This paper is in the following e-collection/theme issue:

eHealth Literacy / Digital Literacy (546) Demographics of Users, Social & Digital Divide (933) Use and User Demographics of mHealth (412) Internet Access and Digital Technology Use in an Elderly Population (339) Cognitive Training for the Elderly (156)

Published on in Vol 28 (2026)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/83955, first published .
Digital Engagement and Cognitive Function Among Older Adults in China: Cross-Sectional Questionnaire Study and Moderated Mediation Model Analysis

Digital Engagement and Cognitive Function Among Older Adults in China: Cross-Sectional Questionnaire Study and Moderated Mediation Model Analysis

Digital Engagement and Cognitive Function Among Older Adults in China: Cross-Sectional Questionnaire Study and Moderated Mediation Model Analysis

Authors of this article:

Yongqi Du1 Author Orcid Image ;   Qing Niu1 Author Orcid Image ;   Gangrui Tan1 Author Orcid Image ;   Jianqian Chao1 Author Orcid Image ;   Shengxuan Jin1 Author Orcid Image ;   Leixia Wang1 Author Orcid Image
Article Authors Cited by Tweetations Metrics

    Original Paper

    Health Management Research Center, School of Public Health, Southeast University, Nanjing, Jiangsu, China

    *these authors contributed equally

    Corresponding Author:

    Jianqian Chao, PhD, Prof Dr

    Health Management Research Center

    School of Public Health

    Southeast University

    No. 87, Dingjiaqiao

    Gulou District

    Nanjing, Jiangsu, 210009

    China

    Phone: 86 18736361189

    Email: jqchao1230220@163.com


    Background: Given the global demographic shifts and rapid digitalization, digital engagement has emerged as a critical determinant of healthy aging. While previous research has linked digital engagement to cognitive outcomes, the underlying mechanisms remain underexplored among Chinese older adults.

    Objective: This study aimed to analyze the relationships between digital engagement and cognitive function among older adults in China through a moderated mediation model guided by the technological reserve hypothesis, with digital health literacy (DHL) and social support as mediators and living arrangements as a moderator.

    Methods: We conducted a cross-sectional questionnaire survey using stratified multistage sampling from June to November 2024, including 8123 participants aged 55 years and older. Digital engagement, defined as older adults’ use of contemporary digital technologies to support routine daily activities, autonomy, independence, and social inclusion, was assessed using a multidimensional questionnaire. The Chinese eHealth Literacy Scale, the 3-item short version of the Perceived Social Support Scale, and the Mini-Cog test were used to assess DHL, social support, and cognitive function. Guided by a directed acyclic graph based on the technological reserve hypothesis, mediation and moderated mediation analyses were performed using the PROCESS macro in SPSS (IBM Corp) with 5000 bootstrap resamples.

    Results: Digital engagement was positively associated with cognitive function among older adults (β=0.241, 95% CI 0.216-0.265). This association was partially mediated by DHL (β=0.059, 95% CI 0.049-0.069) and social support (β=0.012, 95% CI 0.008-0.016), with the combined indirect effects accounting for 29.5% of the total effect (β=0.071, 95% CI 0.061-0.082). Additionally, living arrangements significantly moderated the associations between digital engagement and cognitive function (β=0.109, 95% CI 0.052-0.166), digital engagement and DHL (β=0.063, 95% CI 0.014-0.112), and digital engagement and social support (β=0.151, 95% CI 0.089-0.212). These effects were stronger among older adults living alone.

    Conclusions: This study contributes to the understanding of cognitive aging in the digital environment from the perspective of the technological reserve hypothesis and digital engagement. Digital engagement influenced cognitive function via DHL and social support, and these associations of digital engagement with cognitive function, DHL, and social support were stronger among older adults living alone. Digital health interventions and public health policies should target both DHL and social support among older populations and prioritize older adults living alone.

    J Med Internet Res 2026;28:e83955

    doi:10.2196/83955

    Keywords

    digital engagement; cognitive function; digital health literacy; social support; living arrangements; moderated mediation model; technological reserve hypothesis 


    Background

    Cognitive function is a critical determinant of dementia, functional independence, quality of life, and health care burden [1]. With an increasingly aged population, cognitive impairment and dementia have become major health and social issues worldwide [2]. Research has indicated that there are about 15.07 million patients with dementia in the population aged 60 and older in China, while the prevalence of mild cognitive impairment is 15.54%, and the number of patients is 38.77 million [3]. The disease burden of dementia and cognitive impairment is huge. The estimated total annual costs of dementia in China will reach 114.2 billion US dollars in 2030 [4]. Since there is currently no effective pharmaceutical treatment for dementia and mild cognitive impairment, it is important to identify modifiable intermediate risk factors that could prevent cognitive decline [5].

    With rapid digitalization and the widespread integration of technology into daily life, digital technologies have emerged as a potential determinant of healthy aging. In China, as of 2025, the number of internet users aged 60 and older has reached 161 million, accounting for nearly 14.4% of all internet users [6]. Many studies have indicated that older people are competent and skilled users of digital technologies [7-9]. Consequently, the concept of digital engagement has been introduced to emphasize the breadth and extent of digital technology use among older people [10]. Every day, digital engagement provides new opportunities for older adults to address age-related cognitive decline. Engagement in cognitively challenging activities, such as learning new digital skills or knowledge, plays a protective role against age-related cognitive decline [11,12]. Meanwhile, access to communication technology and social media facilitates interpersonal interactions and enhances social support [13], which helps maintain cognitive health in older adulthood.

    Against this background, exploring the association between digital technology use and cognitive health in later life has become an important research focus. However, the cognitive impact of digital technology use in China has not been sufficiently studied and understood [7,14]. First, research has focused on how access to the internet relates to cognitive function and the associations between use frequency in specific domains and cognitive outcomes [8,15-17]. Many studies in China have investigated the effect of internet or social media use on cognitive function [18,19]. But limited studies give attention to the concept of digital engagement [20] and comprehensively measure the dimensions and frequency of digital technology use. As digital technologies have become increasingly integrated into older adults’ daily lives, it is important to shift research focus from use to meaningful digital engagement to better understand the cognitive effects of digital technology use [7].

    Second, although evidence has established the efficacy of digital health interventions for cognitive decline and cognitive impairment, including dementia [21-23], little is known about how they lead to an improvement in symptoms or behavior. The identification of these mediating mechanisms would be useful for tailoring interventions that specifically target these pathways, improving intervention effectiveness. Some studies in China have estimated the mediating roles of physical activity [14] and social support [20,24]. However, few studies have simultaneously examined the roles of multiple mediators. Including multiple mediators can better reflect real-world mechanisms, help understand the relative importance of different intervention pathways, and reduce bias [25].

    Third, while digital technologies become increasingly integrated into older adults’ everyday life, growing urbanization and economic reforms in China have transformed intergenerational living arrangements patterns [26]. However, limited studies in China have examined how the association between digital technology use and cognitive function may vary by living arrangements. As the number of older adults living alone in China increases, examining the moderating role of living arrangements in this association is meaningful for developing targeted interventions.

    The technological reserve hypothesis provides a theoretical framework for addressing these gaps. This hypothesis, developed by Benge and Scullin, focuses on how digital technology use can counteract cognitive decline and reduce disease burden [27-29]. Technological reserve is defined as “the development of a culture and environment of technology use in older adults that can buffer against the impact of cognitive decline on day-to-day activities” [27]. Further study developed the technological reserve concept and summarized 3 central pathways through which digital technology may prevent cognitive decline [28,30]. First, technology can generate cognitive complexity by engaging older adults in cognitively demanding activities that strengthen cognitive reserve [12,31,32]. By enabling access to diverse information sources (eg, online health information), promoting mentally stimulating activities, and requiring continual learning and adaptation, digital technologies help sustain and challenge cognitive capacities [33]. Second, technology fosters social connection and engagement, which are well-established protective factors against cognitive decline [34]. Through platforms such as social media, messaging apps, and video calls, older adults can maintain social ties, reduce loneliness, and access emotional and instrumental support. Finally, technologies can function as cognitive prosthetics by directly compensating for lapses in memory and executive function, particularly those involved in completing activities of daily living. For example, smartphone apps can deliver reminders for medication adherence [35].

    Guided by the technological reserve hypothesis, this study aimed to examine the mediating effect of digital health literacy (DHL) and social support on the relationship between digital engagement and cognitive function, as well as the moderating effect of living arrangements on the relationships among digital engagement, DHL, social support, and cognitive function.

    Theoretical Framework

    Digital Engagement and Cognitive Function

    Within the technological reserve framework, digital technology use as a modifiable lifestyle behavior is a critical factor that can promote better cognitive outcomes than would be expected based on age, brain injury, or disease stage [30]. In this study, we adopted the term “digital engagement” to define digital technology use among older adults. Digital engagement among older adults refers to their use of contemporary digital technologies and devices to carry out routine and enjoyable everyday activities that support autonomy, independence, and social inclusion [8]. This concept emphasizes how older adults integrate information and communication technologies into daily activities and information-seeking behaviors rather than focusing on limitations [36]. Research has investigated the potential association between digital engagement and cognitive function among older adults. Although some studies suggest potential risks such as sleep disruption or social isolation [37-39], the prevailing evidence supports that digital engagement is positively linked to cognitive function [26,40,41]. Empirical findings generally suggest that regular use of digital technologies (such as social media and online social networking) is positively associated with better cognitive outcomes [42-44]. These benefits are often attributed to increased cognitive stimulation, enhanced social connectivity, and greater engagement in mentally active tasks afforded by digital technology [45,46]. Longitudinal studies further suggest that consistent internet use is associated with slower cognitive decline and a lower subsequent risk of dementia compared with nonuse [47,48]. Meta-analyses of randomized controlled trials also support the effectiveness of digital interventions in improving specific cognitive domains [49,50]. Despite growing evidence, the mechanisms through which digital engagement benefits cognition remain insufficiently understood.

    The Mediating Role of DHL

    DHL is the ability to seek, understand, evaluate, and apply health information from digital sources to support health-related decision-making [51]. Within the technological reserve framework, DHL can strengthen cognition by engaging older adults in cognitively demanding processes such as evaluating online resources, learning new digital skills, and applying health information in daily life. These processes involve active learning, adaptive reasoning, and problem-solving, which are consistent with mechanisms that sustain cognitive reserve [30]. Moreover, empirical studies support this pathway. Higher DHL is associated with greater adoption of preventive health behaviors, better management of chronic conditions, improved adherence to treatment, and more informed health decisions [52-55]. Such behaviors not only enhance health outcomes but also contribute to maintaining and preserving cognitive function in later life. Thus, DHL may mediate the association between digital engagement and cognitive function.

    The Mediating Role of Social Support

    Within the technological reserve framework, another plausible pathway operates through social connectivity. Social connectivity refers to the structural and functional aspects of individuals’ social relationships, and in later life, is often reflected through social support received from their networks [56-58]. Socioemotional selectivity theory points out that social participation requires a certain cost investment, and members who engage in social participation are bound to consider cost-benefit issues [59]. For older adults, declining physical and cognitive abilities raise the cost of offline social participation, leading to a gradual reduction in face-to-face interactions [60]. Digital technologies offer alternative and more accessible avenues for maintaining social support [61]. Some empirical studies have shown that digital engagement is positively associated with increased social support in later life [61-63]. Social support, in turn, is a well-established protective factor for cognitive function: Older adults with stronger support networks tend to perform better cognitively and face a lower risk of cognitive decline or dementia [64-66]. Thus, social support may serve as a mediator linking digital engagement to cognitive outcomes.

    The Moderating Role of Living Arrangements

    Economic reforms and urbanization in China since the 1980s have profoundly reshaped family structures, particularly impacting older adults. This shift aligns with modernization theory, predicting smaller families and fewer older adults co-residing with children [67]. Consequently, more older adults live only with a spouse or alone [68]. Given the central role of family in Chinese culture, the study shifted to investigate the moderating role of living arrangements. Within the framework of the technological reserve hypothesis, the cognitive benefits of digital engagement are expected to vary across social contexts that shape baseline access to cognitive and social resources. Living arrangements represent a contextual factor in later life, as co-residence with others may provide routine cognitive stimulation and social interaction, whereas living alone is often associated with reduced offline engagement. Consequently, digital engagement may play a more pronounced compensatory role for older adults living alone by supplementing limited in-person cognitive and social resources. This theoretical perspective provides a rationale for examining living arrangements as a moderator in the association between digital engagement and cognitive function. Additionally, living arrangements may shape both the opportunities and the need for engaging with digital technology [69]. Older adults living alone often rely on digital technologies to maintain social ties, bridge social gaps, and manage independent living [70,71]. In contrast, those in multigenerational households may experience “proxy internet use” (eg, reliance on family members for online tasks), reducing direct engagement and the attendant cognitive stimulation [72]. Digital engagement may therefore be especially protective for those living alone.

    Hypotheses

    Guided by the technological reserve hypothesis, this study tested a moderated mediation model to examine whether digital engagement is associated with cognitive function through DHL and social support, and whether these pathways are moderated by living arrangements. Based on the theoretical framework and prior empirical evidence, we propose the following hypotheses:

    1. Hypothesis 1: higher digital engagement is correlated with greater cognitive function among older adults.
    2. Hypothesis 2: higher digital engagement is associated with higher DHL among older adults.
    3. Hypothesis 3: higher DHL is correlated with greater cognitive function among older adults.
    4. Hypothesis 4: DHL is a mediator between digital engagement and cognitive function among older adults.
    5. Hypothesis 5: higher digital engagement is associated with greater social support among older adults.
    6. Hypothesis 6: greater social support is correlated with greater cognitive function among older adults.
    7. Hypothesis 7: social support is a mediator between digital engagement and cognitive function among older adults in China.
    8. Hypothesis 8: living arrangements moderate the associations of digital engagement with cognitive function, DHL, and social support.

    To guide the analyses, we specified a directed acyclic graph (DAG) illustrating the hypothesized relationships among digital engagement, cognitive function, DHL, social support, and living arrangements (Figure 1).

    ‎
    Figure 1. The hypothesized moderated mediation model.

    Study Design and Sampling Procedures

    This study used data collected through a large-scale, cross-sectional survey conducted concurrently by 5 academic teams affiliated with 4 major universities in China. To ensure methodological uniformity, all participating sites adhered to a unified research protocol during the implementation phase.

    A stratified, multistage sampling framework was used to enhance representativeness across regions with different levels of socioeconomic development. China was first stratified into eastern, central, and western regions, which reflect well-documented gradients in economic development, urbanization, and digital infrastructure. One to 2 provinces were randomly selected from each region. The final sample included Hubei (central China), Shandong and Jiangsu (eastern China), and Guangxi (western China), thereby capturing substantial regional heterogeneity in demographic structure and digital development. Within each selected province, 1 to 2 urban or county-level administrative units were further sampled based on local economic conditions, followed by cluster sampling of communities or villages.

    Sample size estimation followed the standard formula for proportion-based calculations: , where uα represents the critical value for a 95% CI (uα=1.96), p is the estimated proportion of older internet users based on the China Internet Network Information Center’s 51st Statistical Report [73], q is the complementary proportion (q=1–p), and d denotes the allowable error (1.2%). Based on these parameters, the minimum required sample size was calculated as 6616. To account for possible nonresponses and invalid questionnaires, a 20% oversampling rate was applied, resulting in a target sample of approximately 7940 individuals.

    Data Collection and Quality Control

    Fieldwork was conducted from June to November 2024 by trained surveyors in collaboration with local village committees or community service offices. Face-to-face interviews were administered at participants’ homes using standardized paper questionnaires. The interviews collected information on sociodemographic characteristics, digital technology use, digital literacy, cognitive function, and quality of life. All surveyors received centralized training to ensure consistent questionnaire administration and interpretation. Upon completion of each interview, field supervisors performed a thorough review of the questionnaires to check for completeness, internal consistency, and data accuracy before submission for entry.

    Participants

    Eligible participants were older adults who met the following inclusion criteria: (1) aged 55 years and older, (2) had resided in the sampled community or village for at least 6 months, and (3) were able to communicate effectively with investigators. Exclusion criteria included: (1) individuals temporarily absent from their households during the survey period, (2) those diagnosed with terminal illnesses, and (3) those who declined to participate. After excluding incomplete responses, duplicate entries, and respondents younger than 55 years, 8302 valid questionnaires remained. Among these, 179 participants (2.2%) had missing values on at least 1 analytic variable and were excluded from the main analyses. The final analytic sample consisted of 8123 participants (Figure 2).

    ‎
    Figure 2. Flow diagram of participant recruitment and data exclusion, resulting in a final analytic sample of N=8123.

    Measurements

    Cognitive Function

    Cognitive function was assessed using the Mini-Cog test. The Mini-Cog test is a rapid, valid, and reliable screening tool for cognitive impairment [74]. The Mini-Cog Test includes a 3-word recall task (scored 0-3) and the clock drawing test (scored 0-2). The total score ranges from 0 to 5. The Mini-Cog test has demonstrated good screening performance in community-dwelling older adults in China [75]. In this study, the Mini-Cog total score was used as a continuous measure of cognitive function, with higher scores indicating better cognitive performance.

    Digital Engagement

    Digital engagement was measured using a self-reported scale developed to capture older adults’ frequency of participation in various digital activities. The scale included eight items of digital behaviors: (1) social communication (eg, using WeChat [Tencent] voice or video calls), (2) experience sharing (eg, posting on WeChat Moments, QQ Zone [Tencent], or Weibo [Sina Corporation]), (3) leisure and entertainment (eg, playing online games, listening to music, or watching videos), (4) online transactions (eg, transferring money, making payments, booking services, or trading stocks), (5) information seeking (eg, searching for travel information or reading news), (6) online learning or training, (7) online civic participation (eg, participating in online polls, petitions, or rights protection), and (8) political engagement (eg, online voting or leaving messages on government websites). Participants rated the frequency of each activity on a 5-point Likert scale ranging from 1 (never) to 5 (always). Higher scores indicated higher digital engagement. The scale demonstrated good internal consistency in this sample (Cronbach α=0.876). Although the scale covers multiple items of digital activities, this study conceptualized digital engagement as an overall behavioral tendency reflecting the breadth and extent of digital technology use in daily life. This approach is consistent with the technological reserve hypothesis and the concept of digital engagement, which emphasize cumulative and sustained engagement. Therefore, a composite digital engagement score was used in the analyses.

    DHL

    DHL was assessed using the eHealth Literacy Scale (eHEALS), a widely validated instrument developed by Norman and Skinner to measure individuals’ self-perceived skills in locating, evaluating, and applying electronic health information to health-related problems [51]. The eHEALS consists of 8 items rated on a 5-point Likert scale (1=strongly disagree to 5=strongly agree), reflecting domains such as awareness of available online health resources, confidence in using the internet for health decision-making, and the ability to discern high-quality digital health content. Given the linguistic and cultural differences between the original instrument and the target population of older adults in mainland China, we used the simplified Chinese version (C-eHEALS) translated and validated by Ma and Wu [76]. The C-eHEALS has been confirmed to have good psychometric properties and can therefore be used to evaluate eHealth literacy in Chinese older populations [77]. In this study, the C-eHEALS demonstrated excellent internal consistency, with a Cronbach α coefficient of 0.986, indicating high reliability for use among Chinese older adults.

    Social Support

    Social support was assessed using the 3-item short version of the Perceived Social Support Scale (PSSS-3), which was developed and validated by Wu et al [78] specifically for use among the Chinese general population. This abbreviated scale was derived from the original 12-item Chinese version of the Multidimensional Scale of Perceived Social Support (MSPSS), originally adapted by Jiang [79] from the version developed by Zimet et al [80]. The PSSS-3 includes 1 item from each of the 3 core dimensions, family support, friend support, and significant others, selected based on the highest factor loadings in a large-scale national sample. The Cronbach α of PSSS-3 in this study was 0.868, demonstrating good internal consistency.

    Living Arrangements

    Living arrangements were measured as a binary variable indicating whether the older adult lived alone, and were assessed using the following question: “What are your current living arrangements?” Those who reported living alone were coded as 1, and those who reported living with others were coded as 0.

    Control Variables

    The prior study indicates that demographic and health factors have close links with cognitive function and suggests that these factors should be included in pertinent research [81]. In this study, gender, age, current place of residence, marital status, education, and number of chronic diseases were controlled as covariates.

    Statistical Analysis

    All analyses were performed using SPSS (version 27; IBM Corp). We first examined the extent and pattern of missing data for all analytic variables. The proportion of missing values for each variable ranged from 0.08% to 1.2% and the overall proportion of missing data was 2.2% (Figure 2). Little’s Missing Completely at Random test was conducted using the missing value analysis procedure in SPSS 27. The test indicated that the missing values were independent of the observed or unobserved values (=14.893; P=.06). Given the low proportion and completely random patterns of missingness, we performed complete-case analyses based on listwise deletion.

    Descriptive statistics summarized sample characteristics, with continuous variables reported as mean (SD) and categorical variables as frequencies and percentages. Pearson correlations examined associations among digital engagement, cognitive function, DHL, social support, and living arrangements. Multicollinearity was assessed by the variance inflation factor (VIF), with VIF>5 indicating collinearity. The relationships among variables were specified according to a DAG based on the technological reserve hypothesis (Figure 1) and analyzed using PROCESS models 4 and 8 with 5000 bootstrapped resamples. Effects were considered significant if the 95% bias–corrected CI did not include 0. All models controlled for age, gender, current place of residence, marital status, education, and number of chronic diseases. Continuous variables were standardized prior to analysis. Statistical tests were 2-tailed with α=.05.

    Ethical Considerations

    The study was reviewed and approved by the Medical Ethics Committee of Zhongda Hospital, Southeast University (approval number 2024ZDSYLL294-Y01). Written informed consent was obtained from all participants before they participated in the study, and they were provided with the opportunity to withdraw at any time during and after the survey. To protect privacy and confidentiality, electronic data were de-identified and stored on password-protected devices accessible only to the research team. No images or supplementary materials in this manuscript contain information that could identify individual participants. There was no compensation for the participants in our study survey.


    Demographic Characteristics of the Participants

    The final sample consisted of 8123 older adults, with an average age of 71.03 (SD 8.39) years. Men accounted for 42.46% (3449/8123) of the participants. Among the participants, 3990 (49.12%) lived in urban areas, and 4133 (50.88%) lived in rural areas. Most respondents were married (6338/8123, 78.03%), and 51.73% (4202/8123) of the participants had attained a middle school education or above. Overall, 75.28% (6115/8123) of the participants reported at least 1 chronic condition. Summary statistics for main variables, including digital engagement, DHL, social support, living arrangements, and cognitive function, are presented in Table 1.

    Table 1. Participant characteristics and descriptive statistics for study variables (N=8123).
    VariablesValues
    Sex, n (%)

    		Male3449 (42.46)

    		Female4674 (57.54)
    Age (years), mean (SD)71.03 (8.39)
    Current place of residence, n (%)

    		Urban3990 (49.12)

    		Rural4133 (50.88)
    Marital status, n (%)

    		Married6338 (78.03)

    		Unmarried1785 (21.97)
    Education, n (%)

    		Primary school or under3921 (48.27)

    		Middle or high school3462 (42.62)

    		College or above740 (9.11)
    Chronic diseases, n (%)

    		02008 (24.72)

    		13477 (42.80)

    		≥22638 (32.48)
    Digital engagement, mean (SD)17.37 (8.03)
    DHLa, mean (SD)20.18 (10.59)
    Social support, mean (SD)16.71 (3.22)
    Living arrangements, n (%)

    		Living alone1166 (14.35)

    		Living with others6957 (85.65)
    Cognitive function, mean (SD)3.57 (1.49)

    aDHL: digital health literacy.

    Preliminary Correlation Analysis

    Table 2 presents the Pearson correlation coefficients among the main variables. Digital engagement was significantly and positively correlated with cognitive function (r=0.365, P<.001), social support (r=0.081, P<.001), and DHL (r=0.575, P<.001), but negatively associated with living arrangements (r=–0.101, P<.001). Cognitive function was also positively correlated with social support (r=0.131, P<.001) and DHL (r=0.347, P<.001), and negatively correlated with living arrangements (r=–0.069, P<.001). VIFs indicated no multicollinearity among digital engagement (VIF=1.732), DHL (VIF=1.666), social support (VIF=1.027), living arrangements (VIF=1.823), and cognitive function.

    Table 2. Pearson correlation matrix of digital engagement, digital health literacy, social support, living arrangements, and cognitive function (N=8123).
    VariablesDigital engagementDHLaSocial supportLiving arrangementsCognitive function
    Digital engagement

    		r10.5750.081–0.1010.365

    		P value—b<.001<.001<.001<.001
    DHL

    		r0.57510.127–0.0730.347

    		P value<.001—<.001<.001<.001
    Social support

    		r0.0810.1271–0.0450.131

    		P value<.001<.001—<.001<.001
    Living arrangements

    		r–0.101–0.073–0.0451–0.069

    		P value<.001<.001<.001—<.001
    Cognitive function

    		r0.3650.3470.131–0.0691

    		P value<.001<.001<.001<.001—

    aDHL: digital health literacy.

    bNot applicable.

    Mediation Analysis

    Guided by the DAG-specified conditional process model, we first used model 4 of the PROCESS macro for SPSS [82] to test Hypotheses 1 to 7. The total effect of digital engagement on cognitive function was 0.241 (95% CI 0.216-0.265), of which the direct effect accounted for 70.5% (β=0.170, 95% CI 0.143-0.196) and the combined indirect effects by DHL and social support accounted for 29.5% (β=0.071, 95% CI 0.061-0.082). The findings indicated a moderate but statistically meaningful mediation by DHL and social support.

    Table 3 and Figure 3 present the results of the mediation analysis. As shown in model 3, the direct effect of digital engagement on cognitive function was significant (Model 3: β=0.170, 95% CI 0.143-0.196; P<.001), thus supporting Hypothesis 1. Digital engagement also demonstrated a significant and positive association with DHL (Model 1: β=0.400, 95% CI 0.379-0.420; P<.001), supporting Hypothesis 2. Additionally, DHL was significantly and positively related to cognitive function (Model 3: β=0.148, 95% CI 0.123-0.174; P<.001), supporting Hypothesis 3. Furthermore, digital engagement was positively and significantly correlated with social support (Model 2: β=0.129, 95% CI 0.103-0.155; P<.001), supporting Hypothesis 5. Social support, in turn, showed a significant and positive correlation with cognitive function (Model 3: β=0.091, 95% CI 0.071-0.112; P<.001), supporting Hypothesis 6.

    Table 3. Mediation analysis of the association between digital engagement and cognitive function through digital health literacy and social support, adjusted for covariates (PROCESS Model 4; N=8123).
    Model 1a (DHLb)Model 2c (social support)Model 3d (cognitive function)

    		βe (95% CI)P valueβ (95% CI)P valueβ (95% CI)P value
    Explanatory variable

    		Digital engagement0.400 (0.379 to 0.420)<.0010.129 (0.103 to 0.155)<.0010.170 (0.143 to 0.196)<.001
    Mediator variables

    		DHL—f———0.148 (0.123 to 0.174)<.001

    		Social support————0.091 (0.071 to 0.112)<.001
    Control variables

    		Gender–0.035 (–0.070 to –0.001).0470.169 (0.125 to 0.213)<.001–0.044 (–0.085 to –0.003).03

    		Age–0.041 (–0.060 to –0.021)<.0010.065 (0.041 to 0.089)<.001–0.095 (–0.117 to –0.072)<.001

    		Current place of residence0.321 (0.280 to 0.362)<.001–0.465 (–0.518 to –0.413)<.0010.048 (–0.003 to 0.098).06

    		Marital status0.039 (–0.004 to 0.082).0760.159 (0.104 to 0.213)<.0010.134 (0.084 to 0.184)<.001

    		Education0.294 (0.261 to 0.327)<.0010.172 (0.131 to 0.213)<.0010.179 (0.140 to 0.218)<.001

    		Chronic diseases–0.124 (–0.147 to –0.101)<.001–0.078 (–0.107 to –0.048)<.0010.034 (0.007 to 0.062).01
    Constant–0.471 (–0.563 to –0.380)<.001–0.353 (–0.469 to –0.237)<.001–0.383 (–0.491 to –0.275)<.001

    aF7, 8115=811.099; R2=0.412.

    bDHL: digital health literacy.

    cF7, 8115=65.515; R2=0.054.

    dF9, 8113=214.765; R2=0.192.

    eβ: standardized regression coefficient.

    fNot applicable.

    ‎
    Figure 3. Mediation model of the association between digital engagement and cognitive function through digital health literacy and social support; values are standardized coefficients adjusted for covariates (PROCESS Model 4; N=8123; ***P<.001).

    These findings suggest that DHL and social support play a partial mediating role in the relationship between digital engagement and cognitive function, with indirect effects of 0.059 (95% CI 0.049-0.069) and 0.012 (95% CI 0.008-0.016), respectively, supporting Hypotheses 4 and 7. Combining 2 mediation effects, the total indirect effect was 0.071 (95% CI 0.061-0.082). The bootstrap test results for indirect effects are reported in Table 4.

    Table 4. Bootstrap estimates of indirect effects of digital engagement on cognitive function through digital health literacy and social support (PROCESS Model 4; N=8123).
    Indirect effects pathEffectsSE95% CIProportion of effects (%)
    Total indirect effect0.0710.0050.061-0.082100
    DEa→DHLb→CFc0.0590.0050.049-0.06983.10
    DE→SSd→CF0.0120.0020.008-0.01616.90

    aDE: digital engagement.

    bDHL: digital health literacy.

    cCF: cognitive function.

    dSS: social support.

    Moderated Mediation Analysis

    To examine the moderated mediation effects involving living arrangements, we used Model 8 of the PROCESS macro for SPSS [82], using 5000 bootstrap resamples and a 95% bias-corrected CI. The results are reported in Table 5. The analysis revealed a significant and positive interaction effect between digital engagement and living arrangements on cognitive function (Model 6: β=0.109, 95% CI 0.052-0.166; P<.001), suggesting a moderating role of living arrangements. As illustrated in Figure 4, the beneficial association between digital engagement and cognitive performance was stronger among older adults who lived alone, relative to those who lived with others.

    Table 5. Moderated mediation analysis testing moderation by living arrangements in the associations between digital engagement and digital health literacy, digital engagement and social support, digital engagement and cognitive function, adjusted for covariates (PROCESS Model 8; N=8123).
    VariablesModel 4a (DHLb)Model 5c (social support)Model 6d (cognitive function)

    		βe (95% CI)P valueβ (95% CI)P valueβ (95% CI)P value
    Explanatory variable

    		Digital engagement0.391 (0.370 to 0.413)<.0010.109 (0.082 to 0.137)<.0010.156 (0.129 to 0.183)<.001
    Mediator variables

    		DHL—f———0.147 (0.121 to 0.172)<.001

    		Social support————0.089 (0.069 to 0.110)<.001
    Moderating variables

    		Living arrangements0.054 (–0.011 to 0.119).100.058 (–0.024 to 0.141).170.154 (0.078 to 0.230)<.001

    		DE×LAg0.063 (0.014 to 0.112).010.151 (0.089 to 0.212)<.0010.109 (0.052 to 0.166)<.001
    Control variables

    		Gender–0.036 (–0.070 to –0.001).0440.167 (0.123 to 0.211)<.001–0.044 (–0.085 to –0.003).04

    		Age–0.040 (–0.060 to –0.021)<.0010.065 (0.041 to 0.089)<.001–0.094 (–0.117 to –0.072)<.001

    		Current place of residence0.321 (0.280 to 0.362)<.001–0.465 (–0.517 to –0.413)<.0010.047 (–0.003 to 0.097).07

    		Marital status0.063 (0.007 to 0.119).030.175 (0.103 to 0.247)<.0010.210 (0.144 to 0.276)<.001

    		Education0.292 (0.259 to 0.324)<.0010.167 (0.126 to 0.208)<.0010.176 (0.137 to 0.215)<.001

    		Chronic diseases–0.123 (–0.146 to –0.100)<.001–0.077 (–0.106 to –0.047)<.0010.034 (0.007 to 0.061).01
    Constant–0.492 (–0.591 to –0.393)<.001–0.360 (0.485 to –0.234)<.001–0.456 (–0.572 to –0.339)<.001

    aF9, 8113=632.208; R2=0.412.

    bDHL: digital health literacy.

    cF9, 8113=53.680; R2=0.056.

    dF11, 8111=178.558; R2=0.195.

    eβ: standardized regression coefficient.

    fNot applicable.

    gDE×LA: the interaction term between digital engagement and living arrangements.

    ‎
    Figure 4. The moderating effect of living arrangements on the association between digital engagement and cognitive function. The difference in simple slopes indicated that the association between digital engagement and cognitive function was significantly stronger for individuals living alone than for those living with others (PROCESS Model 8, N=8123).

    In addition to the interaction effect on the direct path, the results also revealed significant moderating effects of living arrangements on the first stages of both mediation pathways. Specifically, the interaction term between digital engagement and living arrangements significantly predicted DHL (Model 4: β=0.063, 95% CI 0.014-0.112; P=.01 and social support (Model 5: β=0.151, 95% CI 0.089-0.212; P<.001). As illustrated in Figures 5 and 6, the beneficial association of digital engagement with both DHL and social support was stronger among older adults who lived alone, relative to those who lived with others. These findings indicate that living arrangements moderate the associations of digital engagement with cognitive function, DHL, and social support, supporting Hypothesis 8.

    ‎
    Figure 5. The moderating effect of living arrangements on the association between digital engagement and digital health literacy. The difference in simple slopes indicated that the association between digital engagement and digital health literacy was significantly stronger for individuals living alone than for those living with others (PROCESS Model 8, N=8123).
    ‎
    Figure 6. The moderating effect of living arrangements on the association between digital engagement and social support. The difference in simple slopes indicated that the association between digital engagement and social support was significantly stronger for individuals living alone than for those living with others (PROCESS Model 8, N=8123).

    Table 6 reports the conditional indirect effects of digital engagement on cognitive function by each path. For the DHL mediator, the indirect effect of digital engagement on cognitive function was 0.057 (95% CI 0.048-0.068) among participants living with others and 0.067 (95% CI 0.054-0.080) among those living alone. The corresponding index of moderated mediation was 0.009 (95% CI 0.003-0.016), indicating a significantly stronger indirect effect by DHL for participants living alone. Similarly, for the social support mediator, the indirect effect was 0.010 (95% CI 0.007-0.013) for those living with others and 0.023 (95% CI 0.016-0.032) for those living alone. The index of moderated mediation for the social support pathway was 0.013 (95% CI 0.007-0.021). In all cases, the 95% CI values excluded zero, indicating that both indirect effects were significantly stronger among older adults living alone.

    Table 6. Indices of moderated mediation for two conditional indirect effects of digital engagement on cognitive function by living arrangements (PROCESS Model 8; N=8123).
    Conditional indirect effects pathEffectsSE95% CI
    DEa→DHLb→CFc (living with others)0.0570.0060.048-0.068
    DE→DHL→CF (living alone)0.0670.0070.054-0.080
    Index of the moderated mediation0.0090.0030.003-0.016
    DE→SSd→CF (living with others)0.0100.0020.007-0.013
    DE→SS→CF (living alone)0.0230.0040.016-0.032
    Index of the moderated mediation0.0130.0030.007-0.021

    aDE: digital engagement.

    bDHL: digital health literacy.

    cCF: cognitive function.

    dSS: social support.


    Principal Findings

    Guided by the technological reserve hypothesis and using a large, community-based sample of older adults in China, this study investigated the mechanisms underlying the association between digital engagement and cognitive function among older Chinese adults. We found that higher digital engagement was associated with better cognitive performance. DHL and social support partially mediated this association, and the combined indirect effects accounted for 29.5% of this association. Living arrangements moderated both the direct and indirect pathways, with stronger benefits among older adults living alone. These findings extend prior work on technology use and cognition in later life and broaden the application of the technological reserve hypothesis in the Chinese context.

    The Association Between Digital Engagement and Cognitive Function

    Digital engagement was significantly and positively associated with cognitive function. This supports the technological reserve hypothesis that digital technology use is a modifiable behavioral factor that can promote better cognitive outcomes [30]. It also aligns with prior work linking internet or computer use to cognition [83-88] and with recent studies in China demonstrating that internet use and digital activities enhance cognitive function [14,47]. By adopting the construct of digital engagement rather than a simple use-versus-nonuse dichotomy, our study advances the field by situating technology use within the everyday life context of older adults, emphasizing how they integrate information and communication technologies into their ongoing activities, social interaction, and information seeking [36]. This finding implies that encouraging sustained and meaningful digital engagement is a promising strategy for public health and aging policies aiming to strengthen cognitive function among older adults.

    The Mediating Role of DHL and Social Support

    Consistent with the cognitive-stimulation pathway posited by the technological reserve hypothesis, higher digital engagement was associated with higher DHL, which in turn related to better cognitive performance [28,30]. Notably, the DHL pathway accounted for the majority of the total indirect effect, indicating that health-related digital competencies may be a primary mechanism linking engagement to cognition. Specifically, engaging with digital technology improves DHL because digital skills are among the core skills of DHL [89,90]. In turn, higher DHL denotes a stronger capacity to seek, understand, appraise, and apply health information [91]. These processes involve engaging with cognitively complex information [30], helping older adults build cognitive reserve. This finding advances the eHealth Literacy Model, which posits that DHL is underpinned by cognition [92], and indicates that DHL also serves as a tool that shapes cognition through ongoing, cognitively complex digital activities. Accordingly, interventions should combine user-friendly interfaces with structured, progressive training in cognitively complex digital tasks, ensuring that everyday digital engagement serves as sustained cognitive stimulation. Practical examples include stepwise smartphone or tablet training delivered in community settings (eg, locating health information from reliable sources, evaluating credibility and misinformation, and applying information to everyday self-management tasks).

    Social support mediated the association between digital engagement and cognition, consistent with the social-connectivity pathway [28,30]. This finding aligns with previous research showing that digital engagement has the potential to enhance cognitive function among older individuals by addressing feelings of loneliness and improving the social support they receive from relatives and friends [20]. Specifically, digital engagement enables cheap and easy communication between older adults in distant communities, increasing social connections, overcoming social and spatial barriers, and providing a convenient way to stay in touch with families, friends, and the outside world [62]. In turn, better social support is associated with better cognitive outcomes in older adults [65,93-96]. This finding underscores that interventions should help older adults form and maintain digital social ties so that online interactions translate into perceived social support and, ultimately, better cognitive outcomes. For example, programs could incorporate facilitated online peer groups and a “Digital Buddy system” to help older adults translate online interactions into perceived support [97].

    The Moderating Role of Living Arrangements

    Our study further revealed that living arrangements played a significant moderating role in the associations of digital engagement with cognitive function, DHL, and social support. Compared with older adults who live with others, those living alone experienced a significantly stronger positive effect of digital technology engagement on cognitive function, consistent with the previous studies [26,98]. This moderating effect was significantly present in both mediating pathways: older adults living alone gained greater benefits in terms of DHL and social support from digital engagement than those living with others. Specifically, older adults living alone, due to a lack of effective offline social interactions, are more reliant on virtual social networks facilitated by digital technologies [60]. This reliance partially compensates for the reduced social support associated with solitary living, thereby mitigating its negative impact on cognitive function [70]. Additionally, older adults who live alone are less likely to engage in proxy internet use [72] and thus rely more on themselves to use digital devices (eg, searching for health information online). Furthermore, since older adults living alone are less often burdened with caregiving responsibilities for grandchildren, they have more freedom and time to engage with digital technologies [26]. Our findings suggest that digital engagement serves as a more efficacious strategy for mitigating cognitive decline among older adults living alone compared to those living with others.

    Our finding is broadly consistent with other international evidence. A cohort study in America reported that transitioning into Internet use was associated with better cognitive function and slower cognitive decline, and that these benefits were more pronounced among older adults living alone than among those living with others [84]. Additionally, a 2-country longitudinal study in Sweden and the Netherlands observed less decline in global cognition among baseline internet users after adjustment for living situation [86]. Beyond cognitive outcomes, findings based on the Survey of Health, Ageing and Retirement in Europe further indicate that internet use can attenuate the association between living alone and loneliness across different European welfare regimes, implying that digital engagement may buffer psychosocial vulnerabilities of solitary living [99]. Taken together, although the prevalence and social meaning of living alone differ across cultures, converging evidence supports that digital engagement may serve as a compensatory resource for older adults with constrained offline or household-based support.

    This moderation finding has practical implications for intervention design. Digital inclusion initiatives to help older people adapt to digital technologies should prioritize this vulnerable group. An integrated community-based approach may be especially useful: individual digital coaching (eg, guided practice in health information seeking) coupled with structured social support (eg, online group chats). Including a “living alone” priority within such initiatives may help maximize equity and potential cognitive benefits, while also addressing social isolation risks that have been recognized as a public health and policy concern.

    Limitations and Future Research

    Despite these contributions, several limitations should be acknowledged. First, the cross-sectional design precludes causal inferences. Although our models adjusted for a set of covariates, endogeneity, reverse causation, and unmeasured confounding cannot be fully ruled out. Thus, our results only show associations, not causality. Future studies should use longitudinal designs with extended follow-up periods to elucidate temporal dynamics and disentangle potential reverse causation. Second, reliance on self-reported measures introduces the risk of recall bias, especially among participants with cognitive impairments, despite our use of validated instruments to attenuate this issue. Third, digital engagement was operationalized as a composite measure. Thus, this study could not disentangle potentially differential effects of specific types of digital activities on cognitive function. Finally, due to data constraints, living arrangements were operationalized as solitary versus nonsolitary living, precluding differentiation among various household compositions (eg, living with a spouse, children, or extended family). Given the important role of family structures in the well-being of older adults in China, future research should refine classifications of living arrangements to better explore their moderating effects.

    Conclusions

    This study contributes to the understanding of cognitive aging in the digital environment from the perspective of the technological reserve hypothesis and digital engagement. First, it offers an innovative framework based on the technological reserve hypothesis for understanding the moderating and mediating mechanisms of DHL, social support, and living arrangements. Second, it advances previous assessment methods of digital technology application by using a comprehensive measure. Our results increase understanding of the mechanisms underlying the cognitive effects of digital technology use and provide insights for designing digital health interventions and public health policies.

    Acknowledgments

    The authors would like to express their sincere gratitude to all participants in this study, as well as to the staff of the community and village committees who facilitated the fieldwork. This study was supported by the Major Project of the National Social Science Fund of China (grant number 23&ZD188). The funder had no involvement in the study design, data collection, analysis, interpretation, or the writing of the manuscript.

    Funding

    No external financial support or grants were received from any public, commercial, or not-for-profit entities for the research, authorship, or publication of this article.

    Data Availability

    The datasets generated or analyzed during this study are not publicly available due to restrictions under existing Data Use Agreements, but are available from the corresponding author on reasonable request.

    Conflicts of Interest

    None declared.

    1. Wang Q, Ni J, Guan Y, Liu X, Li M, Xue H, et al. Associations of depression and symptomatic knee osteoarthritis with cognitive function among middle-aged and older adults: evidence from CHARLS in China. J Gerontol Soc Work Routledge. Jul 28, 2025:1-17. [CrossRef] [Medline]
    2. World Alzheimer report 2024: global changes in attitudes to dementia. Alzheimer’s Disease International. 2024. URL: https://www.alzint.org/resource/world-alzheimer-report-2024/ [accessed 2025-07-25]
    3. Jia L, Du Y, Chu L, Zhang Z, Li F, Lyu D, et al. COAST Group. Prevalence, risk factors, and management of dementia and mild cognitive impairment in adults aged 60 years or older in China: a cross-sectional study. Lancet Public Health. Dec 2020;5(12):e661-e671. [FREE Full text] [CrossRef] [Medline]
    4. Xu J, Wang J, Wimo A, Fratiglioni L, Qiu C. The economic burden of dementia in China, 1990-2030: implications for health policy. Bull World Health Organ. 2017;95(1):18-26. [FREE Full text] [CrossRef] [Medline]
    5. Shah H, Albanese E, Duggan C, Rudan I, Langa KM, Carrillo MC, et al. Research priorities to reduce the global burden of dementia by 2025. Lancet Neurol. Nov 2016;15(12):1285-1294. [CrossRef] [Medline]
    6. The 56th statistical report on China's internet development [Web page in Chinese]. China Internet Network Information Center. URL: https://www3.cnnic.cn/n4/2025/0721/c88-11328.html [accessed 2025-07-31]
    7. Kebede A, Ozolins L, Holst H, Galvin K. Digital engagement of older adults: scoping review. J Med Internet Res. 2022;24(12):e40192. [FREE Full text] [CrossRef] [Medline]
    8. Aleti T, Figueiredo B, Reid M, Martin D, Sheahan J, Hjorth L. Older adults' digital competency, digital risk perceptions and frequency of everyday digital engagement. Inf Technol People. 2025;38(8):118. [CrossRef]
    9. Xu L, Ng D, Lee C, Peng P, Chu S. A systematic review of digital literacy in lifelong learning for older adults: challenges, strategies, and learning outcomes. Education Tech Research Dev. 2025;73(6):3627-3674. [CrossRef]
    10. Iqbal S, Fischl C, Asai R. Older persons’ social participation, health and well-Being through digital engagement. Act Adapt Aging Routledge. 2025;49(4):534-563. [CrossRef]
    11. Chan M, Haber S, Drew L, Park D. Training older adults to use tablet computers: does it enhance cognitive function? Gerontologist. 2016;56(3):475-484. [FREE Full text] [CrossRef] [Medline]
    12. Almeida-Meza P, Steptoe A, Cadar D. Is engagement in intellectual and social leisure activities protective against dementia risk? Evidence from the English Longitudinal Study of Ageing. J Alzheimers Dis. 2021;80(2):555-565. [FREE Full text] [CrossRef] [Medline]
    13. Czaja S, Boot W, Charness N, Rogers W, Sharit J. Improving social support for older adults through technology: findings from the PRISM randomized controlled trial. Gerontologist. May 08, 2018;58(3):467-477. [FREE Full text] [CrossRef] [Medline]
    14. Wang J, Zhang N, Huang C, Wu Q, Tong J. Internet use, physical activity, and cognitive function in Chinese older adults: a cross-lagged panel analysis. Front Aging Neurosci. 2025;17:1579874. [FREE Full text] [CrossRef] [Medline]
    15. Hua Z, Wang F. Association between WeChat use and memory performance among older adults in China: the mediating role of depression. Behav Sci (Basel). 2022;12(9):323. [FREE Full text] [CrossRef] [Medline]
    16. Zhou Y, Abuduxukuer K, Wang C, Wei J, Shi W, Li Y, et al. et al. WeChat usage and preservation of cognitive functions in middle-aged and older Chinese adults: indications from a nationally representative survey, 2018-2020. BMC Public Health. 2024;24(1):1783. [FREE Full text] [CrossRef] [Medline]
    17. Chen B, Yang C, Ren S, Li P, Zhao J. Relationship between internet use and cognitive function among middle-aged and older Chinese adults: 5-year longitudinal study. J Med Internet Res. 2024;26:e57301. [FREE Full text] [CrossRef] [Medline]
    18. Yu D, Fiebig D. Internet use and cognition among middle-aged and older adults in China: a cross-lagged panel analysis. J Econ Ageing. 2020;17:100262. [CrossRef]
    19. Ding L, Lu J, Ma X. WeChat use, cognitive function, and depressive symptoms: examining longitudinal relationships among older Chinese adults from a national survey. Mass Commun Soc. 2025:1-21. [CrossRef]
    20. Liu Z, Li Z. Relationships between digital engagement and the mental health of older adults: evidence from China. PLoS One. 2024;19(8):e0308071. [FREE Full text] [CrossRef] [Medline]
    21. Li A, Qiang W, Li J, Geng Y, Qiang Y, Zhao J. Effectiveness of an exergame-based training program on physical and cognitive function in older adults with cognitive impairment: a randomized controlled trial in rural China. BMC Geriatr. 2025;25(1):892. [FREE Full text] [CrossRef] [Medline]
    22. Wen X, Song S, Tian H, Cui H, Zhang L, Sun Y, et al. et al. Intervention of computer-assisted cognitive training combined with occupational therapy in people with mild cognitive impairment: a randomized controlled trial. Front Aging Neurosci. 2024;16:1384318. [FREE Full text] [CrossRef] [Medline]
    23. Cheng Z, Zhou M, Sabran K. Mobile app-based interventions to improve the well-being of people with dementia: a systematic literature review. Assist Technol. 2024;36(1):64-74. [CrossRef] [Medline]
    24. Yu X, Ang S, Zhang Y. Exploring rural-urban differences in the association between internet use and cognitive functioning among older adults in China. J Gerontol B Psychol Sci Soc Sci. 2024;79(4):gbad195. [FREE Full text] [CrossRef] [Medline]
    25. Preacher KJ, Hayes AF. Asymptotic and resampling strategies for assessing and comparing indirect effects in multiple mediator models. Behav Res Methods. 2008;40(3):879-891. [FREE Full text] [CrossRef] [Medline]
    26. Li Y, Han W, Hu M. Does internet access make a difference for older adults' cognition in urban China? The moderating role of living arrangements. Health Soc Care Community. 2022;30(4):e909-e920. [CrossRef] [Medline]
    27. Benge JF, Scullin MK. Implications for technological reserve development in advancing age, cognitive impairment, and dementia. Behav Brain Sci. 2020;43:e157. [CrossRef] [Medline]
    28. Wolff JL, Benge JF, Cassel CK, Monin JK, Reuben DB. Emerging topics in dementia care and services. J Am Geriatr Soc. 2021;69(7):1763-1773. [FREE Full text] [CrossRef] [Medline]
    29. Benge JF, Kiselica AM, Aguirre A, Hilsabeck RC, Douglas M, Paydarfar D, et al. et al. Technology use and subjective cognitive concerns in older adults. Arch Gerontol Geriatr. 2023;106:104877. [FREE Full text] [CrossRef] [Medline]
    30. Benge JF, Scullin MK. A meta-analysis of technology use and cognitive aging. Nat Hum Behav. 2025;9(7):1405-1419. [FREE Full text] [CrossRef] [Medline]
    31. Stern Y, Arenaza-Urquijo EM, Bartrés-Faz D, Belleville S, Cantilon M, Chetelat G, et al. et al. Whitepaper: defining and investigating cognitive reserve, brain reserve, and brain maintenance. Alzheimers Dement. 2020;16(9):1305-1311. [FREE Full text] [CrossRef] [Medline]
    32. Stern Y, Albert M, Barnes CA, Cabeza R, Pascual-Leone A, Rapp PR. A framework for concepts of reserve and resilience in aging. Neurobiol Aging. 2023;124:100-103. [FREE Full text] [CrossRef] [Medline]
    33. Krell-Roesch J, Vemuri P, Pink A, Roberts RO, Stokin GB, Mielke MM, et al. et al. Association between mentally stimulating activities in late life and the outcome of incident mild cognitive impairment, with an analysis of the APOE e4 genotype. JAMA Neurol. 2017;74(3):332-338. [FREE Full text] [CrossRef] [Medline]
    34. Penninkilampi R, Casey A, Singh MF, Brodaty H. The association between social engagement, loneliness, and risk of dementia: a systematic review and meta-analysis. J Alzheimers Dis. 2018;66(4):1619-1633. [CrossRef] [Medline]
    35. Scullin MK, Jones WE, Phenis R, Beevers S, Rosen S, Dinh K, et al. et al. Using smartphone technology to improve prospective memory functioning: a randomized controlled trial. J Am Geriatr Soc. 2022;70(2):459-469. [FREE Full text] [CrossRef] [Medline]
    36. Quan-Haase A, Martin K, Schreurs K. Interviews with digital seniors: ICT use in the context of everyday life. Inf Commun Soc. 2016;19(5):691-707. [CrossRef]
    37. Ackermann K, Awaworyi Churchill S, Smyth R. Broadband internet and cognitive functioning. Econ Rec Hoboken. 2023;99(327):536-563. [FREE Full text] [CrossRef]
    38. Schacter DL. Media, technology, and the sins of memory. Mem Mind Media. 2022;1:e1. [FREE Full text] [CrossRef] [Medline]
    39. Small GW, Lee J, Kaufman A, Jalil J, Siddarth P, Gaddipati H, et al. et al. Brain health consequences of digital technology use
. Dialogues Clin Neurosci. 2020;22(2):179-187. [FREE Full text] [CrossRef] [Medline]
    40. Medlock S, Eslami S, Askari M, Arts DL, Sent D, de Rooij SE, et al. et al. Health information-seeking behavior of seniors who use the internet: a survey. J Med Internet Res. 2015;17(1):e10. [FREE Full text] [CrossRef] [Medline]
    41. Bielak AAM. How can we not 'lose it' if we still don't understand how to 'use it'? Unanswered questions about the influence of activity participation on cognitive performance in older age--a mini-review. Gerontology. 2010;56(5):507-519. [CrossRef] [Medline]
    42. Yildirim E, Ogel-Balaban H. Cognitive functions among healthy older adults using online social networking. Appl Neuropsychol Adult. 2023;30(4):401-408. [CrossRef] [Medline]
    43. Cotten SR, Schuster AM, Seifert A. Social media use and well-being among older adults. Curr Opin Psychol. 2022;45:101293. [CrossRef] [Medline]
    44. Han M, Tan XY, Lee R, Lee JK, Mahendran R. Impact of social media on health-related outcomes among older adults in Singapore: qualitative study. JMIR Aging. 2021;4(1):e23826. [FREE Full text] [CrossRef] [Medline]
    45. Ihle A, Bavelier D, Maurer J, Oris M, Kliegel M. Internet use in old age predicts smaller cognitive decline only in men. Sci Rep. 2020;10(1):8969. [FREE Full text] [CrossRef] [Medline]
    46. Firth J, Torous J, Stubbs B, Firth JA, Steiner GZ, Smith L, et al. et al. The "online brain": how the internet may be changing our cognition. World Psychiatry. 2019;18(2):119-129. [FREE Full text] [CrossRef] [Medline]
    47. Deng C, Shen N, Li G, Zhang K, Yang S. Digital isolation and dementia risk in older adults: longitudinal cohort study. J Med Internet Res. 2025;27:e65379. [FREE Full text] [CrossRef] [Medline]
    48. Choi NG, Dinitto DM. Internet use among older adults: association with health needs, psychological capital, and social capital. J Med Internet Res. 2013;15(5):e97. [FREE Full text] [CrossRef] [Medline]
    49. Chen C, Huang N, Hu B, Zhang M, Yuan J, Guo J. The effectiveness of digital technology interventions for cognitive function in older adults: a systematic review and meta-analysis of randomized controlled trials. Geroscience. 2025;47(1):653-683. [CrossRef] [Medline]
    50. Gao Y, Liu N. Effects of digital technology-based serious games interventions for older adults with mild cognitive impairment: a meta-analysis of randomised controlled trials. Age Ageing. 2025;54(4):afaf080. [CrossRef] [Medline]
    51. Norman CD, Skinner HA. eHEALS: the eHealth Literacy Scale. J Med Internet Res. 2006;8(4):e27. [FREE Full text] [CrossRef] [Medline]
    52. Xin Y, Weina H, Yan D. Digital literacy impacts quality of life among older adults through hierarchical mediating mechanisms. Sci Rep. 2025;15(1):19288. [FREE Full text] [CrossRef] [Medline]
    53. Liu S, Lu Y, Wang D, He X, Ren W, Kong D, et al. et al. Impact of digital health literacy on health-related quality of life in Chinese community-dwelling older adults: the mediating effect of health-promoting lifestyle. Front Public Health. 2023;11:1200722. [FREE Full text] [CrossRef] [Medline]
    54. Arias López MDP, Ong BA, Borrat Frigola X, Fernández AL, Hicklent RS, Obeles AJT, et al. et al. Digital literacy as a new determinant of health: a scoping review. PLOS Digit Health. 2023;2(10):e0000279. [FREE Full text] [CrossRef] [Medline]
    55. Xie L, Zhang S, Xin M, Zhu M, Lu W, Mo PK. Electronic health literacy and health-related outcomes among older adults: a systematic review. Prev Med. 2022;157:106997. [CrossRef] [Medline]
    56. Santini ZI, Jose PE, York Cornwell E, Koyanagi A, Nielsen L, Hinrichsen C, et al. et al. Social disconnectedness, perceived isolation, and symptoms of depression and anxiety among older Americans (NSHAP): a longitudinal mediation analysis. Lancet Public Health. 2020;5(1):e62-e70. [FREE Full text] [CrossRef] [Medline]
    57. Domènech-Abella J, Mundó J, Haro JM, Rubio-Valera M. Anxiety, depression, loneliness and social network in the elderly: longitudinal associations from The Irish Longitudinal Study on Ageing (TILDA). J Affect Disord. 2019;246:82-88. [CrossRef] [Medline]
    58. Cohen S, Wills TA. Stress, social support, and the buffering hypothesis. Psychological Bulletin. 1985;98(2):310-357. [CrossRef] [Medline]
    59. Carstensen LL. Social and emotional patterns in adulthood: support for socioemotional selectivity theory. Psychol Aging. 1992;7(3):331-338. [CrossRef] [Medline]
    60. Cheng M, Su W, Li H, Li L, Xu M, Zhao X, et al. et al. Factors influencing the social participation ability of rural older adults in China: a cross-sectional study. Front Public Health. 2022;10:1001948. [FREE Full text] [CrossRef] [Medline]
    61. Sun K, Zhou J. Understanding the impacts of internet use on senior citizens’ social participation in China: evidence from longitudinal panel data. Telemat Inform. 2021;59:101566. [CrossRef]
    62. Du X, Liao J, Ye Q, Wu H. Multidimensional internet use, social participation, and depression among middle-aged and elderly Chinese individuals: nationwide cross-sectional study. J Med Internet Res. 2023;25:e44514. [FREE Full text] [CrossRef] [Medline]
    63. He T, Huang C, Li M, Zhou Y, Li S. Social participation of the elderly in China: the roles of conventional media, digital access and social media engagement. Telemat Inform. 2020;48:101347. [CrossRef]
    64. Long C, Yang W, Glaser K. Social support, cognition, and mental health among older people in China: a longitudinal life course study. Soc Sci Med. 2025;381:118279. [FREE Full text] [CrossRef] [Medline]
    65. Ma T, Liao J, Ye Y, Li J. Social support and cognitive activity and their associations with incident cognitive impairment in cognitively normal older adults. BMC Geriatr. 2024;24(1):38. [FREE Full text] [CrossRef] [Medline]
    66. Joyce J, Ryan J, Owen A, Hu J, McHugh Power J, Shah R, et al. et al. Social isolation, social support, and loneliness and their relationship with cognitive health and dementia. Int J Geriatr Psychiatry. 2022;37(1):00. [FREE Full text] [CrossRef] [Medline]
    67. Ragab E, Ghannam A. Modernisation in Arab societies: the theoretical and analytical view. Int J Sociol Soc Policy. 2001;21:99-131. [CrossRef]
    68. Zeng Y, Wang Z. Dynamics and policy implications of family households and elderly living arrangements in China. American Enterprise Institute. 2019. URL: http://www.jstor.org/stable/resrep24663.5 [accessed 2025-07-30]
    69. Cáceres RB, Chaparro AC. Age for learning, age for teaching: the role of inter-generational, intra-household learning in internet use by older adults in Latin America. Inf, Commun Soc. 2017:1-17. [FREE Full text] [CrossRef]
    70. Silva P, Matos AD, Martinez-Pecino R. Can the internet reduce the loneliness of 50+ living alone? Inf, Commun Soc. 2022;25(1):17-33. [FREE Full text] [CrossRef]
    71. Berner J, Aartsen M, Deeg D. Predictors in starting and stopping internet use between 2002 and 2012 by Dutch adults 65 years and older. Health Informatics J. 2019;25(3):715-730. [FREE Full text] [CrossRef] [Medline]
    72. Grošelj D, Reisdorf BC, Petrovčič A. Obtaining indirect internet access: an examination how reasons for internet non-use relate to proxy internet use. Telecommunications Policy. 2019;43(3):213-224. [CrossRef]
    73. The 51st statistical report on China's Internet development [Web page in Chinese]. China Internet Network Information Center. 2023. URL: https://www3.cnnic.cn/n4/2023/0302/c199-10755.html [accessed 2025-09-04]
    74. Adhikari SP, Dev R, Borson S. Modifying the Mini-Cog to screen for cognitive impairment in nonliterate individuals. Int J Alzheimers Dis. 2021;2021:5510093. [FREE Full text] [CrossRef] [Medline]
    75. Yang L, Yan J, Jin X, Jin Y, Yu W, Xu S, et al. et al. Screening for dementia in older adults: comparison of Mini-Mental State Examination, Mini-Cog, Clock Drawing Test and AD8. PLoS One. 2016;11(12):e0168949. [FREE Full text] [CrossRef] [Medline]
    76. Ma Z, Wu M. The psychometric properties of the Chinese eHealth Literacy Scale (C-eHEALS) in a Chinese rural population: cross-sectional validation study. J Med Internet Res. 2019;21(10):e15720. [FREE Full text] [CrossRef] [Medline]
    77. Xu RH, Zhou L, Lu SY, Wong EL, Chang J, Wang D. Psychometric validation and cultural adaptation of the simplified Chinese eHealth Literacy Scale: cross-sectional study. J Med Internet Res. 2020;22(12):e18613. [FREE Full text] [CrossRef] [Medline]
    78. Wu Y, Tang J, Du Z, Chen K, Wang F, Sun X, et al. et al. Development of a short version of the perceived social support scale: based on classical test theory and ant colony optimization. BMC Public Health. 2025;25(1):232. [FREE Full text] [CrossRef] [Medline]
    79. Jiang Q. Perceived Social Support Scale. Chin J Behav Med Sci. 2001;10(10):41-43. [FREE Full text]
    80. Zimet G, Dahlem N, Zimet S, Farley G. The Multidimensional Scale of Perceived Social Support. J Pers Assess Routledge. Mar 1988;52(1):30-41. [CrossRef]
    81. Wu X, Tang Y, He Y, Wang Q, Wang Y, Qin X. Prevalence of cognitive impairment and its related factors among Chinese older adults: an analysis based on the 2018 CHARLS data. Front Public Health. 2024;12:1500172. [FREE Full text] [CrossRef] [Medline]
    82. Hayes AF. Introduction to Mediation, Moderation, and Conditional Process Analysis: A Regression-Based Approach. New York, London. The Guilford Press; Jan 24, 2022.
    83. Cho G, Betensky RA, Chang VW. Internet usage and the prospective risk of dementia: a population-based cohort study. J Am Geriatr Soc. 2023;71(8):2419-2429. [CrossRef] [Medline]
    84. Kim YK, Han SH. Internet use and cognitive functioning in later life: focus on asymmetric effects and contextual factors. Gerontologist. 2022;62(3):425-435. [FREE Full text] [CrossRef] [Medline]
    85. Kamin ST, Lang F. Internet use and cognitive functioning in late adulthood: longitudinal findings from the Survey of Health, Ageing and Retirement in Europe (SHARE). J Gerontol B Psychol Sci Soc Sci. 2020;75(3):534-539. [CrossRef] [Medline]
    86. Berner J, Comijs H, Elmståhl S, Welmer AK, Sanmartin Berglund J, Anderberg P, et al. et al. Maintaining cognitive function with internet use: a two-country, six-year longitudinal study. Int Psychogeriatr. 2019;31(07):929-936. [FREE Full text] [CrossRef]
    87. d'Orsi E, Xavier AJ, Rafnsson SB, Steptoe A, Hogervorst E, Orrell M. Is use of the internet in midlife associated with lower dementia incidence? Results from the English Longitudinal Study of Ageing. Aging Ment Health. 2018;22(11):1525-1533. [FREE Full text] [CrossRef] [Medline]
    88. Klimova B. Use of the internet as a prevention tool against cognitive decline in normal aging. Clin Interv Aging Dove Medical Press Ltd. 2016;11:1231-1237. [FREE Full text] [CrossRef] [Medline]
    89. Li S, Cui G, Yin Y, Xu H. Associations between health literacy, digital skill, and eHealth literacy among older Chinese adults: a cross-sectional study. Digit Health. 2023;9:20552076231178431. [FREE Full text] [CrossRef] [Medline]
    90. Norman CD, Skinner HA. eHealth literacy: essential skills for consumer health in a networked world. J Med Internet Res. 2006;8(2):e9. [FREE Full text] [CrossRef] [Medline]
    91. Jung SO, Son YH, Choi E. E-health literacy in older adults: an evolutionary concept analysis. BMC Med Inform Decis Mak. 2022;22(1):28. [FREE Full text] [CrossRef] [Medline]
    92. El Benny ME, Kabakian-Khasholian T, El-Jardali F, Bardus M. Application of the eHealth literacy model in digital health interventions: scoping review. J Med Internet Res. 2021;23(6):e23473. [FREE Full text] [CrossRef] [Medline]
    93. Zhang C, Tang D, Wang Y, Jiang S, Liu X. Community support and promoting cognitive function for the elderly. Front Psychol. 2022;13:942474. [FREE Full text] [CrossRef] [Medline]
    94. Li B, Guo Y, Deng Y, Zhao S, Li C, Yang J, et al. et al. Association of social support with cognition among older adults in China: a cross-sectional study. Front Public Health. 2022;10:947225. [FREE Full text] [CrossRef] [Medline]
    95. Wang Y, Li J, Fu P, Jing Z, Zhao D, Zhou C. Social support and subsequent cognitive frailty during a 1-year follow-up of older people: the mediating role of psychological distress. BMC Geriatr. 2022;22(1):162. [FREE Full text] [CrossRef] [Medline]
    96. Tariq A, Beihai T, Abbas N, Ali S, Yao W, Imran M. Role of perceived social support on the association between physical disability and symptoms of depression in senior citizens of Pakistan. Int J Environ Res Public Health. 2020;17(5):1485. [FREE Full text] [CrossRef] [Medline]
    97. Kwan RYC, Ng F, Lai M, Wong D, Chan S. The effects of Digital Buddy programme on older adults' mental well-being: study protocol for a multi-centre, cluster randomized controlled trial. Trials. 2023;24(1):95. [FREE Full text] [CrossRef] [Medline]
    98. Zhong R, Ning W. Impact of living arrangements and internet use on the mental health of Chinese older adults. Front Public Health. 2024;12:1395181. [FREE Full text] [CrossRef] [Medline]
    99. Silva P, Delerue Matos A, Martinez-Pecino R. The contribution of the Internet to reducing social isolation in individuals aged 50 years and older: quantitative study of data from the Survey of Health, Ageing and Retirement in Europe. J Med Internet Res. 2022;24(1):e20466. [FREE Full text] [CrossRef] [Medline]

    ‎
    C-eHEALS: Chinese version of the eHealth Literacy Scale
    DAG: directed acyclic graph
    DHL: digital health literacy
    eHEALS: eHealth Literacy Scale
    MSPSS: Multidimensional Scale of Perceived Social Support
    PSSS-3: 3-item short version of the Perceived Social Support Scale
    VIF: variance inflation factor

    Edited by S Brini; submitted 11.Sep.2025; peer-reviewed by E Calatayud, S Dong; comments to author 17.Nov.2025; accepted 24.Dec.2025; published 20.Jan.2026.

    Copyright

    ©Yongqi Du, Qing Niu, Gangrui Tan, Jianqian Chao, Shengxuan Jin, Leixia Wang. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 20.Jan.2026.

    This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research (ISSN 1438-8871), is properly cited. The complete bibliographic information, a link to the original publication on https://www.jmir.org/, as well as this copyright and license information must be included.