Smartphone Addiction, Depression, and Anxiety Among Chinese University Students: Network Analysis

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Data may be preliminary. 6 March 2025 V1 Latest version Share on Smartphone Addiction, Depression, and Anxiety Among Chinese University Students: Network Analysis Authors : Hong Luo 0009-0007-9695-0033 , Xinglian Wang , Hao Ren , Xingning An , Guixia Liu , Shixin Yu , Jin Zhang , Xuebin Wen , Ghalia Zainab Jafri , Xiufen Zhong , Xiangyang Zhang , and Haitang Qiu [email protected] Authors Info & Affiliations https://doi.org/10.22541/au.174123935.58196613/v1 431 views 138 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Growing studies have revealed associations between smartphone addiction, depression and anxiety. However, most studies failed to examine the complex symptom-level connection, limiting insights into how specific smartphone addiction symptoms interact with mental health outcomes. This study aims to identify central and bridge symptoms within the smartphone addiction-depression-anxiety network among Chinese university students. A nationwide sample of 99,941 Chinese university students completed the Smartphone Addiction Scale for College Students (SAS-C), the Center for Epidemiologic Studies Depression Scale (CES-D), and the Generalized Anxiety Disorder Scale (GAD-7). Network analysis was employed to estimate symptom connections, centrality, and bridge symptoms. Withdrawal behavior (SA1), depressed mood (D3), everything is an effort (D4), nervous (A1), and uncontrollable worry (A2) emerged as central symptoms. Excessive app use (SA5), D10 (could not get going), D4 (everything is an effort), and A1 (nervous) served as critical bridge symptoms linking smartphone addiction with depression and anxiety. This study reveals the complex interactions between smartphone addiction and depression and anxiety symptoms in university students. Targeted interventions addressing everything an effort, nervous, withdrawal behavior, and excessive app use may disrupt the comorbidity cycle. Smartphone Addiction, Depression, and Anxiety Among Chinese University Students: Network Analysis Hong Luo 1,2# , Xinglian Wang 1,2# , Hao Ren 1,2,3 , Xingning An 1,2 , Guixia Liu 1,2 , Shixin Yu 1,2 , Jin Zhang 1,2 , Xuebin Wen 1,2 , Chali Zainab Jafri 1,2 , Xiufen Zhong 1,2,4 , Xiangyang Zhang 5* , Haitang Qiu 1,2* 1 Department of Psychiatry, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China. 2 Department of psychiatry, Key Laboratory of Major Brain Disease and AgingResearch (Ministry of Education), Chongqing Medical University, Chongqing, 400016, China. 3 Department of psychiatry, Chongging Changshou District, Mental Health CenterChongqing, China 4 Department of psychiatry, Chongging Mental Health Center, Chongging. China 5 Department of psychiatry, Hefei Fourth People’s Hospital; Anhui Mental HealthCenter; Affiliated Mental Health Center of Anhui Medical University; Hefei, China # These authors contributed equally to this work. * Corresponding author: Xiangyang Zhang Department of Psychiatry, Hefei Fourth People’s Hospital (Anhui Mental Health Center), Affiliated Mental Health Center of Anhui Medical University, 316 Huangshan Road, Hefei, China E-mail: [email protected] Haitang Qiu, Department of Psychiatry, The First Affiliated Hospital of Chongqing Medical University, 1 Youyi Road, Yuzhong District, Chongqing, China E-mail: [email protected] Abstract Growing studies have revealed associations between smartphone addiction, depression and anxiety. However, most studies failed to examine the complex symptom-level connection, limiting insights into how specific smartphone addiction symptoms interact with mental health outcomes. This study aims to identify central and bridge symptoms within the smartphone addiction-depression-anxiety network among Chinese university students. A nationwide sample of 99,941 Chinese university students completed the Smartphone Addiction Scale for College Students (SAS-C), the Center for Epidemiologic Studies Depression Scale (CES-D), and the Generalized Anxiety Disorder Scale (GAD-7). Network analysis was employed to estimate symptom connections, centrality, and bridge symptoms. Withdrawal behavior (SA1), depressed mood (D3), everything is an effort (D4), nervous (A1), and uncontrollable worry (A2) emerged as central symptoms. Excessive app use (SA5), D10 (could not get going), D4 (everything is an effort), and A1 (nervous) served as critical bridge symptoms linking smartphone addiction with depression and anxiety. This study reveals the complex interactions between smartphone addiction and depression and anxiety symptoms in university students. Targeted interventions addressing everything an effort, nervous, withdrawal behavior, and excessive app use may disrupt the comorbidity cycle. Introduction With the rapid advancement of global digitalization, smartphones have become an indispensable component of university students’ daily lives. As of June 2024, China’s internet penetration rate reached 78.0%, with individuals aged 20–29 accounting for 13.5% of its 1.1 billion users(”China Internet Network Information Center. Statistical Report on the Development of China’s Internet Network,” 2024). As smartphone use grows, smartphone addiction has emerged as a new issue. According to a multinational survey by Leung et al., the prevalence of smartphone addiction among university students worldwide ranges from 4.05% to 27.4%(Leung, 2008). In China, approximately 33.8% university students exhibit smartphone dependency, with an estimated 21.3% of undergraduate students experiencing smartphone addiction(Long et al., 2016). Notably, this proportion is rapidly increasing(Santander-Hernández, Peralta, Guevara-Morales, Díaz-Vélez, & Valladares-Garrido, 2022). Previous studies have identified smartphone addiction as an independent risk factor for depression and anxiety, while anxiety and depressive symptoms have also been shown to be significant predictors of smartphone addiction(Coyne, Stockdale, & Summers, 2019; Daimer, Mihatsch, Neufeld, Murray, & Knolle, 2022). Several theories have attempted to explain this bidirectional relationship. For instance, the Compensatory Internet Use Theory(Kardefelt-Winther, 2014) posits that individuals may use smartphones as a coping mechanism to alleviate real-life problems and negative emotions, which can inadvertently lead to addictive behaviors that exacerbate depression and anxiety. Similarly, the Interaction of Person-Affect-Cognition-Execution Model (I-PACE model) (Brand et al., 2019; Brand, Young, Laier, Woelfling, & Potenza, 2016) highlights that the development and maintenance of smartphone addiction result from the interplay of individual traits, emotional and cognitive responses, and executive functions. Specifically, the I-PACE model emphasizes that negative emotions, maladaptive coping strategies, and impaired executive functioning collectively contribute to the persistence and intensification of addictive behaviors, leading to heightened loneliness and emotional distress. Additionally, the Social Displacement Hypothesis suggests that excessive smartphone use for online interactions may reduce time spent on face-to-face communication, leading to social withdrawal and diminished capacity to address real-world problems, thereby increasing the risk of depression and anxiety(Bessière, Kiesler, Kraut, & Boneva, 2008). However, these theoretical frameworks remain constrained by unidirectional conceptualizations (e.g., addiction as cause vs. psychopathology as consequence), failing to account for the temporal symptom dynamics and reinforcement loops that sustain comorbidity. To better elucidate the interplay between smartphone addiction, depression, and anxiety symptoms, network analysis provides a novel and robust statistical approach to explore the relationships among psychological symptoms. From the network perspective, mental disorders are not viewed as unified diagnoses, but as systems of interacting symptoms(D. Borsboom, 2017; Hofmann, Curtiss, & McNally, 2016). Network analysis represents psychological variables or symptoms as nodes and the relationships between them as edges, with ”central symptoms”(”Network analysis of multivariate data in psychological science,” 2021) exerting a significant influence on other nodes and ”bridge symptoms” (Jones, Ma, & McNally, 2021) linking different symptom clusters and driving comorbidity. Identifying these central and bridge symptoms can provide valuable insights for early intervention and more effective psychological treatment. Emerging network studies have begun mapping symptom-level interactions between digital addiction and mental health. Studies on medical students identified sleep problems and anhedonia as core symptoms bridging smartphone addiction with depression and anxiety(Z. Chen et al., 2024), while research involving left-behind children detected autonomic hyperarousal (e.g., tachycardia) as physiological bridge symptoms(Shen, Zhou, Liao, McDonnell, & Wang, 2024). Nevertheless, several critical limitations constrain current understanding of university populations. On one hand, existing networks predominantly derive from clinical or culturally specific subgroups (e.g., medical students, left-behind children), failing to represent the psychosocial complexity of mainstream university ecosystems. The exclusion of moderating variables such as academic discipline and learning environments may compromises ecological generalizability. Additionally, core and bridging symptoms have been insufficiently explored, with little attention given to how factors like withdrawal behavior and excessive app use influence these relationships. Critical mechanisms underlying symptom propagation remain unexamined, particularly withdrawal-triggered emotional cascades, where abrupt smartphone abstinence induces neurocognitive stress reactivity, amplifying anxiety sensitivity and depressive rumination, and compensatory overuse cycles, wherein anxiety-driven app engagement paradoxically reinforces depressive affect through reward system desensitization. Therefore, this study aims to employ network analysis to explore the complex relationship between smartphone addiction and depressive and anxiety symptoms among Chinese university students. By constructing a more targeted symptom network, this study seeks to identify the central and bridging symptoms more accurately, addressing gaps in previous research on this population. The findings will offer a theoretical basis for developing effective psychological interventions and provide valuable insights for improving the psychological well-being of university students. 2. Methods 2.1 Study Design and Participants This study was a nationwide online survey conducted among Chinese university students between April 13 and April 23, 2020. Details of the survey procedure have been published elsewhere(Y. Chen et al., 2022) . Participants were required to meet the following inclusion criteria: (1) currently enrolled university students in mainland China, (2) the ability to comprehend the study content, and (3) voluntary participation. The study was approved by the Ethics Committee of the Institute of Psychology, Chinese Academy of Sciences (2020YFC200300000). All participants provided informed consent prior to the survey. 2.2 Measures 2.2.1 Smartphone Addiction Smartphone addiction was assessed using the Smartphone Addiction Scale for College Students (SAS-C)(Su et al., 2014), a 22-item scale rated on a 5-point Likert scale (1 = strongly disagree to 5 = strongly agree). The scale comprises six factors: withdrawal behavior, salient behavior, social comfort, negative effect, application using, and application update. Each factor represents a component node of smartphone addiction. SAS-C has demonstrated good reliability and validity among Chinese university students(Su et al., 2014) . In the current study, the Cronbach’s alpha for SAS-C was 0.920. 2.2.2 Anxiety Symptoms Anxiety symptoms were measured using the Generalized Anxiety Disorder Scale (GAD-7). This 7-item scale uses a 4-point Likert scale (0 = not at all to 3 = nearly every day). A score above 10 indicates the potential presence of anxiety symptoms(Loewe et al., 2008). In this study, the Cronbach’s alpha for GAD-7 was 0.922. 2.2.3 Depressive Symptoms Depressive symptoms were assessed using the 10-item version of the Center for Epidemiological Studies Depression Scale (CES-D-10)(Yu, Lin, & Hsu, 2013). The 10 items are rated on a 4-point Likert scale (0 = rarely or none of the time to 3 = most or all of the time). The CES-D-10 is widely used in general populations, demonstrating high reliability and validity. Scores of 10 or higher indicate the potential presence of depressive symptoms. In this study, the Cronbach’s alpha for CES-D-10 was 0.718. 2.3 Data Analysis To examine the relationships among smartphone addiction, depressive symptoms, and anxiety symptoms, we constructed a symptom network comprising 23 nodes. A Gaussian Graphical Model (GGM) (Costantini et al., 2015) was employed, combining graphical lasso and an extended Bayesian Information Criterion (J. Chen & Chen, 2008). GGM is an undirected network where edges represent partial correlations between nodes. In the visual network, red edges indicate negative correlations, blue edges indicate positive correlations, and thicker edges signify stronger connections(Denny Borsboom & Cramer, 2013). Expected Influence (EI) was used as the primary metric for centrality, as it is particularly suitable for networks with both positive and negative edges(Robinaugh, Millner, & McNally, 2016). EI reflects the sum of all edge values connected to a given node, with higher values indicating greater importance within the network. To assess the influence of smartphone addiction on depressive and anxiety symptoms, the Bridge Expected Influence (BEI) was calculated. BEI, defined as the sum of edge weights connecting a node to other clusters, reflects a symptom’s role in linking different symptom clusters. Nodes with high BEI values are more likely to act as bridges, facilitating the interaction between clusters. In this study, all nodes were grouped into three clusters: depressive symptoms (10 items from CES-D-10), anxiety symptoms (7 items from GAD-7), and smartphone addiction (6 items from SAS-C). The stability and accuracy of the network estimation were evaluated using bootstrapping methods (nBoots = 2,000) to compute 95% confidence intervals for edge weights (Efron, 1979). Centrality stability was assessed using the centrality stability coefficient (CS-coefficient). According to Epskamp et al., a CS-coefficient > 0.25 indicates adequate stability, while > 0.5 indicates good stability(Sacha Epskamp, Denny Borsboom, & Eiko I. Fried, 2018). All data analyses were performed using SPSS 26.0 and R version 4.1.2, utilizing the qgraph(Epskamp, Cramer, Waldorp, Schmittmann, & Borsboom, 2012) and bootnet(S. Epskamp, D. Borsboom, & E. I. Fried, 2018) R packages for network estimation and stability analysis. 3. Results 3.1 Smartphone Addiction, Anxiety, and Depressive Symptoms This study analyzed data from 99,941 Chinese university students (M = 20.09, SD = 1.37, age range: 16–27 years). The mean score for smartphone addiction symptoms was 56.94 (SD = 14.25). The mean scores for depressive and anxiety symptoms were 10.26 (SD = 4.51) and 2.95 (SD = 3.96), respectively. Using the CES-D-10 cutoff score of 10, approximately 56.2% of students (n = 56,130) exhibited significant depressive symptoms. For anxiety, 6.4% of students (n = 6,393) reported significant symptoms based on the GAD-7. The description of each symptom of smartphone addiction, as well as depression and anxiety were presented in Table 1. Table1. The means, standard deviations, and BEI values of nodes in the smartphone addiction–anxiety and depression network. Nodes Abbreviation M SD BEI Anxiety symptoms (GAD-7) Feeling nervous, anxious, or on the edge Nervous 0.47 0.69 0.25 Not able to stop or control worrying Uncontrollable worry 0.38 0.65 0.13 Worrying too much about different things Excessive worry 0.61 0.88 0.14 Having trouble relaxing Trouble relaxing 0.43 0.70 0.07 So restless that it is hard to sit still Restless 0.32 0.61 0.07 Easily annoyed/irritable Irritable 0.45 0.68 0.18 Feeling afraid as if something awful might happen Feeling afraid 0.28 0.58 0.14 Depression symptoms (CES-D-10) Bothered by things Bothering 0.89 0.80 0.19 Trouble keeping mind Trouble focus 1.46 0.90 0.11 Felt depressed Depressed 0.85 0.81 0.14 Fell like everything is an effort Everything is an effort 0.77 0.79 0.22 Hopeful about the future Hopeful 1.82 1.01 0.08 Felt fearful Fearful 0.60 0.73 0.20 Restless sleep Sleep quality 0.76 0.87 0.13 Felt happy Happy 1.87 0.97 0.13 Felt lonely Loney 0.80 0.86 0.19 Could not get going Could not get going 0.43 0.69 0.22 Smartphone Addiction (SAS-C) Negative psychological or behavioral reactions when not participating in smartphone activities Withdrawal behavior 17.62 5.44 0.12 Smartphone use takes center stage in both mental and behavioral activity Salient behavior 8.28 2.54 0.04 Role of smartphones in interpersonal interactions Social comfort 6.59 2.31 0.06 Reduced work or study efficiency due to excessive smartphone use Negative effect 10.98 3.23 0.08 Overuse of smartphone APP APP using 9.40 2.70 0.22 Excessive attention to APP updates APP update 4.08 1.63 0.02 3.2 Network Structure The symptom network for smartphone addiction, depressive symptoms, and anxiety symptoms is illustrated in Figure 1. Among the 253 potential edges in the network, 139 edges had non-zero weights, indicating significant partial correlations between nodes. Figure 1. A) The visual network structure of smartphone addiction, anxiety and depression among college students. B) The nodes for this network: A1-A7 represents 7 items of GAD-7, D1-D10 represents 10 items of CES-D-10, and SA1-SA6 represents 6 items of SAS-C. Note. In the diagram symptom, nodes with stronger connections are closer to each other. Yellow nodes represent anxiety symptoms measured with the GAD-7 scale, green nodes represent depressive symptoms measured with the CES-D-10 scale, and blue nodes represent smartphone addiction measured with SAS-C. The blue lines represent positive correlations, and the red lines represent negative correlations. The edge thickness represents the strength of the association between symptom nodes. A1 (Nervous): Feeling nervous, anxious, or on the edge; A2(uncontrollable worry): Not able to stop or control worrying; A3(Excessive worry): Worrying too much about different things; A4(Trouble relaxing): Having trouble relaxing; A5(Restless): So restless that it is hard to sit still; A6(Irritable): Easily annoyed/irritable; A7(Feeling afraid): Feeling afraid as if something awful might happen; D1(Bothering): Bothered by things; D2(Trouble focus): Trouble keeping mind; D3(Depressed): Felt depressed; D4(Everything is an effort): Fell like everything is an effort; D5(Hopeful): Hopeful about the future; D6(Fearful): Felt fearful; D7(Sleep quality): Restless sleep; D8(Happy): Felt happy; D9(Loney): Felt lonely; D10(Could not get going): Could not get going; SA1(Withdrawal behavior): Negative psychological or behavioral reactions when not participating in smartphone activities; SA2(Salient behavior): Smartphone use takes center stage in both mental and behavioral activity; SA3(Social comfort): Role of smartphones in interpersonal interactions; SA4(Negative effect): Reduced work or study efficiency due to excessive smartphone use; SA5(APP using): Overuse of smartphone APP; SA6(APP update): Excessive attention to APP updates. In the network analysis, the strongest connections among smartphone addiction symptoms were observed between SA1 (withdrawal behavior) and SA5 (APP using) (weight = 0.31), SA1 (withdrawal behavior) and SA6 (APP update) (weight = 0.29), SA1 (withdrawal behavior) and SA4 (negative effect) (weight = 0.28), and SA2 (salient behavior) and SA4 (negative effect) (weight = 0.29). Among anxiety symptoms, the most robust associations were between A2 (uncontrollable worry) and A1 (nervous) (weight = 0.32), A5 (restless) and A4 (trouble relaxing) (weight = 0.27), and A5 (restless) and A7 (afraid) (weight = 0.24). For depression symptoms, the strongest links were between D5 (hopeful) and D8 (happy) (weight = 0.53), D4 (everything is an effort) and D3 (depressed) (weight = 0.29), and D10 (get going) and D9 (lonely) (weight = 0.23). In Figure 1, the predictability of each node is indicated by the surrounding rings. The 95% bootstrap confidence intervals were relatively narrow, suggesting that the smartphone addiction-anxiety-depression network is accurate (Supplementary Figure 1). Supplementary Figure 2 presents the bootstrap difference tests for edge weights. 3.3 Central and Bridge Nodes The EI and BEI of the smartphone addiction, depression and anxiety symptom network are illustrated in Figure 2 and 3. Nodes SA1 (withdrawal behavior), D3 (depressed), A2 (uncontrollable worry), D4 (everything is an effort), and A1 (nervous) exhibit the highest EI, indicating their strong connections with other nodes within the network. Conversely, nodes D2 (trouble focus) and D8 (loney) have the lowest EI, suggesting weaker connections and minimal influence within the network. The CS coefficient for node EI is 0.95, exceeding 0.5, which signifies sufficient stability of this centrality measure (refer to Supplementary Figure 3). Supplementary Figure 4 presents the bootstrap difference test for node EI. Regarding bridge nodes, the results indicate that SA5 (APP usage), SA1 (withdrawal behavior), D4 (everything is an effort), D10 (could not get going), and A1 (nervous) have the highest BEI. Notably, SA5 (APP use) may play a pivotal role in activating anxiety and depression symptoms. The CS coefficient for BEI is 0.95, surpassing 0.5, demonstrating adequate stability of this centrality measure (see Supplementary Figure 5). Supplementary Figure 6 displays the bootstrap difference test for node BEI. Figure 2. Centrality plot depicted the expected influence of each variable chosen in the network. Figure 3. Centrality plot depicted the bridge expected influence of each variable chosen in the network. 4. Discussion This study is the first to employ network analysis on a large sample of university students to investigate the interactions between smartphone addiction and symptoms of anxiety and depression. Compared to traditional correlation and regression analyses, network analysis offers a more comprehensive and detailed understanding of the relationships among symptoms or components, facilitating the identification of distinct roles of various symptoms within the network. In the constructed networks, the strongest connections were observed within respective disorder communities. Nodes SA1 (withdrawal behavior), D3 (depressed), D4 (everything is an effort), A2 (uncontrollable worry), and A1 (nervous) emerged as central symptoms, while bridge symptoms included SA5 (APP using), D10 (could not get going), D4 (everything is an effort), and A1 (nervous). 4.1 Core Symptoms and Their Role in the Network of Smartphone Addiction, Depression, and Anxiety Symptoms In the current study, SA1 (withdrawal behavior) exhibited the highest centrality in the network, indicating its significant role in the comorbidity mechanism between smartphone addiction and depression and anxiety symptoms. Withdrawal, operationally defined as psychological distress upon smartphone abstinence(Shoukat, 2019), transcends mere behavioral persistence—it reflects a neurocognitive failure in emotion regulation circuitry. This aligns with ICD-11’s conceptualization of behavioral addictions as disorders of reward processing dysregulation(Tsai, Lu, Hsiao, Hu, & Yen, 2020), wherein withdrawal symptoms signify disrupted dopaminergic homeostasis rather than simple habit formation. Empirical support emerges from recent studies. Jingjing Wang et al.’s identification of abstinence-induced anxiety in Chinese rural adolescents(Wang et al., 2023), and Hong Cai et al.’s finding of offline dysphoria in internet addiction networks(Cai et al., 2022), collectively validate withdrawal’s transdiagnostic potency. These findings resonate with the Compensatory Internet Use Theory(Kardefelt-Winther, 2014), which posits smartphone dependency as a maladaptive coping strategy for real-life stressors. Contemporary university students navigate a triad of challenges encompassing rigorous academic demands, intricate social dynamics, and consequential career planning. These compounding stressors may predispose them to utilize smartphones as a maladaptive coping mechanism, particularly within institutional environments characterized by intense scholarly competition and precarious employment prospects(X. Y. Wei et al., 2023). Simultaneously, the transitional autonomy of collegiate life, which is marked by diminished parental oversight and academic supervision, creates behavioral permeability that facilitates excessive digital engagement(Fromme, Corbin, & Kruse, 2008; Young, 2004). As the structural linchpin in our model, SA1 (withdrawal behavior) demonstrates smartphone addiction’s Janus-faced nature. It emerges as the phenotypic expression of underlying emotional dysregulation (proximal cause), while simultaneously establishing maladaptive neural pathways that exacerbate regulatory impairments (distal consequence), thereby creating an escalating spiral of technological dependency. The study also found that D4 (everything is an effort), D3 (depressed), and D10 (could not get going) are key symptoms in the network of smartphone addiction, depression, and anxiety comorbidity. D4 (everything is an effort) not only emerged as a central symptom in the network but also as a bridging symptom, indicating its pivotal role in connecting depressive symptoms with smartphone addiction and anxiety. D3 (depressed) and D10 (could not get going) serve as important central symptoms and bridging symptoms, respectively. These symptoms are closely associated with mental health deteriorating, functional impairment, and lack of social support(Réus, de Moura, Silva, Resende, & Quevedo, 2018), and are significantly linked to the etiology of smartphone addiction(Pizzagalli, Iosifescu, Hallett, Ratner, & Fava, 2008). This configuration aligns with Jichao Jia et al.’s(Jia, Tong, Wang, & Fang, 2024) identification of energy depletion-physical dysfunction as the strongest bridge between digital overuse and depression. Prior research has shown that depression and addiction to the internet for escape or emotional relief play a bridging role between internet addiction and depression(Sit et al., 2023). Xinyi Wei et al.(X. Wei et al., 2025) also highlighted that ”escaping negative moods” is a bridging symptom connecting problematic smartphone use and depression symptoms. Contrary to existing literature, attention problems did not emerge as a core symptom in the network of smartphone addiction, depression, and anxiety symptoms in this study(Guo et al., 2023). This discrepancy may reflect the paradoxical arousal effect, wherein hyperstimulating smartphone engagement transiently masks underlying attention regulation deficits through dopaminergic hyperactivation, while simultaneously eroding metacognitive awareness of impairment through instant gratification reinforcement. Through the I-PACE model lens, D4’s dual role epitomizes the pathogenic loop. As a core depression phenotype characterized by anergia and avolition(Billones, Kumar, & Saligan, 2020; Roehr, 2013), it simultaneously depletes psychological resources for adaptive coping, and enhances susceptibility to smartphone reward salience through effort-cost computation distortion(X. Y. Wei et al., 2023). This creates a self-amplifying cycle where depressive anhedonia (D3) and behavioral inertia (D10) drive compensatory digital engagement, which in turn exacerbates real-world disengagement through social skill atrophy(Chern & Huang, 2018; Hur & Shin, 2023). A1 (nervous) and A2 (uncontrollable worry) were also identified as core symptoms in this study, consistent with previous findings(Z. H. Chen et al., 2024; Elhai, Dvorak, Levine, & Hall, 2017b; Wang et al., 2023). A1 (nervous) further plays an important bridging role in the network. Anxiety symptoms such as A1 (nervous) and A2 (uncontrollable worry) increase attentional bias toward digital reassurance-seeking, which paradoxically reinforces interoceptive sensitivity through intermittent reward schedules(Elhai, Dvorak, Levine, & Hall, 2017a). Our findings aligns with the Social Displacement Hypothesis framework. When experiencing deficits in offline social interactions, individuals frequently adopt social media and instant messaging as compensatory strategies. Crucially, the presence of anxiety symptoms (A1 and A2) intensifies this compensatory pattern, predisposing individuals to increasingly substitute face-to-face communication with virtual interactions(Kwak, Cho, & Kim, 2022; Rozgonjuk, Levine, Hall, & Elhai, 2018). This behavioral shift creates a self-perpetuating cycle that exacerbates smartphone dependence. SA5 (APP Using) is another important bridging symptom that plays a critical role in linking smartphone addiction to anxiety and depression symptoms. This node operationalizes maladaptive dependency on social platforms and instant messaging systems, characterized by compulsive checking behaviors and platform immersion. Instant feedback mechanisms from applications (such as likes and comments) reinforce dependency while simultaneously increasing the risk of anxiety and depression(Marino, Gini, Vieno, & Spada, 2018; Mazzeo, Weinstock, Vashro, Henning, & Derrigo, 2024). Interventions targeting SA5 (APP Using), such as time management and cognitive-behavioral adjustments, may effectively break the vicious cycle between smartphone addiction and mental health issues(Kuss et al., 2018; Lin et al., 2016; Panova & Lleras, 2016). In summary, interventions targeting key symptoms such as withdrawal Behavior, everything an effort, depressed, could not get going, nervous, uncontrollable worry, and excessive app use may help alleviate emotional distress and disrupt the vicious cycle between smartphone addiction and mental health issues. Through strategies such as mindfulness therapy, time management, emotional regulation training, and behavioral activation, individuals may be able to enhance their sense of self-efficacy in daily life, reduce reliance on smartphones, and improve their overall mental health(Goldberg et al., 2018; Gross, 2002; Jacobson, Martell, & Dimidjian, 2001). 4.2 Limitations of the Study While this study offers valuable insights into the relationships among smartphone addiction, depression, and anxiety symptoms in university students, several limitations exist. First, the cross-sectional design precludes the determination of causal relationships among symptoms. Future research could employ longitudinal designs to track changes in mental health among university students, providing a better understanding of the dynamic relationships among smartphone addiction, depression, and anxiety symptoms. Second, the study relied on self-report questionnaires to assess symptoms of depression, anxiety, and smartphone addiction. Although these instruments possess good reliability and validity, self-reports may be influenced by social desirability or recall biases. Future studies could incorporate objective data (e.g., smartphone usage statistics) to enhance the accuracy of findings. Additionally, we did not collect detailed information on participants’ specific smartphone usage behaviors and time allocation, which are crucial for understanding the psychological mechanisms of smartphone addiction. Future research should explore types of smartphone use (e.g., social media, gaming, video watching) and usage patterns to more precisely assess the impact of different smartphone activities on mental health. 5. Conclusion This study identifies the core symptoms within the network of smartphone addiction, depression, and anxiety among Chinese university students: withdrawal behavior, depressed, everything an effort, uncontrollable worry, and nervous. Key bridging symptoms include excessive app use, withdrawal behavior, could not get going, everything is an effort, and nervous. These findings offer new insights into the complex interplay of mental health issues among university students. The identified core and bridging symptoms hold significant clinical relevance and may serve as critical targets for mental health interventions and treatments. List of abbreviations SAS-C: Smartphone Addiction Scale for College Students CES-D-10: the 10-item version of the Center for Epidemiologic Studies Depression Scale GAD-7: Generalized Anxiety Disorder Scale I-PACE model: the Interaction of Person-Affect-Cognition-Execution Model EI: Expected Influence BEI: Bridge Expected Influence A1: Feeling nervous, anxious, or on the edge A2: Not able to stop or control worrying A3: Worrying too much about different things A4: Having trouble relaxing A5: So restless that it is hard to sit still A6: Easily annoyed/irritable A7: Feeling afraid as if something awful might happen D1: Bothered by things D2: Trouble keeping mind D3: Felt depressed D4: Fell like everything is an effort D5: Hopeful about the future D6: Felt fearful D7: Restless sleep D8: Felt happy D9: Felt lonely D10: Could not get going SA1: Negative psychological or behavioral reactions when not participating in smartphone activities SA2: Smartphone use takes center stage in both mental and behavioral activity SA3: Role of smartphones in interpersonal interactions SA4: Reduced work or study efficiency due to excessive smartphone use SA5: Overuse of smartphone APP SA6: Excessive attention to APP updates Acknowledgments We are grateful to the participants of this study for their participation. Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Financial Support None. Availability of data and materials The data-sets analyzed during this study are available from the corresponding author on reasonable request. Author contributions Hong Luo and Xinglian Wang contributed to the conception and design of the study. Hao Ren, Xingning An, Guixia Liu, Shixin Yu, Jin Zhang, Xuebin Wen collected and collated data. Chali Zainab Jafri and Xiufen Zhong performed sample measurements. Hong Luo contributed to the analysis, interpretation of data and authored drafts of the article. Xiangyang Zhang and Haitang Qiu revised the manuscript. All authors read and approved the final manuscript. 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Quality & Quantity, 47 (3), 1511-1518. doi:10.1007/s11135-011-9604-0 Supplementary Material File (figure s1.tif) Download 5.54 MB File (figure s2.tif) Download 8.25 MB File (figure s3.tif) Download 7.42 MB File (figure s4.tif) Download 7.46 MB File (figure s5.tif) Download 7.43 MB File (figure s6.tif) Download 6.68 MB File (figure1.tif) Download 16.40 MB File (figure2.tif) Download 8.71 MB File (figure3.tif) Download 8.79 MB File (table1.docx) Download 14.42 KB Information & Authors Information Version history V1 Version 1 06 March 2025 Copyright This work is licensed under a Non Exclusive No Reuse License. Authors Affiliations Hong Luo 0009-0007-9695-0033 Chongqing Medical University View all articles by this author Xinglian Wang Chongqing Medical University View all articles by this author Hao Ren Chongqing Medical University View all articles by this author Xingning An Chongqing Medical University View all articles by this author Guixia Liu Chongqing Medical University View all articles by this author Shixin Yu Chongqing Medical University View all articles by this author Jin Zhang Chongqing Medical University View all articles by this author Xuebin Wen Chongqing Medical University View all articles by this author Ghalia Zainab Jafri Chongqing Medical University View all articles by this author Xiufen Zhong Chongqing Medical University View all articles by this author Xiangyang Zhang Hefei Fourth People's Hospital Anhui Mental HealthCenter Affiliated Mental Health Center of Anhui Medical University View all articles by this author Haitang Qiu [email protected] Chongqing Medical University View all articles by this author Metrics & Citations Metrics Article Usage 431 views 138 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Hong Luo, Xinglian Wang, Hao Ren, et al. 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