Beyond the individual: A dyadic longitudinal study of internet use, social participation, and depressive symptoms in older couples

Abstract

Background

While the link between internet use and depressive symptoms in older adults is studied, research often overlooks the interdependent nature of couples. This study examines the longitudinal actor and partner effects of internet use on depressive symptoms among older couples, testing social participation as a key mediating mechanism.

Methods

Using a multistage, stratified probability sampling method, data were drawn from 4878 heterosexual married couples participating in the 2013, 2015, and 2018 waves of the China Health and Retirement Longitudinal Study. A longitudinal dyadic analysis was conducted using structural equation modeling to test an Actor-Partner Interdependence Mediation Model.

Results

For both husbands and wives, their own internet use was associated with lower depressive symptoms, a relationship fully mediated by their own increased social participation (actor-actor effects). Crucially, significant asymmetric partner effects emerged. A husband's internet use was associated with a substantial reduction in his wife's depressive symptoms (β = −0.959, p = .039), indicating a practically meaningful protective effect. This benefit operated both directly and indirectly by increasing the wife's social participation (β = −0.072, p = .026). However, a wife's internet use had no significant effect on her husband's depression.

Conclusions

The mental health benefits of digital engagement extend beyond the individual user to their spouse, operating through enhanced social participation. These findings underscore the importance of dyadic, gender-sensitive approaches when developing interventions to promote digital literacy and social engagement to improve well-being in later life.

Introduction

Depression represents one of the most prevalent and debilitating mental health conditions among older adults worldwide, affecting approximately 7% of the global elderly population and imposing substantial burdens on individuals, families, and healthcare systems. ¹ Beyond its direct impact on quality of life, late-life depression significantly increases risks of cognitive decline, cardiovascular disease, and premature mortality, while also elevating healthcare costs and caregiver burden. ² The multifactorial etiology of geriatric depression encompasses biological vulnerabilities, chronic medical conditions, social isolation, and psychosocial stressors, necessitating a comprehensive understanding of its determinants to develop effective prevention and intervention strategies.

The global demographic transition toward population aging has intensified scholarly and policy attention to late-life mental health. In China, this transition has been particularly rapid and pronounced. According to the National Bureau of Statistics, individuals aged 60 and above comprised 22.0% of China's total population by 2024, with those aged 65+ reaching 220 million (15.6% of the population), officially marking China's entry into a moderately aging society. ³ This demographic shift, coupled with rapid social and technological changes, has created unprecedented challenges for maintaining psychological well-being among Chinese older adults, making the identification of modifiable risk and protective factors increasingly urgent.

The digital revolution has introduced internet use as a novel and potentially significant factor influencing older adults’ mental health. However, empirical evidence regarding the relationship between internet use and depression among the elderly remains inconsistent and theoretically underdeveloped. Some studies suggest that internet use can enhance older adults’ social connectivity, facilitate intergenerational communication, and provide access to health information and emotional support, thereby reducing isolation and depressive symptoms. ⁴ Conversely, other research indicates that excessive or inappropriate internet use may exacerbate depressive tendencies through mechanisms such as social substitution, information overload, and disrupted sleep patterns. ⁵ Still other investigations have found non-linear or null associations, suggesting that the relationship may be more complex than initially conceptualized. ⁶

The psychosocial mechanism between Internet use and depression may be a key factor in the difference. Although the existing literature has recorded a variety of psychosocial mechanisms as a potential link between Internet use and mental health outcomes, covering social support, ⁷ cognitive participation, self-efficacy, and loneliness, ⁸ this study is based on the research results of the “Internet Paradox,” that is, the impact of Internet use on mental health depends on whether it is used to maintain and strengthen existing social networks, especially focusing on the dimension of social participation. ⁹ Compared to psychological states like loneliness, social participation is an observable, upstream behavior. ¹⁰ This suggests that internet use primarily influences mental health through its impact on these tangible social activities. Social participation, a key protective factor against depression, also serves as a mediating variable linking internet use and mental health. ¹¹ Internet use may amplify or constrain social participation through different patterns. Moderate internet use and information access can improve psychological well-being in older adults, but excessive screen time may negatively impact mental health through mechanisms such as cognitive overload, social substitution, and physiological disruption. Empirical studies have demonstrated that distinct internet use patterns differentially influence social participation among older adults.^9,12 Given the complexity of social participation as a mediating variable, further research is necessary to better understand the intricate dynamics among individual behaviors, social interactions, and mental health.

Furthermore, the vast majority of research on internet use and mental health among older adults has adopted an individual-centered approach, overlooking the fundamental reality that aging often occurs within the context of long-term intimate relationships. Marriage represents the most significant and enduring social relationship for most older adults, serving as a primary source of social support, shared activities, and emotional regulation. ¹³ Within family systems theory, couples function as interdependent dyads where one partner's behaviors, experiences, and well-being directly influence the other's through processes of emotional contagion, behavioral modeling, shared environmental exposure, and mutual support provision.^14,15

The Actor-Partner Interdependence Model (APIM) provides a robust theoretical and methodological framework for understanding these dyadic processes by simultaneously examining how individual characteristics affect both one's own outcomes (actor effects) and one's partner's outcomes (partner effects). ¹⁶ In the context of internet use and depressive symptoms, this framework suggests four potential pathways: (a) actor mediation effects, where one's internet use influences one's own depressive symptoms through changes in one's own social participation; (b) actor-partner mediation effects, where one's internet use influences one's partner's depressive symptoms through changes in one's own social participation; (c) partner-actor mediation effects, where one's internet use influences one's own depressive symptoms through changes in one's partner's social participation; and (d) partner mediation effects, where one's internet use influences one's partner's depressive symptoms through changes in one's partner's social participation.

Despite the theoretical plausibility and practical importance of these dyadic processes, empirical research examining how couples’ internet use patterns jointly influence their mental health outcomes remains remarkably scarce. This gap is particularly problematic given evidence that spouses often share similar technology adoption patterns, jointly negotiate household internet use rules, and experience both benefits and challenges of digital engagement within their shared domestic environment. ¹⁷ Moreover, asymmetrical internet use between spouses may create relationship tensions, communication barriers, or feelings of exclusion that could adversely affect both partners’ mental health. ¹⁸

The present study addresses these critical research gaps by examining the complex longitudinal relationships among internet use, social participation, and depression within older married couples using data from the China Health and Retirement Longitudinal Study (CHARLS). Specifically, we test a comprehensive APIM mediation model to determine whether and how spouses’ internet use at baseline influences their own and their partners’ depression three years later through changes in social participation at the intermediate time point. This investigation advances theoretical understanding of digital technology's role in late-life mental health by: (a) adopting a dyadic perspective that acknowledges the interdependent nature of couples’ experiences; (b) employing a longitudinal design that enables causal inference; (c) examining social participation as a potential mediating mechanism; and (d) testing multiple pathways of influence simultaneously within a comprehensive statistical framework. This investigation advances theoretical understanding of digital technology's role in late-life mental health by demonstrating how internet use functions as a relational resource within couples, with important implications for designing digital health interventions that account for interpersonal impacts.

Materials and methods

Data and sample

This study utilized longitudinal data from the CHARLS, specifically waves from 2013, 2015, and 2018. CHARLS is a nationally representative survey designed to examine the health and socioeconomic status of adults aged 45 and above in China. ¹⁹ The survey employs a multistage, stratified probability sampling design covering 28 provinces, 150 counties/districts, and 450 villages/communities across China. CHARLS collects comprehensive information on demographics, health status and behaviors, social activities, family structure, household finances, and other relevant variables for understanding the aging process. Importantly, both members of couples completed identical questionnaires, creating valuable matched-pair data for examining interpersonal dynamics. The CHARLS was approved by the Biomedical Ethics Committee of Peking University after each round of investigation (IRB00001052-11015). All participants signed the informed consent. This study followed the STROBE observational study reporting guidelines.

For our analysis, we focused on three consecutive waves of CHARLS data to establish temporal precedence for testing causal relationships in our mediation model. We deliberately excluded the 2020 wave to avoid potential confounding effects from the COVID-19 pandemic. The sample selection process is illustrated in Figure 1. The 2013 baseline survey included 18,612 participants. We identified 7790 spousal dyads and excluded same-sex couples (n = 2) and those lost to follow-up (n = 2209). From the resulting 5579 longitudinal dyads, we further excluded couples with missing depressive symptom scores (n = 573) or where either spouse was aged under 45 (n = 128). The final analytical sample comprised 4878 couples.

Figure 1.

The flow chart of the inclusion and exclusion of the study population.

Measurements

Internet use

Internet use served as the independent variable in our study and was measured in 2013. The variable was measured with the single question “Have you used the Internet in the past month?” Responses were coded as binary values: “yes” = 1 (Internet user) and “no” = 0 (nonuser). This binary measurement approach has been validated as a standard indicator and is widely used in existing research examining Internet adoption and digital divide issues among older populations.^20,21

Depressive symptoms

The outcome variable was the couple's depressive symptoms in 2018. We used the 10-item Center for Epidemiological Studies Depression Scale (CESD-10), which has demonstrated strong reliability and validity in Chinese older adult populations.^22,23 The scale asked participants to report how frequently during the past week they experienced ten specific symptoms, including feeling bothered, having trouble concentrating, feeling depressed, experiencing effort in daily activities, feeling hopeful about the future (reverse-coded), feeling fearful, sleeping restlessly, feeling happy (reverse-coded), feeling lonely, and inability to “get going.” Responses ranged from 0 (less than 1 day) to 3 (5–7 days) on a four-point scale. Total scores were calculated by summing all items (range: 0–30), with higher scores indicating greater depressive symptomatology. The scale demonstrated good internal consistency in this study (Cronbach's alpha = 0.84).

Social participation

The mediating variable was the couple's social participation in 2015. It was measured using items that asked respondents: “Have you done any of these activities in the last month?” We included 10 distinct types of social participation activities: (1) interacting with friends; (2) playing Ma-jong, chess, cards, or attending community clubs; (3) providing help to family, friends, or neighbors who do not live with the respondent; (4) attending sports, social, or other clubs; (5) participating in community-related organizations; (6) engaging in voluntary or charity work; (7) caring for sick or disabled adults who do not live with the respondent; (8) attending educational or training courses; (9) stock investment; and (11) other social activities. We excluded item 10 (Internet use) from our social participation measure to avoid conceptual overlap with our independent variable. For each activity, participation was coded as 1 and non-participation as 0. The sum of all items created a social participation index ranging from 0 to 10, which has been validated in prior studies to reflect the diversity and extent of social engagement.^11,24

Covariates

Following established research on internet use and mental health among older adults,^11,25 we included several individual-level and household-level covariates that have been shown to influence the relationships between internet use, social participation, and depressive symptoms. Individual-level demographic covariates included age (continuous), educational attainment (primary, secondary, or tertiary education), and labor force status (employed, retired, or other). Health-related covariates encompassed multimorbidity status (number of chronic conditions), smoking behavior (never, former, or current smoker) and drinking status (yes/no). At the household level, we controlled for residential location (urban/rural), household size (continuous), per capita consumption as a proxy for economic status (continuous), and proximity to adult children (living near children: yes/no). All covariates were measured in 2013 to maintain temporal precedence in our longitudinal analysis framework.

Analytic strategy

We conducted comprehensive descriptive analyses to characterize the study sample. Given that married couples represent distinguishable dyads, we presented descriptive statistics separately for wives and husbands, including means and standard deviations for continuous measures and frequencies with percentages for categorical variables. To evaluate gender-based differences within couples, we utilized paired t-tests for continuous variables and Chi-square tests for categorical variables. Spousal concordance and interdependence were assessed using Pearson correlation coefficients for continuous measures and Cohen's kappa coefficients for categorical measures. Bivariate associations among the key study variables were examined through correlation analyses. We calculated phi coefficients for relationships between binary variables, point-biserial correlations for associations between binary and continuous variables, and Pearson correlation coefficients for relationships among continuous variables.

The hypothesized Actor-Partner Interdependence Mediation Model (APIMeM) was tested using structural equation modeling (SEM). SEM represents a robust analytical framework for simultaneously examining complex multivariate relationships while accounting for measurement error. The APIM framework is well-suited for dyadic research as it allows for the estimation of both actor effects (how an individual's characteristics influence their own outcomes) and partner effects (how an individual's characteristics influence their partner's outcomes).^26,27 Our mediation model specifically examined whether social participation mediates the relationship between internet use and depressive symptoms, both within individuals (actor-actor pathways) and across partners (actor-partner and partner-actor pathways). All analyses were conducted using R with the lavaan package and maximum likelihood estimation.

Result

Characteristics of study participants

Descriptive characteristics of the older couples are presented in Table 1. The study included 4878 older couples with distinct demographic profiles. Wives averaged 55.89 years (SD = 8.12) and husbands 58.06 years (SD = 8.37). Educational attainment was predominantly at the primary level, with 91.7% of wives and 83.7% of husbands having completed only elementary education. Employment patterns differed between spouses, as 69.4% of wives and 80.7% of husbands remained in the workforce, while retirement rates were higher among wives (28.9%) compared to husbands (18.7%). Health behaviors showed marked disparities: smoking prevalence was substantially higher in husbands (48.1% current smokers) than wives (3.3%), and similar patterns emerged for alcohol consumption (59.5% vs 14.9%). Multimorbidity affected approximately 70% of participants, with internet usage remaining low across both groups. Household characteristics revealed that 65.0% of couples lived in rural settings, with an average household size of 3.79 members and most families (88.0%) residing near their adult children.

Table 1.

Descriptive characteristics of the older couples (N = 4878).

Measures	Wives	Husbands	Dyad	Gender differences	Spousal concordance
Individual-level demographic
Internet use, yes, n (%)	147 (3.0)	240 (4.9)		χ2 = 719.40; p ≤ .001	κ = 0.37
Depressive symptoms, mean (SD)	9.39 (6.66)	7.24 (5.80)		t = −20.69; p ≤ .001	r = 0.33
Age, mean (SD)	55.89 (8.12)	58.06 (8.37)		t = 41.33; p ≤ .001	r = 0.90
Social participation	0.85 (1.08)	0.95 (1.15)		t = 4.97; p ≤ .001	r = 0.27
Drinking, yes, n (%)	728 (14.9)	2897 (59.5)		χ2 = 75.85; p ≤ .001	κ = 0.08
Education, n (%)				χ2 = 669.81; p ≤ .001	κ = 0.23
Primary	4475 (91.7)	4082 (83.7)
Secondary	361 (7.4)	706 (14.5)
Tertiary	42 (0.9)	90 (1.8)
Labor force status, n (%)				χ2 = 664.06; p ≤ .001	κ = 0.34
Employed	3348 (69.4)	3902 (80.7)
Other	82 (1.7)	27 (0.6)
Retired	1394 (28.9)	906 (18.7)
Smoking status, n (%)				χ2 = 24.11; p ≤ .001	κ = 0.03
Current	156 (3.3)	1465 (48.1)
Former	75 (1.6)	569 (18.7)
Never	4525 (95.1)	1009 (33.2)
Multimorbidity, n (%)				χ2 = 146.79; p ≤ .001	κ = 0.08
None	1305 (29.4)	1462 (32.8)
1 type	1300 (29.3)	1339 (30.0)
2 types	899 (20.3)	895 (20.1)
3 or more types	928 (20.9)	766 (17.2)
Household-level characteristic
Residency, rural, n (%)			3172 (65.0)
Living near children, yes, n (%)			4244 (88.0)
Household size, mean (SD)			3.79 (1.76)
Per capita consumption, mean (SD)			10,580.95 (13,035.04)

Notes: CI: confidence interval; SD: standard deviation. To examine gender differences, paired t test was used for continuous measures while Chi-square test was used for categorical measures. To examine spousal concordance, Pearson's correlation coefficient was used for continuous measures while Cohen's kappa coefficient was used for categorical measures.

Notable gender disparities emerged across multiple domains. Husbands demonstrated higher educational achievement, greater workforce participation, and more frequent engagement in health-risk behaviors including smoking and drinking. Conversely, wives reported elevated depressive symptoms (9.39 vs. 7.24) but showed greater social participation levels. Spousal similarity was strongest for age (r = 0.90), reflecting typical age-matching in marriages, followed by moderate concordance in employment status (κ = 0.34) and internet use (κ = 0.37). Health-related concordance varied considerably, with depressive symptoms showing moderate correlation (r = 0.33) while behavioral factors like smoking (κ = 0.03) and drinking (κ = 0.08) demonstrated weak spousal alignment.

Correlations between internet use, social participation, and depressive symptoms

Inter-correlations of study variables are presented in Table 2. Significant correlations were observed between husbands’ and wives’ internet use (r = 0.387, p < .01), social participation (r = 0.275, p < .01), and depressive symptoms (r = 0.328, p < .01), confirming non-independence within dyads. Cross-variable associations revealed that internet use was positively correlated with social participation for both husbands (r = 0.194, p < .001) and wives (r = 0.206, p < .001). Negative associations emerged between internet use and depressive symptoms across all dyadic combinations (r range: −0.068 to −0.090, all p < .001). Similarly, social participation showed inverse relationships with depressive symptoms, with within-spouse correlations being stronger (husbands: r = −0.102; wives: r = −0.099) than cross-spouse associations (r range: −0.057 to −0.073, all p < .001). These patterns suggest interconnected relationships between digital engagement, social behaviors, and psychological well-being within older couples.

Table 2.

Inter-correlations of the study variables for older couples.

		Internet use		Social participation		Depressive symptoms
		Husband	Wife	Husband	Wife	Husband	Wife
Internet use	Husband	1
	Wife	0.387***	1
Social participation	Husband	0.194***	0.121***	1
	Wife	0.152***	0.206***	0.275***	1
Depressive symptoms	Husband	−0.082***	−0.068***	−0.102***	−0.073***	1
	Wife	−0.090***	−0.083***	−0.057***	−0.099***	0.328***	1

Notes: For correlation analyses, phi coefficients were calculated between binary variables; point-biserial correlations were computed between binary and continuous variables; and Pearson’s correlation coefficients were used for relationships between continuous variables. ***p < .001.

APIMeM analysis

As shown in Figure 2, the final APIMeM model examines the dual effects of Internet use and social participation on quality of life. The model demonstrated acceptable overall fit with most indices meeting established criteria: CFI = 0.998, TLI = 0.996, RMSEA = 0.058. Although the χ²/df ratio was elevated (17.28), this indicator can be inflated in large sample studies and should be interpreted with caution as a supplementary fit index rather than a primary criterion for model evaluation. ²⁸ As an alternative to evaluate model fit, CFI, TLI, and RMSEA account for both model complexity and sample size. The combined results of these metrics provide a more comprehensive assessment of the model's overall fit. ²⁹

Figure 2.

SEM results examining the mediating role of social participation in the association between internet use and depressive symptoms among older couples. *p < .05; **p < .01; ***p < .001.

Notes: Model goodness-of-fit indices: χ²/df = 17.28; Comparative Fit Index (CFI) = 0.998; Tucker–Lewis index (TLI) = 0.996; Root Mean Square Error of Approximation (RMSEA) = 0.058. Models were adjusted for baseline individual-level demographic (i.e., age, education, and labor force status) and health (i.e., multimorbidity, drinking behavior, and smoking status) characteristics, as well as household-level covariates (i.e., household size, per capita consumption, residency, and living near children). Solid lines indicate significant, dashed lines indicate non-significant.

Direct and indirect effects are reported in Table 3. The analysis revealed complex patterns of actor and partner effects within couples. For actor effects, husbands showed significant total effects of internet use on their own depressive symptoms (β = −0.901, 95% CI [−1.690, −0.111], p = .003), which was primarily mediated through enhanced social participation (actor-actor simple indirect effect: β = −0.209, 95% CI [−0.313, −0.104], p < .001). Similarly, wives’ internet use significantly reduced their own depressive symptoms through increased social participation (actor-actor simple indirect effect: β = −0.246, 95% CI [−0.380, −0.112], p < .001), though the total effect was non-significant (β = −0.519, 95% CI [−1.664, 0.627], p = .375). Notably, direct effects of internet use on depressive symptoms were non-significant for both spouses after accounting for mediation pathways (husbands: β = −0.666, 95% CI [−1.459, 0.126], p = .100; wives: β = −0.272, 95% CI [−1.421, 0.878], p = .643).

Table 3.

SEM results examining the actor and partner effects of internet use, and mediation role of social participation in the relationship between internet use and depressive symptoms.

Effect		Label	Estimates	P	95%CI
Actor effect
Husband	Total effect	Actor total effect	−0.901	.003	[−1.690, −0.111]
	Total IE	Actor total IE	−0.234	.000	[−0.344, −0.124]
	Actor-actor simple IE	Actor-mediated actor effect	−0.209	.000	[−0.313, −0.104]
	Partner-partner simple IE	Partner-mediated actor Effect	−0.026	.150	[−0.060, 0.009]
	Direct effect	Actor direct effect	−0.666	.100	[−1.459, 0.126]
Wife	Total effect	Actor total effect	−0.519	.375	[−1.664, 0.627]
	Total IE	Actor total IE	−0.247	.000	[−0.381, −0.113]
	Actor-actor simple IE	Actor-mediated actor effect	−0.246	.000	[−0.380, −0.112]
	Partner-Partner simple IE	Partner-mediated actor effect	−0.001	.847	[−0.015, 0.012]
	Direct effect	Actor direct effect	−0.272	.643	[−1.421, 0.878]
Partner effect
Husband	Total effect	Partner total effect	−0.491	.336	[−1.492, 0.509]
	Total IE	Partner total IE	−0.113	.069	[−0.234, 0.009]
	Actor-partner simple IE	Actor-mediated partner effect	−0.087	.095	[−0.189, 0.015]
	Partner-actor simple IE	Partner-mediated partner effect	−0.026	.445	[−0.092, 0.040]
	Direct effect	Partner direct effect	−0.379	.459	[−1.382, 0.624]
Wife	Total effect	Partner total effect	−1.042	.024	[−1.949, −0.135]
	Total IE	Partner total IE	−0.083	.176	[−0.204, 0.037]
	Actor-partner simple IE	Actor-mediated partner effect	−0.011	.835	[−0.113, 0.091]
	Partner-actor simple IE	Partner-mediated partner effect	−0.072	.026	[−0.136, −0.009]
	Direct effect	Partner direct effect	−0.959	.039	[−1.870, −0.048]

Notes: SEM: structural equation model; CI: confidence interval; IE: indirect effect.

Regarding partner effects, husbands’ internet use demonstrated a significant total effect on wives’ depressive symptoms (β = −1.042, 95% CI [−1.949, −0.135], p = .024), with both direct (β = −0.959, 95% CI [−1.870, −0.048], p = .039) and indirect pathways contributing to this effect. The indirect pathway operated through husbands’ internet use enhancing wives’ social participation, which subsequently reduced wives’ depressive symptoms (partner-actor simple indirect effect: β = −0.072, 95% CI [−0.136, −0.009], p = .026). Conversely, wives’ internet use showed no significant partner effects on husbands’ depressive symptoms (β = −0.491, 95% CI [−1.492, 0.509], p = .336).

Sensitivity analysis

To assess the robustness of our findings, we conducted three sensitivity analyses with full results reported in Supplementary Materials. First, we re-estimated the APIMeM using internet use frequency categories (not regularly, almost every week, almost daily) instead of the binary measure. Results showed that actor-mediated actor effects remained significant across all frequency levels for both husbands and wives, while husband-to-wife partner effects through wives’ social participation emerged as significant only with weekly or daily use, suggesting that sustained engagement is necessary for interpersonal spillover effects (Supplementary Table S1). Second, we controlled for baseline (2013) depressive symptoms and social participation to test whether internet use predicts genuine changes over time. Despite substantial attenuation in effect sizes, actor-mediated actor effects remained significant for both spouses, and critically, the husband-to-wife partner effect persisted, confirming genuine dynamic interpersonal processes rather than mere selection effects (Supplementary Table S2). Third, we excluded health behavior covariates (smoking and drinking) to examine potential over adjustment bias. Results were nearly identical to the main analysis, with actor-mediated actor effects unchanged and husband-to-wife partner effects remaining significant, indicating that social participation operates as the primary mediating mechanism independently of health behavior pathways (Supplementary Table S3).

Discussion

Using a nationally representative longitudinal dataset spanning three waves of data collection, this study represents one of the first empirical investigations to examine the complex interplay between internet use, social participation, and depressive symptoms among older couples in China. The findings reveal significant actor and partner effects within dyadic relationships, with social participation serving as a crucial mediating mechanism linking digital engagement to psychological well-being in later life.

The correlation analysis revealed important patterns of spousal concordance that provide the foundation for understanding dyadic effects within older couples. Significant correlations were observed between husbands’ and wives’ internet use, social participation, and depressive symptoms, confirming the interdependent nature of these variables within couples. The moderate to strong spousal correlations in these key variables indicate that couples tend to share similar patterns of digital engagement, social activity levels, and psychological well-being, supporting the theoretical rationale for examining actor-partner effects in this population. ¹⁷ These correlations also suggest that interventions targeting one spouse are likely to have cascading effects on the partner, highlighting the importance of considering dyadic approaches in research and practice.

Cross-variable associations further illuminated the interconnected relationships between digital engagement, social behaviors, and psychological well-being within older couples. Internet use was positively correlated with social participation for both husbands and wives, while negative associations emerged between internet use and depressive symptoms across all dyadic combinations. Similarly, social participation showed inverse relationships with depressive symptoms, with within-spouse correlations being stronger than cross-spouse associations. These patterns suggest that digital engagement, social participation, and psychological well-being are closely intertwined within the context of older marriages, forming a complex web of relationships that operate both within individuals and between spouses.

The APIMeM analysis revealed complex patterns of actor and partner effects that illuminate how internet use influences depressive symptoms within older couples. For actor effects, the results demonstrated that internet use significantly reduced depressive symptoms for husbands, with this relationship being fully mediated through enhanced social participation. An analysis of German aging survey data supported this conclusion. ¹² However, some studies have pointed out that online use may damage social networks, weaken interpersonal relationships, and have a negative impact on mental health. ⁹ Social engagement is a key factor in producing different results. This finding suggests that internet adoption among older men serves as a gateway to broader social engagement, potentially through accessing online communities, maintaining contact with distant family members, or participating in digital social activities that translate into offline social connections.^30,31 The mediation pattern indicates that social participation represents a key mechanism through which digital engagement benefits psychological well-being.

For wives, internet use also significantly reduced depressive symptoms through increased social participation, though the total effect was non-significant. This pattern suggests that while the mechanism linking internet use to psychological well-being operates similarly for both spouses, wives may encounter additional barriers stemming from traditional family role expectations and domestic pressures that constrain their digital engagement and subsequently attenuate the overall psychological benefits of internet use on their mental health. ³² The significant indirect effect indicates that when wives do engage with internet technologies, they experience meaningful improvements in social participation that subsequently reduce depressive symptoms. However, the non-significant total effect may reflect barriers that limit wives’ ability to fully realize the psychological benefits of digital engagement. ³³

The analysis revealed particularly important partner effects that highlight the interdependent nature of older couples’ psychological well-being. Husbands’ internet use demonstrated a substantial impact on wives’ depressive symptoms through both direct and indirect pathways. The direct partner effect suggests that husbands’ internet use benefits wives’ psychological well-being through mechanisms beyond social participation, potentially including improved household communication, enhanced access to health information, or reduced caregiving burden as husbands become more digitally self-sufficient. ³⁴ Additionally, the significant indirect partner effect indicates that husbands’ internet use enhances wives’ social participation, which subsequently reduces wives’ depressive symptoms. This finding suggests that husbands’ digital engagement may create ripple effects within the household, potentially through sharing online resources, facilitating wives’ digital learning, or creating joint opportunities for social connection. ³⁵

In addition, the significant direct effect of husband's use of the Internet on his wife's depressive symptoms (β = −0.959) was compared with the smaller indirect effect through social participation (β = −0.072), indicating that other unmeasured mechanisms may play an important role. Although social participation represents a proven pathway, several other mechanisms may contribute to the observed direct effects, such as helping spouses search for health information ³⁶ ; expand communication channels between husband and wife to promote marriage harmony. ³⁷

While our study found a positive mate effect, we must also acknowledge that the impact of marital relationships on technology use and social participation is complex, some studies have shown that the close relationship between husband and wife can transmit both positive and negative effects. When one party faces external pressure, the pressure will spill into the marriage interaction. ³⁸ Individuals are thus prone to become irritated, withdrawn or lack of support, which in turn leads to more marital conflicts or reduces the relationship satisfaction of both parties. The pain of one party, to a large extent, becomes the cause of the pain of the other party.

The asymmetrical nature of these partner effects warrants particular attention. While husbands’ internet use significantly influenced wives’ psychological outcomes, wives’ internet use showed no reciprocal partner effects on husbands’ depressive symptoms. This gender asymmetry may reflect traditional relationship dynamics in older Chinese marriages, where husbands’ activities and resources often have broader implications for household functioning and decision-making. ³³

This gender asymmetry is consistent with established research on gendered power dynamics in older Chinese marriages. Prior studies have documented the persistence of traditional gender roles, often described as “men outside, women inside.” ³⁹ According to this framework, husbands tend to retain dominant influence over major household decisions, such as finances and the adoption of new technologies. ⁴⁰ Wives, conversely, often manage daily domestic affairs. Therefore, a husband's adoption of the internet (a major resource) may function as a form of household-level information gatekeeping; his use may bring new information and resources into the entire household, thereby influencing his wife's social opportunities and well-being. Conversely, a wife's internet use, if perceived as secondary, may have its benefits confined more to her personal sphere, failing to produce a significant partner effect on the husband. While direct comparisons are limited by the scarcity of dyadic studies in other cultural contexts, our finding of asymmetric partner effects aligns with research in Western societies documenting gendered patterns in technology adoption and use within couples. ⁴¹

Another possible reason is the socioeconomic disparities between husband and wife. Our sample shows a significant difference in education level: 91.7% of wives only received primary education, while the proportion of husbands was 83.7%. This educational disadvantage may limit wives’ literacy, confidence and self-efficacy in the application of digital technology, making it difficult for them to transform technological advantages into means to enhance their husbands’ well-being. ⁴²

The mediating role of social participation provides crucial insights into the mechanisms through which internet use influences psychological well-being in later life. The consistent finding that indirect effects operated through enhanced social participation for both actor and partner effects supports the social compensation hypothesis, which posits that internet use helps older adults maintain and expand social networks that might otherwise decline due to age-related constraints.^43,44 For both husbands and wives, the pathway from internet use to reduced depressive symptoms was explained by increased social participation, suggesting that digital technologies serve as tools for social connection rather than sources of social replacement or withdrawal. This finding has important implications for understanding how older adults integrate technology into their social lives and challenges stereotypes about digital engagement leading to social isolation in later life.

This study has several limitations that should be acknowledged. First, our binary measure of internet use captured basic access rather than specific behaviors. Although our supplementary analysis of usage frequency confirms the robustness of the results, we lacked data on the specific purpose of engagement. Different digital activities likely activate distinct pathways; for instance, interactive social networking may drive social participation more effectively than passive information-seeking, while unmeasured excessive duration could conversely lead to social displacement. Future research should distinguish these specific dimensions to clarify their differential impacts on well-being. Second, the sample was limited to older adults who remained married across all three waves; those whose spouses died or who experienced marital dissolution were excluded, potentially limiting the generalizability of the partner effects observed. Third, this study did not examine the underlying mechanisms that explain why husbands’ internet use has partner effects while wives’ does not. Further exploratory analyses investigating potential pathways (e.g., technology sharing, joint online activities, changes in household dynamics) could be instructive. Fourth, study was conducted exclusively in China, a context characterized by rapid digitalization and distinct familial norms. While this offers valuable insights into a rapidly aging population, the generalizability of our findings to other cultural or economic settings remains uncertain. Future research should explore using Harmonized data for cross-border comparative analysis to examine the universality of the observed dyadic effects. Finally, a key limitation is our model's reliance on only baseline (2013) internet use, which does not account for “dynamic adoption” (i.e., new users post-2013) during a period of rapid growth. This omission potentially introduces confounding bias, and future research should use methods like cross-lagged panel models to analyze the complete longitudinal trajectories.

Despite these limitations, this study carries broad implications for aging research and policy development. Theoretically, the current findings demonstrate the importance of adopting dyadic perspectives in understanding digital health interventions for older adults. The benefits of internet use are not limited to individual users but extend to their spouses through complex interdependent processes, highlighting the need for couple-based approaches to promoting digital engagement in later life.

In terms of policy implications, these findings underscore the need for targeted interventions to address the digital divide among older adults. The results suggest that promoting internet access and digital literacy among older men may have particularly broad benefits, improving not only their own psychological well-being but also that of their wives. Consequently, policymakers and practitioners should consider gender-sensitive approaches to digital inclusion programs, recognizing that interventions targeting husbands may yield dual benefits for couples.

The findings also suggest that digital literacy programs should explicitly incorporate social participation components to maximize their mental health benefits. Interventions that teach older adults not only how to use technology but also how to leverage digital tools for social connection may be more effective than those focusing solely on technical skills.^45,46 Additionally, the mediating role of social participation highlights the importance of ensuring that digital interventions complement rather than replace traditional forms of social engagement.

In terms of practical, practitioners not only need to exert the positive effects of digital technology, but also consider potential risks. When providing mental health services for elderly partners, the digital participation of both parties should be routinely assessed. One party’s Internet use habits may become a protective factor, and may also lead to social conflicts that affect the mental health of their partners.

Conclusion

This longitudinal study examined the actor and partner effects of internet use on depressive symptoms among older couples, revealing social participation as a critical mediating mechanism. Results indicated that internet use reduced depressive symptoms for both husbands and wives through enhanced social participation, with husbands’ internet use additionally demonstrating significant partner effects on wives’ psychological well-being. These findings confirm the Internet's role as a facilitator of digital health for older adults, emphasize the importance of adopting dyadic perspectives in digital health research and developing couple-based interventions to promote internet adoption and social participation among older adults. As digital technologies become increasingly integral to healthcare and social connection, addressing the digital divide among older couples represents both a public health priority and an opportunity to enhance psychological well-being in later life.