From Bytes to Bodies: How Does Internet Use Affect Subjective Health?

Abstract

Although an extensive research tradition has examined the impact of Internet access on individual well-being, limited attention has been given to its effect on subjective health. This study addresses this gap by utilizing data from the Chinese General Social Survey to explore the association between these two indicators in a developing country scenario. We find a significant positive correlation between Internet use and improvements in subjective health. Furthermore, this paper shows that Internet use can increase subjective health through income effect, health literacy, social relations, and the learning effect. Heterogeneity analysis indicates that the beneficial impact is more pronounced among women and individuals with lower education levels compared to men and those with higher education. Moreover, individuals in less developed regions experience greater improvements in subjective health from Internet access. Additionally, this study confirms that using the Internet as a primary source of information leads to greater improvements in subjective health than traditional media, and both work-related and leisure-related Internet use are confirmed to improve individuals’ subjective health. These findings suggest that expanding Internet access, particularly in economically disadvantaged areas, can serve as an effective strategy for developing countries to enhance public health outcomes.

Plain language summary

The Internet has become an essential part of people’s daily lives. This study explores how Internet use impacts subjective health by utilizing data from the Chinese General Social Survey. The results show that Internet use is beneficial for improving Chinese residents’ subjective health by increasing their income, cultivating health literacy, maintaining social relations, and enhancing learning activities. Besides, the conclusions indicate that the positive correlation between Internet use and subjective health may be more pronounced for the economically and socially vulnerable population. This study provides several policy implications for developing countries to improve public health through digital innovations

Keywords

Internet use subjective health health outcome well-being China

Introduction

Recent decades have witnessed the rapid development of information and communication technologies (ICTs), bringing about tremendous changes in economic and social systems worldwide (Abdulqadir & Asongu, 2022; Basnet & Donou-Adonsou, 2016; Chau et al., 2024; Işik & Alkaya, 2017; Niebel, 2018; Zhang, Cai, et al., 2019). In particular, with the widespread adoption of the Internet, nearly every aspect of human activity has become closely intertwined with digital networks. According to International Telecommunication Union (ITU), the global Internet penetration rate had reached 68% in 2024, a significant increase from just 16% in 2005 (ITU, 2025). The rapid diffusion of the Internet has profoundly influenced not only material aspects of life, such as income distribution (Bauer, 2018; Canh et al., 2020; Nie et al., 2024; Zhang & Gong, 2024), consumption (Wang & Hao, 2018; Zhang et al., 2022a), and the job market (Beard et al., 2012; Castellacci & Viñas-Bardolet, 2019), but also includes the public attitudes and perceptions toward the physical world and daily activities (Huang & Chen, 2022; Mitchell et al., 2011; Zhang, Cheng, et al., 2019, 2020) . Against this backdrop, exploring the connection between Internet use and individual welfare and cognition, as well as understanding the underlying mechanisms, is of great practical significance for public policy interventions.

Previous studies have long examined the relationship between Internet use and subjective well-being (SWB). However, there is still no clear consensus on this topic. First of all, the influence of digital technology on people’s SWB should depend on the purpose, intensity, and the content of online activities (Castellacci & Tveito, 2018; Longstreet & Brooks, 2017; Nakamura et al., 2012; Nie et al., 2017; Phu & Gow, 2019). Moreover, research has also shown that SWB is determined by people’s attitudes towards the information on the Internet (Zhang, Cheng, & Yu, 2020). Furthermore, the impacts of Internet use on SWB may differ significantly across populations. For example, it can be determined by users’ demographic characteristics and their socioeconomic status (Castellacci & Tveito, 2018; Hughes & Burke, 2018). Finally, differences in the indicators used to measure SWB (e.g., life satisfaction and subjective depression) may lead to substantial heterogeneity in the observed “happiness effect” of Internet use (Lu & Kandilov, 2021; Zhang, Cheng, & Yu, 2020).

Therefore, the present study aims to extend the underlying strand of research by estimating the influence of Internet use on subjective health, a self-reported indicator of an individual’s health status (Zhao et al., 2023), which is similar to and strongly associated with SWB (Bollen et al., 2021; Habibov & Afandi, 2016; Kekäläinen et al., 2020; Tiliouine, 2009). Specifically, by adopting the Chinese General Social Survey (CGSS) covering the period 2010 to 2021, this work estimates an ordered probit model and adopts the conditional mixed process (CMP) estimator to address potential endogeneity. We find that Internet use has a statistically significant and positive correlation with Chinese residents’ subjective health. The contributions of our study are as follows: firstly, it enriches literature on the association between Internet adoption with individual well-being by exploring how it affects people’s subjective health. Moreover, by using a pooled cross-sectional dataset from 2010 to 2021, this study provides a long-term analysis of this issue; secondly, it disaggregates the influences of Internet use on subjective health by individual characteristics, regional differences, information access channels, and the purpose of Internet use. The findings should provide additional information to reveal the internal relationship between the two indicators; thirdly, this paper further identifies four mechanisms through which Internet use improves individual subjective health: income effect, health literacy, social relations, and the learning effect. The conclusions are conducive to offer several implications for improving health outcomes in developing countries through targeted health interventions leveraging Internet technologies.

Literature Review and Theoretical Mechanism

Literature Review

Determinants of Individual Health Status

As a primary component of human capital, health is the foundation of human survival and critical to individual well-being and the sustainable development of nations (Angner et al., 2009; Cattaneo et al., 2009; Li & Jiang, 2023; Ljunge, 2016; Perneger et al., 2004; Zhou, Qin, & Liu, 2020). As Grossman (1972) suggested in his classic health capital model, health capital functions not only as a consumer good that enhances individual utility, but also as an investment good that contributes to income generation. The modern mainstream economic literature has widely established that health capital is a key driver of economic growth (e.g., Aísa & Pueyo, 2004; Bhargava et al., 2001; Fumagalli et al., 2024; Lucas, 1988; Romer, 1986; Well, 2007). Conversely, the deterioration of public health or emerging health threats poses significant challenges to regional economic development. For example, according to the Financing for Sustainable Development Report 2021, the COVID-19 pandemic may put many countries around the world at risk of losing a decade of development gains (United Nations, 2021).

An individual’s health status is determined by multiple factors (Zhang et al., 2022b). The prior research can be broadly categorized into the following aspects: (1) external environment and health outcomes. Within this strand, a growing body of research indicates that ecological degradation is closely linked to adverse health outcomes and increased healthcare costs (Aloi & Tournemaine, 2011; Do et al., 2018; Leibowitz, 2004; Lu et al., 2017); (2) economic development and its health impacts. One prominent area of focus is the impact of urbanization on public health. Some studies argued that urban areas often have abundant medical resources, advanced equipment, and skilled professionals, which collectively improve health outcomes as urbanization progresses and populations shift to cities (Liu et al., 2003; Galea et al., 2005; Vlahov et al., 2007). However, other research highlights the negative health consequences of urbanization, including higher rates of obesity, diabetes, and hypertension (Goryakin et al., 2017; Popkin, 1999; Van de Poel et al., 2012); (3) educational attainment and health outcomes. There is strong evidence supporting a correlation between higher education levels and better health outcomes, as education not only enhances economic opportunities but also influences health-related decisions and behaviors, including smoking, alcohol consumption, physical activity, and sexual practices (Belfield & Kelly, 2013; Cutler et al., 2010; Dursun et al., 2018; Groot et al., 2007; Keats, 2018; Ross & Wu, 1995); (4) health policy intervention and public health expenditure (e.g., Arthur & Oaikhenan, 2017; Edeme et al., 2017; Kim & Lane, 2013; Novignon et al., 2012); (5) other socioeconomic and non-economic determinants of health outcomes, such as health literacy (Paasche-Orlow & Wolf, 2007), tourism development (Godovykh & Ridderstaat, 2020), politics (Navarro, 2008), early experiences (Chen & Zhou, 2007; Cheng et al., 2024), water quality (Gundry et al., 2004; Zhang, 2012), etc.

Objective Health and Subjective Health

Both objective health and subjective health are widely used to measure public health status in previous literature. In general, objective health refers to the physical and biochemical indicators obtained through medical examinations or laboratory tests, along with other observable metrics. For example, at the micro level, objective health indicators can be the number of surgeries, whether a person has chronic disease, and body mass index (BMI) (Chen & Liu, 2022; Elran-Barak et al., 2019; Li et al., 2018; Linn & Linn, 1980; Wu et al., 2013). At the macro level, proxies such as infant mortality rate, life expectancy, HIV/AIDS-related mortality, and total population mortality are often utilized to represent objective health (see as Oduyemi et al., 2021; Oladosu et al., 2022; Vu, 2020; Zhang et al., 2022b)

In contrast, subjective health refers to an individual’s self-assessment of their health status, often known as self-reported health. Subjective health is important because it encompasses not only physical and mental health but also cognitive well-being, capturing potential health risks that objective indicators might overlook (Bollen et al., 2021). For example, younger populations generally exhibit lower disease prevalence, making objective indicators less effective for assessing their health status. Subjective health is particularly valuable in such cases, providing insights into health conditions that are difficult to quantify. As defined by World Health Organization (WHO, 1948), ‘good’health is a state of complete physical, psychological and social wellbeing and not merely the absence of disease or infirmity.

Effect of Internet Access on Health Status

As a representative general-purpose technology (GPT), Internet technology has increasingly integrated with the medical field in recent years (Croll, 2011; Zhang et al., 2022b). The advancements of digital technology and the Internet of Things (IoT) have driven the development of innovative solutions that provide comprehensive support for residents’ health needs (Andargoli, 2021). For instance, the advent of electronic health (e-health) systems is regarded as a pivotal innovation to improve medical services (Altab et al., 2022). Additionally, the Internet has become an essential channel for governments and public health institutions to advance healthcare reforms and disseminate health-related policies efficiently. Moreover, with the growing popularity of platforms such as WeRun on WeChat and menstrual tracking apps, the Internet is playing an increasingly important role in shaping personal health management practices.

Against this backdrop, a substantial body of research has explored the impact of Internet diffusion on public health outcomes, with mixed conclusions. From a macro perspective, studies by Lee et al. (2016), Dutta et al. (2019), Majeed and Khan (2019), Rana et al. (2020), and Zhang et al. (2022b) suggested that Internet expansion can improve public health outcomes, such as increasing life expectancy and reducing mortality rates. At the micro level, de Jong et al. (2014) found that Internet-based asynchronous communication improves the management of chronic conditions. Using data on older adults in Japan, Nakagomi et al. (2022) confirmed a positive relationship between Internet usage and subsequent health improvements among the elderly.

Inversely, other studies argued that the popularity of the Internet does not necessarily lead to improvements in health outcomes, especially the effects of excessive Internet use or Internet addiction. Research indicated that excessive use of the Internet may reduce an individual’s interpersonal ability (Akbulut, 2013; Gür et al., 2015) and increase their over-dependence on the Internet, which negatively affects psychological well-being and productivity (Bélanger et al., 2011; Kuss & Griffiths, 2011; Gür et al., 2015; Li et al., 2014). There is also evidence that excessive Internet use is associated with the sleep deprivation (Do et al., 2013). Additionally, over-reliance on social networking sites has been shown to have many negative effects on individuals, such as neglect of personal life, escapism, mood instability, and hiding online behaviors (Kuss & Griffiths, 2011; Valenzuela et al., 2014).

Theoretical Mechanism: Internet Use and Subjective Health

This paper elaborates the association mechanism between Internet use and an individual’s subjective health from four paths (see as in Figure 1).

Figure 1.

Relationship between Internet use and subjective health.

The first mechanism is the income effect. According to Grossman’s health capital model (Grossman, 1972), increased income plays a critical role in enhancing individuals’ perceptions of health. Higher income not only allows for greater material consumption but also facilitates access to leisure and recreational activities that contribute to overall well-being. Moreover, it enables individuals to adopt effective health management strategies when facing health risks, thereby boosting their ability and confidence to address potential future challenges. Over the past few decades, the Internet has become a pivotal tool in the labor market. A substantial body of economic research has demonstrated that Internet use significantly contributes to higher income levels in the labor force (e.g., DiMaggio & Bonikowski, 2008; Falck et al., 2021; Zhou, Cui, & Zhang, 2020). Furthermore, the Internet provides disadvantaged populations with a wider range of resources and opportunities to increase their income, such as promoting online consumption activities (Zhang et al., 2022a). Therefore, Internet use may enhance subjective health by increasing personal income.

The second pathway through which Internet use influences individual subjective health is by enhancing health literacy. In the digital age, the Internet has become a dominant source of information, revolutionizing the way knowledge is stored, disseminated and accessed. Its advantages, such as low transmission costs and wide accessibility, have facilitated the spread of modern health-related knowledge, raising awareness of potential health risks associated with unhealthy habits like smoking and excessive drinking (Zhang et al., 2022b). Du et al. (2020) found that during the COVID-19 pandemic, individuals increasingly turned to the Internet for emotional support and health-related information. Similarly, Suziedelyte (2012) has shown that searching for online health information can further increase users’ demand for health services. Furthermore, unlike the pre-digital era, when medical information was largely confined to professionals, the Internet can provide convenient medical knowledge and resources for people to engage in health management activities (Rice, 2006; Ybarra & Suman, 2006; Zhang et al., 2022b; Zucco et al., 2018). For example, various health-related Internet applications can help users record and track their health data (such as physical activity, diet, and sleep patterns), as well as provide individuals with additional personalized health advice (such as medication reminders and health goal setting). According to planned behavior theory (Ajzen, 1991), this accessibility and technological innovations can enhance individual perceived behavioral control, thereby increasing their behavioral motivation to make informed health decisions and take action on positive health management activities.

The third mechanism by which Internet use impacts subjective health lies in its influence on social relations. An extensive body of research underscored the importance of social connections in promoting subjective health (see as Berkman, 1995; Holt-Lunstad, 2018; Lin, 1999; Su & Ferraro, 1997; Tsur et al., 2019). Some literature indicated that social relations can affect people’s participation in health promoting behaviors and provide crucial social support during health challenges (Melchior et al., 2003; Tay et al., 2013). Other studies even suggested that social relations can predict mortality (Avlund et al., 1998; Helweg-Larsen et al., 2003; Rasulo et al., 2005). As a powerful communication tool and platform, the Internet eliminates space-time barriers and helps individuals build stronger social networks. This is particularly beneficial for marginalized groups, providing them with broader social support. For instance, social media platforms and search engines facilitate connections between individuals with shared experiences, thus greatly improving one’s ability to obtain social support. In this regard, using data from seven European countries, Wangberg et al. (2008) demonstrated that Internet use can predict positive subjective health outcomes by strengthening social support networks.

The fourth pathway is through the learning effect. Lifelong learning has long been recognized as a critical factor in personal health (e.g., Hammond, 2004; Narushima, 2008; Narushima et al., 2018). Learning activities stimulate intellectual engagement, foster critical thinking, promote adaptability, and help individuals navigate evolving social demands (Hammond, 2004). Coleman (2017) suggested that even a short time of reading can significantly reduce people’s stress. The Internet has revolutionized knowledge dissemination, drastically reducing educational costs, expanding access to learning opportunities, and making lifelong learning more attainable. According to self-determination theory (Deci & Ryan, 1985), the Internet greatly promotes autonomy by providing rich personalized learning resources and platforms, enabling learners to freely choose content and methods according to their interests and needs. In addition, by incorporating online discussion boards and interactive games, the Internet platform creates an interactive learning experience that enhances the fun and engagement of learning and helps to increase an individual’s intrinsic motivation to learn.

Based on the above analysis, we propose the following research hypotheses:

H1: Internet use is positively correlated with individual subjective health.

H2: Internet use can improve an individual’s subjective health via income effect.

H3: Internet use can improve an individual’s subjective health via health literacy.

H4: Internet use can improve an individual’s subjective health via social relations

H5: Internet use can improve an individual’s subjective health via learning effects

Methodology

Material

The empirical data for this study is sourced from CGSS. The survey is mainly performed by the Renmin University of China. Each year, CGSS conducts face-to-face interviews with more than 10,000 respondents, collecting information that reflects China’s economic and social development from multiple dimensions. The CGSS has gained significant social influence, serving not only as an important reference for government policymaking but also as a key resource for academic research on Chinese social issues (e.g., Ma & Chen, 2020; Zhang, Cheng, et al., 2020). To date, CGSS has released data in three phases. The first phase, initiated in 2003, includes five annual surveys, while the second phase, starting in 2010, encompasses six annual surveys. The third phase started in 2021, and only data conducted in 2021 have been published so far. Since the first phase lacks data on respondents’ Internet usage behavior, this study utilizes data from the second phase and third phase. Specifically, it includes data from the CGSS conducted in 2010, 2012, 2013, 2015, 2017, 2018, and 2021, forming a pooled cross-sectional dataset (the 2011 survey does not include Internet use information).

Benchmark Model

We adopt the following econometric model to investigate the relationship between Internet use and an individual’s subjective health:

Subjective_healt h_{i} = a_{0} + β_{1} Internet us e_{i} + g X_{i} + ε_{i}

(1)

where $subjective_healt h_{i}$ is the dependent variable, representing the respondents’ subjective assessment of their health status. It is an ordered variable ranging from 1 (very unhealthy) to 5 (very healthy). Consistent with Gong et al. (2020), Zhang, Cheng, and Yu, (2020), and Zhao et al. (2022), the key independent variable of interest, $Internet us e_{i}$ , is set as a binary variable with 1 for Internet users and 0 otherwise. Vector $X_{i}$ contains several control variables discussed in the subjective health literature. Referencing to Evans et al. (2020) and Acosta-González and Marcenaro-Gutiérrez, (2021), the control variables in our study mainly comprise several demographic and socioeconomic characteristic variables, including gender, age, age squared, ethnicity, political belief, hukou, marital status, education. Additionally, dummy variables for province and year are controlled to account for temporal and regional heterogeneity. The parameters $a_{0}$ and $ε_{i}$ represent the constant term and error term, respectively. Our parameter of interest, β₁, is hypothesized to be significantly positive, indicating a beneficial effect of Internet use on subjective health.

The dataset has been processed as follows: samples with uncertain information about the respondent’s answer and samples with missing values for all variables are removed. Finally, a total of 78,900 samples are obtained in the benchmark model analysis. Table 1 reports the detailed information on above variables. It can be found that the average subjective health was 3.5677 and 50.06% of the samples were Internet users. The average age of the sample was about 50, with a male ratio of 48.02%. Moreover, the proportion of urban residents in our sample is 62.27%, and the proportion of married population is 76.99%, while more than half of the samples have only received the education of junior high school and below.

Table 1.

Descriptive Statistics of Variables.

Variable	Definition	Mean	SD
Subjective_health	Five categories: very unhealthy = 1 and very healthy = 5	3.5677	1.0938
Internet use	Using the Internet = 1, otherwise = 0	0.5006	0.5000
Gender	Male = 1, female = 0	0.4802	0.4996
Age	The actual age of the respondents in the year they were surveyed	49.8977	16.6933
Ethnicity	Han = 1, else = 0	0.9200	0.2713
Political belief	Party member =1, else =0	0.1136	0.3173
Hukou	Urban =1, rural = 0	0.6227	0.4847
Marital status (ref: divorce or widowed)
Unmarried	Unmarried = 1, else = 0	0.1156	0.3197
In marriage	In marriage = 1, else = 0	0.7699	0.4209
Education (ref: illiteracy)
Primary school	Primary school = 1, else = 0	0.2282	0.4197
Junior high school	Junior high school = 1, else = 0	0.2821	0.4500
Senior high school	Senior high school = 1, else = 0	0.1853	0.3886
College and above	College and above = 1, else = 0	0.1748	0.3798
Year dummies (ref: 2010)
2012	Year is 2012 = 1, else = 0	0.1484	0.3555
2013	Year is 2013 = 1, else = 0	0.1433	0.3504
2015	Year is 2015 = 1, else = 0	0.1376	0.3445
2017	Year is 2017 = 1, else = 0	0.1589	0.3656
2018	Year is 2018 = 1, else = 0	0.1613	0.3678
2021	Year is 2021 = 1, else = 0	0.1027	0.3035

Mediating Variable

As discussed in the theoretical section, we attempt to examine how Internet use affects subjective health through four channels: income effect, health literacy, social relations, and the learning effect. To achieve this, our work adopts the mediating effect model to identify the above four influence channels (Baron & Kenny, 1986). Specifically, the mediating effect model involves two key steps. In the first step, we estimate the impact of Internet use on the mediating variables (e.g., income, health literacy, social relations, and learning activities). In the second step, we incorporate both Internet use and the mediating variables into Equation (1) to assess their combined influence on subjective health.

According to CGSS, the four mediating variables are set as follows: (1) the mediating variable for income effect is measured by the logarithm of personal total income plus 1 (denoted by income); (2) the frequency of residents participating in physical exercise is employed as a proxy for health literacy. This indicator is an ordered variable ranging from “never = 1” to “every day = 5” (denoted by exercise); (3) the mediating variable for social relations is measured by the frequency of social activities conducted by respondents over the past year. The variable is an ordered variable with “never = 1”, “rarely = 2”, “sometimes = 3”, “often = 4”, and “very frequently = 5” (denoted by social); 4) the mediating variable for learning effect is obtained by asking the question about the frequency of learning activities carried out by the respondents in the past year. Similar to social relations variable, it is an ordered variable scale from “never = 1” to “very frequently = 5” (denoted by study).

Internet Use and Individual Subjective Health: Statistical Description

Figure 2 shows the average subjective health for Internet users and non-users. The results indicate that the average subjective health for Internet users is 3.90, which is significantly (verified by t-test) higher than that for non-Internet users (3.23).

Figure 2.

Average subjective health for Internet users and non-users.

Table 2 reports the average subjective health scores across various population groups. First of all, the average subjective health of different populations varies. Specifically, men and urban residents report higher average subjective health scores compared to women and rural residents, respectively. Additionally, the average subjective health decreases with age but increases with the education level. Differences in subjective health are also observed across marital status, with unmarried individuals reporting the highest average subjective health, followed by married individuals, while divorced or widowed individuals report the lowest scores. Furthermore, across all population subgroups, respondents who use the Internet consistently report higher average subjective health scores than those who do not.

Table 2.

Average Subjective Health for Different Populations.

	Non-Internet users	Internet users	Total sample
Male	3.32	3.95	3.65
Female	3.16	3.85	3.50
Urban	3.31	3.91	3.69
Rural	3.17	3.86	3.37
Age < 45	3.77	4.11	4.04
65 > Age > 44	3.26	3.62	3.41
Age > 64	2.96	3.31	3.01
Divorce or widowed	2.96	3.48	3.08
Unmarried	3.12	4.19	4.06
In marriage	3.30	3.85	3.57
Illiteracy	2.92	3.46	2.97
Primary school	3.19	3.58	3.27
Junior high school	3.45	3.84	3.65
Senior high school	3.46	3.93	3.80
College and above	3.47	4.06	4.02

Figures 3 and 4 present the average subjective health for Internet users and non-users across years and regions, respectively. The results still indicate that respondents using the Internet report higher average subjective health than their comparators. Besides, the average subjective health status also varies by regions and years, indicating that it is necessary to consider the temporal and regional heterogeneity in our model.

Figure 3.

Average subjective health for Internet users and non-users across regions.

Figure 4.

Average subjective health for Internet users and non-users across years.

Results and Discussions

Baseline Results

Considering that subjective health is an ordered variable from 1 to 5, we adopt the ordered probit model to estimate Equation (1). Column (1) in Table 3 reports the results without including control variables and it shows a statistically significant positive correlation between Internet use and subjective health. After incorporating control variables, the results in column (2) show that Internet users are 4.36% more likely to feel “very healthy” compared to non-Internet users. Furthermore, columns (3)-(7) give estimation results using the annual survey data separately. Results suggest that using the Internet can significantly improve people’s subjective health for each year. Therefore, research hypothesis H1 is verified. Our results are consistent with Khan et al. (2016), Wangberg et al. (2008), and Wan et al. (2022), who suggested that Internet use is conducive to improving individual health. Moreover, the findings in our study also supports the growing body of literature emphasizing the role of Internet use (or smartphone use) in empowering people to maintain subjective well-being (see as Greyling, 2018; Nie et al., 2021; Li & Zhou, 2021).

Table 3.

Baseline Regression Result: Internet Use and Subjective Health (Marginal Effect).

Variable	Total sample		2010	2012	2013	2015	2017	2018	2021
Variable	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)	(9)
Internet use	0.1978^***(0.0022)	0.0436***(0.0028)	0.0399***(0.0080)	0.0232***(0.0070)	0.0408***(0.0084)	0.0440***(0.0075)	0.0454***(0.0065)	0.0697***(0.0066)	0.0460***(0.0087)
Gender		0.0317***(0.0020)	0.0434***(0.0057)	0.0433***(0.0051)	0.0348***(0.0060)	0.0304***(0.0055)	0.0241***(0.0045)	0.0275***(0.0047)	0.0208***(0.0058)
Age		−0.0133^***(0.0004)	−0.0159^***(0.0012)	−0.0141^***(0.0010)	−0.0126^***(0.0012)	−0.0124^***(0.0011)	−0.0123^***(0.0009)	−0.0112^***(0.0009)	−0.0124^***(0.0011)
Age squared		0.0001^***(0.0000)	0.0001^***(0.0000)	0.0001^***(0.0000)	0.0001^***(0.0000)	0.0001^***(0.0000)	0.0001^***(0.0000)	0.0001^***(0.0000)	0.0001^***(0.0000)
Ethnicity		−0.0023(0.0042)	−0.0263^**(0.0117)	−0.0252^**(0.0114)	−0.0433^***(0.0128)	−0.0078(0.0121)	−0.0153(0.0101)	0.0168(0.0106)	−0.0046(0.0126)
Political belief		0.0236^***(0.0032)	0.0134(0.0090)	0.0123(0.0079)	0.0330^***(0.0097)	0.0387^***(0.0093)	0.0319^***(0.0071)	0.0200^***(0.0076)	0.0285^***(0.0089)
Hukou		0.0251^***(0.0024)	0.0098(0.0071)	0.0243^***(0.0062)	0.0081(0.0073)	0.0243^***(0.0066)	0.0346^***(0.0058)	0.0390^***(0.0061)	0.0203^***(0.0065)
Marital status(ref: divorce or widowed)
Unmarried		−0.0191^***(0.0046)	−0.0269^*(0.0140)	−0.0305^**(0.0123)	−0.0415^***(0.0143)	−0.0178(0.0141)	−0.0133(0.0107)	−0.0097(0.0113)	−0.0035(0.0134)
In marriage		0.0214^***(0.0032)	0.0298^***(0.0096)	0.0141^*(0.0083)	0.0149(0.0099)	0.0137(0.0092)	0.0188^***(0.0072)	0.0191^**(0.0077)	0.0201^**(0.0094)
Education(ref: illiteracy)
Primary school		0.0297^***(0.0032)	0.0452^***(0.0091)	0.0428^***(0.0076)	0.0155(0.0099)	0.0365^***(0.0095)	0.0289^***(0.0081)	0.0193^**(0.0081)	0.0254^**(0.0117)
Junior high school		0.0586^***(0.0034)	0.0800^***(0.0097)	0.0680^***(0.0082)	0.0511^***(0.0108)	0.0507^***(0.0101)	0.0622^***(0.0085)	0.0387^***(0.0085)	0.0467^***(0.0118)
Senior high school		0.0699^***(0.0040)	0.0862^***(0.0112)	0.0858^***(0.0097)	0.0708^***(0.0126)	0.0634^***(0.0115)	0.0697^***(0.0097)	0.0468^***(0.0094)	0.0607^***(0.0131)
College and above		0.0701^***(0.0045)	0.0978^***(0.0136)	0.0909^***(0.0115)	0.0572^***(0.0140)	0.0611^***(0.0129)	0.0881^***(0.0105)	0.0457^***(0.0104)	0.0515^***(0.0141)
Province dummy	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
Year dummy	Yes	Yes	—	—	—	—	—	—	—
N	78,900	78,900	11,659	11,711	11,307	10,858	12,540	12,724	8,101

Note.^*, ^**, ^*** represents 10%, 5%, and 1% levels. Robust standard errors are reported in parentheses. Data are from the Chinese General Social Survey from 2010 to 2021. Results are marginal effect of each variable on subjective health at 5.

As for the control variables, we find a significant positive correlation between gender and subjective health, indicating that men feel healthier than women in China. The result is consistent with the most well-established research on gender differences in subjective health (e.g., Assari, 2014; Eriksen et al., 1998). Age has a U-shaped relationship with the subjective health. In terms of different marital status, we find a positive association between marriage and higher subjective health, indicating that marriage plays a significant role in enhancing the happiness and well-being of Chinese residents. Political identity is significantly positively correlated with subjective health which can be attributed to party membership serving as an important form of social capital that influences personal material wealth, life perspectives, and psychological cognition in China. Hukou is positively correlated with subjective health, (significant at the 1% level), confirming that compared with rural residents, the subjective health of urban residents is higher. This can be explained by the fact that urban areas in China have more developed medical security system and more accessible living service facilities than rural areas. Consistent with Ross and Van Willigen (1997), Baron-Epel and Kaplan (2001), and Assari (2018), our study finds that access to education significantly improves Chinese residents’ subjective health. These findings underscore the importance of socioeconomic and demographic factors in shaping subjective health outcomes.

Robustness Checks

This paper conducts several robustness checks to ensure the reliability of above finding. Firstly, the key independent variable used in baseline model regression is a binary variable. Considering that the intensity of Internet use may vary among individuals, we replace the key independent variable with the frequency of Internet use (denoted by Internet_frequency). The indicator is an ordered variable ranging from “never = 1” to “every day = 5”. The results in Table 4 indicate that Internet_frequency is still positively associated with subjective health.

Table 4.

Robustness Check: Frequency of Internet Use and Subjective Health (Marginal Effect).

Variable	Total sample		2010	2012	2013	2015	2017	2018	2021
Variable	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)	(9)
Internet_frequency	0.0626^***(0.0006)	0.0145^***(0.0009)	0.0125^***(0.0029)	0.0086^***(0.0024)	0.0134^***(0.0029)	0.0161^***(0.0025)	0.0154^***(0.0020)	0.0191^***(0.0020)	0.0172^***(0.0025)
Control variable	No	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
Province dummy	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
Year dummy	Yes	Yes	—	—	—	—
N	78,900	78,900	11,659	11,711	11,307	10,858	12,540	12,724	8,101

Note.^*** represents 1% level. Robust standard errors are reported in parentheses. Data are from the Chinese General Social Survey from 2010 to 2021. Results are marginal effect of each variable on subjective health at 5.

Secondly, we replace the dependent variable with specific aspects of respondents’ subjective health. Two variables are constructed to capture the impact of health problems on their work activities and subjective mental health, denoted by subjective_job health and subjective_mental health, respectively. Specifically, subjective_job health is obtained by asking respondents whether their work or other daily activities were affected by health problems during the past 4 weeks. Analogously, subjective_mental health is constructed by asking respondents how often they felt depressed in the past 4 weeks. These two indicators are both ordered variables with “always = 1” to “never = 5”. Results in Table 5 show that Internet use is positively correlated with subjective_job and subjective_mental health, suggesting that using the Internet can improve an individual’s subjective health in work domain and subjective mental health.

Table 5.

Robustness Check: Replacing the Dependent Variable.

Variable	Subjective_job health		Subjective_mental health
Variable	(1)	(2)	(3)	(4)
Internet use	0.2394^***(0.0027)	0.0480^***(0.0039)	0.0955^***(0.0028)	0.0263^***(0.0038)
Control variable	No	Yes	No	Yes
Province dummy	Yes	Yes	Yes	Yes
Year dummy	Yes	Yes	Yes	Yes
N	78,715	78,715	78,728	78,728

Thirdly, we exploit other methods to estimate the relationship between Internet use and subjective health, as shown in Table 6. Columns (1) to (4) indicate that the coefficients of the key independent variable are still significantly positive under the generalized ordered logit regression and OLS estimator.

Table 6.

Robustness Check: Adopting Other Estimation Method.

Variable	Generalized ordered logit regression		OLS
Variable	(1)	(2)	(3)	(4)
Internet use	0.1928^***(0.0022)	0.0414^***(0.0027)	0.7197^***(0.0077)	0.1682^***(0.0101)
Control variable	No	Yes	No	Yes
Province dummy	Yes	Yes	Yes	Yes
Year dummy	Yes	Yes	Yes	Yes
N	78,900	78,900	78,900	78,900

Note.^*** represents 1% level. Standard errors are reported in parentheses. Data are from the Chinese General Social Survey from 2010 to 2021. Results are marginal effect of each variable on subjective health at 5.

Fourthly, Internet use in our study may be endogenous because of several unobservable factors that can influence both the residents’ Internet use and subjective health (e.g., personal preferences and early experience). Therefore, the instrumental variable method is adopted to address the endogeneity problem. In existing literature, regional informatization development level or aggregation indicator is widely used as the proxy of the instrumental variable for Internet use or Internet penetration (Castellacci & Viñas-Bardolet, 2019; Zhang et al., 2022a; Zheng et al., 2019). In general, whether a person uses the Internet is partly determined by the regional informatization level. Nevertheless, the development level of regional informatization, which often depends on government investment policies should be exogenous to an individual’s subjective health. Therefore, we utilize fixed broadband penetration in respondents’ province to construct an instrumental variable. To further ensure the exogeneity of that instrumental variable, we lag fixed broadband penetration by 3 years. Notably, we also add instrumental variable to the baseline model and run a OLS regression, as shown in column (1) of Table 7. The results suggest that the coefficient of the instrumental variable is not significant, indicating that after controlling for Internet use, the instrumental variable should have no direct effect on subjective health. This finding supports, to some extent, that the instrumental variable meets the exclusion restrictions (Zhang et al., 2022a).

Table 7.

Internet Use and Subjective Health: CMP Model.

		CMP model
	Exclusion restriction test	Internet use	Subjective_health
Variable	(1)	(2)	(3)
Internet use	0.1679^***(0.0101)		0.0558^***(0.0077)
Fixed broadband penetration	−0.0955(0.1136)	0.6925^***(0.0071)
Atanhrho_12		−0.0303^*(0.0179)
Control variable	Yes	Yes	Yes
N	78,900	78,900	78,900

Because subjective health is an ordered variable, this paper uses the conditional mixed process estimator (CMP) to conduct the instrumental variable estimation (Roodman, 2011). In our study, the CMP model comprises a probit model to predict the probability of an individual using the Internet and an ordered probit to estimate the association between Internet use and subjective health. CMP model employs the maximum likelihood method to jointly estimate Internet use model and subjective health model, and generate the correlation coefficients (as shown in Atanhrho_12 test) of their error terms to determine whether an endogeneity problem exists. As shown in columns (2) and (3), the results indicate that instrumental variable is positively correlated with Internet use, and Internet use is also significantly positively correlated with subjective health, further supporting the main conclusion.

Heterogeneity Analysis

The previous sections have examined the correlation between Internet use and individuals’ subjective health in the full sample. However, the relationship may exhibit heterogeneity across different populations and regions. Therefore, this paper further analyzes the impact of Internet use on individual subjective health by introducing interaction terms between Internet use and socioeconomic characteristics into the benchmark model.

As shown in Table 8, column (1) reports the results of adding the interaction term between Internet use and education into the benchmark model. Education is recoded as a binary variable: 1 if respondents have received senior high school education or above and 0 otherwise (denoted as high education). Column (2) presents the gender differences in the health effects of Internet use, while column (3) examines whether the relationships vary by age. Accordingly, the sample is divided into three age groups: young (age < 45), middle-aged (45 ≤ age < 65), and old-aged (age ≥ 65), with two corresponding dummy variables.

Table 8.

Internet Use and Subjective Health: Heterogeneity Analyses By Education, Gender, and Age.

Variable	Subjective_health
Variable	(1)	(2)	(3)
Internet use	0.0562^***(0.0030)	0.0488^***(0.0034)	0.0593^***(0.0031)
Internet use × high education	−0.0227^***(0.0050)
Internet use × gender		−0.0105^***(0.0039)
Internet use × middle-aged			0.0077^**(0.0034)
Internet use × old-aged			0.0011(0.0044)
Control variable	Yes	Yes	Yes
Province dummy	Yes	Yes	Yes
Year dummy	Yes	Yes	Yes
N	78,900	78,900	78,900

Note. ^**, and ^*** represents 5% and 1% levels. Robust standard errors are reported in parentheses. Data are from the Chinese General Social Survey from 2010 to 2021. Results are marginal effect of each variable on subjective health at 5.

First, the interaction term between Internet use and education is significantly and negatively correlated with subjective health. This indicates that Internet use has a greater facilitating effect on subjective health of individuals with lower education levels. A possible explanation is that, compared with the lower-educated population, individuals with higher education levels generally have higher incomes and stronger health awareness. Therefore, the Internet offers lower-educated individuals more opportunities to improve their health by compensating for material disadvantages. For example, these benefits can include increasing their income through non-agricultural employment and providing better access to medical services and products via online healthcare platforms. Second, regarding gender differences, column (2) confirms that the correlation between Internet use and subjective health is more pronounced for women. This may be because the Internet provides Chinese women with more job opportunities, medical information, and social networks, helping to offset their traditionally disadvantaged position in society. Third, column (3) shows that the association between Internet use and subjective health does not differ significantly between younger and older adults. However, Internet use has a stronger health-enhancing effect on middle-aged individuals. Interestingly, compared with younger people, the elderly often have lower Internet access rates. In recent years, many developing countries have been entering an aging society. This is especially true in rural areas, where the migration of young laborers to non-agricultural jobs has left many elderly individuals living alone, leading to inadequate care for their physical and mental health. Therefore, the finding may imply that the Internet holds significant potential for improving health outcomes in aging societies.

Table 9 presents the results of adding the interaction terms between Internet use and regional dummy variables into the benchmark model. Column (1) examines the urban-rural differences, while column (2) explores regional disparities among eastern China, central China, western China, and the northeastern China. The results reveal that the positive impact of Internet use on subjective health is stronger in rural areas compared to urban areas. Additionally, this positive effect is more pronounced in central China, western China, and northeastern China than that in eastern China. In general, China’s urban and eastern regions generally have better medical resources and higher levels of economic development than rural areas and the central and western regions. Thus, the conclusions suggest that Internet access is expected to reduce health inequalities between regions within China.

Table 9.

Internet Use and Subjective Health: Heterogeneity Analyses by Regions.

Variable	Subjective_health
Variable	(1)	(2)
Internet use	0.0579^***(0.0041)	0.0423^***(0.0028)
Internet use × Hukou	−0.0220^***(0.0045)
Internet use × Central China		0.0199^***(0.0033)
Internet use × Western China		0.0261^***(0.0034)
Internet use × Northeastern China		0.0249^***(0.0043)
Control variable	Yes	Yes
Province dummy	Yes	Yes
Year dummy	Yes	Yes
N	78,900	78,900

Information Access Channels, Purpose of Internet Use and Subjective Health

As an emerging medium for information access, the Internet differs significantly from traditional media such as newspapers and television. For example, the Internet allows users to conveniently search for health-related information in real time. With advancements in online healthcare and big data technologies, the Internet can provide people with more professional health management and medical information services and plays an increasingly important role in safeguarding public health. With that in mind, we further explore how residents’ different information access channels affect their subjective health. Specifically, based on the question in CGSS “which is your most important information source media (including newspaper, magazine, radio, television, Internet, and mobile phone customization)”, we categorize personal information access channels into five dummy variables. Table 10 presents the impact of these channels on subjective health. The results show that using the Internet as the primary source of information has a significantly stronger positive effect on subjective health compared to traditional media such as newspapers, magazines, radio, television, and mobile phone customization.

Table 10.

Information Access Channels and Subjective Health.

Variable	Subjective_health
Information access channels (ref: newspaper)
Magazine	0.0071(0.0245)
Radio	−0.0406^***(0.0083)
Television	0.0019(0.0056)
Internet	0.0223^***(0.0060)
Mobile phone customization	0.0023(0.0104)
Control variable	Yes
Province dummy	Yes
Year dummy	Yes
N	77,089

Note. ^*** represents 1% level. Robust standard errors are reported in parentheses. Data are from the Chinese General Social Survey from 2010 to 2021. Results are marginal effect of each variable on subjective health at 5.

In addition, considering that differences in the purpose of Internet use may determine the association between Internet use and subjective health, this paper further examines the impact of different types of Internet use purposes on subjective health. Due to data limitations, we only examine the impact of the use of the Internet at leisure time (such as for entertainment, chat, and video watching) and the use of the Internet at work on subjective health. Specifically, we construct a binary variable that reflects whether respondents use the Internet in their leisure time (0 for never use, 1 for otherwise) and add it to the baseline model. Table 11 shows that Internet use at leisure time can significantly improve subjective health. Moreover, the coefficient of the key independent variable is still significantly positive after controlling for the variable of Internet use at leisure time, indicating that the use of the Internet at work can also significantly promote an individual’s subjective health (Gong et al., 2020).

Table 11.

Purpose of Internet Use and Subjective Health.

Variable	Subjective_health
Internet	0.0252^***(0.0048)
Internet use at leisure time	0.0224^***(0.0048)
Control variable	Yes
Province dummy	Yes
Year dummy	Yes
N	78,793

Mechanism Analysis

As aforementioned, we further examine the mechanism through which Internet use affects subjective health by analyzing four pathways. In this section, we remove the missing values for all mediating variables and 71,987 samples are used for the analysis. Table 12 shows the estimation results that determine the relationship between Internet use and mediating variables. The findings show that Internet use is positively correlated with all four mediating variables, indicating that Internet use can enhance income, foster healthy habits, increase social interaction, and promote learning activities.

Table 12.

The Relationship Between Internet Use and Mediating Variables.

Variable	Income	Exercise	Social	Study
Variable	(1)	(2)	(3)	(4)
Internet use	0.5861^***(0.0344)	0.0685^***(0.0025)	0.0179^***(0.0010)	0.0252^***(0.0007)
Control variable	Yes	Yes	Yes	Yes
Province dummy	Yes	Yes	Yes	Yes
Year dummy	Yes	Yes	Yes	Yes
N	71,987	71,987	71,987	71,987

Note: ^*** represents 1% level. Robust standard errors are reported in parentheses. Data are from the Chinese General Social Survey from 2010 to 2021.

Table 13 reports the results on the association of subjective health with Internet use and mediating variables. It indicates that both Internet use and the four mediating variables have significant positive correlation with individual subjective health. The findings suggest that Internet use can improve an individual’s subjective health via income effect, health literacy, social relations, and learning effect. Therefore, the research hypotheses H2-H5 are supported.

Table 13.

Internet Use and Subjective Health: Mechanism Checks.

Variable	Subjective_health
Variable	(1)	(2)	(3)	(4)	(5)
Internet use	0.0396^***(0.0029)	0.0384^***(0.0029)	0.0392^***(0.0029)	0.0373^***(0.0029)	0.0286^***(0.0029)
Income	0.0063^***(0.0003)				0.0061^***(0.0003)
Exercise		0.0144^***(0.0007)			0.0115^***(0.0007)
Social			0.0235^***(0.0010)		0.0207^***(0.0010)
Study				0.0161^***(0.0012)	0.0093^***(0.0012)
Control variable	Yes	Yes	Yes	Yes	Yes
Province dummy	Yes	Yes	Yes	Yes	Yes
Year dummy	Yes	Yes	Yes	Yes	Yes
N	71,987	71,987	71,987	71,987	71,987

Note.*** represents 1% level. Robust standard errors are reported in parentheses. Data are from the Chinese General Social Survey from 2010 to 2021. Results are marginal effect of each variable on subjective health at 5.

Conclusion, Policy Implications and Limitations

Health is fundamental to human survival and has long been a focus for researchers and policymakers. Understanding the factors influencing both subjective and objective health is crucial for improving public welfare and developing effective health intervention policies that promote economic sustainability. In recent decades, information technologies, particularly the Internet, have become integral to both work and daily life. This study conducts an empirical analysis using CGSS data to explore the relationship between Internet use and subjective health, responding to the growing integration of Internet technology in the health sector. Our findings confirm that Internet use significantly enhances Chinese residents’ subjective health. Furthermore, we identify four key mechanisms through which this effect occurs: increased income, enhanced health literacy, strengthened social relationships, and greater engagement in learning activities. Additionally, our study reveals that the association between Internet use and subjective health varies across various demographic and regional groups. Specifically, Internet use has a more pronounced positive impact on women and individuals with lower education levels compared to men and those with higher educational attainment. Moreover, this effect is stronger in economically underdeveloped regions compared to more developed areas. Finally, individuals who primarily use the Internet to access information report higher levels of subjective health than those who rely on traditional information sources.

The finding in our study that Internet use can improve an individual’s subjective health has certain policy implications. Today, many potential threats to human health persist, particularly in developing countries where medical resources are insufficient and health literacy is lacking. In general, a healthy individual must not only maintain physical health, but also good mental health, the ability to adapt to society, and strong moral character. Therefore, our study underlines the importance of promoting information technology to improve public health. Moreover, the finding that Internet use has a more significant positive effect on subjective health in economically less developed areas highlights the potential of Internet penetration to compensate for the lack of medical infrastructure in these regions. In addition, our paper identifies several strategies for improving public health through the Internet platforms. These strategies may include: (1) strengthening Internet-related skills training to increase the income and well-being of disadvantaged populations; (2) disseminating modern health knowledge and improving citizens’ health literacy through online platforms; (3) providing health-related social support and enhancing social capital through online networks; (4) enhancing Internet-based learning platforms to promote the concept of lifelong learning among the public.

However, this paper has several limitations. First, it uses cross-sectional data rather than panel data, which makes it difficult to examine the long-term dynamic relationship between Internet use and individuals’ subjective health. Theoretically, changes in behavior and cognition due to Internet use are a dynamic and long-term process. Moreover, using data after 2021 to reveal recent changes in the relationship between Internet use and subjective health is also encouraged, especially in the post-pandemic era. This is important because the COVID-19 pandemic has significantly changed Internet usage patterns and may introduce new factors that affect health outcomes. Second, although we use instrumental variables to address endogeneity, the cross-sectional nature of the data omits certain individual and temporal factors, making it difficult to accurately establish the causal relationship between Internet use and improvements in subjective health. Third, due to data limitations, the dependent variable in this study is an ordered variable while the independent variable is binary variable. As a result, we cannot assess how specific online content or activities affect subjective health. Fourth, the sample only includes individuals aged 17 and over, and future research could explore whether Internet use affects subjective health for adolescents. Finally, this paper focuses only on how Internet use improves subjective health through income effect, health literacy, social relations, and learning effect. Future studies could identify other aspects of Internet use that may negatively affect subjective health and further explore whether the improvement effect of Internet use on individuals’ subjective health will further lead to changes in their objective health indicators.

Footnotes

ORCID iD

Huirong Zhang

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the National Social Science Foundation of China (18BJY108), Shanghai Pujiang Program (22PJC042), the Chinese Ministry of Education Youth Fund Project (23YJC630228), and the Fundamental Research Funds for the Central Universities (2023ECNU-YYJ040).

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Data Availability Statement

The data used in this study are publicly available from the Chinese General Social Survey (CGSS) at .

References

Abdulqadir

I. A.

Asongu

S. A.

(2022). The asymmetric effect of internet access on economic growth in sub-Saharan Africa. Economic Analysis and Policy, 73, 44–61. https://doi.org/10.1016/j.eap.2021.12.009

Acosta-González

H. N.

Marcenaro-Gutiérrez

O. D.

(2021). The relationship between subjective well-being and self-reported health: Evidence from Ecuador. Applied Research in Quality of Life, 16, 1961–1981. https://doi.org/10.1007/s11482-020-09832-1

Ajzen

(1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179–211. https://doi.org/10.1016/0749-5978(91)90020-T

Aísa

Pueyo

(2004). Endogenous longevity, health and economic growth: A slow growth for a longer life. Economics Bulletin, 9(3), 1–10.

Akbulut

(2013). Developmental implications of computer and internet use among children and adolescents. Trakya University Journal of Education, 3(2), 53–68.

Aloi

Tournemaine

(2011). Growth effects of environmental policy when pollution affects health. Economic Modelling, 28(4), 1683–1695. https://doi.org/10.1016/j.econmod.2011.02.011

Altab

H. M.

Bosompem

Okae-Adjei

Avornyo

Boadi

E. A.

Boadi

V. A.

Say

(2022). Personal and organisational resources on electronic health usage intentions in an emerging economy. Telematics and Informatics, 71, 101832. https://doi.org/10.1016/j.tele.2022.101832

Angner

Ray

M. N.

Saag

K. G.

Allison

J. J.

(2009). Health and happiness among older adults: A community-based study. Journal of Health Psychology, 14(4), 503–512. https://doi.org/10.1177/1359105309103570

Andargoli

A. E.

(2021). E-Health in Australia: A synthesis of thirty years of e-Health initiatives. Telematics and Informatics, 56, 101478. https://doi.org/10.1016/j.tele.2020.101478

10.

Arthur

Oaikhenan

H. E.

(2017). The effects of health expenditure on health outcomes in Sub-Saharan Africa (SSA). African Development Review, 29(3), 524–536. https://doi.org/10.1111/1467-8268.12281

11.

Assari

(2018). Blacks’ diminished return of education attainment on subjective health; Mediating effect of income. Brain Sciences, 8(9), 176. https://doi.org/10.3390/brainsci8090176

12.

Assari

(2014). Cross-country variation in additive effects of socio-economics, health behaviors, and comorbidities on subjective health of patients with diabetes. Journal of Diabetes & Metabolic Disorders, 13, Article 1. https://doi.org/10.1186/2251-6581-13-1

13.

Avlund

Damsgaard

M. T.

Holstein

B. E.

(1998). Social relations and mortality: An eleven-year follow-up study of 70-year-old men and women in Denmark. Social Science & Medicine, 47(5), 635–643. https://doi.org/10.1016/S0277-9536(98)00167-6

14.

Baron

R. M.

Kenny

D. A.

(1986). The moderator–mediator variable distinction in social psychological research: Conceptual, strategic, and statistical considerations. Journal of Personality and Social Psychology, 51(6), 1173–1182. https://doi.org/10.1037/0022-3514.51.6.1173

15.

Baron-Epel

Kaplan

(2001). General subjective health status or age-related subjective health status: Does it make a difference? Social Science & Medicine, 53(10), 1373–1381. https://doi.org/10.1016/S0277-9536(00)00428-0

16.

Basnet

H. C.

Donou-Adonsou

(2016). Internet, consumer spending, and credit card balance: Evidence from US consumers. Review of Financial Economics, 30, 11–22. https://doi.org/10.1016/j.rfe.2016.08.001

17.

Bauer

J. M.

(2018). The Internet and income inequality: Socio-economic challenges in a hyperconnected society. Telecommunications Policy, 42(4), 333–343. https://doi.org/10.1016/j.telpol.2017.05.009

18.

Bhargava

Jamison

D. T.

Lau

L. J.

Murray

C. J.

(2001). Modeling the effects of health on economic growth. Journal of Health Economics, 20(3), 423–440. https://doi.org/10.1016/S0167-6296(01)00073-X

19.

Beard

T. R.

Ford

G. S.

Saba

R. P.

Seals

R. A.

Jr. (2012). Internet use and job search. Telecommunications Policy, 36(4), 260–273. https://doi.org/10.1016/j.telpol.2011.12.001

20.

Bélanger

R. E.

Akre

Berchtold

Michaud

P. A.

(2011). A U-shaped association between intensity of Internet use and adolescent health. Pediatrics, 127(2), e330–e335. https://doi.org/10.1542/peds.2010-1235

21.

Belfield

C. R.

Kelly

I. R.

(2013). Early education and health outcomes of a 2001 US birth cohort. Economics & Human Biology, 11(3), 310–325. https://doi.org/10.1016/j.ehb.2013.03.006

22.

Berkman

L. F.

(1995). The role of social relations in health promotion. Psychosomatic Medicine, 57(3), 245–254. https://doi.org/10.1097/00006842-199505000-00006

23.

Bollen

K. A.

Gutin

Halpern

C. T.

Harris

K. M.

(2021). Subjective health in adolescence: Comparing the reliability of contemporaneous, retrospective, and proxy reports of overall health. Social Science Research, 96, 102538. https://doi.org/10.1016/j.ssresearch.2021.102538

24.

Canh

N. P.

Schinckus

Thanh

S. D.

Ling

F. C. H.

(2020). Effects of the internet, mobile, and land phones on income inequality and the Kuznets curve: Cross country analysis. Telecommunications Policy, 44(10), 102041. https://doi.org/10.1016/j.telpol.2020.102041

25.

Croll

P. R.

(2011). Determining the privacy policy deficiencies of health ICT applications through semi-formal modelling. International Journal of Medical Informatics, 80(2), e32–e38. https://doi.org/10.1016/j.ijmedinf.2010.10.006

26.

Castellacci

Tveito

(2018). Internet use and well-being: A survey and a theoretical framework. Research Policy, 47(1), 308–325. https://doi.org/10.1016/j.respol.2017.11.007

27.

Castellacci

Viñas-Bardolet

(2019). Internet use and job satisfaction. Computers in Human Behavior, 90, 141–152. https://doi.org/10.1016/j.chb.2018.09.001

28.

Cattaneo

M. D.

Galiani

Gertler

P. J.

Martinez

Titiunik

(2009). Housing, health, and happiness. American Economic Journal: Economic Policy, 1(1), 75–105. https://doi.org/10.1257/pol.1.1.75

29.

Chau

T. N.

Hong

N. V. T.

Thu

T. P. T.

Ramsawak

Thien

N. N.

(2024). Re-examining the effects of information and communication technology on economic growth. Technology in Society, 78, 102646. https://doi.org/10.1016/j.techsoc.2024.102646

30.

Chen

Liu

(2022). The effect of internet access on body weight: Evidence from China. Journal of Health Economics, 85, 102670. https://doi.org/10.1016/j.jhealeco.2022.102670

31.

Chen

Zhou

L. A.

(2007). The long-term health and economic consequences of the 1959–1961 famine in China. Journal of Health Economics, 26(4), 659–681. https://doi.org/10.1016/j.jhealeco.2006.12.001

32.

Cheng

Wang

N. N.

(2024). Long-term mental health cost of the Great Chinese Famine. Health Economics, 33(1), 121–136. https://doi.org/10.1002/hec.4753

33.

Coleman

(2017, February). Lifelong learning is good for your health, your wallet, and your social life. Harvard Business Review. https://hbr.org/2017/02/lifelong-learning-is-good-for-your-health-your-wallet-and-your-social-life

34.

Cutler

D. M.

Lleras-Muney

(2010). Understanding differences in health behaviors by education. Journal of Health Economics, 29(1), 1–28. https://doi.org/10.1016/j.jhealeco.2009.10.003

35.

Deci

E. L.

Ryan

R. M.

(1985). Intrinsic motivation and self-determination in human behavior. Plenum Press.

36.

de Jong

C. C.

Ros

W. J.

Schrijvers

(2014). The effects on health behavior and health outcomes of Internet-based asynchronous communication between health providers and patients with a chronic condition: A systematic review. Journal of Medical Internet Research, 16(1), e19. https://doi.org/10.2196/jmir.2876

37.

DiMaggio

Bonikowski

(2008). Make money surfing the web? The impact of Internet use on the earnings of US workers. American Sociological Review, 73(2), 227–250. https://doi.org/10.1177/000312240807300203

38.

Q. T.

Joshi

Stolper

(2018). Can environmental policy reduce infant mortality? Evidence from the Ganga Pollution Cases. Journal of Development Economics, 133, 306–325. https://doi.org/10.1016/j.jdeveco.2018.04.003

39.

Y. K.

Shin

Bautista

M. A.

Foo

(2013). The associations between self-reported sleep duration and adolescent health outcomes: What is the role of time spent on Internet use? Sleep Medicine, 14(2), 195–200. https://doi.org/10.1016/j.sleep.2012.11.013

40.

Yang

King

R. B.

Yang

Chi

(2020). COVID-19 increases online searches for emotional and health-related terms. Applied Psychology: Health and Well-Being, 12(4), 1039–1053. https://doi.org/10.1111/aphw.12234

41.

Dursun

Cesur

Mocan

(2018). The impact of education on health outcomes and behaviors in a middle-income, low-education country. Economics & Human Biology, 31, 94–114. https://doi.org/10.1016/j.ehb.2018.07.002

42.

Dutta

U. P.

Gupta

Sengupta

P. P.

(2019). ICT and health outcome nexus in 30 selected Asian countries: Fresh evidence from panel data analysis. Technology in Society, 59, 101184. https://doi.org/10.1016/j.techsoc.2019.101184

43.

Edeme

R. K.

Emecheta

Omeje

M. O.

(2017). Public health expenditure and health outcomes in Nigeria. American Journal of Biomedical and Life Sciences, 5(5), 96–102. https://doi.org/10.11648/j.ajbls.20170505.13

44.

Elran-Barak

Weinstein

Beeri

M. S.

Ravona-Springer

(2019). The associations between objective and subjective health among older adults with type 2 diabetes: The moderating role of personality. Journal of Psychosomatic Research, 117, 41–47. https://doi.org/10.1016/j.jpsychores.2019.01.001

45.

Eriksen

H. R.

Svendarod

Ursin

(1998). Prevalence of subjective health complaints in the Nordic European countries in 1993. The European Journal of Public Health, 8(4), 294–298. https://doi.org/10.1093/eurpub/8.4.294

46.

Evans

M. D. R.

Kelley

C. G. E.

Kelley

S. M. C.

(2020). Income inequality in the great recession did not harm subjective health in Europe, 2003–2012. Applied Research in Quality of Life, 15(5), 1451–1473. https://doi.org/10.1007/s11482-019-09741-3

47.

Falck

Heimisch-Roecker

Wiederhold

(2021). Returns to ICT skills. Research Policy, 50(7), 104064. https://doi.org/10.1016/j.respol.2021.104064

48.

Fumagalli

Pintor

M. P.

Suhrcke

(2024). The impact of health on economic growth: A narrative literature review. Health Policy, 143, 105039. https://doi.org/10.1016/j.healthpol.2024.105039

49.

Galea

Freudenberg

Vlahov

(2005). Cities and population health. Social Science & Medicine, 60(5), 1017–1033. https://doi.org/10.1016/j.socscimed.2004.06.036

50.

Godovykh

Ridderstaat

(2020). Health outcomes of tourism development: A longitudinal study of the impact of tourism arrivals on residents’ health. Journal of Destination Marketing & Management, 17, 100462. https://doi.org/10.1016/j.jdmm.2020.100462

51.

Gong

Zhang

Cheng

Wang

(2020). Internet use encourages pro-environmental behavior: Evidence from China. Journal of Cleaner Production, 256, 120725. https://doi.org/10.1016/j.jclepro.2020.120725

52.

Goryakin

Rocco

Suhrcke

(2017). The contribution of urbanization to non-communicable diseases: Evidence from 173 countries from 1980 to 2008. Economics & Human Biology, 26, 151–163. https://doi.org/10.1016/j.ehb.2017.03.004

53.

Greyling

(2018). Internet access and its relationship to subjective well-being in a developing region. South African Journal of Economic and Management Sciences, 21(1), 1–12. https://doi.org/10.4102/sajems.v21i1.2146

54.

Groot

Van Den Brink

H. M.

(2007). The health effects of education. Economics of Education Review, 26(2), 186–200. https://doi.org/10.1016/j.econedurev.2005.09.002

55.

Grossman

(1972). The demand for health: A theoretical and empirical investigation. NBER Books.

56.

Gundry

Wright

Conroy

(2004). A systematic review of the health outcomes related to household water quality in developing countries. Journal of Water and Health, 2(1), 1–13. https://doi.org/10.2166/wh.2004.0001

57.

Gür

Yurt

Bulduk

Atagöz

(2015). Internet addiction and physical and psychosocial behavior problems among rural secondary school students. Nursing & Health Sciences, 17(3), 331–338. https://doi.org/10.1111/nhs.12194

58.

Habibov

Afandi

(2016). Does life satisfaction determine subjective health? Applied Research in Quality of Life, 11(2), 413–428. https://doi.org/10.1007/s11482-014-9367-1

59.

Hammond

(2004). Impacts of lifelong learning upon emotional resilience, psychological and mental health: Fieldwork evidence. Oxford Review of Education, 30(4), 551–568. https://doi.org/10.1080/0305498042000306636

60.

Helweg-Larsen

Kjøller

Thoning

(2003). Do age and social relations moderate the relationship between self-rated health and mortality among adult Danes? Social Science & Medicine, 57(7), 1237–1247. https://doi.org/10.1016/S0277-9536(02)00404-0

61.

Holt-Lunstad

(2018). Why social relationships are important for physical health: A systems approach to understanding and modifying risk and protection. Annual Review of Psychology, 69, 437–458. https://doi.org/10.1146/annurev-psych-122216-011902

62.

Huang

Chen

(2022). The impact of internet use on community participation of older adults: Evidence from China. SAGE Open, 12(2). https://doi.org/10.1177/21582440221097387

63.

Hughes

Burke

(2018). Sleeping with the frenemy: How restricting ‘bedroom use’ of smartphones impacts happiness and wellbeing. Computers in Human Behavior, 85, 236–244. https://doi.org/10.1016/j.chb.2018.03.032

64.

Işik

Alkaya

S. A.

(2017). Internet use and psychosocial health of school aged children. Psychiatry Research, 255, 204–208. https://doi.org/10.1016/j.psychres.2017.05.011

65.

International Telecommunication Union (ITU). (2025). Individuals using the Internet. https://www.itu.int/en/ITU-D/Statistics/Pages/stat/default.aspx

66.

Keats

(2018). Women’s schooling, fertility, and child health outcomes: Evidence from Uganda’s free primary education program. Journal of Development Economics, 135, 142–159. https://doi.org/10.1016/j.jdeveco.2018.07.006

67.

Kekäläinen

Freund

A. M.

Sipilä

Kokko

(2020). Cross-sectional and longitudinal associations between leisure time physical activity, mental well-being and subjective health in middle adulthood. Applied Research in Quality of Life, 15(4), 1099–1116. https://doi.org/10.1007/s11482-019-09741-4

68.

Khan

A. S.

ur Rahman

Qazi

L. T.

(2016). The relationship between internet usage, socioeconomic status, subjective health and social status. Business & Economic Review, 8, 67–82.

69.

Kim

T. K.

Lane

S. R.

(2013). Government health expenditure and public health outcomes: A comparative study among 17 countries and implications for US health care reform. American International Journal of Contemporary Research, 3(9), 8–13.

70.

Kuss

D. J.

Griffiths

M. D.

(2011). Online social networking and addiction—A review of the psychological literature. International Journal of Environmental Research and Public Health, 8(9), 3528–3552. https://doi.org/10.3390/ijerph8093528

71.

Lee

M. H.

Liu

P. Y.

Lio

M. C.

(2016). The impact of the diffusion of information and communication technology on health: A cross-country study. Applied Research in Quality of Life, 11(2), 471–491. https://doi.org/10.1007/s11482-014-9377-3

72.

Leibowitz

A. A.

(2004). The demand for health and health concerns after 30 years. Journal of Health Economics, 23(4), 663–671. https://doi.org/10.1016/j.jhealeco.2004.06.006

73.

Jiang

(2023). Research on stress, resilience, and mental health of grass-roots leading cadres from the perspective of modernization of grass-roots governance. Journal of Statistics and Information, 38(6), 117–128. (in Chinese)

74.

Zhou

(2021). Internet use and Chinese older adults’ subjective well-being (SWB): The role of parent-child contact and relationship. Computers in Human Behavior, 119, 106725. https://doi.org/10.1016/j.chb.2021.106725

75.

Chen

(2018). Laugh and grow fat: Happiness affects body mass index among urban Chinese adults. Social Science & Medicine, 208, 55–63. https://doi.org/10.1016/j.socscimed.2018.05.024

76.

Zhang

Wang

(2014). Internet addiction among elementary and middle school students in China: A nationally representative sample study. Cyberpsychology, Behavior, and Social Networking, 17(2), 111–116. https://doi.org/10.1089/cyber.2012.0482

77.

Lin

(1999). Building a network theory of social capital. Connections, 22(1), 28–51.

78.

Linn

B. S.

Linn

M. W.

(1980). Objective and self-assessed health in the old and very old. Social Science & Medicine. Part A: Medical Psychology & Medical Sociology, 14(4), 311–315. https://doi.org/10.1016/0271-7123(80)90044-7

79.

Liu

G. G.

Peng

A. Z.

(2003). Urbanization and health care in rural China. Contemporary Economic Policy, 21(1), 11–24. https://doi.org/10.1093/cep/21.1.11

80.

Ljunge

(2016). Migrants, health, and happiness: Evidence that health assessments travel with migrants and predict well-being. Economics & Human Biology, 22, 35–46. https://doi.org/10.1016/j.ehb.2016.03.002

81.

Longstreet

Brooks

(2017). Life satisfaction: A key to managing internet & social media addiction. Technology in Society, 50, 73–77. https://doi.org/10.1016/j.techsoc.2017.05.003

82.

Kandilov

I. T.

(2021). Does mobile internet use affect the subjective well-being of older Chinese adults? An instrumental variable quantile analysis. Journal of Happiness Studies, 22, 3137–3156. https://doi.org/10.1007/s10902-020-00332-0

83.

Z. N.

Chen

Hao

Wang

Song

Mok

T. M.

(2017). The dynamic relationship between environmental pollution, economic development and public health: Evidence from China. Journal of Cleaner Production, 166, 134–147. https://doi.org/10.1016/j.jclepro.2017.08.010

84.

Lucas

R. E. J.

(1988). On the mechanics of economic development. Journal of Monetary Economics, 22(1), 3–42. https://doi.org/10.1016/0304-3932(88)90168-7

85.

Chen

(2020). Openness, rural-urban inequality, and happiness in China. Economic Systems, 44(4), 100834. https://doi.org/10.1016/j.ecosys.2020.100834

86.

Majeed

M. T.

Khan

F. N.

(2019). Do information and communication technologies (ICTs) contribute to health outcomes? An empirical analysis. Quality & Quantity, 53(1), 183–206. https://doi.org/10.1007/s11135-018-0745-1

87.

Melchior

Berkman

L. F.

Niedhammer

Chea

Goldberg

(2003). Social relations and self-reported health: A prospective analysis of the French Gazel cohort. Social Science & Medicine, 56(8), 1817–1830. https://doi.org/10.1016/S0277-9536(02)00180-1

88.

Mitchell

M. E.

Lebow

J. R.

Uribe

Grathouse

Shoger

(2011). Internet use, happiness, social support and introversion: A more fine-grained analysis of person variables and internet activity. Computers in Human Behavior, 27(5), 1857–1861. https://doi.org/10.1016/j.chb.2011.04.008

89.

Nakamura

Ohara

Kouda

Fujita

Mase

Miyawaki

Okita

Ishikawa

(2012). Combined influence of media use on subjective health in elementary school children in Japan: A population-based study. BMC Public Health, 12, 1–10. https://doi.org/10.1186/1471-2458-12-432

90.

Narushima

(2008). More than nickels and dimes: The health benefits of a community-based lifelong learning programme for older adults. International Journal of Lifelong Education, 27(6), 673–692. https://doi.org/10.1080/02601370802408334

91.

Narushima

Liu

Diestelkamp

(2018). Lifelong learning in active ageing discourse: Its conserving effect on wellbeing, health and vulnerability. Ageing & Society, 38(4), 651–675. https://doi.org/10.1017/S0144686X16001136

92.

Nakagomi

Shiba

Kawachi

Ide

Nagamine

Kondo

Hanazato

Kondo

(2022). Internet use and subsequent health and well-being in older adults: An outcome-wide analysis. Computers in Human Behavior, 130, 107156. https://doi.org/10.1016/j.chb.2021.107156

93.

Navarro

(2008). Politics and health: A neglected area of research. European Journal of Public Health, 18(4), 354–355. https://doi.org/10.1093/eurpub/ckn031

94.

Nie

Feng

Chen

(2024). Theoretical basis and empirical facts of digital technology innovation empowering common prosperity. Journal of Statistics and Information, 39(12), 99–111. (in Chinese)

95.

Nie

Sousa-Poza

(2021). The relationship between smartphone use and subjective well-being in rural China. Electronic Commerce Research, 21, 983–1009. https://doi.org/10.1007/s10660-020-09436-1

96.

Nie

Sousa-Poza

Nimrod

(2017). Internet use and subjective well-being in China. Social Indicators Research, 132, 489–516. https://doi.org/10.1007/s11205-016-1297-1

97.

Niebel

(2018). ICT and economic growth–Comparing developing, emerging and developed countries. World Development, 104, 197–211. https://doi.org/10.1016/j.worlddev.2017.11.024

98.

Novignon

Olakojo

S. A.

Nonvignon

(2012). The effects of public and private health care expenditure on health status in sub-Saharan Africa: New evidence from panel data analysis. Health Economics Review, 2(1), 1–8. https://doi.org/10.1186/2191-1991-2-22

99.

Oduyemi

G. O.

Owoeye

Adekoya

O. B.

(2021). Health outcomes and the resource curse paradox: The experience of African oil-rich countries. Resources Policy, 73, 102201. https://doi.org/10.1016/j.resourpol.2021.102201

100.

Oladosu

A. O.

Chanimbe

Anaduaka

U. S.

(2022). Effect of public health expenditure on health outcomes in Nigeria and Ghana. Health Policy OPEN, 3, 100072. https://doi.org/10.1016/j.hpopen.2022.100072

101.

Paasche-Orlow

M. K.

Wolf

M. S.

(2007). The causal pathways linking health literacy to health outcomes. American Journal of Health Behavior, 31(1), S19–S26. https://doi.org/10.5555/ajhb.2007.31.supp.S19

102.

Perneger

T. V.

Hudelson

P. M.

Bovier

P. A.

(2004). Health and happiness in young Swiss adults. Quality of Life Research, 13(1), 171–178. https://doi.org/10.1023/B:QURE.0000015305.21008.c6

103.

Phu

Gow

A. J.

(2019). Facebook use and its association with subjective happiness and loneliness. Computers in Human Behavior, 92, 151–159. https://doi.org/10.1016/j.chb.2018.11.015

104.

Popkin

B. M.

(1999). Urbanization, lifestyle changes and the nutrition transition. World Development, 27(11), 1905–1916. https://doi.org/10.1016/S0305-750X(99)00094-7

105.

Rana

R. H.

Alam

Gow

(2020). Health outcome and expenditure in low-income countries: Does increasing diffusion of information and communication technology matter?. Information Technology for Development, 26(3), 506–524. https://doi.org/10.1080/02681102.2020.1745829

106.

Rasulo

Christensen

Tomassini

(2005). The influence of social relations on mortality in later life: A study on elderly Danish twins. The Gerontologist, 45(5), 601–608. https://doi.org/10.1093/geront/45.5.601

107.

Rice

R. E.

(2006). Influences, usage, and outcomes of Internet health information searching: Multivariate results from the Pew surveys. International Journal of Medical Informatics, 75(1), 8–28. https://doi.org/10.1016/j.ijmedinf.2005.07.032

108.

Romer

P. M.

(1986). Increasing return and long-run growth. Journal of Political Economy, 94(5), 1002–1037. https://doi.org/10.1086/261420

109.

Roodman

(2011). Fitting fully observed recursive mixed-process models with cmp. The Stata Journal, 11(2), 159–206. https://doi.org/10.1177/1536867X1101100202

110.

Ross

C. E.

Van Willigen

(1997). Education and the subjective quality of life. Journal of Health and Social Behavior, 38(3), 275–297. https://doi.org/10.2307/2955371

111.

Ross

C. E.

C. L.

(1995). The links between education and health. American Sociological Review, 60(5), 719–745. https://doi.org/10.2307/2096319

112.

Y. P.

Ferraro

K. F.

(1997). Social relations and health assessments among older people: Do the effects of integration and social contributions vary cross-culturally? The Journals of Gerontology Series B: Psychological Sciences and Social Sciences, 52(1), S27–S36. https://doi.org/10.1093/geronb/52B.1.S27

113.

Suziedelyte

(2012). How does searching for health information on the Internet affect individuals’ demand for health care services? Social Science & Medicine, 75(10), 1828–1835. https://doi.org/10.1016/j.socscimed.2012.07.022

114.

Tay

Tan

Diener

Gonzalez

(2013). Social relations, health behaviors, and health outcomes: A survey and synthesis. Applied Psychology: Health and Well-Being, 5(1), 28–78. https://doi.org/10.1111/aphw.12000

115.

Tiliouine

(2009). Health and subjective wellbeing in Algeria: A developing country in transition. Applied Research in Quality of Life, 4(2), 223–238. https://doi.org/10.1007/s11482-009-9073-y

116.

Tsur

Stein

J. Y.

Levin

Siegel

Solomon

(2019). Loneliness and subjective physical health among war veterans: Long term reciprocal effects. Social Science & Medicine, 234, 112373. https://doi.org/10.1016/j.socscimed.2019.112373

117.

United Nations. (2021). Financing for sustainable development report 2021. https://unosd.un.org/sites/unosd.un.org/files/financing_for_sustainable_development_report_2021_0.pdf

118.

Valenzuela

Halpern

Katz

J. E.

(2014). Social network sites, marriage well-being and divorce: Survey and state-level evidence from the United States. Computers in Human Behavior, 36, 94–101. https://doi.org/10.1016/j.chb.2014.03.034

119.

Van de Poel

O'Donnell

Van Doorslaer

(2012). Is there a health penalty of China’s rapid urbanization? Health Economics, 21(4), 367–385. https://doi.org/10.1002/hec.1710

120.

Vlahov

Freudenberg

Proietti

Ompad

Quinn

Nandi

Galea

(2007). Urban as a determinant of health. Journal of Urban Health, 84(1), 16–26. https://doi.org/10.1007/s11524-007-9169-3

121.

T. V.

(2020). Economic complexity and health outcomes: A global perspective. Social Science & Medicine, 265, 113480. https://doi.org/10.1016/j.socscimed.2020.113480

122.

Wan

Lighthall

N. R.

Paulson

(2022). Subjective markers of successful aging and change in Internet use among older adults: The distinctive role of subjective health. Computers in Human Behavior, 127, 107064. https://doi.org/10.1016/j.chb.2021.107064

123.

Wang

Hao

(2018). Does internet penetration encourage sustainable consumption? A cross-national analysis. Sustainable Production and Consumption, 16, 237–248. https://doi.org/10.1016/j.spc.2018.08.002

124.

Wangberg

S. C.

Andreassen

H. K.

Prokosch

H. U.

Santana

S. M. V.

Sørensen

Chronaki

C. E.

(2008). Relations between Internet use, socio-economic status (SES), social support and subjective health. Health Promotion International, 23(1), 70–77. https://doi.org/10.1093/heapro/dam039

125.

Well

D. N.

(2007). Accounting for the effect of health on economic growth. The Quarterly Journal of Economics, 122(3), 1265–1306. https://doi.org/10.1162/qjec.122.3.1265

126.

World Health Organization (WHO). (1948). Preamble to the constitution of the World Health Organization. https://www.who.int/about/governance/constitution

127.

Wang

Zhao

Yan

(2013). The relationship between self-rated health and objective health status: A population-based study. BMC Public Health, 13(1), 1–9. https://doi.org/10.1186/1471-2458-13-320

128.

Ybarra

M. L.

Suman

(2006). Help seeking behavior and the Internet: A national survey. International Journal of Medical Informatics, 75(1), 29–41. https://doi.org/10.1016/j.ijmedinf.2005.07.029

129.

Zhang

(2012). The impact of water quality on health: Evidence from the drinking water infrastructure program in rural China. Journal of Health Economics, 31(1), 122–134. https://doi.org/10.1016/j.jhealeco.2011.08.008

130.

Zhang

Cai

Cheng

Zhang

Zhu

(2019a). Association of Internet use with attitudes toward food safety in China: A cross-sectional study. International Journal of Environmental Research and Public Health, 16(21), 4162. https://doi.org/10.3390/ijerph16214162

131.

Zhang

Cheng

Mei

Wang

(2020). Internet use and individuals’ environmental quality evaluation: Evidence from China. Science of The Total Environment, 710, 136290. https://doi.org/10.1016/j.scitotenv.2019.136290

132.

Zhang

Cheng

(2020). Internet use and lower life satisfaction: The mediating effect of environmental quality perception. Ecological Economics, 176, 106725. https://doi.org/10.1016/j.ecolecon.2020.106725

133.

Zhang

Cheng

Wei

Gong

(2019). Internet use and the satisfaction with governmental environmental protection: Evidence from China. Journal of Cleaner Production, 212, 1025–1035. https://doi.org/10.1016/j.jclepro.2018.12.099

134.

Zhang

Gong

(2024). How mobile social networks affect rural household income: Evidence from China. Kybernetes. https://doi.org/10.1108/K-05-2024-1395

135.

Zhang

Gong

Zhang

(2022a). Mobile payment and rural household consumption: Evidence from China. Telecommunications Policy, 46(3), 102276. https://doi.org/10.1016/j.telpol.2021.102276

136.

Zhang

Gong

Zhang

(2022b). ICT diffusion and health outcome: Effects and transmission channels. Telematics and Informatics, 67, 101755. https://doi.org/10.1016/j.tele.2021.101755

137.

Zhao

Kim

J. E.

Moon

Nam

E. W.

(2023). Social engagement and subjective health among older adults in South Korea: Evidence from the Korean Longitudinal Study of Aging (2006–2018). SSM - Population Health, 21, 101341. https://doi.org/10.1016/j.ssmph.2023.101341

138.

Zhao

Jiao

(2022). The impact of Internet use on labor wage distortions: Empirical evidence from China. SAGE Open, 12(2). https://doi.org/10.1177/21582440221099290

139.

Zheng

Duan

Ward

M. R.

(2019). The effect of broadband internet on divorce in China. Technological Forecasting and Social Change, 139, 99–114. https://doi.org/10.1016/j.techfore.2018.10.017

140.

Zhou

Qin

Liu

G. G.

(2020). Relative economic status and mental health among Chinese adults: Evidence from the China Health and Retirement Longitudinal Study. Review of Development Economics, 24(4), 1312–1332. https://doi.org/10.1111/rode.12692

141.

Zhou

Cui

Zhang

(2020). Internet use and rural residents’ income growth. China Agricultural Economic Review, 12(2), 315–327. https://doi.org/10.1108/CAER-12-2018-0276

142.

Zucco

Lavano

Anfosso

Bianco

Pileggi

Pavia

(2018). Internet and social media use for antibiotic-related information seeking: Findings from a survey among adult population in Italy. International Journal of Medical Informatics, 111, 131–139. https://doi.org/10.1016/j.ijmedinf.2017.12.005