Mobile Internet, Physical Exercise and Depression Levels: Mediating Mechanism and Empirical Examination

Mobile Internet, Physical Exercise and Depression Levels: Mediating Mechanism and Empirical Examination | 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 Article Mobile Internet, Physical Exercise and Depression Levels: Mediating Mechanism and Empirical Examination yue sun, zhenghao hou This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8146436/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Mental health has become a central pillar of national health strategies worldwide, aligning with the World Health Organization’s (WHO) vision of “health for all.” Drawing on data from the 2020–2022 China Family Panel Studies (CFPS) and applying a two-way fixed-effects model, this study empirically examines the impact of mobile internet use on residents’ depression levels and explores the underlying behavioral transmission mechanisms. The results show that mobile internet use significantly reduces depressive symptoms, and the findings remain robust after addressing endogeneity concerns. Further analysis reveals that mobile internet use not only directly alleviates depression but also indirectly reduces depressive symptoms by increasing individuals’ frequency of physical exercise. Additional heterogeneity analyses indicate that the depression-reducing effect of mobile internet use is more pronounced among individuals with abnormal BMI, non-agricultural workers, and those lacking pension security. These findings suggest the need to strengthen functional digital inclusion, enhance psychological health interventions for key groups, and promote innovative models of health promotion driven by internet technologies. Health sciences/Health care Humanities/Health humanities Biological sciences/Psychology Social science/Psychology mobile internet depression physical exercise mental health Introduction With accelerated social rhythms and mounting psychological pressures, depression has become a major public health challenge. According to the World Health Organization (2025), depression and other mental disorders are leading causes of global disability, contributing to sustained health losses and significant socioeconomic burdens while exhibiting increasing prevalence and younger onset trends(Vos et al., 2012 ; Whiteford et al., 2013 ). In China, rapid social transformation and intensified competition have led to widespread depressive emotions across all age groups, characterized by persistence and concealment (Tian et al., 2025 ), which severely affect work efficiency, quality of life, and social functioning (Woo et al., 2011 ). The Healthy China 2030 Planning Outline (State Council of the People’s Republic of China, 2016 ) explicitly calls for strengthening and standardizing mental health service systems. Therefore, identifying effective approaches to alleviate depression and enhance mental health literacy is of critical importance. Against the backdrop of rapid digital development, the widespread application of mobile internet technologies such as the internet and artificial intelligence has become a key force reshaping individuals’ social trust, behavioral patterns and psychological well-being(McDool et al. 2020 ; Liu et al., 2025). These technologies not only redefine everyday social interactions and break down barriers to information access(Nakagomi et al., 2022 ), but also integrate deeply into residents’ health management through personalized recommendations and intelligent health monitoring(Audi et al., 2025 ) thereby supporting the formation of healthy behaviors(Korp, 2006 ) and creating new opportunities for improving mental health. However, significant differences exist in residents’ preferences for and dependence on online information, leading to heterogeneous effects of internet use on psychological health and leaving its specific influence pathways insufficiently understood. On the one hand, the internet may relieve depressive symptoms by fostering social connections and empowering health behavior(Chen and Gao, 2023 ); on the other hand, it may exacerbate psychological stress due to information overload, overreliance, or the deepening of the digital divide. Consequently, examining the relationship between mobile internet use and depressive tendencies, as well as clarifying the mediating transmission mechanism of physical exercise, is of critical importance. Physical exercise is widely recognized as an effective means of alleviating depression. Physiologically, exercise stimulates the release of endorphins and dopamine, which counteract anxiety and sadness while enhancing pleasure responses (Harber and Sutton, 1984 ; Gorrell et al., 2022 ). Psychologically, exercise diverts attention from negative thoughts and provides emotional relief. Furthermore, achieving exercise goals fosters a sense of accomplishment, which is essential in combating depressive emotions (Anderson and Shivakumar, 2013 ; Petruzzello et al., 1991 ). Overall, existing research has laid a theoretical foundation for understanding how mobile internet use may improve psychological health, but it has primarily focused on the general relationships among internet use, physical exercise, and mental well-being. Systematic investigation is still lacking regarding the specific mechanisms through which mobile internet use promotes exercise behavior and subsequently enhances residents’ psychological health. Although mobile internet use has shown positive effects in promoting health behaviors, the theoretical and empirical evidence remains limited, and insufficient attention has been given to its deeper psychological impacts. As mobile internet becomes fully integrated into daily life and digitalization becomes the social norm, it is necessary to examine the pathways through which mobile internet use affects depression levels from the perspective of behavioral mediation. This approach helps reveal its potential psychological regulatory functions and its role in health promotion within contemporary social contexts. In summary, this study draws on data from the China Family Panel Studies (CFPS) and constructs a two-way fixed-effects model and a mediation model to systematically analyze the impact of mobile internet use on residents’ depression levels and to test the mediating role of physical exercise behavior. At the same time, it further examines, from the perspective of individual heterogeneity, the differential effects of internet use and physical exercise across various population groups, with the aim of revealing the internal mechanism through which mobile internet improves mental health by fostering positive health behaviors, thereby providing a theoretical basis and policy implications for mental health promotion in the digital era. This study makes two main contributions: First, it extends existing research on the relationship between internet use and mental health from the perspective of behavioral mechanisms. Prior literature has predominantly focused on the direct effects of internet use on mental health, while paying relatively little attention to its behavioral transmission pathways. Through empirical analysis, this study finds that mobile internet use not only directly reduces depression levels but also exerts a significant indirect effect by increasing the frequency of physical exercise, thus revealing a behavioral mediation mechanism through which the internet influences mental health. Second, it deepens the understanding of the psychological effects of internet use from the perspective of group heterogeneity. Based on subgroup analyses along three dimensions—BMI status, employment type, and pension security—this study shows that the mental health effects of internet use differ substantially across groups, thereby providing empirical support for more targeted mental health interventions. Literature review The Worsening Trend of Depression Depressive disorder (also referred to as depression) is a common mental disorder characterized by persistent low mood, loss of pleasure, or diminished interest in daily activities(World Health Organization, 2025). According to the World Health Organization (2025), approximately one in seven people worldwide suffers from some form of mental disorder, and depressive disorder is among the leading contributors to the global burden of disease, exerting extensive impacts on individual health, social relationships, and economic development. Against the backdrop of social transformation and intensified competition, mental health problems among residents exhibit spillover and diffusion characteristics, with depressive emotions increasingly spreading across diverse groups and social contexts. Existing research indicates that adolescents face academic pressure and peer comparison, working adults experience performance pressure and role conflict, and older adults are prone to emotional distress due to difficulties adapting to retirement and declining physical functions. Symptoms of depression have shown an upward trend across these populations(Qu et al., 2024 ; Zhou et al., 2020 ; Liu et al., 2023 ). Accordingly, analyses by Tian et al. ( 2025 ) based on the Global Burden of Disease Study 2021 indicate that the number of depressive disorder cases in China increased from approximately 34.4 million in 1990 to 53.1 million in 2021, with the associated disease burden continuing to rise. Moreover, psychological health has become a critical dimension influencing Chinese residents’ subjective well-being, second only to physical health. Against this backdrop, depression has increasingly become a central issue in social governance and public policy. The Healthy China 2030 Planning Outline(Central Committee of the Communist Party of China, 2016) explicitly emphasizes the need to promote mental health and implement comprehensive interventions, highlighting the importance of systematic measures that address social support, lifestyle factors, and behavioral interventions. Consequently, exploring feasible behavioral pathways—particularly those that alleviate depression by fostering positive and healthy behaviors—has become a shared focus of both academic research and policy development. The Impact of Mobile Internet Use There remains considerable disagreement within the academic community regarding the direction of the effect of mobile internet use on depressive tendencies. One body of research highlights its positive value, arguing that the internet can buffer negative emotions through informational and emotional support, thereby reducing the risk of depression(Griffiths et al., 2010 ). Another line of inquiry contends that problematic or excessive internet use is closely associated with elevated depression levels, and that this relationship is significantly moderated by usage patterns and individual characteristics(Cai et al., 2023 ). Bessière et al. ( 2010 ) found that levels of social support shape the psychological effects of internet use: for individuals with high social support, online behavior is negatively associated with depression, whereas no such pattern is observed among those with low support. Cotten (2012) pointed out that the Internet can effectively alleviate depressive symptoms among retired older adults; however, Moreno et al. ( 2015 ) observed that problematic use significantly increases depression levels among female college students. Existing studies also emphasize the stratifying effects of age and urban–rural contexts on the psychological outcomes of internet use. Wang and Shi ( 2024 ) found that Internet use, by facilitating access to family support, significantly reduces depressive symptoms among older adults; Sun (2022) further noted that this effect is more pronounced in rural populations; and Zhang ( 2022 ) argued that internet use can attenuate the negative impact of occupational status on depression among urban residents. Moreover, usage motivation constitutes another key mechanism affecting outcomes. Internet use aimed at interacting with family and friends has been shown to alleviate depressive symptoms, whereas search behaviors driven by health-related anxiety may amplify negative emotions(Zhang et al., 2023 ; Mathes et al., 2018 ). Adolescents and individuals with internet addiction are also more likely to enter a negative cycle in which depressive symptoms worsen during use(Morrison and Gore, 2010 ). Overall, existing research has examined the complex effects of internet use on depressive tendencies from multiple perspectives, including social support, identity heterogeneity, urban–rural differences, and usage motivations, yet the findings remain inconsistent. Although many studies acknowledge the potential psychological regulatory function of the internet, they do not provide a systematic explanation of the underlying mechanisms, nor do they offer structural analyses of how its effects vary across different population groups. Furthermore, research exploring how internet use interacts with individuals’ real-life behaviors—such as social participation and physical activity—to influence mental health remains limited. These gaps indicate that there is still considerable room for advancing mechanism development and variable integration, thereby offering important avenues for further investigation into the dynamic pathways linking internet use, behavioral patterns, and psychological well-being. The Role of Physical Exercise Physical exercise, as a behavioral approach that integrates both physiological regulation and psychological adjustment, has been widely demonstrated to exert a positive effect on alleviating depressive symptoms(Liu et al., 2024 ). Physiologically, exercise promotes the release of endorphins and other biochemical substances, enhancing emotional stability and one’s sense of bodily control(Harber and Sutton, 1984 ; Gorrell et al., 2022 ). Psychologically, it serves as an effective alternative behavior for coping with stress, and its collective nature can strengthen social connectedness and feelings of belonging, thereby indirectly improving mental well-being(White et al., 2024 ). Theoretically, behavioral activation theory posits that physical exercise can interrupt the maladaptive cycle of “behavioral inhibition–negative emotion–social withdrawal”(Ekers et al., 2014 ), while self-determination theory emphasizes that exercise satisfies the basic psychological needs for autonomy, competence, and relatedness, producing deeper benefits for mental health(Ng et al., 2012 ). Empirical studies further validate the mediating psychological effects of physical activity. Recent research has shown that Internet use significantly increases the likelihood and frequency of physical activity among older adults, and also enhances their exercise adherence, thereby contributing to a reduction in depressive symptoms (Guo et al., 2025 ). Among adolescents, the interactive functions and peer-encouragement mechanisms embedded in social media platforms have been found to strengthen the continuity of physical exercise, supporting improvements in emotional regulation and psychological resilience (Goodyear et al., 2021 ). In addition, empirical evidence indicates that digital health tools such as exercise applications can improve exercise compliance through real-time feedback and online community support, which in turn helps reduce anxiety and depressive symptoms (Romeo et al., 2019 ). Overall, with the support of digital media environments, the mental-health benefits of physical activity have become increasingly prominent.In summary, existing research has provided a relatively systematic understanding of the mediating potential of physical exercise in the relationship between internet use and depressive tendencies, yet the conditions under which this pathway operates and the boundaries of its mechanisms remain insufficiently clarified. On the one hand, although theoretical models suggest that exercise alleviates depression by activating positive behaviors and satisfying psychological needs, empirical studies have not adequately examined its applicability across different patterns of internet use or among different population groups. On the other hand, how individuals in digital environments translate online content into exercise motivation, and how such motivation evolves into sustained behavior that ultimately improves psychological well-being, remains underexplored, particularly in terms of dynamic pathways and mechanism validation. Therefore, future studies should further investigate the role of physical exercise within the “digital media–behavioral execution–psychological regulation” chain, identify both the facilitating and hindering factors within this process, and thereby enrich the multidimensional understanding of how internet use influences mental health. Research Questions Existing studies have separately demonstrated both the positive and negative effects of mobile internet use on depression levels(Griffiths et al., 2010 ; Cai et al., 2023 ), as well as the beneficial role of physical exercise in emotional regulation(Harber and Sutton, 1984 ). However, research integrating these two domains remains limited. Most literature focuses on the direct pathways linking internet use to mental health, while overlooking the mediating role played by behavioral variables. Although some empirical studies suggest that the internet can promote exercise frequency through mechanisms such as online check-ins and social incentives(Goodyear et al., 2021 ; Romeo et al., 2019 ), a systematic mediation model explaining how these behavioral changes translate into improvements in psychological well-being has yet to be established. From a theoretical perspective, behavioral activation theory and self-determination theory respectively highlight the crucial role of physical exercise in interrupting cycles of negative emotion and satisfying basic psychological needs(Ekers et al., 2014 ; Ng et al., 2012 ). Empirically, prior work also indicates that internet use can stimulate exercise motivation(Guo et al., 2025 ), and that exercise itself contributes to reducing depressive symptoms(White et al., 2024 ). Moreover, clear differences exist in how various population groups use the internet and engage in exercise. For instance, older adults enhance social support through internet use and thereby improve psychological health(Wang and Shi, 2024 ), whereas adolescents are more vulnerable to psychological risks associated with internet addiction(Morrison and Gore, 2010 ). Yet, whether the mediating effect of physical exercise varies across age groups remains insufficiently examined, and systematic comparative research on this issue is still lacking. Building on the existing literature, although prior studies have provided preliminary theoretical and empirical support for the “internet use–physical exercise–mental health” chain, they still lack a logically complete explanation of the mechanism pathway and structural validation from the perspective of group heterogeneity. Therefore, this study adopts a mediation-effect framework and conducts empirical analysis around three core questions: (1) Does mobile internet use significantly reduce residents’ depression levels? (2) Does physical exercise serve as a mediating mechanism in this relationship? (3) Does this mechanism pathway vary across different age groups? By uncovering the bridging role of physical exercise between internet use and mental health, this study aims to enhance understanding of the behavioral pathways linking internet use and mental health and to provide targeted policy insights. Methodology Data This study utilizes microdata from the China Family Panel Survey (CFPS), implemented by the Institute of Social Science Survey (ISSS) at Peking University, along with its corresponding indicator system. The CFPS covers all provinces in China except Hong Kong, Macao, and Taiwan, and adopts a multistage, stratified, probability-proportional-to-size sampling design, ensuring strong national representativeness and reliability. In the sample processing stage, respondents aged 18–70 were selected as the analysis population. Samples with missing or abnormal values in key variables—such as depression scores, physical exercise frequency, and mobile internet use—were excluded, and extreme values in the calculated Body Mass Index (BMI) variable were removed to ensure the robustness of the estimates. To balance data representativeness with availability, this study uses CFPS data from 2020 to 2022 to construct an unbalanced panel dataset, yielding a total of 16,901 valid observations. The dependent variable in this study is residents’ depression tendency score, measured using two indicators: cesd20sc and cesd8. These indicators are derived from the Center for Epidemiologic Studies Depression Scale (CES-D), which evaluates individual depressive symptoms. Both measures reflect the degree of depression through specific numerical scores, with higher scores indicating higher levels of depressive symptoms. The explanatory variables in this study are “use of mobile devices to access the internet” and “internet use.” The primary explanatory variable, “use of mobile devices to access the internet,” focuses on online activity conducted through mobile terminals such as smartphones and tablets. A value of 1 indicates that the respondent accesses the internet via such devices, while 0 indicates no such behavior. The secondary variable, “internet use,” captures internet activity within a broader scope, covering all types of terminals—including mobile devices and computers—with 1 representing use and 0 representing non-use. Together, these indicators comprehensively reflect residents’ internet access and usage status. The mediating variable in this study is “frequency of physical exercise in the past 12 months,” which is divided into eight levels. Corresponding to the eight response options in the questionnaire (“How often did you participate in physical exercise or fitness activities in the past 12 months?”—never; less than once per month on average; more than once per month but less than once per week; 1–2 times per week on average; 3–4 times per week on average; 5 or more times per week; once per day; twice or more per day), values from 0 to 7 are assigned sequentially. Higher values represent higher exercise frequency. Depression levels are easily influenced by factors such as age, physical condition, employment, family circumstances, and economic status. Therefore, this study includes a set of control variables, including gender, age, years of education, marital status, household registration type, employment status, household size, and health status (including self-rated health and whether BMI is within the normal range). In addition, to control for fixed regional differences that may affect the results, this study also incorporates county-level regional attributes and year variables. The specific definitions of all variables are presented in Table 1 . Table 1 Variable Definitions Variable Type Variable Meaning Variable Definition Dependent Variables Depression Index 1 cesd20sc (score) Depression Index 2 cesd8 (score) Explanatory Variable Internet Use Whether the respondent uses mobile devices (e.g., mobile phone, tablet) to access the Internet: 1 = yes; 0 = no; Whether the respondent uses the Internet: 1 = yes; 0 = no Instrumental Variable Internet Access of Other Residents within the Region The Internet access rate of other villagers or community residents (number of people using the Internet ÷ total number of people) Mediating Variable Exercise Frequency Participation frequency in physical exercise or recreational sports activities during the past 12 months: 0. Never 1. On average, less than once per month 2. On average, at least once per month but less than once per week 3. On average, 1–2 times per week 4. On average, 3–4 times per week 5. On average, 5 times per week or more 6. Once per day 7. Twice per day or more Control Variable Gender 1 = male; 0 = female Age in years Age at the time of survey (years) Registered Residence Type 1 = rural; 0 = urban Education Years of education Marital Status 1 = married; 0 = unmarried Employment Status Whether the respondent has a full-time job: 1 = yes; 0 = no Household Income Total household income (log-transformed) Household Size Number of household members Health Status Self-rated health status 1 = BMI within normal range (18.5 ≤ BMI ≤ 23.9); 0 = BMI outside normal range (BMI < 18.5 or BMI ≥ 23.9) The descriptive statistics of the variables are presented in Table 2 . This study includes 16,901 observations, and the distribution of each variable is generally reasonable. For the dependent variables, the mean value of cesd20sc is 33.61 with a standard deviation of 8.226, ranging from 22 to 72. The mean value of cesd8 is 13.77 with a standard deviation of 4.140, ranging from 8 to 32. These results indicate that there is notable variation in depression levels among the respondents. For the explanatory variables, the mean values of the two indicators measuring internet use are 0.729 and 0.732, indicating that approximately 72% of the respondents use the internet or access it via mobile devices, while about 28% do not use the internet at all. The mediating variable, exercise frequency, has a mean of 1.639 and a standard deviation of 2.319, suggesting substantial variation in residents’ exercise habits and indicating that most respondents do not engage in physical activity frequently. In terms of control variables, the mean value of gender is 0.548, indicating a slightly higher proportion of men than women. The mean age is 46.13 years, covering primarily adults to middle-aged and older groups. The mean value of household registration type is 0.830, showing a higher proportion of rural residents. The average years of education is 9.566, which approximates the level of junior secondary schooling. The mean value of marital status is 0.822, suggesting that married residents constitute the majority. The mean value of employment status is 0.813, indicating that most residents hold full-time jobs. The mean of the logarithm of household income is 11.15, and the average household size is 3.619 persons. Regarding health indicators, the mean value of self-rated health status is 3.061 on a five-point scale, the proportion of normal BMI is 0.521, and the distribution of health status across the sample is relatively balanced. Table 2 Descriptive Statistics of Variables Variables Variable Meaning Mean Std. Dev. Min Max Dependent variables Depression Index 1 33.62 8.241 22 72 Depression Index 2 13.77 4.148 8 32 Explanatory variable Mobile Internet use 0.720 0.449 0 1 Internet use 0.723 0.448 0 1 Instrumental variable Internet access of other residents within the region 0.723 0.166 0 1 Mediating variable Exercise frequency 1.637 2.326 0 7 Control variables Gender 0.550 0.498 0 1 Age in years 46.66 12.94 18 70 Registered residence type 0.831 0.375 0 1 Years of education 9.444 4.516 0 22 Marital status 0.828 0.377 0 1 Employment status 0.811 0.391 0 1 Household income 11.14 1.156 0 16.59 Household size 3.659 1.900 1 15 Self-rated health status 3.049 1.154 1 5 BMI in normal range 0.519 0.500 0 1 Econometric Models To examine the impact of mobile Internet use on residents’ mental health and to verify the existence of the transmission pathway of “mobile Internet use - frequency of physical exercise - residents’ mental health,” a bidirectional fixed-effects model is constructed to empirically analyze the effect of Internet use on residents’ depressive symptoms, while further exploring the mediating role of exercise frequency in this relationship. First, Model (1) is constructed to examine the direct impact of mobile Internet use on residents’ mental health: First, Model (1) is constructed to examine the direct impact of mobile Internet use on residents’ mental health: （1） Second, taking the frequency of physical exercise as the dependent variable, Model (2) is constructed to examine the effect of mobile Internet use on residents’ exercise frequency: （2） Finally, Model (3) incorporates both mobile Internet use and the frequency of physical exercise to investigate whether exercise frequency serves as a mediating factor in the relationship between mobile Internet use and residents’ mental health: （3） Among these variables, Cesd it denotes the depression scale score of individual i in year t ; M_web it indicates whether individual i used mobile Internet in year t; E_freq it represents the exercise frequency of individual i in year t ; and X it is a set of control variables, including gender, age, years of education, marital status, household registration type, employment status, household size, household income, and health status. δ i denotes the region fixed effects, θ t denotes the time fixed effects, and ε it is the random error term. Discussion of Endogeneity Considering the possibility of a directional causal relationship between mobile Internet use and depressive symptoms—namely, residents with lower levels of depression may be more inclined to use mobile Internet for social interaction and entertainment, or there may be other unobserved variables that simultaneously affect both factors and thus lead to endogeneity bias in the baseline regression results—this study employs the proportion of other villagers’ (or community residents’) Internet access as an instrumental variable, which reflects the overall level of Internet penetration within the community. The rationale for selecting this variable as an instrument is based on two considerations: first, regarding relevance, the Internet access status of other residents in the same community can influence an individual’s mobile Internet use through social network effects, information diffusion, and similar mechanisms, thereby meeting the requirement that the instrumental variable must be correlated with the core explanatory variable; second, concerning exogeneity, an individual’s depressive symptoms constitute a micro-level psychological state that does not exert a reverse influence on the community-level Internet penetration rate, and this variable is unlikely to be affected by other unobserved individual characteristics, thus satisfying the assumption that the instrumental variable is exogenous to the error term. This approach effectively alleviates endogeneity concerns, enhances the reliability of the estimation results, and provides a more robust basis for interpreting the impact of mobile Internet use on depressive symptoms. Results The Impact of Mobile Internet Use Table 3 reports the baseline regression results of the model. Regardless of whether control variables are included, the estimated coefficients of the core explanatory variable—mobile Internet use—are significantly negative at the 1 percent level, indicating that the use of mobile Internet can significantly reduce residents’ depressive symptoms, and the direction of the results remains unchanged when the dependent variable is replaced (cesd20sc or cesd8 depression scores). Without control variables (Columns 1 and 3), the regression coefficient of mobile Internet use is − 1.389 for cesd20sc and − 0.694 for cesd8. After including control variables (Columns 2 and 4), the absolute values of the coefficients decrease, with coefficients of − 0.654 for cesd20sc and − 0.330 for cesd8, both still significant at the 1 percent level, indicating that even after controlling for individual and household characteristics, the inhibitory effect of Internet use on depressive symptoms remains robust. The regression results of the control variables are generally consistent with theoretical expectations. Male residents exhibit significantly lower levels of depression than females, which may be related to gender differences in emotional expression and social role allocation. Age is significantly negatively associated with depressive symptoms, meaning that depression tends to decrease slightly as individuals grow older, possibly because accumulated life experience facilitates psychological adjustment. Years of education and household income (log) both show significant negative effects on depression; improvements in educational attainment and increases in income may alleviate depressive symptoms by enhancing cognitive capacity and reducing financial stress. Better self-rated health and being married are associated with significantly lower levels of depression, as good physical condition and stable marital relationships provide psychological support and help mitigate depressive feelings. Residents with full-time employment exhibit significantly higher depression levels than those without full-time jobs, which may be related to work pressure and workplace competition. Urban–rural differences and household size are not key factors influencing residents’ depressive symptoms. Table 3 Benchmark Regression Results (1) (2) (3) (4) Variables cesd20sc score cesd20sc score cesd8 score cesd8 score Mobile Internet Use -1.389 *** (0.174) -0.654 *** (0.181) -0.694 *** (0.087) -0.330 *** (0.091) Gender -1.009 *** (0.145) -0.509 *** (0.073) Age -0.067 *** (0.007) -0.034 *** (0.004) Years of Education -0.232 *** (0.020) -0.116 *** (0.010) Health Status -2.088 *** (0.063) -1.052 *** (0.032) Marital Status -2.571 *** (0.225) -1.291 *** (0.113) Employment Status 0.522 ** (0.184) 0.269 ** (0.092) Registered residence type 0.140 (0.190) 0.076 (0.096) Household Size -0.027 (0.043) -0.014 (0.022) Household Income (log) -0.391*** (0.071) -0.197*** (0.036) Constant 34.620*** (0.153) 52.391*** (0.904) 14.272*** (0.077) 23.228*** (0.454) Number of observations 16 142 16 142 16 142 16 142 Rsquared 0.090 0.207 0.090 0.207 Adj-Rsquared 0.041 0.164 0.042 0.164 Year FE YES YES YES YES County FE YES YES YES YES Notes : *, **, *** denote significance at the 0.1, 0.05, and 0.01 level, respectively. IV Regression Results Table 4 reports the first-stage and second-stage regression results of the instrumental variable estimation for the effect of Internet use on the cesd20sc score. The first-stage regressions (1a) and (2a) show that the instrumental variable—“the Internet access rate of other villagers (community residents)”—has a significant impact on the core explanatory variable, indicating a strong correlation between the instrumental variable and Internet use. The Kleibergen–Paap rk Wald F statistics are 194.961 and 169.892, both far exceeding the critical value of 16.38 under a 10 percent maximal relative bias, and the p-values of the Kleibergen–Paap rk LM test are both 0.0000, suggesting that the instrumental variable satisfies the relevance and identification assumptions, with no problems of under-identification or weak instruments; thus, the chosen instrument is valid. The second-stage regressions (1b) and (2b) reveal the effect of Internet use on the cesd20sc score and show that Internet use significantly reduces the cesd20sc score. Whether control variables are included or not, the estimated coefficients of the core explanatory variable—mobile Internet use—remain significantly negative, indicating that after addressing endogeneity and controlling for individual and household characteristics, the depressive-reducing effect of Internet use remains robust. Table 4 Instrumental Variable Regression Results (1a) Mobile Internet Use (1b) cesd20sc score (2a) Mobile Internet Use (2b) cesd20sc score Other villagers’ (community residents’) Internet access -4.268*** (0.306) -3.098*** (0.238) Mobile Internet use -1.783*** (0.357) -1.349** (0.479) Gender -0.001 (0.006) -1.011*** (0.145) Age -0.012*** (0.000) -0.077*** (0.009) Years of education 0.020*** (0.001) -0.217*** (0.023) Health status -0.004 (0.003) -2.091*** (0.063) Marital status 0.062*** (0.009) -2.526*** (0.227) Employment status -0.031*** (0.008) 0.505** (0.184) Registered residence type -0.046*** (0.008) 0.106 (0.191) Household size -0.006** (0.002) -0.032 (0.043) Household income (log) 0.037*** (0.004) -0.363*** (0.073) Number of observations 16142 16142 16142 16142 Rsquared 0.254 0.005 0.437 0.133 Adj-Rsquared 0.214 -0.048 0.407 0.086 Year FE YES YES YES YES County FE YES YES YES YES Kleibergen-Paap rk Wald F 194.961[16.38] 169.892[16.38] Kleibergen-Paap rk LM P = 0.0000 P = 0.0000 Notes : *, **, *** denote significance at the 0.1, 0.05, and 0.01 level, respectively. Heterogeneity Analysis To examine group heterogeneity in the effect of Internet use on depressive symptoms, this study conducts a heterogeneity analysis from three dimensions—BMI status, employment type, and pension coverage—and the regression results are presented in Table 5 . Among these groups, mobile Internet use has a significant depressive-reducing effect on residents with abnormal BMI (including overweight, obesity, or underweight). On the one hand, digital technology provides them with more convenient channels for social interaction, helping to break social isolation and rebuild emotional connections, thereby alleviating the sense of social exclusion that may arise from body-shape concerns. On the other hand, the Internet enables access to extensive professional information about body management and exercise, which lowers the barriers to action and provides clearer guidance for behavioral adjustments. Such clarity can substantially reduce depressive emotions stemming from self-doubt, thus exerting a significant negative impact on depression levels. Second, the depressive-reducing effect of mobile Internet use is stronger among non-agricultural workers. This may stem from the high compatibility between Internet functions and the “high-pressure, fast-paced” nature of their work: the Internet’s information and entertainment features can satisfy fragmented information and entertainment needs, alleviate work-related irritability, anxiety, and other negative emotions in a timely manner, and expand channels for exercise, social interaction, and emotional expression, all of which play a positive role in improving emotional states and reducing depressive risk. In contrast, although agricultural work is physically demanding, its rhythm is often synchronized with natural cycles and tends to be more regular and autonomous. Moreover, farmers’ social support networks may rely more on real-life interactions with neighbors, relatives, and local communities rather than virtual online networks, resulting in a nonsignificant effect on their depression levels. Notably, the depressive-alleviating effect of Internet use is more pronounced among residents without pension coverage. Compared with individuals who possess pension security, this group often faces greater uncertainty and emotional stress when contemplating their future livelihood. In this context, Internet platforms play an important role in psychological adjustment. On the one hand, through government service platforms, short videos, public accounts, and similar channels, the Internet lowers the informational threshold for understanding pension-related policies, enabling individuals without pension coverage to obtain clearer and timelier policy information, thereby easing confusion and anxiety caused by information asymmetry. On the other hand, the Internet offers online social spaces and virtual support networks that help individuals rebuild a sense of social connectedness through emotional support and experience sharing, partially compensating for the lack of offline social support systems. These mechanisms jointly form a psychological buffering channel for groups without pension coverage, thereby strengthening the regulatory effect of Internet use on their depressive symptoms. In contrast, when individuals’ basic pension needs are already secured by institutional arrangements, the marginal alleviating effect of Internet use becomes relatively weaker. Table 5 Heterogeneity Analysis (1) (2) (3) (4) (5) (6) Variables cesd20sc score bmigood Bmibad Agricultural Work Non-agricultural Work With Pension Without Pension Mobile Internet use -0.440 (0.256) -0.867*** (0.256) 0.099 (0.285) -1.136*** (0.270) -0.397 (0.244) -0.848** (0.284) Gender -0.972*** (0.202) -0.980*** (0.214) -0.573** (0.184) -1.734*** (0.270) -1.100*** (0.184) -0.907*** (0.237) Age -0.065*** (0.010) -0.069*** (0.010) -0.080*** (0.010) -0.051*** (0.014) -0.041*** (0.011) -0.078*** (0.010) Years of education -0.251*** (0.029) -0.210*** (0.029) -0.248*** (0.028) -0.193*** (0.036) -0.201*** (0.027) -0.261*** (0.032) Health status -2.165*** (0.090) -2.018*** (0.091) -2.047*** (0.088) -2.008*** (0.103) -1.995*** (0.081) -2.167*** (0.101) Marital status -2.164*** (0.292) -2.996*** (0.353) -2.263*** (0.282) -3.806*** (0.479) -2.942*** (0.302) -2.302*** (0.342) Employment status 0.365 (0.255) 0.604* (0.268) 0.008 (0.273) 0.285 (0.460) 0.009 (0.296) 0.742** (0.259) Registered residence type 0.243 (0.264) 0.214 (0.276) -0.059 (0.226) 0.229 (0.636) 0.233 (0.238) 0.019 (0.317) Household size -0.102 (0.059) 0.048 (0.064) -0.011 (0.060) 0.013 (0.072) -0.012 (0.059) -0.039 (0.063) Household income (log) -0.308** (0.096) -0.464*** (0.110) -0.436*** (0.108) -0.412*** (0.121) -0.342*** (0.098) -0.417*** (0.105) Constant 51.863*** (1.259) 52.702*** (1.355) 52.787*** (1.333) 53.321*** (1.754) 50.643*** (1.227) 53.452*** (1.355) Number of observations 8 233 7 600 9 239 5 238 9 500 6 351 Squared 0.217 0.231 0.208 0.229 0.217 0.240 Adj-Squared 0.151 0.170 0.136 0.185 0.156 0.174 Year FE YES YES YES YES YES YES County FE YES YES YES YES YES YES Notes : *, **, *** denote significance at the 0.1, 0.05, and 0.01 level, respectively. Mediation Effect To reveal the mechanism through which Internet use affects residents’ depressive symptoms, this study selects exercise frequency as the mediating variable and employs a stepwise regression approach to test the mediation effect. The regression results are presented in Table 6 . First, the analysis above has verified the significant negative effect of the core explanatory variable—mobile Internet use—on the dependent variable, depressive symptom scores, indicating that Internet use significantly reduces depression levels and thus provides the basis for testing the mediation effect. Second, Columns 1 and 3 of Table 6 present the effect of the core explanatory variable on the mediating variable. Whether control variables are included or not, mobile Internet use shows a significantly positive effect on exercise frequency, indicating that Internet use significantly encourages residents to increase physical activity. Finally, Columns 2 and 4 of Table 6 show the effects of simultaneously including the core explanatory variable and the mediating variable on the dependent variable. With and without control variables, mobile Internet use continues to significantly reduce depression levels; however, the total coefficient (− 0.569) is smaller in magnitude than the coefficient obtained when the mediating variable is not included (− 0.654), indicating that exercise frequency serves as a partial mediator in the relationship between Internet use and residents’ depressive symptoms. The above results indicate that mobile Internet use not only has a direct alleviating effect on residents’ depressive symptoms but also indirectly reduces depression by increasing the frequency of physical exercise. Specifically, mobile Internet use provides residents with multiple supportive conditions for engaging in physical activity, such as access to information and opportunities for social interaction, which facilitate the initiation and maintenance of exercise behaviors. In turn, the increase in exercise frequency transmits the influence of Internet use on depressive symptoms, forming an indirect effect pathway in which exercise frequency serves as a mediating mechanism. Table 6 Mediation Regression Results (1) (2) (3) (4) Variables Exercise frequency cesd20sc score Exercise frequency cesd20sc score Mobile Internet use 0.369 *** (0.046) -1.276 *** (0.174) 0.449 *** (0.051) -0.569 ** (0.181) Exercise frequency -0.306 *** (0.031) -0.189 *** (0.030) Gender 0.098 * (0.040) -0.991 *** (0.144) Age 0.040 *** (0.002) -0.060 *** (0.007) Years of education 0.096 *** (0.006) -0.214 *** (0.021) Health status 0.070 *** (0.017) -2.075 *** (0.063) Marital status -0.190 ** (0.058) -2.607 *** (0.225) Employment status -0.716 *** (0.056) 0.387 * (0.185) Registered residence type -0.416 *** (0.059) 0.061 (0.190) Household size -0.055 *** (0.012) -0.037 (0.043) Household income (log) 0.150 *** (0.019) -0.363 *** (0.071) Constant 1.371 *** (0.038) 35.039 *** (0.159) -2.089 *** (0.243) 51.995 *** (0.907) Number of observations 16 142 16 142 16 142 16 142 Rsquared 0.127 0.096 0.196 0.209 Adj-Rsquared 0.081 0.048 0.153 0.166 Year FE YES YES YES YES County FE YES YES YES YES Notes : *, **, *** denote significance at the 0.1, 0.05, and 0.01 level, respectively. Robustness Test To verify the reliability of the baseline regression results, this study conducts a robustness check by replacing the explanatory variable (Table 7 ). The original indicator of “mobile Internet use” is replaced with the indicator of “Internet use,” and the regression results show that the core conclusions remain valid. In Column (1), the regression coefficient of the substituted Internet use variable on the depression score cesd20sc is − 0.691 and significantly negative at the 1 percent level, indicating a clear depressive-reducing effect of Internet use. Column (2) shows that the regression coefficient of the substituted Internet use variable on the mediating variable (exercise frequency) is 0.460 and significantly positive at the 1 percent level, confirming the robustness of the Internet’s promoting effect on exercise frequency. Column (3), which simultaneously includes the substituted Internet use variable and exercise frequency, shows that the regression coefficient of Internet use on cesd20sc is − 0.604 (significant at the 1 percent level), with an absolute value smaller than − 0.691 in Column (1), and the regression coefficient of exercise frequency is − 0.189 (significant at the 1 percent level). These findings again verify the partial mediating effect of exercise frequency. The directions and significance levels of the control variables remain unchanged, providing further evidence that the core conclusions of this study are robust. Table 7 Robustness Check Results (Alternative Mediator Variable) (1) (2) (3) Variables cesd20sc score Exercise frequency cesd20sc score Mobile Internet use -0.691*** (0.182) 0.460*** (0.051) -0.604*** (0.183) Exercise frequency -0.189*** (0.030) Gender -1.009*** (0.145) 0.097* (0.040) -0.991*** (0.144) Age -0.068*** (0.007) 0.040*** (0.002) -0.060*** (0.007) Years of education -0.231*** (0.020) 0.095*** (0.006) -0.213*** (0.021) Health status -2.088*** (0.063) 0.070*** (0.017) -2.075*** (0.063) Marital status -2.569*** (0.225) -0.191** (0.058) -2.605*** (0.225) Employment status 0.521** (0.184) -0.716*** (0.056) 0.386* (0.185) Registered residence type 0.137 (0.190) -0.415*** (0.059) 0.059 (0.190) Household size -0.027 (0.043) -0.055*** (0.012) -0.038 (0.043) Household income (log) -0.389*** (0.071) 0.149*** (0.019) -0.361*** (0.071) Constant 52.415*** (0.904) -2.095*** (0.243) 52.019*** (0.907) Number of observations 16 142 16 142 16 142 Rsquared 0.207 0.196 0.209 Adj-Rsquared 0.164 0.153 0.166 Year FE YES YES YES County FE YES YES YES Notes : *, **, *** denote significance at the 0.1, 0.05, and 0.01 level, respectively. Table 8 presents the robustness test results obtained by conducting 1,000 Bootstrap replications to further verify the validity of the mediating mechanism of exercise frequency. The results show that the confidence intervals of the indirect effect—through which Internet use influences residents’ depressive symptoms by increasing exercise frequency—do not include zero, indicating a significant mediation effect. This finding corroborates the preceding analysis and confirms that the conclusion regarding the partial mediating role of exercise frequency in the relationship between Internet use and residents’ depressive symptoms is robust. Table 8 Bootstrap Estimation Results of Indirect Effects Statistic Estimate z-value P>|z| 95% Confidence Interval Direct Effect -0.391 -2.31 0.021 [-0.723, -0.059] Indirect Effec -0.092 -5.75 0.000 [-0.124, -0.061] Based on the assumption that the relationship between the treatment effect and unobservable variables can be inferred from its relationship with observable variables, Oster ( 2019 ) argues that the absolute value of the estimated coefficient of the core explanatory variable typically decreases after adding more control variables, implying that even if unobservable omitted variables exist, their influence on the estimation results is likely limited. Although this study has addressed endogeneity concerns and incorporated bidirectional fixed effects, it cannot completely eliminate the endogeneity arising from unobservable omitted variables. Therefore, to further mitigate the potential influence of such factors, this study applies the coefficient stability test proposed by Oster ( 2019 ), and the test results are presented in Table 9 . The specific procedures are as follows: first, R max is set to 1.3 times the baseline regression R² (0.269), and δ is set to 1, after which the feasible range of the adjusted coefficient β ∗ for the core explanatory variable is estimated; second, with R max set to 1.3 times the baseline regression R² (0.269), β ∗ is set to 0, and the value of δ is calculated accordingly. The first row of Table 9 reports the results of the first procedure, where the range of β ∗ does not include zero, indicating that the test is passed. The second row corresponds to the second procedure, and if the calculated δ exceeds 1, the test is considered passed. The results in Table 9 show that both procedures successfully pass the test, thereby confirming the robustness of the main conclusions of this study—namely, that omitted variables do not affect the validity of the empirical results. Table 9 Coefficient Stability Test Results Method Parameter Setting Judgment Criterion Actual Estimation Result Passed or No (1) R max =0.269, δ = 1 The interval does not contain 0 [-1.603, -0.691] Yes (2) R max =0.269, β ∗ = 0 The value of δ is greater than 1 1.460 Yes Conclusion and Discussion Against the backdrop of growing concern for mental health and the accelerating process of digitalization, understanding how Internet use affects residents’ depressive symptoms and through which channels this influence occurs has become increasingly urgent. Using microdata from the China Family Panel Studies (CFPS) for 2020–2022, this study examines the impact of mobile Internet use on residents’ depressive tendencies, explores the mediating role of exercise frequency, and further investigates heterogeneous effects across different groups. The main conclusions are as follows: First, mobile Internet use has a significant inhibitory effect on residents’ depressive symptoms. The baseline regression results show that, regardless of whether control variables are included, mobile Internet use significantly reduces residents’ cesd20sc and cesd8 depression scores, and this negative effect remains robust after addressing endogeneity concerns and controlling for individual and household characteristics. This indicates that in the digital era, mobile Internet has become an important instrument for alleviating depressive emotions, offering new avenues for psychological adjustment through functions such as information acquisition and social interaction. Second, exercise frequency plays a partial mediating role in the relationship between mobile Internet use and residents’ depressive symptoms. The mediation analysis shows that mobile Internet use not only directly reduces depression levels but also indirectly alleviates depressive emotions by increasing the frequency of physical exercise. Specifically, mobile Internet use significantly promotes residents’ exercise frequency, and the subsequent increase in exercise frequency further reduces depression scores, forming a transmission pathway in which exercise frequency serves as a mediating mechanism. This finding confirms the essential mediating role of physical exercise within digital health interventions. Third, the impact of mobile Internet use on depressive symptoms exhibits significant heterogeneity across different groups. In terms of physiological characteristics, the depressive-alleviating effect of mobile Internet use is more pronounced among residents with abnormal BMI. From the perspective of occupational attributes, its depressive‐reducing effect is stronger for non-agricultural workers. Regarding social security status, the improvement in depressive symptoms is more substantial among individuals without pension coverage. These findings indicate that the psychological health effects of mobile Internet use are moderated by individual characteristics and life contexts, and that different groups exhibit varying psychological needs and differential responses to Internet use. Policy Implications Based on the above research findings, this study proposes the following policy recommendations: First, improve mobile Internet infrastructure and the public service system to fully leverage the role of digital technologies in promoting mental health. On the one hand, government agencies and Internet enterprises should jointly develop integrated platforms that combine psychological assessment, emotional counseling, and health information services in order to enhance the accessibility and inclusiveness of public mental health services. On the other hand, investment in network infrastructure in rural and remote areas should be increased, accompanied by digital skills training to narrow the “digital divide,” ensuring that residents in different regions can obtain equal access to high-quality psychological support resources. At the same time, a content review and professional evaluation mechanism should be established to standardize online mental health service protocols and ensure the scientific accuracy and safety of related information. Second, drawing on the mediating role of physical exercise, an integrated online–offline health promotion model should be developed. It is advisable to encourage the creation of specialized fitness apps that provide personalized exercise plans based on users’ physical characteristics and enhance exercise adherence through functions such as activity tracking, peer supervision, and goal incentives. Communities and online platforms should be coordinated to organize offline activities such as fitness challenges and charity runs to strengthen residents’ sense of participation in physical activity. Internet-based exercise guidance should be incorporated into community health services, with trained professionals providing scientific fitness counseling for residents. These efforts can help establish a virtuous cycle of “online guidance plus offline practice” and fully unlock the depressive-relieving benefits of physical exercise. Third, differentiated mental health intervention strategies should be implemented for distinct population groups. Tailored support measures should be designed in accordance with each group’s Internet use patterns and psychological needs. For individuals with abnormal BMI, online health management programs should be established that integrate exercise guidance, nutritional interventions, and psychological counseling. For non-agricultural workers, modular stress-management tools suitable for fragmented time use should be developed, utilizing short videos and virtual communities to provide psychological relief and emotional support. For residents without pension coverage, digital service packages that combine emotional assessment, exercise guidance, and policy interpretation should be offered to enhance their sense of psychological security and social connectedness. Research Limitations This study still has several limitations that warrant further refinement in future research. The measurement of Internet use and mobile Internet use in this paper focuses solely on the dimension of “whether individuals use the Internet,” without incorporating usage duration, and future studies may conduct more nuanced analyses as data availability improves. As digital technologies continue to develop, the role of the Internet in promoting mental health will become increasingly prominent. Despite the limitations noted above, this study provides important insights into the factors influencing residents’ mental health and the pathways of intervention in the digital era. Declarations Data availability The datasets generated during and/or analyzed during the current study are available from the corresponding author upon reasonable request. Ethics declarations Competing interests The authors declare no competing interests. Ethical approval This article does not contain any studies with human participants performed by any of the authors. Informed consent This article does not contain any studies with human participants performed by any of the authors. Acknowledgements There was no dedicated funding to conduct this study. I acknowledge all the participants for giving out information that enriched the manuscript. Author Contribution YS: writing—original draft, conceptualization, methodology, data resources. ZH: writing—original draft, supervision, methodology, modeling, programming, data resources. All authors reviewed the manuscript. 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Int J Environ Res Public Health 17(19):6954 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-8146436","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":558506657,"identity":"4bbbc380-d657-4419-8aa2-e600a301abe3","order_by":0,"name":"yue sun","email":"","orcid":"","institution":"Yeungnam University","correspondingAuthor":false,"prefix":"","firstName":"yue","middleName":"","lastName":"sun","suffix":""},{"id":558506668,"identity":"63ce248b-b9dd-4ab1-86eb-483755267a4f","order_by":1,"name":"zhenghao hou","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5klEQVRIiWNgGAWjYBACAyBmhjIYH4O5DAnEa2E2ZjAwIE0LmzSET0CLOXuP8eeCijt229kPH6suKPjDwM+eY8DwcwduLZY9ZwyMZ5x5lryzJy3t9gygwyR73hgw9p7B47AbOQbJvG2Hkw0O5Jjd5gFqAYkwM7bh13KY9x9Qy/k3ZsUgLfZEaDFs5m04bAdkmDGDbZEgpOXMsWJmnmOHEwxuPEuWnmFgzCNx5lnBwV58Wo43b/7MU3PY3uB88sHPBX/k5Pjbkzc++IlHCwwkNkAZPCDiAGENDAz2xCgaBaNgFIyCEQoA8nBPvOLe8e4AAAAASUVORK5CYII=","orcid":"","institution":"Beijing Institute of Technology","correspondingAuthor":true,"prefix":"","firstName":"zhenghao","middleName":"","lastName":"hou","suffix":""}],"badges":[],"createdAt":"2025-11-18 14:08:41","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8146436/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8146436/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":98250080,"identity":"c269bf08-9786-48ee-86fb-cf6101cb3578","added_by":"auto","created_at":"2025-12-15 16:46:59","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":62558,"visible":true,"origin":"","legend":"","description":"","filename":"MOBILEINTERNETPHYSICALEXERCISEANDDEPRESSIONLEVELSMEDIATINGMECHANISMANDEMPIRICALEXAMINATION.docx","url":"https://assets-eu.researchsquare.com/files/rs-8146436/v1/e22c186ea01ac1320f18ed9d.docx"},{"id":98250079,"identity":"652cb530-8d8c-4532-9f1a-665ef640b777","added_by":"auto","created_at":"2025-12-15 16:46:58","extension":"json","order_by":1,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":4199,"visible":true,"origin":"","legend":"","description":"","filename":"077e5bc4908c455cb500e44a3fa5784b.json","url":"https://assets-eu.researchsquare.com/files/rs-8146436/v1/973d81b885737ebc226e7883.json"},{"id":98250081,"identity":"af2dd953-7065-4388-a335-80a83cff7623","added_by":"auto","created_at":"2025-12-15 16:46:59","extension":"xml","order_by":2,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":171947,"visible":true,"origin":"","legend":"","description":"","filename":"077e5bc4908c455cb500e44a3fa5784b1enriched.xml","url":"https://assets-eu.researchsquare.com/files/rs-8146436/v1/513abad7d2338981f89f2789.xml"},{"id":98433423,"identity":"f796c8b8-39f2-48e8-b542-3f54b557cc50","added_by":"auto","created_at":"2025-12-17 16:50:44","extension":"xml","order_by":3,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":170336,"visible":true,"origin":"","legend":"","description":"","filename":"077e5bc4908c455cb500e44a3fa5784b1structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-8146436/v1/68f0aa517c0a7510e96dbf41.xml"},{"id":98250082,"identity":"a9e3098f-359b-4b3c-9fe8-713c987dd99a","added_by":"auto","created_at":"2025-12-15 16:46:59","extension":"html","order_by":4,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":173961,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8146436/v1/0d0be33ecf39535bc384fd3a.html"},{"id":101569977,"identity":"c1a6ad91-2792-4f3b-81c4-79d636752f21","added_by":"auto","created_at":"2026-01-31 15:10:42","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1314677,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8146436/v1/51ef9615-dd6b-4025-b7d4-af5dbbf8bcc4.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Mobile Internet, Physical Exercise and Depression Levels: Mediating Mechanism and Empirical Examination","fulltext":[{"header":"Introduction","content":"\u003cp\u003eWith accelerated social rhythms and mounting psychological pressures, depression has become a major public health challenge. According to the World Health Organization (2025), depression and other mental disorders are leading causes of global disability, contributing to sustained health losses and significant socioeconomic burdens while exhibiting increasing prevalence and younger onset trends(Vos et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Whiteford et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). In China, rapid social transformation and intensified competition have led to widespread depressive emotions across all age groups, characterized by persistence and concealment (Tian et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), which severely affect work efficiency, quality of life, and social functioning (Woo et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). The Healthy China 2030 Planning Outline (State Council of the People\u0026rsquo;s Republic of China, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) explicitly calls for strengthening and standardizing mental health service systems. Therefore, identifying effective approaches to alleviate depression and enhance mental health literacy is of critical importance.\u003c/p\u003e\u003cp\u003eAgainst the backdrop of rapid digital development, the widespread application of mobile internet technologies such as the internet and artificial intelligence has become a key force reshaping individuals\u0026rsquo; social trust, behavioral patterns and psychological well-being(McDool et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Liu et al., 2025). These technologies not only redefine everyday social interactions and break down barriers to information access(Nakagomi et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), but also integrate deeply into residents\u0026rsquo; health management through personalized recommendations and intelligent health monitoring(Audi et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) thereby supporting the formation of healthy behaviors(Korp, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2006\u003c/span\u003e) and creating new opportunities for improving mental health. However, significant differences exist in residents\u0026rsquo; preferences for and dependence on online information, leading to heterogeneous effects of internet use on psychological health and leaving its specific influence pathways insufficiently understood. On the one hand, the internet may relieve depressive symptoms by fostering social connections and empowering health behavior(Chen and Gao, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2023\u003c/span\u003e); on the other hand, it may exacerbate psychological stress due to information overload, overreliance, or the deepening of the digital divide. Consequently, examining the relationship between mobile internet use and depressive tendencies, as well as clarifying the mediating transmission mechanism of physical exercise, is of critical importance.\u003c/p\u003e\u003cp\u003ePhysical exercise is widely recognized as an effective means of alleviating depression. Physiologically, exercise stimulates the release of endorphins and dopamine, which counteract anxiety and sadness while enhancing pleasure responses (Harber and Sutton, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e1984\u003c/span\u003e; Gorrell et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Psychologically, exercise diverts attention from negative thoughts and provides emotional relief. Furthermore, achieving exercise goals fosters a sense of accomplishment, which is essential in combating depressive emotions (Anderson and Shivakumar, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Petruzzello et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e1991\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eOverall, existing research has laid a theoretical foundation for understanding how mobile internet use may improve psychological health, but it has primarily focused on the general relationships among internet use, physical exercise, and mental well-being. Systematic investigation is still lacking regarding the specific mechanisms through which mobile internet use promotes exercise behavior and subsequently enhances residents\u0026rsquo; psychological health. Although mobile internet use has shown positive effects in promoting health behaviors, the theoretical and empirical evidence remains limited, and insufficient attention has been given to its deeper psychological impacts. As mobile internet becomes fully integrated into daily life and digitalization becomes the social norm, it is necessary to examine the pathways through which mobile internet use affects depression levels from the perspective of behavioral mediation. This approach helps reveal its potential psychological regulatory functions and its role in health promotion within contemporary social contexts.\u003c/p\u003e\u003cp\u003eIn summary, this study draws on data from the China Family Panel Studies (CFPS) and constructs a two-way fixed-effects model and a mediation model to systematically analyze the impact of mobile internet use on residents\u0026rsquo; depression levels and to test the mediating role of physical exercise behavior. At the same time, it further examines, from the perspective of individual heterogeneity, the differential effects of internet use and physical exercise across various population groups, with the aim of revealing the internal mechanism through which mobile internet improves mental health by fostering positive health behaviors, thereby providing a theoretical basis and policy implications for mental health promotion in the digital era. This study makes two main contributions: First, it extends existing research on the relationship between internet use and mental health from the perspective of behavioral mechanisms. Prior literature has predominantly focused on the direct effects of internet use on mental health, while paying relatively little attention to its behavioral transmission pathways. Through empirical analysis, this study finds that mobile internet use not only directly reduces depression levels but also exerts a significant indirect effect by increasing the frequency of physical exercise, thus revealing a behavioral mediation mechanism through which the internet influences mental health. Second, it deepens the understanding of the psychological effects of internet use from the perspective of group heterogeneity. Based on subgroup analyses along three dimensions\u0026mdash;BMI status, employment type, and pension security\u0026mdash;this study shows that the mental health effects of internet use differ substantially across groups, thereby providing empirical support for more targeted mental health interventions.\u003c/p\u003e"},{"header":"Literature review","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eThe Worsening Trend of Depression\u003c/h2\u003e\u003cp\u003eDepressive disorder (also referred to as depression) is a common mental disorder characterized by persistent low mood, loss of pleasure, or diminished interest in daily activities(World Health Organization, 2025). According to the World Health Organization (2025), approximately one in seven people worldwide suffers from some form of mental disorder, and depressive disorder is among the leading contributors to the global burden of disease, exerting extensive impacts on individual health, social relationships, and economic development. Against the backdrop of social transformation and intensified competition, mental health problems among residents exhibit spillover and diffusion characteristics, with depressive emotions increasingly spreading across diverse groups and social contexts. Existing research indicates that adolescents face academic pressure and peer comparison, working adults experience performance pressure and role conflict, and older adults are prone to emotional distress due to difficulties adapting to retirement and declining physical functions. Symptoms of depression have shown an upward trend across these populations(Qu et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Zhou et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Liu et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Accordingly, analyses by Tian et al. (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) based on the Global Burden of Disease Study 2021 indicate that the number of depressive disorder cases in China increased from approximately 34.4\u0026nbsp;million in 1990 to 53.1\u0026nbsp;million in 2021, with the associated disease burden continuing to rise. Moreover, psychological health has become a critical dimension influencing Chinese residents\u0026rsquo; subjective well-being, second only to physical health.\u003c/p\u003e\u003cp\u003eAgainst this backdrop, depression has increasingly become a central issue in social governance and public policy. The Healthy China 2030 Planning Outline(Central Committee of the Communist Party of China, 2016) explicitly emphasizes the need to promote mental health and implement comprehensive interventions, highlighting the importance of systematic measures that address social support, lifestyle factors, and behavioral interventions. Consequently, exploring feasible behavioral pathways\u0026mdash;particularly those that alleviate depression by fostering positive and healthy behaviors\u0026mdash;has become a shared focus of both academic research and policy development.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eThe Impact of Mobile Internet Use\u003c/h3\u003e\n\u003cp\u003eThere remains considerable disagreement within the academic community regarding the direction of the effect of mobile internet use on depressive tendencies. One body of research highlights its positive value, arguing that the internet can buffer negative emotions through informational and emotional support, thereby reducing the risk of depression(Griffiths et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Another line of inquiry contends that problematic or excessive internet use is closely associated with elevated depression levels, and that this relationship is significantly moderated by usage patterns and individual characteristics(Cai et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Bessi\u0026egrave;re et al. (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) found that levels of social support shape the psychological effects of internet use: for individuals with high social support, online behavior is negatively associated with depression, whereas no such pattern is observed among those with low support. Cotten (2012) pointed out that the Internet can effectively alleviate depressive symptoms among retired older adults; however, Moreno et al. (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) observed that problematic use significantly increases depression levels among female college students. Existing studies also emphasize the stratifying effects of age and urban\u0026ndash;rural contexts on the psychological outcomes of internet use. Wang and Shi (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) found that Internet use, by facilitating access to family support, significantly reduces depressive symptoms among older adults; Sun (2022) further noted that this effect is more pronounced in rural populations; and Zhang (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) argued that internet use can attenuate the negative impact of occupational status on depression among urban residents. Moreover, usage motivation constitutes another key mechanism affecting outcomes. Internet use aimed at interacting with family and friends has been shown to alleviate depressive symptoms, whereas search behaviors driven by health-related anxiety may amplify negative emotions(Zhang et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Mathes et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Adolescents and individuals with internet addiction are also more likely to enter a negative cycle in which depressive symptoms worsen during use(Morrison and Gore, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2010\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eOverall, existing research has examined the complex effects of internet use on depressive tendencies from multiple perspectives, including social support, identity heterogeneity, urban\u0026ndash;rural differences, and usage motivations, yet the findings remain inconsistent. Although many studies acknowledge the potential psychological regulatory function of the internet, they do not provide a systematic explanation of the underlying mechanisms, nor do they offer structural analyses of how its effects vary across different population groups. Furthermore, research exploring how internet use interacts with individuals\u0026rsquo; real-life behaviors\u0026mdash;such as social participation and physical activity\u0026mdash;to influence mental health remains limited. These gaps indicate that there is still considerable room for advancing mechanism development and variable integration, thereby offering important avenues for further investigation into the dynamic pathways linking internet use, behavioral patterns, and psychological well-being.\u003c/p\u003e\n\u003ch3\u003eThe Role of Physical Exercise\u003c/h3\u003e\n\u003cp\u003ePhysical exercise, as a behavioral approach that integrates both physiological regulation and psychological adjustment, has been widely demonstrated to exert a positive effect on alleviating depressive symptoms(Liu et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Physiologically, exercise promotes the release of endorphins and other biochemical substances, enhancing emotional stability and one\u0026rsquo;s sense of bodily control(Harber and Sutton, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e1984\u003c/span\u003e; Gorrell et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Psychologically, it serves as an effective alternative behavior for coping with stress, and its collective nature can strengthen social connectedness and feelings of belonging, thereby indirectly improving mental well-being(White et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Theoretically, behavioral activation theory posits that physical exercise can interrupt the maladaptive cycle of \u0026ldquo;behavioral inhibition\u0026ndash;negative emotion\u0026ndash;social withdrawal\u0026rdquo;(Ekers et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), while self-determination theory emphasizes that exercise satisfies the basic psychological needs for autonomy, competence, and relatedness, producing deeper benefits for mental health(Ng et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Empirical studies further validate the mediating psychological effects of physical activity. Recent research has shown that Internet use significantly increases the likelihood and frequency of physical activity among older adults, and also enhances their exercise adherence, thereby contributing to a reduction in depressive symptoms (Guo et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Among adolescents, the interactive functions and peer-encouragement mechanisms embedded in social media platforms have been found to strengthen the continuity of physical exercise, supporting improvements in emotional regulation and psychological resilience (Goodyear et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). In addition, empirical evidence indicates that digital health tools such as exercise applications can improve exercise compliance through real-time feedback and online community support, which in turn helps reduce anxiety and depressive symptoms (Romeo et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Overall, with the support of digital media environments, the mental-health benefits of physical activity have become increasingly prominent.In summary, existing research has provided a relatively systematic understanding of the mediating potential of physical exercise in the relationship between internet use and depressive tendencies, yet the conditions under which this pathway operates and the boundaries of its mechanisms remain insufficiently clarified. On the one hand, although theoretical models suggest that exercise alleviates depression by activating positive behaviors and satisfying psychological needs, empirical studies have not adequately examined its applicability across different patterns of internet use or among different population groups. On the other hand, how individuals in digital environments translate online content into exercise motivation, and how such motivation evolves into sustained behavior that ultimately improves psychological well-being, remains underexplored, particularly in terms of dynamic pathways and mechanism validation. Therefore, future studies should further investigate the role of physical exercise within the \u0026ldquo;digital media\u0026ndash;behavioral execution\u0026ndash;psychological regulation\u0026rdquo; chain, identify both the facilitating and hindering factors within this process, and thereby enrich the multidimensional understanding of how internet use influences mental health.\u003c/p\u003e\n\u003ch3\u003eResearch Questions\u003c/h3\u003e\n\u003cp\u003eExisting studies have separately demonstrated both the positive and negative effects of mobile internet use on depression levels(Griffiths et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Cai et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), as well as the beneficial role of physical exercise in emotional regulation(Harber and Sutton, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e1984\u003c/span\u003e). However, research integrating these two domains remains limited. Most literature focuses on the direct pathways linking internet use to mental health, while overlooking the mediating role played by behavioral variables. Although some empirical studies suggest that the internet can promote exercise frequency through mechanisms such as online check-ins and social incentives(Goodyear et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Romeo et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), a systematic mediation model explaining how these behavioral changes translate into improvements in psychological well-being has yet to be established. From a theoretical perspective, behavioral activation theory and self-determination theory respectively highlight the crucial role of physical exercise in interrupting cycles of negative emotion and satisfying basic psychological needs(Ekers et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Ng et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Empirically, prior work also indicates that internet use can stimulate exercise motivation(Guo et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), and that exercise itself contributes to reducing depressive symptoms(White et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Moreover, clear differences exist in how various population groups use the internet and engage in exercise. For instance, older adults enhance social support through internet use and thereby improve psychological health(Wang and Shi, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), whereas adolescents are more vulnerable to psychological risks associated with internet addiction(Morrison and Gore, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Yet, whether the mediating effect of physical exercise varies across age groups remains insufficiently examined, and systematic comparative research on this issue is still lacking.\u003c/p\u003e\u003cp\u003eBuilding on the existing literature, although prior studies have provided preliminary theoretical and empirical support for the \u0026ldquo;internet use\u0026ndash;physical exercise\u0026ndash;mental health\u0026rdquo; chain, they still lack a logically complete explanation of the mechanism pathway and structural validation from the perspective of group heterogeneity. Therefore, this study adopts a mediation-effect framework and conducts empirical analysis around three core questions: (1) Does mobile internet use significantly reduce residents\u0026rsquo; depression levels? (2) Does physical exercise serve as a mediating mechanism in this relationship? (3) Does this mechanism pathway vary across different age groups? By uncovering the bridging role of physical exercise between internet use and mental health, this study aims to enhance understanding of the behavioral pathways linking internet use and mental health and to provide targeted policy insights.\u003c/p\u003e"},{"header":"Methodology","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eData\u003c/h2\u003e\u003cp\u003eThis study utilizes microdata from the China Family Panel Survey (CFPS), implemented by the Institute of Social Science Survey (ISSS) at Peking University, along with its corresponding indicator system. The CFPS covers all provinces in China except Hong Kong, Macao, and Taiwan, and adopts a multistage, stratified, probability-proportional-to-size sampling design, ensuring strong national representativeness and reliability. In the sample processing stage, respondents aged 18\u0026ndash;70 were selected as the analysis population. Samples with missing or abnormal values in key variables\u0026mdash;such as depression scores, physical exercise frequency, and mobile internet use\u0026mdash;were excluded, and extreme values in the calculated Body Mass Index (BMI) variable were removed to ensure the robustness of the estimates. To balance data representativeness with availability, this study uses CFPS data from 2020 to 2022 to construct an unbalanced panel dataset, yielding a total of 16,901 valid observations.\u003c/p\u003e\u003cp\u003eThe dependent variable in this study is residents\u0026rsquo; depression tendency score, measured using two indicators: cesd20sc and cesd8. These indicators are derived from the Center for Epidemiologic Studies Depression Scale (CES-D), which evaluates individual depressive symptoms. Both measures reflect the degree of depression through specific numerical scores, with higher scores indicating higher levels of depressive symptoms.\u003c/p\u003e\u003cp\u003eThe explanatory variables in this study are \u0026ldquo;use of mobile devices to access the internet\u0026rdquo; and \u0026ldquo;internet use.\u0026rdquo; The primary explanatory variable, \u0026ldquo;use of mobile devices to access the internet,\u0026rdquo; focuses on online activity conducted through mobile terminals such as smartphones and tablets. A value of 1 indicates that the respondent accesses the internet via such devices, while 0 indicates no such behavior. The secondary variable, \u0026ldquo;internet use,\u0026rdquo; captures internet activity within a broader scope, covering all types of terminals\u0026mdash;including mobile devices and computers\u0026mdash;with 1 representing use and 0 representing non-use. Together, these indicators comprehensively reflect residents\u0026rsquo; internet access and usage status.\u003c/p\u003e\u003cp\u003eThe mediating variable in this study is \u0026ldquo;frequency of physical exercise in the past 12 months,\u0026rdquo; which is divided into eight levels. Corresponding to the eight response options in the questionnaire (\u0026ldquo;How often did you participate in physical exercise or fitness activities in the past 12 months?\u0026rdquo;\u0026mdash;never; less than once per month on average; more than once per month but less than once per week; 1\u0026ndash;2 times per week on average; 3\u0026ndash;4 times per week on average; 5 or more times per week; once per day; twice or more per day), values from 0 to 7 are assigned sequentially. Higher values represent higher exercise frequency.\u003c/p\u003e\u003cp\u003eDepression levels are easily influenced by factors such as age, physical condition, employment, family circumstances, and economic status. Therefore, this study includes a set of control variables, including gender, age, years of education, marital status, household registration type, employment status, household size, and health status (including self-rated health and whether BMI is within the normal range). In addition, to control for fixed regional differences that may affect the results, this study also incorporates county-level regional attributes and year variables. The specific definitions of all variables are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\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\u003eVariable Definitions\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\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=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable Type\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eVariable Meaning\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eVariable Definition\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\u003eDependent Variables\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDepression Index 1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ecesd20sc (score)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDepression Index 2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ecesd8 (score)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eExplanatory Variable\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eInternet Use\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eWhether the respondent uses mobile devices (e.g., mobile phone, tablet) to access the Internet: 1\u0026thinsp;=\u0026thinsp;yes; 0\u0026thinsp;=\u0026thinsp;no;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eWhether the respondent uses the Internet: 1\u0026thinsp;=\u0026thinsp;yes; 0\u0026thinsp;=\u0026thinsp;no\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInstrumental Variable\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eInternet Access of Other Residents within the Region\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eThe Internet access rate of other villagers or community residents (number of people using the Internet\u0026thinsp;\u0026divide;\u0026thinsp;total number of people)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMediating Variable\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eExercise Frequency\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eParticipation frequency in physical exercise or recreational sports activities during the past 12 months:\u003c/p\u003e\u003cp\u003e0. Never\u003c/p\u003e\u003cp\u003e1. On average, less than once per month\u003c/p\u003e\u003cp\u003e2. On average, at least once per month but less than once per week\u003c/p\u003e\u003cp\u003e3. On average, 1\u0026ndash;2 times per week\u003c/p\u003e\u003cp\u003e4. On average, 3\u0026ndash;4 times per week\u003c/p\u003e\u003cp\u003e5. On average, 5 times per week or more\u003c/p\u003e\u003cp\u003e6. Once per day\u003c/p\u003e\u003cp\u003e7. Twice per day or more\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"9\" rowspan=\"10\"\u003e\u003cp\u003eControl Variable\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGender\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1\u0026thinsp;=\u0026thinsp;male; 0\u0026thinsp;=\u0026thinsp;female\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAge in years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAge at the time of survey (years)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRegistered Residence Type\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1\u0026thinsp;=\u0026thinsp;rural; 0\u0026thinsp;=\u0026thinsp;urban\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEducation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYears of education\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMarital Status\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1\u0026thinsp;=\u0026thinsp;married; 0\u0026thinsp;=\u0026thinsp;unmarried\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEmployment Status\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eWhether the respondent has a full-time job: 1\u0026thinsp;=\u0026thinsp;yes; 0\u0026thinsp;=\u0026thinsp;no\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHousehold Income\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eTotal household income (log-transformed)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHousehold Size\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNumber of household members\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eHealth Status\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSelf-rated health status\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1\u0026thinsp;=\u0026thinsp;BMI within normal range (18.5\u0026thinsp;\u0026le;\u0026thinsp;BMI\u0026thinsp;\u0026le;\u0026thinsp;23.9); 0\u0026thinsp;=\u0026thinsp;BMI outside normal range (BMI\u0026thinsp;\u0026lt;\u0026thinsp;18.5 or BMI\u0026thinsp;\u0026ge;\u0026thinsp;23.9)\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\u003eThe descriptive statistics of the variables are presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. This study includes 16,901 observations, and the distribution of each variable is generally reasonable. For the dependent variables, the mean value of cesd20sc is 33.61 with a standard deviation of 8.226, ranging from 22 to 72. The mean value of cesd8 is 13.77 with a standard deviation of 4.140, ranging from 8 to 32. These results indicate that there is notable variation in depression levels among the respondents.\u003c/p\u003e\u003cp\u003eFor the explanatory variables, the mean values of the two indicators measuring internet use are 0.729 and 0.732, indicating that approximately 72% of the respondents use the internet or access it via mobile devices, while about 28% do not use the internet at all. The mediating variable, exercise frequency, has a mean of 1.639 and a standard deviation of 2.319, suggesting substantial variation in residents\u0026rsquo; exercise habits and indicating that most respondents do not engage in physical activity frequently.\u003c/p\u003e\u003cp\u003eIn terms of control variables, the mean value of gender is 0.548, indicating a slightly higher proportion of men than women. The mean age is 46.13 years, covering primarily adults to middle-aged and older groups. The mean value of household registration type is 0.830, showing a higher proportion of rural residents. The average years of education is 9.566, which approximates the level of junior secondary schooling. The mean value of marital status is 0.822, suggesting that married residents constitute the majority. The mean value of employment status is 0.813, indicating that most residents hold full-time jobs. The mean of the logarithm of household income is 11.15, and the average household size is 3.619 persons. Regarding health indicators, the mean value of self-rated health status is 3.061 on a five-point scale, the proportion of normal BMI is 0.521, and the distribution of health status across the sample is relatively balanced.\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 of Variables\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=\"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\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\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eVariable Meaning\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMean\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eStd. Dev.\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eMin\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eMax\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\u003e\u003cem\u003eDependent variables\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDepression Index 1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e33.62\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e8.241\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e72\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDepression Index 2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e13.77\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.148\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e32\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u003cem\u003eExplanatory variable\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMobile Internet use\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.720\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.449\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eInternet use\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.723\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.448\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eInstrumental variable\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eInternet access of other residents within the region\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.723\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.166\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eMediating variable\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eExercise frequency\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.637\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.326\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"9\" rowspan=\"10\"\u003e\u003cp\u003e\u003cem\u003eControl variables\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGender\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.550\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.498\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAge in years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e46.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e12.94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e70\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRegistered residence type\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.831\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.375\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYears of education\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e9.444\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.516\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e22\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMarital status\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.828\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.377\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEmployment status\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.811\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.391\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHousehold income\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e11.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.156\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e16.59\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHousehold size\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.659\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.900\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e15\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSelf-rated health status\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.049\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.154\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBMI in normal range\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.519\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.500\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1\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\n\u003ch3\u003eEconometric Models\u003c/h3\u003e\n\u003cp\u003eTo examine the impact of mobile Internet use on residents\u0026rsquo; mental health and to verify the existence of the transmission pathway of \u0026ldquo;mobile Internet use - frequency of physical exercise - residents\u0026rsquo; mental health,\u0026rdquo; a bidirectional fixed-effects model is constructed to empirically analyze the effect of Internet use on residents\u0026rsquo; depressive symptoms, while further exploring the mediating role of exercise frequency in this relationship.\u003c/p\u003e\u003cp\u003eFirst, Model (1) is constructed to examine the direct impact of mobile Internet use on residents\u0026rsquo; mental health:\u003c/p\u003e\u003cp\u003eFirst, Model (1) is constructed to examine the direct impact of mobile Internet use on residents\u0026rsquo; mental health:\u003c/p\u003e\n\u003cp\u003e\u003cimg width=\"405\" height=\"21\" src=\"data:image/png;base64,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\" alt=\"image\"\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;（1）\u003c/p\u003e\n\u003cp\u003eSecond, taking the frequency of physical exercise as the dependent variable, Model (2) is constructed to examine the effect of mobile Internet use on residents\u0026rsquo; exercise frequency:\u003c/p\u003e\n\u003cp\u003e\u003cimg width=\"410\" height=\"21\" src=\"data:image/png;base64,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\" alt=\"image\"\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;（2）\u003c/p\u003e\n\u003cp\u003eFinally, Model (3) incorporates both mobile Internet use and the frequency of physical exercise to investigate whether exercise frequency serves as a mediating factor in the relationship between mobile Internet use and residents\u0026rsquo; mental health:\u003c/p\u003e\n\u003cp\u003e\u003cimg width=\"455\" height=\"21\" src=\"data:image/png;base64,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\" alt=\"image\"\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;（3）\u003c/p\u003e\n\u003cp\u003eAmong these variables, \u003cem\u003eCesd\u003csub\u003eit\u003c/sub\u003e\u003c/em\u003e denotes the depression scale score of individual \u003cem\u003ei\u003c/em\u003e in year \u003cem\u003et\u003c/em\u003e; \u003cem\u003eM_web\u003csub\u003eit\u003c/sub\u003e\u003c/em\u003e\u003csub\u003e\u0026nbsp;\u003c/sub\u003eindicates whether individual \u003cem\u003ei\u003c/em\u003e used mobile Internet in year t; \u003cem\u003eE_freq\u003csub\u003eit\u0026nbsp;\u003c/sub\u003e\u003c/em\u003erepresents the exercise frequency of individual \u003cem\u003ei\u003c/em\u003e in year \u003cem\u003et\u003c/em\u003e; and \u003cem\u003eX\u003csub\u003eit\u0026nbsp;\u003c/sub\u003e\u003c/em\u003eis a set of control variables, including gender, age, years of education, marital status, household registration type, employment status, household size, household income, and health status. \u003cem\u003e\u0026delta;\u003csub\u003ei\u003c/sub\u003e\u0026nbsp;\u003c/em\u003edenotes the region fixed effects,\u003cem\u003e\u0026nbsp;\u0026theta;\u003csub\u003et\u0026nbsp;\u003c/sub\u003e\u003c/em\u003edenotes the time fixed effects, and \u003cem\u003e\u0026epsilon;\u003csub\u003eit\u0026nbsp;\u003c/sub\u003e\u003c/em\u003eis the random error term.\u003c/p\u003e\n\u003ch3\u003eDiscussion of Endogeneity\u003c/h3\u003e\n\u003cp\u003eConsidering the possibility of a directional causal relationship between mobile Internet use and depressive symptoms\u0026mdash;namely, residents with lower levels of depression may be more inclined to use mobile Internet for social interaction and entertainment, or there may be other unobserved variables that simultaneously affect both factors and thus lead to endogeneity bias in the baseline regression results\u0026mdash;this study employs the proportion of other villagers\u0026rsquo; (or community residents\u0026rsquo;) Internet access as an instrumental variable, which reflects the overall level of Internet penetration within the community. The rationale for selecting this variable as an instrument is based on two considerations: first, regarding relevance, the Internet access status of other residents in the same community can influence an individual\u0026rsquo;s mobile Internet use through social network effects, information diffusion, and similar mechanisms, thereby meeting the requirement that the instrumental variable must be correlated with the core explanatory variable; second, concerning exogeneity, an individual\u0026rsquo;s depressive symptoms constitute a micro-level psychological state that does not exert a reverse influence on the community-level Internet penetration rate, and this variable is unlikely to be affected by other unobserved individual characteristics, thus satisfying the assumption that the instrumental variable is exogenous to the error term. This approach effectively alleviates endogeneity concerns, enhances the reliability of the estimation results, and provides a more robust basis for interpreting the impact of mobile Internet use on depressive symptoms.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eThe Impact of Mobile Internet Use\u003c/h2\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e reports the baseline regression results of the model. Regardless of whether control variables are included, the estimated coefficients of the core explanatory variable\u0026mdash;mobile Internet use\u0026mdash;are significantly negative at the 1 percent level, indicating that the use of mobile Internet can significantly reduce residents\u0026rsquo; depressive symptoms, and the direction of the results remains unchanged when the dependent variable is replaced (cesd20sc or cesd8 depression scores). Without control variables (Columns 1 and 3), the regression coefficient of mobile Internet use is \u0026minus;\u0026thinsp;1.389 for cesd20sc and \u0026minus;\u0026thinsp;0.694 for cesd8. After including control variables (Columns 2 and 4), the absolute values of the coefficients decrease, with coefficients of \u0026minus;\u0026thinsp;0.654 for cesd20sc and \u0026minus;\u0026thinsp;0.330 for cesd8, both still significant at the 1 percent level, indicating that even after controlling for individual and household characteristics, the inhibitory effect of Internet use on depressive symptoms remains robust.\u003c/p\u003e\u003cp\u003eThe regression results of the control variables are generally consistent with theoretical expectations. Male residents exhibit significantly lower levels of depression than females, which may be related to gender differences in emotional expression and social role allocation. Age is significantly negatively associated with depressive symptoms, meaning that depression tends to decrease slightly as individuals grow older, possibly because accumulated life experience facilitates psychological adjustment. Years of education and household income (log) both show significant negative effects on depression; improvements in educational attainment and increases in income may alleviate depressive symptoms by enhancing cognitive capacity and reducing financial stress. Better self-rated health and being married are associated with significantly lower levels of depression, as good physical condition and stable marital relationships provide psychological support and help mitigate depressive feelings. Residents with full-time employment exhibit significantly higher depression levels than those without full-time jobs, which may be related to work pressure and workplace competition. Urban\u0026ndash;rural differences and household size are not key factors influencing residents\u0026rsquo; depressive symptoms.\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\u003eBenchmark Regression Results\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\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=\"left\" 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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(1)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(2)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(3)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(4)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ecesd20sc score\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ecesd20sc score\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ecesd8\u003c/p\u003e\u003cp\u003escore\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ecesd8\u003c/p\u003e\u003cp\u003escore\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMobile Internet Use\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-1.389\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(0.174)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.654\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(0.181)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.694\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(0.087)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.330\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(0.091)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGender\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-1.009\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(0.145)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.509\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(0.073)\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=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.067\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(0.007)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.034\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(0.004)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYears of Education\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.232\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(0.020)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.116\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(0.010)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHealth Status\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-2.088\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(0.063)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-1.052\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(0.032)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMarital Status\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-2.571\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(0.225)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-1.291\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(0.113)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEmployment Status\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.522\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(0.184)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.269\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(0.092)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRegistered residence type\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.140\u003c/p\u003e\u003cp\u003e(0.190)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.076\u003c/p\u003e\u003cp\u003e(0.096)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHousehold Size\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.027\u003c/p\u003e\u003cp\u003e(0.043)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.014\u003c/p\u003e\u003cp\u003e(0.022)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHousehold Income (log)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.391***\u003c/p\u003e\u003cp\u003e(0.071)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.197***\u003c/p\u003e\u003cp\u003e(0.036)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eConstant\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e34.620***\u003c/p\u003e\u003cp\u003e(0.153)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e52.391***\u003c/p\u003e\u003cp\u003e(0.904)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e14.272***\u003c/p\u003e\u003cp\u003e(0.077)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e23.228***\u003c/p\u003e\u003cp\u003e(0.454)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNumber of observations\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e16 142\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e16 142\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e16 142\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e16 142\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRsquared\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.090\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.207\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.090\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.207\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAdj-Rsquared\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.041\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.164\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.042\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.164\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYear FE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCounty FE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003cb\u003eNotes\u003c/b\u003e: *, **, *** denote significance at the 0.1, 0.05, and 0.01 level, respectively.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eIV Regression Results\u003c/h2\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e reports the first-stage and second-stage regression results of the instrumental variable estimation for the effect of Internet use on the cesd20sc score. The first-stage regressions (1a) and (2a) show that the instrumental variable\u0026mdash;\u0026ldquo;the Internet access rate of other villagers (community residents)\u0026rdquo;\u0026mdash;has a significant impact on the core explanatory variable, indicating a strong correlation between the instrumental variable and Internet use. The Kleibergen\u0026ndash;Paap rk Wald F statistics are 194.961 and 169.892, both far exceeding the critical value of 16.38 under a 10 percent maximal relative bias, and the p-values of the Kleibergen\u0026ndash;Paap rk LM test are both 0.0000, suggesting that the instrumental variable satisfies the relevance and identification assumptions, with no problems of under-identification or weak instruments; thus, the chosen instrument is valid. The second-stage regressions (1b) and (2b) reveal the effect of Internet use on the cesd20sc score and show that Internet use significantly reduces the cesd20sc score. Whether control variables are included or not, the estimated coefficients of the core explanatory variable\u0026mdash;mobile Internet use\u0026mdash;remain significantly negative, indicating that after addressing endogeneity and controlling for individual and household characteristics, the depressive-reducing effect of Internet use remains robust.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003e\u0026emsp;Instrumental Variable Regression Results\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\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=\"left\" 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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(1a)\u003c/p\u003e\u003cp\u003eMobile Internet Use\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(1b) cesd20sc score\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(2a)\u003c/p\u003e\u003cp\u003eMobile Internet Use\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(2b) cesd20sc score\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOther villagers\u0026rsquo; (community residents\u0026rsquo;) Internet access\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-4.268***\u003c/p\u003e\u003cp\u003e(0.306)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-3.098***\u003c/p\u003e\u003cp\u003e(0.238)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMobile Internet use\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-1.783***\u003c/p\u003e\u003cp\u003e(0.357)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-1.349**\u003c/p\u003e\u003cp\u003e(0.479)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGender\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\u003cp\u003e-0.001\u003c/p\u003e\u003cp\u003e(0.006)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-1.011***\u003c/p\u003e\u003cp\u003e(0.145)\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=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.012***\u003c/p\u003e\u003cp\u003e(0.000)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.077***\u003c/p\u003e\u003cp\u003e(0.009)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYears of education\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\u003cp\u003e0.020***\u003c/p\u003e\u003cp\u003e(0.001)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.217***\u003c/p\u003e\u003cp\u003e(0.023)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHealth status\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\u003cp\u003e-0.004\u003c/p\u003e\u003cp\u003e(0.003)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-2.091***\u003c/p\u003e\u003cp\u003e(0.063)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMarital status\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\u003cp\u003e0.062***\u003c/p\u003e\u003cp\u003e(0.009)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-2.526***\u003c/p\u003e\u003cp\u003e(0.227)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEmployment status\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\u003cp\u003e-0.031***\u003c/p\u003e\u003cp\u003e(0.008)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.505**\u003c/p\u003e\u003cp\u003e(0.184)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRegistered residence type\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\u003cp\u003e-0.046***\u003c/p\u003e\u003cp\u003e(0.008)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.106\u003c/p\u003e\u003cp\u003e(0.191)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHousehold size\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\u003cp\u003e-0.006**\u003c/p\u003e\u003cp\u003e(0.002)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.032\u003c/p\u003e\u003cp\u003e(0.043)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHousehold income (log)\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\u003cp\u003e0.037***\u003c/p\u003e\u003cp\u003e(0.004)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.363***\u003c/p\u003e\u003cp\u003e(0.073)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNumber of observations\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e16142\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e16142\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e16142\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e16142\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRsquared\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.254\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.005\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.437\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.133\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAdj-Rsquared\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.214\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.048\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.407\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.086\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYear FE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCounty FE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eKleibergen-Paap rk Wald F\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e194.961[16.38]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e169.892[16.38]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eKleibergen-Paap rk LM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eP\u0026thinsp;=\u0026thinsp;0.0000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eP\u0026thinsp;=\u0026thinsp;0.0000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003cb\u003eNotes\u003c/b\u003e: *, **, *** denote significance at the 0.1, 0.05, and 0.01 level, respectively.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003eHeterogeneity Analysis\u003c/h2\u003e\u003cp\u003eTo examine group heterogeneity in the effect of Internet use on depressive symptoms, this study conducts a heterogeneity analysis from three dimensions\u0026mdash;BMI status, employment type, and pension coverage\u0026mdash;and the regression results are presented in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e.\u003c/p\u003e\u003cp\u003eAmong these groups, mobile Internet use has a significant depressive-reducing effect on residents with abnormal BMI (including overweight, obesity, or underweight). On the one hand, digital technology provides them with more convenient channels for social interaction, helping to break social isolation and rebuild emotional connections, thereby alleviating the sense of social exclusion that may arise from body-shape concerns. On the other hand, the Internet enables access to extensive professional information about body management and exercise, which lowers the barriers to action and provides clearer guidance for behavioral adjustments. Such clarity can substantially reduce depressive emotions stemming from self-doubt, thus exerting a significant negative impact on depression levels.\u003c/p\u003e\u003cp\u003eSecond, the depressive-reducing effect of mobile Internet use is stronger among non-agricultural workers. This may stem from the high compatibility between Internet functions and the \u0026ldquo;high-pressure, fast-paced\u0026rdquo; nature of their work: the Internet\u0026rsquo;s information and entertainment features can satisfy fragmented information and entertainment needs, alleviate work-related irritability, anxiety, and other negative emotions in a timely manner, and expand channels for exercise, social interaction, and emotional expression, all of which play a positive role in improving emotional states and reducing depressive risk. In contrast, although agricultural work is physically demanding, its rhythm is often synchronized with natural cycles and tends to be more regular and autonomous. Moreover, farmers\u0026rsquo; social support networks may rely more on real-life interactions with neighbors, relatives, and local communities rather than virtual online networks, resulting in a nonsignificant effect on their depression levels.\u003c/p\u003e\u003cp\u003eNotably, the depressive-alleviating effect of Internet use is more pronounced among residents without pension coverage. Compared with individuals who possess pension security, this group often faces greater uncertainty and emotional stress when contemplating their future livelihood. In this context, Internet platforms play an important role in psychological adjustment. On the one hand, through government service platforms, short videos, public accounts, and similar channels, the Internet lowers the informational threshold for understanding pension-related policies, enabling individuals without pension coverage to obtain clearer and timelier policy information, thereby easing confusion and anxiety caused by information asymmetry. On the other hand, the Internet offers online social spaces and virtual support networks that help individuals rebuild a sense of social connectedness through emotional support and experience sharing, partially compensating for the lack of offline social support systems. These mechanisms jointly form a psychological buffering channel for groups without pension coverage, thereby strengthening the regulatory effect of Internet use on their depressive symptoms. In contrast, when individuals\u0026rsquo; basic pension needs are already secured by institutional arrangements, the marginal alleviating effect of Internet use becomes relatively weaker.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003e\u0026emsp;Heterogeneity Analysis\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=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" 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=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(1)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(2)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(3)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(4)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(5)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003e(6)\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\u003eVariables\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"6\" nameend=\"c7\" namest=\"c2\"\u003e\u003cp\u003ecesd20sc score\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ebmigood\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eBmibad\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eAgricultural Work\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNon-agricultural Work\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eWith Pension\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eWithout Pension\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMobile Internet use\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.440\u003c/p\u003e\u003cp\u003e(0.256)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.867***\u003c/p\u003e\u003cp\u003e(0.256)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.099\u003c/p\u003e\u003cp\u003e(0.285)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-1.136***\u003c/p\u003e\u003cp\u003e(0.270)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.397\u003c/p\u003e\u003cp\u003e(0.244)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-0.848**\u003c/p\u003e\u003cp\u003e(0.284)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGender\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.972***\u003c/p\u003e\u003cp\u003e(0.202)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.980***\u003c/p\u003e\u003cp\u003e(0.214)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.573**\u003c/p\u003e\u003cp\u003e(0.184)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-1.734***\u003c/p\u003e\u003cp\u003e(0.270)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-1.100***\u003c/p\u003e\u003cp\u003e(0.184)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-0.907***\u003c/p\u003e\u003cp\u003e(0.237)\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=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.065***\u003c/p\u003e\u003cp\u003e(0.010)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.069***\u003c/p\u003e\u003cp\u003e(0.010)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.080***\u003c/p\u003e\u003cp\u003e(0.010)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.051***\u003c/p\u003e\u003cp\u003e(0.014)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.041***\u003c/p\u003e\u003cp\u003e(0.011)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-0.078***\u003c/p\u003e\u003cp\u003e(0.010)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYears of education\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.251***\u003c/p\u003e\u003cp\u003e(0.029)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.210***\u003c/p\u003e\u003cp\u003e(0.029)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.248***\u003c/p\u003e\u003cp\u003e(0.028)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.193***\u003c/p\u003e\u003cp\u003e(0.036)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.201***\u003c/p\u003e\u003cp\u003e(0.027)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-0.261***\u003c/p\u003e\u003cp\u003e(0.032)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHealth status\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-2.165***\u003c/p\u003e\u003cp\u003e(0.090)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-2.018***\u003c/p\u003e\u003cp\u003e(0.091)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-2.047***\u003c/p\u003e\u003cp\u003e(0.088)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-2.008***\u003c/p\u003e\u003cp\u003e(0.103)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-1.995***\u003c/p\u003e\u003cp\u003e(0.081)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-2.167***\u003c/p\u003e\u003cp\u003e(0.101)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMarital status\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-2.164***\u003c/p\u003e\u003cp\u003e(0.292)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-2.996***\u003c/p\u003e\u003cp\u003e(0.353)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-2.263***\u003c/p\u003e\u003cp\u003e(0.282)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-3.806***\u003c/p\u003e\u003cp\u003e(0.479)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-2.942***\u003c/p\u003e\u003cp\u003e(0.302)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-2.302***\u003c/p\u003e\u003cp\u003e(0.342)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEmployment status\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.365\u003c/p\u003e\u003cp\u003e(0.255)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.604*\u003c/p\u003e\u003cp\u003e(0.268)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.008\u003c/p\u003e\u003cp\u003e(0.273)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.285\u003c/p\u003e\u003cp\u003e(0.460)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.009\u003c/p\u003e\u003cp\u003e(0.296)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.742**\u003c/p\u003e\u003cp\u003e(0.259)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRegistered residence type\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.243\u003c/p\u003e\u003cp\u003e(0.264)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.214\u003c/p\u003e\u003cp\u003e(0.276)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.059\u003c/p\u003e\u003cp\u003e(0.226)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.229\u003c/p\u003e\u003cp\u003e(0.636)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.233\u003c/p\u003e\u003cp\u003e(0.238)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.019\u003c/p\u003e\u003cp\u003e(0.317)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHousehold size\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.102\u003c/p\u003e\u003cp\u003e(0.059)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.048\u003c/p\u003e\u003cp\u003e(0.064)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.011\u003c/p\u003e\u003cp\u003e(0.060)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.013\u003c/p\u003e\u003cp\u003e(0.072)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.012\u003c/p\u003e\u003cp\u003e(0.059)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-0.039\u003c/p\u003e\u003cp\u003e(0.063)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHousehold income (log)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.308**\u003c/p\u003e\u003cp\u003e(0.096)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.464***\u003c/p\u003e\u003cp\u003e(0.110)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.436***\u003c/p\u003e\u003cp\u003e(0.108)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.412***\u003c/p\u003e\u003cp\u003e(0.121)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.342***\u003c/p\u003e\u003cp\u003e(0.098)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-0.417***\u003c/p\u003e\u003cp\u003e(0.105)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eConstant\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e51.863***\u003c/p\u003e\u003cp\u003e(1.259)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e52.702***\u003c/p\u003e\u003cp\u003e(1.355)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e52.787***\u003c/p\u003e\u003cp\u003e(1.333)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e53.321***\u003c/p\u003e\u003cp\u003e(1.754)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e50.643***\u003c/p\u003e\u003cp\u003e(1.227)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e53.452***\u003c/p\u003e\u003cp\u003e(1.355)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNumber of observations\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8 233\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7 600\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e9 239\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5 238\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e9 500\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e6 351\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSquared\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.217\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.231\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.208\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.229\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.217\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.240\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAdj-Squared\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.151\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.170\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.136\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.185\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.156\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.174\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYear FE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCounty FE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u003cb\u003eNotes\u003c/b\u003e: *, **, *** denote significance at the 0.1, 0.05, and 0.01 level, respectively.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003eMediation Effect\u003c/h2\u003e\u003cp\u003eTo reveal the mechanism through which Internet use affects residents\u0026rsquo; depressive symptoms, this study selects exercise frequency as the mediating variable and employs a stepwise regression approach to test the mediation effect. The regression results are presented in Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e.\u003c/p\u003e\u003cp\u003eFirst, the analysis above has verified the significant negative effect of the core explanatory variable\u0026mdash;mobile Internet use\u0026mdash;on the dependent variable, depressive symptom scores, indicating that Internet use significantly reduces depression levels and thus provides the basis for testing the mediation effect. Second, Columns 1 and 3 of Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e present the effect of the core explanatory variable on the mediating variable. Whether control variables are included or not, mobile Internet use shows a significantly positive effect on exercise frequency, indicating that Internet use significantly encourages residents to increase physical activity. Finally, Columns 2 and 4 of Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e show the effects of simultaneously including the core explanatory variable and the mediating variable on the dependent variable. With and without control variables, mobile Internet use continues to significantly reduce depression levels; however, the total coefficient (\u0026minus;\u0026thinsp;0.569) is smaller in magnitude than the coefficient obtained when the mediating variable is not included (\u0026minus;\u0026thinsp;0.654), indicating that exercise frequency serves as a partial mediator in the relationship between Internet use and residents\u0026rsquo; depressive symptoms.\u003c/p\u003e\u003cp\u003eThe above results indicate that mobile Internet use not only has a direct alleviating effect on residents\u0026rsquo; depressive symptoms but also indirectly reduces depression by increasing the frequency of physical exercise. Specifically, mobile Internet use provides residents with multiple supportive conditions for engaging in physical activity, such as access to information and opportunities for social interaction, which facilitate the initiation and maintenance of exercise behaviors. In turn, the increase in exercise frequency transmits the influence of Internet use on depressive symptoms, forming an indirect effect pathway in which exercise frequency serves as a mediating mechanism.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003e\u0026emsp;Mediation Regression Results\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\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=\"left\" 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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(1)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(2)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(3)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(4)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eExercise frequency\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ecesd20sc score\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eExercise frequency\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ecesd20sc score\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMobile Internet use\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.369\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(0.046)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-1.276\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(0.174)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.449\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(0.051)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.569\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(0.181)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eExercise frequency\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.306\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(0.031)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.189\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(0.030)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGender\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\u003cp\u003e0.098\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(0.040)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.991\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(0.144)\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=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.040\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(0.002)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.060\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(0.007)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYears of education\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\u003cp\u003e0.096\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(0.006)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.214\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(0.021)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHealth status\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\u003cp\u003e0.070\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(0.017)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-2.075\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(0.063)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMarital status\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\u003cp\u003e-0.190\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(0.058)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-2.607\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(0.225)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEmployment status\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\u003cp\u003e-0.716\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(0.056)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.387\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(0.185)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRegistered residence type\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\u003cp\u003e-0.416\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(0.059)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.061\u003c/p\u003e\u003cp\u003e(0.190)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHousehold size\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\u003cp\u003e-0.055\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(0.012)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.037\u003c/p\u003e\u003cp\u003e(0.043)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHousehold income (log)\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\u003cp\u003e0.150\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(0.019)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.363\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(0.071)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eConstant\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.371\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(0.038)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e35.039\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(0.159)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-2.089\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(0.243)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e51.995\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(0.907)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNumber of observations\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e16 142\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e16 142\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e16 142\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e16 142\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRsquared\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.127\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.096\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.196\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.209\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAdj-Rsquared\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.081\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.048\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.153\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.166\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYear FE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCounty FE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003cb\u003eNotes\u003c/b\u003e: *, **, *** denote significance at the 0.1, 0.05, and 0.01 level, respectively.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003eRobustness Test\u003c/h2\u003e\u003cp\u003eTo verify the reliability of the baseline regression results, this study conducts a robustness check by replacing the explanatory variable (Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). The original indicator of \u0026ldquo;mobile Internet use\u0026rdquo; is replaced with the indicator of \u0026ldquo;Internet use,\u0026rdquo; and the regression results show that the core conclusions remain valid. In Column (1), the regression coefficient of the substituted Internet use variable on the depression score cesd20sc is \u0026minus;\u0026thinsp;0.691 and significantly negative at the 1 percent level, indicating a clear depressive-reducing effect of Internet use. Column (2) shows that the regression coefficient of the substituted Internet use variable on the mediating variable (exercise frequency) is 0.460 and significantly positive at the 1 percent level, confirming the robustness of the Internet\u0026rsquo;s promoting effect on exercise frequency. Column (3), which simultaneously includes the substituted Internet use variable and exercise frequency, shows that the regression coefficient of Internet use on cesd20sc is \u0026minus;\u0026thinsp;0.604 (significant at the 1 percent level), with an absolute value smaller than \u0026minus;\u0026thinsp;0.691 in Column (1), and the regression coefficient of exercise frequency is \u0026minus;\u0026thinsp;0.189 (significant at the 1 percent level). These findings again verify the partial mediating effect of exercise frequency. The directions and significance levels of the control variables remain unchanged, providing further evidence that the core conclusions of this study are robust.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003e\u0026emsp;Robustness Check Results (Alternative Mediator Variable)\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=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(1)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(2)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(3)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ecesd20sc score\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eExercise frequency\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ecesd20sc score\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMobile Internet use\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.691***\u003c/p\u003e\u003cp\u003e(0.182)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.460***\u003c/p\u003e\u003cp\u003e(0.051)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.604***\u003c/p\u003e\u003cp\u003e(0.183)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eExercise frequency\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\u003cp\u003e-0.189***\u003c/p\u003e\u003cp\u003e(0.030)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGender\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-1.009***\u003c/p\u003e\u003cp\u003e(0.145)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.097*\u003c/p\u003e\u003cp\u003e(0.040)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.991***\u003c/p\u003e\u003cp\u003e(0.144)\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=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.068***\u003c/p\u003e\u003cp\u003e(0.007)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.040***\u003c/p\u003e\u003cp\u003e(0.002)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.060***\u003c/p\u003e\u003cp\u003e(0.007)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYears of education\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.231***\u003c/p\u003e\u003cp\u003e(0.020)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.095***\u003c/p\u003e\u003cp\u003e(0.006)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.213***\u003c/p\u003e\u003cp\u003e(0.021)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHealth status\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-2.088***\u003c/p\u003e\u003cp\u003e(0.063)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.070***\u003c/p\u003e\u003cp\u003e(0.017)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-2.075***\u003c/p\u003e\u003cp\u003e(0.063)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMarital status\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-2.569***\u003c/p\u003e\u003cp\u003e(0.225)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.191**\u003c/p\u003e\u003cp\u003e(0.058)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-2.605***\u003c/p\u003e\u003cp\u003e(0.225)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEmployment status\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.521**\u003c/p\u003e\u003cp\u003e(0.184)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.716***\u003c/p\u003e\u003cp\u003e(0.056)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.386*\u003c/p\u003e\u003cp\u003e(0.185)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRegistered residence type\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.137\u003c/p\u003e\u003cp\u003e(0.190)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.415***\u003c/p\u003e\u003cp\u003e(0.059)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.059\u003c/p\u003e\u003cp\u003e(0.190)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHousehold size\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.027\u003c/p\u003e\u003cp\u003e(0.043)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.055***\u003c/p\u003e\u003cp\u003e(0.012)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.038\u003c/p\u003e\u003cp\u003e(0.043)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHousehold income (log)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.389***\u003c/p\u003e\u003cp\u003e(0.071)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.149***\u003c/p\u003e\u003cp\u003e(0.019)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.361***\u003c/p\u003e\u003cp\u003e(0.071)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eConstant\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e52.415***\u003c/p\u003e\u003cp\u003e(0.904)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-2.095***\u003c/p\u003e\u003cp\u003e(0.243)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e52.019***\u003c/p\u003e\u003cp\u003e(0.907)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNumber of observations\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e16 142\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e16 142\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e16 142\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRsquared\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.207\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.196\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.209\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAdj-Rsquared\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.164\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.153\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.166\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYear FE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCounty FE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003cb\u003eNotes\u003c/b\u003e: *, **, *** denote significance at the 0.1, 0.05, and 0.01 level, respectively.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e presents the robustness test results obtained by conducting 1,000 Bootstrap replications to further verify the validity of the mediating mechanism of exercise frequency. The results show that the confidence intervals of the indirect effect\u0026mdash;through which Internet use influences residents\u0026rsquo; depressive symptoms by increasing exercise frequency\u0026mdash;do not include zero, indicating a significant mediation effect. This finding corroborates the preceding analysis and confirms that the conclusion regarding the partial mediating role of exercise frequency in the relationship between Internet use and residents\u0026rsquo; depressive symptoms is robust.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab8\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003e\u0026emsp;Bootstrap Estimation Results of Indirect Effects\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\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=\"\u0026minus;\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStatistic\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEstimate\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003ez-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eP\u0026gt;|z|\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e95% Confidence Interval\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDirect Effect\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.391\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-2.31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.021\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c5\"\u003e\u003cp\u003e[-0.723, -0.059]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIndirect Effec\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.092\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-5.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c5\"\u003e\u003cp\u003e[-0.124, -0.061]\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\u003eBased on the assumption that the relationship between the treatment effect and unobservable variables can be inferred from its relationship with observable variables, Oster (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) argues that the absolute value of the estimated coefficient of the core explanatory variable typically decreases after adding more control variables, implying that even if unobservable omitted variables exist, their influence on the estimation results is likely limited. Although this study has addressed endogeneity concerns and incorporated bidirectional fixed effects, it cannot completely eliminate the endogeneity arising from unobservable omitted variables. Therefore, to further mitigate the potential influence of such factors, this study applies the coefficient stability test proposed by Oster (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), and the test results are presented in Table\u0026nbsp;\u003cspan refid=\"Tab9\" class=\"InternalRef\"\u003e9\u003c/span\u003e. The specific procedures are as follows: first, \u003cem\u003eR\u003c/em\u003e\u003csub\u003e\u003cem\u003emax\u003c/em\u003e\u003c/sub\u003e is set to 1.3 times the baseline regression \u003cem\u003eR\u0026sup2;\u003c/em\u003e (0.269), and \u003cem\u003eδ\u003c/em\u003e is set to 1, after which the feasible range of the adjusted coefficient \u003cem\u003eβ\u003c/em\u003e\u003csup\u003e\u003cem\u003e\u0026lowast;\u003c/em\u003e\u003c/sup\u003e for the core explanatory variable is estimated; second, with \u003cem\u003eR\u003c/em\u003e\u003csub\u003e\u003cem\u003emax\u003c/em\u003e\u003c/sub\u003e set to 1.3 times the baseline regression \u003cem\u003eR\u0026sup2;\u003c/em\u003e (0.269), \u003cem\u003eβ\u003c/em\u003e\u003csup\u003e\u003cem\u003e\u0026lowast;\u003c/em\u003e\u003c/sup\u003e is set to 0, and the value of \u003cem\u003eδ\u003c/em\u003e is calculated accordingly. The first row of Table\u0026nbsp;\u003cspan refid=\"Tab9\" class=\"InternalRef\"\u003e9\u003c/span\u003e reports the results of the first procedure, where the range of \u003cem\u003eβ\u003c/em\u003e\u003csup\u003e\u003cem\u003e\u0026lowast;\u003c/em\u003e\u003c/sup\u003e does not include zero, indicating that the test is passed. The second row corresponds to the second procedure, and if the calculated \u003cem\u003eδ\u003c/em\u003e exceeds 1, the test is considered passed. The results in Table\u0026nbsp;\u003cspan refid=\"Tab9\" class=\"InternalRef\"\u003e9\u003c/span\u003e show that both procedures successfully pass the test, thereby confirming the robustness of the main conclusions of this study\u0026mdash;namely, that omitted variables do not affect the validity of the empirical results.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab9\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 9\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003e\u0026emsp;Coefficient Stability Test Results\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\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=\"left\" 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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMethod\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eParameter Setting\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eJudgment Criterion\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eActual Estimation Result\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003ePassed or No\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e(1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eR\u003c/em\u003e\u003csub\u003e\u003cem\u003emax\u003c/em\u003e\u003c/sub\u003e=0.269, δ\u0026thinsp;=\u0026thinsp;1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eThe interval does not contain 0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e[-1.603, -0.691]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e(2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eR\u003c/em\u003e\u003csub\u003e\u003cem\u003emax\u003c/em\u003e\u003c/sub\u003e=0.269, β\u003csup\u003e\u0026lowast;\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eThe value of δ is greater than 1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.460\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eYes\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"},{"header":"Conclusion and Discussion","content":"\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003cp\u003eAgainst the backdrop of growing concern for mental health and the accelerating process of digitalization, understanding how Internet use affects residents\u0026rsquo; depressive symptoms and through which channels this influence occurs has become increasingly urgent. Using microdata from the China Family Panel Studies (CFPS) for 2020\u0026ndash;2022, this study examines the impact of mobile Internet use on residents\u0026rsquo; depressive tendencies, explores the mediating role of exercise frequency, and further investigates heterogeneous effects across different groups. The main conclusions are as follows:\u003c/p\u003e\u003cp\u003eFirst, mobile Internet use has a significant inhibitory effect on residents\u0026rsquo; depressive symptoms. The baseline regression results show that, regardless of whether control variables are included, mobile Internet use significantly reduces residents\u0026rsquo; cesd20sc and cesd8 depression scores, and this negative effect remains robust after addressing endogeneity concerns and controlling for individual and household characteristics. This indicates that in the digital era, mobile Internet has become an important instrument for alleviating depressive emotions, offering new avenues for psychological adjustment through functions such as information acquisition and social interaction.\u003c/p\u003e\u003cp\u003eSecond, exercise frequency plays a partial mediating role in the relationship between mobile Internet use and residents\u0026rsquo; depressive symptoms. The mediation analysis shows that mobile Internet use not only directly reduces depression levels but also indirectly alleviates depressive emotions by increasing the frequency of physical exercise. Specifically, mobile Internet use significantly promotes residents\u0026rsquo; exercise frequency, and the subsequent increase in exercise frequency further reduces depression scores, forming a transmission pathway in which exercise frequency serves as a mediating mechanism. This finding confirms the essential mediating role of physical exercise within digital health interventions.\u003c/p\u003e\u003cp\u003eThird, the impact of mobile Internet use on depressive symptoms exhibits significant heterogeneity across different groups. In terms of physiological characteristics, the depressive-alleviating effect of mobile Internet use is more pronounced among residents with abnormal BMI. From the perspective of occupational attributes, its depressive‐reducing effect is stronger for non-agricultural workers. Regarding social security status, the improvement in depressive symptoms is more substantial among individuals without pension coverage. These findings indicate that the psychological health effects of mobile Internet use are moderated by individual characteristics and life contexts, and that different groups exhibit varying psychological needs and differential responses to Internet use.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003ePolicy Implications\u003c/h2\u003e\u003cp\u003eBased on the above research findings, this study proposes the following policy recommendations:\u003c/p\u003e\u003cp\u003eFirst, improve mobile Internet infrastructure and the public service system to fully leverage the role of digital technologies in promoting mental health. On the one hand, government agencies and Internet enterprises should jointly develop integrated platforms that combine psychological assessment, emotional counseling, and health information services in order to enhance the accessibility and inclusiveness of public mental health services. On the other hand, investment in network infrastructure in rural and remote areas should be increased, accompanied by digital skills training to narrow the \u0026ldquo;digital divide,\u0026rdquo; ensuring that residents in different regions can obtain equal access to high-quality psychological support resources. At the same time, a content review and professional evaluation mechanism should be established to standardize online mental health service protocols and ensure the scientific accuracy and safety of related information.\u003c/p\u003e\u003cp\u003eSecond, drawing on the mediating role of physical exercise, an integrated online\u0026ndash;offline health promotion model should be developed. It is advisable to encourage the creation of specialized fitness apps that provide personalized exercise plans based on users\u0026rsquo; physical characteristics and enhance exercise adherence through functions such as activity tracking, peer supervision, and goal incentives. Communities and online platforms should be coordinated to organize offline activities such as fitness challenges and charity runs to strengthen residents\u0026rsquo; sense of participation in physical activity. Internet-based exercise guidance should be incorporated into community health services, with trained professionals providing scientific fitness counseling for residents. These efforts can help establish a virtuous cycle of \u0026ldquo;online guidance plus offline practice\u0026rdquo; and fully unlock the depressive-relieving benefits of physical exercise.\u003c/p\u003e\u003cp\u003eThird, differentiated mental health intervention strategies should be implemented for distinct population groups. Tailored support measures should be designed in accordance with each group\u0026rsquo;s Internet use patterns and psychological needs. For individuals with abnormal BMI, online health management programs should be established that integrate exercise guidance, nutritional interventions, and psychological counseling. For non-agricultural workers, modular stress-management tools suitable for fragmented time use should be developed, utilizing short videos and virtual communities to provide psychological relief and emotional support. For residents without pension coverage, digital service packages that combine emotional assessment, exercise guidance, and policy interpretation should be offered to enhance their sense of psychological security and social connectedness.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003eResearch Limitations\u003c/h2\u003e\u003cp\u003eThis study still has several limitations that warrant further refinement in future research. The measurement of Internet use and mobile Internet use in this paper focuses solely on the dimension of \u0026ldquo;whether individuals use the Internet,\u0026rdquo; without incorporating usage duration, and future studies may conduct more nuanced analyses as data availability improves. As digital technologies continue to develop, the role of the Internet in promoting mental health will become increasingly prominent. Despite the limitations noted above, this study provides important insights into the factors influencing residents\u0026rsquo; mental health and the pathways of intervention in the digital era.\u003c/p\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated during and/or analyzed during the current study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics declarations\u003c/strong\u003e\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\u003eEthical approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis article does not contain any studies with human participants performed by any of the authors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInformed consent\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis article does not contain any studies with human participants performed by any of the authors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThere was no dedicated funding to conduct this study. I acknowledge all the participants for giving out information that enriched the manuscript.\u003c/p\u003e\n\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eYS: writing\u0026mdash;original draft, conceptualization, methodology, data resources. ZH: writing\u0026mdash;original draft, supervision, methodology, modeling, programming, data resources. All authors reviewed the manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAudi AS, Fernandes AE, Coelho GSDMA, Moura AMDSHD, Pepe RB, Cercato C, Mancini MC (2025) Effect of a mobile phone application for dietary self-monitoring on obesity in adolescents: a pilot randomized controlled trial. Eur J Clin Nutr, 1\u0026ndash;8\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAnderson E, Shivakumar G (2013) Effects of exercise and physical activity on anxiety. Front Psychiatry 4:27\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBessi\u0026egrave;re K, Pressman S, Kiesler S, Kraut R (2010) Effects of internet use on health and depression: a longitudinal study. J Med Internet Res, 12(1), e6\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCai Z, Mao P, Wang Z, Wang D, He J, Fan X (2023) Associations between problematic internet use and mental health outcomes of students: a meta-analytic review. Adolesc Res Rev 8(1):45\u0026ndash;62\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChen Y, Gao Q (2023) Effects of social media self-efficacy on informational use, loneliness, and self-esteem of older adults. Int J Human\u0026ndash;Computer Interact 39(5):1121\u0026ndash;1133\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCotten SR, Ford G, Ford S, Hale TM (2012) Internet use and depression among older adults. Comput Hum Behav 28(2):496\u0026ndash;499\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eEkers D, Webster L, Van Straten A, Cuijpers P, Richards D, Gilbody S (2014) Behavioural activation for depression; an update of meta-analysis of effectiveness and sub group analysis. PLoS ONE, 9(6), e100100\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGoodyear VA, Wood G, Skinner B, Thompson JL (2021) The effect of social media interventions on physical activity and dietary behaviours in young people and adults: a systematic review. Int J Behav Nutr Phys Activity 18(1):72\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGorrell S, Shott ME, Frank GK (2022) Associations between aerobic exercise and dopamine-related reward-processing: Informing a model of human exercise engagement. 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Int J Environ Res Public Health 17(19):6954\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"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":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"mobile internet, depression, physical exercise, mental health","lastPublishedDoi":"10.21203/rs.3.rs-8146436/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8146436/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eMental health has become a central pillar of national health strategies worldwide, aligning with the World Health Organization\u0026rsquo;s (WHO) vision of \u0026ldquo;health for all.\u0026rdquo; Drawing on data from the 2020\u0026ndash;2022 China Family Panel Studies (CFPS) and applying a two-way fixed-effects model, this study empirically examines the impact of mobile internet use on residents\u0026rsquo; depression levels and explores the underlying behavioral transmission mechanisms. The results show that mobile internet use significantly reduces depressive symptoms, and the findings remain robust after addressing endogeneity concerns. Further analysis reveals that mobile internet use not only directly alleviates depression but also indirectly reduces depressive symptoms by increasing individuals\u0026rsquo; frequency of physical exercise. Additional heterogeneity analyses indicate that the depression-reducing effect of mobile internet use is more pronounced among individuals with abnormal BMI, non-agricultural workers, and those lacking pension security. These findings suggest the need to strengthen functional digital inclusion, enhance psychological health interventions for key groups, and promote innovative models of health promotion driven by internet technologies.\u003c/p\u003e","manuscriptTitle":"Mobile Internet, Physical Exercise and Depression Levels: Mediating Mechanism and Empirical Examination","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-15 16:46:54","doi":"10.21203/rs.3.rs-8146436/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"457c2abd-935e-45d6-a780-38b768fedc4f","owner":[],"postedDate":"December 15th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":59452302,"name":"Health sciences/Health care"},{"id":59452303,"name":"Humanities/Health humanities"},{"id":59452304,"name":"Biological sciences/Psychology"},{"id":59452305,"name":"Social science/Psychology"}],"tags":[],"updatedAt":"2026-01-31T15:10:14+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-15 16:46:54","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8146436","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8146436","identity":"rs-8146436","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}