6\u003c/li\u003e\n\u003cli\u003eHevey, D. (2018). Network analysis: a brief overview and tutorial. \u003cem\u003eHealth Psychology \u0026amp; Behavioral Medicine, 6\u003c/em\u003e(1), 301\u0026ndash;328. https://doi.org/10.1080/21642850.2018.1521283\u003c/li\u003e\n\u003cli\u003eHerbell, K., \u0026amp; Zauszniewski, J. A. (2019). Stress experiences and mental health of pregnant women: The mediating role of social support. \u003cem\u003eIssues in Mental Health Nursing, 40\u003c/em\u003e(7), 613\u0026ndash;620. https://doi.org/10.1080/01612840.2019.1565873\u003c/li\u003e\n\u003cli\u003eHobel, C. J., Goldstein, A., \u0026amp; Barrett, E. S. (2008). Psychosocial stress and pregnancy outcome. \u003cem\u003eClinical Obstetrics \u0026amp; Gynecology, 51\u003c/em\u003e(2), 333\u0026ndash;348. https://doi.org/10.1097/GRF.0b013e31817397b8\u003c/li\u003e\n\u003cli\u003eInsan, N., Weke, A., Forrest, S., \u0026amp; Rankin, J. (2022). Social determinants of antenatal depression and anxiety among women in South asia: A systematic review \u0026amp; meta-analysis. \u003cem\u003ePLoS ONE, 17\u003c/em\u003e(2), e0263760. https://doi.org/10.1371/journal.pone.0263760\u003c/li\u003e\n\u003cli\u003eLancaster, C. A., Gold, K. J., Flynn, H. A., Yoo, H., Marcus, S. M., \u0026amp; Davis, M. M. (2010). Risk factors for depressive symptoms during pregnancy: a systematic review [published correction appears in Am J Obstet Gynecol. 2011;205(3):236]. \u003cem\u003eAmerican Journal of Obstetrics and Gynecology, 202\u003c/em\u003e(1), 5\u0026ndash;14. https://doi.org/10.1016/j.ajog.2009.09.007\u003c/li\u003e\n\u003cli\u003eLemus, A., Vogel, S. C., Greaves, A. N., \u0026amp; Brito, N. H. (2022). Maternal anxiety symptoms associated with increased behavioral synchrony in the early postnatal period. \u003cem\u003eInfancy, 27\u003c/em\u003e(4), 821\u0026ndash;835. https://doi.org/10.1111/infa.12473\u003c/li\u003e\n\u003cli\u003eLi, W., Zhao, N., Yan, X., et al. (2022). Network analysis of depression, anxiety, posttraumatic stress symptoms, insomnia, pain, and fatigue in clinically stable older patients with psychiatric disorders during the COVID-19 outbreak. \u003cem\u003eJournal of Geriatric Psychiatry and Neurology, 35\u003c/em\u003e(2), 196\u0026ndash;205. https://doi.org/10.1177/08919887221078559\u003c/li\u003e\n\u003cli\u003eLin, I. H., Huang, C. Y., Chou, S. H., \u0026amp; Shih, C. L. (2022). Efficacy of prenatal yoga in the treatment of depression and anxiety during pregnancy: A systematic review and Meta-Analysis. \u003cem\u003eInternational Journal of Environmental Research and Public Health, 19\u003c/em\u003e(9), 5368. https://doi.org/10.3390/ijerph19095368\u003c/li\u003e\n\u003cli\u003eLiu, S., Yan, Y., Gao, X., et al. (2017). Risk factors for postpartum depression among Chinese women: path model analysis. \u003cem\u003eBMC Pregnancy and Childbirth, 17\u003c/em\u003e(1), 133. https://doi.org/10.1186/s12884-017-1320-x\u003c/li\u003e\n\u003cli\u003eMeng, W., Shalayiding, S., Wang, X., Sailike, B., \u0026amp; Jiang, T. (2025). Relationship between prenatal anxiety, depression, pregnancy stress and their social determinants. \u003cem\u003eBMC Psychology, 13\u003c/em\u003e, 905. https://doi.org/10.1186/s40359-025-03237-3\u003c/li\u003e\n\u003cli\u003eO\u0026rsquo;Hara, M. W., \u0026amp; McCabe, J. E. (2013). Postpartum depression: current status and future directions. \u003cem\u003eAnnual Review of Clinical Psychology, 9\u003c/em\u003e, 379\u0026ndash;407. https://doi.org/10.1146/annurev-clinpsy-050212-185612\u003c/li\u003e\n\u003cli\u003eQi, W., Liu, Y., Lv, H., Ge, J., Meng, Y., Zhao, N., \u0026amp; Hu, J. (2022). Effects of family relationship and social support on the mental health of Chinese postpartum women. \u003cem\u003eBMC Pregnancy and Childbirth, 22\u003c/em\u003e(1), 65. https://doi.org/10.1186/s12884-022-04392-w\u003c/li\u003e\n\u003cli\u003eShalayiding, S., Meng, W., Wang, X., Sailike, B., \u0026amp; Jiang, T. (2024). Symptom network differences in school adjustment and anxiety-depression-stress in adolescents: a gender-based perspective. \u003cem\u003eBMC Public Health, 24\u003c/em\u003e(1), 3189. https://doi.org/10.1186/s12889-024-20718-zh\u003c/li\u003e\n\u003cli\u003eShea, A. K., Streiner, D. L., Fleming, A., Kamath, M. V., Broad, K., \u0026amp; Steiner, M. (2007). The effect of depression, anxiety and early life trauma on the cortisol awakening response during pregnancy: preliminary results. \u003cem\u003ePsychoneuroendocrinology, 32\u003c/em\u003e(8\u0026ndash;10), 1013\u0026ndash;1020. https://doi.org/10.1016/j.psyneuen.2007.07.006\u003c/li\u003e\n\u003cli\u003eSidebottom, A. C., Harrison, P. A., Godecker, A., \u0026amp; Kim, H. (2012). Validation of the patient health questionnaire (PHQ)-9 for prenatal depression screening. \u003cem\u003eArchives of Women\u0026apos;s Mental Health, 15\u003c/em\u003e(5), 367\u0026ndash;374. https://doi.org/10.1007/s00737-012-0295-x\u003c/li\u003e\n\u003cli\u003eSmythe, K. L., Petersen, I., \u0026amp; Schartau, P. (2022). Prevalence of perinatal depression and anxiety in both parents: A systematic review and Meta-analysis. \u003cem\u003eJAMA Network Open, 5\u003c/em\u003e(6), e2218969. https://doi.org/10.1001/jamanetworkopen.2022.18969\u003c/li\u003e\n\u003cli\u003eVidhan, D., Rohilla, J., Dhiman, V., \u0026amp; Khoiwal, K. (2025). Associations between maternal anxiety and depression during pregnancy and obstetric outcomes: A cross-sectional study. \u003cem\u003eCureus\u003c/em\u003e. https://doi.org/10.7759/cureus.86541\u003c/li\u003e\n\u003cli\u003eVoerman, E., Santos, S., Inskip, H., et al. (2019). Association of gestational weight gain with adverse maternal and infant outcomes. \u003cem\u003eJAMA, 321\u003c/em\u003e(17), 1702\u0026ndash;1715. https://doi.org/10.1001/jama.2019.4667\u003c/li\u003e\n\u003cli\u003eXiabidan Tuxunjiang, Li Ling, Zhang Wei, Bahadana Selik, Gulijanati Wumaier, Jiang Ting. (2023). The mediating effect of psychological resilience between pregnancy stress and prenatal depression in pregnant women. Journal of Central South University (Medical Edition), 48(4), 557\u0026ndash;564. https://doi.org/10.11817/j.issn.1672-7347.2023.220338\u003c/li\u003e\n\u003cli\u003eZelkowitz, P., \u0026amp; Papageorgiou, A. E. (2012). Easing maternal anxiety: an update. \u003cem\u003eWomen\u0026apos;s Health (London, England), 8\u003c/em\u003e(2), 205\u0026ndash;213. https://doi.org/10.2217/whe.11.96\u003c/li\u003e\n\u003cli\u003eZeng, Y., Cui, Y., \u0026amp; Li, J. (2015). Prevalence and predictors of antenatal depressive symptoms among Chinese women in their third trimester: a cross-sectional survey. \u003cem\u003eBMC Psychiatry, 15\u003c/em\u003e, 66. https://doi.org/10.1186/s12888-015-0452-7\u003c/li\u003e\n\u003cli\u003eZhang, D., Cui, J., Niu, Y., et al. (2024, December 4). Maternal mental health dataset (Version 5) [Data set]. Science Data Bank. https://doi.org/10.57760/sciencedb.11251\u003c/li\u003e\n\u003cli\u003eZhang, L., Yang, Y. T., Li, M. D., et al. (2022). The prevalence of suicide ideation and predictive factors among pregnant women in the third trimester. BMC Pregnancy and Childbirth, 22(1), 266.\u003c/li\u003e\n\u003cli\u003eZigmond, A. S., \u0026amp; Snaith, R. P. (1983). The hospital anxiety and depression scale. Acta Psychiatrica Scandinavica, 67(6), 361\u0026ndash;370. https://doi.org/10.1111/j.1600-0447.1983.tb09716.x\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-pregnancy-and-childbirth","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"prch","sideBox":"Learn more about [BMC Pregnancy and Childbirth](http://bmcpregnancychildbirth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/prch/default.aspx","title":"BMC Pregnancy and Childbirth","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Pregnancy-related stress (PPS), Anxiety, Depression, Hospital Anxiety and Depression Scale (HADS), Network analysis, Second trimester of pregnancy","lastPublishedDoi":"10.21203/rs.3.rs-7903480/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7903480/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003ePrenatal stress, anxiety, and depressive symptoms exhibit high prevalence among pregnant women, posing serious threats to maternal physical and mental health, pregnancy outcomes, and offspring development. This study employed network analysis to construct and compare pregnancy stress-anxiety-depression networks with topics and dimensions as nodes. Centrality analysis identified core nodes, and the network's stability was assessed using the bootstrap method. Results revealed that pregnancy stress, anxiety, and depression form a tightly interwoven interactive network. At the item level, \"Worry about not receiving sufficient psychological support\" emerged as the most central node within the pregnancy stress network. At the dimension level, the role dimension exhibited the highest centrality in the pregnancy stress network, while the depression dimension served as the core of the hospital anxiety and depression network. Further analysis showed that the centrality stability coefficients of all networks exceeded 0.5, indicating robust and reliable network structures. This study offers new insights into the interactive mechanisms of negative emotions during pregnancy, and the identified core symptoms provide key targets for precise clinical interventions.\u003c/p\u003e","manuscriptTitle":"Network Analysis of Stress, Anxiety, and Depression During Pregnancy: An Integrated Perspective on Topics and Dimensions","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-01 13:52:32","doi":"10.21203/rs.3.rs-7903480/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-02-26T13:11:09+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"18529932821163367643108946980174478343","date":"2026-02-10T05:51:32+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-03T14:49:14+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"94110198246480671907022285185950461388","date":"2025-11-30T12:16:48+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"41589613038945650291423439042449256287","date":"2025-11-26T05:32:34+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-11-25T15:08:11+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-10-23T00:24:07+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-10-23T00:23:27+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Pregnancy and Childbirth","date":"2025-10-20T08:04:52+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-pregnancy-and-childbirth","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"prch","sideBox":"Learn more about [BMC Pregnancy and Childbirth](http://bmcpregnancychildbirth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/prch/default.aspx","title":"BMC Pregnancy and Childbirth","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"ed01c992-b974-49b4-b8b8-056fdadda457","owner":[],"postedDate":"December 1st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-12-01T13:52:32+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-01 13:52:32","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7903480","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7903480","identity":"rs-7903480","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}