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2
(Choi et al., 2012; Cotten et al., 2012, 2013, 2014; Hajek & König,
2022). Internet use can also promote older adults’ social participation,
thus improving their quality of life (Quinn et al., 2016) and mental
health (Cotten et al., 2014). Therefore, from a theoretical perspective,
Internet use is positively associated with a lower incidence of depressive
symptoms. According to the China Internet Network Information Center
(CINIC; 2021), as of December 2020, there were 989 million Chinese
netizens. However, significant age cohort differences are evident
because virtually all young and middle-aged adults in China use the
Internet, whereas a considerable proportion of older adults do not have
Internet access, especially rural older adults. There are two other note
worthy changes. First, the number of Internet users aged over 60 years
has increased from 7.3 million (4.4% of China’s total older adult pop
ulation) in 2009 to 60.6 million (23.8%) in 2019 (CINIC, 2021). Thus,
the number of older-adult Internet users shows a trend of continuous
growth. Further, there are numerous new Internet users in addition to
the constant Internet users. Second, although the proportion of Internet
users in rural areas had increased from 2016 to 2020, the number of
rural users is far lower than the number of urban users, especially among
older adults (CINIC, 2021).
Several empirical studies have explored the relationship between
Internet use and older adults’ depressive symptoms (Bessière et al.,
2010; Cotten et al., 2012, 2014; Elliot et al., 2014; Kouvonen et al.,
2021; Lam et al., 2020). However, the following research gaps remain.
First, the findings are inconsistent: While most Western studies have
reported that Internet use decreases the probability of depression in
older adults (Cotten et al., 2012, 2014; Kouvonen et al., 2021), other
studies have reported an increase or no difference (Bessière et al., 2010;
Elliot et al., 2014; Lam et al., 2020). Second, few studies have explored
the mechanisms that link Internet use and depressive symptoms among
older adults. Third, despite the rapid increase in the number and pro
portion of older-adult Internet users, there is a lack of discussion
regarding the association between Internet use and depressive symp
toms for constant users and new users. Lastly, some studies have
explored the different effects of Internet use on the psychological
well-being of rural and urban older adults (Long et al., 2020); however,
the mechanisms of rural/urban status in older adults’ Internet use and its
influence on depressive symptoms remain under-explored.
To fill these research gaps, this study explores the mechanism be
tween Internet use and older adults’ depressive symptoms using a
sample of adults aged over 60 years from a large, nationally represen
tative dataset in China. It adopts social capital theory to support the
research hypotheses and is specifically interested in the potential role of
social networks (i.e., family and friendship networks) and social
participation (i.e., volunteering and non-volunteering participation) in
mediating the relationship between constant and new Internet use as
well as older adults’ levels of depressive symptoms. This study also in
vestigates whether rural/urban differences exist in the pathway. The
findings can aid in the formulation of policies to promote Internet use
among older adults in a positive manner; for example, social services can
provide measures to help older adults with technology adoption.
2. Theoretical background and hypotheses development
First, this study reviews previous studies on Internet use and older
adults’ depression. Then, it proposes the theoretical and empirical
background for the research hypotheses regarding Internet use, social
capital, and depression. Finally, this study reviews the differences in
rural/urban status for the relationship between Internet use and older
adults’ depression.
2.1. Association between Internet use and depression
Studies from multiple countries have found a negative association
between Internet use and older adults’ depressive symptoms (Cotten
et al., 2012, 2014; Hajek & König, 2022; Jun & Kim, 2017; Kouvonen
et al., 2021). For example, a large prospective study on American older
adults found that Internet use influenced depression by decreasing
loneliness and social isolation (Cotten et al., 2014). Meanwhile, a
German study of middle-aged and older adults during the COVID-19
pandemic showed that less frequent contact with friends and relatives
via the Internet was positively associated with psychosocial factors,
including increased loneliness, lower life satisfaction, and more
depressive symptoms (Hajek & König, 2022). However, some studies
have indicated no association, or a positive association, between
Internet use and depression among older adults (Bessière et al., 2010;
Elliot et al., 2014; Lam et al., 2020; Perlis et al., 2021). A meta-analysis
revealed that Internet interventions are effective for decreasing loneli
ness, but not depression (Choi et al., 2012). Another study found that
Internet use was not directly related to depressive symptoms or
well-being, but acted as a moderator (Elliot et al., 2014). Alarmingly,
some studies have found that excessive Internet use could increase
feelings of loneliness and depression over time (Yao & Zhong, 2014) and
cause Internet addiction (Dieris-Hirche et al., 2017).
Meanwhile, some Chinese studies have explored the relationship
between Internet use and depressive symptoms (Liao et al., 2020; Liu
et al., 2020; Mu et al., 2021; Wang et al., 2019; Xie et al., 2021; Zhang
et al., 2021). Some have indicated that Internet use is negatively asso
ciated with the risk of depression among older adults, and that a higher
frequency of Internet use results in a lower likelihood of suffering from
depression (Liao et al., 2020; Yuan, 2021). Some possible mechanisms
have been proposed to explain the benefits of Internet use for older
adults. First, the Internet provides ready access to information, offers
opportunities for entertainment, and allows for communication among
family and friends (Pan & Jordan-Marsh, 2010). Second, health
information-seeking is embedded in the everyday health practices of
older adults, and the Internet can provide a channel to obtain greater
healthcare utilization (Suziedelyte, 2012). Third, Internet use is also
regarded as a means to acquire new knowledge, especially for Chinese
older adults (Pan & Jordan-Marsh, 2010). Conversely, another view has
indicated that Internet use affects older adults’ mental health and in
creases the incidence of depressive symptoms (Xie et al., 2021). There
are some reasons to account for this correlation. Internet use is an
effective tool to strengthen members’ social networks and is an asset for
the provision of social support, but it also decreases face-to-face inter
action and social trust, which can damage family relationships (Sabatini
& Sarracino, 2017). Moreover, irrational attitudes toward Internet use
could harm their mental health (Xie et al., 2021). Both viewpoints
indicate that Internet use is associated with depressive symptoms.
In addition, Internet users are strongly heterogeneous across
different sub-groups, which could lead to imprecise analysis (Macedo,
2017). We suggest continuity theory as a possible explanation of why
some older adults go online. This theory proposes that Internet use is an
adaptive choice to maintain the internal and external structures of older
adults’ psychological and social environments (McMellon & Schiffman,
2000). However, some studies have empirically demonstrated that older
adults are more anxious and less confident in Internet use (Kelley et al.,
1999), which is especially true for new Internet users. Moreover, the
lack of consensus on the association between Internet use and depres
sion is further challenged by new Internet users’ irrational attitudes
toward Internet use and unreasonable Internet use, which may cause
Internet addiction and problematic use. Meanwhile, constant Internet
users can rationally and actively use the Internet. Therefore, the het
erogeneity between new and constant users poses a challenge to the
study of the relationship between Internet usage and depressive symp
toms. In response, this study examines the association between constant
and new older-adult Internet users in China and proposes:
H1. Internet use is associated with depressive symptoms in older
adults, categorized as either constant or new Internet users.
R. Jing et al.
3. Computers in Human Behavior 138 (2023) 107480
3
2.2. Mediating role of structural social capital in the relationship between
Internet use and older adults’ depression
As discussed above, most extant studies have concluded that Internet
use is associated with mental health in older adults; however, the un
derlying mechanism remains unknown. Meanwhile, Internet use may
be, both directly and indirectly, related to depressive symptoms through
intermediary variables. Social capital is one such potential intermediary
variable in the relationship between Internet use and depressive symp
toms. The more common definition of social capital from Putnam et al.
(1993) is that social capital refers to the nature and extent of one’s
involvement in various informal networks and formal civic organiza
tions, which can improve the efficiency of the society by facilitating
coordinated actions. Social capital theory demonstrates that some abil
ities and values originate from social networks and participation, which
can create both instrumental and emotional benefits for people (Sum
et al., 2008). Social capital has been widely demonstrated to be related
to people’s mental health, especially for depression (Bassett & Moore,
2013; Lee & Kim, 2014), and evidence shows that social capital has a
greater effect on the mental health of older adults than that of younger
adults (Muckenhuber et al., 2013).
Social capital has structural and cognitive components (Bassett &
Moore, 2013; Nyqvist et al., 2014). The cognitive part typically includes
measures of trust, norms, reciprocity, and perceptions of surrounding
social environments (Legh-Jones & Moore, 2012). However, there is still
doubt that the cognitive component is an indirect or a proxy measure for
social capital and is closer to social cohesion than social capital (Car
piano, 2006). The structural part describes the networks, relationships,
and institutions that link people and groups together, which directly
assess how resources are allocated within social networks for personal
benefit (Carpiano, 2006). Structural social capital mainly comprises two
concepts (Muennig et al., 2013; Quan-Haase & Wellman, 2002): the first
concept, concerning social networks, refers to interpersonal communi
cation patterns such as visits, encounters, phone calls, and social events;
the second concept, concerning social participation, refers to the degree
of people’s active and passive involvement in their communities,
including through volunteering and leisure activities. The two concepts
of structural social capital are also based on some theoretical founda
tions. From the informal-formal dimension, a social network is informal
behavior that seems to require little commitment or preparation,
whereas social participation concerns more formal relationships that
require planning and intentionality (Kikuchi & Coleman, 2012). Based
on the types of ties, a social network can be considered to have strong or
bonding ties that are easily maintained and connect the closest family
and friends. Meanwhile, social participation is a weak or bridging tie
that connects individuals who are at a distance from each other in a
given network (Teilmann, 2012). Social networks, as bonding ties, offer
the kind of cohesion and security necessary to support mental health
(Coleman et al., 2022), whereas social participation, with broader net
works, improves mental health by conveying more diverse information
(Coleman et al., 2022) and introducing new ideas and opportunities
(Sørensen, 2016).
Social networks refer to one’s interactions within informal, bonding,
and strong ties. The existing studies were mainly carried out from two
aspects: family network and friendship network (Eriksson & Ng, 2015;
Muennig et al., 2013; Quan-Haase & Wellman, 2002). Internet use is
reportedly significantly related to social networks, but the direction of
the relationship between Internet use and social networks remains
controversial. Some researchers believe that Internet use helps enhance
communication capabilities because of its technical advantages. Spe
cifically, the Internet can overcome time and spatial restrictions, thus
enhancing opportunities for social connectedness. It promotes mean
ingful contact with close relatives and friends, focusing on the quality,
not the quantity of interaction (De Jong Gierveld et al., 2015). Regular
contact with relatives and friends via the Internet can enhance one’s
communication within these networks (Hajek & König, 2022).
Meanwhile, older adults, whose social networks shrink, especially after
retirement, can use the Internet to communicate with family and friends
and thus, gain access to valuable social support (Francis et al., 2016). In
addition, other studies have indicated that using the Internet’s enter
tainment and information capabilities disengages people from their
families and friends (Quan-Haase & Wellman, 2002). Sum et al. (2008)
showed that older adults reported greater family disengagement and
reduced social activity when using the Internet to talk to strangers,
which therefore threatens social life. In conclusion, from various per
spectives, Internet use could either supplement or detract from social
networks. Some theories and studies have further highlighted the rela
tionship between social networks and depression. The main effect model
has shown that social networks provide people with regular positive
experiences and a set of stable, socially rewarding roles in the commu
nity. This kind of support could be related to mental health because of its
positive effect (Cohen & Wills, 1985). The buffering model has indicated
that social networks assess the perceived availability of interpersonal
resources that are responsive to the needs elicited by stressful events
(Cohen & Wills, 1985). If this is suitable for older adults, the Internet can
serve as an economical channel to help them maintain and develop so
cial connections, which can further decrease feelings of depression (Lee
& Kim, 2014). Meanwhile, a study investigating the structural compo
nents of an individual’s network found that people who were centrally
located within their social networks were less likely to be depressed than
those located on the periphery. Further, an individual’s extent of
depressive symptoms in a given period was strongly correlated with the
extent of such symptoms among their friends and neighbors (Rosenquist
et al., 2011). Based on the analysis above, this study proposes:
H2. Social networks can mediate the relationship between Internet use
and depressive symptoms in older adults in terms of family and friend
ship networks.
Social participation refers to one’s interactions within formal,
bridging, and weak ties. Participation in volunteering and participation
in non-volunteering (leisure) activities were regarded as the general
categories of social capital (Muennig et al., 2013; Vogelsang, 2016).
There are different views on the effects of Internet use on social
participation. On the one hand, greater Internet use undermines the
basis of social engagement and participation (Kraut et al., 1998).
Further, Internet use facilitates a greater range of communication and
involvement, thus reducing interest in the local community and politics
(Quan-Haase & Wellman, 2002), consequently decreasing the size of the
social circle, and finally, intensifying loneliness (Kraut et al., 1998). On
the other hand, Internet use promotes older adults’ social participation,
which improves their sense of belonging or control, provides them with
a greater sense of self-esteem (Thoits, 2011), improves their level of
social adjustment (Quan-Haase et al., 2017), and enhances their mental
health (Liu et al., 2020). Meanwhile, activity theory of aging proposes
that an individual loses a specific role and function because of aging or
retirement (Havighurst, 1961). Older adults can gain roles by partici
pating in activities, and successful aging requires older adults to replace
their roles as they age. Social participation is one way to acquire new
roles for older adults, which has been reported to have a negative
relationship with depression (Chen & Zhu, 2022). Based on the above,
this study proposes:
H3. Social participation can mediate the relationship between Internet
use and depressive symptoms in older adults in terms of volunteering
and non-volunteering participation.
2.3. Differences in the relationship between Internet use and depression in
rural and urban older adults
Exploring the relationship between Internet use and the psycholog
ical well-being of Chinese rural and urban older adults is particularly
important. Owing to the long-term, dual rural–urban structure in LMICs
R. Jing et al.
4. Computers in Human Behavior 138 (2023) 107480
4
such as China, the development of the Internet has led to a digital divide
between rural and urban areas (Long et al., 2020). Social compensation
hypothesis posits that using the Internet to meet new people and
participate in online groups has an augmentative effect on those with
impoverished social resources (Bessière et al., 2008). This is important
because the rural Chinese live in an “acquaintance society” and
communicate with their family and friends more frequently than their
urban counterparts (Long et al., 2020). Social networks were found to be
marginally weaker in urban areas (Sørensen, 2016), and the Internet
could provide a solution. In addition, because of poor infrastructure and
lack of support resources in rural areas, rural and urban residents may
not enjoy the benefits of Internet use equally (Berner et al., 2015; Hong
& Cho, 2016). While the Internet, as a newly-developed technology,
allows for the allocation of resources, rural residents lack access to it,
while urban residents benefit from its use (Long et al., 2020). Therefore,
this study proposes:
H4. Social networks can mainly mediate the relationship between
Internet use and depressive symptoms among urban older adults.
There are many left-behind older adults or empty-nesters in rural
China (He et al., 2016), and rural older adults who live far from cities
lack social communication and have less social participation (Vogel
sang, 2016). The Internet can be an effective way of building and
retaining social ties for rural older adults. According to Sun et al. (2022),
Internet use can reduce rural older adults’ depression not only directly
but also indirectly through the mediating role of social activity. Mean
while, some studies have found a statistically significant association
between attending religious, artistic, or cultural activities and health
improvement, but only for rural residents (Green & Elliott, 2010;
Vogelsang, 2016). In considering the types of ties, bonding social capital
(i.e., social networks) was found to be significantly higher in rural areas,
but there was a scarcity of bridging social capital (i.e., social partici
pation; Sørensen, 2016). Based on the above, this study proposes:
H5. Social participation can mainly mediate the relationship between
Internet use and depressive symptoms among rural older adults.
All five hypotheses are shown in Fig. 1.
3. Methods
3.1. Data and sample
The data were obtained from the China Longitudinal Aging Social
Survey (CLASS) conducted by the National Survey Research Center and
the Center for Population and Development Studies at Renmin Univer
sity of China. The CLASS is a nationally representative and biennially
longitudinal survey conducted in 28 provinces (autonomous regions and
municipalities) of mainland China, including 134 counties (districts and
county-level cities) and 462 rural villages (urban communities).
Detailed information regarding the data collection procedure is avail
able at http://class.ruc.edu.cn/. The CLASS targets randomly selected
individuals aged over 60 years and conducts face-to-face interviews with
each participant after informing them of the survey instructions and
receiving their informed consent. The CLASS was launched in 2014 and
was followed up in 2016 and 2018. The 2014 baseline survey was not
designed to collect information on Internet use; therefore, this study
only used the 2016 (second wave) and 2018 (third wave) surveys for
analysis. Of the 11471 participants in the 2016 wave, 1821 participants
were excluded from the 2018 wave because of death or dropping out.
The 2018 wave’s response rate was approximately 84.1%. Eight obser
vations with missing values for the dependent and independent vari
ables were excluded, resulting in a final sample of 9642 Chinese older
adults.
Fig. 1. This study’s conceptual framework.
R. Jing et al.
5. Computers in Human Behavior 138 (2023) 107480
5
3.2. Measures
3.2.1. Depressive symptoms
This study quantified the measurement of depressive symptoms
using the Chinese version of the Center for Epidemiologic Studies-
Depression (CES-D) scale (Radloff, 1977). This nine-item scale assesses
six types of negative emotions and somatic syndromes (loneliness,
sadness, worthlessness, boredom, loss of appetite, and lack of sleep) and
three types of positive emotions (happiness, enjoyment of life, and
pleasure). The participants were asked how frequently they experienced
these symptoms in the past week, and the responses were scored on a
three-point Likert-type scale (0 = rarely, 1 = sometimes, 2 = most of the
time). This study reverse-scored the positive emotions and then summed
the item scores. This created overall scores for the depressive symptoms
that ranged from 0 to 18, with a higher score representing a higher risk
of depression. The CES-D scale has been widely validated for Chinese
older adults (Li et al., 2019, 2021). The Cronbach’s alpha value for the
2018 wave was approximately 0.73, indicating a reasonable level of
consistency.
3.2.2. Internet use
Following several previous studies (Cotten et al., 2012; Heo et al.,
2015; Khalaila & Vitman-Schorr, 2018; Li & Zhou, 2021), this study
determined Internet use based on whether the participants used a device
(e.g., smartphones, computers, and tablets) to go online. Therefore, the
measurement of Internet use was a dichotomous variable, equaling zero
for non-users and one for users. Further, this study determined the
changes in Internet use between the 2016 and 2018 waves. This study
defined constant Internet users as participants who self-reported
Internet use in both survey waves, and new Internet users as partici
pants who had not used the Internet in the 2016 wave but had used the
Internet in the 2018 wave.
3.2.3. Structural social capital
Considering that social capital is a contextual phenomenon that
cannot be directly observed (Giordano et al., 2011), this study measured
structural social capital using latent constructs. This study systemati
cally conceptualized structural social capital in two forms: social net
works and social participation, in line with several previous studies
(Eriksson & Ng, 2015; Hikichi et al., 2020; Muennig et al., 2013). For
social networks, this study used questions from the Lubben Social
Network scale (Lubben et al., 2006) to construct the relevant variables.
The family network was assessed by asking the participants the
following questions that concerned their families or relatives: (a) “How
many people do you see or hear from at least once a month?” (b) “How
many people can you openly speak your mind to?” and (c) “How many
people can you call on for help when needed?” The friendship network
was assessed by asking participants three comparable questions about
their number of friends. The responses were coded as 0 for “none,” 1 for
“one,” 2 for “two,” 3 for “three or four,” 4 for “five to eight,” and 5 for
“nine or more.”
For social participation, this study used participants’ responses to
questions regarding the frequency of their participation in social activ
ities in the past year to construct two sub-scales. This study assessed
volunteering participation based on the following seven items in the
2018 wave: (a) “community patrol,” (b) “providing help to older adults
who do not live with you (e.g., doing housework),” (c) “environment
protection,” (d) “dispute resolution,” (e) “chatting with people who do
not live with you,” (f) “professional services (e.g., medical consulta
tion),” and (g) “caring for children who do not live with you.” Several
additional activity types were included in the 2018 wave, such as: (a)
“conducting religious activities,” (b) “attending an educational or
training course,” (c) “public singing or playing an instrument,” and (d)
“square-dancing”; these activity types constituted the latent construct of
non-volunteering participation as they were not considered to reflect
voluntary interactions. Each response was measured on a five-point
Likert-type scale ranging from 0 (never) to 4 (almost every day).
3.2.4. Covariates
Referring to previous studies (Phifer & Murrell, 1986; Tang et al.,
2020; Zhang & Zhao, 2021), this study included the following socio
economic and health variables to control for potential confounders in
the analysis. The participants’ self-reported agricultural/non-agricul
tural household registration statuses were used to verify the rural/urban
differences (0 = rural, 1 = urban). The additional covariates included
sex (0 = female, 1 = male), age (0 = 60–69, 1 = 70–79, and 2 = 80 years
or older), education (0 = illiterate, 1 = primary school, and 2 = junior
high school or higher), marital status (0 = living without a spouse, 1 =
living with a spouse), household size measured by the number of
co-residents with family members/others, employment status (0 =
non-working, 1 = currently working), pension (0 = no pension, 1 =
pension), family wealth (0 = has family wealth, 1 = has no family
wealth), body mass index (BMI; measured by dividing weight in kilo
grams by height in meters squared, with a higher score indicating a
higher level of being overweight), the Instrumental Activities of Daily
Living (IADL) score measured by the IADL scale with a higher summed
score representing a higher level of functional health (Lawton & Brody,
1969), and the cognition index measured by the Short Portable Mental
Health questionnaire with a higher summed score indicating a higher
level of cognitive health (Pfeiffer, 1975).
3.3. Statistical analysis
The descriptive statistics comprised the characteristics of the total
sample and other sub-groups regarding Internet use, and included non-
users, total users, constant users, and new users. The continuous vari
ables were described using the mean and standard deviation (SD), and
the categorical variables were described using the number and per
centage (%).
This study used structural equation modeling (SEM) to examine the
hypothesized mediating effects of structural social capital on the asso
ciation between Internet use and depressive symptoms in older adults. It
used multiple variables for the latent construct to reduce the measure
ment error and increase the accuracy of the results (Kline, 2015). As a
multi-dimensional concept, structural social capital requires various
assessment approaches for full examination (Almedom, 2005). This
study used social networks and social participation as the most impor
tant aspects of structural social capital. To gain a better understanding of
the potential mechanisms by which Internet use might be associated
with depressive symptoms through social networks and social partici
pation, this study employed SEM in three stages. First, this study built a
structural model (Model 1) that included the Internet use variable, two
primary latent constructs of the structural social capital variable (i.e.,
social networks and social participation), depressive symptoms, and
covariates to examine the effects of Internet use on depressive symp
toms. Second, we further investigated the mediating role of the
sub-dimensions of social networks and social participation in the asso
ciation between Internet use and older adults’ depressive symptoms.
This study built a supplementary structural model (Model 2) that
included four structural social capital variables (i.e., family network,
friendship network, volunteering participation, and non-volunteering
participation) to test the varied mediating roles of different structural
social capital in Internet use and depressive symptoms. Model 2 fol
lowed a similar methodology as in Model 1. Third, based on Model 2,
this study conducted a multigroup analysis to evaluate whether the
differences in the mediating effects between the rural and urban par
ticipants were significant.
This study used multiple indexes and related cutoff points to assess
the model fit, including: (a) the comparative fit index (CFI), with values
greater than 0.90 indicating a close model fit (Bentler, 1990); (b) the
root mean square error of approximation (RMSEA), with values less than
0.06 representing a good model fit (Hooper et al., 2008); and (c) the
R. Jing et al.
6. Computers in Human Behavior 138 (2023) 107480
6
standardized root mean square residual (SRMR), with values less than
0.08 showing a satisfactory model fit (Hooper et al., 2008). All analyses
were conducted using Stata 16.0 (Stata Corporation, College Station, TX,
U.S.A.).
Ethical approval
This study, which involved human participants, was reviewed and
approved by the Ethics Committee of Renmin University of China. All
participants provided written informed consent, and the study was
conducted in accordance with the Declaration of Helsinki.
4. Results
4.1. Characteristics of the study population
Table 1 displays the sample characteristics of the 2018 survey.
Overall, 16.7% of the participants used the Internet, and the proportion
of constant Internet users was lower than that of new Internet users
(7.06% vs. 9.68%, respectively). Compared with non-users, Internet
users exhibited a relatively lower incidence of depressive symptoms
(5.65 vs. 7.74, respectively), and the mean CES-D score for constant
Internet users was lower than that of new Internet users (5.23 vs. 5.96,
respectively). There were statistically significant differences in social
networks and social participation between Internet users and non-users
(P < 0.001). Compared with non-users, Internet users exhibited a
stronger family network score (7.84 vs. 7.30, respectively), friendship
network score (7.01 vs. 6.19, respectively), volunteering participation
score (2.05 vs. 0.99, respectively), and non-volunteering participation
score (0.72 vs. 0.19, respectively). The social network score for constant
Internet users was higher than that for new Internet users (15.13 vs.
14.66, respectively), whereas new Internet users exhibited a slightly
higher social participation score than constant users (2.86 vs. 2.65,
respectively), particularly for volunteering participation.
Overall, 56.9% of the participants lived in rural areas; 50.4% were
males; and 47.5%, 36.2%, and 16.3% were aged 60–69, 70–79, and 80+
years, respectively. Regarding education, 31.8%, 36.5%, and 31.7% of
the participants were categorized as illiterate, primary school, and ju
nior high school or higher, respectively. Furthermore, 68.4% of the
participants were married; 24.4% were working; 74.8% had a pension;
and 60.7% possessed family wealth. The total sample had a mean (SD)
BMI, IADL, and cognition index of 22.33 (2.49), 9.01 (2.17), and 12.86
(3.63), respectively.
4.2. Mediating effects of structural social capital as two latent constructs
Fig. 2 presents the results for the standardized solutions based on
Model 1, which treats structural social capital as two latent constructs (i.
e., social networks and social participation). The model testing indicates
that it provides an acceptable fit for the data (CFI = 0.900, RMSEA =
0.059, SRMR = 0.040). Specifically, Model 1 explains 21.5% of the
variance in the depressive symptoms among older adults in China.
Both constant and new Internet users exhibited statistically signifi
cant direct effects on depressive symptoms (β = − 0.108, and β =
− 0.100, respectively; P < 0.001). Meanwhile, the effects of Internet use
on depressive symptoms are partially mediated by structural social
capital. Compared with non-users, constant Internet users had a higher
social network score (β = 0.038, P < 0.001), leading to a reduced risk of
depressive symptoms (β = − 0.094, P < 0.001). However, constant
Internet use was not significantly correlated with a higher likelihood of
social participation. Conversely, new Internet users were positively
associated with having a higher social network score (β = 0.023, P =
0.044), but negatively associated with having a higher likelihood of
social participation (β = − 0.109, P < 0.001). The results of each
pathway in Model 1 are displayed in Table S1, and Fig. S1 shows the
association between Internet use (total Internet users vs. non-users) and
Table 1
Descriptive statistics of the sample characteristics (N = 9642).
Variables Total Non-users Internet users
Total Constant New
N 9642
(100.00)
8028
(83.26)
1614
(16.74)
681
(7.06)
933
(9.68)
Depressive
symptoms
7.39
(3.63)
7.74
(3.56)*** a
5.65
(3.48)
5.23
(3.21)
5.96
(3.64)
Social network
indexb
13.71
(5.46)
13.49
(5.45)*** a
14.86
(5.33)
15.13
(5.15)
14.66
(5.45)
Family network
indexb
7.39
(2.82)
7.30
(2.83)*** a
7.84
(2.70)
7.86
(2.67)
7.83
(2.72)
Friendship
network indexb
6.33
(3.21)
6.19
(3.21)*** a
7.01
(3.12)
7.27
(2.95)
6.83
(3.23)
Social
participation
indexb
1.45
(1.68)
1.18
(1.40)*** a
2.77
(2.27)
2.65
(2.30)
2.86
(2.24)
Volunteering
participation
indexb
1.17
(1.14)
0.99
(0.98)*** a
2.05
(1.40)
1.93
(1.43)
2.15
(1.37)
Non-
volunteering
participation
indexb
0.28
(0.63)
0.19
(0.50)*** a
0.72
(0.94)
0.72
(0.94)
0.72
(0.94)
Rural/urban status *** a
Rural 5491
(56.9)
5021
(62.5)
470
(29.1)
97 (14.2) 373
(40.0)
Urban 4151
(43.1)
3007
(37.5)
1144
(70.9)
584
(85.8)
560
(60.0)
Sex
Female 4778
(49.6)
3998
(49.8)
780
(48.3)
340
(49.9)
440
(47.2)
Male 4864
(50.4)
4030
(50.2)
834
(51.7)
341
(50.1)
493
(52.8)
Age *** a
60–69 years 4583
(47.5)
3349
(41.7)
1234
(76.5)
519
(76.2)
715
(76.6)
70–79 years 3489
(36.2)
3169
(39.5)
320
(19.8)
123
(18.1)
197
(21.1)
80+ years 1570
(16.3)
1510
(18.8)
60 (3.7) 39 (5.7) 21
(2.3)
Education *** a
Illiterate 3064
(31.8)
2916
(36.3)
148
(9.2)
47 (6.9) 101
(10.8)
Primary school 3519
(36.5)
3114
(38.8)
405
(25.1)
138
(20.3)
267
(28.6)
Junior high
school or higher
3059
(31.7)
1998
(24.9)
1061
(65.7)
496
(72.8)
565
(60.6)
Marital status *** a
Living without a
spouse
3046
(31.6)
2764
(34.4)
282
(17.5)
118
(17.3)
164
(17.6)
Living with a
spouse
6596
(68.4)
5264
(65.6)
1332
(82.5)
563
(82.7)
769
(82.4)
Household size 2.61
(1.23)
2.63
(1.25)*** a
2.51
(1.12)
2.46
(1.07)
2.56
(1.15)
Employment status
Non-working 7291
(75.6)
6061
(75.5)
1230
(76.2)
592
(86.9)
638
(68.4)
Currently
working
2351
(24.4)
1967
(24.5)
384
(23.8)
89 (13.1) 295
(31.6)
Pension *** a
No pension 2432
(25.2)
2210
(27.5)
222
(13.8)
69 (10.1) 153
(16.4)
Has a pension 7210
(74.8)
5818
(72.5)
1392
(86.2)
612
(89.9)
780
(83.6)
Family wealth *** a
No family wealth 3793
(39.3)
3545
(44.2)
248
(15.4)
92 (13.5) 156
(16.7)
Has family
wealth
5849
(60.7)
4483
(55.8)
1366
(84.6)
589
(86.5)
777
(83.3)
BMI 22.33
(2.49)
22.21
(2.51)*** a
22.9
(2.33)
23.07
(2.44)
22.78
(2.24)
IADL 9.01
(2.17)
8.90
(2.28)*** a
9.57
(1.32)
9.6
(1.31)
9.55
(1.33)
Cognition index 12.86
(3.63)
12.56
(3.75)*** a
14.38
(2.45)
14.74
(2.28)
14.11
(2.54)
R. Jing et al.
7. Computers in Human Behavior 138 (2023) 107480
7
depressive symptoms (see the supplementary materials).
4.3. Mediating effects of structural social capital as four latent constructs
Fig. 3 presents the results for the standardized solutions of Model 2,
which treats structural social capital as four latent constructs (i.e.,
family network, friendship network, volunteering participation, and
non-volunteering participation). The results show that the model fit is
Note: a
*, **, and *** indicate a statistically significant difference (P < 0.001, P <
0.01, and P < 0.05, respectively) between Internet users and non-users based on
a chi-square test or t-test. b
For the convenience of the description, the summed
scores for social networks and social participation sub-scales were created, with
a higher score indicating a higher level of engagement.
Fig. 2. Solutions for Model 1 regarding Internet use (constant and new), structural social capital (as two latent constructs), and depressive symptoms (N = 9642).
Note: All the path coefficients are standardized. Solid lines indicate statistically significant paths (P < 0.05); dotted lines indicate non-significant paths. ***P < 0.001,
**P < 0.01, and * P < 0.05.
Fig. 3. Solutions for Model 2 regarding Internet use (constant and new), structural social capital (as four latent constructs), and depressive symptoms (N = 9642).
Note: All the path coefficients are standardized and adjusted for rural/urban status, sex, age, education, marital status, household size, employment status, pension,
family wealth, BMI, IADL, and cognition index. Solid lines indicate statistically significant paths (P < 0.05); dotted lines indicate non-significant paths. ***P < 0.001,
**P < 0.01, *P < 0.05.
R. Jing et al.
8. Computers in Human Behavior 138 (2023) 107480
8
acceptable (CFI = 0.950, RMSEA = 0.044, SRMR = 0.021). Further,
Model 2 explains 42.5% of the variance in the depressive symptoms
among older adults.
Regarding social networks, only the friendship network plays a
partial mediating role in the association between constant Internet use
and depressive symptoms (β = − 0.122, P < 0.001); the results indicated
that the family network does not have a mediating effect. Regarding
social participation, new Internet users were less likely to participate in
volunteering activities (β = − 0.108, P < 0.001), whereas constant
Internet users showed no significant relation, which was associated with
having fewer depressive symptoms (β = − 0.120, P < 0.001). Compared
to non-users, both constant and new Internet users exhibited greater
participation in non-volunteering activities (β = 0.223, and β = 0.288,
respectively; P < 0.001); Internet use affected depressive symptoms
through non-volunteering participation (β = − 0.031, P = 0.047).
Table 2 shows the direct, indirect, and total effects of Internet use.
The total effects of constant and new Internet use on depressive symp
toms were significantly negative (β = − 1.582, P < 0.001; β = − 1.101, P
< 0.001). The direct effects were negative for both constant and new
Internet use (β = − 1.420, P < 0.001; β = − 1.138, P < 0.001). Only
constant Internet use showed a statistically significant indirect effect on
depressive symptoms among older adults in China (β = − 0.162, P =
0.004). The results of each pathway in Model 2 are displayed in
Table S2. Fig. S2 shows the varied mediating roles of different structural
social capital in the Internet use (total Internet users vs. non-users) and
depressive symptoms (see the supplementary materials).
4.4. Multigroup analysis for rural/urban differences
This study tested whether the relationships between Internet use,
structural social capital, and depressive symptoms differed between
rural and urban older adults using a multigroup structural model
(Fig. 4). Both the family and friendship networks significantly mediated
the relationships between Internet use and depressive symptoms among
urban older adults (P < 0.01). Among rural participants, only constant
Internet use was significantly associated with depressive symptoms
through the friendship network (P < 0.001). Differing from the urban
sample, the indirect effects of constant and new Internet use on
depressive symptoms were partially mediated by non-volunteering
participation among the rural older adults (P < 0.05). The results of
each pathway of the multigroup model are displayed in Table S3, and
the multigroup analysis—to evaluate whether the differences in the
mediating effects between the rural and urban participants (total
Internet users vs. non-users) were significant—are presented in Fig. S3
(see the supplementary materials).
5. Discussion
This study uses a relatively large sample of Chinese older adults to
examine the latent constructs of structural social capital (i.e., social
networks and social participation) as the main mediating pathways that
link constant and new Internet use with depressive symptoms. It also
tests the potential rural/urban differences in these mediatory effects.
This study found that Internet use was directly associated with fewer
depressive symptoms in constant and new Internet users; thus, H1 is
supported. This is consistent with the previous literature regarding the
direct relationship between Internet use and depressive symptoms
among older adults. For example, Xie et al. (2021) used the China Family
Panel Study data to explore the causal effects of older adults’ Internet
use on their depression levels and found that Internet use significantly
affected depression levels—after adjusting for 10 key confounding
covariates using propensity score matching. The correlation between the
variety, frequency, and type of Internet use and the risk of depression
was measured in China’s context (Liao et al., 2020). As older Internet
users are an increasingly diverse population group, the current study
further revealed that both constant and new Internet users were posi
tively associated with the decline in depressive symptoms.
More importantly, this study revealed structural social capital as an
important mediating factor between Internet use and depressive symp
toms among Chinese older adults; thus, H2 and H3 are partially sup
ported. Specifically, constant Internet use affected depressive symptoms
through the promotion of social networks possibly because Internet use
increases social contact, overcomes social and spatial barriers, and acts
as a convenient way to maintain contact with both friends and the
outside world (Cotten et al., 2012, 2013; Liao et al., 2020). The Internet
is an important, but not a dominant, communication tool for contacting
friends and family. E-mail, chat, and other communication capabilities
supplement social contact by helping people organize meetings and
social events, and thus, fill communication gaps (Wellman & Hay
thornthwaite, 2002). The Internet also enables inexpensive and conve
nient communication among distant communities with shared interests,
thus facilitating a digital revolution by restoring a sense of community
through distant as well as close interpersonal connections, and by
providing information resources on diverse topics (Quan-Haase &
Wellman, 2002). However, the current study was unable to prove this
mechanism for new Internet users because this group requires more time
to build social contacts and is less competent at obtaining social support
through the Internet (Sum et al., 2008). When new Internet users
become more constant users, a stable influencing mechanism between
social networks and depressive symptoms may be revealed.
In this study, being a new Internet user was also negatively associ
ated with social participation, and a further investigation revealed that
new Internet users had limited volunteering participation. A possible
explanation is that new Internet users have an increased propensity for
Internet addiction (Dieris-Hirche et al., 2017), which could reduce their
volunteering participation. Further, if older adults spend more time
online, public spaces for interactions and socializing will become less
relevant (Quan-Haase & Wellman, 2002); this is more likely to apply to
new Internet users. Therefore, from the structural social capital
perspective, two paths for the impact of Internet use on older adults’
depressive symptoms were identified, but the negative effect usually
occurred in new Internet users because of their problematic use. In the
long run, the negative effect would become weaker. Thus, policymakers
should strengthen the interventions for new Internet users to avoid the
negative impacts of Internet use. Meanwhile, this study found that
Internet use positively affected the decrease in depressive symptoms
through non-volunteering participation. Compared with non-users, both
constant and new Internet users participated more in leisure activities.
Other studies have found that Internet use improves participation in
community involvement (Liu et al., 2020; Wellman et al., 2001), and
that older-adult Internet users can obtain activities-related information,
which can, in turn, be used to design and control some of the activities.
However, the current study confirmed that Internet use mainly pro
moted non-volunteering participation; that is, older adults’ Internet use
was strongly and positively correlated with different leisure activities.
This study found rural/urban differences in the mediating role of
structural social capital and the relationship between Internet use and
depressive symptoms in China; thus, H4 and H5 are supported. This
study also found a mediating effect of social networks—including family
and friendship networks—on Internet use and depressive symptoms,
mainly in urban older adults. A possible explanation is that compared
with rural users, urban users can obtain more information from the
Table 2
Standardized direct, indirect, and total effects on depressive symptoms in Model
2 (N = 9642).
Direct Indirect Total
Constant Internet use → Depressive
symptoms
− 1.420*** − 0.162** − 1.582***
New Internet use → Depressive symptoms − 1.138*** 0.037 − 1.101***
Note: ***P < 0.001, **P < 0.01, *P < 0.05.
R. Jing et al.
9. Computers in Human Behavior 138 (2023) 107480
9
Internet and are more likely to procure social contacts; they can also
achieve better benefits from using the Internet because of their pre-
existing knowledge (Long et al., 2020). However, the mediating ef
fects of the social participation pathway, especially for non-volunteering
social participation, only focused on rural Internet users. Previous
studies have shown that attending religious, artistic, or cultural activ
ities is significantly associated with better mental health, but only for
rural residents (Green & Elliott, 2010; Vogelsang, 2016). Moreover, the
mediating effects could be associated with the different Internet habits
between urban and rural older adults, as urban older adults use the
Internet predominantly for communication, while their rural counter
parts use it primarily for entertainment. Furthermore, in previous
studies, social networks were found to be significantly more prevalent in
rural areas, whereas social participation was found to be marginally
higher in urban areas (Sørensen, 2016). Therefore, an increase in the
Internet use for older adults in rural and urban areas is meant to
compensate for the lack of social capital.
This study presents a mechanism for the effects of constant and new
Internet use on older adults’ depressive symptoms from the perspective
of structural social capital, which has important policy implications and
can help in the implementation of interventions by the society, gov
ernment, health systems, and ICT. First, both constant and new Internet
use showed significant total and direct effects on depressive symptoms,
which indicates the benefits of increasing the perceived ease and
perceived usefulness of Internet use (Braun, 2013). In this regard, to
improve the perceived ease of Internet use, the government continues to
Fig. 4. Solutions for the multigroup model regarding Internet use (constant and new), structural social capital (as four latent constructs), and depressive symptoms
among rural and urban older adults in China (N = 9642). Note: All the path coefficients are standardized and adjusted for sex, age, education, marital status,
household size, employment status, pension, family wealth, BMI, IADL, and cognition index. Solid lines indicate statistically significant paths (P < 0.05); dotted lines
indicate non-significant paths. ***P < 0.001, **P < 0.01, *P < 0.05.
R. Jing et al.
10. Computers in Human Behavior 138 (2023) 107480
10
develop the digital infrastructure; communities offer teaching courses
on Internet use for older adults; and ICT industries develop more
convenient and faster applications and create age-friendly interfaces to
facilitate Internet use. Meanwhile, to improve the perceived usefulness
of Internet use, the government has been increasing public awareness;
ICT industries have been promoting the trial and innovation of new
Internet technologies; and the health system has been attempting to
provide older adults access to healthcare through the Internet when
seeking medical assistance. In addition, it is necessary to create an at
mosphere for older adults to learn how to use the Internet and help them
bridge the digital divide. Second, this study revealed that new Internet
users face barriers to Internet use, including potentially problematic use,
which reduces social participation. This finding suggests the importance
of early intervention for new users and the need to advocate for
non-excessive online socializing to avoid negative health consequences
(Jiang & Song, 2022), such as setting usage time limits and public ser
vice advertisements on certain Internet applications. Third, rural/urban
differences in the mediating effect of structural social capital in the as
sociation between Internet use and older adults’ depressive symptoms
were identified, suggesting that specific policies on Internet use should
be proposed for rural and urban older adults. For rural older adults, it is
necessary to continue to popularize Internet use, provide more training
on Internet skills, and further expand their online social functions. For
urban older adults, society and communities must encourage them to
use the Internet to participate actively in social activities, and thereby
promote mental health.
To the best of our knowledge, this study is the first to evaluate the
association between constant and new Internet users and depressive
symptoms among Chinese older adults. The strengths of this study
include the following: A large and nationally representative sample of
Chinese older adults in two waves of the CLASS was used. The moder
ating effects of two constructs of structural social capital (i.e., social
networks and social participation) were explored. The differences be
tween urban and rural older adults were assessed. However, this study
has the following limitations: First, it was unable to include the fre
quency, variety, and purpose of online Internet use. Second, the SEMs
did not prove any causal effects, but allowed this study to test the
theoretical models and examine how well they described the observed
data. Hence, this study only tested theoretically informed models.
6. Conclusion
In this study’s context, Internet use was significantly associated with
fewer depressive symptoms, and social capital played a mediating role in
the association between Internet use and depressive symptoms. Specif
ically, friendship networks and non-volunteering participation played a
partial mediating role in the association between constant Internet use
and depressive symptoms. New Internet users were less likely to
participate in voluntary activities than non-users, and only became more
involved in leisure activities to improve their depressive symptoms.
Social networks and non-volunteering participation acted as mediators
in the association between urban and rural Internet users with depres
sive symptoms, respectively. This study’s findings indicate that the
government, ICT industries, health systems, and communities should
increase the perceived ease and usefulness of Internet use, advocate for
non-excessive online socializing of new users, and work toward policies
relevant to Internet use for rural and urban older adults.
Credit author statement
Rize Jing: Conceptualization, Methodology, Formal analysis,
Writing - Original Draft, Writing- Reviewing & Editing. Guangzhao Jin:
Formal analysis, Visualization, Writing- Reviewing & Editing. Yalong
Guo: Validation. Yiyang Zhang: Validation. Long Li: Conceptualiza
tion, Methodology, Formal analysis, Funding acquisition, Supervision,
Visualization, Writing - Reviewing & Editing.
Declaration of competring interest
The authors declare that they have no known competing financial
interests or personal relationships that could have appeared to influence
the work reported in this paper.
Data availability
Data will be made available on request.
Acknowledgments
The authors would like to thank all participants and staff of the China
Longitudinal Aging Social Survey (CLASS).
Abbreviations
(LMICs) low- and middle-income countries
(CINIC) China Internet Network Information Center
(CLASS) China Longitudinal Aging Social Survey
(CES-D) Center for Epidemiologic Studies-Depression
(BMI) body mass index
(IADL) instrumental activities of daily living
(SD) standard deviation
(SEM) structural equation modeling
(CFI) comparative fit index
(SRMR) standardized root mean square residual
(ICT) information and communications technology
Appendix A. Supplementary data
Supplementary data to this article can be found online at https://doi.
org/10.1016/j.chb.2022.107480.
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