Abstract
Background and objectives
The COVID-19 pandemic exacerbated the public health concerns of social isolation and loneliness for older people who are vulnerable due to their health conditions and more restrictive social measures. However studies revealed that many older adults demonstrated high resilience and remained emotionally stable during the pandemic, particularly those who had a broad engagement with online technology that could compensate for their isolation. Yet, little empirical research has examined explicitly the association between online engagement and loneliness among older adults, and the role resilience played in this relationship during the pandemic. This study contributed to the literature by addressing these research gaps.
Research design and methods
This study investigated the relationships between online engagement (sum of involvement in 31 online activities), resilience (sum of positive experiences and personal growth during COVID-19) and loneliness (mean of 11-items from the revised version of the UCLA loneliness scale) among community-dwelling older people (aged 60+), using national survey data from the 2020 Health and Retirement Study (HRS) collected during the COVID-19 pandemic (N = 3,552).
Results and conclusion
Online engagement was negatively associated with levels of loneliness (β = -0.080, 95% CI [-0.118, -0.047]), and this association was partially mediated by levels of resilience (β = -0.023, 95% CI [-0.031, -0.016]. The findings suggested that a broad integration of online technology into daily-life may have helped older people combat loneliness during the pandemic, and resilience could be one important mechanism that linked this association.
Keywords: COVID-19, Internet, Digital engagement, Loneliness, Resilience, Posttraumatic growth
1. Introduction
The COVID-19 pandemic is an unprecedented global public health crisis with profound health and social consequences for people at all ages (Long et al., 2022; World Health Organization, 2020). Older people are particularly at a higher risk for health complications and mortality caused by the virus (Centers for Disease Control and Prevention, 2021; Wu, 2020). Sheltering-in-place and social (physical) distancing, measures intended to protect people from virus infection, may exacerbate the pre-pandemic public health concerns of social isolation and loneliness. Since many older adults are engaging in more stringent social restrictions due to health vulnerabilities, they may experience a higher degree of social isolation and loneliness (Smith and Lim, 2020; Wu, 2020).
While numerous studies report increased loneliness among older adults following the COVID-19 outbreak (Atzendorf and Gruber, 2022; Choi et al., 2021; Hu and Qian, 2021), a considerable amount of other evidence suggests many older adults proved to be resilient, adaptive to the crisis, and able to remain emotionally strong in the face of challenges during the pandemic (Finlay et al., 2021; Kotwal et al., 2021; Luchetti et al., 2020; Peng and Roth, 2021). Studies reveal that many older people, despite their physical vulnerability, actually demonstrate strong adaptive coping skills and high emotional regulation during the pandemic, reflecting a high degree of resilience (Finlay et al., 2021; Luchetti et al., 2020; Rossi et al., 2021).
A growing number of older adults have taken up or expanded their use of online technology during the pandemic in an effort to maintain social connections and continue to engage in their daily activities (AARP, 2021; Newmark et al., 2021). AARP's latest Tech Trend and the 50-Plus study reports that 70% of adults aged 50 and over in the U.S. used video chat weekly, while less than half of this age group used video chat in 2019. Moreover, there is significant growth in using smartphones to order groceries (from 6% to 24%) and communicate with medical professionals (from 28% to 40%). Online technology provides substantial capacity for helping older people access needed resources. Online platforms, such as WeChat, WhatsApp, Facebook, and other online communities, help older people a) exchange information and resources across a broad social circle, b) participate in online activities, c) access services for daily living needs, and d) access medical care through telehealth options (Conroy et al., 2020; Kotwal et al., 2021; Mano, 2020; Park et al., 2021). These digital engagements may help boost resilience and reduce loneliness during stressful times associated with the pandemic (Conroy et al., 2020; Kamalpour et al., 2020; Morrow-Howell et al., 2020; Newmark et al., 2021).
Yet, there is no consensus in the scientific literature regarding the benefits of online communication for older people's emotional health. Several recent studies report that more virtual communication via phone, email and video-chat may be linked to higher levels of loneliness during the COVID-19 pandemic (Atzendorf and Gruber, 2022; Choi et al., 2021; Hu and Qian, 2021). Kotwal et al. (2021) conducted an in-depth mixed-method longitudinal investigation of older people’ experiences with loneliness during the pandemic and revealed that those who experienced increased levels of loneliness, or experienced severe loneliness over time, were more likely to report discomfort with technology. Therefore, it is probably not just whether older people use online technology, but also to what extent older people use online technologies and incorporate them into their daily lives (online engagement) that matters more with respect to reducing loneliness during the pandemic.
Little research reports on the relationship between older people's online engagement and their levels of loneliness during the COVID-19 pandemic (Kotwal et al., 2021), and even fewer reports on whether resilience plays a mediating role (Newmark et al., 2021). This study aims to address these research gaps by investigating the associations among online engagement, resilience, and loneliness in older adults by using national survey data from the 2020 Health and Retirement Study (HRS) collected during COVID-19 pandemic. This study takes advantage of a special supplement to the HRS regarding pandemic behaviors and characteristics. The findings from this study may inform future research and public health practice by underscoring the relationships between ICT competency and engagement, resilience, and loneliness among older people.
2. Literature review
2.1. Resilience and loneliness among older people during the COVID-19
Resilience is conceptualized as the ability or capacity to bounce back from difficult experiences and positively adapt to the changing demands of stressful situations (Prince-Embury, 2013; Southwick et al., 2014). Unlike previous conceptualizations of resilience as a psychological trait or innate quality (distinguished as “resiliency” by Luthar et al., 2000), more recent conceptualizations and empirical research view resilience as a dynamic process that can be developed or enhanced through interactions between internal factors and the external environment (Prince-Embury, 2013; Windle, 2011). The internal factors include key personal resources such as education, problem-solving skills, a perception of self-efficacy, and a sense of meaning-in-life (Bandura, 1989; Bonnell et al., 2011). External factors are environmental resources that include positive social relationships (social support), community and societal resources (e.g., senior centers), material resources (income, assets), and previous experience with hardship (Bonanno et al., 2007; Southwick et al., 2014). These resources and their interaction not only help promote a person's resilience in overcoming current life challenges but also may facilitate profound personal growth as a function of struggling through a traumatic experience (described as posttraumatic growth by Tedeschi and Calhoun, 2004), that leads to a higher level resilience when facing future adversities (Weathers et al., 2016).
Old age is often associated with a variety of challenges, such as the loss of a partner or other family members, declining health, limited mobility, and dependence on others. These challenges increase the likelihood of an involuntary and often irreversible reduction in the size and composition of older adults' social network and their social participation, as well as a decrease in intimate relationships. These changes, in turn, can lead to a higher chance of experiencing loneliness, a detrimental emotional experience related to a subjective evaluation of one's social relationships in terms of quantity and/or quality (de Jong Gierveld, 1998; Hawkley and Cacioppo, 2010; Perlman and Peplau, 1981).
Yet, empirical evidence shows that many older adults, despite various losses and deteriorating health, may actually maintain and even improve their psychological well-being (Steptoe et al., 2015). Research reveals that resilience may play a critical role in the well-being of these older adults (MacLeod et al., 2016; Jeste et al., 2013). Empirical evidence has also demonstrated that higher resilience in later life is associated with greater longevity, lower risk of mortality risk and morbidity, improved life satisfaction, and a lower level of emotional distress such like loneliness (Jakobsen et al., 2020).
The recent COVID-19 pandemic has been a traumatic experience for many people, especially for older people, due to their high vulnerability to virus and also the stringent social distancing measures which may lead to a new and worse experience of social isolation and loneliness (Smith and Lim, 2020; Wu, 2020). Although numerous empirical studies report increased levels of loneliness among older adults during COVID-19 pandemic (Atzendorf and Gruber, 2022; Choi et al., 2021; Hu and Qian, 2021), there are other studies suggesting that many older adults are adjusting to COVID-19 restrictions, demonstrating remarkable resilience (Celdrán et al., 2021; Kotwal et al., 2021; Luchetti et al., 2020; Peng and Roth, 2021; Whitehead and Torossian, 2021). Luchetti et al. (2020) conducted a longitudinal study on loneliness during the pandemic and find no significant mean-level changes in loneliness across the period from late-January to April 2020. They also show older adults reported less loneliness compared to younger age groups and only experienced a slight increase in the levels of loneliness at the beginning of the pandemic, but the increase leveled off in the following assessment. Peng and Roth (2021) conducted a fixed effects analysis, using the 2016 and 2020 waves of the Health and Retirement Study to identify within-person change in loneliness before and during the pandemic. They report no significant change in loneliness among U.S. middle-aged and older adults (aged 50+) generally, but people who lacked digital connections report an increase in loneliness during the pandemic.
2.2. Online engagement, resilience, and loneliness
During the COVID-19 pandemic, ICT emerged as a valuable resource to help older people cope with social restrictions and other life challenges (Morrow-Howell et al., 2020). In a qualitative study by Newmark et al. (2021), older participants reported that learning online tools, such as Zoom and telehealth platforms, helped them develop or maintain their social connections and access to various virtual services and support programs during the pandemic. Older adults reported appreciating the connectivity that technology made possible during the pandemic, which protected them from feelings of being isolated and lonely. Kotwal et al. (2021), employing a mixed-method longitudinal survey, revealed that poor emotional coping (being less resilient) and discomfort with new technologies were two main themes for those who experienced persistent or increased loneliness.
Empirical evidence has revealed that using online technology promotes several key factors related to resilience, including social support, self-efficacy, and a sense of meaning in life (Bolton et al., 2016; Hofer et al., 2019; Lee et al., 2021; Kamalpour et al., 2020). Specifically, Kamalpour et al. (2020) performed a systematic review of scientific literature about online communities and resilience, and they found that engaging in online communities may have multiple benefits for older people, such as promoting social support, self-empowerment, and improvement in overall well-being. Other studies revealed that online communication tools, such as Facebook, Zoom, WeChat, and WhatsApp, offer a means for family and close friends to see each other digitally and remain emotionally connected (Yu et al., 2020; Zhang et al., 2021). Additionally, by tapping into online resources (e.g., information, services), older adults may access information about health behaviors, medical services, lifestyle options, news, and cultural events (Hofer et al., 2019), as well as gain a sense of control over their lives (Lee et al., 2021). Other research noted that learning and sharing new Internet skills provides a feeling of competency and autonomy for older adults (Nimrod, 2014). Last, participation in online support groups, social activities, and volunteering activities provided new social roles and a sense of meaning in life during difficult times (Kamalpour et al., 2020; Von Humboldt et al., 2020).
In essence, online technologies offer opportunities for older people to expand their internal and external resources, such as self-efficacy and social connections, which are critical factors that promote resilience in the face of life's challenges. Internet skills vary greatly among older users, and this has important implications for how they experience Internet use and whether online engagement has the potential to positively affect their well-being. However, for older adults with limited skills for navigating the Internet, using online technology may lead to more stress (Kotwal et al., 2021; Lee et al., 2021). A more nuanced measure of Internet skills and online activities is necessary to capture the breadth of Internet use that includes a range of online activities across various life domains, along with the frequency of use (Leukel et al., 2021; Wei, 2012). This more inclusive concept is labeled “online engagement” in this study. Online engagement reflects the mastery of an array of digital technology skills and represents the extent to which older adults integrate the Internet into their daily lives. Thus, older adults who are proficient with online engagement may have a greater capacity to adapt to current and future physical and emotional challenges, demonstrating more resilience during the COVID-19 pandemic. In sum, resilience and technology use have consistently been shown to be two important factors that reduce risk of loneliness among older adults during the pandemic (Conroy et al., 2020; Kotwal et al., 2021; Lee et al., 2021; Mano, 2020). Yet, the relationships between these have not been reported in the current literature (Kotwal et al., 2021; Newmark et al., 2021).
3. The present study
The present study aims to contribute to the current scientific literature by investigating the association between online engagement during the pandemic and loneliness among community-dwelling older adults (aged 60 and above). This study also examines the mediating role of resilience. Prior research findings demonstrate that various online engagements lead to beneficial social and personal outcomes, including connectedness with family, friends, and community (Szabo et al., 2019; Zhang et al., 2021), as well as promotion of self-efficacy, independence, and meaning-in-life (Kamalpour et al., 2020; Lee et al., 2021). Thus, we expect higher online engagement may reduce loneliness among older people. Furthermore, resilience theory and empirical evidence suggests that online engagement results in more resilience (Bolton et al., 2016; Hofer et al., 2019), and higher levels of resilience may offer protection for older adults’ emotional well-being (Conroy et al., 2020; Morrow-Howell et al., 2020). Hence, resilience could be one potential pathway linking online engagement and loneliness. We propose the following hypotheses.
Hypothesis 1
Online engagement is negatively associated with levels of loneliness in older adults at the time of the COVID-19 pandemic.
Hypothesis 2
Resilience mediates the relationship between online engagement and levels of loneliness. Higher online engagement promotes resilience and increased resilience leads to lower levels of loneliness.
4. Methods
4.1. Data and study sample
This cross-sectional study used data from the 2020 Health and Retirement Study (HRS; Version 2.0). HRS is a nationally representative panel survey of community-dwelling Americans over the age of 50 (sponsored by the National Institute on Aging, grant number NIA U01AG009740), conducted by the Survey Rese arch Center of the Institute for Social Research (ISR) at the University of Michigan biennially since 1992. The survey was designed using stratified, multistage cluster area probability sampling with Black and Hispanic respondents oversampled (Heeringa and Connor, 1995). Following the initial cohort (born 1931–1941) recruited in 1992, multiple cohorts were added to the sample, including the Asset and Health Dynamics of the Oldest Old (AHEAD, born 1924), the Children of the Depression or CODA (born 1924–1930), the War Babies (born 1942–1947). Subsequently, every six years' new birth cohorts were added (the Early Baby Boomers born 1948–1953, the Mid Baby Boomers born 1954–1959, and the Late Baby Boomers born 1960–1965). The response rates over the years have been consistently above 80% (Sonnega et al., 2014). Since 2006, a random subsample (rotating 50%) of respondents was assigned to enhanced face-to-face interviews (switched to phone interview in 2020 due to COVID-19 social restriction) and was given a self-administered Psychological and Life Style Leave-Behind Questionnaire (LBQ) to complete afterward. The LBQ contains measures of respondents’ evaluations of their subjective well-being, social relationships, personality, lifestyles, and beliefs (Smith et al., 2017). In 2020, additional COVID-19-related questions were added to the LBQ, along with the COVID-19 module. The COVID-19 data were collected between March 2020 and June 2021.
The sample for this study included respondents who completed the 2020 HRS LBQ (n = 4670). After excluding respondents who were not community-dwelling (n = 41), who were proxy respondents (n = 122), who were younger than 60 years old (n = 918), as well as cases with missing information on loneliness (n = 37), the final study sample included 3552 respondents.
4.2. Measures
Loneliness was measured with an 11-item revised version of the UCLA loneliness scale (Russell, 1996; Smith et al., 2017), which has been validated as a measure for the multi-faceted construct of loneliness (Russell, 1996; Hawkley et al., 2005). Respondents were asked to rate how often they had the following positive feelings: “in tune with the people around you,” “there are people you can talk to,” “there are people you can turn to,” “there are people who really understand you,” “there are people you feel close to,” “part of a group of friends,” and “have a lot in common with the people around you,” as well as four negative feelings: “lack companionship,” “being left out,” “being isolated from others,” and “feeling alone,” which are reverse coded. The responses (0 = often, 1 = some of the time, 2 = hardly ever or never) were averaged to create a loneliness scale (range = 0–2), with higher scores indicated higher levels of loneliness (α = .88). If more than five items were missing, the final score was set to missing (Smith et al., 2017). In this study, loneliness was used as a continuous measure following strategies employed in other studies (Peng and Roth, 2021).
Online engagement was measured with several online activities in which respondents were involved, along with activity use frequency, in order to capture the breadth of Internet use (Leukel et al., 2021; Wei, 2012). In the LBQ of 2020 HRS, 31 questions were asked about how often respondents used the Internet to do any of the following activities: connecting face-to-face with family and friends, using social network sites or apps to network with people, blogging, texting, sharing photos or videos, talking to doctors, getting health information, reading news, shopping, paying bills, calling rides, entertaining, and other activities. The scores for each item ranged from 0 to 5 with 0 indicating “never” to 5 indicating “daily.” An index of online engagement was generated by summing the scores for all Internet involvements, with higher scores indicating higher levels of online engagement (range = 0–124; α = .92).
Resilience was operationalized as a construct that includes post-traumatic growth as the growth dimension of resilience, which has been adopted by many researchers (APA, 2020; Lepore and Revenson, 2006). Resilience was measured by summing responses to six statements in the LBQ COVID-19 questionnaire about positive experiences and personal growth since the COVID-19 pandemic. The six statements included “tend to recover quickly after difficult times,” “have learned some positive things about myself,” “found greater meaning in work and other activities,” “feel more in touch with people in the community,” “found new ways to connect socially with others,” and “become more appreciative of things that was taken for granted before.” These items are consistent with the Brief Resilience Scale (Smith et al., 2008) and the Short Form of the Post-traumatic Growth Inventory (PTGI-SF; Cann et al., 2010). Responses to each item ranged from 0 (strongly disagree) to 5 (strongly agree). The resilience score was created by summing the responses of the six items, with higher scores indicating higher levels of resilience (range = 0–30; α = .80).
Control variables included a list of characteristics reported in the scientific literature as associated with loneliness and/or resilience, including age, gender, education, marital status, race-ethnic status, living arrangements, social interactions, self-rated health, financial satisfaction, cognitive function, and work status (Aartsen and Jylha, 2011; Cohen-Mansfield et al., 2016; Bui et al., 2021; Hawkley et al., 2008; Zhang et al., 2021). Age is measured in years (60–99). Gender is represented as a dichotomous variable (1 = female, 0 = male). Education is measured as years of schooling (range = 0–17). The race-ethnic status variable is recoded from the race and Hispanic variables into the following four categories: “non-Hispanic White (reference group),” “non-Hispanic Black,” “non-Hispanic other races,” “Hispanic (any race).” Marital status was coded dichotomously with 1 = married and 0 = not married. Type of living arrangement is represented by a dichotomous variable, 1 = lived alone and 0 = lived with others. Self-rated health is based on respondents’ rating of their overall health ranging from 0 – poor to 4 – excellent. Cognitive function was measured by episodic memory (Weir et al., 2014), which is a summation of correct responses for immediate and delayed word recall tests (range = 0–20). Financial satisfaction ranged from 0 (not at all satisfied) to 4 (completely satisfied). In-person and phone contact were operationalized with the frequency of contact with non-residential children, other family members, and friends. Response options ranged from 0 (seldom) to 5 (one to more times a week). In-person and phone contact measures were based on averaged scores across the three relationship types, respectively (range = 0–5). Work status was measured dichotomously (1 = worked for pay, 0 = did not work for pay). Finally, concerns regarding the COVID-19 pandemic (referring to as COVID concerns) were included in this study, consistent with the loneliness study by Peng and Roth (2021). In this study, COVID concerns included five aspects, including health of self, health of family, financial situation, obtaining help when needed, and the uncertainty about the future with responses scaled from 0 – not worried at all to 10 – very worried. The COVID concerns variable was created by summing the responses of the five concerns, with higher scores indicating higher levels of COVID concerns (range = 0–50; α = .86).
4.3. Analytic strategy
Descriptive statistics were first estimated for the key variables and control variables. To assess the relationship between online engagement and loneliness (Hypothesis 1) and the mediation effects of resilience (Hypothesis 2), path analysis within the structural equation modeling (SEM) framework was employed. In the SEM analysis, the following paths were estimated simultaneously: resilience was regressed on online engagement with covariates, and loneliness was regressed on resilience and online engagement with covariates. Model fit was evaluated using the comparative fit index (CFI, >0.95) and the root mean squared error of approximation (RMSEA, <0.05). The mediation (indirect) effects were estimated using maximum likelihood (ML) techniques with a bootstrapping method (the number of draws was 2000), which addressed the non-normality of variables in the model, and provided a better test (BCBOOTSTRAP) for the significance of the indirect effects from Internet engagement to loneliness via resilience (Kline, 2016). All variable manipulations and data formatting were performed in SPSS 28.0 and data analyses were conducted in Mplus7.4 (Muthén and Muthén, 1998–2015). Missing data for the independent variables was handled using a full information maximum likelihood (FIML) procedure.
5. Results
5.1. Study sample descriptive statistics
The descriptive characteristics of the study sample are displayed in Table 1 . The mean loneliness score was 0.53 (range = 0–2), which demonstrated that respondents, on average, had low to moderate levels of loneliness. The average aggregated score of participation in various online activities (online engagement) was 47.41 (range = 0–112). Also, respondents showed a mean resilience score of 18.76 (range = 0–30).
Table 1.
Descriptive statistics for the study sample (2020 HRS).
| Variables | M | (SD) | Range |
|---|---|---|---|
| Outcome | |||
| Loneliness | 0.53 | (0.43) | 0–2 |
| Predictor | |||
| Online engagement | 47.41 | (22.05) | 0–112 |
| Mediators | |||
| Resilience | 18.76 | (6.04) | 0–30 |
| Covariates | |||
| Age | 72.16 | (8.55) | 60–99 |
| Female, % | 59.60 | ||
| Years of education | 13.30 | (2.91) | 0–17 |
| Race, % | |||
| Non-Hispanic White | 66.41 | ||
| Non-Hispanic Black | 17.95 | ||
| Non-Hispanic other race | 3.31 | ||
| Hispanic | 12.33 | ||
| Married, % | 56.16 | ||
| Lived alone, % | 26.03 | ||
| Self-rated health | 2.16 | (0.98) | 0–4 |
| Episodic memory | 10.24 | (3.61) | 0–20 |
| Financial satisfaction | 2.59 | (1.08) | 0–4 |
| Frequency of meeting-in-person | 2.54 | (1.13) | 0–5 |
| Frequency of phone calls | 3.62 | (0.96) | 0–5 |
| Worked for pay, % | 26.40 | ||
| COVID-19 concerns | 23.86 | (12.78) | 0–50 |
Notes. N = 3,552.
On average, respondents were 72.16 years old. Over half of the respondents were female (59.6%), non-Hispanic White (66.4%), and married (56.2%). The mean years of education was 13.30 (range = 0–17), and the mean episodic memory score was 10.24 (range = 0–20). On average, respondents had a self-reported health score of 2.16 (range = 0–4) and a financial satisfaction score of 2.59 (range = 0–4). Notably, on a scale of 0–5, the average score for the frequency of meeting children/family/friends in-person was 2.54, which was less than once or twice a month; and the frequency of speaking on the phone is 3.62, which was close to once or twice a week. Additionally, about 26.4% of respondents worked for pay, and respondents, on average, had COVID concerns score of 23.86 (range = 0–50).
5.2. The associations between online engagement, resilience, and loneliness
To examine the association between online engagement and loneliness, as well as the mediating effect of resilience, a mediated path model was estimated. The fit indices suggested an adequate model fit with comparative fit index (CFI) = 1.00, a root mean square error of approximation (RMSEA) = .00, and an over-identification (df = 1). The unstandardized regression results showed that the effect size for the association between online engagement and resilience was 0.043 (p < .001); the effect size for the association between resilience and loneliness was -0.010 (p < .001); and the direct effect size for the relationship between online engage and loneliness was small, but also significant (-0.001, p < .01). In order to compare the relative magnitude of the effects for variables based on different measurement characteristics, standardized parameters were presented in our figures and tables. Specifically, in Fig. 1 , online engagement had a direct and negative association with levels of loneliness (β = -0.057, p < .01). As for the mediating pathway of resilience, higher online engagement was associated with higher levels of resilience (β = 0.157, p < .001), and a higher level of resilience was associated with lower levels of loneliness (β = -0.146, p < .001). In Table 2 , the indirect effect via resilience (estimated using bootstrapping) was statistically significant (β = -0.023, 95% CI [-0.031, -0.016]). The total effect (β = -0.080, 95% CI [-0.118, -0.047]) was statistically significant, as well. These results indicated that online engagement was significantly associated with loneliness, which supported Hypothesis 1, and resilience partially mediated the association between online engagement and loneliness, thus Hypothesis 2 was supported.
Fig. 1.
Direct and Indirect Paths between Online engagement and Loneliness
Notes. N = 3,552. RMSEA = .00; CFI = 1.00. Standardized coefficients are reported.Total Effect: -0.080***. Indirect effect via resilience: -0.023***.*p < .05. **p < .01. ***p < .001.
Table 2.
Direct and indirect effects of online engagement via resilience on loneliness.
| Estimate | 95% [CI] | ||
|---|---|---|---|
| Direct effect | |||
| Online engagement → Loneliness | -0.057 | ** | [-0.095, -0.024] |
|
Indirect effect | |||
| Online engagement → Resilience → Loneliness | -0.023 | *** | [-0.031, -0.016] |
| Total Effect | -0.080 | *** | [-0.118, -0.047] |
Notes. N = 3,552. Standardized coefficients are reported. Confidence intervals (CI) for direct and indirect effects were estimated using a bootstrapping procedure. Model controls for age, gender, education, race and ethnicity, marital status, living arrangement, self-rated health, episodic memory, financial satisfaction, frequencies of in-person contact and phone contact, work status, and COVID concerns.
*p < .05. **p < .01. ***p < .001.
The direct effects of online engagement and the covariates on resilience and loneliness are presented in Table 3 . Notably, in addition to online engagement, other factors were significantly associated with levels of resilience and loneliness. Specifically, the factors that linked to higher levels of resilience included older age, being female, being from a minority race-ethnic group, being married, having good or better self-rated health and better cognition, working for pay, having higher financial satisfaction, and more frequent in-person contact with close social ties (children, relatives, and friends). Furthermore, factors that were negatively associated with loneliness included older age, being in good health, having better cognition, working for pay, higher financial satisfaction, and more frequent in-person and phone contact with close social ties, whereas having high COVID concerns was associated with higher levels of loneliness.
Table 3.
Direct effects of online engagement and covariates on resilience and loneliness.
| Variables | Resilience |
Loneliness |
||||
|---|---|---|---|---|---|---|
| β | (SE) | β | (SE) | |||
| Predictor | ||||||
| Online Engagement | 0.157 | *** | (0.020) | -0.057 | ** | (0.018) |
| Mediators | ||||||
| Resilience | – | – | -0.146 | *** | (0.016) | |
| Covariates | ||||||
| Age | 0.046 | * | (0.020) | -0.061 | ** | (0.018) |
| Female | 0.050 | ** | (0.017) | -0.007 | (0.016) | |
| Years of education | -0.002 | (0.022) | -0.003 | (0.018) | ||
| Non-Hispanic Blacka | 0.166 | *** | (0.018) | -0.019 | (0.017) | |
| Non-Hispanic other racea | 0.032 | (0.017) | 0.032 | (0.016) | ||
| Hispanica | 0.147 | *** | (0.020) | 0.000 | (0.018) | |
| Married | 0.058 | * | (0.025) | -0.110 | *** | (0.022) |
| Lived alone | 0.068 | ** | (0.024) | 0.030 | (0.022) | |
| Self-rated health | 0.097 | *** | (0.019) | -0.101 | *** | (0.017) |
| Episodic memory | 0.046 | * | (0.019) | -0.064 | *** | (0.017) |
| Financial satisfaction | 0.101 | *** | (0.019) | -0.173 | *** | (0.017) |
| Frequency of meeting-in-person | 0.042 | * | (0.021) | -0.139 | *** | (0.018) |
| Frequency of phone calls | 0.030 | (0.021) | -0.122 | *** | (0.019) | |
| Worked for pay | 0.076 | *** | (0.017) | -0.051 | ** | (0.016) |
| COVID-19 concerns | – | – | 0.119 | *** | (0.018) | |
| R2 | .108 | .251 | ||||
Notes. N = 3,552. RMSEA = .00; CFI = 1.00. Standardized coefficients are reported.
*p < .05, **p < .01, ***p < .001.
Reference group = non-Hispanic White.
We further engaged in supplemental analysis to determine if the operationalization of online engagement with different purposes affected our results. We divided the online engagement variable into three domains (1) online information seeking (e.g., news, local events, health information): and online leisure activities (e.g., playing puzzles and games, listening to music, and watching video); (2) online instrumental services (e.g., shopping, banking, talking to doctors, calling for a ride); and (3) online communication (e.g., messaging, sharing photos, social media, and video chat). Results showing the associations between these three domains of online engagement, resilience and loneliness are presented graphically in Supplementary Figs. 1–3. The findings showed that all three domains of online engagement have similar indirect effects on loneliness via resilience, with online information-seeking and leisure activity having a somewhat larger effect than the other two domains (online communication: β = -0.018, p < .001; online information and leisure activity: β = -0.020, p < .001; online instrumental services: β = -0.016, p < .001). However, the direct effect and the total effect for the association between online instrumental services and loneliness were not statistically significant (direct effect: β = -0.008, p = .624; total effect: β = -0.024, p = .152), while the direct effect and the total effect for online communication (direct effect: β = -0.052, p < .01; total effect: β = -0.071, p < .001), and for online information seeking and leisure activity (direct effect: β = -0.063, p < .001; total effect: β = -0.084, p < .001) were statistically significant, with online information seeking and leisure activity showing slightly larger effects.
6. Discussion
This study investigated the association between online engagement and loneliness, along with the mediating role of resilience. The findings indicated that older adults who engaged in a wider range of online activities experienced lower levels of loneliness during the COVID-19 pandemic. Additionally, the results showed that resilience may be one of the underlying mechanisms for this association. That is, frequently participating in a wide range of online activities for various purposes may increase levels of resilience for older adults, and in turn, this increased resilience may protect older adults from experiencing heightened levels of loneliness during the pandemic. These findings extend the existing literature and provide new evidence for the association between online engagement and loneliness (Kotwal et al., 2021; Newmark et al., 2021; Szabo et al., 2019), as well as for the potential protective effect of resilience for loneliness in older adults, which is an emerging research field that calls for more empirical studies (Jakobsen et al., 2020; Kamalpour et al., 2020; Tan et al., 2021).
Further examination of the different domains of online engagement (online information seeking and leisure online instrumental service, and online communication) all consistently revealed a significant mediating effect of resilience, but exhibited different direct and total effects with respect to loneliness. Online instrumental activity did not show significant direct and total associations with loneliness, while online communication, as well as online information seeking and leisure activities were negatively related to loneliness. These results were in line with a recent study reporting that using the Internet for different purposes was associated with loneliness differently: using the Internet for social purposes (communication) was linked to lower levels of loneliness but for instrumental purposes was not related to loneliness (Szabo et al., 2019). However, contrary to the Szabo and colleagues study, our study found that using the Internet for information seeking and leisure activities during the COVID-19 pandemic was significantly associated with lower levels of loneliness and the effect was even slightly larger than that of online communication. This finding suggested that gaining information and enjoying leisure activities may play an important role in reducing emotion distress among older people in the time of pandemic. Hence, the association between various forms of online engagement and emotional well-being could be conditioned by social context and situational factors.
6.1. Contributions
This study advanced the scientific literature in two ways. First, this study was among the first to investigate the relationship between a more comprehensive measure of online engagement than most other studies and levels of loneliness in the context of the COVID-19 pandemic. Most current research focuses on either a dichotomous Internet use measure or specific domains of ICT, such as online communication or information seeking (Hu and Qian, 2021; Yu et al., 2020; Zhang et al., 2021). Due to data limitations, little attention has been paid to the emotional experiences of older adults through the incorporation of a more comprehensive ICT measure that included a wide range of life domains, including communication with others, accomplishing daily needs, entertainment, religious services, and seeking general information and local, national and world news. Using national data collected during the COVID-19 pandemic, this study empirically tested and supported the hypothesis that more online engagement was associated with a reduction in feelings of loneliness, albeit the effect sizes were small. However, we suggest that to combat loneliness among older adults, the results show that promoting online engagement, combined with other interventions, may contribute to older adults’ well-being. Additionally, decomposing online engagement into three subdomains in our sensitivity analysis contributed to a deeper understanding of how various forms of online engagement (communication, information/leisure, and instrumental services) were related to loneliness among older people, particularly during the COVID-19 pandemic.
The second major contribution of this study is the examination of the mediating role of a measure of resilience that includes information about post-traumatic growth to help explain the relationship between online engagement and loneliness. Although a few researchers have implied that resilience might explain stable levels of loneliness during the pandemic (Luchetti et al., 2020; Peng and Roth, 2021; Whitehead and Torossian, 2021), there has been no explicit empirical investigation of the association between resilience and loneliness in older adults, as well as the potential effect of online engagement for promoting resilience. Drawing insights from theory and findings from prior empirical investigations of resilience (Bolton et al., 2016; Jakobsen et al., 2020; Kamalpour et al., 2020; Newmark et al., 2021; Southwick et al., 2014), we posited that mastering ICT skills and integrating a broad range of online activities into daily life may be related to an increase in older adults’ internal and external resources that helps promote resilience, and increased resilience is associated with less risk of experiencing emotional distress including higher levels of loneliness during the COVID-19 pandemic. These expectations were empirically tested and supported by this current study. Furthermore, the results suggested that the association between online engagement and resilience may be stronger than the association between resilience and loneliness. More research is needed to confirm this possibility.
6.2. Limitations and future research
Several limitations in this study are noted, and these should be addressed in future studies, where possible. First, because resilience and online engagement were only measured in the 2020 wave of HRS, the present study employed a cross-sectional research design, which, along with issues of omitted variable bias, makes it impossible to establish the causal directions between online engagement, resilience, and loneliness; thus, reverse causality was possible. That is, people who are more resilient may have better integrated online technology into their daily life to compensate for the lack of physical interactions during the pandemic. Future research examining the relationship among these variables with mediation models should employ longitudinal designs to help provide a more in-depth understanding of the directional relationships among these key concepts. Second, excluding respondents who were missing information for the loneliness measure may have introduced selection bias because this group of respondents may be lonelier compared with other respondents. Also, because we employed data for community-dwelling older adults only, the findings could not be generalized to older adults who were institutionalized and might experience higher levels of loneliness, especially during the pandemic. Further, we did not explore what potential factors would lead to more online engagement among older adults, such as the accessibility of online technology and social economic factors, which is an important topic for future research. Moreover, there were some interesting results regarding the covariates in this study that should be further explored in future research. For example, older age and being a member of a race-ethnic minority group were linked to higher levels of resilience, which may result from more exposure to challenging life experiences, and in turn, provide protection for them when facing difficult situations. Future research should also investigate other potential mediators for the link between online engagement and loneliness, such as sense of control, social support, and social engagement. Finally, because the 2020 HRS COVID-19 data were collected between March 2020 and June 2021, we were unable to specifically identify the effects of the ebb and flow of the pandemic and we were unable to determine if the collection of HRS data was impacted by the circumstances surrounding mandated lock-downs.
7. Conclusions
This study made important contributions to the current scientific literature by examining the association between online engagement with loneliness and the potential mediating role of resilience. The findings of this study can inform policymakers and practitioners who want to look beyond whether older adults are using the Internet, and to be more attentive to how broadly older adults use online technologies and integrate these technologies into their daily lives. Furthermore, it is important to recognize the significant association between online engagement and resilience, as well as the association between resilience and loneliness, which may provide insights about how to utilize technology to boost resilience and mitigate loneliness for older people, especially during the times of large scale, long-lasting public health emergencies.
Funding
This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.
Credit author statement
Kunyu Zhang: Conceptualization, Methodology, Data curation, Formal analysis, Writing-Reviewing-Editing; Jeffrey A. Burr: Conceptualization, Methodology, Writing-Reviewing-Editing; Jan E. Mutchler: Conceptualization, Writing-Reviewing-Editing; Jiehua Lu: Conceptualization, Writing-Reviewing-Editing.
Declaration of competing 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.
Handling Editor: Blair T. Johnson
Footnotes
Supplementary data to this article can be found online at https://doi.org/10.1016/j.socscimed.2023.116026.
Appendix A. Supplementary data
The following is the Supplementary data to this article.
Data availability
Data will be made available on request.
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Supplementary Materials
Data Availability Statement
Data will be made available on request.