Digital Stress and Depression among Chinese College Students: A Cross- Sectional Study

Digital Stress and Depression among Chinese College Students: A Cross- Sectional Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Digital Stress and Depression among Chinese College Students: A Cross- Sectional Study Xi Li, Ziqi Guan, Wang Zhe, Xiaolong Wang, Xin Wang, Juzhe Xi This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9296856/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract Backgrounds With the integration of digital technologies, individuals are increasingly exposed to high-volume information flows, multi-platform communication, and online performance pressures that can lead to digital stress, a novel risk factor for mental health. Although existing studies have preliminarily demonstrated a negative association between digital stress and mental well-being, empirical evidence remains limited regarding the underlying mechanisms through which digital stress may lead to depressive symptoms via a sequential chain involving positive affect, negative affect, and loneliness. This study therefore tests the chained mediating effects of these variables in the association between digital stress and depression among Chinese college students, aiming to inform theory and practice for mental health interventions in higher education. Methods A cross-sectional survey was conducted from October to November 2024 among 1,231 undergraduates from five universities across eastern, central, and western China. Participants completed measures of digital stress, positive/negative emotions, loneliness, and depression. Correlation and chain mediation analyses (using PROCESS for SPSS) were performed, controlling for age, gender, and family background. Results Digital stress was significantly correlated with all variables. Both positive and negative emotions, as well as loneliness, independently mediated the link between digital stress and depression. Furthermore, digital stress had a direct effect on depression and indirect effects through two chain mediation pathways: positive emotion → loneliness and negative emotion → loneliness. Conclusions Digital stress impacts depression through multiple psychological pathways. These findings offer a novel theoretical framework for understanding digital-era depression and inform strategies for developing integrated digital mental health support systems in educational contexts. Digital stress Loneliness Depression College students Figures Figure 1 Figure 2 Introduction According to the International Telecommunication Union (2024), as of 2024, the number of global internet users exceeded 5.5 billion, accounting for approximately 68% of the world’s population[ 1 ]. In China, this digital transformation is particularly striking. The 55th Statistical Report on Internet Development in China released by the China Internet Network Information Center showed that the number of Chinese internet users reached 1.108 billion, with an internet penetration rate of 78.6%[ 2 ]. Beyond traditional stressors encountered in daily life, individuals are increasingly exposed to new digital-era stressors, such as information overload, social comparison, and fear of missing out (FOMO) [ 3 ]. Digital stress refers to the persistent psychological tension experienced when using digital technologies (e.g., social media, instant messaging), resulting from factors such as information overload, social comparison, and the pressure of expected responsiveness[ 4 ]. Empirical studies have consistently shown that digital stress has become an important risk factor for mental health issues, including anxiety, depression, and loneliness [ 5 – 7 ]. Within the context of deep digital immersion, college students face elevated risks of loneliness and depression. A large-scale U.S. survey of over 80,000 undergraduates reported that 58% experienced loneliness while a national survey in China revealed a depression detection rate of 39.7% among college students[ 8 ]. As “digital natives,” students are heavily engaged with digital platforms for learning, socializing, and entertainment[ 9 ]. Meanwhile, they are situated in Erikson’s psychosocial stage of “identity vs. role confusion”. Combined with the relatively unstructured environment of higher education in China, these factors may heighten susceptibility to digital addiction and information overload, positioning students as a high-risk group for digital stress [ 10 ]. Digital stress has been shown to increase loneliness, which is itself a strong predictor of depression[ 11 , 12 ]. Depression, in turn, impairs executive functioning and academic performance and represents a major risk factor for suicide among university students[ 13 – 15 ]. Therefore, elucidating the mechanisms through which digital stress contributes to depression—both directly and indirectly via loneliness—is of critical importance for safeguarding students’ mental health. Digital Stress and Depression The concept of stress originated from Selye’s theory of the general adaptation syndrome, which describes the body’s nonspecific physiological response to environmental demands [ 16 ]. With the rise of cognitive psychology, stress research shifted toward the cognitive–environmental interaction model. According to Lazarus and Folkman’s transactional model, stress is defined as an individual’s subjective experience of demands posed by a stressor in relation to perceived coping resources. Stressors can be external (e.g., life events, trauma) or internal (e.g., intrapersonal conflict) [ 17 ]. In the digital era, technological use has emerged as a new stressor. The earliest concept of technostress was introduced by Brod in1984, who defined it as stress arising from adaptation to new technologies[ 18 ]. Digital stress was defined as the subjective stress associated with social media use, encompassing physiological, emotional, and behavioral responses [ 19 ]. Building on this conceptualization, a four-factor model of digital stress was proposed [ 20 ], which includes connectivity stress, recognition anxiety, fear of missing out, and information overload. This model was subsequently expanded to incorporate a fifth factor: online vigilance [ 4 ]. Stressor–emotion model emphasizes that stressors influence psychological outcomes via emotional responses[ 21 ]. Building on this framework, digital stress may evoke emotional disturbances that increase the risk of depression. College students are particularly vulnerable to this dynamic. From a cultural change perspective, it was reported that the surge in adolescent depression rates since 2011 coincided with the widespread adoption of smartphones [ 22 ], with heavy users exhibiting twice the risk of depression. Longitudinal studies further confirm the sustained impact of digital stress on depressive symptoms [ 7 ]. Core elements of digital stress (e.g., recognition anxiety, information overload, availability pressure) are moderately correlated with psychological distress ( r = 0.26–0.34) [ 23 ]. Evidence also suggests that students in collectivist cultures are more prone to digital stress arising from social comparison [ 24 ]. A study of Chinese undergraduates reported that digital stress had a direct effect of 0.38 on depression [ 25 ]. These findings suggest that digital stress is a significant risk factor for depression, though its underlying mechanisms warrant further exploration. The Mediating Role of Emotion Emotion, defined as subjective experience and physiological response to internal and external stimuli, is often conceptualized along two dimensions: positive and negative emotions [ 26 , 27 ]. Negative emotion serves as a “psychological alarm system” and is positively associated with adverse mental health outcomes, such as depressive states and suicidal ideation[ 28 ]. By contrast, positive emotion enhances psychological resilience through the broaden-and-build effect, thereby protecting individuals from mental disorders (Fredrickson, 2001). The stressor-emotion model also posits that emotional states mediate the impact of stressors on outcomes[ 21 ]. Recent network analyses show that under digital stress (e.g., digital fatigue), emotion regulation strategies form specific connectivity patterns, with emotional [ 21 ]suppression demonstrating the strongest centrality and directly linking digital fatigue to depression [ 29 ]. Specifically, recognition anxiety triggered by digital stress heightens negative emotions, while the constant processing of fragmented digital information increases cognitive load and depletes resources for positive emotion processing [ 30 , 31 ]. Based on Zautra et al. (2005) dynamic model of affect digital stress intensifies negative emotion while undermining the regulatory function of positive emotion. Thus, both positive and negative emotions may mediate the relationship between digital stress and depression. The Mediating Role of Loneliness Loneliness is a subjective psychological experience arising when perceived social needs exceed the quality of actual social interactions [ 32 ]. It is not only a negative emotional state but also a social stressor that impacts physical and mental health through physiological [ 33 , 34 ], cognitive [ 35 ], and behavioral pathways [ 36 ]. Digital technologies have reshaped traditional social ecosystems and contributed to rising loneliness levels. The notion of “alone together” captures the paradox of digital sociality [ 37 ]. While online interactions may temporarily alleviate loneliness by fostering new connections, excessive reliance on digital platforms as an escape from offline “social pain” often displaces face-to-face interactions, paradoxically intensifying loneliness [ 38 , 39 ]. Moreover, algorithmic recommendation systems amplify social comparison [ 40 ], lower self-esteem [ 41 ], and trigger negative emotional reactions [ 42 ], leading to social withdrawal. Extensive evidence links loneliness to depression, establishing it as a major predictor of depressive symptoms [ 43 – 45 ]. Neuroimaging studies further demonstrate that loneliness and depression share overlapping neural substrates underlying sadness, worthlessness, and anhedonia, suggesting shared cognitive biases and emotion-regulation deficits as possible mechanisms[ 46 ]. Thus, loneliness may represent both an outcome of digital stress and a proximal risk factor for depression. The Chain Mediating Roles of Emotion and Loneliness According to the social functional theory of emotion, emotions are not merely immediate responses to stimuli but also shape the quality of social interactions, thereby mediating psychological outcomes[ 47 , 48 ]. Empirical findings indicate that individuals’ responses to positive and negative emotions are closely associated with their loneliness levels [ 49 ]. Digital stress–induced negative emotion may heighten perceptions of social risk and exacerbate social isolation[ 7 , 31 ]. Conversely, positive emotional experiences (e.g., gratitude, joy) may foster prosocial motivation and enhance emotional expression thereby maintaining high-quality social connections, reducing loneliness, and lowering depression risk [ 48 , 49 ]. Taken together, prior research suggests close associations among digital stress, emotion, loneliness, and depression. However, the mechanisms by which digital stress influences depression through the interplay of emotion and loneliness remain unclear. This study proposes a chain mediation model in which emotional states (negative/positive) and loneliness sequentially mediate the impact of digital stress on depression: digital stress alters emotional regulation, which influences social cognition and behavior, thereby elevating loneliness and ultimately depression. A theoretical model (Fig. 1 ) was constructed and tested to examine the following hypotheses: Hypothesis 1 Digital stress is significantly associated with depression in college students. Hypothesis 2 Positive and negative emotions independently mediate the relationship between digital stress and depression, with negative emotion exerting a positive mediation effect and positive emotion exerting a negative mediation effect. Hypothesis 3 Loneliness independently mediates the relationship between digital stress and depression. Hypothesis 4 Emotion and loneliness jointly form chain mediating pathways: “digital stress → positive emotion → loneliness → depression” and “digital stress → negative emotion → loneliness → depression.” Methods Participants Data were collected via the Questionnaire Star platform. A total of 1,330 questionnaires were distributed using a combination of convenience and snowball sampling, and 1,231 valid responses were retained. Questionnaires were excluded if they were (a) incomplete, (b) showed patterned responding (e.g., selecting the same option across Likert items), or (c) failed embedded attention-check items. The valid response rate was 92.56%. Among the participants, 655 were female (53.2%) and 576 (46.8%) were male, with a mean age of 19.32 years ( SD = 1.34). Students from eastern, central, and western regions accounted for 37.6%, 14.6%, and 47.8% of the sample, respectively. Regarding academic year, 604 participants were freshmen (49.1%), 368 were sophomores (29.9%), 193 were juniors (15.7%), 50 were seniors (4.1%), and 16 were postgraduates (1.3%). The study protocol was reviewed and approved by the Ethics Committee of Shanghai University of Medicine & Health Sciences. All participants provided informed consent prior to participation, and questionnaires were completed anonymously. Measures Demographic information Data were collected on participants’ demographic characteristics, including age, gender, family residence, household income, university location (eastern, central, or western China), and academic major (medical vs. non-medical). Loneliness Scale (ULS-6) The original UCLA Loneliness Scale (ULS) was developed by Russell et al(1980)., and revised twice to form the third edition (ULS-3). A simplified 8-item version (ULS-8) war introduced and widely applied in international contexts [ 51 ]. In China, the ULS-8 was adapted by removing two reverse-scored items, resulting in the 6-item version (ULS-6) [ 52 ], Each item is rated on a 4-point Likert scale (1 = “Never,” 4 = “Often”), with higher scores indicating greater loneliness. The ULS-6 has been validated as a rapid screening tool for loneliness among Chinese adults, with a Cronbach’s α of 0.89, demonstrating good consistency [ 53 ]. The Cronbach’s α in this study was 0.94. Digital Stress Scale (RC-DSS) The Digital Stress Scale (DSS) was developed by[ 4 ] to measure stress related to social media use among adolescents and young adults (ages 14–30). It consists of 24 items covering five dimensions: connectivity stress, fear of missing out (FOMO), online vigilance, recognition anxiety, and information overload. The DSS was revised for Chinese populations by adding a social comparison dimension, resulting in the Revised Chinese Digital Stress Scale (RC-DSS), which comprises 31 items. It uses a 5-point Likert scale (1 = Never, 5 = Always), with higher scores indicating greater digital stress. This scale has shown strong psychometric properties in Chinese samples[ 54 ].In the present study, Cronbach’s α was 0.97. Positive and Negative Affect Schedule (PANAS) The Positive and Negative Affect Schedule (PANAS) was developed by[ 55 ], to assess two dimensions of emotional states: positive affect (PA) and negative affect (NA). The scale includes 20 items, with 10 items for PA and 10 for NA. Responses are given on a 5-point Likert scale (1 = “Very slightly or not at all,” 5 = “Extremely”). Higher PA scores indicate high energy and pleasant affect, whereas lower PA scores indicate dullness or reduced positive mood. Higher NA scores reflect distress and aversive mood states, while lower NA scores indicate calmness. The PANAS has shown strong reliability and validity across diverse populations[ 56 ]. In this study, Cronbach’s α for the Positive Affect (PA) subscale was .93, and for the Negative Affect (NA) subscale was .91, indicating good internal consistency for both scales. Patient Health Questionnaire-9 (PHQ-9) The PHQ-9 is a widely used tool for screening and assessing depressive symptoms [ 57 ]. It contains nine items covering domains such as anhedonia, low mood, sleep disturbances, fatigue, appetite changes, self-worth, concentration difficulties, psychomotor changes, and suicidal ideation. Each item is rated from 0 (“Not at all”) to 3 (“Nearly every day”) based on experiences in the past two weeks. Higher total scores indicate greater risk of depression. The Cronbach’s α in this study was 0.92. Statistical methods Data were analyzed using SPSS 23.0. Descriptive statistics (mean ± standard deviation), independent samples t -tests, one-way ANOVAs, and Pearson correlations were conducted. Mediation analyses were performed using PROCESS macro [ 58 ]. All continuous variables were standardized (z-scores) to eliminate scale effects on regression coefficients. The significance of indirect effects was tested using bias-corrected percentile bootstrapping with 5,000 resamples to compute 95% confidence intervals (CI). Indirect effects were considered significant if the CI did not include zero. Sociodemographic variables (gender, age, socioeconomic status) were included as covariates to control for potential confounding effects. Results Common Method Bias Test To assess common method bias, Harman’s single-factor test was conducted [ 59 ]. The first unrotated factor accounted for 28.67% of the total variance, which is below the critical threshold of 40%. Therefore, common method bias was not a serious concern in this study and remained within an acceptable range. Demographic Characteristics of the Study Sample Table 1 presents the demographic characteristics of the 1,231 valid participants. The sample consisted of 655 females (53.2%) and 576 males (46.8%), with a mean age of 19.32 years (SD = 1.34). Regarding geographic distribution, 463 participants (37.6%) were from eastern China, 180 (14.6%) from central China, and 588(47.8%) from western China. Most participants were freshmen (604, 49.1%), followed by sophomores (368, 29.9%), juniors (193, 15.7%), seniors (50, 4.1%), and postgraduate students (16, 1.3%). In terms of academic major, 390 participants (31.7%) reported being in medical-related fields, whereas 841 (68.3%) were from non-medical majors. Table 1 Demographic Characteristics of Participants ( N = 1,231) Variable Category Frequency ( n ) Percentage (%) Gender Male 576 46.8 Female 655 53.2 Only Child Status Yes 375 30.46 No 856 69.54 Hometown Location Rural 735 59.71 Urban 496 40.29 Academic Year Freshman 604 49.06 Sophomore 368 29.89 Junior 193 15.69 Senior 50 4.06 Postgraduate 16 1.30 University Location Eastern 463 37.61 Central 180 14.62 Western 588 47.77 Medical-related Major Yes 390 31.68 No 841 68.32 Descriptive Statistics and Bivariate Correlations The means, standard deviations, and bivariate correlations of the study variables are presented in Table 2 . Digital stress was significantly negatively correlated with positive emotion ( r = − 0.18, p < 0.01), and significantly positively correlated with negative emotion ( r = 0.38, p < 0.01), loneliness ( r = 0.54, p < 0.01), and depression ( r = 0.43, p < 0.01). Negative emotion was positively correlated with both loneliness ( r = 0.34, p < 0.01) and depression ( r = 0.62, p < 0.01). Finally, loneliness showed a significant positive correlation with depression ( r = 0.42, p < 0.01). Table 2 Descriptive statistics and intercorrelations among study variables Digital Stress M ± SD Digital Stress Positive Emotion Negative Emotion Loneliness Depression 98.35 ± 29.10 1 Positive Emotion 21.98 ± 4.55 -0.18 ** 1 Negative Emotion 14.57 ± 4.91 0.38 ** -0.21 ** 1 Loneliness 11.96 ± 4.57 0.54 ** -0.37 ** 0.34 ** 1 Depression 7.87 ± 5.62 0.43 ** -0.28 ** 0.62 ** 0.42 ** 1 * p < 0.05, ** p < 0.01 4.3 Chain Mediation Analysis To further examine the mechanisms linking digital stress and depression, positive and negative emotions as well as loneliness—variables significantly correlated with depression—were included as mediators. The results are presented in Table 3 . The total effect of digital stress on depression was significant (β = 0.44, SE = 0.026, t = 16.96, 95% CI [0.39, 0.49]). Both the direct effect (β = 0.16, SE = 0.026, t = 6.18, 95% CI [0.11, 0.21]) and the indirect effect ( β = 0.28, SE = 0.025, p < 0.001, 95% CI [0.23, 0.33]) were significant. Analysis of independent mediating pathways revealed that digital stress exerted significant indirect effects on depression via positive emotion (β = 0.02, SE = 0.007, 95% CI [0.01, 0.04]), negative emotion (β = 0.19, SE = 0.023, 95% CI [0.15, 0.24]), and loneliness (β = 0.06, SE = 0.015, 95% CI [0.03, 0.09]). In the chain mediation pathways, digital stress significantly predicted depression through positive emotion → loneliness (β = 0.007, SE = 0.002, 95% CI [0.003, 0.011]) and through negative emotion → loneliness (β = 0.006, SE = 0.002, 95% CI [0.002, 0.011]). These findings indicate that positive emotion, negative emotion, and loneliness not only independently mediated the association between digital stress and depression, but also jointly contributed to significant chain mediation effects. The final serial model is depicted in Fig. 2 . Covariates including age, family background, and gender were controlled. Among these, gender exerted a significant negative effect on depression (β = − 0.17, SE = 0.052, t = − 3.21, p < 0.001), indicating that male students reported significantly lower depression levels than female students. Neither age nor socioeconomic status had a significant effect on depression. Table 3 Direct and indirect effects of digital stress on depression Predictors Estimated effect SE R 2 t 95% Bias-Corrected CI Outcome: Positive Emotion 0.04 *** Digital Stress -0.20 *** 0.028 -6.92 -0.25 to -0.14 Age -0.00 0.029 -0.09 -0.06 to 0.05 Income 0.07 0.029 2.37 0.01 to 0.12 Sex 0.16 * 0.056 2.90 0.05 to 0.27 Outcome: Negative Emotion 0.15 *** Digital Stress 0.39 *** 0.027 14.58 0.34 to 0.44 Age 0.01 0.027 0.49 -0.04 to 0.07 Income -0.03 0.027 -1.07 -0.08 to 0.02 Sex -0.17 ** 0.053 -3.24 -0.28 to -0.07 Outcome: Loneliness 0.39 *** Digital Stress 0.44 *** 0.025 17.74 0.39 to 0.49 Positive Emotion -0.27 *** 0.023 -11.49 -0.31 to -0.22 Negative Emotion 0.12 *** 0.025 5.04 0.08 to 0.17 Age -0.06 ** 0.023 -2.75 -0.11 to -0.02 Income -0.05 * 0.023 -2.11 -0.09 to -0.00 Sex 0.09 0.045 1.91 -0.00 to 0.18 Outcome: Depression 0.46 *** Digital Stress 0.16 *** 0.026 6.18 0.11 to 0.21 Positive Emotion -0.10 *** 0.023 -4.35 -0.14 to -0.05 Negative Emotion 0.49 *** 0.023 21.07 0.45 to 0.54 Loneliness 0.13 *** 0.027 4.71 0.07 to 0.18 Age 0.01 0.022 0.40 -0.03 to 0.05 Income -0.04 0.022 -1.63 -0.08 to 0.01 Sex -0.07 0.042 -1.58 -0.15 to 0.02 Indirect effects Total indirect effect 0.28 *** 0.025 0.23 to 0.33 Digital Stress→ Positive Emotion→Depression 0.02 ** 0.007 0.01 to 0.04 Digital Stress→Negative Emotion→Depression 0.19 *** 0.023 0.15 to 0.24 Digital Stress→Loneliness→Depression 0.06 *** 0.015 0.03 to 0.09 Digital Stress→Positive Emotion→Loneliness→Depression 0.007 ** 0.002 0.003 to 0.011 Digital Stress→Negative Emotion→Loneliness→Depression 0.006 ** 0.002 0.002to 0.011 Total effect 0.44 *** 0.026 16.96 0.39 to 0.49 * p < 0.05, ** p < 0.01, *** p < 0.001. Discussion In the current digital era, while technology provides college students with convenience in learning and daily life, it simultaneously poses challenges to mental health[ 60 ]. Digital stress, as a unique stressor of the digital age, has been shown to be significantly associated with depression and loneliness [ 6 ]. Building upon the stressor–emotion model [ 61 ], the present study further investigated the impact of digital stress on depression and revealed the chain mediating roles of positive emotion, negative emotion, and loneliness. Consistent with Hypothesis 1 , our findings demonstrated a significant positive association between digital stress and depression, in line with previous studies [ 6 , 25 ]. Digital stress may serve as a crucial psychosocial pathway linking digital media use and mental health outcome[ 20 ]. Historically, research on digital stress focused on workplace contexts, where it has been closely linked to burnout and stress responses such as irritability, sleep disturbances, anxiety, and depression [ 31 , 62 ]. Today, however, college students represent a particularly vulnerable group. They rely heavily on digital technologies for online courses, academic collaboration, social interaction, and project completion. Constant exposure to massive amounts of information can result in cognitive overload and emotional strain [ 63 ]. Moreover, the tendency for upward social comparison on social media—where content is often idealized—may distort self-perceptions and induce depressive symptoms [ 64 ]. In addition, perpetual online vigilance and dependence on digital devices can disrupt circadian rhythms[ 65 ], and impair academic performance [ 66 ], which elevate depression risk. These findings highlight the urgent need for educational institutions to pay greater attention to students’ digital stress and its associated psychological consequences. Mediation analyses supported Hypothesis 2 : both positive and negative emotions significantly mediated the relationship between digital stress and depression, confirming the applicability of the stressor–emotion model [ 61 ] in digital contexts. Stress and emotional changes are closely linked [ 67 ], and cognitive appraisals further shape these responses [ 68 ] Under digital stressors such as excessive social comparison and information bombardment, students may adopt maladaptive cognitive strategies (e.g., avoidance), which foster negative emotions. Positive emotions typically serve as a “psychological buffer” [ 69 ]. However, persistent exposure to digital stress may deplete emotional resources, suppressing the generation and maintenance of positive affect [ 70 ]. Once this buffer collapses, students become more vulnerable to negative emotions, thereby increasing depression risk. Support was also found for Hypothesis 3 : loneliness significantly mediated the relationship between digital stress and depression. According to the theory of stress and coping [ 17 ] stress represents an adaptive response to environmental demands. When digital stressors (e.g., availability pressure, recognition anxiety) exceed students’ coping capacity, they may reduce social engagement as a defense mechanism. For example, a diary study found that daily stress predicted same-day loneliness among students [ 71 ]. Excessive reliance on online social interaction can crowd out offline relationships; online interactions are often superficial and lack the emotional depth required to meet psychological needs[ 72 ]. Moreover, digital platforms amplify opportunities for social comparison, a process particularly salient during the developmental stage of identity formation, which may increase dissatisfaction and distort self-perceptions [ 73 ],Excessive information flow further induces social fatigue, intensifying feelings of loneliness [ 74 ]. Over time, accumulated loneliness, especially in the absence of social support, has been shown to predict depressive outcomes [ 11 ]. Most importantly, this study revealed that positive and negative emotions, together with loneliness, formed chain mediating pathways between digital stress and depression—a mechanism that has received limited prior attention. Recent literature distinguishes between functional and dysfunctional uses of digital technologies: functional use (e.g., efficient learning, prosocial communication) promotes well-being, whereas dysfunctional use (e.g., excessive immersion, passive exposure to negative content) contributes to adverse outcomes[ 75 ]. College students, with limited self-regulation and information-filtering capacity, are more likely to engage in dysfunctional usage patterns, thereby exacerbating digital stress, negative emotions, and loneliness. Moreover, algorithms on digital platforms may amplify the spread of negative emotions, reinforcing emotional contagion [ 76 , 77 ]. Students experiencing negative emotions under digital stress may withdraw from social interactions, further intensifying loneliness. Notably, momentary ecological assessment studies suggest that transient loneliness not only co-occurs with depression but also predicts elevated depressive affect several hours later [ 78 ]. Interestingly, we also found that digital stress had a significant negative effect on positive emotion, which in turn negatively predicted loneliness, ultimately forming a weak positive chain effect on depression. At first glance, this appears inconsistent with the broaden-and-build theory [ 69 ], which posits that positive emotions buffer against stress. A plausible explanation is that the “destructive” intensity of digital stress overwhelms the protective role of positive emotions, suppressing their expected buffering and broadening effects. Consequently, the pathway of “reduced positive emotion → heightened loneliness → increased depression” emerges. For example, online social feedback such as “likes” may temporarily enhance positive emotions, but unmet expectations regarding feedback can paradoxically trigger deeper negative affect. This finding underscores the dual role of emotion and social interaction in mediating mental health outcomes under digital stress and highlights the need for interventions that simultaneously target emotion regulation and the cultivation of meaningful social connections. In conclusion, the present study has theoretical and practical implications. At the theoretical level, it supports the applicability of the stressor–emotion model [ 61 ] in digital contexts, and demonstrates a chain‑mediated effect of positive affect, negative affect, and loneliness in the association between digital stress and depression. This finding extends the emotion–social mechanism framework through which digital stress affects mental health, offering an integrated perspective for understanding youth mental health risks in the era of digitalization. At the practical level, the results indicate that universities and relevant authorities should prioritize comprehensive prevention and management of digital stress among college students. Key strategies include enhancing digital literacy and emotion regulation education, fostering high‑quality offline social connections, and mitigating the cumulative burden of loneliness. Furthermore, a dual‑track intervention strategy that integrates emotion regulation and social support is recommended to strengthen students’ psychological resilience in digital environments and to disrupt the processes through which digital stress contributes to depression. Several limitations should be noted. First, this study employed a cross-sectional design, which only captures the associations among digital stress, emotional states, loneliness, and depression at a single time point, thereby precluding causal inferences. Future studies could adopt longitudinal cohort designs or cross-lagged panel models to track these variables across multiple time points and clarify their dynamic interrelations. Second, data were primarily collected through self-report, which is susceptible to recall bias and social desirability effects, potentially undermining reliability. Future research should incorporate objective measures (e.g., digital device usage logs, cortisol levels) alongside self-reports, using multimodal data integration approaches to provide a more comprehensive assessment of digital stress, emotional states, and depressive symptoms. Conclusions This study explored the underlying associations between digital stress and depression among college students. Findings indicate that digital stress is a critical risk factor for depression, exerting both direct and indirect effects via multiple psychological mechanisms. Specifically: Digital stress reduced positive emotion, which directly predicted depression and indirectly contributed to it through increased loneliness. Digital stress heightened negative emotion, which directly increased depression and indirectly exacerbated it through greater loneliness. Digital stress directly intensified loneliness, thereby elevating depression risk. Overall, this study underscores the potential threat of digital stress to students’ mental health. Theoretically, it provides a new perspective for understanding depression in the digital era and highlights loneliness and emotion as key mechanisms. Practically, the findings inform the design of tailored interventions. For instance, universities should integrate digital mental health education into general curricula, enhance digital literacy, and promote adaptive coping strategies. Artificial intelligence technologies may be leveraged to improve the precision of digital stress assessment, enable early warning, and deliver AI-driven interventions targeting emotion regulation and social connection enhancement. Optimizing digital ecosystems to reduce information overload is also crucial. Multi-stakeholder collaboration is needed to address digital stress and safeguard students’ psychological well-being while strengthening their capacity to adapt to the digital society. Abbreviations ULS UCLA Loneliness Scale ULS-3 Revised third edition of the UCLA Loneliness Scale ULS-8 Simplified 8-item version of the UCLA Loneliness Scale ULS-6 6-item Chinese-adapted version of the UCLA Loneliness Scale DSS Digital Stress Scale RC-DSS Revised Chinese Digital Stress Scale PANAS Positive and Negative Affect Schedule PA Positive Affect subscale of PANAS NA Negative Affect subscale of PANAS PHQ-9 Patient Health Questionnaire-9 Declarations Acknowledgements We thank staff at the five schools for helping to contact, and all the participants in this study. Authors’ contributions All authors have contributed significantly the paper. XL and ZQG were responsible for the development of this particular study, performed statistical analyses and wrote the first draft. All authors participated in the study design, and questionnaire design. JZX, WZ were responsible for manuscript reviewing. All authors read and approved the final manuscript. Fundings This study was supported by Brain Science and Brain-like Intelligence Technology - National Science and Technology Major Project (Grant Number: 2021ZD0200500), the National Social Science Foundation of China (Grant Number: 24ASH014), the Program for Professor of Special Appointment at Shanghai Institutions of Higher Education (Grant Number: TP2022029), the East China Normal University Medicine and Health Joint Fund (Grant Number: 2022JKXYD05001), and the Special Project of Shanghai Municipal Education Commission (Grant Number: 2023RX07).The funding institutions had no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. Data availability The datasets generated and/or analyzed during the current study are not publicly available due to the need to protect individual privacy but are available from the corresponding author on reasonable request. Ethics approval and consent to participate This study was approved by the Research Ethics Committee, Shanghai University of Medicine and Health Sciences (2024-SZKT-04). Written informed consent was obtained from all participants before the start of the study. As all participants were university students and were considered to have full capacity to provide consent, their own informed consent was sufficient, and no consent from legal guardians or representatives was required. The study was conducted in strict accordance with the ethical standards of the Declaration of Helsinki. Authorship and copyright All authors confirm that the submitted manuscript is an original contribution and has not been previously published, that it is not under consideration for publication elsewhere, and that, if accepted, will not be published elsewhere in similar form in any language. We also confirm that all authors contributed significantly to the study. Consent for publication Not applicable. This manuscript does not contain any individual person’s data in a form that would compromise anonymity. Competing interests The authors declare no competing interests Author details 1 Shanghai Key Laboratory of Mental Health and Psychological Crisis Intervention, Affiliated Mental Health Center (ECNU), School of Psychology and Cognitive Science, East China Normal University, Shanghai, 200062,China 2 School of medical instrumentation, Shanghai University of Medicine and Health Sciences, Shanghai, 201318,China 3 Positive Education China Academy (PECA) of Han-Jing Institute for Studies in Classics, Juzhe Xi’s Master Workroom of Shanghai School Mental Health Service, China Research Institute of Care and Education of Infants and Young Children, East China Normal University, Shanghai, 200062,China 4 College of Education Science, Kashgar University, Kashgar, 844000,China 5 School of Humanities and Management, Wannan Medical University,Wuhu, 241002,China *Correspondence ： Juzhe Xi, School of Psychology and Cognitive Science, East China Normal University, 3663, North Zhongshan Road, Shanghai 200062, China. 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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-9296856","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":624304113,"identity":"5b368af6-2da9-4728-80ee-6e5e7d6b50ae","order_by":0,"name":"Xi Li","email":"","orcid":"","institution":"East China Normal University","correspondingAuthor":false,"prefix":"","firstName":"Xi","middleName":"","lastName":"Li","suffix":""},{"id":624304114,"identity":"e1b9c4f6-5ef8-47c8-9112-c28ff40065f8","order_by":1,"name":"Ziqi Guan","email":"","orcid":"","institution":"East China Normal University","correspondingAuthor":false,"prefix":"","firstName":"Ziqi","middleName":"","lastName":"Guan","suffix":""},{"id":624304115,"identity":"372e6ded-ede4-4120-ac5e-71a8e40fd8e6","order_by":2,"name":"Wang Zhe","email":"","orcid":"","institution":"East China Normal University","correspondingAuthor":false,"prefix":"","firstName":"Wang","middleName":"","lastName":"Zhe","suffix":""},{"id":624304116,"identity":"810170e7-f946-4f26-ad1b-309aa4e7851a","order_by":3,"name":"Xiaolong Wang","email":"","orcid":"","institution":"Kashgar University","correspondingAuthor":false,"prefix":"","firstName":"Xiaolong","middleName":"","lastName":"Wang","suffix":""},{"id":624304117,"identity":"82d2706f-84f1-465d-b304-7a2919023e36","order_by":4,"name":"Xin Wang","email":"","orcid":"","institution":"Wannan Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xin","middleName":"","lastName":"Wang","suffix":""},{"id":624304118,"identity":"a96079b4-530c-40b2-8522-3a354d0628cb","order_by":5,"name":"Juzhe Xi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA30lEQVRIiWNgGAWjYBACxgYGhgMf0AQIazk4gyQtIMDMQ5IW5v4zhodtd9QlNrA3P3vMw2Aju+EA87MH+B12LOFw7pnDiQ08x8yNeRjSjDccYDM3wKulsfnA4dy2A4kNEjls0jwMhxM3HOBhk8CrpZmx4bBlG9Bh8m9AWv4ToaWN+cBhIAG0hQek5QARWnrYEg72njls3MaTZiY5xyDZeOZhNjO8Wgz7zxh/+LmjTraf/fAziTcVdrJ9x5uf4dfSwACOC8c2MBcUVMz41AOBPANEiz0BdaNgFIyCUTCSAQDAjEevtg+VPQAAAABJRU5ErkJggg==","orcid":"","institution":"East China Normal University","correspondingAuthor":true,"prefix":"","firstName":"Juzhe","middleName":"","lastName":"Xi","suffix":""}],"badges":[],"createdAt":"2026-04-02 01:53:28","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9296856/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9296856/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":107833527,"identity":"e1f024a1-201d-4745-a455-b3d86179cd65","added_by":"auto","created_at":"2026-04-26 15:39:46","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":12900,"visible":true,"origin":"","legend":"\u003cp\u003eChain mediation model of positive emotion, negative emotion, and loneliness in the relationship between digital stress and depression.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-9296856/v1/a83dfe209720271e52c0e58b.png"},{"id":107833311,"identity":"3b83deb0-0c4e-4028-bdf6-2d0e4cb81259","added_by":"auto","created_at":"2026-04-26 15:39:12","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":28097,"visible":true,"origin":"","legend":"\u003cp\u003eResults of the chain mediation model of positive emotion, negative emotion, and loneliness in the relationship between digital stress and depression.\u003c/p\u003e\n\u003cp\u003e**p \u0026lt; 0.01, *** p \u0026lt; 0.001\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-9296856/v1/4dccffa0f5cd89f65620a9e9.png"},{"id":107833675,"identity":"cf7ab0e3-e85c-4c6a-a89b-ff9e3d5240d1","added_by":"auto","created_at":"2026-04-26 15:40:17","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":572432,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9296856/v1/b189f41b-d34c-45a4-b940-60d5459ae9df.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Digital Stress and Depression among Chinese College Students: A Cross- Sectional Study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAccording to the \u003cem\u003eInternational Telecommunication Union\u003c/em\u003e (2024), as of 2024, the number of global internet users exceeded 5.5\u0026nbsp;billion, accounting for approximately 68% of the world\u0026rsquo;s population[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. In China, this digital transformation is particularly striking. The 55th \u003cem\u003eStatistical Report on Internet Development in China\u003c/em\u003e released by \u003cem\u003ethe China Internet Network Information Center\u003c/em\u003e showed that the number of Chinese internet users reached 1.108\u0026nbsp;billion, with an internet penetration rate of 78.6%[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Beyond traditional stressors encountered in daily life, individuals are increasingly exposed to new digital-era stressors, such as information overload, social comparison, and fear of missing out (FOMO) [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Digital stress refers to the persistent psychological tension experienced when using digital technologies (e.g., social media, instant messaging), resulting from factors such as information overload, social comparison, and the pressure of expected responsiveness[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Empirical studies have consistently shown that digital stress has become an important risk factor for mental health issues, including anxiety, depression, and loneliness [\u003cspan additionalcitationids=\"CR6\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eWithin the context of deep digital immersion, college students face elevated risks of loneliness and depression. A large-scale U.S. survey of over 80,000 undergraduates reported that 58% experienced loneliness while a national survey in China revealed a depression detection rate of 39.7% among college students[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. As \u0026ldquo;digital natives,\u0026rdquo; students are heavily engaged with digital platforms for learning, socializing, and entertainment[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Meanwhile, they are situated in Erikson\u0026rsquo;s psychosocial stage of \u0026ldquo;identity vs. role confusion\u0026rdquo;. Combined with the relatively unstructured environment of higher education in China, these factors may heighten susceptibility to digital addiction and information overload, positioning students as a high-risk group for digital stress [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Digital stress has been shown to increase loneliness, which is itself a strong predictor of depression[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Depression, in turn, impairs executive functioning and academic performance and represents a major risk factor for suicide among university students[\u003cspan additionalcitationids=\"CR14\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Therefore, elucidating the mechanisms through which digital stress contributes to depression\u0026mdash;both directly and indirectly via loneliness\u0026mdash;is of critical importance for safeguarding students\u0026rsquo; mental health.\u003c/p\u003e\n\u003ch3\u003eDigital Stress and Depression\u003c/h3\u003e\n\u003cp\u003eThe concept of stress originated from Selye\u0026rsquo;s theory of the general adaptation syndrome, which describes the body\u0026rsquo;s nonspecific physiological response to environmental demands [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. With the rise of cognitive psychology, stress research shifted toward the cognitive\u0026ndash;environmental interaction model. According to Lazarus and Folkman\u0026rsquo;s transactional model, stress is defined as an individual\u0026rsquo;s subjective experience of demands posed by a stressor in relation to perceived coping resources. Stressors can be external (e.g., life events, trauma) or internal (e.g., intrapersonal conflict) [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. In the digital era, technological use has emerged as a new stressor. The earliest concept of technostress was introduced by Brod in1984, who defined it as stress arising from adaptation to new technologies[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Digital stress was defined as the subjective stress associated with social media use, encompassing physiological, emotional, and behavioral responses [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Building on this conceptualization, a four-factor model of digital stress was proposed [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], which includes connectivity stress, recognition anxiety, fear of missing out, and information overload. This model was subsequently expanded to incorporate a fifth factor: online vigilance [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eStressor\u0026ndash;emotion model emphasizes that stressors influence psychological outcomes via emotional responses[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Building on this framework, digital stress may evoke emotional disturbances that increase the risk of depression. College students are particularly vulnerable to this dynamic. From a cultural change perspective, it was reported that the surge in adolescent depression rates since 2011 coincided with the widespread adoption of smartphones [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e], with heavy users exhibiting twice the risk of depression. Longitudinal studies further confirm the sustained impact of digital stress on depressive symptoms [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Core elements of digital stress (e.g., recognition anxiety, information overload, availability pressure) are moderately correlated with psychological distress (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.26\u0026ndash;0.34) [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Evidence also suggests that students in collectivist cultures are more prone to digital stress arising from social comparison [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. A study of Chinese undergraduates reported that digital stress had a direct effect of 0.38 on depression [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. These findings suggest that digital stress is a significant risk factor for depression, though its underlying mechanisms warrant further exploration.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eThe Mediating Role of Emotion\u003c/h2\u003e \u003cp\u003eEmotion, defined as subjective experience and physiological response to internal and external stimuli, is often conceptualized along two dimensions: positive and negative emotions [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Negative emotion serves as a \u0026ldquo;psychological alarm system\u0026rdquo; and is positively associated with adverse mental health outcomes, such as depressive states and suicidal ideation[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. By contrast, positive emotion enhances psychological resilience through the broaden-and-build effect, thereby protecting individuals from mental disorders (Fredrickson, 2001).\u003c/p\u003e \u003cp\u003eThe stressor-emotion model also posits that emotional states mediate the impact of stressors on outcomes[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Recent network analyses show that under digital stress (e.g., digital fatigue), emotion regulation strategies form specific connectivity patterns, with emotional [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]suppression demonstrating the strongest centrality and directly linking digital fatigue to depression [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Specifically, recognition anxiety triggered by digital stress heightens negative emotions, while the constant processing of fragmented digital information increases cognitive load and depletes resources for positive emotion processing [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Based on Zautra et al. (2005) dynamic model of affect digital stress intensifies negative emotion while undermining the regulatory function of positive emotion. Thus, both positive and negative emotions may mediate the relationship between digital stress and depression.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eThe Mediating Role of Loneliness\u003c/h3\u003e\n\u003cp\u003eLoneliness is a subjective psychological experience arising when perceived social needs exceed the quality of actual social interactions [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. It is not only a negative emotional state but also a social stressor that impacts physical and mental health through physiological [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e], cognitive [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e], and behavioral pathways [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eDigital technologies have reshaped traditional social ecosystems and contributed to rising loneliness levels. The notion of \u0026ldquo;alone together\u0026rdquo; captures the paradox of digital sociality [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. While online interactions may temporarily alleviate loneliness by fostering new connections, excessive reliance on digital platforms as an escape from offline \u0026ldquo;social pain\u0026rdquo; often displaces face-to-face interactions, paradoxically intensifying loneliness [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Moreover, algorithmic recommendation systems amplify social comparison [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e], lower self-esteem [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e], and trigger negative emotional reactions [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e], leading to social withdrawal.\u003c/p\u003e \u003cp\u003eExtensive evidence links loneliness to depression, establishing it as a major predictor of depressive symptoms [\u003cspan additionalcitationids=\"CR44\" citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. Neuroimaging studies further demonstrate that loneliness and depression share overlapping neural substrates underlying sadness, worthlessness, and anhedonia, suggesting shared cognitive biases and emotion-regulation deficits as possible mechanisms[\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. Thus, loneliness may represent both an outcome of digital stress and a proximal risk factor for depression.\u003c/p\u003e\n\u003ch3\u003eThe Chain Mediating Roles of Emotion and Loneliness\u003c/h3\u003e\n\u003cp\u003eAccording to the social functional theory of emotion, emotions are not merely immediate responses to stimuli but also shape the quality of social interactions, thereby mediating psychological outcomes[\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. Empirical findings indicate that individuals\u0026rsquo; responses to positive and negative emotions are closely associated with their loneliness levels [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. Digital stress\u0026ndash;induced negative emotion may heighten perceptions of social risk and exacerbate social isolation[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Conversely, positive emotional experiences (e.g., gratitude, joy) may foster prosocial motivation and enhance emotional expression thereby maintaining high-quality social connections, reducing loneliness, and lowering depression risk [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. Taken together, prior research suggests close associations among digital stress, emotion, loneliness, and depression. However, the mechanisms by which digital stress influences depression through the interplay of emotion and loneliness remain unclear. This study proposes a chain mediation model in which emotional states (negative/positive) and loneliness sequentially mediate the impact of digital stress on depression: digital stress alters emotional regulation, which influences social cognition and behavior, thereby elevating loneliness and ultimately depression. A theoretical model (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) was constructed and tested to examine the following hypotheses:\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eHypothesis 1\u003c/strong\u003e \u003cp\u003eDigital stress is significantly associated with depression in college students.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eHypothesis 2\u003c/strong\u003e \u003cp\u003ePositive and negative emotions independently mediate the relationship between digital stress and depression, with negative emotion exerting a positive mediation effect and positive emotion exerting a negative mediation effect.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eHypothesis 3\u003c/strong\u003e \u003cp\u003eLoneliness independently mediates the relationship between digital stress and depression.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eHypothesis 4\u003c/strong\u003e \u003cp\u003eEmotion and loneliness jointly form chain mediating pathways: \u0026ldquo;digital stress \u0026rarr; positive emotion \u0026rarr; loneliness \u0026rarr; depression\u0026rdquo; and \u0026ldquo;digital stress \u0026rarr; negative emotion \u0026rarr; loneliness \u0026rarr; depression.\u0026rdquo;\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eParticipants\u003c/h2\u003e \u003cp\u003eData were collected via the Questionnaire Star platform. A total of 1,330 questionnaires were distributed using a combination of convenience and snowball sampling, and 1,231 valid responses were retained. Questionnaires were excluded if they were (a) incomplete, (b) showed patterned responding (e.g., selecting the same option across Likert items), or (c) failed embedded attention-check items. The valid response rate was 92.56%.\u003c/p\u003e \u003cp\u003eAmong the participants, 655 were female (53.2%) and 576 (46.8%) were male, with a mean age of 19.32 years (\u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.34). Students from eastern, central, and western regions accounted for 37.6%, 14.6%, and 47.8% of the sample, respectively. Regarding academic year, 604 participants were freshmen (49.1%), 368 were sophomores (29.9%), 193 were juniors (15.7%), 50 were seniors (4.1%), and 16 were postgraduates (1.3%).\u003c/p\u003e \u003cp\u003eThe study protocol was reviewed and approved by the Ethics Committee of Shanghai University of Medicine \u0026amp; Health Sciences. All participants provided informed consent prior to participation, and questionnaires were completed anonymously.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eMeasures\u003c/h2\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003eDemographic information\u003c/h2\u003e \u003cp\u003eData were collected on participants\u0026rsquo; demographic characteristics, including age, gender, family residence, household income, university location (eastern, central, or western China), and academic major (medical vs. non-medical).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e\n\u003ch3\u003eLoneliness Scale (ULS-6)\u003c/h3\u003e\n\u003cp\u003eThe original UCLA Loneliness Scale (ULS) was developed by Russell et al(1980)., and revised twice to form the third edition (ULS-3). A simplified 8-item version (ULS-8) war introduced and widely applied in international contexts [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. In China, the ULS-8 was adapted by removing two reverse-scored items, resulting in the 6-item version (ULS-6) [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e], Each item is rated on a 4-point Likert scale (1 = \u0026ldquo;Never,\u0026rdquo; 4 = \u0026ldquo;Often\u0026rdquo;), with higher scores indicating greater loneliness. The ULS-6 has been validated as a rapid screening tool for loneliness among Chinese adults, with a Cronbach\u0026rsquo;s α of 0.89, demonstrating good consistency [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]. The Cronbach\u0026rsquo;s α in this study was 0.94.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eDigital Stress Scale (RC-DSS)\u003c/h2\u003e \u003cp\u003eThe Digital Stress Scale (DSS) was developed by[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e] to measure stress related to social media use among adolescents and young adults (ages 14\u0026ndash;30). It consists of 24 items covering five dimensions: connectivity stress, fear of missing out (FOMO), online vigilance, recognition anxiety, and information overload. The DSS was revised for Chinese populations by adding a social comparison dimension, resulting in the Revised Chinese Digital Stress Scale (RC-DSS), which comprises 31 items. It uses a 5-point Likert scale (1\u0026thinsp;=\u0026thinsp;Never, 5\u0026thinsp;=\u0026thinsp;Always), with higher scores indicating greater digital stress. This scale has shown strong psychometric properties in Chinese samples[\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e].In the present study, Cronbach\u0026rsquo;s α was 0.97.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003ePositive and Negative Affect Schedule (PANAS)\u003c/h2\u003e \u003cp\u003eThe Positive and Negative Affect Schedule (PANAS) was developed by[\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e], to assess two dimensions of emotional states: positive affect (PA) and negative affect (NA). The scale includes 20 items, with 10 items for PA and 10 for NA. Responses are given on a 5-point Likert scale (1 = \u0026ldquo;Very slightly or not at all,\u0026rdquo; 5 = \u0026ldquo;Extremely\u0026rdquo;). Higher PA scores indicate high energy and pleasant affect, whereas lower PA scores indicate dullness or reduced positive mood. Higher NA scores reflect distress and aversive mood states, while lower NA scores indicate calmness. The PANAS has shown strong reliability and validity across diverse populations[\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]. In this study, Cronbach\u0026rsquo;s α for the Positive Affect (PA) subscale was .93, and for the Negative Affect (NA) subscale was .91, indicating good internal consistency for both scales.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003ePatient Health Questionnaire-9 (PHQ-9)\u003c/h2\u003e \u003cp\u003eThe PHQ-9 is a widely used tool for screening and assessing depressive symptoms [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e]. It contains nine items covering domains such as anhedonia, low mood, sleep disturbances, fatigue, appetite changes, self-worth, concentration difficulties, psychomotor changes, and suicidal ideation. Each item is rated from 0 (\u0026ldquo;Not at all\u0026rdquo;) to 3 (\u0026ldquo;Nearly every day\u0026rdquo;) based on experiences in the past two weeks. Higher total scores indicate greater risk of depression. The Cronbach\u0026rsquo;s α in this study was 0.92.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eStatistical methods\u003c/h2\u003e \u003cp\u003eData were analyzed using SPSS 23.0. Descriptive statistics (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation), independent samples \u003cem\u003et\u003c/em\u003e-tests, one-way ANOVAs, and Pearson correlations were conducted. Mediation analyses were performed using PROCESS macro [\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e]. All continuous variables were standardized (z-scores) to eliminate scale effects on regression coefficients. The significance of indirect effects was tested using bias-corrected percentile bootstrapping with 5,000 resamples to compute 95% confidence intervals (CI). Indirect effects were considered significant if the CI did not include zero. Sociodemographic variables (gender, age, socioeconomic status) were included as covariates to control for potential confounding effects.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eCommon Method Bias Test\u003c/h2\u003e \u003cp\u003eTo assess common method bias, Harman\u0026rsquo;s single-factor test was conducted [\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e]. The first unrotated factor accounted for 28.67% of the total variance, which is below the critical threshold of 40%. Therefore, common method bias was not a serious concern in this study and remained within an acceptable range.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eDemographic Characteristics of the Study Sample\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents the demographic characteristics of the 1,231 valid participants. The sample consisted of 655 females (53.2%) and 576 males (46.8%), with a mean age of 19.32 years (SD\u0026thinsp;=\u0026thinsp;1.34). Regarding geographic distribution, 463 participants (37.6%) were from eastern China, 180 (14.6%) from central China, and 588(47.8%) from western China. Most participants were freshmen (604, 49.1%), followed by sophomores (368, 29.9%), juniors (193, 15.7%), seniors (50, 4.1%), and postgraduate students (16, 1.3%). In terms of academic major, 390 participants (31.7%) reported being in medical-related fields, whereas 841 (68.3%) were from non-medical majors.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDemographic Characteristics of Participants (\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1,231)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCategory\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFrequency (\u003cem\u003en\u003c/em\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePercentage (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e576\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e46.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e655\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e53.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eOnly\u003c/p\u003e \u003cp\u003eChild Status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e375\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e30.46\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e856\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e69.54\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eHometown Location\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRural\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e735\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e59.71\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUrban\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e496\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e40.29\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eAcademic Year\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFreshman\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e604\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e49.06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSophomore\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e368\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e29.89\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eJunior\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e193\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e15.69\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSenior\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePostgraduate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eUniversity Location\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEastern\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e463\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e37.61\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCentral\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e180\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e14.62\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWestern\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e588\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e47.77\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eMedical-related Major\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e390\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e31.68\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e841\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e68.32\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eDescriptive Statistics and Bivariate Correlations\u003c/h2\u003e \u003cp\u003eThe means, standard deviations, and bivariate correlations of the study variables are presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Digital stress was significantly negatively correlated with positive emotion (\u003cem\u003er\u003c/em\u003e = \u0026minus;\u0026thinsp;0.18, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01), and significantly positively correlated with negative emotion (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.38, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01), loneliness (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.54, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01), and depression (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.43, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01). Negative emotion was positively correlated with both loneliness (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.34, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01) and depression (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.62, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01). Finally, loneliness showed a significant positive correlation with depression (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.42, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDescriptive statistics and intercorrelations among study variables\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eDigital Stress\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eM\u003c/em\u003e\u0026thinsp;\u0026plusmn;\u0026thinsp;\u003cem\u003eSD\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDigital Stress\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePositive Emotion\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNegative Emotion\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLoneliness\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eDepression\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e98.35\u0026thinsp;\u0026plusmn;\u0026thinsp;29.10\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePositive Emotion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e21.98\u0026thinsp;\u0026plusmn;\u0026thinsp;4.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.18\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNegative Emotion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e14.57\u0026thinsp;\u0026plusmn;\u0026thinsp;4.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.38\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.21\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLoneliness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e11.96\u0026thinsp;\u0026plusmn;\u0026thinsp;4.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.54\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.37\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.34\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDepression\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e7.87\u0026thinsp;\u0026plusmn;\u0026thinsp;5.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.43\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.28\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.62\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.42\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u003csup\u003e*\u003c/sup\u003e\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, \u003csup\u003e**\u003c/sup\u003e \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e4.3 Chain Mediation Analysis\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTo further examine the mechanisms linking digital stress and depression, positive and negative emotions as well as loneliness\u0026mdash;variables significantly correlated with depression\u0026mdash;were included as mediators. The results are presented in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eThe total effect of digital stress on depression was significant (β\u0026thinsp;=\u0026thinsp;0.44, \u003cem\u003eSE\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.026, \u003cem\u003et\u003c/em\u003e\u0026thinsp;=\u0026thinsp;16.96, 95% CI [0.39, 0.49]). Both the direct effect (β\u0026thinsp;=\u0026thinsp;0.16, \u003cem\u003eSE\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.026, \u003cem\u003et\u003c/em\u003e\u0026thinsp;=\u0026thinsp;6.18, 95% CI [0.11, 0.21]) and the indirect effect \u003cem\u003e(\u003c/em\u003eβ\u0026thinsp;=\u0026thinsp;0.28, \u003cem\u003eSE\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.025, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, 95% CI [0.23, 0.33]) were significant.\u003c/p\u003e \u003cp\u003eAnalysis of independent mediating pathways revealed that digital stress exerted significant indirect effects on depression via positive emotion (β\u0026thinsp;=\u0026thinsp;0.02, \u003cem\u003eSE\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.007, 95% CI [0.01, 0.04]), negative emotion (β\u0026thinsp;=\u0026thinsp;0.19, \u003cem\u003eSE\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.023, 95% CI [0.15, 0.24]), and loneliness (β\u0026thinsp;=\u0026thinsp;0.06, \u003cem\u003eSE\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.015, 95% CI [0.03, 0.09]).\u003c/p\u003e \u003cp\u003eIn the chain mediation pathways, digital stress significantly predicted depression through positive emotion \u0026rarr; loneliness (β\u0026thinsp;=\u0026thinsp;0.007, \u003cem\u003eSE\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.002, 95% CI [0.003, 0.011]) and through negative emotion \u0026rarr; loneliness (β\u0026thinsp;=\u0026thinsp;0.006, \u003cem\u003eSE\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.002, 95% CI [0.002, 0.011]). These findings indicate that positive emotion, negative emotion, and loneliness not only independently mediated the association between digital stress and depression, but also jointly contributed to significant chain mediation effects. The final serial model is depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eCovariates including age, family background, and gender were controlled. Among these, gender exerted a significant negative effect on depression (β = \u0026minus;\u0026thinsp;0.17, \u003cem\u003eSE\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.052, \u003cem\u003et\u003c/em\u003e = \u0026minus;\u0026thinsp;3.21, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), indicating that male students reported significantly lower depression levels than female students. Neither age nor socioeconomic status had a significant effect on depression.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDirect and indirect effects of digital stress on depression\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePredictors\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEstimated effect\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eSE\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eR\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003et\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e95% Bias-Corrected CI\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOutcome: Positive Emotion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.04\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDigital Stress\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.20\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.028\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-6.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.25 to -0.14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.029\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.06 to 0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIncome\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.029\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.01 to 0.12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.16\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.056\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.05 to 0.27\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOutcome: Negative Emotion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.15\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDigital Stress\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.39\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.027\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e14.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.34 to 0.44\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.027\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.04 to 0.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIncome\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.027\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-1.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.08 to 0.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.17\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.053\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-3.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.28 to -0.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOutcome: Loneliness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.39\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDigital Stress\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.44\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e17.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.39 to 0.49\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePositive Emotion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.27\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-11.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.31 to -0.22\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNegative Emotion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.12\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.08 to 0.17\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.06\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-2.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.11 to -0.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIncome\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.05\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-2.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.09 to -0.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.045\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.00 to 0.18\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOutcome: Depression\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.46\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDigital Stress\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.16\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.026\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.11 to 0.21\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePositive Emotion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.10\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-4.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.14 to -0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNegative Emotion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.49\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e21.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.45 to 0.54\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLoneliness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.13\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.027\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.07 to 0.18\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.03 to 0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIncome\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-1.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.08 to 0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.042\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-1.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.15 to 0.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndirect effects\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal indirect effect\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.28\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.23 to 0.33\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDigital Stress\u0026rarr; Positive Emotion\u0026rarr;Depression\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.02\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.01 to 0.04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDigital Stress\u0026rarr;Negative Emotion\u0026rarr;Depression\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.19\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.15 to 0.24\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDigital Stress\u0026rarr;Loneliness\u0026rarr;Depression\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.06\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.03 to 0.09\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDigital Stress\u0026rarr;Positive Emotion\u0026rarr;Loneliness\u0026rarr;Depression\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.007\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.003 to 0.011\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDigital Stress\u0026rarr;Negative Emotion\u0026rarr;Loneliness\u0026rarr;Depression\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.006\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.002to 0.011\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal effect\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.44\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.026\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e16.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.39 to 0.49\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003csup\u003e*\u003c/sup\u003e \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, \u003csup\u003e**\u003c/sup\u003e\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u003cb\u003e\u0026lt;\u003c/b\u003e\u0026thinsp;0.01,\u003csup\u003e***\u003c/sup\u003e \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e "},{"header":"Discussion","content":"\u003cp\u003eIn the current digital era, while technology provides college students with convenience in learning and daily life, it simultaneously poses challenges to mental health[\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e]. Digital stress, as a unique stressor of the digital age, has been shown to be significantly associated with depression and loneliness [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Building upon the stressor\u0026ndash;emotion model [\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e], the present study further investigated the impact of digital stress on depression and revealed the chain mediating roles of positive emotion, negative emotion, and loneliness.\u003c/p\u003e \u003cp\u003eConsistent with Hypothesis \u003cspan refid=\"FPar1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, our findings demonstrated a significant positive association between digital stress and depression, in line with previous studies [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Digital stress may serve as a crucial psychosocial pathway linking digital media use and mental health outcome[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Historically, research on digital stress focused on workplace contexts, where it has been closely linked to burnout and stress responses such as irritability, sleep disturbances, anxiety, and depression [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e]. Today, however, college students represent a particularly vulnerable group. They rely heavily on digital technologies for online courses, academic collaboration, social interaction, and project completion. Constant exposure to massive amounts of information can result in cognitive overload and emotional strain [\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e]. Moreover, the tendency for upward social comparison on social media\u0026mdash;where content is often idealized\u0026mdash;may distort self-perceptions and induce depressive symptoms [\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e]. In addition, perpetual online vigilance and dependence on digital devices can disrupt circadian rhythms[\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e], and impair academic performance [\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e], which elevate depression risk. These findings highlight the urgent need for educational institutions to pay greater attention to students\u0026rsquo; digital stress and its associated psychological consequences.\u003c/p\u003e \u003cp\u003eMediation analyses supported Hypothesis \u003cspan refid=\"FPar2\" class=\"InternalRef\"\u003e2\u003c/span\u003e: both positive and negative emotions significantly mediated the relationship between digital stress and depression, confirming the applicability of the stressor\u0026ndash;emotion model [\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e] in digital contexts. Stress and emotional changes are closely linked [\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e], and cognitive appraisals further shape these responses [\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e] Under digital stressors such as excessive social comparison and information bombardment, students may adopt maladaptive cognitive strategies (e.g., avoidance), which foster negative emotions. Positive emotions typically serve as a \u0026ldquo;psychological buffer\u0026rdquo; [\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e]. However, persistent exposure to digital stress may deplete emotional resources, suppressing the generation and maintenance of positive affect [\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e]. Once this buffer collapses, students become more vulnerable to negative emotions, thereby increasing depression risk.\u003c/p\u003e \u003cp\u003eSupport was also found for Hypothesis \u003cspan refid=\"FPar3\" class=\"InternalRef\"\u003e3\u003c/span\u003e: loneliness significantly mediated the relationship between digital stress and depression. According to the theory of stress and coping [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] stress represents an adaptive response to environmental demands. When digital stressors (e.g., availability pressure, recognition anxiety) exceed students\u0026rsquo; coping capacity, they may reduce social engagement as a defense mechanism. For example, a diary study found that daily stress predicted same-day loneliness among students [\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e]. Excessive reliance on online social interaction can crowd out offline relationships; online interactions are often superficial and lack the emotional depth required to meet psychological needs[\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e]. Moreover, digital platforms amplify opportunities for social comparison, a process particularly salient during the developmental stage of identity formation, which may increase dissatisfaction and distort self-perceptions [\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e],Excessive information flow further induces social fatigue, intensifying feelings of loneliness [\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e]. Over time, accumulated loneliness, especially in the absence of social support, has been shown to predict depressive outcomes [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eMost importantly, this study revealed that positive and negative emotions, together with loneliness, formed chain mediating pathways between digital stress and depression\u0026mdash;a mechanism that has received limited prior attention. Recent literature distinguishes between functional and dysfunctional uses of digital technologies: functional use (e.g., efficient learning, prosocial communication) promotes well-being, whereas dysfunctional use (e.g., excessive immersion, passive exposure to negative content) contributes to adverse outcomes[\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e]. College students, with limited self-regulation and information-filtering capacity, are more likely to engage in dysfunctional usage patterns, thereby exacerbating digital stress, negative emotions, and loneliness. Moreover, algorithms on digital platforms may amplify the spread of negative emotions, reinforcing emotional contagion [\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e, \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e]. Students experiencing negative emotions under digital stress may withdraw from social interactions, further intensifying loneliness. Notably, momentary ecological assessment studies suggest that transient loneliness not only co-occurs with depression but also predicts elevated depressive affect several hours later [\u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eInterestingly, we also found that digital stress had a significant negative effect on positive emotion, which in turn negatively predicted loneliness, ultimately forming a weak positive chain effect on depression. At first glance, this appears inconsistent with the broaden-and-build theory [\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e], which posits that positive emotions buffer against stress. A plausible explanation is that the \u0026ldquo;destructive\u0026rdquo; intensity of digital stress overwhelms the protective role of positive emotions, suppressing their expected buffering and broadening effects. Consequently, the pathway of \u0026ldquo;reduced positive emotion \u0026rarr; heightened loneliness \u0026rarr; increased depression\u0026rdquo; emerges. For example, online social feedback such as \u0026ldquo;likes\u0026rdquo; may temporarily enhance positive emotions, but unmet expectations regarding feedback can paradoxically trigger deeper negative affect. This finding underscores the dual role of emotion and social interaction in mediating mental health outcomes under digital stress and highlights the need for interventions that simultaneously target emotion regulation and the cultivation of meaningful social connections.\u003c/p\u003e \u003cp\u003eIn conclusion, the present study has theoretical and practical implications. At the theoretical level, it supports the applicability of the stressor\u0026ndash;emotion model [\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e] in digital contexts, and demonstrates a chain‑mediated effect of positive affect, negative affect, and loneliness in the association between digital stress and depression. This finding extends the emotion\u0026ndash;social mechanism framework through which digital stress affects mental health, offering an integrated perspective for understanding youth mental health risks in the era of digitalization. At the practical level, the results indicate that universities and relevant authorities should prioritize comprehensive prevention and management of digital stress among college students. Key strategies include enhancing digital literacy and emotion regulation education, fostering high‑quality offline social connections, and mitigating the cumulative burden of loneliness. Furthermore, a dual‑track intervention strategy that integrates emotion regulation and social support is recommended to strengthen students\u0026rsquo; psychological resilience in digital environments and to disrupt the processes through which digital stress contributes to depression.\u003c/p\u003e \u003cp\u003eSeveral limitations should be noted. First, this study employed a cross-sectional design, which only captures the associations among digital stress, emotional states, loneliness, and depression at a single time point, thereby precluding causal inferences. Future studies could adopt longitudinal cohort designs or cross-lagged panel models to track these variables across multiple time points and clarify their dynamic interrelations. Second, data were primarily collected through self-report, which is susceptible to recall bias and social desirability effects, potentially undermining reliability. Future research should incorporate objective measures (e.g., digital device usage logs, cortisol levels) alongside self-reports, using multimodal data integration approaches to provide a more comprehensive assessment of digital stress, emotional states, and depressive symptoms.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThis study explored the underlying associations between digital stress and depression among college students. Findings indicate that digital stress is a critical risk factor for depression, exerting both direct and indirect effects via multiple psychological mechanisms. Specifically:\u003c/p\u003e \u003cp\u003eDigital stress reduced positive emotion, which directly predicted depression and indirectly contributed to it through increased loneliness.\u003c/p\u003e \u003cp\u003eDigital stress heightened negative emotion, which directly increased depression and indirectly exacerbated it through greater loneliness.\u003c/p\u003e \u003cp\u003eDigital stress directly intensified loneliness, thereby elevating depression risk.\u003c/p\u003e \u003cp\u003eOverall, this study underscores the potential threat of digital stress to students\u0026rsquo; mental health. Theoretically, it provides a new perspective for understanding depression in the digital era and highlights loneliness and emotion as key mechanisms. Practically, the findings inform the design of tailored interventions. For instance, universities should integrate digital mental health education into general curricula, enhance digital literacy, and promote adaptive coping strategies. Artificial intelligence technologies may be leveraged to improve the precision of digital stress assessment, enable early warning, and deliver AI-driven interventions targeting emotion regulation and social connection enhancement. Optimizing digital ecosystems to reduce information overload is also crucial. Multi-stakeholder collaboration is needed to address digital stress and safeguard students\u0026rsquo; psychological well-being while strengthening their capacity to adapt to the digital society.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eULS UCLA Loneliness Scale\u003c/p\u003e\u003cp\u003eULS-3 Revised third edition of the UCLA Loneliness Scale\u003c/p\u003e\u003cp\u003eULS-8 Simplified 8-item version of the UCLA Loneliness Scale\u003c/p\u003e\u003cp\u003eULS-6 6-item Chinese-adapted version of the UCLA Loneliness Scale\u003c/p\u003e\u003cp\u003eDSS Digital Stress Scale\u003c/p\u003e\u003cp\u003eRC-DSS Revised Chinese Digital Stress Scale\u003c/p\u003e\u003cp\u003ePANAS Positive and Negative Affect Schedule\u003c/p\u003e\u003cp\u003ePA Positive Affect subscale of PANAS\u003c/p\u003e\u003cp\u003eNA Negative Affect subscale of PANAS\u003c/p\u003e\u003cp\u003ePHQ-9 Patient Health Questionnaire-9\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank staff at the five schools for helping to contact, and all the participants in this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors have contributed significantly the paper. XL and ZQG were responsible for the development of this particular study, performed statistical analyses and wrote the first draft. All authors participated in the study design, and questionnaire design. JZX, WZ were responsible for manuscript reviewing. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFundings\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by Brain Science and Brain-like Intelligence Technology - National Science and Technology Major Project (Grant Number: 2021ZD0200500), the National Social Science Foundation of China (Grant Number: 24ASH014), the Program for Professor of Special Appointment at Shanghai Institutions of Higher Education (Grant Number: TP2022029), the East China Normal University Medicine and Health Joint Fund (Grant Number: 2022JKXYD05001), and the Special Project of Shanghai Municipal Education Commission (Grant Number: 2023RX07).The funding institutions had no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and/or analyzed during the current study are not publicly available due to the need to protect individual privacy but are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was approved by the Research Ethics Committee, Shanghai University of Medicine and Health Sciences (2024-SZKT-04). Written informed consent was obtained from all participants before the start of the study. As all participants were university students and were considered to have full capacity to provide consent, their own informed consent was sufficient, and no consent from legal guardians or representatives was required. The study was conducted in strict accordance with the ethical standards of the Declaration of Helsinki.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthorship and copyright\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors confirm that the submitted manuscript is an original contribution and has not been previously published, that it is not under consideration for publication elsewhere, and that, if accepted, will not be published elsewhere in similar form in any language. We also confirm that all authors contributed significantly to the study.\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\u0026rsquo;s data in a form that would compromise anonymity.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor details\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e1\u003c/sup\u003e Shanghai Key Laboratory of Mental Health and Psychological Crisis Intervention, Affiliated Mental Health Center (ECNU), School of Psychology and Cognitive Science, East China Normal University, Shanghai, 200062,China\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e2\u0026nbsp;\u003c/sup\u003eSchool of medical instrumentation, Shanghai University of Medicine and Health Sciences, Shanghai, 201318,China\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e3\u003c/sup\u003ePositive Education China Academy (PECA) of Han-Jing Institute for Studies in Classics, Juzhe Xi\u0026rsquo;s Master Workroom of Shanghai School Mental Health Service, China Research Institute of Care and Education of Infants and Young Children, East China Normal University, Shanghai, 200062,China\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e4\u003c/sup\u003eCollege of Education Science, Kashgar University, Kashgar, 844000,China\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e5\u003c/sup\u003eSchool of Humanities and Management, Wannan Medical University,Wuhu, 241002,China\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e*Correspondence\u003c/strong\u003e\u003cstrong\u003e：\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eJuzhe Xi, School of Psychology and Cognitive Science, East China Normal University, 3663, North Zhongshan Road, Shanghai 200062, China.\u003c/p\u003e\n\u003cp\u003eE-mail: [email protected]\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eInternational Telecommunication Union. 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J Affect Disord. 2024;360:376\u0026ndash;86. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jad.2024.05.148\u003c/span\u003e\u003cspan address=\"10.1016/j.jad.2024.05.148\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\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-psychology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"psyo","sideBox":"Learn more about [BMC Psychology](http://bmcpsychology.biomedcentral.com/)","snPcode":"","submissionUrl":"","title":"BMC Psychology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Digital stress, Loneliness, Depression, College students","lastPublishedDoi":"10.21203/rs.3.rs-9296856/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9296856/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackgrounds\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWith the integration of digital technologies, individuals are increasingly exposed to high-volume information flows, multi-platform communication, and online performance pressures that can lead to digital stress, a novel risk factor for mental health. Although existing studies have preliminarily demonstrated a negative association between digital stress and mental well-being, empirical evidence remains limited regarding the underlying mechanisms through which digital stress may lead to depressive symptoms via a sequential chain involving positive affect, negative affect, and loneliness. This study therefore tests the chained mediating effects of these variables in the association between digital stress and depression among Chinese college students, aiming to inform theory and practice for mental health interventions in higher education.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA cross-sectional survey was conducted from October to November 2024 among 1,231 undergraduates from five universities across eastern, central, and western China. Participants completed measures of digital stress, positive/negative emotions, loneliness, and depression. Correlation and chain mediation analyses (using PROCESS for SPSS) were performed, controlling for age, gender, and family background.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDigital stress was significantly correlated with all variables. Both positive and negative emotions, as well as loneliness, independently mediated the link between digital stress and depression. Furthermore, digital stress had a direct effect on depression and indirect effects through two chain mediation pathways: positive emotion → loneliness and negative emotion → loneliness.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDigital stress impacts depression through multiple psychological pathways. These findings offer a novel theoretical framework for understanding digital-era depression and inform strategies for developing integrated digital mental health support systems in educational contexts.\u003c/p\u003e","manuscriptTitle":"Digital Stress and Depression among Chinese College Students: A Cross- Sectional Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-26 15:38:20","doi":"10.21203/rs.3.rs-9296856/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewersInvited","content":"","date":"2026-04-16T12:15:06+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-16T12:14:18+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-04-06T12:41:34+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-04-05T02:27:32+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Psychology","date":"2026-04-05T02:22:13+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-psychology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"psyo","sideBox":"Learn more about [BMC Psychology](http://bmcpsychology.biomedcentral.com/)","snPcode":"","submissionUrl":"","title":"BMC Psychology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"af41ecd8-804c-4376-8f8d-b06cd26a540e","owner":[],"postedDate":"April 26th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-26T15:38:20+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-26 15:38:20","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9296856","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9296856","identity":"rs-9296856","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}