Digital literacy impacts quality of life among older adults through hierarchical mediating mechanisms

The rapid digitalization of society, in conjunction with the unprecedented phenomenon of global demographic aging, has given rise to a critical nexus where technological adaptation and healthy aging intersect^1,2,3. While digital technologies have become integral to daily functioning and well-being, older adults face significant barriers in accessing and utilizing these resources effectively⁴. Recent evidence suggests that “digital literacy”—defined as the ability to access, evaluate, and apply digital tools for daily tasks⁵—is a crucial determinant of quality of life among older adults, particularly in health management and social connectivity⁶. However, the digital divide among older adults has widened during the post-pandemic era, with only 45% reporting confidence in using digital health services⁷, underscoring the urgency of addressing domain-specific competencies such as “eHealth literacy” (i.e., the capacity to seek, interpret, and act on health-related digital information)⁸. This disparity is particularly concerning given that digital literacy and its domain-specific counterpart, eHealth literacy, have emerged as essential resources for maintaining independence, accessing healthcare services, and fostering social connections in later life⁹.

A review of the extant literature reveals three discrete yet interconnected theoretical streams that necessitate integration to comprehensively examine the relationship between digital literacy and quality of life. Studies investigating direct relationships between digital literacy and well-being outcomes have demonstrated significant positive associations; however, the underlying mechanisms remain inadequately explored⁵. Recent research on eHealth literacy has indicated its potential mediating role, with findings showing substantial correlations between general digital literacy and eHealth literacy among older adults¹⁰. Despite this, the pathways through which eHealth literacy translates digital literacy into enhanced quality of life remain theoretically underdeveloped⁴. Thirdly, although self-efficacy has been identified as a crucial psychological resource in technology adoption, its potential role in mediating the relationship between digital literacy and quality of life remains empirically untested¹¹. Furthermore, extant research has primarily examined these factors in isolation, neglecting potential sequential mediation effects that might better explain the complex process through which digital literacy enhances older adults’ quality of life⁹. The fragmentation of literature in this area necessitates the development of an integrated theoretical framework capable of simultaneously accounting for both domain-specific competencies (eHealth literacy) and psychological resources (self-efficacy) in explaining the digital literacy-quality of life relationship¹².

In order to address the research gaps that have been identified, three complementary theoretical frameworks are integrated. First, Social Cognitive Theory¹³ (Bandura, 1986) posits that behavioral capabilities (digital literacy) influence outcomes (quality of life) through both direct pathways and indirect mechanisms involving psychological resources (self-efficacy)¹⁴. Second, the Digital Health Engagement Framework conceptualizes eHealth literacy as a domain-specific capability that mediates the relationship between general digital literacy and health-related outcomes¹⁵. Finally, the Technology-Enhanced Psychological Empowerment Model theorizes the sequential nature of digital empowerment processes¹⁶. To visualize this integration, Fig. 1 illustrates how these frameworks complement one another, mapping the hypothesized pathways from digital literacy to quality of life. According to this integrated theoretical framework, digital literacy enhances quality of life through three mechanisms: (1) direct effects representing immediate behavioral capabilities; (2) parallel mediation through both eHealth literacy and self-efficacy; and (3) sequential mediation wherein eHealth literacy strengthens self-efficacy, which subsequently enhances quality of life¹⁰.

Integrated theoretical framework. 1. Social Cognitive Theory (Direct pathway: Digital literacy → Quality of life). 2. Digital Health Engagement Framework (Parallel mediation: Digital literacy → eHealth literacy/Self-efficacy→ Quality of life). 3. Technology-Enhanced Psychological Empowerment Model (Sequential mediation: Digital literacy → eHealth literacy → Self-efficacy → Quality of life). The model highlights how domain-specific competencies (eHealth literacy) and psychological resources (self-efficacy) operate in tandem to translate digital skills into enhanced well-being. Note: This study’s conceptual model synthesizes three frameworks:

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This study aims to investigate how digital literacy enhances quality of life among older adults through these hypothesized pathways: direct effects, parallel mediation via eHealth literacy and self-efficacy, and sequential mediation where eHealth literacy precedes self-efficacy. Based on our integrated theoretical framework, we hypothesize that: (H1) digital literacy positively predicts quality of life; (H2) eHealth literacy mediates the relationship between digital literacy and quality of life; (H3) self-efficacy mediates the relationship between digital literacy and quality of life; and (H4) there exists a sequential mediation where digital literacy enhances eHealth literacy, which subsequently strengthens self-efficacy, ultimately improving quality of life.

This research advances theoretical understanding of digital empowerment processes while providing actionable insights for designing age-specific interventions. We extend current knowledge by integrating previously fragmented theoretical perspectives into a comprehensive framework explaining the complex pathways through which digital literacy enhances well-being¹⁷. Our sequential mediation model demonstrates how domain-specific competencies and psychological resources work together to translate digital literacy into improved quality of life¹⁸, addressing critical gaps identified in recent systematic reviews regarding the design of digital literacy interventions for older adults, particularly in healthcare contexts¹⁵.

Participants and sampling

This study employed a stratified random sampling approach across three geographical regions of China: Eastern (Guangdong and Zhejiang provinces), Central (Hubei and Anhui provinces), and Western (Sichuan and Guangxi provinces). The study design was cross-sectional, focusing on community-dwelling older adults aged ≥ 60 years, consistent with the World Health Organization’s definition of older populations¹⁹. Inclusion criteria required participants to: (1) reside independently (non-institutionalized), (2) have no severe cognitive impairment (Mini-Mental State Examination score ≥ 18), and (3) possess basic digital device usage experience (e.g., smartphone ownership). Exclusion criteria included severe sensory impairments (uncorrected vision/hearing loss) or acute health conditions limiting participation.

In accordance with Kline’s (2023) guidelines for structural equation modeling (SEM), a minimum sample size of 800 participants was targeted to ensure adequate statistical power (1-β = 0.90) for detecting medium effect sizes (Cohen’s δ = 0.30)³⁰. Community centers were selected to represent diverse socioeconomic contexts, with 62% located in urban areas and 38% in rural townships. Participant recruitment numbers by region were as follows: Eastern (n = 412, 40.6%), Central (n = 328, 32.3%), and Western (n = 276, 27.1%). The study protocol was approved by the Ethics Committee of Youjiang Medical University for Nationalities (NO.20240103) and was conducted in accordance with the Declaration of Helsinki. Research assistants, who were professional and not affiliated with the study, collaborated with administrators from community centers to recruit participants. Prior to data collection, participants were briefed on the study’s purpose, confidentiality protocols, and voluntary nature of participation. Written informed consent was obtained from all participants.

Data collection procedures

Data collection was conducted between January and March 2024 through standardized group sessions in community centers. All instruments underwent rigorous cultural adaptation to ensure relevance for Chinese older adults. This included: (1) forward-backward translation by bilingual experts, (2) cognitive interviews with 30 older adults to assess item comprehension, and (3) pilot testing to validate psychometric properties. While Chinese versions of some scales exist (e.g., WHOQOL-BREF), the Self-Rating Digital Literacy Scale (SRDLS-OA) required new adaptations due to its focus on culturally specific dimensions like digital payment awareness, which lacks equivalent validated measures in China.

Research assistants, trained in geriatric survey administration, provided detailed instructions and assistance when needed. Questionnaires took approximately 30 min to complete. Response quality was monitored through attention check items and completion time thresholds.

Measures

All measures were translated following standardized back-translation procedures, with cultural adaptations validated through cognitive interviews with older adults (n = 30) during pilot testing.

Digital literacy

Digital literacy was assessed using the Self-Rating Digital Literacy Scale for Older Adults (SRDLS-OA)²⁰, a 35-item instrument measuring three dimensions: digital practice skills (12 items), digital learning awareness (12 items), and digital payment consciousness (11 items). Responses were recorded on a 5-point Likert scale (1 = “strongly disagree” to 5 = “strongly agree”). The scale demonstrated strong psychometric properties in our sample (Cronbach’s α = 0.87; composite reliability = 0.89). Confirmatory factor analysis supported the three-factor structure (χ²/df = 2.24, CFI = 0.93, TLI = 0.93, RMSEA = 0.08 [90% CI: 0.07, 0.09]).

Quality of life

The World Health Organization Quality of Life Brief Version (WHOQOL-BREF) was employed to assess quality of life across four domains: physical health (7 items), psychological well-being (6 items), social relationships (3 items), and environment (8 items). Items were rated on a 5-point scale (1 = “very dissatisfied” to 5 = “very satisfied”). Internal consistency was satisfactory (α = 0.76–0.88 across subscales), with good model fit indices (χ²/df = 3.91, CFI = 0.92, RMSEA = 0.07 [90% CI: 0.06, 0.08]).

eHealth literacy

eHealth literacy was measured using the updated eHealth Literacy Scale (eHEALS-R)⁸, comprising 8 items assessing individuals’ combined knowledge, comfort, and perceived skills at finding, evaluating, and applying electronic health information. Responses ranged from 1 (“strongly disagree”) to 5 (“strongly agree”). The scale showed excellent reliability (α = 0.91) and construct validity (χ²/df = 2.66, CFI = 0.95, RMSEA = 0.06 [90% CI: 0.05, 0.07]).

Self-efficacy

The revised General Self-Efficacy Scale (GSE-R) was used to assess perceived self-efficacy²¹. This 10-item unidimensional scale measures individuals’ beliefs in their ability to handle challenging situations. Items were rated on a 5-point scale (1 = “strongly disagree” to 5 = “strongly agree”). Psychometric analysis supported the scale’s reliability (α = 0.84) and factorial validity (χ²/df = 3.78, CFI = 0.93, RMSEA = 0.04 [90% CI: 0.03, 0.05]).

Statistical analysis

Data analysis followed a systematic protocol using SPSS 26.0 for preliminary analyses, AMOS 24.0 for structural equation modeling, and PROCESS macro for mediation testing:

1. (1)

   Preliminary checks: Following established methodological guidelines, we conducted preliminary analyses including missing data evaluation (Little’s MCAR test), common method bias assessment through Harman’s single-factor test (variance explained by first factor = 37.59%, below 40% threshold), and multicollinearity diagnostics (VIF < 3.0).

2. (2)

   Measurement model validation: The measurement model was validated through confirmatory factor analysis, followed by structural model testing using maximum likelihood estimation. Model fit was assessed using three established indices²²: (i) χ²/df ratio: Values < 3 indicate acceptable fit, as lower ratios suggest smaller discrepancies between observed and model-implied covariance matrices; (ii) Comparative Fit Index (CFI): Values > 0.95 signify excellent fit, reflecting ≥ 95% improvement over a baseline null model; (iii) Root Mean Square Error of Approximation (RMSEA): Values < 0.06 denote close fit, with 90% confidence intervals excluding 0.08.

3. (3)

   Structural model testing: Sequential mediation effects were examined using bootstrap procedures with 5000 resamples and bias-corrected 95% confidence intervals²³. Multi-group analyses were conducted to assess demographic invariance, with measurement equivalence established across gender and education levels (ΔCFI < 0.01).

Preliminary analyses and descriptive statistics

The final sample comprised 1,016 older adults, with a response rate of 87.3%, and a balanced gender distribution (50.39% male, n = 512; 49.61% female, n = 504). The mean age of the participants was 67.11 years (SD = 5.92, range = 60–85). Preliminary analyses revealed significant regional differences in education levels (Eastern: 43.2% high school or above vs. Western: 18.1%; χ² = 29.7, p < 0.001) and prior digital exposure (Eastern: 68% smartphone ownership vs. Western: 42%; χ² = 18.3, p < 0.001) (Table 1).

Prior to hypothesis testing, data were screened for normality and multivariate outliers following established protocols²⁴. Skewness (-0.89 to 0.76) and kurtosis (-1.12 to 1.45) values were within acceptable ranges. To account for potential confounding effects, gender and educational attainment were included as covariates in the structural equation model. These variables were selected based on prior evidence highlighting their robust associations with digital literacy and eHealth literacy in older Chinese populations^25,26. Other sociodemographic factors (e.g., marital status, chronic disease status) were excluded due to non-significant correlations with the primary variables in preliminary analyses (p > 0.05).

Descriptive statistics and zero-order correlations among study variables are presented in Table 2. Digital literacy demonstrated significant positive correlations with eHealth literacy (r = 0.53, p < 0.01), self-efficacy (r = 0.42, p < 0.01), and quality of life (r = 0.44, p < 0.01). Additionally, eHealth literacy was positively associated with both self-efficacy (r = 0.39, p < 0.01) and quality of life (r = 0.47, p < 0.01), while self-efficacy showed a significant positive correlation with quality of life (r = 0.48, p < 0.01).

Measurement model evaluation

Confirmatory factor analysis supported the distinctiveness of study constructs. The four-factor measurement model demonstrated excellent fit: χ²/df = 2.65, RMSEA = 0.05 [90% CI: 0.04, 0.06], CFI = 0.96, TLI = 0.95. All standardized factor loadings were significant (λ range: 0.65–0.89, p < 0.001), supporting convergent validity. Measurement invariance was established across gender and education levels (ΔCFI < 0.01).

Structural model and hypothesis testing

The hypothesized structural model demonstrated good fit: χ²/df = 2.65, RMSEA = 0.05 [90% CI: 0.04, 0.06], CFI = 0.96, TLI = 0.95. While the RMSEA slightly exceeded the 0.06 threshold, its upper CI (0.09) remained below 0.10, indicating adequate approximation error for complex models with large samples²².

To systematically evaluate the proposed model, we explicitly linked each hypothesis to its corresponding statistical outcomes (Table 3; Fig. 2).

Path analysis model of digital literacy, eHealth literacy, self-efficacy and quality of life.

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1. (1)

   Direct Effect of Digital Literacy on Quality of Life: Digital literacy exhibited a robust direct effect on quality of life (β = 0.56, p < 0.01, 95% CI [0.51, 0.79]), accounting for 73.7% of the total effect. This finding strongly supports H1, indicating that digital literacy independently enhances older adults’ well-being beyond mediated pathways.

2. (2)

   Parallel Mediation via eHealth Literacy: The indirect effect through eHealth literacy was statistically significant (β = 0.09, p < 0.01, 95% CI [0.05, 0.15]), explaining 11.8% of the total effect. This supports H2, confirming that digital literacy improves quality of life partly by equipping older adults with health-specific digital competencies.

3. (3)

   Parallel Mediation via Self-Efficacy: Self-efficacy mediated the relationship between digital literacy and quality of life (β = 0.08, p < 0.01, 95% CI [0.07, 0.16]), contributing 10.5% of the total effect. These results validate H3, demonstrating that digital literacy fosters psychological empowerment, which in turn enhances life satisfaction.

4. (4)

   Sequential Mediation: Digital Literacy → eHealth Literacy → Self-Efficacy → Quality of Life: A significant sequential pathway emerged (β = 0.03, p < 0.01, 95% CI [0.02, 0.06]), representing 3.95% of the total effect. This hierarchical mediation supports H4, illustrating that eHealth literacy serves as a cognitive bridge, enabling digital literacy to amplify self-efficacy and ultimately improve quality of life.

5. (5)

   Sensitivity Analyses: To ensure robustness, we re-ran the model excluding covariates (gender, education). Results remained consistent (Δβ < 0.02 for all paths), reinforcing confidence in the hypothesized pathways²².

This study contributes to our understanding of digital literacy’s role in enhancing older adults’ quality of life through multiple pathways. By integrating three theoretical frameworks—Social Cognitive Theory, Digital Health Engagement Framework, and Technology-Enhanced Psychological Empowerment Model—we provide a comprehensive analysis of both direct and indirect mechanisms linking digital literacy to well-being outcomes in older populations.

Theoretical implications of integrated pathways

Our findings demonstrate that digital literacy influences quality of life through a complex interplay of direct and mediated pathways. Contrary to unidimensional conceptualizations that treat digital literacy as either a technical skill or a psychological resource, our sequential mediation model reveals a hierarchical process whereby digital competencies enhance both domain-specific abilities and broader psychological resources²⁷. The substantial direct effect (73.68% of total effect) indicates that digital literacy serves as a fundamental enabling resource, providing older adults with immediate capabilities for information access, social connectivity, and daily task management. This direct pathway exceeds effect sizes reported in previous studies (ranging from β = 0.31 to β = 0.45), potentially reflecting the heightened importance of digital competencies in China’s rapidly digitalizing environment⁵.

The parallel and sequential mediation pathways identified in our study advance the Technology-Enhanced Psychological Empowerment Model by demonstrating how digital literacy operates through both specialized competencies and broader psychological resources²⁸. The significant indirect effects through eHealth literacy (11.84%) and self-efficacy (10.53%) suggest that digital literacy enhances quality of life not only through direct capabilities but also by equipping older adults with health-specific digital skills and strengthening their general self-efficacy beliefs^29,30. Particularly noteworthy is the sequential pathway (3.95% of total effect) where digital literacy enhances eHealth literacy, which subsequently strengthens self-efficacy, creating a cascading effect on quality of life^31,32. This finding resolves theoretical debates about whether digital literacy primarily enhances well-being through direct capabilities or mediated resources, showing that both mechanisms coexist and interact in a hierarchical manner.

The contextual significance of our findings is amplified by China’s distinctive digital ecosystem. Unlike Western societies with established alternative support systems, Chinese older adults increasingly rely on digital platforms for essential services (e.g., telemedicine, mobile payments). This context may explain the pronounced direct effect observed in our study, as digital competencies become fundamental for maintaining independence and accessing vital resources. Simultaneously, the mediating roles of eHealth literacy and self-efficacy highlight how domain-specific skills and psychological resources complement these direct effects, creating multiple pathways through which digital literacy enhances quality of life.

Practical implications for digital literacy interventions

Our integrated pathway model offers several practical implications for designing digital literacy interventions tailored to older adults’ needs. First, the substantial direct effect suggests that basic digital skills training remains essential for immediate improvement in quality of life³³. However, the significant mediating pathways indicate that interventions focusing solely on technical skills may overlook important psychological and domain-specific mechanisms³⁴.

The parallel mediation through eHealth literacy (11.84%) and self-efficacy (10.53%) suggests that interventions should adopt a multidimensional approach incorporating both health-specific digital applications and psychological empowerment strategies³⁵. For instance, digital literacy programs could include modules on accessing health information online (enhancing eHealth literacy) alongside structured mastery experiences that build confidence in using digital tools (strengthening self-efficacy). The sequential pathway further suggests that these components should be strategically sequenced, with domain-specific applications serving as stepping stones toward broader psychological empowerment.

The regional differences identified in our sample—with Eastern regions showing higher education levels (43.2% vs. 18.1%) and smartphone ownership (68% vs. 42%) compared to Western regions—highlight the need for contextually sensitive interventions. For digitally marginalized groups, such as rural older adults in Western China, prioritizing eHealth literacy training may yield maximal impact due to its gateway role in the sequential pathway. For those with basic digital skills, integrating self-efficacy reinforcement could amplify quality-of-life outcomes.

Several limitations warrant consideration when interpreting our findings. First, our cross-sectional design precludes causal inferences, necessitating longitudinal investigations to establish temporal precedence in the hypothesized pathways. Second, while our multi-regional sampling strengthens generalizability within China, cultural variations in digital literacy development patterns require cross-cultural validation. Third, our reliance on self-report measures may introduce social desirability bias, though our methodological safeguards (e.g., anonymous administration, attention checks) mitigate this concern.

Future research should employ longitudinal designs to examine the temporal dynamics of digital empowerment processes, particularly how domain-specific competencies evolve into broader psychological resources over time. Additionally, exploring potential moderating effects of cultural and contextual factors would enhance our understanding of how digital literacy pathways vary across different populations. Investigating additional mediating mechanisms, such as digital social capital and technological anxiety, could further enrich our theoretical framework. Finally, intervention studies testing the practical implications of our sequential mediation model would provide valuable evidence for designing more effective digital literacy programs for older adults.

This study provides empirical evidence for understanding the mechanisms through which digital literacy influences older adults’ quality of life through a sequential mediation model. Our findings demonstrate that digital literacy affects quality of life through three distinct pathways: a substantial direct effect (73.68% of total effect), parallel mediation through eHealth literacy (11.84%) and self-efficacy (10.53%), and a sequential mediation pathway (3.95%) where eHealth literacy serves as a cognitive bridge between digital literacy and self-efficacy. These findings advance theoretical understanding by demonstrating how domain-specific competencies and psychological resources work in concert to translate digital literacy into enhanced quality of life outcomes.

Our integrated pathway model offers practical guidance for designing more effective digital literacy interventions that address both technical skills and psychological empowerment. By recognizing the hierarchical nature of digital empowerment processes, policymakers and practitioners can develop targeted programs that strategically sequence different components to maximize impact on older adults’ well-being. As societies continue to digitalize, understanding these complex pathways becomes increasingly crucial for ensuring that older adults can fully benefit from digital technologies and maintain a high quality of life in an increasingly connected world.

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Authors and Affiliations

1. Guangxi University, Nanning, 530004, China

   Yang Xin

2. Guangxi University of Finance and Economics, Nanning, 530007, China

   Hu Weina

3. Youjiang Medical University For Nationalities, Baise, 533000, China

   Deng Yan

4. Guangxi Medical University, Nanning, 530021, China

   Deng Yan

Contributions

Y.X. writes the paper, H.W.N. writes the paper, D.Y. writes the paper, D.Y. designs the plan, H.W.N. questionnaire survey and article revision, Y.X. data processing, H.W.N. data processing, H.W.N. questionnaire survey, Y.X. questionnaire survey, D.Y. consults materials, Y.X. & D.Y. funding support and research direction confirmation, D.Y. funding support.

Corresponding author

Correspondence to Deng Yan.

Competing interests

The authors declare no competing interests.

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