What this is
- This research evaluates the relationship between Internet use and depression among older adults in China.
- It specifically examines how mediates this relationship.
- The study utilizes data from the 2018 China Health and Retirement Longitudinal Study (CHARLS) involving 4645 older adults.
Essence
- Internet use among older adults in China is associated with reduced depressive symptoms, primarily through increased . The effect is particularly pronounced in females, rural residents, and those aged 70-79.
Key takeaways
- Internet use significantly reduces depressive symptoms in older adults. This reduction is linked to enhanced , which helps to alleviate feelings of isolation.
- The positive impact of Internet use is stronger among older women, those living in rural areas, and individuals with lower education levels, indicating varying benefits across demographics.
- Interventions should focus on improving Internet accessibility and usability for older adults to promote mental well-being and social engagement.
Caveats
- The study's cross-sectional design limits causal inferences about the relationship between Internet use and depression.
- Data on social capital was limited, suggesting a need for more comprehensive measures in future research.
Definitions
- Social participation: Active involvement in social activities, which can enhance social networks and support mental health.
- CES-D Score: A scale measuring depressive symptoms, with scores ranging from 10 to 40; higher scores indicate more severe depression.
AI simplified
Background
Driven by the one-child policy and rapid industrialization, China’s population structure has undergone profound changes, and it has the largest scale and growth rate of older people population in the world. According to the Seventh Census in 2020, the number of people aged 60 and above was 264 million, accounting for 18.7%, of whom 191 million were aged 65 and above, accounting for 13.50% [43]. Chinese society was about to enter the stage of deep aging. The biggest problem associated with the elderly society is the elderly health problem, especially the mental health. Depression and its associated health problems such as diabetes, disability and suicide were threatening the health and quality of life of older people in China [55, 56]. The report released by the World Health Organization showed that the risk of depression peaks in middle and old age, and the older you are, the more likely you are to develop depression [62]. A meta-analysis also showed that the proportion of Chinese older people with depressive symptoms were as high as 23.6% [33]. In this context, mental disorders such as geriatric depression not only seriously affected the life quality of older people, but also increased the financial and mental stress of their families, while inevitably adding to the medical burden and resource strain on the whole community [69].
A growing body of research has been exploring risk factors for depression in old age, ranging from biological characteristics, behavioral traits, socio-economic status, family structure, living arrangements to community environment and more [2, 30]. Sociodemographic characteristics such as age, gender, education level, marital status and physical condition were all associated with individuals’ subjective well-being and affect their mental health [16]. The study of older men in Europe has shown that physical activity and moderate alcohol consumption could prevent depression in older people [5]. Other studies have also found that smoking [6], drinking [22, 25, 35], diet and sleep [38] were associated with depression. Family financial support and urban environment also affected the mental health of them [41]. Also, the “vascular depression hypothesis”, widowhood, lower socioeconomic levels, the transition from an active career to retirement, and the presence of chronic conditions such as diabetes have all been tested for their impact on depression in old age [30]. Furthermore, numerous studies have found that political factors such as the degree of democratic development [8, 17, 54] and government public services [7], and natural environmental factors such as geography and air quality [57] were all associated with subjective well-being and depression.
Among these factors, the Internet use was now attracting attention. The Internet was becoming more and more common among older people and has become an important part of daily life [34]. By June 2020, the number of Internet users in China has reached 940 million, of which the elderly users over 60 accounted for 10.3% [9].
In China, as a result of the COVID-19 crisis, people were actively complying with the epidemic prevention and control regulations, consciously reducing the number of offline meetings and gatherings, and starting to gradually move many of their daily activities online, such as online medical consultations, online work, online shopping, online chatting, etc. For older people, some of them were affected by the epidemic and began to change their perspectives on technology and the Internet, gradually accepting and learning to use the Internet to adapt to their lives now and beyond. Although the Chinese government has introduced a series of policies and measures to help and promote Internet access among older people, there were still some people who were unwilling or unable to use the Internet due to their age, lifestyle habits, literacy level and physical condition [4, 53], resulting in them being gradually left behind by the rapid development of society. So, in the Internet era, did the Internet affect the mental health of older people, and did Internet use increase well-being or depression? This has become an important social issue worldwide, and the answer to this question would go some way to helping older people adapt better to the Internet age and safeguard their mental health.
In recent years, with the gradual popularization of the Internet, the positive effect of Internet use on the health of older people has attracted the attention of many scholars. On the one hand, as an emerging technology, the Internet could provide elderly users with health care related information [61], online entertainment resources (such as videos and songs) and convenient shopping consumption [31, 64], so that they had better life experience and higher health level [39]. On the other hand, they used the Internet to maintain parent-child relations and family ties, maintain interaction with friends [69], broaden the scope of interpersonal communication and social participation [18, 67], strengthen social connection with others [14], and strengthen the social capital of them [40], which helps to reduce the risk of loneliness and depression and improve the well-being of them [11, 52].
However, the existing empirical conclusions related to Internet use and depression of older people were not consistent. Most showed that Internet use had a protective effect on depression of them in developed countries and Chinese society [12, 14, 34, 37, 55, 56, 65, 66, 69]. Some studies using randomized intervention experiments also showed that providing Internet training and expanding access to older people could significantly reduce depression and improve positive attitudes [50, 59]. However, some studies have found that there was no significant evidence that Internet use affected the mental health of older people [24, 61], and even wrong use would have an adverse impact on the mental health [21, 28, 29, 45].
Although existing studies have analyzed the impact of Internet use on health, there were few empirical tests of the specific mechanisms by which Internet use improved health and reduced the risk of depression [14, 40, 69]. As a structural social capital, social participation may be an important intermediary variable for Internet use to reduce depression of older people. Conceptually, social participation meant that people actively participated in various social activities, such as friend communication, community interaction and voluntary service, so as to form and expand social networks [13, 60]. Theoretically, in the process of participating in social activities, older people could obtain economic and emotional support, a sense of identity and self-esteem through communication and interaction with others, which was helpful to improve their mental health [1, 23]. Empirically, the existing studies showed that social participation could reduce the sense of social isolation, loneliness [10, 13, 15, 20, 26, 36, 55, 56]. Moreover, some studies have also confirmed that Internet use could improve the scope and frequency of social activities, maintain and establish social relationship networks, and expand individual social capital by strengthening contacts with friends and communities [3, 37]. Therefore, based on the previous study [65], this paper proposed that Internet use can improve social participation and strengthen social capital of older people, so as to reduce the risk of depression.
Overall, there was growing academic interest in the health effects of Internet use by older people. Based on this, using the data from the 2018 wave of the China Health And Retirement Longitudinal Study, the aim of this study was to explore the association between Internet use and depression among older people in China, and to further investigate whether the association in a socially unevenly developed Chinese scenario differs across different characteristics of older people. Also, we examined the mediating role of social participation, an overlooked mediating variable.
Method
Data source
Flow chart of the study population selection process
Variable design
Dependent variable
The dependent variable of this paper was the CES-D Score of older people. The Center of Epidemiological Survey-Depression Scale (CES-D) was adopted to measure the mental health, a scale commonly used to investigate depressive symptoms in the general population [8, 48]. The scale has demonstrated high levels of internal consistency across samples and concurrent validity in both developed and developing countries [42]. Also, we confirmed the good reliability and validity of the Chinese version of CES-D with Cronbach’s alpha, which reached 0.815 in Chinese sample data of CHARLS [32]. In CHARLS, respondents were asked to answer ten questions, including two positive emotion items and eight depression items. Each item on the scale has four response options, including ‘Rarely or none of the time (<1 day)’, ‘Some or a little of the time (1–2 days)’, ‘Occasionally or a moderate amount of time (3–4 days)’, and ‘Most or all of the time (5–7 days)’ over the past week. Assign the answers under the item of depression as integers between 1 and 4 respectively, and carry out corresponding reverse assignment for the answers under the positive item. In this study, the CES-D score ranged from 10 to 40, with higher scores indicating higher degrees of depressive symptoms.
Independent variable
Internet use was the independent variable of this study. In the survey, respondents were asked “whether there have been Internet activities in the past month”, and we set the answer to “yes” = 1 and “no” = 0.
Mediating variable
This paper hypothesized that social participation was an intermediary variable for Internet use to reduce the depression level of older people, which is measured by 8 items.Then we summed up the frequency of respondents’ participation in activities and carried out standardization.The larger the value, the higher the level of social participation. 1 2
Control variables
| Variable | Variable definition and assignment |
|---|---|
| Dependent variable | |
| CES-D score (Depressive status) | continuous variable (CES-D score range 10 to 40) the higher the score, the more serious the depression. |
| Independent variable | |
| Internet use | yes = 1, no = 0 |
| Control variables | |
| Gender | man = 1, woman = 0 |
| Age | actual age at the time of interview survey year - respondent’s year of birth |
| Education level | primary school and below =1, junior high school = 2 senior high school = 3, higher education = 4 |
| Marital status | married = 1, separated, single, divorced or widowed = 0 |
| Smoking status | yes = 1, no = 0 |
| Drinking status | yes = 1, no = 0 |
| Residence | urban = 1, rural = 0 |
| Religious belief | yes = 1, no = 0 |
| Political status | the Chinese Communist party member = 1, other = 0 |
| Air quality status | completely satisfied = 5, very satisfied = 4, somewhat satistied = 3, not very satisfied = 2, not at all satisfied = 1 |
| Medical insurance | covered medical insurance = 1, without medical insurance = 0 |
| Mediating variable | |
| Social capital | Standardized social capital index |
Methods
Ordinary least squares (OLS)
Given that the CES-D score used in this study was continuous variable, we used the Ordinary Least Sqaure (OLS) model to examine the relationship. The model was constructed as followed:1\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${Score}_{it}={\alpha}_1+{\beta}_1{Internet}_{it}+{\gamma}_1{X}_{it}+{\delta}_{it}$$\end{document}Scoreit=α1+β1Internetit+γ1Xit+δitWhere Scoreit represented the CES-D score of older people, Internetit represented the use of Internet, Xit was other control variables affecting the depression, and δit was a random error term. α1 denoted the intercept term. β1 and γ1 represented the regression coefficient for the corresponding variable.
Propensity score matching model (PSM)
Theoretically, whether older people used the Internet was a self-selection behavior, so there may be a selective bias. The Propensity Score Matching method was used to test the robustness to determine the net impact of Internet use on depression. The setting mode was as followed:2\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${y}_i=\left(1-{D}_i\right){y}_{0i}+{D}_i{y}_{1i}={y}_{0i}+\left({y}_{1i}-{y}_{0i}\right){D}_i$$\end{document}yi=1-Diy0i+Diy1i=y0i+y1i-y0iDi3\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$ATT=E\left[{y}_{1i}-{y}_{0i}|{D}_i=1,P(X)\right]=E\left[{y}_{1i}|{D}_i=1,P(X)\right]-E\left[{y}_{0i}|{D}_i=1,P(X)\right]$$\end{document}ATT=Ey1i-y0i|Di=1,P(X)=Ey1i|Di=1,P(X)-Ey0i|Di=1,P(X)Wherey1irepresented the score of older people using the Internet and y0i represented the score of older people who did not use the Internet; The processing variable Di = {0, 1} indicated whether the respondent used the Internet, where 1 represented the treatment group and 0 represented the control group.
Mediating effect model
In order to explore the mechanism of the impact of Internet use on depression of older people, the mediating effect model was constructed with reference to the method of Wen et al. [58]. The models were set as followed:4\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$Social\hbox{-} {capital}_{it}={\alpha}_2+{\beta}_2{Internet}_{it}+{\gamma}_2{X}_{it}+{\phi}_{it}$$\end{document}Social-capitalit=α2+β2Internetit+γ2Xit+ϕit5\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${Score}_{it}={\alpha}_3+{\beta}_3{Internet}_{it}+{\lambda}_1 Social\hbox{-} {capital}_{it}+{\gamma}_3{X}_{it}+{\varphi}_{it}$$\end{document}Scoreit=α3+β3Internetit+λ1Social-capitalit+γ3Xit+φitWhere α2represented the influence coefficient of Internet use on mediating variable, and α3 represented the influence coefficient of Internet use on CES-D score of older people after adding mediating variable.
Results
Descriptive analysis
| Varname | Mean | Mean-Diff | T value | ||
|---|---|---|---|---|---|
| Total(= 4645)N | Not using the Internet (= 4312)N | Using the Internet (= 333)N | |||
| CES-D score | 19.268 | 19.548 | 15.637 | 3.912*** | 10.013 |
| Gender | 0.223 | 0.215 | 0.318 | −0.103*** | −4.354 |
| Age | 68.168 | 68.333 | 66.024 | 2.309*** | 6.607 |
| Education level | 1.374 | 1.294 | 2.408 | −1.114*** | −27.448 |
| Marital status | 0.785 | 0.78 | 0.841 | −0.060*** | −2.588 |
| Smoking status | 0.072 | 0.068 | 0.123 | −0.055*** | −3.781 |
| Drinking status | 0.205 | 0.192 | 0.366 | −0.174*** | −7.632 |
| Residence | 0.315 | 0.278 | 0.79 | −0.511*** | −20.188 |
| Religious belief | 0.131 | 0.133 | 0.108 | 0.025 | 1.279 |
| Political status | 0.085 | 0.068 | 0.297 | −0.229*** | −14.776 |
| Air quality status | 3.192 | 3.216 | 2.883 | 0.333*** | 7.143 |
| Medical insurance | 0.974 | 0.972 | 0.994 | −0.022** | −2.400 |
| Social capital | 0.052 | −0.013 | 0.904 | −0.918*** | −15.933 |
Regression results
| Variable | Model 1 | Model 2 |
|---|---|---|
| Internet use | −3.912*** | − 1.800*** |
| (0.294) | (0.332) | |
| Gender | −1.715*** | |
| (0.254) | ||
| Age | −0.005 | |
| (0.017) | ||
| Education level | −0.913*** | |
| (0.138) | ||
| Marital status | −1.282*** | |
| (0.260) | ||
| Smoking status | 1.119*** | |
| (0.372) | ||
| Drinking status | −0.995*** | |
| (0.238) | ||
| Residence | −2.146*** | |
| (0.230) | ||
| Religious belief | −0.773*** | |
| (0.287) | ||
| Political status | −0.458 | |
| (0.348) | ||
| Air quality status | −1.312*** | |
| (0.126) | ||
| Medical insurance | 0.253 | |
| (0.563) | ||
| N | 4645 | 4645 |
| R2 | 0.021 | 0.103 |
Propensity score matching analysis
| Variable | Mean | %bias | T-test | V(T)/V(C) | ||
|---|---|---|---|---|---|---|
| Treated | Control | t | P> | t | | |||
| Gender | 0.312 | 0.312 | 0 | 0 | 1 | . |
| Age | 66.095 | 66.422 | −5.60 | −0.75 | 0.453 | 0.88 |
| Education level | 2.379 | 2.388 | −1.00 | −0.11 | 0.916 | 0.78* |
| Marital status | 0.838 | 0.872 | −8.60 | −1.22 | 0.223 | . |
| Smoking status | 0.122 | 0.104 | 6.3 | 0.74 | 0.46 | |
| Drinking status | 0.358 | 0.321 | 8.3 | 0.99 | 0.322 | |
| Residence | 0.786 | 0.807 | −5.00 | −0.68 | 0.497 | |
| Religious belief | 0.107 | 0.104 | 0.9 | 0.13 | 0.899 | |
| Political status | 0.284 | 0.3 | −4.10 | −0.43 | 0.668 | |
| Air quality status | 2.899 | 2.884 | 1.9 | 0.25 | 0.801 | 0.98 |
| Medical insurance | 0.994 | 0.994 | 0 | 0 | 1 | . |
| Matching method | Treatment group | Control group | ATT | Bootstrap standard error | T value |
|---|---|---|---|---|---|
| K-nearest neighbor matching | 15.661 | 17.439 | −1.779*** | 0.446 | −3.40 |
| Radius matching | 15.666 | 17.432 | −1.766*** | 0.363 | −4.51 |
| K-nearest neighbor matching in caliper | 15.666 | 17.456 | −1.790*** | 0.514 | −3.42 |
| Kernel matching | 15.661 | 17.536 | −1.875*** | 0.322 | −5.05 |
| Local linear regression matching | 15.661 | 17.519 | −1.858*** | 0.365 | −3.55 |
Heterogeneity analysis
| Variable | Age | Gender | |||
|---|---|---|---|---|---|
| Model 3 | Model 4 | Model 5 | Model 6 | Model 7 | |
| Age = 60–69 | Age = 70–79 | Age ≥ 80 | Man | Woman | |
| Internet use | −1.606*** | −2.402*** | 0.607 | −1.552*** | − 1.837*** |
| (0.391) | (0.711) | (1.823) | (0.515) | (0.419) | |
| Control variable | Yes | Yes | Yes | Yes | Yes |
| N | 3020 | 1345 | 280 | 1035 | 3610 |
| R2 | 0.094 | 0.126 | 0.131 | 0.089 | 0.088 |
| Variable | Education | Residence | |||
| Model 8 | Model 9 | Model 10 | Model 11 | Model 12 | |
| Primary school and below | Junior middle school | High school and above | Urban | Rural | |
| Internet use | −2.316*** | −1.453** | −1.687*** | −1.569*** | −2.655*** |
| (0.619) | (0.574) | (0.543) | (0.371) | (0.728) | |
| Control variable | Yes | Yes | Yes | Yes | Yes |
| N | 3548 | 648 | 449 | 1463 | 3182 |
| R2 | 0.071 | 0.092 | 0.076 | 0.081 | 0.075 |
Mechanism analysis
| Variable | Model 13 | Model 14 | Model 15 |
|---|---|---|---|
| CES-D score | Social capital | CES-D score | |
| Internet use | −1.800*** | 0.638*** | −1.450*** |
| (0.332) | (0.087) | (0.333) | |
| Social capital | −0.549*** | ||
| (0.090) | |||
| Control variable | Yes | Yes | Yes |
| N | 4645 | 4645 | 4645 |
| R2 | 0.103 | 0.081 | 0.109 |
Discussion
With the worldwide population aging, the mental health of older people has gained an increasing focus. Based on data from the 2018 China Health And Retirement Longitudinal Study, we sought to explore the association between Internet use and depression in older people and to examine the mediating role of social participation. Consistent with most existing research conclusions [14, 34, 66], this study confirmed that Internet use had a protective effect on depression of older people in China. Studies have explained the possible mechanism from two aspects. On the one hand, compared with traditional media, the Internet could provide older people with rich health care information, online entertainment resources, etc., so that they had a better life experience, which was conducive to improving their mental health [61, 63]. On the other hand, with the growth of age, especially after retirement, their original work-related interpersonal relationships disappeared, resulting in the disconnection between old people and society. As an emerging technology, the Internet could help the them maintain and establish social relations, expand the scope of social participation and accumulate social capital across time and space, so as to alleviate the sense of isolation [14, 34].
In fact, with the help of online platforms, older people could actively participate in social activities and strengthen their offline social participation by more convenient access to all kinds of information, which helped to reduce the risk of depression. In addition, Zhu et al. [69] found that the structural social capital characterized by “interaction with friends” did not show a significant mediating effect. Lyu and Sun [40] used the “gift expenditure” of the past year to describe the structural social capital, which had a significant mediating effect on Internet use to improve self-rated health of older people, but the indirect effect accounted for only 2.45%. This may be because in the Chinese society with obvious characteristics of “differential order pattern”, the social capital described by “communicating with friends” and “gift expenditure” was limited to the close acquaintance relationship network, which could not reflect the positive role of Internet use in expanding social communication and participation [3]. With the growth of age, the circle of acquaintances was shrinking, and the Internet can help them participate in various social activities, enhance community connection and establish new social networks [44, 46]. Therefore, this paper used eight types of social activities [36] including informal social participation (such as friend interaction, providing informal help to family and neighbors) and formal social participation (such as community organization activities, volunteer services, etc.) to measure the comprehensive level of social participation of older people, and it was found that active and wider social participation was one of the important channels for Internet use to reduce the risk of depression in older people.
Internet use had a stronger correlation with mental health among middle and lower aged older adult [65, 69]. Relatively older adults could have more learning and technological barriers to Internet use. At the same time, due to the decline of physical function and limited activity ability, they may not be able to participate in community activities. So, the protective effect of Internet use on depression of them was very limited [47, 67]. On the contrary, younger older adults could use the information accessibility of the Internet to maintain an interactive network with friends and even compensate for the network of colleagues and related activity participation lost through retirement [49], thus gaining a sense of identity and psychological belonging, which helped to maintain the mental health of them. From the perspective of education level, older people with lower education level would obtain more health effects in the process of Internet use [27, 39]. This may be because older people groups with different education levels had different social resources and social networks, and the Internet had a greater impact on the lifestyle of those with lower education levels. For example, the emergence of short video applications would strengthen the contact and interaction of this group, expand the social network and improve the level of social adaptation [27]. Compared with older people living in urban, the rural elderly benefited more from the Internet, which was consistent with Liao et al. [34] and Zhu et al. [69]. There may be the following two reasons: firstly, there were great differences in medical resources and health information between them and there was a lack of medical resources in rural areas. As one of the sources of health information, the Internet promoted the health of the rural elderly; Secondly, compared with urban residents, farmers had fewer channels for social activities, so online platforms could effectively make up for the lack of social participation of them to reduce the risk of depression.
Conclusions
In summary, this paper empirically demonstrated the positive impact of Internet use on depressive symptoms among older people in China and the role of social participation in this relationship. Interventions should be developed to help older individuals with depressive symptoms based on the findings of this paper.
Firstly, make full use of the Internet to effectively prevent and treat mental disorders, such as, depression of older people. The Internet accessibility and convenience of older people should be further promoted. In some rural areas, the Chinese elderly, discarded by digital society, generally have poor access to the Internet. However, it was mentioned that modern technology was mainly aimed at young people. There were significant differences between the elderly and young people in terms of physical function and psychological cognition, and the product design was not applicable to the elderly, such as disordered page layout, small web page font, vulgar information content, etc., which brought unfriendly experience to the elderly and damaged their physical and mental health. Therefore, the existing equipment and applications need to be gradually incorporated into the friendly design for the elderly users, especially considering the decline of the elderly users’ visual and auditory abilities, and carrying out adaptation in the aspects of voice, character recognition, font size, etc., so as to improve the experience of the elderly in using the Internet. Besides, other measures were also worth advocating, such as strengthening the review and supervision of relevant contents for the elderly in the Internet, using big data to more accurately tap the potential needs of the elderly, and providing targeted services and commodities for the elderly group.
Secondly, built Internet platform to realize the social participation of the elderly. In platform, the elderly actively carry out online and offline activities suitable for older people, provide special activity venues in the community, and improve the level of social integration of them, so as to maintain and expand the social network and reduce the risk of depression of them.
Finally, due to the group differences of the depression of older people, interventions should be implemented for different types of elderly groups. For example, for older people with poor learning ability, we should help them accept and use the Internet by encouraging family guidance and social workers’ participation, so as to finally bridge the “digital divide”; In view of differences between urban and rural areas, government departments should strengthen the inclusive construction of Internet infrastructure, gradually improve the rural Internet penetration rate, and reduce the differences in Internet use, to promote the healthy and balanced development of all kinds of elderly people, and finally improve the well-being of all older people in the Internet era.
There were several major limitations to be noted in this study. First, we recognized the cross-sectional nature of data in the study, coupled with the fact that this was an observational study, so any conclusions about predictions could only be understood in a statistical sense and did not provide evidence for causality. Second, in order to gain a fuller understanding of the role of social capital in health, more direct and extensive measures of bridging and linking social networks were necessary. However, there was not much more available information in the existing data. Future research was definitely needed to gather more information in this area and to further elucidate how different forms of social capital moderate the relationship between Internet use and health.