How Health Literacy and eHealth Literacy Influence Quality of Life in Older Adults with Chronic Diseases: A Network and Path Analysis

How Health Literacy and eHealth Literacy Influence Quality of Life in Older Adults with Chronic Diseases: A Network and Path Analysis | 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 How Health Literacy and eHealth Literacy Influence Quality of Life in Older Adults with Chronic Diseases: A Network and Path Analysis Hao Zou, Shenglan Zhou, li Jin, Lijun Xie, Yuchen Wang, Haoyang Shi, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8541176/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract Background Despite existing research on factors influencing quality of life (QOL) in older adults with chronic diseases, the underlying mechanisms by which health literacy (HL) and e-health literacy (eHL) contribute to QOL improvement remain underexplored. This study aimed to elucidate the complex relationships among HL, eHL, health behaviors, psychosocial factors, and QOL, and to identify the driving pathways of HL and eHL in enhancing QOL. Methods A cross-sectional study was conducted between March and June 2025 at a tertiary hospital in Xi’an, China. A total of 304 older adults with chronic diseases participated in the study. Participants completed assessments for HL, eHL, cognitive function, frailty, nutrition, physical activity, sleep quality, family function, depression, and QOL (including Physical and Mental Component Summaries, PCS and MCS). Grip strength was also measured. Multiple linear regression, network analysis, and path analysis were employed to determine influencing factors and structural relationships. Results Higher HL and eHL levels were associated with younger age, higher socioeconomic status, longer daily smartphone usage, and having personal interests; HL was additionally linked to better family function. Network and path analyses revealed that depression, frailty, physical activity, grip strength, and family function were primary direct predictors of QOL, PCS and MCS. Depression and frailty were identified as key risk factors, while sleep quality and nutritional status served as significant mediators. Although HL and eHL did not directly influence QOL, they functioned as upstream variables that indirectly improved QOL by positively influencing these intermediary health behaviors and psychosocial factors. Conclusions HL and eHL indirectly enhance QOL in older adults with chronic diseases by driving improvements in health behaviors and psychological status. Interventions should target depression, frailty, sleep quality, and nutrition as critical modifiable factors. Future programs aiming to improve HL and eHL should prioritize older individuals with low socioeconomic status and limited digital experience, incorporating age-friendly designs, social interaction, and family involvement. Health literacy E-health literacy Quality of life Chronic Diseases Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Globally, approximately 80% of older adults suffer from at least one chronic condition, and nearly half experience multimorbidity[ 1 ]. Quality of life (QOL) is a crucial indicator for assessing the burden of chronic diseases[ 2 ]. Unlike traditional physiological metrics (e.g., blood glucose, blood pressure), QOL focuses on patients' subjective health experiences, encompassing physical functioning, psychological state, and social relationships[ 3 ]. This subjective perception often better reflects the real impact of disease on daily life than objective measures. Poor QOL frequently indicates difficulties in disease management and self-care[ 4 , 5 ]. Effective management of chronic diseases requires deep patient engagement, which relies on health literacy (HL) — defined as the ability of individuals to access, understand, evaluate, and apply health information to make health decisions[ 6 ]. Studies report that patients with higher health literacy exhibit better self-care behaviors and healthier lifestyles, which are associated with improved health outcomes[ 7 , 8 ]. With the development of the internet and mobile devices, health literacy has extended to include e-health literacy, which assesses an individual's ability to access, comprehend, evaluate, and apply health information within digital environments[ 9 ]. E-health literacy (eHL) has been found to be significantly associated with cognitive health in older adults, yet most older adults lack adequate eHL[ 10 ]. Both HL and eHL represent essential skills necessary for patients to understand health issues, make informed decisions, and take appropriate actions. Previous research has yielded growing evidence on predictors of QOL in older adults. Identified predictors primarily focus on physiological psychological and behavioral levels, including demographic characteristics, symptom indicators, and lifestyle factors[ 11 – 13 ]. These findings provide a crucial foundation for developing targeted interventions and policies. However, our understanding of the interaction mechanisms among these factors remains limited, particularly concerning the relationships between HL/eHL, these factors, and QOL. Nevertheless, one can envision such a path: higher HL is associated with better QOL because patients with greater HL tend to exhibit healthier behaviors. This could trigger a cascade of effects promoting physical, psychological, and cognitive health. However, this hypothesis requires empirical validation in older adults with chronic conditions. Furthermore, conventional prediction models typically treat variables as independent, additive predictors[ 14 ]. Given that QOL is a multidimensional concept involving physical, social, and psychological domains—factors that often interact complexly[ 15 ]—previous models, constrained by their statistical frameworks, struggle to capture intricate network relationships among variables. Consequently, it remains unclear which variables act as central hubs in the association between health literacy and QOL, nor can we elucidate the pathways through which these variables amplify or buffer each other's effects. Baker proposed a conceptual model illustrating the cascading causal processes through which HL influences health outcomes. This model posits health behaviors and self-efficacy as key mediators between HL and health status[ 16 ]. The relationship between HL and self-efficacy has been validated in our prior work[ 17 ]. The health ecology model is widely used in chronic disease prevention and health promotion research. It emphasizes that individual and population health are influenced by a combination of personal traits, interpersonal networks, and social environments, with interactions among these factors[ 18 ]. Building upon these two models, we constructed the logical framework for this study. We hypothesize that individuals with varying levels of HL/eHL will exhibit differential health behaviors (e.g., physical activity, sleep, nutrition, depressive symptoms, grip strength), ultimately leading to divergent health outcomes (e.g., frailty, cognitive decline, diminished QOL) (Fig. 1 ). These variables were selected based on their previously reported associations with QOL[ 13 , 14 , 19 – 21 ]. To address the limitations of prior research, we employed network analysis to visualize and quantify the structural relationships and centrality among variables. Based on the network analysis results, we used path analysis to verify the mediating variables in the pathways linking HL/eHL to QOL and to elucidate the interaction mechanisms among variables. This approach aims to reveal the multi-level pathways for improving QOL among older adults with chronic diseases from the perspective of HL/eHL. Methods Study design and sample This cross-sectional study employed a convenience sampling strategy. Participants were recruited from the geriatrics department of a tertiary hospital in Xi'an, China, between March and June 2025. Inclusion criteria: (1) Diagnosis of at least one chronic disease with a stable condition; (2) Provision of informed consent to participate voluntarily. Exclusion criteria: (1) Presence of psychiatric disorders or cognitive impairments precluding completion of questionnaires; (2) Age < 60 years. The minimum sample size in network analysis can be calculated using the formula N + N*(N-1)/2[ 22 ]. In this study, a total of 11 nodes were involved (N = 11). Considering the minimum sample size requirement for structural equation modeling (N ≥ 200)[ 23 ]and allowing for a 20% inefficiency rate, a final sample of 304 participants was included. Ethics statement The study adhered to the Declaration of Helsinki and was approved by the Ethics Committee of Xi’an Medical University Second Affiliated Hospital (Approval No.: S-X2Y2024-043). Informed consent was obtained from all participants. Instrument A self-designed questionnaire was developed to collect general information, including demographic characteristics, personal interests (defined as regular hobbies or leisure activities such as reading, music, exercise, or gardening), smartphone usage, chronic disease status, and handgrip strength (measured on the participant’s dominant hand using a handgrip dynamometer; three trials were performed and the maximum value recorded). Socioeconomic status (SES) was derived from three indicators: economic status, educational attainment, and occupational rank, using principal component analysis (PCA). Based on PCA scores and their distribution percentiles (P33 and P67), participants were stratified into high, moderate, and low SES groups. Details of other scales employed in this study are presented in Table 1 . Table 1 The scales used in this study Scale Dimensions Items Scoring method Cronbach’αin this study HeLMS[ 24 ] information acquisition ability, communication interaction ability, health improvement willingness, economic support willingness. 24 5-point Likert scale (24–120); higher score indicate higher health literacy. 0.87 eHEALS[ 10 , 25 ] application ability, evaluation ability, decision-making ability. 8 5-point Likert scale (8–40); higher score indicate higher eHealth literacy. 0.94 MMSE[ 26 ] orientation, registration, attention and calculation, recall, and language. 30 Each correct answer = 1 point (0–30); higher score indicate better cognitive function. 0.63 FRAIL[ 27 ] fatigue, resistance, ambulation, illnesses, and weight loss. 5 “Yes” = 1 point, “No” = 0; total 0 = non-frail, 1–2 = pre-frail, ≥ 3 = frail, higher score indicate higher the risk of frailty. 0.64 MNA[ 28 ] Anthropometry, general assessment, dietary habits, self-assessment. 18 Total score 0–30; higher scores indicate better nutritional status. 0.60 PASE[ 29 ] Leisure-time, household, work-related physical activity. 12 Weighted total score (0−500); higher scores indicate higher physical activity 0.65 PSQI[ 30 ] Subjective sleep quality, latency, duration, efficiency, disturbances, medication use, daytime dysfunction. 19 Total score 0–21; higher scores indicate more severe sleep problems. 0.70 APGAR[ 31 ] Adaptation, Partnership, Growth, Affection, Resolve. 5 Total score 0–10; higher scores indicate better family function level, reflecting greater support, cooperation,emotional connection, and problem-solving ability among family members. 0.93 PHQ−9[ 32 ] Depressive symptoms. 9 Total score 0–27; higher scores indicate more severe depressive symptoms. 0.83 SF−12[ 33 ] Physical Component Summary (PCS): Physical function, role physical, bodily pain, general health, Mental Component Summary (MCS): vitality, social function, role emotion, mental health. 12 PCS and MCS scores both range from 0 to 100, with higher scores indicating better quality of life. 0.92 Note: HeLMS: Health Literacy Management Scale, eHEALS: eHealth Literacy Scale, MMSE: Mini-Mental State Examination, FRAIL: Frail scale, MNA: Mini Nutritional Assessment, PASE: Physical Activity Scale for the Elderly, PSQI: Pittsburgh Sleep Quality Index, APGAR: Family APGAR Index, PHQ−9: Patient Health Questionnaire−9, SF−12: Short Form 12-Item Health Survey. This study used the Chinese versions of the above scales. Data analysis Data analysis was performed using SPSS 26.0, AMOS 29.0, and R 4.5.1. Both HL and eHL scores exhibited skewness and kurtosis values within ± 1, indicating approximately normal distributions. Categorical variables were summarized as frequencies and percentages, while continuous variables not following a normal distribution were presented as medians and quartile spacings ( P 25 , P 75 ). First, we employed independent samples t-tests, one-way analysis of variance (ANOVA), Spearman correlation analysis, and multiple linear regression analysis in SPSS to explore the factors associated with HL and eHL. Second, we utilized the qgraph and bootnet packages in R to construct a Gaussian Graphical Model (GGM). Partial correlation networks were estimated using the Extended Bayesian Information Criterion graphical least absolute shrinkage and selection operator (EBICglasso) algorithm. Centrality metrics—including strength, closeness, betweenness, and expected influence—were calculated using the centralityPlot function. To assess the stability of the network structure, we performed nonparametric bootstrap analyses for edge weight accuracy and case-dropping bootstrap analyses for the robustness of centrality indices. Correlation stability coefficients (CS-coefficients) were computed to evaluate the reliability of the centrality metrics. Finally, based on the network analysis results, path analysis was conducted in AMOS to construct the initial structural model. The model was revised during the fitting process to meet the commonly used fit indices such as x 2 /df < 3, GFI, AGFI, RFI, IFI, CFI, TLI ≥ 0.9 , and RMSEA ≤ 0.05 . The visualization graphics were completed by GraphPad Prism 10, R 4.5.1 and Visio. A two-tailed p-value < 0.05 was considered statistically significant in this study. Results Sociodemographic characteristics The median age of participants in this study was 70 years (66, 75). Among them, 55.3% were male and 44.7% were female. The majority were married (81.9%) and residing in rural areas (52.0%). A total of 58.6% of participants were classified as having low socioeconomic status. Regarding smartphone use, 38.8% of older adults reported using a smartphone for 2–4 hours per day, while 21.1% reported usage of ≥ 4 hours per day. Median scores were 97 (85, 105) for HL and 19 (8, 29) for eHL. The overall median SF-12 score was 100 (89.3, 106.3), with Physical Component Summary (PCS) and Mental Component Summary (MCS) medians of 43 (32.3, 50.8) and 57 (48.7, 62.7), respectively. Additional sociodemographic and health-related characteristics are detailed in Table 2 . Table 2 Sociodemographic characteristics (N = 304) Variables Characteristics Participants N (%)/ \(\:\stackrel{\text{-}}{\text{x}}\) ± SD / M ( P 25 , P 75 ) Gender Female 136 (44.7%) Male 168 (55.3%) SES Low 178 (58.6%) Middle 85 (28.0%) High 41 (13.5%) Marital status Married 249 (81.9%) Unmarried/Divorced/Widowed 55 (18.1%) Place of residence Rural areas 158 (52.0%) Urban areas 146 (48.0%) Daily Smartphone Usage Duration ≤ 1h 72 (23.7%) 1−2h 50 (16.4%) 2−4h 118 (38.8%) ≥ 4h 64 (21.1%) Interest Yes 130 (42.8%) No 174 (57.2%) FR Non-frail 154 (50.7%) Pre-frail 107 (35.2) Frail 43 (14.1%) Frailty Score 0 (0, 2) No. of Chronic Diseases 5 (4, 6) Age 70 (66, 75) HL 94.8 ± 13.7 eHL 19.7 ± 10.2 PR 10 (8, 10) CF 26 (23.25, 26) MNA 24 (21.5, 25.5) PA 93 (52.2, 122.7) SQ 7 (5, 10.8) DP 3 (2, 6) GS 20.2 (14.2, 27.0) QOL 100 (89.3, 106.3) PCS 43 (32.3, 50.8) MCS 57 (48.7, 62.7) Note: HL: health literacy, eHL: e-health literacy, CF: cognitive function, PR: APGAR (family function), PA: physical activity, SQ: sleep quality, DP: depression, MNA: Mini Nutritional Assessment (nutrition status), FR: frail, GS: Grip strength, QOL: quality of life, PCS: Physical Component Summary, MCS: Mental Component Summary. Univariate analysis and multiple linear regression Analysis As shown in Figure. 2, differences in both HL and eHL were observed across various demographic characteristics. Participants who were married ( P < 0.01), reported longer daily smartphone use ( P < 0.001), had personal interests ( P < 0.001), and had higher SES ( P < 0.001) exhibited significantly higher levels of both HL and eHL. Regarding gender, a significant difference was found only in HL, with females scoring lower than males ( P < 0.05). In contrast, place of residence was significantly associated with eHL, with rural residents showing lower eHL scores than urban residents ( P < 0.05), while no significant association was observed between residence and HL. * P < 0.05, ** P < 0.01, *** P < 0.001, **** P < 0.0001. As shown in Figure. 3, age was negatively correlated with both HL ( r s = − 0.21, P < 0.01) and eHL ( r s = − 0.22, P < 0.01). Notably, age demonstrated a stronger negative correlation with the information acquisition dimension of health literacy ( r s = − 0.31, P < 0.01), while no significant associations were found between age and the other HL dimensions. In contrast, age was negatively associated with all dimensions of eHL ( P < 0.05). In addition, family function was positively correlated with overall HL ( r s = 0.26, P < 0.01), as well as with each of its individual dimensions ( P < 0.05). However, no significant associations were observed between family support and eHL. Taking HL and eHL as dependent variables, and the variables with statistical significance in the univariate analysis as independent variables, a multiple linear regression analysis was conducted. The results (Fig. 4 a) showed that family function, age, interest, daily smartphone usage duration, and SES were the influencing factors of HL. Among them, patients without interests had significantly lower HL than those with interests ( P < 0.001); patients with daily smartphone usage durations of 1–2 hours ( P < 0.01), 2–4 hours ( P < 0.01), and ≥ 4 hours ( P < 0.001) had significantly higher HL than those with usage durations of ≤ 1 hour; patients with high SES had significantly higher HL than those with low SES ( P < 0.01); In addition, higher family function was associated with better HL ( P < 0.01), while older age was associated with lower HL ( P < 0.01). For eHL, significant influencing factors included age ( P < 0.01), interests ( P < 0.05), daily smartphone usage duration ( P < 0.05), and SES ( P < 0.05), demonstrating trends similar to those observed for HL. Moreover, participants with moderate SES also had significantly higher eHL than those with low SES. Figure 4 b. Network analysis Figure 5 shows that in any of the networks, there are strong edges connections between HL, eHL and cognitive function, as well as between sleep quality and depression. HL, eHL, family function and cognitive function form a cognitive health network; sleep quality, depression, nutrition, frailty, physical activity and QOL form a somatic-mental health network. All three networks consisted of 11 nodes. In the QOL network, the network density was 0.71 (39/55), with non-zero edge weights ranging from (-0.41-0.45). As shown in Fig. 5a1, there were strong edge connections between depression, frail, and QOL ( -0.41, -0.31). Both frail and depression also showed notable edge connections with nutritional status (-0.23, -0.16), while depression was strongly linked to sleep quality (0.45). These findings suggest that depression and frail serve as key mediators linking other variables to QOL within the network. As shown in Fig. 5a2, depression, frail, and HL exhibited high strength centrality values (1.27, 0.90, and 0.99, respectively). Moreover, HL and eHL demonstrated strong positive expected influence within the network (0.81, 0.60), followed by cognitive function (0.42). In contrast, frail and depression showed strong negative expected influence (-0.84, -0.31). In the PCS network, the network density was 0.74 (41/55), with non-zero edge weights ranging from (-0.37-0.53). As shown in Fig. 5b1, frail and physical activity exhibited strong edge connections with PCS (-0.37, 0.17). In addition, physical activity and frail were also strongly connected to nutritional status (0.15, -0.23). Similar to the QOL network, depression, frail, and HL showed high strength centrality in the PCS network (1.17, 1.04, and 1.00, respectively). HL and eHL demonstrated strong positive expected influence (0.85, 0.63), whereas frail and family function showed strong negative expected influence (-0.60, -0.32) within the network. In the MCS network, the network density was consistent with that of the QOL network, with non-zero edge weights ranging from (-0.44-0.44). As shown in Fig. 5c1, depression, family function, and sleep quality exhibited edge connections with PCS (-0.44, 0.11, and − 0.10, respectively). Additionally, frail, nutritional status, and family function showed strong edge connections with depression (0.18, -0.16, and − 0.12, respectively), suggesting that depression serves as a key mediating node within this network. Figure 5c2 shows that depression, HL, and cognitive function had high strength centrality (1.41, 0.96, and 0.91, respectively). Frail and depression demonstrated strong negative expected influence (-0.46 and − 0.17), while HL and eHL exhibited strong positive expected influence (0.76, 0.59). Network stability was assessed by calculating the correlation stability (CS) coefficient for strength centrality. The CS coefficients for the three networks were 0.60, 0.52, and 0.60, respectively, all above the recommended threshold of 0.5[ 34 ], indicating that the centrality estimates were robust across subsamples Fig. 5a3, b3, c3. Path analysis Figure 6a1 showed that depression had the strongest direct effect on QOL ( β = -0.52), followed by frail ( β = -0.29). Physical activity contributed a positive direct effect of 0.11. Therefore, depression, frail, and physical activity were identified as the main direct predictors of overall QOL ( R² = 0.53). Regarding total effects size, depression exerted the strongest negative impact on quality of life ( ES = -0.59), followed by sleep quality ( ES = -0.38) and frail ( ES = -0.29). In contrast, HL showed the strongest positive total effect on QOL ( ES = 0.25), followed by physical activity ( ES = 0.26) and nutritional status ( ES = 0.23) (Fig. 6a2). As shown in Fig. 6b1, frail had the strongest direct negative effect on the PCS ( β = -0.44). Physical activity and grip strength showed direct positive effects on PCS, with coefficients of 0.17 and 0.12, respectively. Regarding total effects, frail and depression had the strongest negative impacts on PCS ( ES = -0.44 and − 0.18), while physical activity, HL, and nutritional status had the strongest positive effects ( ES = 0.30, 0.17, and 0.15, respectively) (Fig. 6b2). Figure 6c1 showed that depression had a strong direct negative effect on the PCS ( β = -0.61), whereas family function exhibited a direct positive effect ( β = 0.10). In terms of total effects, depression remained the strongest negative impact on MCS ( ES = − 0.61), followed by sleep quality ( ES = − 0.32). Family function and nutritional status had relatively strong positive effects on MCS ( ES = 0.28, 0.26), followed by health literacy ( ES = 0.14) (Fig. 6c2). *P < 0.05, **P < 0.01, ***P < 0.001. Discussion Our model results indicate that depression, frailty, physical activity, grip strength, and family function are the primary direct predictors of overall QOL, PCS, and MCS. HL and eHL serve as key driving factors that indirectly improve QOL/PCS/MCS by positively influencing various health behaviors and psychological factors, consistent with our initial hypotheses. Notably, depression and frailty exhibit high strength centrality and strong negative total effects across all three network and path models, identifying them as key risk factors impacting patients’ QOL. Depression and Frailty: Key Risk Factors Depression exhibited the highest strength centrality in the network, followed by frailty, with both showing negative effects, indicating that they are the most influential risk factors within the network structure. Patients with chronic diseases are at an increased risk of developing depression due to persistent physical symptoms (e.g., pain, functional limitations) and adverse effects of medications[ 35 – 37 ]. In the present study, 36% of participants reported depressive symptoms (PHQ-9 ≥ 5)[ 38 ], which was higher than in previous national surveys (the discrepancy might be attributable to research tools and population characteristics)[ 39 ]. Patients with depression often experience persistent low mood and diminished interest in daily activities. Such emotional distress impairs their ability to experience pleasure and satisfaction, leading to a marked reduction in subjective well-being[ 40 , 41 ]. Depressive individuals may also withdraw from social interactions, exhibit reduced engagement with family members, and experience intensified feelings of isolation—all of which contribute to a lower QOL[ 42 , 43 ]. Furthermore, depressive symptoms may exacerbate the severity of chronic diseases, increase treatment challenges, and ultimately compromise QOL[ 44 ]. In this study, 49% of participants were identified as either pre-frail or frail (Frail ≥ 1). Frailty increases vulnerability to stressors, thereby elevating the risk of falls, disability, and long-term care dependency[ 45 ]. It severely limits an individual’s physical, physiological, and social functioning, further deteriorating QOL[ 46 , 47 ]. Depression and frailty frequently co-occur and may reinforce each other, thereby intensifying the overall disease burden[ 48 ]. Consistent with prior research, 27% of the participants in our study presented with both depression symptom and pre-frailty/frailty. These individuals reported significantly lower QOL compared to those without such comorbidity. Path analyses of PCS and MCS also suggested that depression and frailty can interact with each other, leading older adults patients into a predicament of dual deterioration in both physical and mental health. Therefore, the key to improving the QOL of patients lies in early identification and interruption of the vicious cycle between depression and frailty. HL and eHL: Cognitive Empowerment Drivers HL and eHL demonstrated strong positive influence within the network, indicating that they act as active “empowering agents” that positively drive other nodes. Across all three networks, HL, eHL, and cognitive function formed a stable cognitive health cluster. Consistent with previous findings, eHL was associated with higher levels of cognitive function10. Individuals with high eHL are more likely to actively search for, review and apply health information from digital channels such as the Internet[ 49 ]. During this process, they continuously activate cognitive functions such as learning memory, executive function, verbal organization strategies, attention, and decision-making[ 50 , 51 ]. This continuous cognitive engagement is similar to “exercise” for the brain, which helps to enhance or maintain cognitive reserves and thereby promotes the sustained improvement of cognitive functions[ 51 , 52 ]. HL was positively associated with both eHL and cognitive function. HL and eHL have a conceptual continuity relationship. The latter incorporates both the former and digital skills[ 53 ]. Therefore, people with higher HL are more likely to master and utilize online health resources, thereby improving their cognitive performance. Importantly, the application of HL itself also depends on a collaboration of varying cognitive processes, including memory, numeracy and executive functions. For example, calculating calorie intake, remembering the foods that cause allergies, organizing and dosing of medication, etc[ 54 ]. Furthermore, the decline in cognitive functions may also cause the older adults to feel ashamed and embarrassed, thereby reducing interpersonal communication and leading to lower HL[ 55 ]. Although cognitive function was not directly associated with QOL in this study, it may exert an indirect influence through frailty and depression. Cognitive impairment is common among older adults and easily leads to difficulties in daily activities[ 56 , 57 ]. Lower cognitive function may also increase the risk of psychiatric disorders[ 58 ]or dementia[ 59 ], thereby negatively affecting QOL. Moreover, a bidirectional relationship exists between cognitive decline and frailty, forming a vicious cycle that exacerbates adverse health outcomes among older adults with chronic conditions[ 60 , 61 ]. Taken together, HL, eHL, and cognitive function appear to reinforce each other and collectively form a cognitive-empowerment support system. This system may help disrupt the negative cycle of cognitive decline and frailty, ultimately improving QOL. Path analysis revealed that, in addition to its effects on eHL and cognitive function, HL had significant positive effect on physical activity, grip strength, and family function. A meta-analysis indicated that older adults with adequate HL were approximately 61% more likely to engage in physical activity at least five days per week than those with inadequate HL[ 62 ]. As conceptualized by Nutbeam, HL comprises three domains—functional, communicative, and critical literacy [ 63 ](all of which are captured by the HL instrument used in this study); individuals with high HL are better able to obtain, understand, and apply health information, which not only strengthens their sense of control over health decisions but also, through the development of communicative and critical literacy, enhances their motivation, confidence, and critical thinking skills, enabling them to make informed choices, adapt their lifestyle, and sustain health-promoting behaviors such as regular physical activity[ 64 ]. Regular physical activity, in turn, increases the demand for energy and nutrients[ 65 ], which, with adequate dietary intake, may enhance nutritional status, supporting muscle protein synthesis and functional maintenance[ 66 ]. This process strengthens muscle, as evidenced by higher grip strength levels[ 67 ], reduces frailty risk[ 68 ], and thereby improves patients’ QOL. The communicative literacy of HL emphasizes social skills[ 63 ]. Due to aging and chronic disease-related limitations, older adults often experience a shrinking social network, making family the primary source of emotional and practical support[ 17 , 69 ]. Higher HL is associated with stronger communication skills, which may foster more effective family health communication. This can include receiving advice, sharing health-related experiences, and collaboratively solving health-related problems—all of which enhance perceived familial support[ 70 , 71 ]. Moreover, effective family function has been shown to significantly improve emotional well-being, reduce the occurrence of diseases, and enhance health outcomes[ 72 ]. This was verified in the MCS pathway, where greater family function was associated with lower depression, thereby indirectly promoting MCS. Sleep Quality and Nutritional Status: Critical Mediating Factor Across all three path models, sleep quality consistently showed a significant negative effect on depression, indicating that the poorer the sleep quality, the more obvious the depressive symptoms—a finding corroborated by our previous research[ 73 ]. Mechanistically, sleep disturbances may contribute to depression by activating the hypothalamic-pituitary-adrenal (HPA) axis, elevating proinflammatory cytokine levels (e.g., IL-6 and TNF), disrupting monoamine neurotransmitter systems (e.g., serotonin and dopamine), and perturbing circadian rhythm gene expression, thereby impairing brain emotion regulation and increasing depressive symptoms[ 74 , 75 ]. In both the overall QOL and MCS pathways, sleep quality contributed a relatively high negative total and mediating effects, suggesting that it serves as a key mediator linking various upstream factors to psychological well-being and overall QOL. Notably, findings from the PCS path, HL can influence sleep quality by enhancing family function. On the one hand, it is because good family function can actively facilitate the older adults to share their emotions and pains, and obtain emotional support through effective communication with family members to improve sleep quality[ 72 ]. On the other hand a good family function helps maintain family relationships, provide life support and create a comfortable internal and external environment for better sleep quality[ 76 ]. This pathway highlights how HL, particularly its communicative dimensions, can be translated into concrete, supportive interpersonal environments. These environments, in turn, facilitate healthier sleep behaviors, mitigate depressive symptoms, and ultimately improve MCS. Another noteworthy finding is that nutritional status consistently contributed a relatively high positive total and mediating effect across all three path models. Clearly, in addition to its associations with physical activity and frailty, HL can directly influence nutritional status, thereby alleviating depression and enhancing QOL. Previous studies have reported that higher levels of literacy, numeracy, and HL are associated with the use of food labels and good portion-size estimation skills. This may promote better dietary behaviors, such as choosing nutrient-rich foods, leading to a more balanced overall nutrient intake and thereby improving dietary quality[ 77 – 79 ]. HL also plays a signiicant role in improving dietary adherence, helping patients with chronic diseases follow high-quality dietary patterns and reducing the risks of disease deterioration and death[ 80 ]. Improved nutritional levels can also reduce the risk of depression through various biological pathways, such as reducing chronic inflammation, promoting beneficial gut bacteria, and enhancing neuroprotection[ 81 – 83 ]. In conclusion, HL and eHL indirectly improve the QOL through a series of intermediate factors such as sleep quality, nutritional status, physical activity, depression, family function, and so on, rather than acting directly. On the one hand, it might be that these mediating variables are themselves strongly correlated with and explanatory for QOL, thereby absorbing the effect of HL in the model and attenuating or even masking its direct impact on QOL. On the other hand, this is also in line with the theoretical mechanism: HL primarily acts as a health resource or capability, exerting its influence on QOL gradually by promoting the adoption of healthy behaviors and improvements in health status, rather than producing an immediate direct effect, highlighting its role as a “driver” in improving QOL. Strategies for Improving HL and eHL This study further examined the factors influencing HL and eHL, and found that younger age, higher SES, longer daily smartphone usage, and having personal interests were associated with higher HL and eHL levels. These findings offer valuable guidance for developing future intervention strategies. First, aging is associated with declines in learning ability and information processing capacity, alongside the presence of a digital divide[ 84 , 85 ]. Therefore, for older adults, it is essential to implement age-friendly, intuitive digital literacy training to reduce the barriers to accessing and understanding health information. Second, increasing the frequency of digital engagement may promote the improvement of HL and eHL. For individuals with chronic diseases, combing smart devices with health content such as personalized disease management apps, WeChat mini-programs, or online Q&A platforms to enhance their familiarity with and trust in digital health resources. Third, individuals with higher SES tend to have better HL/eHL, reflecting the influence of resource accessibility and educational attainment on health capabilities[ 86 ]. As such, it is critical to provide more inclusive, equitable health education resources for low-SES populations, with a focus on empowerment-oriented strategies targeting vulnerable groups. In addition, individuals with hobbies or personal interests often show better cognitive functioning, psychological well-being, and social interaction. This suggests that encouraging older adults to cultivate interests, participate in community activities, and increase their sense of engagement in life may serve as an effective approach to improving HL, thereby contributing to better QOL. Finally, family function and HL are mutually reinforcing, forming a positive feedback loop. This indicates that interventions should extend beyond individual-level education to encompass the family context. Approaches such as family-based health education, group discussions, or joint digital training sessions involving family members may enhance the sustainability and emotional support of HL/eHL interventions. Limitations This study has some limitations. First, due to its cross-sectional design and relatively limited sample size, causal relationships among the variables cannot be established. Second, the questionnaire survey in this study was conducted in hospital wards. Some respondents completed the survey in the presence of others, which may have limited their privacy and affected the authenticity of sensitive information, potentially leading to social desirability bias. Third, the data were sourced from a single hospital, which may limit the generalizability and representativeness of the findings due to regional and sample-specific factors. Conclusions In conclusion, this study emphasizes that HL and eHL serve as core upstream factors that promote cognitive empowerment and drive health-related behaviors, indirectly enhance QOL by influencing multiple mediating pathways, including depression, frailty, sleep quality, physical activity, nutrition, and family function. Network and path analyses identified several key modifiable targets, such as depression, frailty, nutritional status and sleep quality, offering empirical support for the development of multi-level and coordinated intervention strategies. Abbreviations HL Health Literacy eHL Electronic Health Literacy QOL Quality of Life PCS Physical Component Summary MCS Mental Component Summary CF Cognitive function APGAR Family function PA Physical activity SQ Sleep Quality DP Depression MNA Mini Nutritional Assessment GS Grip Strength PR: Family APGAR Index (family function); Declarations Acknowledgments We sincerely thank all the participants who took part in this study. An unauthorized version of the Chinese MMSE was used by the study team without permission, however this has now been rectified with PAR. The MMSE is a copyrighted instrument and may not be used or reproduced in whole or in part, in any form or language, or by any means without written permission of PAR (www.parinc.com). Authors’ Contributions Hao Zou and Hong Li contributed to the conceptualization and methodology of the study. Li Jin, Lijun Xie, Yuchen Wang, and Haoyang Shi were responsible for data curation. Formal analysis and visualization was carried out by Hao Zou, and Shenglan Zhou. Hao Zou drafted the original manuscript, while Linlin Zhang and Hong Li critically reviewed and edited the manuscript. Hong Li provided supervision and served as the corresponding author. Funding acquisition was supported by Hao Zou. Data Availability The datasets generated or analyzed during this study are available from the corresponding author on reasonable request. Conflicts of Interes None declared Funding This work was funded by the Shaanxi Provincial Natural Science Foundation Program (General Project, Grant No. 2025JC-YBQN-989). References World population. prospects 2019: highlights. New York: United Nations; 2019. He C, Kong X, Li J, Wang X, Chen X, Wang Y, et al. 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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-8541176","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":577183012,"identity":"de8c31c3-0a2e-45c3-bdfe-7f1087f5cfea","order_by":0,"name":"Hao Zou","email":"","orcid":"","institution":"Fujian Medical University","correspondingAuthor":false,"prefix":"","firstName":"Hao","middleName":"","lastName":"Zou","suffix":""},{"id":577183013,"identity":"5cbe6032-baf3-4403-aaef-4728519f88cf","order_by":1,"name":"Shenglan Zhou","email":"","orcid":"","institution":"Changzhou 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1","display":"","copyAsset":false,"role":"figure","size":199784,"visible":true,"origin":"","legend":"\u003cp\u003eTheoretical hypothetical model.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8541176/v1/48f6ac3ca306460dbdd9611e.png"},{"id":100864363,"identity":"0aff2756-dfd5-4384-9ae4-2044e0c1d64c","added_by":"auto","created_at":"2026-01-22 08:16:06","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1296942,"visible":true,"origin":"","legend":"\u003cp\u003eThe differences in HL and eHL among different demographic groups. a: Univariate analysis of HL across demographic characteristics, b: Univariate analysis of eHL across demographic characteristics.\u003c/p\u003e\n\u003cp\u003e*\u003cem\u003eP\u003c/em\u003e \u0026lt;0.05, **\u003cem\u003eP \u003c/em\u003e\u0026lt;0.01, ***\u003cem\u003eP\u003c/em\u003e \u0026lt;0.001, ****\u003cem\u003eP\u003c/em\u003e \u0026lt;0.0001.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8541176/v1/aff808f6c53fbb0457776c90.png"},{"id":100864366,"identity":"d75a6777-63b9-444d-84fe-72554b7583b9","added_by":"auto","created_at":"2026-01-22 08:16:06","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":240157,"visible":true,"origin":"","legend":"\u003cp\u003eSpearman analysis. \u003cstrong\u003e×\u003c/strong\u003e: Not significant, IAA, CIA, HIW, ESW are the four dimensions of HL, while AA, EA, DA are the three dimensions of eHL. Circles of different colors and sizes represent the magnitude of the correlation coefficients; red indicates positive correlations, while blue indicates negative correlations.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-8541176/v1/eeb7c42258be2ec98f5c197c.png"},{"id":100864368,"identity":"814a3584-4ceb-4355-9f33-ec4b29cbd3b4","added_by":"auto","created_at":"2026-01-22 08:16:06","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":294484,"visible":true,"origin":"","legend":"\u003cp\u003eMultivariate Linear Regression Forest Plot, a: Multivariate linear regression forest plot of HL influencing factors, b: Multivariate linear regression forest plot of eHL influencing factors.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-8541176/v1/a726f3802de704a9971b12c4.png"},{"id":101296548,"identity":"45dad0dc-07bc-4a75-9a93-f7831d901550","added_by":"auto","created_at":"2026-01-28 09:14:26","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":392921,"visible":true,"origin":"","legend":"\u003cp\u003eThe network structure, node centrality, and strength centrality stability. a1, b1, c1: Estimated network structures for QOL, PCS, and MCS, respectively. Edge thickness reflects the strength of associations, with blue lines representing positive correlations and red lines representing negative correlations. a2, b2, c2: Centrality indices (strength, closeness, betweenness, and expected influence) for each node in the QOL, PCS, and MCS networks. a3, b3, c3: Correlation stability (CS) analysis for node strength in the QOL, PCS, and MCS networks, showing average correlation with the original sample across case-dropping bootstraps.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-8541176/v1/c318c50f37d33d0373eca19c.png"},{"id":100950430,"identity":"d76e3788-744e-4a36-92ee-3c9b3c773cb9","added_by":"auto","created_at":"2026-01-23 07:08:09","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":385611,"visible":true,"origin":"","legend":"\u003cp\u003ePotential pathways linking HL and eHL to QOL. a1, b1, c1: Path analysis of HL/eHL on QOL, PCS, and MCS, a2, b2, c2: Standardized direct, indirect, and total effects of each variable on QOL components. The numbers on the arrows represent standardized path coefficients. Blue arrows indicate positive effects, while red arrows indicate negative effects. Solid lines denote statistically significant paths, and dashed lines represent non-significant paths. The width of the arrows reflects the magnitude of the effects.\u003c/p\u003e\n\u003cp\u003e*P\u0026lt;0.05, **P\u0026lt;0.01, ***P\u0026lt;0.001.\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-8541176/v1/4cefd3cc6754d2b7f6328eec.png"},{"id":101942705,"identity":"3eb54afd-3e7a-4ddd-8e11-725448bd70e4","added_by":"auto","created_at":"2026-02-05 09:34:37","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3740602,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8541176/v1/9ce04350-ecba-4cbb-9b95-68309881f268.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"How Health Literacy and eHealth Literacy Influence Quality of Life in Older Adults with Chronic Diseases: A Network and Path Analysis","fulltext":[{"header":"Introduction","content":"\u003cp\u003eGlobally, approximately 80% of older adults suffer from at least one chronic condition, and nearly half experience multimorbidity[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Quality of life (QOL) is a crucial indicator for assessing the burden of chronic diseases[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Unlike traditional physiological metrics (e.g., blood glucose, blood pressure), QOL focuses on patients' subjective health experiences, encompassing physical functioning, psychological state, and social relationships[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. This subjective perception often better reflects the real impact of disease on daily life than objective measures. Poor QOL frequently indicates difficulties in disease management and self-care[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eEffective management of chronic diseases requires deep patient engagement, which relies on health literacy (HL) \u0026mdash; defined as the ability of individuals to access, understand, evaluate, and apply health information to make health decisions[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Studies report that patients with higher health literacy exhibit better self-care behaviors and healthier lifestyles, which are associated with improved health outcomes[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. With the development of the internet and mobile devices, health literacy has extended to include e-health literacy, which assesses an individual's ability to access, comprehend, evaluate, and apply health information within digital environments[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. E-health literacy (eHL) has been found to be significantly associated with cognitive health in older adults, yet most older adults lack adequate eHL[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Both HL and eHL represent essential skills necessary for patients to understand health issues, make informed decisions, and take appropriate actions.\u003c/p\u003e \u003cp\u003ePrevious research has yielded growing evidence on predictors of QOL in older adults. Identified predictors primarily focus on physiological psychological and behavioral levels, including demographic characteristics, symptom indicators, and lifestyle factors[\u003cspan additionalcitationids=\"CR12\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. These findings provide a crucial foundation for developing targeted interventions and policies. However, our understanding of the interaction mechanisms among these factors remains limited, particularly concerning the relationships between HL/eHL, these factors, and QOL. Nevertheless, one can envision such a path: higher HL is associated with better QOL because patients with greater HL tend to exhibit healthier behaviors. This could trigger a cascade of effects promoting physical, psychological, and cognitive health. However, this hypothesis requires empirical validation in older adults with chronic conditions. Furthermore, conventional prediction models typically treat variables as independent, additive predictors[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Given that QOL is a multidimensional concept involving physical, social, and psychological domains\u0026mdash;factors that often interact complexly[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u0026mdash;previous models, constrained by their statistical frameworks, struggle to capture intricate network relationships among variables. Consequently, it remains unclear which variables act as central hubs in the association between health literacy and QOL, nor can we elucidate the pathways through which these variables amplify or buffer each other's effects.\u003c/p\u003e \u003cp\u003eBaker proposed a conceptual model illustrating the cascading causal processes through which HL influences health outcomes. This model posits health behaviors and self-efficacy as key mediators between HL and health status[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. The relationship between HL and self-efficacy has been validated in our prior work[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. The health ecology model is widely used in chronic disease prevention and health promotion research. It emphasizes that individual and population health are influenced by a combination of personal traits, interpersonal networks, and social environments, with interactions among these factors[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Building upon these two models, we constructed the logical framework for this study. We hypothesize that individuals with varying levels of HL/eHL will exhibit differential health behaviors (e.g., physical activity, sleep, nutrition, depressive symptoms, grip strength), ultimately leading to divergent health outcomes (e.g., frailty, cognitive decline, diminished QOL) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). These variables were selected based on their previously reported associations with QOL[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan additionalcitationids=\"CR20\" citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. To address the limitations of prior research, we employed network analysis to visualize and quantify the structural relationships and centrality among variables. Based on the network analysis results, we used path analysis to verify the mediating variables in the pathways linking HL/eHL to QOL and to elucidate the interaction mechanisms among variables. This approach aims to reveal the multi-level pathways for improving QOL among older adults with chronic diseases from the perspective of HL/eHL.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design and sample\u003c/h2\u003e \u003cp\u003eThis cross-sectional study employed a convenience sampling strategy. Participants were recruited from the geriatrics department of a tertiary hospital in Xi'an, China, between March and June 2025. Inclusion criteria: (1) Diagnosis of at least one chronic disease with a stable condition; (2) Provision of informed consent to participate voluntarily. Exclusion criteria: (1) Presence of psychiatric disorders or cognitive impairments precluding completion of questionnaires; (2) Age\u0026thinsp;\u0026lt;\u0026thinsp;60 years. The minimum sample size in network analysis can be calculated using the formula N\u0026thinsp;+\u0026thinsp;N*(N-1)/2[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. In this study, a total of 11 nodes were involved (N\u0026thinsp;=\u0026thinsp;11). Considering the minimum sample size requirement for structural equation modeling (N\u0026thinsp;\u0026ge;\u0026thinsp;200)[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]and allowing for a 20% inefficiency rate, a final sample of 304 participants was included.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eEthics statement\u003c/h3\u003e\n\u003cp\u003e The study adhered to the Declaration of Helsinki and was approved by the Ethics Committee of Xi\u0026rsquo;an Medical University Second Affiliated Hospital (Approval No.: S-X2Y2024-043). Informed consent was obtained from all participants.\u003c/p\u003e\n\u003ch3\u003eInstrument\u003c/h3\u003e\n\u003cp\u003eA self-designed questionnaire was developed to collect general information, including demographic characteristics, personal interests (defined as regular hobbies or leisure activities such as reading, music, exercise, or gardening), smartphone usage, chronic disease status, and handgrip strength (measured on the participant\u0026rsquo;s dominant hand using a handgrip dynamometer; three trials were performed and the maximum value recorded). Socioeconomic status (SES) was derived from three indicators: economic status, educational attainment, and occupational rank, using principal component analysis (PCA). Based on PCA scores and their distribution percentiles (P33 and P67), participants were stratified into high, moderate, and low SES groups. Details of other scales employed in this study are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\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\u003eThe scales used in this study\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\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=\"char\" char=\".\" 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=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eScale\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDimensions\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eItems\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eScoring method\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCronbach\u0026rsquo;αin this study\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeLMS[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003einformation acquisition ability, communication interaction ability, health improvement willingness, economic support willingness.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5-point Likert scale (24\u0026ndash;120); higher score indicate higher health literacy.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.87\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eeHEALS[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eapplication ability, evaluation ability, decision-making ability.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5-point Likert scale (8\u0026ndash;40); higher score indicate higher eHealth literacy.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.94\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMMSE[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eorientation, registration, attention and calculation, recall, and language.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEach correct answer\u0026thinsp;=\u0026thinsp;1 point (0\u0026ndash;30); higher score indicate better cognitive function.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.63\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFRAIL[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003efatigue, resistance, ambulation, illnesses, and weight loss.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026ldquo;Yes\u0026rdquo; = 1 point, \u0026ldquo;No\u0026rdquo; = 0; total 0\u0026thinsp;=\u0026thinsp;non-frail, 1\u0026ndash;2\u0026thinsp;=\u0026thinsp;pre-frail, \u0026ge;\u0026thinsp;3\u0026thinsp;=\u0026thinsp;frail, higher score indicate higher the risk of frailty.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.64\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMNA[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAnthropometry, general assessment, dietary habits, self-assessment.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTotal score 0\u0026ndash;30; higher scores indicate better nutritional status.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.60\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePASE[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLeisure-time, household, work-related physical activity.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWeighted total score (0\u0026minus;500); higher scores indicate higher physical activity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.65\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePSQI[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSubjective sleep quality, latency, duration, efficiency, disturbances, medication use, daytime dysfunction.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTotal score 0\u0026ndash;21; higher scores indicate more severe sleep problems.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.70\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAPGAR[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAdaptation, Partnership, Growth, Affection, Resolve.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTotal score 0\u0026ndash;10; higher scores indicate better family function level, reflecting greater support, cooperation,emotional connection, and problem-solving ability among family members.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.93\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePHQ\u0026minus;9[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDepressive symptoms.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTotal score 0\u0026ndash;27; higher scores indicate more severe depressive symptoms.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.83\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSF\u0026minus;12[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePhysical Component Summary (PCS): Physical function, role physical, bodily pain, general health, Mental Component Summary (MCS): vitality, social function, role emotion, mental health.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePCS and MCS scores both range from 0 to 100, with higher scores indicating better quality of life.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.92\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eNote: HeLMS: Health Literacy Management Scale, eHEALS: eHealth Literacy Scale, MMSE: Mini-Mental State Examination, FRAIL: Frail scale, MNA: Mini Nutritional Assessment, PASE: Physical Activity Scale for the Elderly, PSQI: Pittsburgh Sleep Quality Index, APGAR: Family APGAR Index, PHQ\u0026minus;9: Patient Health Questionnaire\u0026minus;9, SF\u0026minus;12: Short Form 12-Item Health Survey. This study used the Chinese versions of the above scales.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eData analysis\u003c/h2\u003e \u003cp\u003eData analysis was performed using SPSS 26.0, AMOS 29.0, and R 4.5.1. Both HL and eHL scores exhibited skewness and kurtosis values within \u0026plusmn;\u0026thinsp;1, indicating approximately normal distributions. Categorical variables were summarized as frequencies and percentages, while continuous variables not following a normal distribution were presented as medians and quartile spacings (\u003cem\u003eP\u003c/em\u003e\u003csub\u003e\u003cem\u003e25\u003c/em\u003e\u003c/sub\u003e, \u003cem\u003eP\u003c/em\u003e\u003csub\u003e\u003cem\u003e75\u003c/em\u003e\u003c/sub\u003e).\u003c/p\u003e \u003cp\u003eFirst, we employed independent samples t-tests, one-way analysis of variance (ANOVA), Spearman correlation analysis, and multiple linear regression analysis in SPSS to explore the factors associated with HL and eHL. Second, we utilized the qgraph and bootnet packages in R to construct a Gaussian Graphical Model (GGM). Partial correlation networks were estimated using the Extended Bayesian Information Criterion graphical least absolute shrinkage and selection operator (EBICglasso) algorithm. Centrality metrics\u0026mdash;including strength, closeness, betweenness, and expected influence\u0026mdash;were calculated using the centralityPlot function. To assess the stability of the network structure, we performed nonparametric bootstrap analyses for edge weight accuracy and case-dropping bootstrap analyses for the robustness of centrality indices. Correlation stability coefficients (CS-coefficients) were computed to evaluate the reliability of the centrality metrics. Finally, based on the network analysis results, path analysis was conducted in AMOS to construct the initial structural model. The model was revised during the fitting process to meet the commonly used fit indices such as \u003cem\u003ex\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e/df\u0026thinsp;\u0026lt;\u0026thinsp;3, GFI, AGFI, RFI, IFI, CFI, TLI\u0026thinsp;\u0026ge;\u0026thinsp;0.9\u003c/em\u003e, and \u003cem\u003eRMSEA\u0026thinsp;\u0026le;\u0026thinsp;0.05\u003c/em\u003e. The visualization graphics were completed by GraphPad Prism 10, R 4.5.1 and Visio. A two-tailed p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant in this study.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eSociodemographic characteristics\u003c/h2\u003e \u003cp\u003eThe median age of participants in this study was 70 years (66, 75). Among them, 55.3% were male and 44.7% were female. The majority were married (81.9%) and residing in rural areas (52.0%). A total of 58.6% of participants were classified as having low socioeconomic status. Regarding smartphone use, 38.8% of older adults reported using a smartphone for 2\u0026ndash;4 hours per day, while 21.1% reported usage of \u0026ge;\u0026thinsp;4 hours per day. Median scores were 97 (85, 105) for HL and 19 (8, 29) for eHL. The overall median SF-12 score was 100 (89.3, 106.3), with Physical Component Summary (PCS) and Mental Component Summary (MCS) medians of 43 (32.3, 50.8) and 57 (48.7, 62.7), respectively. Additional sociodemographic and health-related characteristics are detailed in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\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\u003eSociodemographic characteristics (N\u0026thinsp;=\u0026thinsp;304)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\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 \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\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eParticipants \u003cem\u003eN\u003c/em\u003e(%)/\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\stackrel{\\text{-}}{\\text{x}}\\)\u003c/span\u003e\u003c/span\u003e\u0026plusmn;\u003cem\u003eSD\u003c/em\u003e/\u003cem\u003eM\u003c/em\u003e (\u003cem\u003eP\u003c/em\u003e\u003csub\u003e\u003cem\u003e25\u003c/em\u003e\u003c/sub\u003e, \u003cem\u003eP\u003c/em\u003e\u003csub\u003e\u003cem\u003e75\u003c/em\u003e\u003c/sub\u003e)\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 \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e136 (44.7%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e168 (55.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e178 (58.6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMiddle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e85 (28.0%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e41 (13.5%)\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 \u003cp\u003eMarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e249 (81.9%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnmarried/Divorced/Widowed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e55 (18.1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlace of residence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRural areas\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e158 (52.0%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUrban areas\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e146 (48.0%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDaily Smartphone Usage Duration\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;1h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e72 (23.7%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u0026minus;2h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e50 (16.4%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u0026minus;4h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e118 (38.8%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;4h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e64 (21.1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInterest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e130 (42.8%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e174 (57.2%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNon-frail\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e154 (50.7%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePre-frail\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e107 (35.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFrail\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e43 (14.1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFrailty Score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0, 2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo. of Chronic Diseases\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5 (4, 6)\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=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e70 (66, 75)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e94.8\u0026thinsp;\u0026plusmn;\u0026thinsp;13.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eeHL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19.7\u0026thinsp;\u0026plusmn;\u0026thinsp;10.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10 (8, 10)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26 (23.25, 26)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24 (21.5, 25.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e93 (52.2, 122.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSQ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7 (5, 10.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 (2, 6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20.2 (14.2, 27.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQOL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e100 (89.3, 106.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePCS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e43 (32.3, 50.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMCS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e57 (48.7, 62.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003eNote: HL: health literacy, eHL: e-health literacy, CF: cognitive function, PR: APGAR (family function), PA: physical activity, SQ: sleep quality, DP: depression, MNA: Mini Nutritional Assessment (nutrition status), FR: frail, GS: Grip strength, QOL: quality of life, PCS: Physical Component Summary, MCS: Mental Component Summary.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eUnivariate analysis and multiple linear regression Analysis\u003c/h3\u003e\n\u003cp\u003eAs shown in Figure. 2, differences in both HL and eHL were observed across various demographic characteristics. Participants who were married (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01), reported longer daily smartphone use (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), had personal interests (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and had higher SES (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) exhibited significantly higher levels of both HL and eHL. Regarding gender, a significant difference was found only in HL, with females scoring lower than males (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). In contrast, place of residence was significantly associated with eHL, with rural residents showing lower eHL scores than urban residents (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), while no significant association was observed between residence and HL.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e*\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, **\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01, ***\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, ****\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001.\u003c/p\u003e \u003cp\u003eAs shown in Figure. 3, age was negatively correlated with both HL (\u003cem\u003er\u003c/em\u003e\u003csub\u003e\u003cem\u003es\u003c/em\u003e\u003c/sub\u003e = \u0026minus;\u0026thinsp;0.21, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01) and eHL (\u003cem\u003er\u003c/em\u003e\u003csub\u003e\u003cem\u003es\u003c/em\u003e\u003c/sub\u003e = \u0026minus;\u0026thinsp;0.22, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01). Notably, age demonstrated a stronger negative correlation with the information acquisition dimension of health literacy (\u003cem\u003er\u003c/em\u003e\u003csub\u003e\u003cem\u003es\u003c/em\u003e\u003c/sub\u003e = \u0026minus;\u0026thinsp;0.31, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01), while no significant associations were found between age and the other HL dimensions. In contrast, age was negatively associated with all dimensions of eHL (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). In addition, family function was positively correlated with overall HL (\u003cem\u003er\u003c/em\u003e\u003csub\u003es\u003c/sub\u003e = 0.26, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01), as well as with each of its individual dimensions (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). However, no significant associations were observed between family support and eHL.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTaking HL and eHL as dependent variables, and the variables with statistical significance in the univariate analysis as independent variables, a multiple linear regression analysis was conducted. The results (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea) showed that family function, age, interest, daily smartphone usage duration, and SES were the influencing factors of HL. Among them, patients without interests had significantly lower HL than those with interests (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001); patients with daily smartphone usage durations of 1\u0026ndash;2 hours (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01), 2\u0026ndash;4 hours (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01), and \u0026ge;\u0026thinsp;4 hours (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) had significantly higher HL than those with usage durations of \u0026le;\u0026thinsp;1 hour; patients with high SES had significantly higher HL than those with low SES (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01); In addition, higher family function was associated with better HL (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01), while older age was associated with lower HL (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01).\u003c/p\u003e \u003cp\u003eFor eHL, significant influencing factors included age (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01), interests (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), daily smartphone usage duration (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), and SES (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), demonstrating trends similar to those observed for HL. Moreover, participants with moderate SES also had significantly higher eHL than those with low SES. Figure\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eNetwork analysis\u003c/h3\u003e\n\u003cp\u003eFigure \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e5\u003c/span\u003e shows that in any of the networks, there are strong edges connections between HL, eHL and cognitive function, as well as between sleep quality and depression. HL, eHL, family function and cognitive function form a cognitive health network; sleep quality, depression, nutrition, frailty, physical activity and QOL form a somatic-mental health network. All three networks consisted of 11 nodes. In the QOL network, the network density was 0.71 (39/55), with non-zero edge weights ranging from (-0.41-0.45). As shown in Fig.\u0026nbsp;5a1, there were strong edge connections between depression, frail, and QOL ( -0.41, -0.31). Both frail and depression also showed notable edge connections with nutritional status (-0.23, -0.16), while depression was strongly linked to sleep quality (0.45). These findings suggest that depression and frail serve as key mediators linking other variables to QOL within the network. As shown in Fig.\u0026nbsp;5a2, depression, frail, and HL exhibited high strength centrality values (1.27, 0.90, and 0.99, respectively). Moreover, HL and eHL demonstrated strong positive expected influence within the network (0.81, 0.60), followed by cognitive function (0.42). In contrast, frail and depression showed strong negative expected influence (-0.84, -0.31).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn the PCS network, the network density was 0.74 (41/55), with non-zero edge weights ranging from (-0.37-0.53). As shown in Fig.\u0026nbsp;5b1, frail and physical activity exhibited strong edge connections with PCS (-0.37, 0.17). In addition, physical activity and frail were also strongly connected to nutritional status (0.15, -0.23). Similar to the QOL network, depression, frail, and HL showed high strength centrality in the PCS network (1.17, 1.04, and 1.00, respectively). HL and eHL demonstrated strong positive expected influence (0.85, 0.63), whereas frail and family function showed strong negative expected influence (-0.60, -0.32) within the network.\u003c/p\u003e \u003cp\u003eIn the MCS network, the network density was consistent with that of the QOL network, with non-zero edge weights ranging from (-0.44-0.44). As shown in Fig.\u0026nbsp;5c1, depression, family function, and sleep quality exhibited edge connections with PCS (-0.44, 0.11, and \u0026minus;\u0026thinsp;0.10, respectively). Additionally, frail, nutritional status, and family function showed strong edge connections with depression (0.18, -0.16, and \u0026minus;\u0026thinsp;0.12, respectively), suggesting that depression serves as a key mediating node within this network. Figure\u0026nbsp;5c2 shows that depression, HL, and cognitive function had high strength centrality (1.41, 0.96, and 0.91, respectively). Frail and depression demonstrated strong negative expected influence (-0.46 and \u0026minus;\u0026thinsp;0.17), while HL and eHL exhibited strong positive expected influence (0.76, 0.59).\u003c/p\u003e \u003cp\u003eNetwork stability was assessed by calculating the correlation stability (CS) coefficient for strength centrality. The CS coefficients for the three networks were 0.60, 0.52, and 0.60, respectively, all above the recommended threshold of 0.5[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e], indicating that the centrality estimates were robust across subsamples Fig.\u0026nbsp;5a3, b3, c3.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003ePath analysis\u003c/h2\u003e \u003cp\u003eFigure\u0026nbsp;6a1 showed that depression had the strongest direct effect on QOL (\u003cem\u003eβ\u003c/em\u003e= -0.52), followed by frail (\u003cem\u003eβ\u003c/em\u003e= -0.29). Physical activity contributed a positive direct effect of 0.11. Therefore, depression, frail, and physical activity were identified as the main direct predictors of overall QOL (\u003cem\u003eR\u0026sup2;\u003c/em\u003e= 0.53). Regarding total effects size, depression exerted the strongest negative impact on quality of life (\u003cem\u003eES\u003c/em\u003e= -0.59), followed by sleep quality (\u003cem\u003eES\u003c/em\u003e= -0.38) and frail (\u003cem\u003eES\u003c/em\u003e= -0.29). In contrast, HL showed the strongest positive total effect on QOL (\u003cem\u003eES\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.25), followed by physical activity (\u003cem\u003eES\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.26) and nutritional status (\u003cem\u003eES\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.23) (Fig.\u0026nbsp;6a2). As shown in Fig.\u0026nbsp;6b1, frail had the strongest direct negative effect on the PCS (\u003cem\u003eβ\u003c/em\u003e= -0.44). Physical activity and grip strength showed direct positive effects on PCS, with coefficients of 0.17 and 0.12, respectively. Regarding total effects, frail and depression had the strongest negative impacts on PCS (\u003cem\u003eES\u003c/em\u003e= -0.44 and \u0026minus;\u0026thinsp;0.18), while physical activity, HL, and nutritional status had the strongest positive effects (\u003cem\u003eES\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.30, 0.17, and 0.15, respectively) (Fig.\u0026nbsp;6b2). Figure\u0026nbsp;6c1 showed that depression had a strong direct negative effect on the PCS (\u003cem\u003eβ\u003c/em\u003e= -0.61), whereas family function exhibited a direct positive effect (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.10). In terms of total effects, depression remained the strongest negative impact on MCS (\u003cem\u003eES\u003c/em\u003e\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.61), followed by sleep quality (\u003cem\u003eES\u003c/em\u003e\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.32). Family function and nutritional status had relatively strong positive effects on MCS (\u003cem\u003eES\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.28, 0.26), followed by health literacy (\u003cem\u003eES\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.14) (Fig.\u0026nbsp;6c2).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e*P\u0026thinsp;\u0026lt;\u0026thinsp;0.05, **P\u0026thinsp;\u0026lt;\u0026thinsp;0.01, ***P\u0026thinsp;\u0026lt;\u0026thinsp;0.001.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eOur model results indicate that depression, frailty, physical activity, grip strength, and family function are the primary direct predictors of overall QOL, PCS, and MCS. HL and eHL serve as key driving factors that indirectly improve QOL/PCS/MCS by positively influencing various health behaviors and psychological factors, consistent with our initial hypotheses. Notably, depression and frailty exhibit high strength centrality and strong negative total effects across all three network and path models, identifying them as key risk factors impacting patients\u0026rsquo; QOL.\u003c/p\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eDepression and Frailty: Key Risk Factors\u003c/h2\u003e \u003cp\u003eDepression exhibited the highest strength centrality in the network, followed by frailty, with both showing negative effects, indicating that they are the most influential risk factors within the network structure. Patients with chronic diseases are at an increased risk of developing depression due to persistent physical symptoms (e.g., pain, functional limitations) and adverse effects of medications[\u003cspan additionalcitationids=\"CR36\" citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. In the present study, 36% of participants reported depressive symptoms (PHQ-9\u0026thinsp;\u0026ge;\u0026thinsp;5)[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e], which was higher than in previous national surveys (the discrepancy might be attributable to research tools and population characteristics)[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Patients with depression often experience persistent low mood and diminished interest in daily activities. Such emotional distress impairs their ability to experience pleasure and satisfaction, leading to a marked reduction in subjective well-being[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. Depressive individuals may also withdraw from social interactions, exhibit reduced engagement with family members, and experience intensified feelings of isolation\u0026mdash;all of which contribute to a lower QOL[\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. Furthermore, depressive symptoms may exacerbate the severity of chronic diseases, increase treatment challenges, and ultimately compromise QOL[\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. In this study, 49% of participants were identified as either pre-frail or frail (Frail\u0026thinsp;\u0026ge;\u0026thinsp;1). Frailty increases vulnerability to stressors, thereby elevating the risk of falls, disability, and long-term care dependency[\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. It severely limits an individual\u0026rsquo;s physical, physiological, and social functioning, further deteriorating QOL[\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. Depression and frailty frequently co-occur and may reinforce each other, thereby intensifying the overall disease burden[\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. Consistent with prior research, 27% of the participants in our study presented with both depression symptom and pre-frailty/frailty. These individuals reported significantly lower QOL compared to those without such comorbidity. Path analyses of PCS and MCS also suggested that depression and frailty can interact with each other, leading older adults patients into a predicament of dual deterioration in both physical and mental health. Therefore, the key to improving the QOL of patients lies in early identification and interruption of the vicious cycle between depression and frailty.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eHL and eHL: Cognitive Empowerment Drivers\u003c/h2\u003e \u003cp\u003eHL and eHL demonstrated strong positive influence within the network, indicating that they act as active \u0026ldquo;empowering agents\u0026rdquo; that positively drive other nodes. Across all three networks, HL, eHL, and cognitive function formed a stable cognitive health cluster. Consistent with previous findings, eHL was associated with higher levels of cognitive function10. Individuals with high eHL are more likely to actively search for, review and apply health information from digital channels such as the Internet[\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. During this process, they continuously activate cognitive functions such as learning memory, executive function, verbal organization strategies, attention, and decision-making[\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. This continuous cognitive engagement is similar to \u0026ldquo;exercise\u0026rdquo; for the brain, which helps to enhance or maintain cognitive reserves and thereby promotes the sustained improvement of cognitive functions[\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. HL was positively associated with both eHL and cognitive function. HL and eHL have a conceptual continuity relationship. The latter incorporates both the former and digital skills[\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]. Therefore, people with higher HL are more likely to master and utilize online health resources, thereby improving their cognitive performance. Importantly, the application of HL itself also depends on a collaboration of varying cognitive processes, including memory, numeracy and executive functions. For example, calculating calorie intake, remembering the foods that cause allergies, organizing and dosing of medication, etc[\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]. Furthermore, the decline in cognitive functions may also cause the older adults to feel ashamed and embarrassed, thereby reducing interpersonal communication and leading to lower HL[\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]. Although cognitive function was not directly associated with QOL in this study, it may exert an indirect influence through frailty and depression. Cognitive impairment is common among older adults and easily leads to difficulties in daily activities[\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e]. Lower cognitive function may also increase the risk of psychiatric disorders[\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e]or dementia[\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e], thereby negatively affecting QOL. Moreover, a bidirectional relationship exists between cognitive decline and frailty, forming a vicious cycle that exacerbates adverse health outcomes among older adults with chronic conditions[\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e, \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e]. Taken together, HL, eHL, and cognitive function appear to reinforce each other and collectively form a cognitive-empowerment support system. This system may help disrupt the negative cycle of cognitive decline and frailty, ultimately improving QOL.\u003c/p\u003e \u003cp\u003ePath analysis revealed that, in addition to its effects on eHL and cognitive function, HL had significant positive effect on physical activity, grip strength, and family function. A meta-analysis indicated that older adults with adequate HL were approximately 61% more likely to engage in physical activity at least five days per week than those with inadequate HL[\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e]. As conceptualized by Nutbeam, HL comprises three domains\u0026mdash;functional, communicative, and critical literacy [\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e](all of which are captured by the HL instrument used in this study); individuals with high HL are better able to obtain, understand, and apply health information, which not only strengthens their sense of control over health decisions but also, through the development of communicative and critical literacy, enhances their motivation, confidence, and critical thinking skills, enabling them to make informed choices, adapt their lifestyle, and sustain health-promoting behaviors such as regular physical activity[\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e]. Regular physical activity, in turn, increases the demand for energy and nutrients[\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e], which, with adequate dietary intake, may enhance nutritional status, supporting muscle protein synthesis and functional maintenance[\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e]. This process strengthens muscle, as evidenced by higher grip strength levels[\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e], reduces frailty risk[\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e], and thereby improves patients\u0026rsquo; QOL. The communicative literacy of HL emphasizes social skills[\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e]. Due to aging and chronic disease-related limitations, older adults often experience a shrinking social network, making family the primary source of emotional and practical support[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e]. Higher HL is associated with stronger communication skills, which may foster more effective family health communication. This can include receiving advice, sharing health-related experiences, and collaboratively solving health-related problems\u0026mdash;all of which enhance perceived familial support[\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e, \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e]. Moreover, effective family function has been shown to significantly improve emotional well-being, reduce the occurrence of diseases, and enhance health outcomes[\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e]. This was verified in the MCS pathway, where greater family function was associated with lower depression, thereby indirectly promoting MCS.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eSleep Quality and Nutritional Status: Critical Mediating Factor\u003c/h2\u003e \u003cp\u003eAcross all three path models, sleep quality consistently showed a significant negative effect on depression, indicating that the poorer the sleep quality, the more obvious the depressive symptoms\u0026mdash;a finding corroborated by our previous research[\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e]. Mechanistically, sleep disturbances may contribute to depression by activating the hypothalamic-pituitary-adrenal (HPA) axis, elevating proinflammatory cytokine levels (e.g., IL-6 and TNF), disrupting monoamine neurotransmitter systems (e.g., serotonin and dopamine), and perturbing circadian rhythm gene expression, thereby impairing brain emotion regulation and increasing depressive symptoms[\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e, \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e]. In both the overall QOL and MCS pathways, sleep quality contributed a relatively high negative total and mediating effects, suggesting that it serves as a key mediator linking various upstream factors to psychological well-being and overall QOL. Notably, findings from the PCS path, HL can influence sleep quality by enhancing family function. On the one hand, it is because good family function can actively facilitate the older adults to share their emotions and pains, and obtain emotional support through effective communication with family members to improve sleep quality[\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e]. On the other hand a good family function helps maintain family relationships, provide life support and create a comfortable internal and external environment for better sleep quality[\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e]. This pathway highlights how HL, particularly its communicative dimensions, can be translated into concrete, supportive interpersonal environments. These environments, in turn, facilitate healthier sleep behaviors, mitigate depressive symptoms, and ultimately improve MCS.\u003c/p\u003e \u003cp\u003eAnother noteworthy finding is that nutritional status consistently contributed a relatively high positive total and mediating effect across all three path models. Clearly, in addition to its associations with physical activity and frailty, HL can directly influence nutritional status, thereby alleviating depression and enhancing QOL. Previous studies have reported that higher levels of literacy, numeracy, and HL are associated with the use of food labels and good portion-size estimation skills. This may promote better dietary behaviors, such as choosing nutrient-rich foods, leading to a more balanced overall nutrient intake and thereby improving dietary quality[\u003cspan additionalcitationids=\"CR78\" citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e]. HL also plays a signiicant role in improving dietary adherence, helping patients with chronic diseases follow high-quality dietary patterns and reducing the risks of disease deterioration and death[\u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e80\u003c/span\u003e]. Improved nutritional levels can also reduce the risk of depression through various biological pathways, such as reducing chronic inflammation, promoting beneficial gut bacteria, and enhancing neuroprotection[\u003cspan additionalcitationids=\"CR82\" citationid=\"CR81\" class=\"CitationRef\"\u003e81\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e83\u003c/span\u003e]. In conclusion, HL and eHL indirectly improve the QOL through a series of intermediate factors such as sleep quality, nutritional status, physical activity, depression, family function, and so on, rather than acting directly. On the one hand, it might be that these mediating variables are themselves strongly correlated with and explanatory for QOL, thereby absorbing the effect of HL in the model and attenuating or even masking its direct impact on QOL. On the other hand, this is also in line with the theoretical mechanism: HL primarily acts as a health resource or capability, exerting its influence on QOL gradually by promoting the adoption of healthy behaviors and improvements in health status, rather than producing an immediate direct effect, highlighting its role as a \u0026ldquo;driver\u0026rdquo; in improving QOL.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eStrategies for Improving HL and eHL\u003c/h2\u003e \u003cp\u003eThis study further examined the factors influencing HL and eHL, and found that younger age, higher SES, longer daily smartphone usage, and having personal interests were associated with higher HL and eHL levels. These findings offer valuable guidance for developing future intervention strategies. First, aging is associated with declines in learning ability and information processing capacity, alongside the presence of a digital divide[\u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e84\u003c/span\u003e, \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e85\u003c/span\u003e]. Therefore, for older adults, it is essential to implement age-friendly, intuitive digital literacy training to reduce the barriers to accessing and understanding health information. Second, increasing the frequency of digital engagement may promote the improvement of HL and eHL. For individuals with chronic diseases, combing smart devices with health content such as personalized disease management apps, WeChat mini-programs, or online Q\u0026amp;A platforms to enhance their familiarity with and trust in digital health resources. Third, individuals with higher SES tend to have better HL/eHL, reflecting the influence of resource accessibility and educational attainment on health capabilities[\u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e86\u003c/span\u003e]. As such, it is critical to provide more inclusive, equitable health education resources for low-SES populations, with a focus on empowerment-oriented strategies targeting vulnerable groups. In addition, individuals with hobbies or personal interests often show better cognitive functioning, psychological well-being, and social interaction. This suggests that encouraging older adults to cultivate interests, participate in community activities, and increase their sense of engagement in life may serve as an effective approach to improving HL, thereby contributing to better QOL. Finally, family function and HL are mutually reinforcing, forming a positive feedback loop. This indicates that interventions should extend beyond individual-level education to encompass the family context. Approaches such as family-based health education, group discussions, or joint digital training sessions involving family members may enhance the sustainability and emotional support of HL/eHL interventions.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eLimitations\u003c/h2\u003e \u003cp\u003eThis study has some limitations. First, due to its cross-sectional design and relatively limited sample size, causal relationships among the variables cannot be established. Second, the questionnaire survey in this study was conducted in hospital wards. Some respondents completed the survey in the presence of others, which may have limited their privacy and affected the authenticity of sensitive information, potentially leading to social desirability bias. Third, the data were sourced from a single hospital, which may limit the generalizability and representativeness of the findings due to regional and sample-specific factors.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn conclusion, this study emphasizes that HL and eHL serve as core upstream factors that promote cognitive empowerment and drive health-related behaviors, indirectly enhance QOL by influencing multiple mediating pathways, including depression, frailty, sleep quality, physical activity, nutrition, and family function. Network and path analyses identified several key modifiable targets, such as depression, frailty, nutritional status and sleep quality, offering empirical support for the development of multi-level and coordinated intervention strategies.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 22.8571%;\"\u003e\n \u003cp\u003eHL \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77.1429%;\"\u003e\n \u003cp\u003eHealth Literacy\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 22.8571%;\"\u003e\n \u003cp\u003eeHL \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77.1429%;\"\u003e\n \u003cp\u003eElectronic Health Literacy\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 22.8571%;\"\u003e\n \u003cp\u003eQOL \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77.1429%;\"\u003e\n \u003cp\u003eQuality of Life\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 22.8571%;\"\u003e\n \u003cp\u003ePCS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77.1429%;\"\u003e\n \u003cp\u003ePhysical Component Summary\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 22.8571%;\"\u003e\n \u003cp\u003eMCS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77.1429%;\"\u003e\n \u003cp\u003eMental Component Summary\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 22.8571%;\"\u003e\n \u003cp\u003eCF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77.1429%;\"\u003e\n \u003cp\u003eCognitive function\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 22.8571%;\"\u003e\n \u003cp\u003eAPGAR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77.1429%;\"\u003e\n \u003cp\u003eFamily function\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 22.8571%;\"\u003e\n \u003cp\u003ePA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77.1429%;\"\u003e\n \u003cp\u003ePhysical activity\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 22.8571%;\"\u003e\n \u003cp\u003eSQ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77.1429%;\"\u003e\n \u003cp\u003eSleep Quality\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 22.8571%;\"\u003e\n \u003cp\u003eDP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77.1429%;\"\u003e\n \u003cp\u003eDepression\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 22.8571%;\"\u003e\n \u003cp\u003eMNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77.1429%;\"\u003e\n \u003cp\u003eMini Nutritional Assessment\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 22.8571%;\"\u003e\n \u003cp\u003eGS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77.1429%;\"\u003e\n \u003cp\u003eGrip Strength\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003ePR: Family APGAR Index (family function);\u0026nbsp;\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch3\u003eAcknowledgments\u003c/h3\u003e\n\u003cp\u003eWe sincerely thank all the participants who took part in this study.\u003c/p\u003e\n\u003cp\u003eAn unauthorized version of the Chinese MMSE was used by the study team without permission,\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ehowever this has now been rectified with PAR. The MMSE is a copyrighted instrument and may not be used or reproduced in whole or in part, in any form or language, or by any means without written permission of PAR (www.parinc.com).\u003c/p\u003e\n\u003ch3\u003eAuthors\u0026rsquo; Contributions\u003c/h3\u003e\n\u003cp\u003eHao Zou and Hong Li contributed to the conceptualization and methodology of the study. Li Jin, Lijun Xie, Yuchen Wang, and Haoyang Shi were responsible for data curation. Formal analysis and visualization was carried out by Hao Zou, and Shenglan Zhou. Hao Zou drafted the original manuscript, while Linlin Zhang and Hong Li critically reviewed and edited the manuscript. Hong Li provided supervision and served as the corresponding author. Funding acquisition was supported by Hao Zou.\u003c/p\u003e\n\u003ch3\u003eData Availability\u003c/h3\u003e\n\u003cp\u003eThe datasets generated or analyzed during this study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003ch3\u003eConflicts of Interes\u003c/h3\u003e\n\u003cp\u003eNone declared\u003c/p\u003e\u003ch3\u003eFunding\u003c/h3\u003e\n\u003cp\u003eThis work was funded by the Shaanxi Provincial Natural Science Foundation Program (General Project, Grant No. 2025JC-YBQN-989).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eWorld population. prospects 2019: highlights. New York: United Nations; 2019.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHe C, Kong X, Li J, Wang X, Chen X, Wang Y, et al. Predictors for quality of life in older adults: network analysis on cognitive and neuropsychiatric symptoms. 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Front Public Health. 2021;9:647230. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fpubh.2021.647230\u003c/span\u003e\u003cspan address=\"10.3389/fpubh.2021.647230\" 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":false,"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":"health-and-quality-of-life-outcomes","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"hqlo","sideBox":"Learn more about [Health and Quality of Life Outcomes](http://hqlo.biomedcentral.com)","snPcode":"12955","submissionUrl":"https://submission.nature.com/new-submission/12955/3","title":"Health and Quality of Life Outcomes","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Health literacy, E-health literacy, Quality of life, Chronic Diseases","lastPublishedDoi":"10.21203/rs.3.rs-8541176/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8541176/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eDespite existing research on factors influencing quality of life (QOL) in older adults with chronic diseases, the underlying mechanisms by which health literacy (HL) and e-health literacy (eHL) contribute to QOL improvement remain underexplored. This study aimed to elucidate the complex relationships among HL, eHL, health behaviors, psychosocial factors, and QOL, and to identify the driving pathways of HL and eHL in enhancing QOL.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eA cross-sectional study was conducted between March and June 2025 at a tertiary hospital in Xi\u0026rsquo;an, China. A total of 304 older adults with chronic diseases participated in the study. Participants completed assessments for HL, eHL, cognitive function, frailty, nutrition, physical activity, sleep quality, family function, depression, and QOL (including Physical and Mental Component Summaries, PCS and MCS). Grip strength was also measured. Multiple linear regression, network analysis, and path analysis were employed to determine influencing factors and structural relationships.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eHigher HL and eHL levels were associated with younger age, higher socioeconomic status, longer daily smartphone usage, and having personal interests; HL was additionally linked to better family function. Network and path analyses revealed that depression, frailty, physical activity, grip strength, and family function were primary direct predictors of QOL, PCS and MCS. Depression and frailty were identified as key risk factors, while sleep quality and nutritional status served as significant mediators. Although HL and eHL did not directly influence QOL, they functioned as upstream variables that indirectly improved QOL by positively influencing these intermediary health behaviors and psychosocial factors.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eHL and eHL indirectly enhance QOL in older adults with chronic diseases by driving improvements in health behaviors and psychological status. Interventions should target depression, frailty, sleep quality, and nutrition as critical modifiable factors. Future programs aiming to improve HL and eHL should prioritize older individuals with low socioeconomic status and limited digital experience, incorporating age-friendly designs, social interaction, and family involvement.\u003c/p\u003e","manuscriptTitle":"How Health Literacy and eHealth Literacy Influence Quality of Life in Older Adults with Chronic Diseases: A Network and Path Analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-22 08:16:01","doi":"10.21203/rs.3.rs-8541176/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-04-13T03:36:29+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-10T06:17:09+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"9593654224291957807213304803384875351","date":"2026-03-21T08:03:21+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-24T19:58:05+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"3474615741717972783051535741742014789","date":"2026-02-03T05:36:44+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-01-20T03:30:07+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-01-10T07:11:51+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-01-10T07:09:04+00:00","index":"","fulltext":""},{"type":"submitted","content":"Health and Quality of Life Outcomes","date":"2026-01-07T11:55:29+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"health-and-quality-of-life-outcomes","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"hqlo","sideBox":"Learn more about [Health and Quality of Life Outcomes](http://hqlo.biomedcentral.com)","snPcode":"12955","submissionUrl":"https://submission.nature.com/new-submission/12955/3","title":"Health and Quality of Life Outcomes","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"d18ee0cf-3ee8-4620-93ef-fb29bdc87614","owner":[],"postedDate":"January 22nd, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-28T11:10:47+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-22 08:16:01","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8541176","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8541176","identity":"rs-8541176","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}