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Abstract

Background

Social capital refers to the valuable resources that individuals can access through their social networks, which are often established within family ties, professional affiliations, community organizations, and neighborhoods. Evidence suggests that there is a positive association between social capital and well-being, health status, and sociability. Research has found a positive association between digital technology use and social capital, though limited attention has been paid to older adults. This systematic review aims to evaluate the association between digital technology use and social capital among older adults.

Methods

Five scientific electronic databases were searched for eligible studies in October 2024. The search strategies were developed by combining the keywords of the three main axes of the research (“older adults,” “digital technology,” and “social capital”) and respective synonyms using Boolean operators. The review protocol was developed in advance and registered in PROSPERO (CRD42024614622).

Results

The research strategy returned a total of 1229 citations from electronic databases, 20 of which met the exclusion and inclusion criteria. All the included studies had a cross-sectional design, and the samples of the included studies ranged from 118 to 9642 individuals. All the included studies found a positive association between digital technology and social capital among older adults.

Conclusions

Digital technology can offer opportunities to maintain or increase social capital, that typically tends to reduce with age. Since social capital positively impacts health and well-being, the importance of enabling older adults to use digital technologies must be considered when designing and implementing health behavior intervention programs.

Keywords

older adults social capital digital technology

Introduction

Social capital is defined as a general public resource that usually exists within connections formed through family ties, work environments, community organizations, and local neighborhoods.^1–3 This concept refers to the various concrete (i.e., money, food, and running errands) and abstract resources (i.e., offering emotional support, giving advice, sharing nuanced information, and making recommendations) that can be accessed through interpersonal relationships.⁴ According to Bourdieu and Coleman, social capital is not an active form of resource; instead, it exists in a dormant state that can be activated in response to particular needs.^2,3 Thus, social capital is regarded as the collection of resources found in one's social ties, their accessibility, and the capability to make use of those resources.⁵

Two distinct frameworks for categorizing social capital have been recognized. The first model makes a distinction between bonding social capital, which gives emotional support through deep connections, and bridging social capital, which relies on weaker ties that assist in gaining knowledge and new perspectives.^6,7 The second approach categorizes social capital into two main components: cognitive and structural. The cognitive component pertains to individuals’ emotional experiences, including trust, reciprocity, a sense of belonging, and personal motivations such as attitudes, sharing, and values, which can be understood as how people view their social relationships. Conversely, the structural component relates to the precedents, regulations, and behavioral norms that emerge from social engagement, along with the roles and networks created through social interactions.^6–8

Essentially, social capital encompasses the resources that individuals gain through their social connections, which can be interpreted as the degree of their social integration and the support they obtain from their community.⁹ A person's social capital is fundamentally rooted in the connections and relationships that form their social networks. Individuals who possess robust and interconnected social networks find it simpler to obtain information and enjoy greater resources, as their network members offer both bridging and bonding support, enhancing their confidence and trust levels.^10,11

Research indicates that social capital is linked to enhanced quality of life and well-being, as well as beneficial factors such as positive emotions, optimism, life satisfaction, trust, and hope, while showing a negative correlation with feelings of loneliness.^12–20

Previous research has tried to assess whether the increasingly massive use of Information and Communication Technologies (ICTs) could have an impact on individuals’ social capital. Initial investigations suggested that online connections were insufficient,^21,22 but with the increasing use of ICTs, particularly through social media platforms, there is now substantial evidence demonstrating how these technologies can aid in acquiring bridging and bonding social capital.^4,23–26

Although previous research has found a positive association between digital technology use and social capital, limited attention has been paid to older adults.²⁷ With the rise of digital technology as a key means of accessing information, engaging in communication, and participating in society within developed nations, it is essential to examine its impact on social resources through an age-related lens. While the adoption of digital technology among older individuals is on the rise, they remain less inclined to utilize it compared to younger.²⁷ With the global rise in the number of older individuals projected to continue in the coming decades, promoting healthy aging has become crucial.²⁸ The growing concerns of social exclusion, isolation, and loneliness are increasingly impacting the health, well-being, and social engagement of older individuals.²⁷ Evidence suggests that senior citizens with elevated social capital levels are likely to be in better health, practice healthier habits, and experience superior physical and social wellness, even if they live alone or have a low income.^29,30 Due to various factors, social capital is crucial for promoting healthy aging, however, its presence tends to diminish as individuals grow older.²⁷ This decline is often linked to life events that frequently involve loss, such as reduced work opportunities, declining physical abilities, and the death of loved ones, all of which can adversely affect one's social capital.^31,32

If digital technology use proves to be positively associated with social capital also in older age, the improvement of digital literacy and a greater use of digital technologies among older individuals might help them maintain high levels of social capital also in the later stages of life. This systematic review aims to evaluate the association between digital technology use and social capital among older adults.

Methods

A systematic review was conducted. The review methods were defined in advance in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines³³ and PRISMA checklist (Appendix A). The review protocol was registered with International Prospective Register of Systematic Reviews (PROSPERO – CRD42024614622).

Search strategy

For the present study, PubMed, PsycINFO, CINAHL, Scopus, and Web of Science databases were systematically searched in October 2024. Search strategies were developed by combining the keywords of the three main axes of the research (“older adults,” “digital technology,” “social capital”) and respective synonyms using Boolean operators. For the PubMed database, a combination of Medical Subject Headings (MeSH) terms and text-words was used. The Medical Subject Headings (MeSH) thesaurus is a controlled and hierarchically organized vocabulary produced by the National Library of Medicine and used for indexing, cataloging, and searching biomedical and health-related information.³⁴ The combination of text-word and MeSH strategies is recommended to achieve the most comprehensive results.³⁵ The search strategies are provided in Appendix B.

Retrieved studies were independently reviewed for eligibility by two authors (GG and AB) in a two-step process: an initial screening was performed based on titles and abstracts, and then full texts were screened. Disagreements between the two reviewers were resolved by consensus. The reference lists of the included studies were also searched to identify additional relevant studies.

Inclusion and exclusion criteria

The review questions were predefined in advance using the PICO (Population, Intervention, Comparison, Outcome) framework.³⁶ The population group was identified in (a) older adults defined as individuals aged 60 years or more or (b) study's samples with the participants’ mean age of 65 years or above; the exposure was related to the use of digital technology including social network sites, apps, smartphones, and the Internet; the comparison was identified in older adults not using digital technology; and the main outcome was the assessment of the measure of the association between the exposure to digital technology and social capital.

Studies were considered for inclusion in the systematic review if they:

assessed the measure of the association between digital technology use and social capital among older adults;

included individuals who were 60 years old and older or samples with a mean age of the participant over 65;

were published from 2000 onwards;

were written in English.

Studies were excluded from the systematic review if they:

targeted exclusively older adults affected by a specific disease or clinical condition;

incorporated theoretical studies or published opinions as their primary source of evidence.

Data extraction and data analysis

A data extraction form based on the research question was developed in Microsoft Excel. The following data were extracted from the included studies: (a) main author, (b) year, (c) objectives, (d) study design, (e) country, (f) characteristics of the population, (g) variables of interest, (h) data sources, (i) methods and statistical analyses, (j) controls and confounders, (k) outcomes, and (l) conclusions. A descriptive analysis was performed to report the characteristics of the included reviews (Table 1) and their outcomes (Table 2).

Table 1.

Association between digital technology use and social capital.

Main author, year, and country	Objectives	Characteristics of the population	Variables of interest and data sources	Outcomes
Barbosa Neves, 2018, Portugal	To examine the association between Internet usage and social capital and whether and how it differs by age	Older adults aged 65 years or more. N = 118, 57% females.	Bonding and bridging social capital Internet usage.	The effect of age on social capital depends on different values of Internet usage: per year, the odds of having a high social capital decreased for Internet nonusers and for Internet light users (compared to heavy users) by 1.7% (p = 0.015) and by 4.5% (p = 0.005), respectively.
Cao, 2023, China	To investigate the association between social capital and eHealth literacy of older adults, and examine its variations in age and gender	Older adults aged 60 years or over. N = 4218, mean age 71.9, 64.8% females	Social capital (a 26-item tool assessing its dimensions of social participation, social support, social connection, trust, cohesion, and reciprocity). Self-perceived eHealth literacy (simplified 5-point Likert Chinese version of eHEALS)	Social participation (β = 0.15, p < 0.001), social connection (β = 0.27, p < 0.001), trust (β = 0.37, p < 0.001), cohesion (β = 0.36, p < 0.001) and reciprocity (β = 0.34, p < 0.001) were all statistically associated with eHealth literacy, while such an association was not observed for social support (p > 0.05). Trust, cohesion, and reciprocity were associated with eHealth literacy among participants from all three age groups (p < 0.05). Social connection was associated to eHealth literacy for participants aged 60–69 and 70–79 years (p < 0.001). Social participation was only linked to eHealth literacy among participants aged 70–79 years after adjustment (p < 0.001). No significant gender differences were identified.
Choi, 2013, United States	To examine whether older adults’ health conditions and psychological and social capital differentiate Internet users from nonusers, and whether the Internet users differed in their types of Internet use based on their health conditions and psychological and social capital.	Older adults aged 65 and over. N = 6680. 30.13% in the 65–69 age group, 26.17% in the 70–74 age group, 19.40% in the 75–79 age group, 14.16% in the 80–84 age group, 10.14% in the 85 or older age group. 55.8% females	Internet use and type of use Social capital measures (having children, having siblings, work for pay, do formal volunteering, provide informal caregiving, visit family/friend, attend religious services, participate in club/classes/other organized activities, go out for enjoyment) Data collected from the National Health and Aging Trends Study (face to face individual interviews for Medicare beneficiaries)	Association between Internet use ad social capital: Having a child (OR 1.47, 95% CI 1.18–1.84, p < 0.001), engagement in paid work (OR 1.65, 95% CI 1.34–2.03, p < 0.001) and formal volunteering (OR 1.33, 95% CI 1.09–1.62, p = 0.01), friend/family visits (OR 1.25, 95% CI 1.03–1.52, p = 0.02), attending organized activities (OR 1.73, 95% CI 1.46–2.05, p < 0.001), and participating in entertainment activities (OR 1.69, 95% CI 1.36–2.08, p < 0.001) were positively associated with Internet use. Having a sibling (OR 0.81, 95% CI 0.68–0.97, p = 0.02) and attending religious services (OR 0.82, 95% CI 0.73–0.94, p = 0.01) were negatively associated with use. Informal caregiving was not a significant correlation. Association between type of Internet use and social capital: formal volunteering was positively associated with Internet use for health-related tasks (OR 1.28, 95% CI 1.05–1.57, p = 0.02). Internet use for shopping/banking activities negatively associated with religious service attendance (OR 0.75, 95% CI 0.62–0.91, p = 0.01). Internet use solely for sending emails/texting was negatively associated with engagement in formal volunteering (OR 0.63, 95% CI 0.43–0.92, p = 0.02)
Cui, 2021, China	To explore the association between social capital (structural and cognitive social capital), eHealth literacy, and the health behaviors of older adults, and to analyze the mediating effect of eHealth literacy	Older adults aged over 60 years. N = 1201, 53.4% females, 51.8% in the age group 60–69 years, 40.4% in the age group 70–79 years, 7.8% in the age group 80 years and over, mean age 70 years.	Social capital (Chinese Shortened Social Capital Scale) eHealth literacy (eHealth Literacy Scale)	Social capital and structural social capital were significantly associated with eHealth literacy (r: 0.21 and 0.36 respectively, p < 0.01) eHealth literacy mediated the association between structural social capital and health behaviors (β = 0.11, p < 0.01)
Cui, 2024, China	To explore the relationship between digital social capital, digital divide, learning ability, and health of older adults.	Older adults aged 60 and over. N = 8099; 52.1% females	Digital social capital (four dimensions: network relationship, network trust, network participation, and network norms) Internet usage Data were collected from the China General Social Survey (CGSS)	Digital social capital is positively associated with Internet usage (Coef. = 0.230, p < 0.01)
Geithner, 2022, Germany	To identify how lifestyles in older age are related to the social positions of very old adults. To evaluate if different forms of capital continue to be related to lifestyles at very old ages, and whether greater access to economic, cultural, and social capital continues to provide advantages when very old adults face severe health impairments	Older adults aged 80 and older. N = 1863. 54.3% in the 80–84 years age group, 30.7% in the 85–89 years age group, 15% in the 90 years or older age group. 63.7% females	Data from the survey on Quality of Life and Subjective Well-being of the Very Old in North Rhine-Westphalia (NRW80+) Social capital (measured by the size of the social network, marital status, and the level of social support) Digital lifestyle (high probability of using computers and the internet on a weekly or daily basis)	The very old adults with a digital lifestyle had higher size of the social network compared to the older adults with a withdrawn lifestyle (OR: 1.1, 95% CI: 1.071–1.137, p < 0.001). The other dimensions of social capital (marital status, and the level of social support) were not significantly different).
Guan, 2024, China	To examine the intention of older adults to engage in preventive behaviors against COVID-19 and the influencing factors, including social, media, and individual factors, within the context of the post epidemic era	Older adults aged 60 years or over. N = 399, mean age 64.93, age range 60–93 years, 50.4% females	Cognitive social capital (trust and reciprocity) and structural social capital (social network and social participation). Cell phone exposure. Data were collected through a web-based-questionnaire	Older adults with higher levels of cognitive social capital were more likely to use the cell phone (β = 0.36; p < 0.001). There was no statistically significant association between structural social capital and cell phone exposure
Nguyen, 2022, United States	To examine the association between older adults’ online social engagement and social capital	Older adults aged 60 and over. N = 1026, mean age: 69.3 years, 57% females	Social capital (offline bonding, offline bridging, and online bridging)Online social engagement Internet experiences and skills	Offline bridging is positively associated with the engagement in the following online activities: looking at photos of others besides family members (β = 0.12, p < 0.01) and at status updates (β = 0.09, p < 0.05).None of the online social engagement activities explains variance in offline bonding. Online bridging was positively associated with six out of seven online social activities: asking questions (β = 0.08, p < 0.05), answering questions (β = 0.08, p < 0.05), checking in on someone (β = 0.09, p < 0.01), looking at photos of others besides family members (β = 0.15, p < 0.001), looking at status updates (β = 0.13, p < 0.01), clicking on links that people shared on social media (β = 0.14, p < 0.001); looking at photos of family members was negatively associated with online bridging (β = −0.08 p < 0.05). Internet skills moderate the association between three online social activities (looking at photos of family members online, looking at people's updates on social media, and clicking on links that people shared on social media) and online bridging but not for offline bonding and bridging
O'Brien, 2021, Canada	To examine the influence of the different facets of social network sites use on bonding and bridging social capital of older adults	Older adults aged 65 or over that use social network sites. N. 330, 70% females, 83% in the age group 65–74, 15% in the age group 75–84, and 2% over the age of 85	Bonding and bridging social capital Social network sites use: active or passive the interaction, the amount of use, the number and type of contacts, and the type of information shared among individuals.	Active use of social network sites positively influences bridging social capital (β=0.188, p < 0.05) and bonding social capital (β=0.178, p < 0.01). Contact breadth: broad contact breadth positively influences bridging social capital (β=0.200, p < 0.001); narrow contact breadth positively influences bonding social capital (β=0.167, p < 0.05). Intensity of use: did not influence bonding and bridging social capital. Type of message content: public information sharing positively influenced bridging social capital (β=0.400, p < 0.001) but not bonding social capital; private information sharing positively influenced bonding social capital (β=0.376, p < 0.001)
Rios, 2019, United States	To investigate whether knowledge of Internet usage among older adults is related to social capital as well as their expectations to obtain social support online.	Older adults aged 60 years and over. N = 1101; mean age 66.11; 49.9% males	Expected Internet support Internet literacy Internet use and frequency Bonding social capital Bridging social capital Dava were collected through an online survey	Internet frequency (but not use) was associated with bonding social capital (β=0.33, p < 0.001) and bridging social capital (β=0.43, p < 0.001) Internet literacy was a significant predictor of bonding social capital (β=0.09, p < 0.001) and bridging social capital (β=0.18 p < 0.001)
Simons, 2023, Netherlands	To explore associations between the use of WhatsApp, social network sites (SNS), and Internet applications and respectively bonding social capital and loneliness	Older adults aged 70 years and over. N = 349, mean age 74.8 years, 59.6% males	Frequencies of use of WhatsApp, social network sites, and Internet applications Bonding social capital (subscale of the Personal Social Capital Scale for Elderly)	WhatsApp use (β=.26, p = 0.003) and Internet use (β=.20, p = 0.020) were positively associated with bonding social capital. Social network sites use was not associated with bonding social capital
Tsai, 2014, Taiwan	To analyze the association between social capital factors, technological factors, and system self-efficacy in telehealth	Older adults who had used a telehealth system for at least one month. N 365, 59.5% women, 41.9% in the 71–80 age group	Perceived ease of use, perceived usefulness, usage intention, and system self-efficacy (belief of one's capability to use the telehealth systems) of a telehealth system. Social capital factors (social trust, institutional trust, social participation) Data collected from a questionnaire administered during in-person interviews.	Social trust (β = 0.197, p < 0.001), institutional trust (β = 0.186, p < 0.001), social participation (β = 0.114, p < 0.05), and system self-efficacy (β = 0.278, p < 0.001) significantly affected perceived ease of use, accounting for 23.7% of the variance among the constructs. Institutional trust (β = 0.177, p < 0.001), social participation (β = 0.214, p < 0.001), and perceived ease of use (β = 0.376, p < 0.001) significantly affected perceived usefulness, accounting for 35.3% of the variance among the constructs. Social trust and system self-efficacy affected perceived usefulness non-significantly. Perceived ease of use (β = 0.216, p < 0.001), and perceived usefulness (β = 0.461, p < 0.001) both significantly affected usage intention, accounting for 35.9% of the variance among the constructs.

Characteristics and findings of the primary level studies.

Table 2.

Association between digital technology use and others health outcomes: social capital as a mediating variable. Characteristics and findings of the primary level studies.

Main author, year, and country	Objectives	Characteristics of the population	Variables of interest and data sources	Outcomes	Social capital as a mediating variable
Cai, 2024, China	To assess the association between e-health literacy, self-identity, social capital, and educational participation motivation	Older adults aged between 65 and 87 years. N = 578; mean age 70.13; 65.92% females.	eHealth literacy (E-Health Literacy Scale) Self-identity (Self Identity Scale) Social capital (Social Capital Scale) Education participation motivation (Educational Participation Motivation Scale) Data were collected from questionnaires distributed at community activity centers and community health examination centers	Positive associations were found among e-health literacy, self-identity, social capital, and educational participation motivation. Coefficient of the association between eHealth literacy and social capital: 0.735, p < 0.01	When e-health literacy, self-identity, and social capital were included in the regression equation, the previously observed predictive effect of e-health literacy on educational participation motivation became statistically insignificant. E-health literacy exhibited a significant direct positive effect on both self-identity (β = 0.65, p < 0.001) and social capital (β = 0.55, p < 0.001). Both self-identity and social capital demonstrated significant positive predictive effects on educational participation motivation (β = 0.35, p < 0.001; β = 0.42, p < 0.001). The total mediating effect value was found to be 0.61.
Duan, 2024, China	To investigate the impact of a digitalization program for rural villages (digital villages) on the subjective well-being of the older adults	Older adults aged 60 or over, living in rural communities. N = 3196; 55% males; mean age: 71.81; SD 6338; min 60 max 98	Subjective Well Being (SWB): data from China Longitudinal Aging Social Survey (CLASS) 2020 Digital villages index (DVI): a proxy variable for digital villages development from the China County Digital Villages Report (CCDVR) 2020. Secondary indicators: digital infrastructure index (DII), digital economy index (DEI), digital governance index (DGI), and digital life index (DLI). Social capital: measured through a six-question scale on family and friends’ networks.	Digital villages’ development significantly contributes to improved subjective well-being of rural older adults. OR: 0.147 p < 0.05, SE: 0.066	Rural digitalization positively supports older adults’ social capital (OR: 0.536, p < 0.01, SE: 0.091). Social capital positively supports the subjective well-being of older adults (OR: 0.013, p < 0.01, SE: 0.002). After including social capital in the regression, rural digitalization still impacts the subjective well-being of older adults (OR: 0.118, p < 0.01, SE: 0.022)
Jiang, 2022, China	To assess the association between internet use and older women's aging process.	Older women aged 60 years and over. N 2713. Mean age 69.96 years. Min 60 max 103 years	Internet use frequency. Social capital Successful aging (no major diseases, no disability, high cognitive functioning, high physical functioning, and active engagement with life). Data collected from the China Longitudinal Aging Social Survey 2016	Successful aging status was significantly associated with an increase in internet use frequency (instrumental variable probit model's coefficient: 0.287, p < 0.001)	Social capital partially mediated the overall association between internet use frequency and successful aging: the indirect effect of internet use frequency was 0.011 (p < 0.01) with 7.83% of the total effect being mediated by social capital
Jing, 2023, China	To examine whether structural social capital mediates the association between constant and new Internet use and depressive symptoms among Chinese older adults, and the potential rural/urban differences	Older adults aged 60 years or over. N = 9642; 47.5% in the 60–69 age range, 36.2% in the 70–79 age range, 16.3% in the 80 years and more age range; 50.4% males	Depressive symptoms Internet use Structural social capital Data collected from the 2016 and 2018 China Longitudinal Aging Social Surveys	Compared with nonusers, Internet users exhibited a lower incidence of depressive symptoms (5.65 vs. 7.74, p < 0.001). Compared with nonusers, Internet users exhibited a stronger family network score (7.84 vs. 7.30, p < 0.001), friendship network score (7.01 vs. 6.19, p < 0.001), volunteering participation score (2.05 vs. 0.99, p < 0.001), and nonvolunteering participation score (0.72 vs. 0.19, p < 0.001).	Structural social capital mediates the association between Internet use and depressive symptoms among constant Internet users. Friendship network mediates the association between constant Internet use and depressive symptoms (β = −0.122, p < 0.001), the family network does not have a mediating effect. 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 nonusers, both constant and new Internet users exhibited greater participation in nonvolunteering activities (β = 0.223, and β = 0.288, respectively; p < 0.001); Internet use affected depressive symptoms through nonvolunteering participation (β = −0.031, p = 0.047). The total effects of constant and new Internet use on depressive symptoms 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 (β = −0.162, p = 0.004).
Li, 2021, China	To examine the association between Internet use, social capital, and depressive symptoms in older adults	Older adults aged 60 years and over. N = 6840; mean age 67.96; 51.33%	Depressive symptoms Internet use Social capital (structural and cognitive) Data collected from the 2018 wave of the China Family Panel Studies	Both Internet use characteristics and social capital components were protective factors for the prevention of depressive symptoms among older adults.	Internet use generated a negatively indirect effect on depressive symptoms mediated by structural social capital (contact with adult children); conversely, Internet access was negatively associated with cognitive social capital (i.e., interpersonal trust and institutional trust) which negatively affected the depressive symptoms
Lyu, 2020, China	To examine the relationship between Internet use and self-rated health among older adults in China, with a particular focus on the mediating role of social capital.	Older adults aged between 60 and 95 years. N = 7193. 64.83% in the 60–69 age range, 28.69% in the 70–79 age range, 6.48% in the 80 years and over age range; 51.01 males.	Self-rated health Internet use (yes/no) Social capital Data collected from the 2018 wave of China Family Panel Studies	Internet use was associated with a higher possibility of being healthy (OR 1.19, p < 0.05)	After introducing the variable of social capital, the direct effect of Internet use on self-rated health was at a significant level (effect = 0.1714, 95% CI: 0.0092–0.3335). The indirect effect of Internet use on self-rated health through increasing gift expenditure (a measure of social capital for the Chinese population) was also at a significant level (effect = 0.0043, 95% CI: 0.0003–0.0128). The ratio of the indirect effect to total effect mediated was 0.0245 (small effect size)
Yang, 2022, China	To test the effect of Internet use on the subjective well-being of older adults	Older adults aged 60 years or over. N = 4463; 52% males. Mean age 68.07; 64.6% in the 60–69 age group, 30.92% in the 70–79 age group, 4.48% in the 80–100 age group	Internet use Subjective well-being Social capital (bonding and bridging) Data were collected from the 2020 China Family Panel Studies	Internet use was positively associated with improvement of subjective well-being (regression coefficient: 0.194, p < 0.01)	The mediating effects of bonding social capital and bridging social capital accounted for 18.69% and 28.17%, respectively
Zhu, 2021, China	To assess the association between Internet use and health among older adults and the mediating role of social capital	Older adults aged 60 years and over. N = 6323; mean age: 69.877; 56.16% in the 60–69 age group, 43.84 in the 70 and over age group; 50.7% males	Internet use Social capital Physical health (ADL scale) and mental health Data collected from the 2018 wave of China Health and Retirement Longitudinal Study	Internet use has a positive association with physical health (coefficient - 0.69 p < 0.01) and mental health (coefficient - 1.891, p < 0.01) of older adults	Internet use has a positive association with the social capital of older adults (coefficient: 0.363 p < 0.01). The association between Internet use and physical health is still significant when considering the mediator. Social capital has no significant association with mental health of older adults

Quality assessment of the included studies

The RoBANS 2 tool³⁷ was used for a critical assessment of the methodological quality of the included studies (Appendix C).

Result

Identified studies

The initial search yielded a total of 1229 citations from electronic databases. After removal of duplicates, 932 titles and abstracts were screened, and 49 full texts were assessed for eligibility. Among them, 29 papers were excluded for the following reasons: 13 studies were not conducted in older adults as defined in the inclusion criteria; nine studies did not evaluate the social capital of the older adults; six studies did not assess the association between digital technology use and social capital; one study was written in a language other than English. Ultimately, 20 studies met the inclusion criteria and were included in the systematic review. No additional studies were included based on the manual search of the reference lists. The literature search is shown in detail in Figure 1.

Figure 1.

The preferred reporting items for systematic reviews and meta-analyses (PRISMA) diagram of the article search.

Characteristics of and findings of the included studies

All the included studies had a cross-sectional design. The studies’ samples ranged from 118 to 9642 individuals. Most of the studies (n = 12) were conducted in China,^8,38–48 three were based in the United States,^9,49,50 three in Europe,^27,32,51 one in Canada,⁵² and one in Taiwan.⁵³

The included studies were divided into two main categories according to the way that the two main variables considered in this systematic review, digital technology use and social capital, were treated in the analyses: 12 studies evaluated the association between digital technology use and social capital,^{8,9,27,32,38–40,49–53} and 8 studies evaluated the association between digital technology use and various health outcomes analyzing the mediating role that social capital played in this association (social capital as a mediator).^41–48

The included studies utilized different variables as a measure of exposure to digital technology: most of them (n = 11) evaluated Internet use of the older adults;^{9,27,38,42,43,45–50} three assessed older adults’ eHealth literacy,^8,40,44 two studies evaluated the participants Social Network Sites use,^32,52 one studied the cell phone exposure,³⁹ one study considered the level of digitalization of the living area,⁴¹ one evaluated the digital lifestyle,⁵¹ and one the participants’ perceptions regarding the use of a telehealth system.⁵³ Regarding the second variable of interest, 10 studies assessed social capital globally,^{8,9,40–42,44,45,47,51,53} five studies evaluated bonding and bridging social capital separately,^{27,43,49,50,52} two studies focused on structural and cognitive social capital,^39,46 one bonding social capital,³² one structural social capital,⁴⁸ and one digital social capital.³⁸

All the included studies found a positive association between digital technology use and social capital among older adults, regardless of the type of digital technology used or subcategory of social capital considered.

Methodological quality of the included studies

The results of the methodological quality assessment are reported in Appendix C. The majority of the studies were evaluated at low risk of bias in the following domains of the RoBANS 2 tool:³⁷ “comparability of the target group,” “target group selection,” “confounders,” “blinding of assessors,” “incomplete outcome data,” and “selective outcome reporting.”

On the other hand, the included studies met one or more of the following RoBANS 2's criteria for “high” risk of bias for inappropriate “measurement of intervention/exposure”: 1) exposures were measured by simply self-response; 2) exposures were measured by unstructured interviews, and 3) measurements were not objectified or standardized properly by utilizing multiple measurements (two times or more), independent measurements by multiple investigators, or using a standardized measurement of exposure, even with no effort to do so.³⁷ This could lead to performance bias due to inappropriate exposure measurement. Also, most of the included studies were judged at high risk of bias for outcome assessment because they met the following criteria for “high” risk of bias: patient-reported outcome that was assessed using only simple self-response:³⁷ this could lead to detection bias due to inappropriate outcome assessment methods.

Discussion

The included studies consistently showed that the use of digital technologies was associated with higher levels of different types of social capital among older adults.

This result suggests that digital technology use, and Internet use in particular, among older adults can contribute to a greater social capital.^8,54 Engaging socially online could actually enhance bonding social capital, as it provides greater chances to interact with loved ones,⁵⁵ while also fostering bridging social capital through the wealth of information and services accessible on the Internet.^9,56

An earlier investigation involving a digital sample of older Australians found that engaging in online communication enhanced their satisfaction with family interactions, facilitated the expansion of their close social circles, and resulted in more face-to-face meetings.⁵⁷ Similarly, interviews conducted with senior users of a social media site showed that they primarily utilized it to keep track of their loved ones’ activities instead of for deep interactions.²⁷ This insight would, however, stimulate direct communication or phone calls, indicating that the social media platform might be facilitating the maintenance and activation of social capital through this awareness.²⁷ Also, studies show that digital media can enhance social engagement for those with limited mobility,^58–60 a common issue for older adults, and can assist in accessing or preserving social relationships.^61,62

O’Brien and colleagues⁵² examined how various features of social networking sites influence interpersonal connections, discovering that the nature of exchanged message, whether they are personal or informational, varies in their effects on social ties. The research indicated that personal messages foster stronger ties in intimate relationships, whereas informational exchanges bolster more tenuous social links.⁵² In this sense, social network sites can help to strengthen the bonding and bridging of social capital of their participants. Moreover, utilizing social media actively, along with the variety and number of connections, contributes positively to social ties, yet a higher level of engagement with these platforms does not appear to affect social relationships, implying that the frequency and length of use do not enhance the social capital of older adults.⁵²

Considering that all the included studies had a cross-sectional design, a bidirectional association might also be at play, implying that older adults with high levels of social capital might be more likely to use digital technologies. In this context, since a person's social connections form the essential foundation of their social capital, individuals with robust and interconnected social circles (such as family, friends, and volunteer companions) are more inclined to receive support in learning to navigate digital technologies, along with both emotional and practical help from their network members.⁹ Engaging in activities with relatives, companions, and other connections is expected to enhance the demand for and perceived value of digital tools as a way to sustain social bonds and integration.⁹ Supporting this hypothesis, previous research revealed that the primary ways individuals engage with digital technology include observing and discussing computer and Internet usage with their children and grandchildren, as well as receiving informal assistance from relatives and friends on navigating these technologies.⁵⁷

In addition, since digital literacy frequently correlates with socioeconomic factors such as educational attainment and income levels,^49,63 implications for studies on digital inequality emerged.^9,27,49 The research conducted by Barbosa-Neves and colleagues supported earlier findings that indicated a positive association between social capital and Internet use, while also showing an inverse association with age.²⁷ At the same time, the Internet appears to mitigate the adverse effects of age on social capital, as older individuals who frequently use the Internet are more likely to possess high social capital compared to their non-using or infrequent-using counterparts.²⁷ Therefore, the Internet appears to have a buffering effect that mitigates the negative association between age and social capital; however, older individuals are still less inclined to embrace the Internet compared to younger demographics, with education being a primary factor influencing Internet usage among seniors.⁶⁴ Consequently, it is primarily individuals with advanced education who would gain from that protective element. Likewise, the research by Choi and DiNitto supports these conclusions, indicating that older adults who are both older and from lower socioeconomic backgrounds are much less prone to use the Internet, including for health-related inquiries.⁹

The findings suggest that although digital technology appears to support the preservation, accumulation, and activation of social capital via its social features, it simultaneously exacerbates certain social disparities, especially related to age and educational background.^9,27 Specifically, engaging in social activities on the Internet can intensify the disparities between those with greater privilege and those with less, particularly in terms of the social capital they can acquire. Studies on digital disparity indicated that individuals are more likely to experience beneficial results from Internet usage when they have the necessary skills to effectively utilize it.⁶⁵ To fully harness the advantages of online social interactions for building social capital, individuals must have enough digital literacy to effectively utilize the Internet for fostering and sustaining social connections.⁶⁶ Supporting older individuals, particularly the most disadvantaged ones, in their use of digital technology to satisfy social demands might mitigate this disparity.⁴⁹ Knowledgeable family members and friends can offer support for older adults in using the Internet for social interactions, complemented by organized initiatives such as computer workshops that have yielded encouraging results.^67–69

Finally, the landscape of security and privacy risks has evolved as fast as digital technologies, creating an urgent need to protect digital devices from key threats through a layered security approach.⁷⁰ Privacy and security concerns can adversely affect the adoption and use of digital technology, and older adults are particularly vulnerable compared to the rest of the population, mainly due to lower digital literacy or cognitive impairment. Strict regulation together with consistent social support for older adults should improve the use of and trust in digital technology while ensuring privacy and security.

Strengths and limitations

This systematic review has some limitations. First, all the included studies had a cross-sectional design; thus, only associations, not causal relationships could be deduced. Secondly, the variables of interest were measured with different scales and instruments across the studies; although, the consistency of the results through all the twenty publications supports the results and the conclusions of this review. Thirdly, limiting the language of publications to English only may have led to a linguistic bias and lack of data on specific cultural contexts that might have different results. Lastly, due to the great heterogeneity of the studies and the lack of standardized measurements, it was not possible to carry out a subgroup analysis concerning the characteristics of the population (age class of the participants, living environment), the type of digital media used and the characteristics of the use (duration, intensity, type of information shared, extent of contacts), as was planned in the study protocol.

Conclusions

Overall, this systematic review indicates that digital technology could play a crucial role in enhancing social connections for older adults, facilitating the renewal, maintenance, and creation of relationships while fostering social capital.

The results highlight some implications for future research and for the development of digital technology training for older adults. Specifically, when providing computer and Internet education for seniors, it is essential to acknowledge the important influence that social support systems have on the learning experiences of older individuals as they engage with digital technology. Children, relatives, and companions can serve as valuable sources of guidance and inspiration when navigating the digital landscape.

Furthermore, training in digital technology for seniors should prioritize specific groups, including older individuals, racial and ethnic minorities, those with limited education and financial resources, individuals who are unmarried and live alone, as well as those experiencing low social integration and support, to effectively bridge the digital gap. Seniors possessing these traits may gain the greatest advantages from the resources available through Internet technology.

Footnotes

Acknowledgements

The authors acknowledge funding from Next Generation EU, in the context of the National Recovery and Resilience Plan, Investment PE8 – Project Age-It: “Ageing Well in an Ageing Society” [DM 1557 11.10.2022]. The views and opinions expressed are only those of the authors and do not necessarily reflect those of the European Union or the European Commission. Neither the European Union nor the European Commission can be held responsible for them.

ORCID iDs

Giulia Grotto

Alessandra Buja

Author contributions

Alessandra Buja: Conceptualization, Validation, Writing – Review & Editing, Visualization, Supervision, Project Administration, Funding Acquisition; Giulia Grotto: Methodology, Software, Formal Analysis, Investigation, Resources, Data curation, Writing – Original Draft; Visualization.

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This paper was developed within the project funded by Next Generation EU - “Age-It - Ageing well in an ageing society” project (PE0000015), National Recovery and Resilience Plan (NRRP) - PE8 - Mission 4, C2, Intervention 1.3.” The views and opinions expressed are only those of the authors and do not necessarily reflect those of the European Union or the European Commission. Neither the European Union nor the European Commission can be held responsible for them. Next Generation EU - “Age-It - Ageing well in an ageing society” project (PE0000015), National Recovery and Resilience Plan (NRRP) - PE8 - Mission 4, C2, Intervention 1.3.”

Declaration of conflicting interests

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

Data availability statement

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

Appendix A. PRISMA 2020 Checklist.

Section and Topic	Item #	Checklist item	Location where item is reported
TITLE
Title	1	Identify the report as a systematic review.	Page 1
ABSTRACT
Abstract	2	See the PRISMA 2020 for Abstracts checklist.	Page 2
INTRODUCTION
Rationale	3	Describe the rationale for the review in the context of existing knowledge.	Page 5, 6
Objectives	4	Provide an explicit statement of the objective(s) or question(s) the review addresses.	Page 6
METHODS
Eligibility criteria	5	Specify the inclusion and exclusion criteria for the review and how studies were grouped for the syntheses.	Page 7, 8
Information sources	6	Specify all databases, registers, websites, organizations, reference lists, and other sources searched or consulted to identify studies. Specify the date when each source was last searched or consulted.	Page 7
Search strategy	7	Present the full search strategies for all databases, registers, and websites, including any filters and limits used.	Page 27–29
Selection process	8	Specify the methods used to decide whether a study met the inclusion criteria of the review, including how many reviewers screened each record and each report retrieved, whether they worked independently, and if applicable, details of automation tools used in the process.	Page 7, 8
Data collection process	9	Specify the methods used to collect data from reports, including how many reviewers collected data from each report, whether they worked independently, any processes for obtaining or confirming data from study investigators, and if applicable, details of automation tools used in the process.	Page 8
Data items	10a	List and define all outcomes for which data were sought. Specify whether all results that were compatible with each outcome domain in each study were sought (e.g., for all measures, time points, analyses), and if not, the methods used to decide which results to collect.	Page 8
Data items	10b	List and define all other variables for which data were sought (e.g., participant and intervention characteristics, funding sources). Describe any assumptions made about any missing or unclear information.	Page 8
Study risk of bias assessment	11	Specify the methods used to assess risk of bias in the included studies, including details of the tool(s) used, how many reviewers assessed each study and whether they worked independently, and if applicable, details of automation tools used in the process.	Page 8, 10, 30
Effect measures	12	Specify for each outcome the effect measure(s) (e.g., risk ratio, mean difference) used in the synthesis or presentation of results.	Not applicable
Synthesis methods	13a	Describe the processes used to decide which studies were eligible for each synthesis (e.g., tabulating the study intervention characteristics and comparing against the planned groups for each synthesis (item #5)).	Page 8
	13b	Describe any methods required to prepare the data for presentation or synthesis, such as handling of missing summary statistics, or data conversions.	Not applicable
	13c	Describe any methods used to tabulate or visually display results of individual studies and syntheses.	Page 8
	13d	Describe any methods used to synthesize results and provide a rationale for the choice(s). If meta-analysis was performed, describe the model(s), method(s) to identify the presence and extent of statistical heterogeneity, and software package(s) used.	Page 8
	13e	Describe any methods used to explore possible causes of heterogeneity among study results (e.g., subgroup analysis, meta-regression).	Not applicable
	13f	Describe any sensitivity analyses conducted to assess robustness of the synthesized results.	Not applicable
Reporting bias assessment	14	Describe any methods used to assess risk of bias due to missing results in a synthesis (arising from reporting biases).	Page 8, 30
Certainty assessment	15	Describe any methods used to assess certainty (or confidence) in the body of evidence for an outcome.	Not applicable
RESULTS
Study selection	16a	Describe the results of the search and selection process, from the number of records identified in the search to the number of studies included in the review, ideally using a flow diagram.	Page 9, 16
Study selection	16b	Cite studies that might appear to meet the inclusion criteria, but which were excluded, and explain why they were excluded.	Page 16
Study characteristics	17	Cite each included study and present its characteristics.	Pages 9, 10
Risk of bias in studies	18	Present assessments of risk of bias for each included study.	Pages 10
Results of individual studies	19	For all outcomes, present, for each study: (a) summary statistics for each group (where appropriate) and (b) an effect estimate and its precision (e.g., confidence/credible interval), ideally using structured tables or plots.	Not applicable
Results of syntheses	20a	For each synthesis, briefly summarise the characteristics and risk of bias among contributing studies.	Pages 10, 30
	20b	Present results of all statistical syntheses conducted. If meta-analysis was done, present for each the summary estimate and its precision (e.g., confidence/credible interval) and measures of statistical heterogeneity. If comparing groups, describe the direction of the effect.	Pages 10
	20c	Present results of all investigations of possible causes of heterogeneity among study results.	Not applicable
	20d	Present results of all sensitivity analyses conducted to assess the robustness of the synthesized results.	Not applicable
Reporting biases	21	Present assessments of risk of bias due to missing results (arising from reporting biases) for each synthesis assessed.	Not applicable
Certainty of evidence	22	Present assessments of certainty (or confidence) in the body of evidence for each outcome assessed.	Not applicable
DISCUSSION
Discussion	23a	Provide a general interpretation of the results in the context of other evidence.	Pages 11–13
	23b	Discuss any limitations of the evidence included in the review.	Page 12, 13
	23c	Discuss any limitations of the review processes used.	Page 12, 13
	23d	Discuss implications of the results for practice, policy, and future research.	Page 13, 14
OTHER INFORMATION
Registration and protocol	24a	Provide registration information for the review, including register name and registration number, or state that the review was not registered.	Page 7
	24b	Indicate where the review protocol can be accessed, or state that a protocol was not prepared.	Page 7
	24c	Describe and explain any amendments to information provided at registration or in the protocol.	Not applicable
Support	25	Describe sources of financial or nonfinancial support for the review, and the role of the funders or sponsors in the review.	Page 15
Competing interests	26	Declare any competing interests of review authors.	Page 15
Availability Of Data, Code And Other Materials	27	Report Which Of The Following Are Publicly Available And Where They Can Be Found: Template Data Collection Forms; Data Extracted From Included Studies; Data Used For All Analyses; Analytic Code; Any Other Materials Used In The Review.	Page 15

Appendix B. Research search strings

Appendix C. Quality assessment of the included studies according to the RoBANS 2 tool. 37

Main author, year	RoBANS 2 domanis
Main author, year	Comparability of the target group	Target group selection	Confounders	Measurement of intervention/ exposure	Blinding of assessors	Outcome assessment	Incomplete outcome data	Selective outcome reporting
Barbosa Neves, 2018	Low risk	Low risk	Low risk	High risk	Low risk	High risk	Low risk	Low risk
Cai, 2024	Low risk	Low risk	Low risk	High risk	Low risk	Low risk	Low risk	Low risk
Cao, 2023	Low risk	Low risk	Low risk	High risk	Low risk	High risk	Low risk	Low risk
Choi, 2013	Low risk	Low risk	Low risk	High risk	Low risk	High risk	Low risk	Low risk
Cui, 2021	Low risk	Low risk	Low risk	High risk	Low risk	Low risk	Low risk	Low risk
Cui, 2024	Low risk	Low risk	Low risk	High risk	Low risk	High risk	Low risk	Low risk
Duan, 2024	Low risk	Low risk	Low risk	High risk	Low risk	High risk	Low risk	Low risk
Geithner, 2022	Low risk	Low risk	Low risk	High risk	Low risk	High risk	Low risk	Low risk
Guan, 2024	Low risk	Low risk	Low risk	High risk	Low risk	High risk	Low risk	Low risk
Jiang, 2022	Low risk	Low risk	Low risk	High risk	Low risk	High risk	Low risk	Low risk
Jing, 2023	Low risk	Low risk	Low risk	High risk	Low risk	High risk	Low risk	Low risk
Li, 2021	Low risk	Low risk	Low risk	High risk	Low risk	High risk	Low risk	Low risk
Lyu, 2020	Low risk	Low risk	Low risk	High risk	Low risk	High risk	Low risk	Low risk
Nguyen, 2022	Low risk	Low risk	Low risk	High risk	Low risk	High risk	Low risk	Low risk
O'Brien, 2021	Low risk	Low risk	High risk	High risk	Low risk	High risk	Low risk	Low risk
Rios, 2019	Low risk	Low risk	Low risk	High risk	Low risk	High risk	Low risk	Low risk
Simons, 2023	Low risk	Low risk	Low risk	High risk	Low risk	Low risk	Low risk	Low risk
Tsai, 2014	Low risk	Low risk	High risk	High risk	Low risk	High risk	Low risk	Low risk
Yang, 2022	Low risk	Low risk	Low risk	High risk	Low risk	High risk	Low risk	Low risk
Zhu, 2021	Low Risk	Low Risk	Low Risk	High Risk	Low Risk	High Risk	Low Risk	Low Risk

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Association between digital technology use and social capital among older adults: A systematic review

Abstract

Background

Methods

Results

Conclusions

Keywords

Introduction

Methods

Search strategy

Inclusion and exclusion criteria

Data extraction and data analysis

Quality assessment of the included studies

Result

Identified studies

Characteristics of and findings of the included studies

Methodological quality of the included studies

Discussion

Strengths and limitations

Conclusions

Footnotes

Acknowledgements

ORCID iDs

Author contributions

Funding

Declaration of conflicting interests

Data availability statement

Appendix A. PRISMA 2020 Checklist.

Appendix B. Research search strings

Appendix C. Quality assessment of the included studies according to the RoBANS 2 tool. 37

References