The association between Internet use and cognitive function among older Chinese adults: a moderated mediation model

The association between Internet use and cognitive function among older Chinese adults: a moderated mediation model | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article The association between Internet use and cognitive function among older Chinese adults: a moderated mediation model Kaiyang Jia, Siyao Song, Bowen Hou This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7069482/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: Previous studies have found a significant link between internet use and cognitive function in older adults. However, the underlying mechanisms of this relationship have not been thoroughly explored. This study aims to investigate the mediating role of social and family-level interactions in the relationship between internet use and cognitive function in older adults, as well as how age moderates this relationship. Methods: The data for this study were derived from the China Health and Retirement Longitudinal Study (CHARLS, N = 7090) organized by the National School of Development at Peking University in 2020. We first examined a simple mediation model in which social participation served as a mediator between internet use and cognitive function. Additionally, age was systematically incorporated as a moderator in the model. Moderated mediation analysis was conducted using the PROCESS macro developed by Hayes. Results: Based on the mediation analysis results, the relationship between internet usage and cognitive function is partially mediated by societal-level interaction and family-level interaction. Specifically, internet usage significantly influences societal-level interaction (B = 0.947, 95% CI = [0.674, 0.892]), and societal-level interaction significantly influences cognitive function (B = 0.196, 95% CI = [0.134, 0.258]). In contrast, internet usage significantly negatively influences family-level interaction (B = -1.880, 95% CI = [-2.619, -1.143]), and family-level interaction significantly influences cognitive function (B = -0.016, 95% CI = [-0.025, -0.007]). Moderator mediation analysis reveals that age not only significantly moderates the direct effect path of internet usage on cognitive function (B = 0.66, 95% CI = [0.07, 1.24]) but also moderates the path through which family-level interaction influences cognitive function (B = 0.12, 95% CI = [0.02, 0.23]). Conclusion: This study provides new insights into the mechanisms linking internet use, social and family interactions, and cognitive function, thereby contributing to existing research on cognitive health. The findings offer valuable references for developing interventions aimed at improving cognitive health in older adults. Internet use Cognitive function Social interaction Chinese elderly Figures Figure 1 Figure 2 Background Global aging has emerged as an increasingly pressing issue. According to the World Health Organization, as of 2023, the global population aged 60 and above exceeds 1 billion, and by 2050, this number is projected to rise to 2.1 billion, accounting for 22% of the global population [ 1 ] . The number of people aged 80 and above will also see a dramatic increase, reaching an estimated 426 million by 2050 [ 2 ] . This rapid demographic shift presents significant challenges for countries worldwide in addressing the aging population. Equally concerning is the cognitive health of the elderly. Currently, over 55 million people worldwide suffer from dementia, with approximately 10 million new cases each year, translating to a new case every 3.2 seconds [ 3 ] . The economic implications of cognitive decline are staggering. According to Alzheimer's Disease International, the global economic cost of dementia has already surpassed $ 1.3 trillion and is expected to rise to $ 2.8 trillion by 2030 [ 4 ] . In conclusion, the acceleration of aging, coupled with the high prevalence of dementia and its rising economic burden, has made the cognitive health of the elderly a critical global public health challenge. As the prevalence of dementia continues to rise, the urgent need to address cognitive health issues has brought academic attention to the factors influencing cognitive function in older adults.Current studies indicate that the preservation and deterioration of cognitive function are mainly affected by three key factors: physical health, mental health, and lifestyle choices.First, regarding physiological health, adults suffering from cardiovascular diseases and heart failure have an increased risk of cognitive decline, whereas higher levels of cardiovascular fitness show protective effects against brain atrophy, reduced cerebral blood flow, and cognitive decline [ 5 ] .One cross-sectional study revealed a significant correlation between oral health and cognitive function in individuals over the age of 60 [ 6 ] .In terms of psychological health, emotional issues like depression and anxiety in older adults can elevate the risk of Alzheimer's disease [ 7 ] . Research has further indicated that loneliness has a negative impact on cognitive function in the elderly, with depressive symptoms acting as a partial mediator of this effect [ 8 ] .Regarding lifestyle, healthy dietary patterns, such as the Mediterranean diet or the The Mediterranean-DASH Diet Intervention for Neurodegenerative Delay (MIND) diet, have been proven to correlate with better cognitive function, helping to reduce the risk of cognitive decline and dementia [ 9 ] . Moreover, aerobic exercise has been shown to have a notably positive impact on global cognitive function in elderly individuals with mild cognitive impairment [ 10 ] . As the internet and smartphones become more prevalent, scholars are increasingly investigating whether internet use could influence cognitive function in the elderly.A longitudinal study in China indicates that elderly internet use is linked to a reduced risk of mild cognitive impairment, with the internet potentially preventing cognitive decline by influencing the volume of the globus pallidus [ 11 ] .Scholars used cross-lagged panel analysis to investigate European seniors, and the data showed significant cross-lagged effects of internet use on cognitive function in elderly people of all age groups [ 12 ] .Besides internet use, Yifan Nie and others discovered through their study that elderly individuals with larger social networks and frequent social activity outperformed their less socially active peers in cognitive function assessments [ 13 ] .Researchers focusing on birth cohorts have also observed that internet use positively impacts cognitive function in older adults, particularly those who live alone, while ceasing internet use may hasten cognitive decline, especially in earlier-born elderly individuals [ 14 ] . Another crucial determinant of cognitive function in the elderly is social interaction. Current studies indicate a positive association between social interaction and cognitive function, with even a small amount of social engagement significantly boosting cognitive function [ 15 ] . Moreover, researchers from South Korea explored the link between social interaction and cognitive function during the Coronavirus Disease 2019 (COVID-19) pandemic and found that cognitive decline in elderly individuals was associated with reduced social interaction due to social distancing measures [ 16 ] . Research conducted in the United States during the COVID-19 pandemic has also confirmed this viewpoint [ 17 ] . Additionally, a separate study highlighted that social interaction greatly influences cognitive abilities in older adults, with increased social interaction playing a role in dementia prevention [ 18 ] . While the majority of current literature supports the notion that social interaction influences cognitive function in the elderly, the mediating effect of social interaction on the relationship between internet use and cognitive function is still unclear. Cognitive aging describes the process by which an individual's cognitive functions decline progressively with advancing age. One could argue that the influence of aging on cognitive function poses a significant challenge for modern societal development [ 19 ] . From existing literature, numerous researchers have concentrated on combating the normal age-associated cognitive decline [ 20 ],[ 21 ] , with efforts to discover the critical factors that can mitigate cognitive deterioration. In summary, the increase in age has been proven to have a strong negative correlation with cognitive decline [ 22 ],[ 23 ] . Consequently, age may act as a potential moderating factor in the relationship between the internet’s role and the barriers to social engagement and activity. China is facing a period of rapid aging, with the population of those aged 65 and older set to rise from 172 million in 2020 to 366 million by 2050, and the proportion of the elderly increasing from 12.0–26.0% [ 24 ] . Although China is experiencing intensified aging, the percentage of elderly internet users has been steadily increasing, rising from 11.3% in 2020 to 14.3% by 2024 [ 25 ],[ 26 ] . China now holds the highest number of dementia patients globally [ 27 ] , with Alzheimer's disease and other dementias (ADOD) being the fifth leading cause of death in the nation [ 28 ] . In this context of rapid aging and expanding internet penetration in China, it becomes crucial to examine how internet usage influences the cognitive functions of the elderly. Therefore, the aim of this study is to explore how internet use influences cognitive function in older adults through a moderated mediation model. We hypothesize that social interaction, as a mediating variable, and age, as a moderating factor, will together affect the relationship between internet use and cognitive function in the elderly. This study will provide new evidence to elucidate the complex interaction between internet use, social interaction, and cognitive function, offering potential pathways for interventions to improve cognitive health in older adults. To be more specific, we proposed the hypotheses as follows: Hypothesis 1 Whether or not older people use the Internet can affect their own cognitive function. Hypothesis 2 Internet use has a specific, indirect effect on cognitive function through social interaction. Hypothesis 3 Age has a specific moderating effect on the path between social interaction and cognitive function. Methods Data and sampling The data for this study are sourced from the China Health and Retirement Longitudinal Study (CHARLS), organized by the National School of Development at Peking University. This survey targets middle-aged and older adults in China aged 45 and above. The data used in this research are drawn from the fifth wave of the CHARLS database, collected in 2020. The survey was conducted across 28 provinces (autonomous regions and municipalities), covering 150 counties and 450 communities (villages), with follow-up visits completed nationwide by 2018. At that time, the sample encompassed a total of 19,000 respondents from 12,400 households. The survey includes comprehensive information on various aspects of older adults’ lives, such as personal background, family structure, health status, employment situation, income and expenditure, and assets, providing a relatively complete set of variables related to internet use among older adults. This study aims to explore the potential impact of internet use on cognitive function in the elderly population. Considering that cognitive function may vary significantly across different age groups, to ensure consistency in the age of the study population, we excluded all respondents under the age of 60 from the dataset. After the age screening, we further processed missing data to ensure the integrity of the data and the reliability of the analytical results. Ultimately, after screening for age and addressing missing values, the final effective sample size for this study was 7090 individuals. Measurements Dependent variable The cognitive function of elderly individuals in China was assessed using the Minimum Mental State Examination (MMSE) from the CHARLS survey. In CHARLS, the MMSE consists of 30 questions assessing areas like daily memory, word recall, and arithmetic. It includes five questions designed to evaluate daily memory by inquiring about the correct recognition of the year, month, day, week, and season. Ten words are randomly read aloud, and respondents are asked to recall them to evaluate immediate memory. After a certain period, they are asked to recall the same words again to assess delayed memory. The participant is instructed to continuously subtract 7 from 100, five times, to evaluate arithmetic skills. For each correct response in the daily memory test, one point is given. In the immediate and delayed memory tests, each recalled word scores one point. In the arithmetic test, one point is awarded for each correct calculation. As a result, cognitive function scores range from 0 to 30, with higher scores indicating better cognitive function. Independent variable The independent variable in this study is internet usage, assessed by asking respondents whether they had used the internet in the past month, as part of the CHARLS survey. Mediator The mediating variable in this study is social interaction, which consists of both family-level and societal-level interaction. Family-level interaction is derived from questions in the CHARLS survey, such as "How often do you see your child when you do not live with them?" and "How often do you communicate with your child via phone, text message, WeChat, letter, or email when not living together?" The responses to these two questions represent the frequency of face-to-face interaction and communications, which are scored and aggregated based on their frequency. The range of this variable is 0–18, with higher values indicating better family-level interaction.The societal-level interaction variable is drawn from the CHARLS questionnaire, asking "Have you participated in any of the following social activities in the past month?" The answer options include: "Visiting or socializing with friends"; "Playing Mahjong, chess, cards, or going to community activity centers"; "Helping relatives, friends, or neighbors who do not live with you"; "Dancing, exercising, or practicing Qigong"; "Participating in community organization activities"; "Volunteering, charity work, or taking care of patients or disabled persons who do not live with you"; and "Attending school or training courses."This study calculates the number and frequency of respondents' social participation to derive a total score for societal-level interaction, which ranges from 0–21, with higher values indicating better societal-level interaction. Moderator In this study, the respondents' age was used as a moderating variable and was measured with a single question: "What is your date of birth?" The respondents' age was then calculated based on their responses. Covariates The regression model in this study controlled for the basic demographic and socioeconomic characteristics of the elderly, such as age, gender, education level, household registration type, marital status, number of children, chronic disease status, Activities of Daily Living (ADL), Instrumental Activities of Daily Living (IADL), and whether they have health insurance. The Cronbach's alpha values for ADL and IADL were 0.879 and 0.876, respectively. Validity analysis revealed KMO values of 0.889 for ADL and 0.796 for IADL, demonstrating good validity. Statistical analysis Data analysis was conducted using SPSS version 27.0 and Hayes' PROCESS macro [ 29 ] . Model 4 was employed to examine the mediating roles of family-level and social-level interactions in the relationship between internet use and cognitive function. Additionally, Model 15 was utilized to test whether age moderates the pathway through which internet use affects cognitive function. Descriptive statistics for the five key variables were presented as means and standard deviations (M ± SD), and Pearson correlation analysis was conducted to evaluate the linear relationships among these variables. To ensure robustness, the analysis applied 10,000 bootstrap resamples. For testing the moderating effect, particular attention was given to the moderation pathway of age in the relationship between internet use and cognitive function. A significance criterion of a 95% confidence interval (CI) was used, and a CI that does not include 0 indicates a significant moderating effect. All statistical tests were two-tailed, with a p-value of less than 0.05 considered statistically significant. Results Sociodemographic characteristics Table 1 shows based on the data collected from a total of 7090 participants, the average age of the respondents was 67.8 years, with a standard deviation of 5.9 years. Regarding gender distribution, the sample was almost equally split, with 3556 male participants (50.16%) and 3534 female participants (49.84%). In terms of education level, 1821 participants (25.68%) were categorized as illiterate, 3208 participants (45.25%) had completed primary school, 1270 participants (17.91%) had a junior high school education, and 791 participants (11.16%) had completed high school or higher education.Among the participants, 5714 individuals (80.59%) have a spouse, while 1,376 individuals (19.41%) do not have a spouse. Table 1 Demographic characteristics of all elderly(N = 7090) Variables Mean SD N (Valid%) Gender Male 3556(50.16%) Female 3534(49.84%) Education level Illiterate 1821(25.68%) Primary school 3208(45.25%) Junior high school 1270(17.91%) High school or above 791(11.16%) Marital status Married 5714(80.59%) Unmarried 1376(19.41%) Age 67.8 5.9 Cognitive function 14.0 5.9 Internet usage 0.2 0.4 Societal-level interactions 10.4 2.7 Family-level interaction 21.6 12.8 Correlations among study variables Table 2 presents the bivariate correlations. Results indicated a significant positive correlation between internet use and cognitive function (r = 0.339, p < 0.01), suggesting that more frequent internet use is associated with higher cognitive performance. The two hypothesized mediators—family-level interaction and social-level interaction—exhibited correlations in opposite directions. Specifically, social-level interaction was positively correlated with both cognitive function (r = 0.154, p < 0.01) and internet use (r = 0.211, p < 0.01), suggesting a potential positive mediating role in the relationship between internet use and cognition. In contrast, family-level interaction showed significant negative correlations with cognitive function (r = -0.132, p < 0.01) and internet use (r = -0.126, p 0.05), suggesting that they may represent distinct dimensions of social interaction. Age, serving as a moderating variable, was significantly negatively correlated with cognitive function (r = -0.159, p < 0.01) and internet use (r = -0.185, p < 0.01), indicating that increased age may be associated with declines in both cognition and internet use frequency. Moreover, age was moderately positively correlated with family-level interaction (r = 0.272, p 0.05), further highlighting potential differences between interaction dimensions among older adults. Table 2 Descriptive statistics and correlations among the key variables Variables 1 2 3 4 5 1 Cognitive function 1 2 Internet usage 0.339 ** 1 3 Family-level interaction -0.132 ** -0.126 ** 1 4 Societal-level interaction 0.154 ** 0.211 ** -0.22 1 5 Age -0.159 ** -0.185 ** 0.272 ** -0.021 1 Note: ** p < 0.01, * p < 0.05 Mediation analysis Table 3 presents the results of the mediation analysis. First, Hypothesis 1 was supported. The direct effect of internet use on cognitive function was significant (B = 2.034, p < 0.001). This indicates that internet use among older adults significantly influences their cognitive functioning. As shown in the results, internet use can directly enhance cognitive performance in later life, thus confirming Hypothesis 1 of this study. Second, Hypothesis 2 was also supported. Internet use had a significant effect on social-level interaction (B = 0.947, p < 0.001), and social-level interaction significantly affected cognitive function (B = 0.196, p < 0.001). This suggests that internet use may indirectly enhance cognitive function by promoting social-level interaction. Notably, internet use also had a significant but negative effect on family-level interaction (B = -1.880, p < 0.001); likewise, family-level interaction had a significant and negative effect on cognitive function (B = -0.016, p < 0.001). These findings indicate that internet use may also indirectly exert a negative impact on cognitive function by reducing family-level interaction. Taken together, the results presented in Table 3 confirm both hypotheses of this study. Internet use not only directly affects cognitive function in older adults, but also exerts a positive indirect effect through social-level interaction and a negative indirect effect through family-level interaction. Table 3 The results of the mediating analysis Model summary B SE t p 95% CI X → Y 2.034 0.147 13.831 < 0.001 [1.746, 2.322] X → M1 0.947 0.068 14.127 < 0.001 [0.674, 0.892] M1 → Y 0.196 0.031 6.233 < 0.001 [0.134, 0.258] X → M2 -1.880 0.377 -4.990 < 0.001 [-2.619, -1.143] M2 → Y -0.016 0.005 -3.436 < 0.001 [-0.025, -0.007] X: Independent variable (Internet usage); M1: Mediator1 (Societal-level interaction);M2: Mediator2 (Family-level interaction); Y: Dependent variable (Cognitive function) Moderated mediation analysis The present study employed Model 15 from the SPSS macro to examine whether age moderates the latter part of the mediation pathway—namely, the effects of the two mediators on the dependent variable—as well as the direct effect of the independent variable on the dependent variable. The results are summarized in Table 4 . First, the direct effect of internet use on cognitive function was significantly positive (B = 1.79, p < 0.001), and the interaction term between internet use and age was also significant (B = 0.66, p < 0.05), indicating that age significantly moderated the direct path from internet use to cognitive function. Specifically, for each one-unit increase in age, the direct effect increased by an average of 0.66 units, suggesting a stronger effect of internet use on cognition among older individuals. Second, in the first half of the mediation pathway, internet use exerted significant effects on both mediators. Specifically, internet use significantly positively predicted social-level interaction (B = 0.78, p < 0.001) and significantly negatively predicted family-level interaction (B = -1.88, p < 0.001). In the latter half of the mediation pathway, social-level interaction had a significant effect on cognitive function (B = 0.19, p < 0.001), and its interaction with age was also significant (B = 0.12, p < 0.05), indicating that age moderated the effect of social-level interaction on cognitive function. Specifically, with each one-unit increase in age, the predictive effect of social-level interaction on cognition increased by an average of 0.12 units, indicating a stronger effect among older individuals. These findings provide support for Hypothesis 3 . In contrast, family-level interaction did not significantly predict cognitive function (B = -0.07, p > 0.05), and its interaction with age was also nonsignificant (B = 0.01, p > 0.05), suggesting that age did not moderate the effect of family-level interaction on cognition. In sum, the model results demonstrate that age significantly moderates both the direct pathway from internet use to cognitive function and the indirect pathway through social-level interaction. The direction of these effects indicates that the positive impacts of internet use and social engagement on cognition become stronger with increasing age, reflecting a quantifiable relationship between age and path strength. In contrast, age did not significantly moderate the pathway from family-level interaction to cognitive function, suggesting that the effect of this pathway remains relatively stable across different age groups. Table 4 Results of the moderated mediation analysis Dependent variable: Societal-level interaction Dependent variable: Family-level interaction Dependent variable: Cognitive function B t 95% CI B t 95% CI B t 95% CI X 0.78 14.12 *** [0.67 ,0.89] -1.88 -4.99 *** [-2.62 ,-1.14 ] 1.79 11.44 *** [1.48, 2.10] Z - - - - - - -0.80 -6.99 *** [-1.03,-0.58] M1 - - - - - - 0.19 6.33 *** [0.14, 0.26] M2 - - - - - - -0.07 -1.53 [-0.01, 0.02] X×Z - - - - - - 0.66 2.20 * [0.07, 1.24] M1×Z - - - - - - 0.12 2.26 * [0.02, 0.23] M2×Z - - - - - - 0.01 1.65 [-0.02, 0.03] X: Independent variable (Internet usage); M1: Mediator1 (Societal-level interaction);M2: Mediator2 (Family-level interaction); Z:Moderator(Age ) Y: Dependent variable (Cognitive function) To further explore the moderating role of age in the relationship between internet use and cognitive function, as well as between social interaction and cognitive function, the present study conducted simple slope analyses across different age groups. The results are illustrated in Figs. 1 and 2.The analysis revealed age-related variation in the direct path: the predictive effect of internet use on cognitive function was relatively weak among younger-old adults (M–1SD), whereas the effect was significantly stronger among older-old adults (M + 1SD). A similar pattern was observed in the latter half of the mediation pathway. Compared with younger-old adults, social-level interaction exhibited greater predictive power for cognitive function among older-old individuals (M + 1SD). Discussion Drawing on data on cognitive function among older adults from the China Health and Retirement Longitudinal Study (CHARLS), this study provides a more nuanced understanding of the relationship between internet use and cognitive function in later life in China. The results indicate a significant positive association between internet use and cognitive function, suggesting that older adults who use the internet tend to exhibit better cognitive performance. This finding supports the first hypothesis of the study.In addition, the analysis revealed significant mediating effects of both social-level and family-level interaction in the relationship between internet use and cognitive function. However, the direction of these mediation effects differed markedly: social-level interaction played a positive role by enhancing cognitive function, whereas family-level interaction exhibited a negative mediation effect, suggesting that internet use may adversely affect cognition by weakening family-based interactions. These findings jointly support the second hypothesis of the study.Finally, the study found that age significantly moderated the latter half of the mediation pathway through social-level interaction. Specifically, the positive impact of social-level interaction on cognitive function became more pronounced with increasing age, indicating that this pathway is particularly salient among older segments of the aging population. This result lends support to the third hypothesis The present study found that internet use significantly positively predicted cognitive function among older adults. In other words, compared with nonusers, older adults who used the internet exhibited better cognitive performance, a finding consistent with previous research [ 11 , 30 ] . In fact, internet use involves a range of cognitively demanding activities, such as information searching, online learning, digital tool operation, and multitasking. These activities require older adults to engage attention, memory, executive function, and problem-solving abilities, thereby providing sustained "cognitive exercise" for the brain.The influence of internet use on cognitive function is attributed to the complex cognitive activities involved in using search engines, such as information filtering, decision-making, and problem-solving, which activate broader brain regions than traditional reading tasks. Particularly, areas such as the frontal lobes, cingulate gyrus, and hippocampus—responsible for complex reasoning, decision-making, and memory integration—are notably engaged. Research has shown that older adults with extensive internet experience activate more than twice the brain regions during search tasks compared to those with less experience, suggesting that internet searching is not only a highly cognitively stimulating activity but may also enhance the plasticity and efficiency of neural circuits through long-term use [ 31 ] .Compared with passive information reception, internet use offers a richer sensory experience and a higher level of cognitive engagement, thereby improving cognitive function in older adults. Thus, it is important to encourage older adults to gradually learn and adapt to internet technologies and to become familiar with internet-based products to better integrate into the digital era. Enterprises should be encouraged to develop age-friendly internet products and to establish a comprehensive system of standards for the age-appropriate design of smart devices to improve product quality and usability. At the governmental level, policies and regulations should be formulated to promote research investment and development efforts focused on enhancing the accessibility and usability of internet technologies for older populations. In exploring the potential mechanisms underlying the relationship between internet use and cognitive function among older adults, our findings suggest that internet use can indirectly enhance cognitive function through increased social interaction. This conclusion also supports the “internet benefits theory”. On one hand, the internet provides users with tools for autonomy and communication, enabling them to overcome physical and social barriers and independently manage interpersonal interactions [ 32 ] . On the other hand, older adults who frequently use the internet are better able to maintain relationships with family and friends, increasing both the frequency and quality of communication [ 33 ] .Moreover, perceived social isolation has been shown to heighten vigilance toward social threats, impair executive function and self-regulation abilities,and chronically activate the hypothalamic-pituitary-adrenal axis and inflammatory responses, thereby damaging brain regions associated with cognitive processing and ultimately leading to cognitive decline [ 34 ] . Accordingly, positive social interaction, as a protective factor against loneliness, may help reduce hypersensitivity to social threats, alleviate physiological stress responses, and enhance the adaptability and plasticity of the nervous system. Together, these processes promote the maintenance and improvement of cognitive function at both neurological and psychological levels among older adults.Thus, integrating these multi-level pathways, it can be concluded that the mechanism by which internet use enhances cognitive function through promoting social interaction has a clear physiological foundation. In exploring potential mechanisms, our findings also suggest that internet use may exert a negative indirect effect on cognitive function by reducing family-level interactions. Prior studies have confirmed that increased time spent online is significantly associated with a decline in communication among family members [ 35 ] . Given that family interaction and the overall family atmosphere can meaningfully influence the cognitive functioning of older adults [ 36 ] , it is plausible that increased internet use may, to some extent, undermine cognitive performance through the weakening of familial engagement. These findings indicate that the influence of internet use on cognitive function in older adults operates through dual pathways: on the one hand, it enhances cognition by promoting social-level interaction; on the other hand, it may exert a negative effect by weakening family interaction. Therefore, while encouraging appropriate internet use among older adults, it is essential to guide them toward greater social engagement and the expansion of external connections, while also emphasizing the maintenance of familial bonds. Such a balanced approach can help prevent emotional estrangement due to technology use and ultimately optimize the overall cognitive benefits of internet engagement. This study further revealed that the direct effect of internet use on cognitive function is significantly moderated by age. This phenomenon can be interpreted from multiple perspectives. First, activity theory posits that individuals maintain their sense of identity and social roles through engagement in social activities, thereby promoting mental and physical well-being. According to this theory, the continuity and substitution of social participation are among the key mechanisms for successful aging. The moderating effect of age in the pathway linking internet use and cognitive function can be understood through this lens. Compared with middle-aged or younger individuals, older adults often face diminished social roles and reduced participation in social activities after retirement, resulting in fewer sources of social engagement and cognitive stimulation. Internet use—particularly for communication, information access, and entertainment—offers older adults novel avenues for social participation and cognitive engagement, functioning as a substitute for traditional activities. This, in turn, is more likely to activate cognitive resources and delay cognitive decline. In short, as age increases and conventional forms of activity decrease, the “cognitive activation” and “social connectivity” benefits of internet use become more pronounced for older adults, thus amplifying the positive effects.In addition, research on neural plasticity and aging supports this interpretation. Although neural plasticity tends to decline with age, older adults retain the capacity to activate underutilized neural networks through learning new skills or engaging with novel technologies such as the internet [ 37 ] . Internet use may serve as a form of “cognitive training,” particularly given its demands on memory, attention, and logical reasoning. These cognitive processes are especially beneficial for older adults, making the positive impact of such engagement more salient in advanced age. In addition to moderating the direct pathway from internet use to cognitive function, age also significantly moderates the effect of social interaction on cognitive function among older adults. According to Stern’s (2002) theory of cognitive reserve, individuals differ in how they manifest cognitive symptoms in the face of brain pathology, depending on their level of cognitive reserve [ 38 ] . Older adults of advanced age typically possess lower cognitive reserve and are thus more vulnerable to age-related neural decline. As a result, social interaction facilitated by internet use—an external source of cognitive stimulation—serves a stronger compensatory function for them. In contrast, younger older adults, who tend to have higher cognitive reserve and more stable cognitive function, are less reliant on external stimulation, and consequently, interventions have a weaker effect.Moreover, the Scaffolding Theory of Aging and Cognition suggests that older adults with lower cognitive reserve rely more heavily on compensatory scaffolding mechanisms, particularly involving the prefrontal cortex, to cope with neural decline [ 37 ] . Through enhanced social engagement, internet use provides cognitive stimulation that activates these scaffolds, thereby improving cognitive performance in high-age older adults. In comparison, younger older adults with greater cognitive reserve and more stable neural function depend less on such scaffolds, and thus experience a weaker cognitive benefit from internet use. Therefore, age moderates the cognitive effects of internet use through differences in cognitive reserve, with older seniors gaining greater benefits. Conclusion This study found that internet use not only directly affects the cognitive function of older adults but also has significant indirect effects through interactions at both the social and family levels. Specifically, internet use facilitates social and family interactions, which in turn enhances cognitive function. Additionally, age plays an important moderating role in this mediation pathway. Notably, the interaction term for social-level interaction significantly moderates the effect on cognitive function, indicating that as age increases, the positive effect of social interaction on cognitive function gradually strengthens. Furthermore, the direct effect of internet use on cognitive function also increases with age, suggesting that older seniors experience more significant cognitive improvements from using the internet. Abbreviations MIND The Mediterranean-DASH Diet Intervention for Neurodegenerative Delay COVID-19 Coronavirus Disease 2019 ADOD Alzheimer's disease and other dementias CHARLS China Health and Retirement Longitudinal Study MMSE Minimum Mental State Examination ADL Activities of Daily Living IADL Instrumental Activities of Daily Living Declarations Ethics approval and consent to participants All participants gave informed written consent and the ethical approval was obtained at the Peking University Institutional Review Board (IRB00001052-11015).The study methodology was carried out following the guidelines of the Declaration of Helsinki. Consent for publication Not applicable. Availability of data and materials The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request. Competing interests The authors declare that they have no competing interests. Funding Not applicable. Clinical Trial Number Not applicable. Authors’ contributions BWH conceived the study, led the research design, collected the data, and critically reviewed the manuscript.SYS contributed to data collection and drafted th e manuscript. KYJ was responsible for data analysis, manuscript writing, and overall manuscript review. All authors have made substantial contributions to the study and approved the final version of the manuscript. Author details 1 Department of Sociology, School of Humanities and Social Sciences, Harbin Engineering University, Harbin, Heilongjiang Province, China. Acknowledgements The authors thank all participants who collaborated in this study.The dataused in this study are from China Health and Retirement Longitudinal Study. References World Health Organization. The global strategy and action plan on ageing and health 2016–2020: Towards a world in which everyone can live a long and healthy life. Geneva: World Health Organization; 2020. World Economic Forum. 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Deary IJ, Corley J, Gow AJ, Harris SE, Houlihan LM, Marioni RE, et al. Age-associated cognitive decline. Br Med Bull. 2009;92:135–52. 10.1093/bmb/ldp033 . Zheng H, Cagney K, Choi Y. Predictors of cognitive functioning trajectories among older Americans: a new investigation covering 20 years of age- and non-age-related cognitive change. PLoS ONE. 2023;18(2):e0281139. 10.1371/journal.pone.0281139 . Feng Z, Glinskaya E, Chen H, Liu Z, Liu Y, Wang L, et al. Long-term care system for older adults in China: policy landscape, challenges, and future prospects. Lancet. 2020;396(10259):1362–72. 10.1016/S0140-6736(20)32136-X . China Internet Network Information Center. The 54th statistical report on China's Internet development. https://www.cnnic.cn . Accessed 8 May 2025. China Internet Network Information Center. The 50th statistical report on China's Internet development. https://www.cnnic.cn . Accessed 8 May 2025. Jia J, Qin Y, Wang F, Qiao Y, Shi S, Wang YJ, et al. Group for the Project of Dementia Situation in China. Dementia in China: epidemiology, clinical management, and research advances. Lancet Neurol. 2020;19(1):81–92. 10.1016/S1474-4422(19)30290-X . Liu Z, Shi D, Cai Y, Zhang T, Song X, Guo Q, et al. Pathophysiology characterization of Alzheimer’s disease in South China’s aging population: for the Greater-Bay-Area Healthy Aging Brain Study (GHABS). Alzheimers Res Ther. 2024;16:84. 10.1186/s13195-024-01458-z . ayes AF, Montoya AK, Rockwood NJ. The analysis of mechanisms and their contingencies: PROCESS versus structural equation modeling. Australas Mark J. 2017;25(1):76–81. 10.1016/j.ausmj.2017.02.001 . Berner J, Comijs H, Elmstahl S, Welmer AK, Sanmartin Berglund J, Anderberg P, et al. Maintaining cognitive function with internet use: a two-country, six-year longitudinal study. Int Psychogeriatr. 2019;31(7):929–36. 10.1017/S1041610219000668 . Small GW, Moody TD, Siddarth P, Bookheimer SY. Your brain on Google: patterns of cerebral activation during internet searching. Am J Geriatr Psychiatry. 2009;17(2):116–26. 10.1097/JGP.0b013e3181953a02 . Amichai-Hamburger Y, McKenna KY, Tal SA. E-empowerment: empowerment by the Internet. Comput Hum Behav. 2008;24(5):1776–89. 10.1016/j.chb.2008.02.002 . Cotten SR, Anderson WA, McCullough BM. Impact of internet use on loneliness and contact with others among older adults: cross-sectional analysis. J Med Internet Res. 2013;15(2):e39. 10.2196/jmir.2306 . Cacioppo JT, Hawkley LC. Perceived social isolation and cognition. Trends Cogn Sci. 2009;13(10):447–54. 10.1016/j.tics.2009.06.005 . Kraut R, Patterson M, Lundmark V, Kiesler S, Mukopadhyay T, Scherlis W. Internet paradox: a social technology that reduces social involvement and psychological well-being? Am Psychol. 1998;53(9):1017–31. 10.1037/0003-066X.53.9.1017 . Xiao C, Mao S, Jia S, Lu N. Research on family relationship and cognitive function among older Hispanic Americans: empirical evidence from the Health and Retirement Study. Hisp J Behav Sci. 2021;43(1–2):95–113. 10.1177/07399863211025419 . Park DC, Reuter-Lorenz P. The adaptive brain: aging and neurocognitive scaffolding. Annu Rev Psychol. 2009;60:173–96. 10.1146/annurev.psych.59 . 103006.093656. Stern Y. What is cognitive reserve? Theory and research application of the reserve concept. J Int Neuropsychol Soc. 2002;8(3):448–60. 10.1017/S1355617702813248 . Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7069482","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":508763862,"identity":"03a67939-de03-4d09-9e05-2ddc4e820913","order_by":0,"name":"Kaiyang Jia","email":"","orcid":"","institution":"Harbin Engineering University","correspondingAuthor":false,"prefix":"","firstName":"Kaiyang","middleName":"","lastName":"Jia","suffix":""},{"id":508763863,"identity":"200f7f7b-03e0-4d41-8b59-d70d4cce315c","order_by":1,"name":"Siyao Song","email":"","orcid":"","institution":"Harbin Engineering University","correspondingAuthor":false,"prefix":"","firstName":"Siyao","middleName":"","lastName":"Song","suffix":""},{"id":508763864,"identity":"dd80f9ba-57f1-49dc-8a41-aa1b562d0aca","order_by":2,"name":"Bowen Hou","email":"data:image/png;base64,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","orcid":"","institution":"Harbin Engineering University","correspondingAuthor":true,"prefix":"","firstName":"Bowen","middleName":"","lastName":"Hou","suffix":""}],"badges":[],"createdAt":"2025-07-08 01:53:05","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7069482/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7069482/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":90422577,"identity":"acb8e7d6-8f65-43e6-b9e1-3402b2da79c7","added_by":"auto","created_at":"2025-09-02 14:12:11","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":116953,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe moderating effect of age on the impact of internet use on cognitive function\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7069482/v1/a97266ff6d8fc84a6db562f9.jpeg"},{"id":90421561,"identity":"cb43ef15-e1fd-4beb-8f6f-cc8cbc134063","added_by":"auto","created_at":"2025-09-02 14:04:11","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":132015,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe moderating effect of age on the impact of societal-level interactions on cognitive function\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7069482/v1/31955e4bbea821deacb9e65a.jpeg"},{"id":91630742,"identity":"01322e36-1d13-443c-9de8-659d12880eb2","added_by":"auto","created_at":"2025-09-18 13:01:16","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1217126,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7069482/v1/43d64458-95e2-44b3-9363-f539321d8bf8.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"The association between Internet use and cognitive function among older Chinese adults: a moderated mediation model","fulltext":[{"header":"Background","content":"\u003cp\u003eGlobal aging has emerged as an increasingly pressing issue. According to the World Health Organization, as of 2023, the global population aged 60 and above exceeds 1\u0026nbsp;billion, and by 2050, this number is projected to rise to 2.1\u0026nbsp;billion, accounting for 22% of the global population\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e. The number of people aged 80 and above will also see a dramatic increase, reaching an estimated 426\u0026nbsp;million by 2050\u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e. This rapid demographic shift presents significant challenges for countries worldwide in addressing the aging population. Equally concerning is the cognitive health of the elderly. Currently, over 55\u0026nbsp;million people worldwide suffer from dementia, with approximately 10\u0026nbsp;million new cases each year, translating to a new case every 3.2 seconds\u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e. The economic implications of cognitive decline are staggering. According to Alzheimer's Disease International, the global economic cost of dementia has already surpassed \u003cspan\u003e$\u003c/span\u003e1.3 trillion and is expected to rise to \u003cspan\u003e$\u003c/span\u003e2.8 trillion by 2030\u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e. In conclusion, the acceleration of aging, coupled with the high prevalence of dementia and its rising economic burden, has made the cognitive health of the elderly a critical global public health challenge.\u003c/p\u003e\u003cp\u003eAs the prevalence of dementia continues to rise, the urgent need to address cognitive health issues has brought academic attention to the factors influencing cognitive function in older adults.Current studies indicate that the preservation and deterioration of cognitive function are mainly affected by three key factors: physical health, mental health, and lifestyle choices.First, regarding physiological health, adults suffering from cardiovascular diseases and heart failure have an increased risk of cognitive decline, whereas higher levels of cardiovascular fitness show protective effects against brain atrophy, reduced cerebral blood flow, and cognitive decline\u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e.One cross-sectional study revealed a significant correlation between oral health and cognitive function in individuals over the age of 60\u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e.In terms of psychological health, emotional issues like depression and anxiety in older adults can elevate the risk of Alzheimer's disease\u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e. Research has further indicated that loneliness has a negative impact on cognitive function in the elderly, with depressive symptoms acting as a partial mediator of this effect\u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e.Regarding lifestyle, healthy dietary patterns, such as the Mediterranean diet or the The Mediterranean-DASH Diet Intervention for Neurodegenerative Delay (MIND) diet, have been proven to correlate with better cognitive function, helping to reduce the risk of cognitive decline and dementia\u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e. Moreover, aerobic exercise has been shown to have a notably positive impact on global cognitive function in elderly individuals with mild cognitive impairment\u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eAs the internet and smartphones become more prevalent, scholars are increasingly investigating whether internet use could influence cognitive function in the elderly.A longitudinal study in China indicates that elderly internet use is linked to a reduced risk of mild cognitive impairment, with the internet potentially preventing cognitive decline by influencing the volume of the globus pallidus\u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e.Scholars used cross-lagged panel analysis to investigate European seniors, and the data showed significant cross-lagged effects of internet use on cognitive function in elderly people of all age groups\u003csup\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e.Besides internet use, Yifan Nie and others discovered through their study that elderly individuals with larger social networks and frequent social activity outperformed their less socially active peers in cognitive function assessments\u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e.Researchers focusing on birth cohorts have also observed that internet use positively impacts cognitive function in older adults, particularly those who live alone, while ceasing internet use may hasten cognitive decline, especially in earlier-born elderly individuals\u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eAnother crucial determinant of cognitive function in the elderly is social interaction. Current studies indicate a positive association between social interaction and cognitive function, with even a small amount of social engagement significantly boosting cognitive function\u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e. Moreover, researchers from South Korea explored the link between social interaction and cognitive function during the Coronavirus Disease 2019 (COVID-19) pandemic and found that cognitive decline in elderly individuals was associated with reduced social interaction due to social distancing measures\u003csup\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e. Research conducted in the United States during the COVID-19 pandemic has also confirmed this viewpoint\u003csup\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e. Additionally, a separate study highlighted that social interaction greatly influences cognitive abilities in older adults, with increased social interaction playing a role in dementia prevention\u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e. While the majority of current literature supports the notion that social interaction influences cognitive function in the elderly, the mediating effect of social interaction on the relationship between internet use and cognitive function is still unclear.\u003c/p\u003e\u003cp\u003eCognitive aging describes the process by which an individual's cognitive functions decline progressively with advancing age. One could argue that the influence of aging on cognitive function poses a significant challenge for modern societal development\u003csup\u003e[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e. From existing literature, numerous researchers have concentrated on combating the normal age-associated cognitive decline\u003csup\u003e[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e],[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e, with efforts to discover the critical factors that can mitigate cognitive deterioration. In summary, the increase in age has been proven to have a strong negative correlation with cognitive decline\u003csup\u003e[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e],[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/sup\u003e. Consequently, age may act as a potential moderating factor in the relationship between the internet\u0026rsquo;s role and the barriers to social engagement and activity.\u003c/p\u003e\u003cp\u003eChina is facing a period of rapid aging, with the population of those aged 65 and older set to rise from 172\u0026nbsp;million in 2020 to 366\u0026nbsp;million by 2050, and the proportion of the elderly increasing from 12.0\u0026ndash;26.0%\u003csup\u003e[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/sup\u003e. Although China is experiencing intensified aging, the percentage of elderly internet users has been steadily increasing, rising from 11.3% in 2020 to 14.3% by 2024\u003csup\u003e[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e],[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/sup\u003e. China now holds the highest number of dementia patients globally\u003csup\u003e[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/sup\u003e, with Alzheimer's disease and other dementias (ADOD) being the fifth leading cause of death in the nation\u003csup\u003e[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]\u003c/sup\u003e. In this context of rapid aging and expanding internet penetration in China, it becomes crucial to examine how internet usage influences the cognitive functions of the elderly. Therefore, the aim of this study is to explore how internet use influences cognitive function in older adults through a moderated mediation model. We hypothesize that social interaction, as a mediating variable, and age, as a moderating factor, will together affect the relationship between internet use and cognitive function in the elderly. This study will provide new evidence to elucidate the complex interaction between internet use, social interaction, and cognitive function, offering potential pathways for interventions to improve cognitive health in older adults.\u003c/p\u003e\u003cp\u003eTo be more specific, we proposed the hypotheses as follows:\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eHypothesis 1\u003c/strong\u003e\u003cp\u003e\u003cem\u003eWhether or not older people use the Internet can affect their own cognitive function.\u003c/em\u003e\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eHypothesis 2\u003c/strong\u003e\u003cp\u003e\u003cem\u003eInternet use has a specific, indirect effect on cognitive function through social interaction.\u003c/em\u003e\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eHypothesis 3\u003c/strong\u003e\u003cp\u003e\u003cem\u003eAge has a specific moderating effect on the path between social interaction and cognitive function.\u003c/em\u003e\u003c/p\u003e\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cb\u003eData and sampling\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe data for this study are sourced from the China Health and Retirement Longitudinal Study (CHARLS), organized by the National School of Development at Peking University. This survey targets middle-aged and older adults in China aged 45 and above. The data used in this research are drawn from the fifth wave of the CHARLS database, collected in 2020. The survey was conducted across 28 provinces (autonomous regions and municipalities), covering 150 counties and 450 communities (villages), with follow-up visits completed nationwide by 2018. At that time, the sample encompassed a total of 19,000 respondents from 12,400 households. The survey includes comprehensive information on various aspects of older adults\u0026rsquo; lives, such as personal background, family structure, health status, employment situation, income and expenditure, and assets, providing a relatively complete set of variables related to internet use among older adults.\u003c/p\u003e\u003cp\u003eThis study aims to explore the potential impact of internet use on cognitive function in the elderly population. Considering that cognitive function may vary significantly across different age groups, to ensure consistency in the age of the study population, we excluded all respondents under the age of 60 from the dataset. After the age screening, we further processed missing data to ensure the integrity of the data and the reliability of the analytical results. Ultimately, after screening for age and addressing missing values, the final effective sample size for this study was 7090 individuals.\u003c/p\u003e\u003cp\u003e\u003cb\u003eMeasurements\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003eDependent variable\u003c/em\u003e\u003c/p\u003e\u003cp\u003eThe cognitive function of elderly individuals in China was assessed using the Minimum Mental State Examination (MMSE) from the CHARLS survey. In CHARLS, the MMSE consists of 30 questions assessing areas like daily memory, word recall, and arithmetic. It includes five questions designed to evaluate daily memory by inquiring about the correct recognition of the year, month, day, week, and season. Ten words are randomly read aloud, and respondents are asked to recall them to evaluate immediate memory. After a certain period, they are asked to recall the same words again to assess delayed memory. The participant is instructed to continuously subtract 7 from 100, five times, to evaluate arithmetic skills. For each correct response in the daily memory test, one point is given. In the immediate and delayed memory tests, each recalled word scores one point. In the arithmetic test, one point is awarded for each correct calculation. As a result, cognitive function scores range from 0 to 30, with higher scores indicating better cognitive function.\u003c/p\u003e\u003cp\u003e\u003cem\u003eIndependent variable\u003c/em\u003e\u003c/p\u003e\u003cp\u003eThe independent variable in this study is internet usage, assessed by asking respondents whether they had used the internet in the past month, as part of the CHARLS survey.\u003c/p\u003e\u003cp\u003e\u003cem\u003eMediator\u003c/em\u003e\u003c/p\u003e\u003cp\u003eThe mediating variable in this study is social interaction, which consists of both family-level and societal-level interaction. Family-level interaction is derived from questions in the CHARLS survey, such as \"How often do you see your child when you do not live with them?\" and \"How often do you communicate with your child via phone, text message, WeChat, letter, or email when not living together?\" The responses to these two questions represent the frequency of face-to-face interaction and communications, which are scored and aggregated based on their frequency. The range of this variable is 0\u0026ndash;18, with higher values indicating better family-level interaction.The societal-level interaction variable is drawn from the CHARLS questionnaire, asking \"Have you participated in any of the following social activities in the past month?\" The answer options include: \"Visiting or socializing with friends\"; \"Playing Mahjong, chess, cards, or going to community activity centers\"; \"Helping relatives, friends, or neighbors who do not live with you\"; \"Dancing, exercising, or practicing Qigong\"; \"Participating in community organization activities\"; \"Volunteering, charity work, or taking care of patients or disabled persons who do not live with you\"; and \"Attending school or training courses.\"This study calculates the number and frequency of respondents' social participation to derive a total score for societal-level interaction, which ranges from 0\u0026ndash;21, with higher values indicating better societal-level interaction.\u003c/p\u003e\u003cp\u003e\u003cem\u003eModerator\u003c/em\u003e\u003c/p\u003e\u003cp\u003eIn this study, the respondents' age was used as a moderating variable and was measured with a single question: \"What is your date of birth?\" The respondents' age was then calculated based on their responses.\u003c/p\u003e\u003cp\u003e\u003cem\u003eCovariates\u003c/em\u003e\u003c/p\u003e\u003cp\u003eThe regression model in this study controlled for the basic demographic and socioeconomic characteristics of the elderly, such as age, gender, education level, household registration type, marital status, number of children, chronic disease status, Activities of Daily Living (ADL), Instrumental Activities of Daily Living (IADL), and whether they have health insurance. The Cronbach's alpha values for ADL and IADL were 0.879 and 0.876, respectively. Validity analysis revealed KMO values of 0.889 for ADL and 0.796 for IADL, demonstrating good validity.\u003c/p\u003e\u003cdiv id=\"Sec2\" class=\"Section2\"\u003e\u003ch2\u003eStatistical analysis\u003c/h2\u003e\u003cp\u003eData analysis was conducted using SPSS version 27.0 and Hayes' PROCESS macro\u003csup\u003e[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]\u003c/sup\u003e. Model 4 was employed to examine the mediating roles of family-level and social-level interactions in the relationship between internet use and cognitive function. Additionally, Model 15 was utilized to test whether age moderates the pathway through which internet use affects cognitive function. Descriptive statistics for the five key variables were presented as means and standard deviations (M\u0026thinsp;\u0026plusmn;\u0026thinsp;SD), and Pearson correlation analysis was conducted to evaluate the linear relationships among these variables. To ensure robustness, the analysis applied 10,000 bootstrap resamples. For testing the moderating effect, particular attention was given to the moderation pathway of age in the relationship between internet use and cognitive function. A significance criterion of a 95% confidence interval (CI) was used, and a CI that does not include 0 indicates a significant moderating effect. All statistical tests were two-tailed, with a p-value of less than 0.05 considered statistically significant.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cem\u003eSociodemographic characteristics\u003c/em\u003e\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows based on the data collected from a total of 7090 participants, the average age of the respondents was 67.8 years, with a standard deviation of 5.9 years. Regarding gender distribution, the sample was almost equally split, with 3556 male participants (50.16%) and 3534 female participants (49.84%). In terms of education level, 1821 participants (25.68%) were categorized as illiterate, 3208 participants (45.25%) had completed primary school, 1270 participants (17.91%) had a junior high school education, and 791 participants (11.16%) had completed high school or higher education.Among the participants, 5714 individuals (80.59%) have a spouse, while 1,376 individuals (19.41%) do not have a spouse.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDemographic characteristics of all elderly(N\u0026thinsp;=\u0026thinsp;7090)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMean\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSD\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eN (Valid%)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGender\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3556(50.16%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3534(49.84%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEducation level\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIlliterate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1821(25.68%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePrimary school\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3208(45.25%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eJunior high school\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1270(17.91%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh school or above\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e791(11.16%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMarital status\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMarried\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e5714(80.59%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUnmarried\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1376(19.41%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e67.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCognitive function\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e14.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInternet usage\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSocietal-level interactions\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e10.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFamily-level interaction\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e21.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e12.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eCorrelations among study variables\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents the bivariate correlations. Results indicated a significant positive correlation between internet use and cognitive function (r\u0026thinsp;=\u0026thinsp;0.339, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), suggesting that more frequent internet use is associated with higher cognitive performance. The two hypothesized mediators\u0026mdash;family-level interaction and social-level interaction\u0026mdash;exhibited correlations in opposite directions. Specifically, social-level interaction was positively correlated with both cognitive function (r\u0026thinsp;=\u0026thinsp;0.154, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) and internet use (r\u0026thinsp;=\u0026thinsp;0.211, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), suggesting a potential positive mediating role in the relationship between internet use and cognition. In contrast, family-level interaction showed significant negative correlations with cognitive function (r = -0.132, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) and internet use (r = -0.126, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), suggesting a potential negative influence in the causal pathway. Additionally, the correlation between family-level and social-level interactions was weak and nonsignificant (r = -0.022, p\u0026thinsp;\u0026gt;\u0026thinsp;0.05), suggesting that they may represent distinct dimensions of social interaction. Age, serving as a moderating variable, was significantly negatively correlated with cognitive function (r = -0.159, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) and internet use (r = -0.185, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), indicating that increased age may be associated with declines in both cognition and internet use frequency. Moreover, age was moderately positively correlated with family-level interaction (r\u0026thinsp;=\u0026thinsp;0.272, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) but showed a weak and nonsignificant correlation with social-level interaction (r = -0.021, p\u0026thinsp;\u0026gt;\u0026thinsp;0.05), further highlighting potential differences between interaction dimensions among older adults.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDescriptive statistics and correlations among the key variables\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1 Cognitive function\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2 Internet usage\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.339\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3 Family-level interaction\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.132\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.126\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e4 Societal-level interaction\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.154\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.211\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e5 Age\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.159\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.185\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.272\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.021\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003cb\u003eNote: **\u003c/b\u003e\u003cb\u003ep\u003c/b\u003e\u0026thinsp;\u003cb\u003e\u0026lt;\u0026thinsp;0.01, *\u003c/b\u003e\u003cb\u003ep\u003c/b\u003e\u0026thinsp;\u003cb\u003e\u0026lt;\u0026thinsp;0.05\u003c/b\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003eMediation analysis\u003c/em\u003e\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e presents the results of the mediation analysis. First, Hypothesis \u003cspan refid=\"FPar1\" class=\"InternalRef\"\u003e1\u003c/span\u003e was supported. The direct effect of internet use on cognitive function was significant (B\u0026thinsp;=\u0026thinsp;2.034, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). This indicates that internet use among older adults significantly influences their cognitive functioning. As shown in the results, internet use can directly enhance cognitive performance in later life, thus confirming Hypothesis \u003cspan refid=\"FPar1\" class=\"InternalRef\"\u003e1\u003c/span\u003e of this study.\u003c/p\u003e\u003cp\u003eSecond, Hypothesis \u003cspan refid=\"FPar2\" class=\"InternalRef\"\u003e2\u003c/span\u003e was also supported. Internet use had a significant effect on social-level interaction (B\u0026thinsp;=\u0026thinsp;0.947, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and social-level interaction significantly affected cognitive function (B\u0026thinsp;=\u0026thinsp;0.196, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). This suggests that internet use may indirectly enhance cognitive function by promoting social-level interaction. Notably, internet use also had a significant but negative effect on family-level interaction (B = -1.880, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001); likewise, family-level interaction had a significant and negative effect on cognitive function (B = -0.016, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). These findings indicate that internet use may also indirectly exert a negative impact on cognitive function by reducing family-level interaction.\u003c/p\u003e\u003cp\u003eTaken together, the results presented in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e confirm both hypotheses of this study. Internet use not only directly affects cognitive function in older adults, but also exerts a positive indirect effect through social-level interaction and a negative indirect effect through family-level interaction.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eThe results of the mediating analysis\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModel summary\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eB\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSE\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003et\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003ep\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e95% CI\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eX \u0026rarr; Y\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2.034\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.147\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e13.831\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e[1.746, 2.322]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eX \u0026rarr; M1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.947\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.068\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e14.127\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e[0.674, 0.892]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eM1 \u0026rarr; Y\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.196\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.031\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e6.233\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e[0.134, 0.258]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eX \u0026rarr; M2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-1.880\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.377\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-4.990\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e[-2.619, -1.143]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eM2 \u0026rarr; Y\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.016\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.005\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-3.436\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e[-0.025, -0.007]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003cb\u003eX: Independent variable (Internet usage); M1: Mediator1 (Societal-level interaction);M2: Mediator2 (Family-level interaction); Y: Dependent variable (Cognitive function)\u003c/b\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003eModerated mediation analysis\u003c/em\u003e\u003c/p\u003e\u003cp\u003eThe present study employed Model 15 from the SPSS macro to examine whether age moderates the latter part of the mediation pathway\u0026mdash;namely, the effects of the two mediators on the dependent variable\u0026mdash;as well as the direct effect of the independent variable on the dependent variable. The results are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e\u003cp\u003eFirst, the direct effect of internet use on cognitive function was significantly positive (B\u0026thinsp;=\u0026thinsp;1.79, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and the interaction term between internet use and age was also significant (B\u0026thinsp;=\u0026thinsp;0.66, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), indicating that age significantly moderated the direct path from internet use to cognitive function. Specifically, for each one-unit increase in age, the direct effect increased by an average of 0.66 units, suggesting a stronger effect of internet use on cognition among older individuals.\u003c/p\u003e\u003cp\u003eSecond, in the first half of the mediation pathway, internet use exerted significant effects on both mediators. Specifically, internet use significantly positively predicted social-level interaction (B\u0026thinsp;=\u0026thinsp;0.78, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and significantly negatively predicted family-level interaction (B = -1.88, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). In the latter half of the mediation pathway, social-level interaction had a significant effect on cognitive function (B\u0026thinsp;=\u0026thinsp;0.19, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and its interaction with age was also significant (B\u0026thinsp;=\u0026thinsp;0.12, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), indicating that age moderated the effect of social-level interaction on cognitive function. Specifically, with each one-unit increase in age, the predictive effect of social-level interaction on cognition increased by an average of 0.12 units, indicating a stronger effect among older individuals. These findings provide support for Hypothesis \u003cspan refid=\"FPar3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. In contrast, family-level interaction did not significantly predict cognitive function (B = -0.07, p\u0026thinsp;\u0026gt;\u0026thinsp;0.05), and its interaction with age was also nonsignificant (B\u0026thinsp;=\u0026thinsp;0.01, p\u0026thinsp;\u0026gt;\u0026thinsp;0.05), suggesting that age did not moderate the effect of family-level interaction on cognition.\u003c/p\u003e\u003cp\u003eIn sum, the model results demonstrate that age significantly moderates both the direct pathway from internet use to cognitive function and the indirect pathway through social-level interaction. The direction of these effects indicates that the positive impacts of internet use and social engagement on cognition become stronger with increasing age, reflecting a quantifiable relationship between age and path strength. In contrast, age did not significantly moderate the pathway from family-level interaction to cognitive function, suggesting that the effect of this pathway remains relatively stable across different age groups.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eResults of the moderated mediation analysis\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"12\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\"\u0026minus;\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003eDependent variable: Societal-level interaction\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e\u003cp\u003eDependent variable: Family-level interaction\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c12\" namest=\"c10\"\u003e\u003cp\u003eDependent variable:\u003c/p\u003e\u003cp\u003eCognitive function\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eB\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003et\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e95% CI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eB\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003et\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003e95% CI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u003cp\u003eB\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c11\"\u003e\u003cp\u003et\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c12\"\u003e\u003cp\u003e95% CI\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eX\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e14.12\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e[0.67 ,0.89]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-1.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-4.99\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c8\"\u003e\u003cp\u003e[-2.62 ,-1.14 ]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e1.79\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e11.44\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\u003cp\u003e[1.48, 2.10]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" 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colname=\"c12\"\u003e\u003cp\u003e[-1.03,-0.58]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eM1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e6.33\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\u003cp\u003e[0.14, 0.26]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eM2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e-0.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e-1.53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\u003cp\u003e[-0.01, 0.02]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eX\u0026times;Z\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e2.20\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\u003cp\u003e[0.07, 1.24]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eM1\u0026times;Z\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e2.26\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\u003cp\u003e[0.02, 0.23]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eM2\u0026times;Z\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e1.65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\u003cp\u003e[-0.02, 0.03]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"12\"\u003e\u003cb\u003eX: Independent variable (Internet usage); M1: Mediator1 (Societal-level interaction);M2: Mediator2 (Family-level interaction); Z:Moderator(Age ) Y: Dependent variable (Cognitive function)\u003c/b\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eTo further explore the moderating role of age in the relationship between internet use and cognitive function, as well as between social interaction and cognitive function, the present study conducted simple slope analyses across different age groups. The results are illustrated in Figs.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and 2.The analysis revealed age-related variation in the direct path: the predictive effect of internet use on cognitive function was relatively weak among younger-old adults (M\u0026ndash;1SD), whereas the effect was significantly stronger among older-old adults (M\u0026thinsp;+\u0026thinsp;1SD). A similar pattern was observed in the latter half of the mediation pathway. Compared with younger-old adults, social-level interaction exhibited greater predictive power for cognitive function among older-old individuals (M\u0026thinsp;+\u0026thinsp;1SD).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eDrawing on data on cognitive function among older adults from the China Health and Retirement Longitudinal Study (CHARLS), this study provides a more nuanced understanding of the relationship between internet use and cognitive function in later life in China. The results indicate a significant positive association between internet use and cognitive function, suggesting that older adults who use the internet tend to exhibit better cognitive performance. This finding supports the first hypothesis of the study.In addition, the analysis revealed significant mediating effects of both social-level and family-level interaction in the relationship between internet use and cognitive function. However, the direction of these mediation effects differed markedly: social-level interaction played a positive role by enhancing cognitive function, whereas family-level interaction exhibited a negative mediation effect, suggesting that internet use may adversely affect cognition by weakening family-based interactions. These findings jointly support the second hypothesis of the study.Finally, the study found that age significantly moderated the latter half of the mediation pathway through social-level interaction. Specifically, the positive impact of social-level interaction on cognitive function became more pronounced with increasing age, indicating that this pathway is particularly salient among older segments of the aging population. This result lends support to the third hypothesis\u003c/p\u003e\u003cp\u003eThe present study found that internet use significantly positively predicted cognitive function among older adults. In other words, compared with nonusers, older adults who used the internet exhibited better cognitive performance, a finding consistent with previous research \u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]\u003c/sup\u003e. In fact, internet use involves a range of cognitively demanding activities, such as information searching, online learning, digital tool operation, and multitasking. These activities require older adults to engage attention, memory, executive function, and problem-solving abilities, thereby providing sustained \"cognitive exercise\" for the brain.The influence of internet use on cognitive function is attributed to the complex cognitive activities involved in using search engines, such as information filtering, decision-making, and problem-solving, which activate broader brain regions than traditional reading tasks. Particularly, areas such as the frontal lobes, cingulate gyrus, and hippocampus\u0026mdash;responsible for complex reasoning, decision-making, and memory integration\u0026mdash;are notably engaged. Research has shown that older adults with extensive internet experience activate more than twice the brain regions during search tasks compared to those with less experience, suggesting that internet searching is not only a highly cognitively stimulating activity but may also enhance the plasticity and efficiency of neural circuits through long-term use \u003csup\u003e[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]\u003c/sup\u003e.Compared with passive information reception, internet use offers a richer sensory experience and a higher level of cognitive engagement, thereby improving cognitive function in older adults. Thus, it is important to encourage older adults to gradually learn and adapt to internet technologies and to become familiar with internet-based products to better integrate into the digital era. Enterprises should be encouraged to develop age-friendly internet products and to establish a comprehensive system of standards for the age-appropriate design of smart devices to improve product quality and usability. At the governmental level, policies and regulations should be formulated to promote research investment and development efforts focused on enhancing the accessibility and usability of internet technologies for older populations.\u003c/p\u003e\u003cp\u003eIn exploring the potential mechanisms underlying the relationship between internet use and cognitive function among older adults, our findings suggest that internet use can indirectly enhance cognitive function through increased social interaction. This conclusion also supports the \u0026ldquo;internet benefits theory\u0026rdquo;. On one hand, the internet provides users with tools for autonomy and communication, enabling them to overcome physical and social barriers and independently manage interpersonal interactions \u003csup\u003e[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]\u003c/sup\u003e. On the other hand, older adults who frequently use the internet are better able to maintain relationships with family and friends, increasing both the frequency and quality of communication \u003csup\u003e[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]\u003c/sup\u003e.Moreover, perceived social isolation has been shown to heighten vigilance toward social threats, impair executive function and self-regulation abilities,and chronically activate the hypothalamic-pituitary-adrenal axis and inflammatory responses, thereby damaging brain regions associated with cognitive processing and ultimately leading to cognitive decline \u003csup\u003e[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]\u003c/sup\u003e. Accordingly, positive social interaction, as a protective factor against loneliness, may help reduce hypersensitivity to social threats, alleviate physiological stress responses, and enhance the adaptability and plasticity of the nervous system. Together, these processes promote the maintenance and improvement of cognitive function at both neurological and psychological levels among older adults.Thus, integrating these multi-level pathways, it can be concluded that the mechanism by which internet use enhances cognitive function through promoting social interaction has a clear physiological foundation.\u003c/p\u003e\u003cp\u003eIn exploring potential mechanisms, our findings also suggest that internet use may exert a negative indirect effect on cognitive function by reducing family-level interactions. Prior studies have confirmed that increased time spent online is significantly associated with a decline in communication among family members \u003csup\u003e[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]\u003c/sup\u003e. Given that family interaction and the overall family atmosphere can meaningfully influence the cognitive functioning of older adults \u003csup\u003e[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]\u003c/sup\u003e, it is plausible that increased internet use may, to some extent, undermine cognitive performance through the weakening of familial engagement.\u003c/p\u003e\u003cp\u003eThese findings indicate that the influence of internet use on cognitive function in older adults operates through dual pathways: on the one hand, it enhances cognition by promoting social-level interaction; on the other hand, it may exert a negative effect by weakening family interaction. Therefore, while encouraging appropriate internet use among older adults, it is essential to guide them toward greater social engagement and the expansion of external connections, while also emphasizing the maintenance of familial bonds. Such a balanced approach can help prevent emotional estrangement due to technology use and ultimately optimize the overall cognitive benefits of internet engagement.\u003c/p\u003e\u003cp\u003eThis study further revealed that the direct effect of internet use on cognitive function is significantly moderated by age. This phenomenon can be interpreted from multiple perspectives. First, activity theory posits that individuals maintain their sense of identity and social roles through engagement in social activities, thereby promoting mental and physical well-being. According to this theory, the continuity and substitution of social participation are among the key mechanisms for successful aging. The moderating effect of age in the pathway linking internet use and cognitive function can be understood through this lens. Compared with middle-aged or younger individuals, older adults often face diminished social roles and reduced participation in social activities after retirement, resulting in fewer sources of social engagement and cognitive stimulation. Internet use\u0026mdash;particularly for communication, information access, and entertainment\u0026mdash;offers older adults novel avenues for social participation and cognitive engagement, functioning as a substitute for traditional activities. This, in turn, is more likely to activate cognitive resources and delay cognitive decline. In short, as age increases and conventional forms of activity decrease, the \u0026ldquo;cognitive activation\u0026rdquo; and \u0026ldquo;social connectivity\u0026rdquo; benefits of internet use become more pronounced for older adults, thus amplifying the positive effects.In addition, research on neural plasticity and aging supports this interpretation. Although neural plasticity tends to decline with age, older adults retain the capacity to activate underutilized neural networks through learning new skills or engaging with novel technologies such as the internet \u003csup\u003e[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]\u003c/sup\u003e. Internet use may serve as a form of \u0026ldquo;cognitive training,\u0026rdquo; particularly given its demands on memory, attention, and logical reasoning. These cognitive processes are especially beneficial for older adults, making the positive impact of such engagement more salient in advanced age.\u003c/p\u003e\u003cp\u003eIn addition to moderating the direct pathway from internet use to cognitive function, age also significantly moderates the effect of social interaction on cognitive function among older adults. According to Stern\u0026rsquo;s (2002) theory of cognitive reserve, individuals differ in how they manifest cognitive symptoms in the face of brain pathology, depending on their level of cognitive reserve \u003csup\u003e[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]\u003c/sup\u003e. Older adults of advanced age typically possess lower cognitive reserve and are thus more vulnerable to age-related neural decline. As a result, social interaction facilitated by internet use\u0026mdash;an external source of cognitive stimulation\u0026mdash;serves a stronger compensatory function for them. In contrast, younger older adults, who tend to have higher cognitive reserve and more stable cognitive function, are less reliant on external stimulation, and consequently, interventions have a weaker effect.Moreover, the Scaffolding Theory of Aging and Cognition suggests that older adults with lower cognitive reserve rely more heavily on compensatory scaffolding mechanisms, particularly involving the prefrontal cortex, to cope with neural decline \u003csup\u003e[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]\u003c/sup\u003e. Through enhanced social engagement, internet use provides cognitive stimulation that activates these scaffolds, thereby improving cognitive performance in high-age older adults. In comparison, younger older adults with greater cognitive reserve and more stable neural function depend less on such scaffolds, and thus experience a weaker cognitive benefit from internet use. Therefore, age moderates the cognitive effects of internet use through differences in cognitive reserve, with older seniors gaining greater benefits.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study found that internet use not only directly affects the cognitive function of older adults but also has significant indirect effects through interactions at both the social and family levels. Specifically, internet use facilitates social and family interactions, which in turn enhances cognitive function. Additionally, age plays an important moderating role in this mediation pathway. Notably, the interaction term for social-level interaction significantly moderates the effect on cognitive function, indicating that as age increases, the positive effect of social interaction on cognitive function gradually strengthens. Furthermore, the direct effect of internet use on cognitive function also increases with age, suggesting that older seniors experience more significant cognitive improvements from using the internet.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eMIND \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;The Mediterranean-DASH Diet Intervention for Neurodegenerative Delay\u003c/p\u003e\n\u003cp\u003eCOVID-19 \u0026nbsp; \u0026nbsp; \u0026nbsp;Coronavirus Disease 2019\u003c/p\u003e\n\u003cp\u003eADOD \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Alzheimer\u0026apos;s disease and other dementias\u003c/p\u003e\n\u003cp\u003eCHARLS \u0026nbsp; \u0026nbsp; \u0026nbsp; China Health and Retirement Longitudinal Study\u003c/p\u003e\n\u003cp\u003eMMSE \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Minimum Mental State Examination\u003c/p\u003e\n\u003cp\u003eADL \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Activities of Daily Living\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIADL \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Instrumental Activities of Daily Living\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participants\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll participants gave informed written consent and the ethical approval was obtained at the Peking University Institutional Review Board (IRB00001052-11015).The study methodology was carried out following the guidelines of the Declaration of Helsinki.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical Trial Number\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBWH conceived the study, led the research design, collected the data, and critically reviewed the manuscript.SYS contributed to data collection and drafted th e manuscript. KYJ was responsible for data analysis, manuscript writing, and overall manuscript review. All authors have made substantial contributions to the study and approved the final version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor details\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e1\u003c/sup\u003eDepartment of Sociology, School of Humanities and Social Sciences, Harbin Engineering University, Harbin, Heilongjiang Province, China.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors thank all participants who collaborated in this study.The dataused in this study are from China Health and Retirement Longitudinal Study.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eWorld Health Organization. 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J Int Neuropsychol Soc. 2002;8(3):448\u0026ndash;60. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1017/S1355617702813248\u003c/span\u003e\u003cspan address=\"10.1017/S1355617702813248\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Internet use, Cognitive function, Social interaction, Chinese elderly","lastPublishedDoi":"10.21203/rs.3.rs-7069482/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7069482/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground:\u003c/h2\u003e\u003cp\u003ePrevious studies have found a significant link between internet use and cognitive function in older adults. However, the underlying mechanisms of this relationship have not been thoroughly explored. This study aims to investigate the mediating role of social and family-level interactions in the relationship between internet use and cognitive function in older adults, as well as how age moderates this relationship.\u003c/p\u003e\u003ch2\u003eMethods:\u003c/h2\u003e\u003cp\u003eThe data for this study were derived from the China Health and Retirement Longitudinal Study (CHARLS, N\u0026thinsp;=\u0026thinsp;7090) organized by the National School of Development at Peking University in 2020. We first examined a simple mediation model in which social participation served as a mediator between internet use and cognitive function. Additionally, age was systematically incorporated as a moderator in the model. Moderated mediation analysis was conducted using the PROCESS macro developed by Hayes.\u003c/p\u003e\u003ch2\u003eResults:\u003c/h2\u003e\u003cp\u003eBased on the mediation analysis results, the relationship between internet usage and cognitive function is partially mediated by societal-level interaction and family-level interaction. Specifically, internet usage significantly influences societal-level interaction (B\u0026thinsp;=\u0026thinsp;0.947, 95% CI = [0.674, 0.892]), and societal-level interaction significantly influences cognitive function (B\u0026thinsp;=\u0026thinsp;0.196, 95% CI = [0.134, 0.258]). In contrast, internet usage significantly negatively influences family-level interaction (B = -1.880, 95% CI = [-2.619, -1.143]), and family-level interaction significantly influences cognitive function (B = -0.016, 95% CI = [-0.025, -0.007]). Moderator mediation analysis reveals that age not only significantly moderates the direct effect path of internet usage on cognitive function (B\u0026thinsp;=\u0026thinsp;0.66, 95% CI = [0.07, 1.24]) but also moderates the path through which family-level interaction influences cognitive function (B\u0026thinsp;=\u0026thinsp;0.12, 95% CI = [0.02, 0.23]).\u003c/p\u003e\u003ch2\u003eConclusion:\u003c/h2\u003e\u003cp\u003eThis study provides new insights into the mechanisms linking internet use, social and family interactions, and cognitive function, thereby contributing to existing research on cognitive health. The findings offer valuable references for developing interventions aimed at improving cognitive health in older adults.\u003c/p\u003e","manuscriptTitle":"The association between Internet use and cognitive function among older Chinese adults: a moderated mediation model","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-02 14:04:07","doi":"10.21203/rs.3.rs-7069482/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"c0a105d2-57f6-4e19-a6c4-933dad489dcc","owner":[],"postedDate":"September 2nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-09-18T12:53:09+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-02 14:04:07","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7069482","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7069482","identity":"rs-7069482","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}